Identify_Customer_Segments.ipynb

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    "# Project: Identify Customer Segments\n",
    "\n",
    "In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.\n",
    "\n",
    "This notebook will help you complete this task by providing a framework within which you will perform your analysis steps. In each step of the project, you will see some text describing the subtask that you will perform, followed by one or more code cells for you to complete your work. **Feel free to add additional code and markdown cells as you go along so that you can explore everything in precise chunks.** The code cells provided in the base template will outline only the major tasks, and will usually not be enough to cover all of the minor tasks that comprise it.\n",
    "\n",
    "It should be noted that while there will be precise guidelines on how you should handle certain tasks in the project, there will also be places where an exact specification is not provided. **There will be times in the project where you will need to make and justify your own decisions on how to treat the data.** These are places where there may not be only one way to handle the data. In real-life tasks, there may be many valid ways to approach an analysis task. One of the most important things you can do is clearly document your approach so that other scientists can understand the decisions you've made.\n",
    "\n",
    "At the end of most sections, there will be a Markdown cell labeled **Discussion**. In these cells, you will report your findings for the completed section, as well as document the decisions that you made in your approach to each subtask. **Your project will be evaluated not just on the code used to complete the tasks outlined, but also your communication about your observations and conclusions at each stage.**"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries here; add more as necessary\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# magic word for producing visualizations in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 0: Load the Data\n",
    "\n",
    "There are four files associated with this project (not including this one):\n",
    "\n",
    "- `Udacity_AZDIAS_Subset.csv`: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).\n",
    "- `Udacity_CUSTOMERS_Subset.csv`: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).\n",
    "- `Data_Dictionary.md`: Detailed information file about the features in the provided datasets.\n",
    "- `AZDIAS_Feature_Summary.csv`: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns\n",
    "\n",
    "Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. You will use this information to cluster the general population into groups with similar demographic properties. Then, you will see how the people in the customers dataset fit into those created clusters. The hope here is that certain clusters are over-represented in the customers data, as compared to the general population; those over-represented clusters will be assumed to be part of the core userbase. This information can then be used for further applications, such as targeting for a marketing campaign.\n",
    "\n",
    "To start off with, load in the demographics data for the general population into a pandas DataFrame, and do the same for the feature attributes summary. Note for all of the `.csv` data files in this project: they're semicolon (`;`) delimited, so you'll need an additional argument in your [`read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) call to read in the data properly. Also, considering the size of the main dataset, it may take some time for it to load completely.\n",
    "\n",
    "Once the dataset is loaded, it's recommended that you take a little bit of time just browsing the general structure of the dataset and feature summary file. You'll be getting deep into the innards of the cleaning in the first major step of the project, so gaining some general familiarity can help you get your bearings."
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the general demographics data.\n",
    "azdias = pd.read_csv('Udacity_AZDIAS_Subset.csv', sep=';')\n",
    "\n",
    "# Load in the feature summary file.\n",
    "feat_info = pd.read_csv('AZDIAS_Feature_Summary.csv', sep=';')"
   ]
  },
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   "execution_count": 3,
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       "(891221, 85)"
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     "execution_count": 3,
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   "source": [
    "# Check the structure of the data after it's loaded (e.g. print the number of\n",
    "# rows and columns, print the first few rows).\n",
    "azdias.shape"
   ]
  },
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       "   AGER_TYP  ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "0        -1                     2          1            2.0   \n",
       "1        -1                     1          2            5.0   \n",
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       "\n",
       "   FINANZ_MINIMALIST  FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  \\\n",
       "0                  3              4                 3               5   \n",
       "1                  1              5                 2               5   \n",
       "2                  1              4                 1               2   \n",
       "3                  4              2                 5               2   \n",
       "4                  4              3                 4               1   \n",
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       "   FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER    ...     PLZ8_ANTG1  PLZ8_ANTG2  \\\n",
       "0                      5                 3    ...            NaN         NaN   \n",
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       "[5 rows x 85 columns]"
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     "execution_count": 4,
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    "azdias.head()"
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       "              attribute information_level         type missing_or_unknown\n",
       "0              AGER_TYP            person  categorical             [-1,0]\n",
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   "source": [
    "feat_info.head()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Tip**: Add additional cells to keep everything in reasonably-sized chunks! Keyboard shortcut `esc --> a` (press escape to enter command mode, then press the 'A' key) adds a new cell before the active cell, and `esc --> b` adds a new cell after the active cell. If you need to convert an active cell to a markdown cell, use `esc --> m` and to convert to a code cell, use `esc --> y`. \n",
    "\n",
    "## Step 1: Preprocessing\n",
    "\n",
    "### Step 1.1: Assess Missing Data\n",
    "\n",
    "The feature summary file contains a summary of properties for each demographics data column. You will use this file to help you make cleaning decisions during this stage of the project. First of all, you should assess the demographics data in terms of missing data. Pay attention to the following points as you perform your analysis, and take notes on what you observe. Make sure that you fill in the **Discussion** cell with your findings and decisions at the end of each step that has one!\n",
    "\n",
    "#### Step 1.1.1: Convert Missing Value Codes to NaNs\n",
    "The fourth column of the feature attributes summary (loaded in above as `feat_info`) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. `[-1,0]`), this will get read in as a string object. You'll need to do a little bit of parsing to make use of it to identify and clean the data. Convert data that matches a 'missing' or 'unknown' value code into a numpy NaN value. You might want to see how much data takes on a 'missing' or 'unknown' code, and how much data is naturally missing, as a point of interest.\n",
    "\n",
    "**As one more reminder, you are encouraged to add additional cells to break up your analysis into manageable chunks.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify missing or unknown data values and convert them to NaNs.\n",
    "for attribute in feat_info['attribute']:\n",
    "\n",
    "    tmp_list = feat_info[feat_info['attribute']==attribute].missing_or_unknown.iloc[0].replace('[','').replace(']','').split(',')\n",
    "    missing_or_unknown_list = [float(x) if x.lstrip('-').isnumeric() else x for x in tmp_list]\n",
    "    #lstrip is to cut the '-' sign in the string\n",
    "    #isnumeric checks whether it is number or not\n",
    "    \n",
    "    azdias[attribute].replace(to_replace=missing_or_unknown_list,\n",
    "                      value=np.nan, inplace=True)\n"
   ]
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       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891218</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891219</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891220</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        AGER_TYP  ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "891216       NaN                   3.0          2            5.0   \n",
       "891217       NaN                   2.0          1            4.0   \n",
       "891218       NaN                   2.0          2            4.0   \n",
       "891219       NaN                   1.0          1            3.0   \n",
       "891220       NaN                   4.0          1            1.0   \n",
       "\n",
       "        FINANZ_MINIMALIST  FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  \\\n",
       "891216                  1              4                 2               5   \n",
       "891217                  3              3                 3               2   \n",
       "891218                  2              4                 2               5   \n",
       "891219                  1              5                 3               5   \n",
       "891220                  4              2                 5               2   \n",
       "\n",
       "        FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER    ...     PLZ8_ANTG1  \\\n",
       "891216                      4                 4    ...            3.0   \n",
       "891217                      2                 3    ...            1.0   \n",
       "891218                      4                 3    ...            4.0   \n",
       "891219                      5                 5    ...            1.0   \n",
       "891220                      1                 5    ...            3.0   \n",
       "\n",
       "        PLZ8_ANTG2  PLZ8_ANTG3  PLZ8_ANTG4  PLZ8_BAUMAX  PLZ8_HHZ  PLZ8_GBZ  \\\n",
       "891216         2.0         0.0         0.0          1.0       2.0       3.0   \n",
       "891217         3.0         3.0         2.0          4.0       5.0       3.0   \n",
       "891218         2.0         0.0         0.0          1.0       3.0       4.0   \n",
       "891219         4.0         3.0         1.0          5.0       1.0       1.0   \n",
       "891220         3.0         1.0         0.0          1.0       4.0       4.0   \n",
       "\n",
       "        ARBEIT  ORTSGR_KLS9  RELAT_AB  \n",
       "891216     NaN          NaN       NaN  \n",
       "891217     4.0          6.0       5.0  \n",
       "891218     2.0          2.0       3.0  \n",
       "891219     4.0          7.0       5.0  \n",
       "891220     3.0          4.0       5.0  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.1.2: Assess Missing Data in Each Column\n",
    "\n",
    "How much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib's [`hist()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html) function to visualize the distribution of missing value counts to find these columns. Identify and document these columns. While some of these columns might have justifications for keeping or re-encoding the data, for this project you should just remove them from the dataframe. (Feel free to make remarks about these outlier columns in the discussion, however!)\n",
    "\n",
    "For the remaining features, are there any patterns in which columns have, or share, missing data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TITEL_KZ                 889061\n",
       "AGER_TYP                 685843\n",
       "KK_KUNDENTYP             584612\n",
       "KBA05_BAUMAX             476524\n",
       "GEBURTSJAHR              392318\n",
       "ALTER_HH                 310267\n",
       "REGIOTYP                 158064\n",
       "KKK                      158064\n",
       "W_KEIT_KIND_HH           147988\n",
       "KBA05_ANTG4              133324\n",
       "KBA05_GBZ                133324\n",
       "MOBI_REGIO               133324\n",
       "KBA05_ANTG1              133324\n",
       "KBA05_ANTG2              133324\n",
       "KBA05_ANTG3              133324\n",
       "PLZ8_GBZ                 116515\n",
       "PLZ8_HHZ                 116515\n",
       "PLZ8_BAUMAX              116515\n",
       "PLZ8_ANTG4               116515\n",
       "PLZ8_ANTG1               116515\n",
       "PLZ8_ANTG3               116515\n",
       "PLZ8_ANTG2               116515\n",
       "SHOPPER_TYP              111196\n",
       "VERS_TYP                 111196\n",
       "HEALTH_TYP               111196\n",
       "NATIONALITAET_KZ         108315\n",
       "PRAEGENDE_JUGENDJAHRE    108164\n",
       "KBA13_ANZAHL_PKW         105800\n",
       "ANZ_HAUSHALTE_AKTIV       99611\n",
       "CAMEO_INTL_2015           99352\n",
       "                          ...  \n",
       "RETOURTYP_BK_S             4854\n",
       "ONLINE_AFFINITAET          4854\n",
       "LP_STATUS_FEIN             4854\n",
       "LP_STATUS_GROB             4854\n",
       "GFK_URLAUBERTYP            4854\n",
       "ALTERSKATEGORIE_GROB       2881\n",
       "FINANZTYP                     0\n",
       "GREEN_AVANTGARDE              0\n",
       "FINANZ_HAUSBAUER              0\n",
       "FINANZ_UNAUFFAELLIGER         0\n",
       "FINANZ_ANLEGER                0\n",
       "FINANZ_SPARER                 0\n",
       "FINANZ_MINIMALIST             0\n",
       "ANREDE_KZ                     0\n",
       "FINANZ_VORSORGER              0\n",
       "SEMIO_FAM                     0\n",
       "SEMIO_SOZ                     0\n",
       "SEMIO_REL                     0\n",
       "SEMIO_MAT                     0\n",
       "SEMIO_VERT                    0\n",
       "SEMIO_LUST                    0\n",
       "SEMIO_ERL                     0\n",
       "SEMIO_KULT                    0\n",
       "SEMIO_RAT                     0\n",
       "SEMIO_KRIT                    0\n",
       "SEMIO_DOM                     0\n",
       "SEMIO_KAEM                    0\n",
       "SEMIO_PFLICHT                 0\n",
       "SEMIO_TRADV                   0\n",
       "ZABEOTYP                      0\n",
       "Length: 85, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform an assessment of how much missing data there is in each column of the\n",
    "# dataset.\n",
    "azdias.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ee6b8dbe0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f145bf390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate patterns in the amount of missing data in each column.\n",
    "azdias.isnull().sum().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_columns = list(azdias.isnull().sum()[azdias.isnull().sum()>300000].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the outlier columns from the dataset. (You'll perform other data\n",
    "# engineering tasks such as re-encoding and imputation later.)\n",
    "             \n",
    "azdias.drop(drop_columns, axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.2: Assess Missing Data in Each Column\n",
    "\n",
    "(Double click this cell and replace this text with your own text, reporting your observations regarding the amount of missing data in each column. Are there any patterns in missing values? Which columns were removed from the dataset?)\n",
    "\n",
    "6 columns have a large amount of missing data\n",
    "most columns missing data below 160000\n",
    "\n",
    "columns removed:\n",
    "TITEL_KZ\n",
    "AGER_TYP\n",
    "KK_KUNDENTYP\n",
    "KBA05_BAUMAX\n",
    "GEBURTSJAHR\n",
    "ALTER_HH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.1.3: Assess Missing Data in Each Row\n",
    "\n",
    "Now, you'll perform a similar assessment for the rows of the dataset. How much data is missing in each row? As with the columns, you should see some groups of points that have a very different numbers of missing values. Divide the data into two subsets: one for data points that are above some threshold for missing values, and a second subset for points below that threshold.\n",
    "\n",
    "In order to know what to do with the outlier rows, we should see if the distribution of data values on columns that are not missing data (or are missing very little data) are similar or different between the two groups. Select at least five of these columns and compare the distribution of values.\n",
    "- You can use seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) function to create a bar chart of code frequencies and matplotlib's [`subplot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html) function to put bar charts for the two subplots side by side.\n",
    "- To reduce repeated code, you might want to write a function that can perform this comparison, taking as one of its arguments a column to be compared.\n",
    "\n",
    "Depending on what you observe in your comparison, this will have implications on how you approach your conclusions later in the analysis. If the distributions of non-missing features look similar between the data with many missing values and the data with few or no missing values, then we could argue that simply dropping those points from the analysis won't present a major issue. On the other hand, if the data with many missing values looks very different from the data with few or no missing values, then we should make a note on those data as special. We'll revisit these data later on. **Either way, you should continue your analysis for now using just the subset of the data with few or no missing values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5eda4ddc50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ee6aa15f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How much data is missing in each row of the dataset?\n",
    "azdias.isnull().sum(axis=1).sort_values(ascending=False).hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write code to divide the data into two subsets based on the number of missing\n",
    "# values in each row.\n",
    "\n",
    "below_threshold = azdias.drop(azdias[azdias.isnull().sum(axis=1)>25].index)\n",
    "above_threshold = azdias.drop(azdias[azdias.isnull().sum(axis=1)<=25].index)\n",
    "\n",
    "\n",
    "#azdias[azdias.isnull().sum(axis=1)>25].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ANREDE_KZ                0\n",
       "FINANZ_MINIMALIST        0\n",
       "FINANZ_SPARER            0\n",
       "FINANZ_VORSORGER         0\n",
       "FINANZ_ANLEGER           0\n",
       "FINANZ_UNAUFFAELLIGER    0\n",
       "FINANZ_HAUSBAUER         0\n",
       "FINANZTYP                0\n",
       "GREEN_AVANTGARDE         0\n",
       "SEMIO_SOZ                0\n",
       "SEMIO_FAM                0\n",
       "SEMIO_REL                0\n",
       "SEMIO_MAT                0\n",
       "SEMIO_VERT               0\n",
       "SEMIO_LUST               0\n",
       "SEMIO_ERL                0\n",
       "SEMIO_KULT               0\n",
       "SEMIO_RAT                0\n",
       "SEMIO_KRIT               0\n",
       "SEMIO_DOM                0\n",
       "SEMIO_KAEM               0\n",
       "SEMIO_PFLICHT            0\n",
       "SEMIO_TRADV              0\n",
       "SOHO_KZ                  0\n",
       "ZABEOTYP                 0\n",
       "ANZ_PERSONEN             0\n",
       "ANZ_TITEL                0\n",
       "HH_EINKOMMEN_SCORE       0\n",
       "WOHNDAUER_2008           0\n",
       "GEBAEUDETYP              0\n",
       "MIN_GEBAEUDEJAHR         0\n",
       "OST_WEST_KZ              0\n",
       "WOHNLAGE                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare the distribution of values for at least five columns where there are\n",
    "# no or few missing values, between the two subsets.\n",
    "below_threshold.isnull().sum()[below_threshold.isnull().sum()==0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ANREDE_KZ                0\n",
       "FINANZ_MINIMALIST        0\n",
       "FINANZ_SPARER            0\n",
       "FINANZ_VORSORGER         0\n",
       "FINANZ_ANLEGER           0\n",
       "FINANZ_UNAUFFAELLIGER    0\n",
       "FINANZ_HAUSBAUER         0\n",
       "FINANZTYP                0\n",
       "GREEN_AVANTGARDE         0\n",
       "SEMIO_SOZ                0\n",
       "SEMIO_FAM                0\n",
       "SEMIO_REL                0\n",
       "SEMIO_MAT                0\n",
       "SEMIO_VERT               0\n",
       "SEMIO_LUST               0\n",
       "SEMIO_ERL                0\n",
       "SEMIO_KULT               0\n",
       "SEMIO_RAT                0\n",
       "SEMIO_KRIT               0\n",
       "SEMIO_DOM                0\n",
       "SEMIO_KAEM               0\n",
       "SEMIO_PFLICHT            0\n",
       "SEMIO_TRADV              0\n",
       "ZABEOTYP                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "above_threshold.isnull().sum()[above_threshold.isnull().sum()==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ee6a08da0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotchart(column):\n",
    "    \n",
    "    ax1 = plt.subplot(1,2,1)\n",
    "    sns.countplot(above_threshold[column], ax=ax1)\n",
    "    ax2 = plt.subplot(1,2,2)\n",
    "    sns.countplot(below_threshold[column], ax=ax2)\n",
    "\n",
    "plotchart('FINANZ_MINIMALIST')\n",
    "#plotchart('FINANZ_SPARER')\n",
    "#FINANZ_SPARER\n",
    "#FINANZ_VORSORGER\n",
    "#FINANZ_ANLEGER\n",
    "#FINANZ_UNAUFFAELLIGER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5edd742f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotchart('FINANZ_SPARER')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5edd6b55f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotchart('FINANZ_VORSORGER')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5edd5f9860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotchart('FINANZ_ANLEGER')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5edd52d7f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotchart('FINANZ_UNAUFFAELLIGER')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.3: Assess Missing Data in Each Row\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your observations regarding missing data in rows. Are the data with lots of missing values are qualitatively different from data with few or no missing values?)\n",
    "\n",
    "The data with lots of missing values has different distribution with data with few or no missing values.\n",
    "One or two values has dominant, a large number."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.2: Select and Re-Encode Features\n",
    "\n",
    "Checking for missing data isn't the only way in which you can prepare a dataset for analysis. Since the unsupervised learning techniques to be used will only work on data that is encoded numerically, you need to make a few encoding changes or additional assumptions to be able to make progress. In addition, while almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (`feat_info`) for a summary of types of measurement.\n",
    "- For numeric and interval data, these features can be kept without changes.\n",
    "- Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).\n",
    "- Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'.\n",
    "\n",
    "In the first two parts of this sub-step, you will perform an investigation of the categorical and mixed-type features and make a decision on each of them, whether you will keep, drop, or re-encode each. Then, in the last part, you will create a new data frame with only the selected and engineered columns.\n",
    "\n",
    "Data wrangling is often the trickiest part of the data analysis process, and there's a lot of it to be done here. But stick with it: once you're done with this step, you'll be ready to get to the machine learning parts of the project!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attribute</th>\n",
       "      <th>information_level</th>\n",
       "      <th>missing_or_unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>categorical</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interval</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mixed</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numeric</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ordinal</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             attribute  information_level  missing_or_unknown\n",
       "type                                                         \n",
       "categorical         21                 21                  21\n",
       "interval             1                  1                   1\n",
       "mixed                7                  7                   7\n",
       "numeric              7                  7                   7\n",
       "ordinal             49                 49                  49"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many features are there of each data type?\n",
    "feat_info.groupby(by='type').count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANREDE_KZ 2\n",
      "CJT_GESAMTTYP 6\n",
      "FINANZTYP 6\n",
      "GFK_URLAUBERTYP 12\n",
      "GREEN_AVANTGARDE 2\n",
      "LP_FAMILIE_FEIN 11\n",
      "LP_FAMILIE_GROB 5\n",
      "LP_STATUS_FEIN 10\n",
      "LP_STATUS_GROB 5\n",
      "NATIONALITAET_KZ 3\n",
      "SHOPPER_TYP 4\n",
      "SOHO_KZ 2\n",
      "VERS_TYP 2\n",
      "ZABEOTYP 6\n",
      "GEBAEUDETYP 7\n",
      "OST_WEST_KZ 2\n",
      "CAMEO_DEUG_2015 9\n",
      "CAMEO_DEU_2015 44\n"
     ]
    }
   ],
   "source": [
    "for attribute in feat_info[feat_info['type']=='categorical'].attribute:\n",
    "    if attribute in azdias.columns:\n",
    "        print(attribute, azdias[attribute].nunique())\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "For categorical data, you would ordinarily need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:\n",
    "- For binary (two-level) categoricals that take numeric values, you can keep them without needing to do anything.\n",
    "- There is one binary variable that takes on non-numeric values. For this one, you need to re-encode the values as numbers or create a dummy variable.\n",
    "- For multi-level categoricals (three or more values), you can choose to encode the values using multiple dummy variables (e.g. via [OneHotEncoder](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)), or (to keep things straightforward) just drop them from the analysis. As always, document your choices in the Discussion section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assess categorical variables: which are binary, which are multi-level, and\n",
    "# which one needs to be re-encoded?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANREDE_KZ [1 2]\n",
      "GREEN_AVANTGARDE [0 1]\n",
      "SOHO_KZ [ nan   1.   0.]\n",
      "VERS_TYP [ nan   2.   1.]\n",
      "OST_WEST_KZ [nan 'W' 'O']\n"
     ]
    }
   ],
   "source": [
    "# access binary level categorical variables\n",
    "for attribute in feat_info[feat_info['type']=='categorical'].attribute:\n",
    "    if attribute in azdias.columns:\n",
    "        if azdias[attribute].nunique() == 2:\n",
    "            print(attribute, azdias[attribute].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'W', 'O'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias['OST_WEST_KZ'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AGER_TYP',\n",
       " 'GEBURTSJAHR',\n",
       " 'TITEL_KZ',\n",
       " 'ALTER_HH',\n",
       " 'KK_KUNDENTYP',\n",
       " 'KBA05_BAUMAX']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add multi-level categorical variables to drop_columns\n",
    "for attribute in feat_info[feat_info['type']=='categorical'].attribute:\n",
    "    if attribute in below_threshold.columns:\n",
    "        if below_threshold[attribute].nunique() > 2:\n",
    "            below_threshold.drop(attribute, axis='columns', inplace=True)\n",
    "            drop_columns.append(attribute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AGER_TYP',\n",
       " 'GEBURTSJAHR',\n",
       " 'TITEL_KZ',\n",
       " 'ALTER_HH',\n",
       " 'KK_KUNDENTYP',\n",
       " 'KBA05_BAUMAX',\n",
       " 'CJT_GESAMTTYP',\n",
       " 'FINANZTYP',\n",
       " 'GFK_URLAUBERTYP',\n",
       " 'LP_FAMILIE_FEIN',\n",
       " 'LP_FAMILIE_GROB',\n",
       " 'LP_STATUS_FEIN',\n",
       " 'LP_STATUS_GROB',\n",
       " 'NATIONALITAET_KZ',\n",
       " 'SHOPPER_TYP',\n",
       " 'ZABEOTYP',\n",
       " 'GEBAEUDETYP',\n",
       " 'CAMEO_DEUG_2015',\n",
       " 'CAMEO_DEU_2015']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ALTERSKATEGORIE_GROB', 'ANREDE_KZ', 'FINANZ_MINIMALIST',\n",
       "       'FINANZ_SPARER', 'FINANZ_VORSORGER', 'FINANZ_ANLEGER',\n",
       "       'FINANZ_UNAUFFAELLIGER', 'FINANZ_HAUSBAUER', 'GREEN_AVANTGARDE',\n",
       "       'HEALTH_TYP', 'LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB',\n",
       "       'PRAEGENDE_JUGENDJAHRE', 'RETOURTYP_BK_S', 'SEMIO_SOZ', 'SEMIO_FAM',\n",
       "       'SEMIO_REL', 'SEMIO_MAT', 'SEMIO_VERT', 'SEMIO_LUST', 'SEMIO_ERL',\n",
       "       'SEMIO_KULT', 'SEMIO_RAT', 'SEMIO_KRIT', 'SEMIO_DOM', 'SEMIO_KAEM',\n",
       "       'SEMIO_PFLICHT', 'SEMIO_TRADV', 'SOHO_KZ', 'VERS_TYP', 'ANZ_PERSONEN',\n",
       "       'ANZ_TITEL', 'HH_EINKOMMEN_SCORE', 'W_KEIT_KIND_HH', 'WOHNDAUER_2008',\n",
       "       'ANZ_HAUSHALTE_AKTIV', 'ANZ_HH_TITEL', 'KONSUMNAEHE',\n",
       "       'MIN_GEBAEUDEJAHR', 'OST_WEST_KZ', 'WOHNLAGE', 'CAMEO_INTL_2015',\n",
       "       'KBA05_ANTG1', 'KBA05_ANTG2', 'KBA05_ANTG3', 'KBA05_ANTG4', 'KBA05_GBZ',\n",
       "       'BALLRAUM', 'EWDICHTE', 'INNENSTADT', 'GEBAEUDETYP_RASTER', 'KKK',\n",
       "       'MOBI_REGIO', 'ONLINE_AFFINITAET', 'REGIOTYP', 'KBA13_ANZAHL_PKW',\n",
       "       'PLZ8_ANTG1', 'PLZ8_ANTG2', 'PLZ8_ANTG3', 'PLZ8_ANTG4', 'PLZ8_BAUMAX',\n",
       "       'PLZ8_HHZ', 'PLZ8_GBZ', 'ARBEIT', 'ORTSGR_KLS9', 'RELAT_AB'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "below_threshold.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(797961, 66)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "below_threshold.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-encode categorical variable(s) to be kept in the analysis.\n",
    "below_threshold = pd.get_dummies(below_threshold, columns=['OST_WEST_KZ'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ALTERSKATEGORIE_GROB', 'ANREDE_KZ', 'FINANZ_MINIMALIST',\n",
       "       'FINANZ_SPARER', 'FINANZ_VORSORGER', 'FINANZ_ANLEGER',\n",
       "       'FINANZ_UNAUFFAELLIGER', 'FINANZ_HAUSBAUER', 'GREEN_AVANTGARDE',\n",
       "       'HEALTH_TYP', 'LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB',\n",
       "       'PRAEGENDE_JUGENDJAHRE', 'RETOURTYP_BK_S', 'SEMIO_SOZ', 'SEMIO_FAM',\n",
       "       'SEMIO_REL', 'SEMIO_MAT', 'SEMIO_VERT', 'SEMIO_LUST', 'SEMIO_ERL',\n",
       "       'SEMIO_KULT', 'SEMIO_RAT', 'SEMIO_KRIT', 'SEMIO_DOM', 'SEMIO_KAEM',\n",
       "       'SEMIO_PFLICHT', 'SEMIO_TRADV', 'SOHO_KZ', 'VERS_TYP', 'ANZ_PERSONEN',\n",
       "       'ANZ_TITEL', 'HH_EINKOMMEN_SCORE', 'W_KEIT_KIND_HH', 'WOHNDAUER_2008',\n",
       "       'ANZ_HAUSHALTE_AKTIV', 'ANZ_HH_TITEL', 'KONSUMNAEHE',\n",
       "       'MIN_GEBAEUDEJAHR', 'WOHNLAGE', 'CAMEO_INTL_2015', 'KBA05_ANTG1',\n",
       "       'KBA05_ANTG2', 'KBA05_ANTG3', 'KBA05_ANTG4', 'KBA05_GBZ', 'BALLRAUM',\n",
       "       'EWDICHTE', 'INNENSTADT', 'GEBAEUDETYP_RASTER', 'KKK', 'MOBI_REGIO',\n",
       "       'ONLINE_AFFINITAET', 'REGIOTYP', 'KBA13_ANZAHL_PKW', 'PLZ8_ANTG1',\n",
       "       'PLZ8_ANTG2', 'PLZ8_ANTG3', 'PLZ8_ANTG4', 'PLZ8_BAUMAX', 'PLZ8_HHZ',\n",
       "       'PLZ8_GBZ', 'ARBEIT', 'ORTSGR_KLS9', 'RELAT_AB', 'OST_WEST_KZ_O',\n",
       "       'OST_WEST_KZ_W'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "below_threshold.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding categorical features. Which ones did you keep, which did you drop, and what engineering steps did you perform?)\n",
    "\n",
    "binary: ANREDE_KZ,GREEN_AVANTGARDE,SOHO_KZ,VERS_TYP,OST_WEST_KZ\n",
    "multi-level: CJT_GESAMTTYP,FINANZTYP,GFK_URLAUBERTYP,LP_FAMILIE_FEIN,\n",
    "LP_FAMILIE_GROB,LP_STATUS_FEIN,LP_STATUS_GROB,NATIONALITAET_KZ,SHOPPER_TYP,\n",
    "ZABEOTYP,GEBAEUDETYP,CAMEO_DEUG_2015,CAMEO_DEU_2015\n",
    "\n",
    "i will drop all multi-level variables :)\n",
    "I keep all binary-level variables\n",
    "I dropped all multi-level variables :)\n",
    "I encoded OST_WEST_KZ\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "There are a handful of features that are marked as \"mixed\" in the feature summary that require special treatment in order to be included in the analysis. There are two in particular that deserve attention; the handling of the rest are up to your own choices:\n",
    "- \"PRAEGENDE_JUGENDJAHRE\" combines information on three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west). While there aren't enough levels to disentangle east from west, you should create two new variables to capture the other two dimensions: an interval-type variable for decade, and a binary variable for movement.\n",
    "- \"CAMEO_INTL_2015\" combines information on two axes: wealth and life stage. Break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables (which, for the purposes of this project, is equivalent to just treating them as their raw numeric values).\n",
    "- If you decide to keep or engineer new features around the other mixed-type features, make sure you note your steps in the Discussion section.\n",
    "\n",
    "Be sure to check `Data_Dictionary.md` for the details needed to finish these tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigate \"PRAEGENDE_JUGENDJAHRE\" and engineer two new variables.\n",
    "movement = {\n",
    "    1: [40,0],\n",
    "    2: [40,1],\n",
    "    3: [50,0],\n",
    "    4: [50,1],\n",
    "    5: [60,0],\n",
    "    6: [60,1],\n",
    "    7: [60,1],\n",
    "    8: [70,0],\n",
    "    9: [70,1],\n",
    "    10: [80,0],\n",
    "    11: [80,1],\n",
    "    12: [80,0],\n",
    "    13: [80,1],\n",
    "    14: [90,0],\n",
    "    15: [90,1],\n",
    "    np.nan: [np.nan, np.nan]\n",
    "}\n",
    "# 0 is Mainstream, 1 is Avantgarde\n",
    "\n",
    "movement[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan == np.nan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "below_threshold['GENERATION'] = below_threshold['PRAEGENDE_JUGENDJAHRE'].apply(lambda x: np.nan if np.isnan(x) else movement[x][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "below_threshold['MOVEMENT'] = below_threshold['PRAEGENDE_JUGENDJAHRE'].apply(lambda x: np.nan if np.isnan(x) else movement[x][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "below_threshold.drop('PRAEGENDE_JUGENDJAHRE', axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Investigate \"CAMEO_INTL_2015\" and engineer two new variables.\n",
    "below_threshold['CAMEO_INTL_2015_tens'] = below_threshold['CAMEO_INTL_2015'].str[0]\n",
    "below_threshold['CAMEO_INTL_2015_ones'] = below_threshold['CAMEO_INTL_2015'].str[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "below_threshold.drop('CAMEO_INTL_2015', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "      <th>OST_WEST_KZ_O</th>\n",
       "      <th>OST_WEST_KZ_W</th>\n",
       "      <th>GENERATION</th>\n",
       "      <th>MOVEMENT</th>\n",
       "      <th>CAMEO_INTL_2015_tens</th>\n",
       "      <th>CAMEO_INTL_2015_ones</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "1                   1.0          2                  1              5   \n",
       "2                   3.0          2                  1              4   \n",
       "3                   4.0          2                  4              2   \n",
       "4                   3.0          1                  4              3   \n",
       "5                   1.0          2                  3              1   \n",
       "\n",
       "   FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  \\\n",
       "1                 2               5                      4                 5   \n",
       "2                 1               2                      3                 5   \n",
       "3                 5               2                      1                 2   \n",
       "4                 4               1                      3                 2   \n",
       "5                 5               2                      2                 5   \n",
       "\n",
       "   GREEN_AVANTGARDE  HEALTH_TYP          ...           PLZ8_GBZ  ARBEIT  \\\n",
       "1                 0         3.0          ...                4.0     3.0   \n",
       "2                 1         3.0          ...                4.0     3.0   \n",
       "3                 0         2.0          ...                4.0     2.0   \n",
       "4                 0         3.0          ...                3.0     4.0   \n",
       "5                 0         3.0          ...                5.0     2.0   \n",
       "\n",
       "   ORTSGR_KLS9  RELAT_AB  OST_WEST_KZ_O  OST_WEST_KZ_W  GENERATION  MOVEMENT  \\\n",
       "1          5.0       4.0              0              1        90.0       0.0   \n",
       "2          5.0       2.0              0              1        90.0       1.0   \n",
       "3          3.0       3.0              0              1        70.0       0.0   \n",
       "4          6.0       5.0              0              1        70.0       0.0   \n",
       "5          3.0       3.0              0              1        50.0       0.0   \n",
       "\n",
       "   CAMEO_INTL_2015_tens  CAMEO_INTL_2015_ones  \n",
       "1                     5                     1  \n",
       "2                     2                     4  \n",
       "3                     1                     2  \n",
       "4                     4                     3  \n",
       "5                     5                     4  \n",
       "\n",
       "[5 rows x 69 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "below_threshold.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove all mixed attributes except PRAEGENDE_JUGENDJAHRE and CAMEO_INTL_2015\n",
    "for attribute in feat_info[feat_info['type']=='mixed'].attribute:\n",
    "    drop_columns.append(attribute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_columns.remove('PRAEGENDE_JUGENDJAHRE')\n",
    "drop_columns.remove('CAMEO_INTL_2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AGER_TYP',\n",
       " 'GEBURTSJAHR',\n",
       " 'TITEL_KZ',\n",
       " 'ALTER_HH',\n",
       " 'KK_KUNDENTYP',\n",
       " 'KBA05_BAUMAX',\n",
       " 'CJT_GESAMTTYP',\n",
       " 'FINANZTYP',\n",
       " 'GFK_URLAUBERTYP',\n",
       " 'LP_FAMILIE_FEIN',\n",
       " 'LP_FAMILIE_GROB',\n",
       " 'LP_STATUS_FEIN',\n",
       " 'LP_STATUS_GROB',\n",
       " 'NATIONALITAET_KZ',\n",
       " 'SHOPPER_TYP',\n",
       " 'ZABEOTYP',\n",
       " 'GEBAEUDETYP',\n",
       " 'CAMEO_DEUG_2015',\n",
       " 'CAMEO_DEU_2015',\n",
       " 'LP_LEBENSPHASE_FEIN',\n",
       " 'LP_LEBENSPHASE_GROB',\n",
       " 'WOHNLAGE',\n",
       " 'KBA05_BAUMAX',\n",
       " 'PLZ8_BAUMAX']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding mixed-value features. Which ones did you keep, which did you drop, and what engineering steps did you perform?)\n",
    "\n",
    "drop PRAEGENDE_JUGENDJAHRE and engineer two new variables, one for generation, one for movement\n",
    "\n",
    "drop CAMEO_INTL_2015 and engineer two new variables, one for its tens digit, one for its ones digit\n",
    "\n",
    "drop all other mixed-value features, because it cannot simply dummy encode them\n",
    "all of them contains several features within one feature, if we need to deal with them, we need to engineer one by one. simply drop all of them makes our job more easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.3: Complete Feature Selection\n",
    "\n",
    "In order to finish this step up, you need to make sure that your data frame now only has the columns that you want to keep. To summarize, the dataframe should consist of the following:\n",
    "- All numeric, interval, and ordinal type columns from the original dataset.\n",
    "- Binary categorical features (all numerically-encoded).\n",
    "- Engineered features from other multi-level categorical features and mixed features.\n",
    "\n",
    "Make sure that for any new columns that you have engineered, that you've excluded the original columns from the final dataset. Otherwise, their values will interfere with the analysis later on the project. For example, you should not keep \"PRAEGENDE_JUGENDJAHRE\", since its values won't be useful for the algorithm: only the values derived from it in the engineered features you created should be retained. As a reminder, your data should only be from **the subset with few or no missing values**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If there are other re-engineering tasks you need to perform, make sure you\n",
    "# take care of them here. (Dealing with missing data will come in step 2.1.)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do whatever you need to in order to ensure that the dataframe only contains\n",
    "# the columns that should be passed to the algorithm functions.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.3: Create a Cleaning Function\n",
    "\n",
    "Even though you've finished cleaning up the general population demographics data, it's important to look ahead to the future and realize that you'll need to perform the same cleaning steps on the customer demographics data. In this substep, complete the function below to execute the main feature selection, encoding, and re-engineering steps you performed above. Then, when it comes to looking at the customer data in Step 3, you can just run this function on that DataFrame to get the trimmed dataset in a single step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AGER_TYP',\n",
       " 'GEBURTSJAHR',\n",
       " 'TITEL_KZ',\n",
       " 'ALTER_HH',\n",
       " 'KK_KUNDENTYP',\n",
       " 'KBA05_BAUMAX',\n",
       " 'CJT_GESAMTTYP',\n",
       " 'FINANZTYP',\n",
       " 'GFK_URLAUBERTYP',\n",
       " 'LP_FAMILIE_FEIN',\n",
       " 'LP_FAMILIE_GROB',\n",
       " 'LP_STATUS_FEIN',\n",
       " 'LP_STATUS_GROB',\n",
       " 'NATIONALITAET_KZ',\n",
       " 'SHOPPER_TYP',\n",
       " 'ZABEOTYP',\n",
       " 'GEBAEUDETYP',\n",
       " 'CAMEO_DEUG_2015',\n",
       " 'CAMEO_DEU_2015',\n",
       " 'LP_LEBENSPHASE_FEIN',\n",
       " 'LP_LEBENSPHASE_GROB',\n",
       " 'WOHNLAGE',\n",
       " 'KBA05_BAUMAX',\n",
       " 'PLZ8_BAUMAX']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "movement = {\n",
    "    1: [40,0],\n",
    "    2: [40,1],\n",
    "    3: [50,0],\n",
    "    4: [50,1],\n",
    "    5: [60,0],\n",
    "    6: [60,1],\n",
    "    7: [60,1],\n",
    "    8: [70,0],\n",
    "    9: [70,1],\n",
    "    10: [80,0],\n",
    "    11: [80,1],\n",
    "    12: [80,0],\n",
    "    13: [80,1],\n",
    "    14: [90,0],\n",
    "    15: [90,1]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(df):\n",
    "    \"\"\"\n",
    "    Perform feature trimming, re-encoding, and engineering for demographics\n",
    "    data\n",
    "    \n",
    "    INPUT: Demographics DataFrame\n",
    "    OUTPUT: Trimmed and cleaned demographics DataFrame\n",
    "    \"\"\"\n",
    "    \n",
    "    # Put in code here to execute all main cleaning steps:\n",
    "    # convert missing value codes into NaNs, ...\n",
    "    \n",
    "    #lstrip is to cut the '-' sign in the string\n",
    "    #isnumeric checks whether it is number or not\n",
    "    for attribute in feat_info['attribute']:\n",
    "        tmp_list = feat_info[feat_info['attribute']==attribute].missing_or_unknown.iloc[0].replace('[','').replace(']','').split(',')\n",
    "        missing_or_unknown_list = [float(x) if x.lstrip('-').isnumeric() else x for x in tmp_list]\n",
    "        df[attribute].replace(to_replace=missing_or_unknown_list,\n",
    "                      value=np.nan, inplace=True)\n",
    "\n",
    "    # remove selected columns and rows, ...    \n",
    "    \n",
    "    df.drop(drop_columns, axis='columns', inplace=True)\n",
    "    \n",
    "    # select, re-encode, and engineer column values.\n",
    "    df = pd.get_dummies(df, columns=['OST_WEST_KZ'])\n",
    "\n",
    "    df['GENERATION'] = df['PRAEGENDE_JUGENDJAHRE'].apply(lambda x: np.nan if np.isnan(x) else movement[x][0])\n",
    "\n",
    "    df['MOVEMENT'] = df['PRAEGENDE_JUGENDJAHRE'].apply(lambda x: np.nan if np.isnan(x) else movement[x][1])\n",
    "\n",
    "    df.drop('PRAEGENDE_JUGENDJAHRE', axis='columns', inplace=True)\n",
    "\n",
    "    df['CAMEO_INTL_2015_tens'] = df['CAMEO_INTL_2015'].str[0]\n",
    "    df['CAMEO_INTL_2015_ones'] = df['CAMEO_INTL_2015'].str[1]\n",
    "    df.drop('CAMEO_INTL_2015', axis=1, inplace=True)\n",
    "    \n",
    "    below_threshold = df.drop(df[df.isnull().sum(axis=1)>25].index)\n",
    "    above_threshold = df.drop(df[df.isnull().sum(axis=1)<=25].index)\n",
    "    \n",
    "    # Return the cleaned dataframe.\n",
    "    return below_threshold\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Feature Transformation\n",
    "\n",
    "### Step 2.1: Apply Feature Scaling\n",
    "\n",
    "Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. Starting from this part of the project, you'll want to keep an eye on the [API reference page for sklearn](http://scikit-learn.org/stable/modules/classes.html) to help you navigate to all of the classes and functions that you'll need. In this substep, you'll need to check the following:\n",
    "\n",
    "- sklearn requires that data not have missing values in order for its estimators to work properly. So, before applying the scaler to your data, make sure that you've cleaned the DataFrame of the remaining missing values. This can be as simple as just removing all data points with missing data, or applying an [Imputer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html) to replace all missing values. You might also try a more complicated procedure where you temporarily remove missing values in order to compute the scaling parameters before re-introducing those missing values and applying imputation. Think about how much missing data you have and what possible effects each approach might have on your analysis, and justify your decision in the discussion section below.\n",
    "- For the actual scaling function, a [StandardScaler](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) instance is suggested, scaling each feature to mean 0 and standard deviation 1.\n",
    "- For these classes, you can make use of the `.fit_transform()` method to both fit a procedure to the data as well as apply the transformation to the data at the same time. Don't forget to keep the fit sklearn objects handy, since you'll be applying them to the customer demographics data towards the end of the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias = pd.read_csv('Udacity_AZDIAS_Subset.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you've not yet cleaned the dataset of all NaN values, then investigate and\n",
    "# do that now.\n",
    "df = clean_data(azdias)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "imp = Imputer()\n",
    "imp = imp.fit(df)\n",
    "clean_df = imp.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply feature scaling to the general population demographics data.\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler = scaler.fit(clean_df)\n",
    "new_df = scaler.transform(clean_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_df = pd.DataFrame(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_df.columns = df.columns\n",
    "pca_df.index = df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "      <th>OST_WEST_KZ_O</th>\n",
       "      <th>OST_WEST_KZ_W</th>\n",
       "      <th>GENERATION</th>\n",
       "      <th>MOVEMENT</th>\n",
       "      <th>CAMEO_INTL_2015_tens</th>\n",
       "      <th>CAMEO_INTL_2015_ones</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>1.537913</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>1.465948</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>-1.277038e-01</td>\n",
       "      <td>0.684613</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>-1.766962</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>-1.277038e-01</td>\n",
       "      <td>-0.789309</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>-8.651676e-01</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-1.170875e+00</td>\n",
       "      <td>-9.982473e-01</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.320266e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-1.550667e+00</td>\n",
       "      <td>-5.902913e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>3.075680e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.320266e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>8.584706e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.170875e+00</td>\n",
       "      <td>-9.982473e-01</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>1.537913</td>\n",
       "      <td>-1.766962</td>\n",
       "      <td>1.465948</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>3.075680e-01</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-8.651676e-01</td>\n",
       "      <td>-5.902913e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-1.625594e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-1.170875e+00</td>\n",
       "      <td>-1.277038e-01</td>\n",
       "      <td>-0.789309</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.320266e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-1.550667e+00</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>0.786963</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.170875e+00</td>\n",
       "      <td>-5.629755e-01</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>-1.550667e+00</td>\n",
       "      <td>8.584706e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.170875e+00</td>\n",
       "      <td>-9.982473e-01</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>-1.550667e+00</td>\n",
       "      <td>1.438124e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>3.075680e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-1.625594e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>3.075680e-01</td>\n",
       "      <td>0.684613</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.320266e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>8.584706e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>1.465948</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>3.075680e-01</td>\n",
       "      <td>0.684613</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>-1.796679e-01</td>\n",
       "      <td>8.584706e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.054859e-16</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>1.178112e+00</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-9.308845e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>-1.766962</td>\n",
       "      <td>0.786963</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-2.174466e+00</td>\n",
       "      <td>-1.433519e+00</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>-1.766962</td>\n",
       "      <td>1.465948</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>-5.629755e-01</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-8.651676e-01</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>3.075680e-01</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.320266e-01</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>-1.796679e-01</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>-1.766962</td>\n",
       "      <td>0.786963</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.054859e-16</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>-5.629755e-01</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>-8.651676e-01</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>-1.766962</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>1.839897e+00</td>\n",
       "      <td>7.428398e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>1.438124e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>1.537913</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>1.465948</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>-1.277038e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.054859e-16</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>3.075680e-01</td>\n",
       "      <td>-0.789309</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.320266e-01</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>8.584706e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>1.537913</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>1.465948</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>-1.433519e+00</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-1.796679e-01</td>\n",
       "      <td>8.584706e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-1.625594e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.054859e-16</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>-9.982473e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.320266e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-3.044230e-16</td>\n",
       "      <td>3.002657e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>7.428398e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>-5.629755e-01</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-1.550667e+00</td>\n",
       "      <td>8.584706e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-1.170875e+00</td>\n",
       "      <td>-5.629755e-01</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-9.308845e-01</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>-1.550667e+00</td>\n",
       "      <td>-5.902913e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>1.537913</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-1.625594e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.261033e+00</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>1.178112e+00</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-1.625594e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>-9.982473e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>-1.550667e+00</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>1.178112e+00</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.320266e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.806254e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>7.428398e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742e+00</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>-8.651676e-01</td>\n",
       "      <td>1.438124e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>1.613383e+00</td>\n",
       "      <td>0.684613</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.320266e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891191</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-2.174466e+00</td>\n",
       "      <td>-9.982473e-01</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891192</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>1.537913</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>1.465948</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-1.625594e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>-1.277038e-01</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-1.796679e-01</td>\n",
       "      <td>-5.902913e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891193</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.261033e+00</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>3.075680e-01</td>\n",
       "      <td>0.684613</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-9.308845e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>8.584706e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891194</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>1.178112e+00</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>-8.651676e-01</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891195</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>7.428398e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.320266e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>1.438124e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891196</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>1.537913</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>1.465948</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>-5.629755e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-8.651676e-01</td>\n",
       "      <td>8.584706e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891197</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-1.625594e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>1.613383e+00</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>-8.651676e-01</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891198</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>1.537913</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>0.786963</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>1.613383e+00</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>-1.550667e+00</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891199</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>1.537913</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>7.428398e-01</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891200</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>1.537913</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>1.465948</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>3.075680e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>-5.902913e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891201</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>7.428398e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891202</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>-1.766962</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.261033e+00</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>1.178112e+00</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891203</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>1.178112e+00</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-9.931367e-16</td>\n",
       "      <td>2.022187e-16</td>\n",
       "      <td>-8.651676e-01</td>\n",
       "      <td>1.438124e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891204</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>0.786963</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>7.428398e-01</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-8.651676e-01</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891205</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.261033e+00</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>7.428398e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-1.796679e-01</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891206</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>0.786963</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>-5.629755e-01</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-8.651676e-01</td>\n",
       "      <td>1.438124e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891207</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-1.625594e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.261033e+00</td>\n",
       "      <td>-2.174466e+00</td>\n",
       "      <td>-1.277038e-01</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.328600e+00</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891208</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-1.625594e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>-1.433519e+00</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.320266e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-1.550667e+00</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891209</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>-1.277038e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891210</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>1.613383e+00</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.320266e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891211</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.261033e+00</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>-1.277038e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.320266e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891212</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-1.625594e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.174106e+00</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>1.613383e+00</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891213</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>1.465948</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-1.625594e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>-5.629755e-01</td>\n",
       "      <td>0.684613</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>1.875412e+00</td>\n",
       "      <td>-1.796679e-01</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891214</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>1.537913</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.261033e+00</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>7.428398e-01</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-8.651676e-01</td>\n",
       "      <td>8.584706e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891215</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>1.537913</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>0.786963</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-1.170875e+00</td>\n",
       "      <td>-1.277038e-01</td>\n",
       "      <td>-0.789309</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-1.796679e-01</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891216</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>1.465948</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>1.337051e-15</td>\n",
       "      <td>-3.865990e-16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891217</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.806254e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>3.075680e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>4.668312e-01</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891218</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>1.465948</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-1.625594e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-1.170875e+00</td>\n",
       "      <td>-1.433519e+00</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>-8.651676e-01</td>\n",
       "      <td>7.619854e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891219</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-1.494591</td>\n",
       "      <td>1.537913</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>1.465948</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-1.625594e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.174106e+00</td>\n",
       "      <td>8.363061e-01</td>\n",
       "      <td>7.428398e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>1.191332e+00</td>\n",
       "      <td>-1.266430e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891220</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-2.698206e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-1.672845e-01</td>\n",
       "      <td>-5.629755e-01</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742e+00</td>\n",
       "      <td>-5.531527e-01</td>\n",
       "      <td>5.058318e-01</td>\n",
       "      <td>8.584706e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>798073 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "1                  -1.766074   0.957905          -1.494591       1.537913   \n",
       "2                   0.201243   0.957905          -1.494591       0.864553   \n",
       "3                   1.184901   0.957905           0.683151      -0.482167   \n",
       "4                   0.201243  -1.043944           0.683151       0.191193   \n",
       "5                  -1.766074   0.957905          -0.042763      -1.155526   \n",
       "6                  -0.782416   0.957905          -1.494591       1.537913   \n",
       "7                  -1.766074  -1.043944          -0.042763       0.191193   \n",
       "8                   0.201243  -1.043944           0.683151       0.864553   \n",
       "9                   0.201243   0.957905          -0.768677       0.864553   \n",
       "10                  0.201243   0.957905          -0.768677      -0.482167   \n",
       "12                  0.201243  -1.043944           1.409065       0.191193   \n",
       "13                 -1.766074   0.957905          -1.494591       0.864553   \n",
       "15                  1.184901   0.957905           0.683151      -1.155526   \n",
       "16                 -1.766074   0.957905           0.683151       0.191193   \n",
       "18                 -0.782416   0.957905          -0.768677       0.864553   \n",
       "19                  0.201243  -1.043944           1.409065      -0.482167   \n",
       "20                 -0.782416   0.957905           0.683151       0.191193   \n",
       "21                 -0.782416  -1.043944          -0.042763       0.864553   \n",
       "22                 -1.766074  -1.043944          -1.494591       1.537913   \n",
       "23                  0.201243  -1.043944           1.409065       0.191193   \n",
       "25                 -1.766074  -1.043944          -0.042763       1.537913   \n",
       "26                  0.201243  -1.043944           1.409065      -0.482167   \n",
       "27                  0.201243  -1.043944          -0.042763       0.191193   \n",
       "28                  0.201243  -1.043944          -0.042763      -0.482167   \n",
       "29                  1.184901   0.957905           1.409065      -1.155526   \n",
       "31                 -0.782416   0.957905          -1.494591       1.537913   \n",
       "32                 -1.766074  -1.043944           1.409065       0.864553   \n",
       "33                 -0.782416   0.957905          -0.768677      -0.482167   \n",
       "34                 -1.766074   0.957905           0.683151      -1.155526   \n",
       "36                  0.201243   0.957905          -0.768677       0.191193   \n",
       "...                      ...        ...                ...            ...   \n",
       "891191              1.184901   0.957905           0.683151      -1.155526   \n",
       "891192             -1.766074   0.957905          -1.494591       1.537913   \n",
       "891193              1.184901  -1.043944           0.683151      -1.155526   \n",
       "891194              0.201243  -1.043944           0.683151       0.191193   \n",
       "891195              1.184901   0.957905          -0.042763      -1.155526   \n",
       "891196             -0.782416   0.957905          -1.494591       1.537913   \n",
       "891197              0.201243   0.957905          -0.042763      -0.482167   \n",
       "891198              0.201243  -1.043944          -0.768677       1.537913   \n",
       "891199             -0.782416  -1.043944          -0.768677       1.537913   \n",
       "891200             -1.766074   0.957905          -1.494591       1.537913   \n",
       "891201              0.201243  -1.043944           0.683151      -0.482167   \n",
       "891202             -0.782416   0.957905          -1.494591       0.864553   \n",
       "891203              1.184901   0.957905           0.683151      -1.155526   \n",
       "891204              0.201243  -1.043944           0.683151       0.191193   \n",
       "891205              1.184901  -1.043944           0.683151      -0.482167   \n",
       "891206             -1.766074   0.957905          -0.042763       0.864553   \n",
       "891207              0.201243   0.957905           1.409065      -1.155526   \n",
       "891208              1.184901  -1.043944           1.409065      -0.482167   \n",
       "891209             -1.766074   0.957905          -1.494591       0.864553   \n",
       "891210              0.201243  -1.043944          -0.042763       0.191193   \n",
       "891211              0.201243  -1.043944          -0.042763      -0.482167   \n",
       "891212              1.184901  -1.043944          -0.042763      -1.155526   \n",
       "891213              1.184901   0.957905          -0.042763       0.191193   \n",
       "891214             -1.766074   0.957905          -1.494591       1.537913   \n",
       "891215             -0.782416   0.957905          -1.494591       1.537913   \n",
       "891216              0.201243   0.957905          -1.494591       0.864553   \n",
       "891217             -0.782416  -1.043944          -0.042763       0.191193   \n",
       "891218             -0.782416   0.957905          -0.768677       0.864553   \n",
       "891219             -1.766074  -1.043944          -1.494591       1.537913   \n",
       "891220              1.184901  -1.043944           0.683151      -0.482167   \n",
       "\n",
       "        FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  \\\n",
       "1              -1.040677        1.465948               0.958614   \n",
       "2              -1.766962       -0.571007               0.244095   \n",
       "3               1.138179       -0.571007              -1.184943   \n",
       "4               0.411893       -1.249992               0.244095   \n",
       "5               1.138179       -0.571007              -0.470424   \n",
       "6              -1.766962        1.465948               0.958614   \n",
       "7               0.411893       -1.249992               0.244095   \n",
       "8              -1.040677        0.786963              -0.470424   \n",
       "9              -1.040677        0.107978               1.673133   \n",
       "10              1.138179        0.107978              -1.184943   \n",
       "12              0.411893       -0.571007               0.958614   \n",
       "13             -0.314392        1.465948               1.673133   \n",
       "15              1.138179       -1.249992              -1.184943   \n",
       "16             -1.766962        0.786963               1.673133   \n",
       "18             -1.766962        1.465948               0.958614   \n",
       "19             -0.314392       -1.249992               0.244095   \n",
       "20             -1.766962        0.786963               1.673133   \n",
       "21             -1.766962       -0.571007               1.673133   \n",
       "22             -0.314392        1.465948               1.673133   \n",
       "23             -0.314392       -0.571007              -0.470424   \n",
       "25             -0.314392        1.465948               0.958614   \n",
       "26              0.411893       -0.571007               0.244095   \n",
       "27              0.411893       -1.249992              -0.470424   \n",
       "28              0.411893        0.107978               0.244095   \n",
       "29              1.138179       -0.571007              -1.184943   \n",
       "31             -1.040677        0.107978               1.673133   \n",
       "32             -1.040677        0.107978               0.958614   \n",
       "33              0.411893       -1.249992               0.244095   \n",
       "34              1.138179       -1.249992              -0.470424   \n",
       "36              0.411893        0.107978              -0.470424   \n",
       "...                  ...             ...                    ...   \n",
       "891191          1.138179       -1.249992              -1.184943   \n",
       "891192         -1.040677        1.465948               0.244095   \n",
       "891193          1.138179       -1.249992              -0.470424   \n",
       "891194          0.411893       -0.571007               0.244095   \n",
       "891195          1.138179       -1.249992              -1.184943   \n",
       "891196         -1.040677        1.465948               0.958614   \n",
       "891197          0.411893       -1.249992               0.244095   \n",
       "891198         -0.314392        0.786963               1.673133   \n",
       "891199         -1.040677        0.107978               0.244095   \n",
       "891200         -0.314392        1.465948               1.673133   \n",
       "891201         -0.314392       -0.571007              -0.470424   \n",
       "891202         -1.766962       -0.571007               1.673133   \n",
       "891203          1.138179       -1.249992               0.244095   \n",
       "891204          0.411893        0.786963              -0.470424   \n",
       "891205          0.411893        0.107978               0.244095   \n",
       "891206         -1.040677        0.786963               0.958614   \n",
       "891207          1.138179       -1.249992              -0.470424   \n",
       "891208          0.411893       -0.571007               0.244095   \n",
       "891209         -1.040677        0.107978               0.958614   \n",
       "891210          0.411893       -0.571007              -0.470424   \n",
       "891211          0.411893        0.107978               0.244095   \n",
       "891212          1.138179       -1.249992              -1.184943   \n",
       "891213         -0.314392        1.465948               0.244095   \n",
       "891214         -1.040677        0.107978               0.244095   \n",
       "891215         -1.040677        0.786963               1.673133   \n",
       "891216         -1.040677        1.465948               0.958614   \n",
       "891217         -0.314392       -0.571007              -0.470424   \n",
       "891218         -1.040677        1.465948               0.958614   \n",
       "891219         -0.314392        1.465948               1.673133   \n",
       "891220          1.138179       -0.571007              -1.184943   \n",
       "\n",
       "        FINANZ_HAUSBAUER  GREEN_AVANTGARDE    HEALTH_TYP  \\\n",
       "1               1.339325         -0.530404  1.085953e+00   \n",
       "2               1.339325          1.885354  1.085953e+00   \n",
       "3              -0.791195         -0.530404 -2.698206e-01   \n",
       "4              -0.791195         -0.530404  1.085953e+00   \n",
       "5               1.339325         -0.530404  1.085953e+00   \n",
       "6              -0.081022         -0.530404 -2.698206e-01   \n",
       "7              -0.791195         -0.530404 -1.625594e+00   \n",
       "8              -0.791195          1.885354  1.085953e+00   \n",
       "9               0.629152          1.885354 -2.698206e-01   \n",
       "10              1.339325         -0.530404 -2.698206e-01   \n",
       "12             -1.501369         -0.530404 -1.625594e+00   \n",
       "13             -0.791195          1.885354  1.085953e+00   \n",
       "15              0.629152         -0.530404 -2.698206e-01   \n",
       "16             -1.501369         -0.530404  1.085953e+00   \n",
       "18             -1.501369         -0.530404 -2.698206e-01   \n",
       "19             -1.501369          1.885354  1.085953e+00   \n",
       "20             -1.501369          1.885354  1.085953e+00   \n",
       "21             -1.501369         -0.530404 -2.698206e-01   \n",
       "22              1.339325         -0.530404 -2.698206e-01   \n",
       "23             -1.501369          1.885354  1.085953e+00   \n",
       "25             -0.081022         -0.530404 -2.698206e-01   \n",
       "26             -1.501369         -0.530404 -1.625594e+00   \n",
       "27             -0.791195          1.885354  1.085953e+00   \n",
       "28             -0.791195         -0.530404  1.085953e+00   \n",
       "29             -0.081022          1.885354 -2.698206e-01   \n",
       "31              0.629152         -0.530404 -1.625594e+00   \n",
       "32             -1.501369          1.885354 -1.625594e+00   \n",
       "33              0.629152         -0.530404 -2.698206e-01   \n",
       "34             -0.081022          1.885354  1.806254e-15   \n",
       "36             -0.081022         -0.530404 -2.698206e-01   \n",
       "...                  ...               ...           ...   \n",
       "891191          0.629152         -0.530404 -2.698206e-01   \n",
       "891192          1.339325         -0.530404 -1.625594e+00   \n",
       "891193          1.339325         -0.530404 -2.698206e-01   \n",
       "891194         -1.501369          1.885354  1.085953e+00   \n",
       "891195          1.339325         -0.530404 -2.698206e-01   \n",
       "891196         -0.081022         -0.530404 -2.698206e-01   \n",
       "891197         -0.081022          1.885354 -1.625594e+00   \n",
       "891198         -0.081022          1.885354  1.085953e+00   \n",
       "891199          0.629152         -0.530404  1.085953e+00   \n",
       "891200          1.339325         -0.530404  1.085953e+00   \n",
       "891201         -0.081022         -0.530404  1.085953e+00   \n",
       "891202          0.629152         -0.530404 -2.698206e-01   \n",
       "891203          0.629152         -0.530404 -2.698206e-01   \n",
       "891204         -1.501369         -0.530404  1.085953e+00   \n",
       "891205          0.629152         -0.530404 -2.698206e-01   \n",
       "891206         -0.791195         -0.530404  1.085953e+00   \n",
       "891207          0.629152          1.885354 -1.625594e+00   \n",
       "891208         -1.501369         -0.530404 -1.625594e+00   \n",
       "891209          0.629152         -0.530404  1.085953e+00   \n",
       "891210         -0.791195         -0.530404  1.085953e+00   \n",
       "891211         -0.791195         -0.530404  1.085953e+00   \n",
       "891212          1.339325         -0.530404 -1.625594e+00   \n",
       "891213         -0.791195          1.885354 -1.625594e+00   \n",
       "891214          0.629152         -0.530404  1.085953e+00   \n",
       "891215          0.629152         -0.530404 -2.698206e-01   \n",
       "891216          0.629152         -0.530404  1.085953e+00   \n",
       "891217         -0.081022         -0.530404  1.806254e-15   \n",
       "891218         -0.081022         -0.530404 -1.625594e+00   \n",
       "891219          1.339325         -0.530404 -1.625594e+00   \n",
       "891220          1.339325         -0.530404 -2.698206e-01   \n",
       "\n",
       "                ...               PLZ8_GBZ        ARBEIT   ORTSGR_KLS9  \\\n",
       "1               ...           5.651132e-01 -1.672845e-01 -1.277038e-01   \n",
       "2               ...           5.651132e-01 -1.672845e-01 -1.277038e-01   \n",
       "3               ...           5.651132e-01 -1.170875e+00 -9.982473e-01   \n",
       "4               ...          -3.479598e-01  8.363061e-01  3.075680e-01   \n",
       "5               ...           1.478186e+00 -1.170875e+00 -9.982473e-01   \n",
       "6               ...           1.478186e+00  8.363061e-01  3.075680e-01   \n",
       "7               ...           5.651132e-01 -1.170875e+00 -1.277038e-01   \n",
       "8               ...          -3.479598e-01 -1.170875e+00 -5.629755e-01   \n",
       "9               ...          -3.479598e-01 -1.170875e+00 -9.982473e-01   \n",
       "10              ...          -3.479598e-01  8.363061e-01  3.075680e-01   \n",
       "12              ...           1.478186e+00 -1.672845e-01  3.075680e-01   \n",
       "13              ...          -3.479598e-01 -1.672845e-01  3.075680e-01   \n",
       "15              ...          -4.054859e-16  8.363061e-01  1.178112e+00   \n",
       "16              ...           5.651132e-01 -2.174466e+00 -1.433519e+00   \n",
       "18              ...          -3.479598e-01 -1.672845e-01 -5.629755e-01   \n",
       "19              ...           5.651132e-01  8.363061e-01  3.075680e-01   \n",
       "20              ...          -4.054859e-16 -1.672845e-01 -5.629755e-01   \n",
       "21              ...          -3.479598e-01  1.839897e+00  7.428398e-01   \n",
       "22              ...          -3.479598e-01  8.363061e-01 -1.277038e-01   \n",
       "23              ...          -4.054859e-16 -1.672845e-01  3.075680e-01   \n",
       "25              ...           1.478186e+00 -1.672845e-01 -1.433519e+00   \n",
       "26              ...          -4.054859e-16  8.363061e-01 -9.982473e-01   \n",
       "27              ...          -3.479598e-01  8.363061e-01  7.428398e-01   \n",
       "28              ...           1.478186e+00 -1.672845e-01 -5.629755e-01   \n",
       "29              ...           5.651132e-01 -1.170875e+00 -5.629755e-01   \n",
       "31              ...          -1.261033e+00  8.363061e-01  1.178112e+00   \n",
       "32              ...           5.651132e-01  8.363061e-01 -9.982473e-01   \n",
       "33              ...          -3.479598e-01 -1.672845e-01  1.178112e+00   \n",
       "34              ...          -3.479598e-01  8.363061e-01  7.428398e-01   \n",
       "36              ...          -3.479598e-01  8.363061e-01  1.613383e+00   \n",
       "...             ...                    ...           ...           ...   \n",
       "891191          ...           1.478186e+00 -2.174466e+00 -9.982473e-01   \n",
       "891192          ...          -3.479598e-01 -1.672845e-01 -1.277038e-01   \n",
       "891193          ...          -1.261033e+00  8.363061e-01  3.075680e-01   \n",
       "891194          ...           5.651132e-01  8.363061e-01  1.178112e+00   \n",
       "891195          ...          -3.479598e-01  8.363061e-01  7.428398e-01   \n",
       "891196          ...          -3.479598e-01 -1.672845e-01 -5.629755e-01   \n",
       "891197          ...           5.651132e-01 -1.672845e-01  1.613383e+00   \n",
       "891198          ...           1.478186e+00 -1.672845e-01  1.613383e+00   \n",
       "891199          ...          -3.479598e-01 -1.672845e-01  7.428398e-01   \n",
       "891200          ...          -3.479598e-01  8.363061e-01  3.075680e-01   \n",
       "891201          ...          -3.479598e-01 -1.672845e-01  7.428398e-01   \n",
       "891202          ...          -1.261033e+00  8.363061e-01  1.178112e+00   \n",
       "891203          ...          -3.479598e-01  8.363061e-01  1.178112e+00   \n",
       "891204          ...           5.651132e-01  8.363061e-01  7.428398e-01   \n",
       "891205          ...          -1.261033e+00 -1.672845e-01  7.428398e-01   \n",
       "891206          ...           1.478186e+00 -1.672845e-01 -5.629755e-01   \n",
       "891207          ...          -1.261033e+00 -2.174466e+00 -1.277038e-01   \n",
       "891208          ...          -3.479598e-01  8.363061e-01 -1.433519e+00   \n",
       "891209          ...           5.651132e-01  8.363061e-01 -1.277038e-01   \n",
       "891210          ...          -3.479598e-01 -1.672845e-01  1.613383e+00   \n",
       "891211          ...          -1.261033e+00 -1.672845e-01 -1.277038e-01   \n",
       "891212          ...          -2.174106e+00 -1.672845e-01  1.613383e+00   \n",
       "891213          ...           1.478186e+00 -1.672845e-01 -5.629755e-01   \n",
       "891214          ...          -1.261033e+00 -1.672845e-01  7.428398e-01   \n",
       "891215          ...           5.651132e-01 -1.170875e+00 -1.277038e-01   \n",
       "891216          ...          -3.479598e-01  1.337051e-15 -3.865990e-16   \n",
       "891217          ...          -3.479598e-01  8.363061e-01  3.075680e-01   \n",
       "891218          ...           5.651132e-01 -1.170875e+00 -1.433519e+00   \n",
       "891219          ...          -2.174106e+00  8.363061e-01  7.428398e-01   \n",
       "891220          ...           5.651132e-01 -1.672845e-01 -5.629755e-01   \n",
       "\n",
       "        RELAT_AB  OST_WEST_KZ_O  OST_WEST_KZ_W    GENERATION      MOVEMENT  \\\n",
       "1       0.684613      -0.517429       0.517429  1.165689e+00 -5.531527e-01   \n",
       "2      -0.789309      -0.517429       0.517429  1.165689e+00  1.875412e+00   \n",
       "3      -0.052348      -0.517429       0.517429 -2.320266e-01 -5.531527e-01   \n",
       "4       1.421574      -0.517429       0.517429 -2.320266e-01 -5.531527e-01   \n",
       "5      -0.052348      -0.517429       0.517429 -1.629742e+00 -5.531527e-01   \n",
       "6      -0.052348      -0.517429       0.517429  4.668312e-01 -5.531527e-01   \n",
       "7      -0.789309      -0.517429       0.517429 -2.320266e-01 -5.531527e-01   \n",
       "8      -0.052348      -0.517429       0.517429  4.668312e-01  1.875412e+00   \n",
       "9      -1.526271      -0.517429       0.517429  1.165689e+00  1.875412e+00   \n",
       "10      1.421574      -0.517429       0.517429 -1.629742e+00 -5.531527e-01   \n",
       "12      0.684613      -0.517429       0.517429 -2.320266e-01 -5.531527e-01   \n",
       "13      0.684613      -0.517429       0.517429  1.165689e+00  1.875412e+00   \n",
       "15      1.421574      -0.517429       0.517429 -9.308845e-01 -5.531527e-01   \n",
       "16     -1.526271      -0.517429       0.517429  1.165689e+00 -5.531527e-01   \n",
       "18     -0.052348      -0.517429       0.517429  4.668312e-01 -5.531527e-01   \n",
       "19     -0.052348      -0.517429       0.517429 -2.320266e-01  1.875412e+00   \n",
       "20     -1.526271      -0.517429       0.517429  4.668312e-01  1.875412e+00   \n",
       "21      1.421574      -0.517429       0.517429  4.668312e-01 -5.531527e-01   \n",
       "22      1.421574      -0.517429       0.517429  1.165689e+00 -5.531527e-01   \n",
       "23     -0.789309      -0.517429       0.517429 -2.320266e-01  1.875412e+00   \n",
       "25      1.421574      -0.517429       0.517429  1.165689e+00 -5.531527e-01   \n",
       "26      1.421574      -0.517429       0.517429 -2.320266e-01 -5.531527e-01   \n",
       "27      1.421574      -0.517429       0.517429  4.668312e-01  1.875412e+00   \n",
       "28     -1.526271      -0.517429       0.517429  4.668312e-01 -5.531527e-01   \n",
       "29     -0.052348      -0.517429       0.517429 -9.308845e-01  1.875412e+00   \n",
       "31      1.421574      -0.517429       0.517429  4.668312e-01 -5.531527e-01   \n",
       "32      1.421574      -0.517429       0.517429  1.165689e+00  1.875412e+00   \n",
       "33      1.421574      -0.517429       0.517429 -2.320266e-01 -5.531527e-01   \n",
       "34      1.421574      -0.517429       0.517429 -1.629742e+00  1.875412e+00   \n",
       "36      0.684613      -0.517429       0.517429 -2.320266e-01 -5.531527e-01   \n",
       "...          ...            ...            ...           ...           ...   \n",
       "891191 -1.526271      -0.517429       0.517429 -1.629742e+00 -5.531527e-01   \n",
       "891192 -1.526271      -0.517429       0.517429  1.165689e+00 -5.531527e-01   \n",
       "891193  0.684613      -0.517429       0.517429 -9.308845e-01 -5.531527e-01   \n",
       "891194  1.421574      -0.517429       0.517429  4.668312e-01  1.875412e+00   \n",
       "891195  1.421574      -0.517429       0.517429 -2.320266e-01 -5.531527e-01   \n",
       "891196  1.421574      -0.517429       0.517429  1.165689e+00 -5.531527e-01   \n",
       "891197  1.421574      -0.517429       0.517429  4.668312e-01  1.875412e+00   \n",
       "891198  1.421574      -0.517429       0.517429  1.165689e+00  1.875412e+00   \n",
       "891199 -0.052348      -0.517429       0.517429  4.668312e-01 -5.531527e-01   \n",
       "891200  1.421574      -0.517429       0.517429  1.165689e+00 -5.531527e-01   \n",
       "891201  1.421574      -0.517429       0.517429  4.668312e-01 -5.531527e-01   \n",
       "891202  1.421574      -0.517429       0.517429  4.668312e-01 -5.531527e-01   \n",
       "891203  1.421574      -0.517429       0.517429 -9.931367e-16  2.022187e-16   \n",
       "891204 -0.052348      -0.517429       0.517429  4.668312e-01 -5.531527e-01   \n",
       "891205  1.421574      -0.517429       0.517429 -1.629742e+00 -5.531527e-01   \n",
       "891206 -0.052348      -0.517429       0.517429  1.165689e+00 -5.531527e-01   \n",
       "891207 -1.526271      -0.517429       0.517429 -2.328600e+00  1.875412e+00   \n",
       "891208  1.421574      -0.517429       0.517429 -2.320266e-01 -5.531527e-01   \n",
       "891209  1.421574      -0.517429       0.517429  1.165689e+00 -5.531527e-01   \n",
       "891210  1.421574      -0.517429       0.517429 -2.320266e-01 -5.531527e-01   \n",
       "891211  1.421574      -0.517429       0.517429 -2.320266e-01 -5.531527e-01   \n",
       "891212  1.421574      -0.517429       0.517429 -1.629742e+00 -5.531527e-01   \n",
       "891213  0.684613      -0.517429       0.517429  4.668312e-01  1.875412e+00   \n",
       "891214 -0.052348      -0.517429       0.517429  1.165689e+00 -5.531527e-01   \n",
       "891215 -0.789309      -0.517429       0.517429  4.668312e-01 -5.531527e-01   \n",
       "891216  0.000000      -0.517429       0.517429  1.165689e+00 -5.531527e-01   \n",
       "891217  1.421574      -0.517429       0.517429  4.668312e-01 -5.531527e-01   \n",
       "891218 -0.052348      -0.517429       0.517429  1.165689e+00 -5.531527e-01   \n",
       "891219  1.421574      -0.517429       0.517429  1.165689e+00 -5.531527e-01   \n",
       "891220  1.421574      -0.517429       0.517429 -1.629742e+00 -5.531527e-01   \n",
       "\n",
       "        CAMEO_INTL_2015_tens  CAMEO_INTL_2015_ones  \n",
       "1               1.191332e+00         -1.266430e+00  \n",
       "2              -8.651676e-01          7.619854e-01  \n",
       "3              -1.550667e+00         -5.902913e-01  \n",
       "4               5.058318e-01          8.584706e-02  \n",
       "5               1.191332e+00          7.619854e-01  \n",
       "6              -8.651676e-01         -5.902913e-01  \n",
       "7              -1.550667e+00          7.619854e-01  \n",
       "8              -1.550667e+00          8.584706e-02  \n",
       "9              -1.550667e+00          1.438124e+00  \n",
       "10              1.191332e+00         -1.266430e+00  \n",
       "12              5.058318e-01          8.584706e-02  \n",
       "13             -1.796679e-01          8.584706e-02  \n",
       "15              5.058318e-01         -1.266430e+00  \n",
       "16              5.058318e-01         -1.266430e+00  \n",
       "18             -8.651676e-01          7.619854e-01  \n",
       "19             -1.796679e-01          7.619854e-01  \n",
       "20             -8.651676e-01          7.619854e-01  \n",
       "21              1.191332e+00          1.438124e+00  \n",
       "22              1.191332e+00         -1.266430e+00  \n",
       "23              5.058318e-01          8.584706e-02  \n",
       "25             -1.796679e-01          8.584706e-02  \n",
       "26             -3.044230e-16          3.002657e-16  \n",
       "27              1.191332e+00         -1.266430e+00  \n",
       "28             -1.550667e+00          8.584706e-02  \n",
       "29             -1.550667e+00         -5.902913e-01  \n",
       "31              1.191332e+00         -1.266430e+00  \n",
       "32             -1.550667e+00          7.619854e-01  \n",
       "33              5.058318e-01         -1.266430e+00  \n",
       "34             -8.651676e-01          1.438124e+00  \n",
       "36              5.058318e-01         -1.266430e+00  \n",
       "...                      ...                   ...  \n",
       "891191          5.058318e-01          7.619854e-01  \n",
       "891192         -1.796679e-01         -5.902913e-01  \n",
       "891193          5.058318e-01          8.584706e-02  \n",
       "891194         -8.651676e-01          7.619854e-01  \n",
       "891195          1.191332e+00          1.438124e+00  \n",
       "891196         -8.651676e-01          8.584706e-02  \n",
       "891197         -8.651676e-01          7.619854e-01  \n",
       "891198         -1.550667e+00          7.619854e-01  \n",
       "891199          1.191332e+00         -1.266430e+00  \n",
       "891200          1.191332e+00         -5.902913e-01  \n",
       "891201          5.058318e-01         -1.266430e+00  \n",
       "891202          5.058318e-01         -1.266430e+00  \n",
       "891203         -8.651676e-01          1.438124e+00  \n",
       "891204         -8.651676e-01          7.619854e-01  \n",
       "891205         -1.796679e-01          7.619854e-01  \n",
       "891206         -8.651676e-01          1.438124e+00  \n",
       "891207          5.058318e-01         -1.266430e+00  \n",
       "891208         -1.550667e+00          7.619854e-01  \n",
       "891209          1.191332e+00         -1.266430e+00  \n",
       "891210          5.058318e-01         -1.266430e+00  \n",
       "891211          1.191332e+00         -1.266430e+00  \n",
       "891212          1.191332e+00         -1.266430e+00  \n",
       "891213         -1.796679e-01          7.619854e-01  \n",
       "891214         -8.651676e-01          8.584706e-02  \n",
       "891215         -1.796679e-01         -1.266430e+00  \n",
       "891216          5.058318e-01         -1.266430e+00  \n",
       "891217          1.191332e+00         -1.266430e+00  \n",
       "891218         -8.651676e-01          7.619854e-01  \n",
       "891219          1.191332e+00         -1.266430e+00  \n",
       "891220          5.058318e-01          8.584706e-02  \n",
       "\n",
       "[798073 rows x 65 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scaling(df):\n",
    "    # use existing object imp\n",
    "    clean_df = imp.transform(df)\n",
    "    #use existing object scaler\n",
    "    new_df = scaler.transform(clean_df)\n",
    "    pca_df = pd.DataFrame(new_df)\n",
    "    pca_df.columns = df.columns\n",
    "    pca_df.index = df.index\n",
    "    return pca_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.1: Apply Feature Scaling\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your decisions regarding feature scaling.)\n",
    "I decided to use imputer to fill in all missing data\n",
    "and use standardscaler to feature scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "On your scaled data, you are now ready to apply dimensionality reduction techniques.\n",
    "\n",
    "- Use sklearn's [PCA](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).\n",
    "- Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's [`plot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html) function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.\n",
    "- Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply PCA to the data.\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=35)\n",
    "pca_X = pca.fit_transform(new_df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=35, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pca_results(full_dataset, pca):\n",
    "\t'''\n",
    "\tCreate a DataFrame of the PCA results\n",
    "\tIncludes dimension feature weights and explained variance\n",
    "\tVisualizes the PCA results\n",
    "\t'''\n",
    "\n",
    "\t# Dimension indexing\n",
    "\tdimensions = dimensions = ['Dimension {}'.format(i) for i in range(1,len(pca.components_)+1)]\n",
    "\n",
    "\t# PCA components\n",
    "\tcomponents = pd.DataFrame(np.round(pca.components_, 4), columns = full_dataset.keys())\n",
    "\tcomponents.index = dimensions\n",
    "\n",
    "\t# PCA explained variance\n",
    "\tratios = pca.explained_variance_ratio_.reshape(len(pca.components_), 1)\n",
    "\tvariance_ratios = pd.DataFrame(np.round(ratios, 4), columns = ['Explained Variance'])\n",
    "\tvariance_ratios.index = dimensions\n",
    "\n",
    "\t# Create a bar plot visualization\n",
    "\tfig, ax = plt.subplots(figsize = (14,8))\n",
    "\n",
    "\t# Plot the feature weights as a function of the components\n",
    "\tcomponents.plot(ax = ax, kind = 'bar');\n",
    "\tax.set_ylabel(\"Feature Weights\")\n",
    "\tax.set_xticklabels(dimensions, rotation=0)\n",
    "\n",
    "\n",
    "\t# Display the explained variance ratios\n",
    "\tfor i, ev in enumerate(pca.explained_variance_ratio_):\n",
    "\t\tax.text(i-0.40, ax.get_ylim()[1] + 0.05, \"Explained Variance\\n          %.4f\"%(ev))\n",
    "\n",
    "\t# Return a concatenated DataFrame\n",
    "\treturn pd.concat([variance_ratios, components], axis = 1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Explained Variance</th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "      <th>OST_WEST_KZ_O</th>\n",
       "      <th>OST_WEST_KZ_W</th>\n",
       "      <th>GENERATION</th>\n",
       "      <th>MOVEMENT</th>\n",
       "      <th>CAMEO_INTL_2015_tens</th>\n",
       "      <th>CAMEO_INTL_2015_ones</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dimension 1</th>\n",
       "      <td>0.1661</td>\n",
       "      <td>-0.1135</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>-0.2171</td>\n",
       "      <td>0.1407</td>\n",
       "      <td>-0.1097</td>\n",
       "      <td>0.0657</td>\n",
       "      <td>0.0669</td>\n",
       "      <td>0.1647</td>\n",
       "      <td>-0.1100</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1700</td>\n",
       "      <td>0.1466</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.1359</td>\n",
       "      <td>0.0605</td>\n",
       "      <td>-0.0605</td>\n",
       "      <td>0.0955</td>\n",
       "      <td>-0.1078</td>\n",
       "      <td>0.2084</td>\n",
       "      <td>-0.1245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 2</th>\n",
       "      <td>0.1325</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.0891</td>\n",
       "      <td>0.0713</td>\n",
       "      <td>-0.2388</td>\n",
       "      <td>0.2346</td>\n",
       "      <td>-0.2052</td>\n",
       "      <td>-0.2301</td>\n",
       "      <td>0.1144</td>\n",
       "      <td>-0.0137</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0678</td>\n",
       "      <td>0.0651</td>\n",
       "      <td>0.0846</td>\n",
       "      <td>0.0611</td>\n",
       "      <td>0.0281</td>\n",
       "      <td>-0.0281</td>\n",
       "      <td>-0.2533</td>\n",
       "      <td>-0.0118</td>\n",
       "      <td>0.0698</td>\n",
       "      <td>0.0051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 3</th>\n",
       "      <td>0.0933</td>\n",
       "      <td>0.0786</td>\n",
       "      <td>-0.3688</td>\n",
       "      <td>0.1564</td>\n",
       "      <td>-0.1061</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>-0.1877</td>\n",
       "      <td>-0.1004</td>\n",
       "      <td>-0.0419</td>\n",
       "      <td>0.0465</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0385</td>\n",
       "      <td>0.0354</td>\n",
       "      <td>0.0447</td>\n",
       "      <td>0.0306</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>-0.0191</td>\n",
       "      <td>-0.1074</td>\n",
       "      <td>0.0445</td>\n",
       "      <td>0.0270</td>\n",
       "      <td>-0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 4</th>\n",
       "      <td>0.0530</td>\n",
       "      <td>-0.0395</td>\n",
       "      <td>0.0299</td>\n",
       "      <td>0.0530</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>-0.1366</td>\n",
       "      <td>0.0981</td>\n",
       "      <td>-0.1062</td>\n",
       "      <td>0.3776</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0660</td>\n",
       "      <td>0.0156</td>\n",
       "      <td>0.2435</td>\n",
       "      <td>0.0952</td>\n",
       "      <td>-0.1996</td>\n",
       "      <td>0.1996</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>0.3775</td>\n",
       "      <td>-0.1106</td>\n",
       "      <td>0.0391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 5</th>\n",
       "      <td>0.0408</td>\n",
       "      <td>-0.0098</td>\n",
       "      <td>0.0237</td>\n",
       "      <td>0.0847</td>\n",
       "      <td>-0.0123</td>\n",
       "      <td>-0.1044</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>-0.0716</td>\n",
       "      <td>-0.0481</td>\n",
       "      <td>0.1424</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3162</td>\n",
       "      <td>0.2428</td>\n",
       "      <td>0.0953</td>\n",
       "      <td>0.0911</td>\n",
       "      <td>0.3956</td>\n",
       "      <td>-0.3956</td>\n",
       "      <td>0.0302</td>\n",
       "      <td>0.1383</td>\n",
       "      <td>-0.0553</td>\n",
       "      <td>0.0815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 6</th>\n",
       "      <td>0.0325</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0243</td>\n",
       "      <td>-0.0546</td>\n",
       "      <td>-0.0023</td>\n",
       "      <td>-0.0506</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>-0.0420</td>\n",
       "      <td>0.1061</td>\n",
       "      <td>0.0226</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1176</td>\n",
       "      <td>-0.1331</td>\n",
       "      <td>-0.1069</td>\n",
       "      <td>-0.2330</td>\n",
       "      <td>0.2460</td>\n",
       "      <td>-0.2460</td>\n",
       "      <td>-0.0076</td>\n",
       "      <td>0.0258</td>\n",
       "      <td>-0.0712</td>\n",
       "      <td>0.0848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 7</th>\n",
       "      <td>0.0292</td>\n",
       "      <td>-0.0018</td>\n",
       "      <td>-0.0236</td>\n",
       "      <td>0.0268</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>0.0401</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>0.0528</td>\n",
       "      <td>-0.1365</td>\n",
       "      <td>-0.0284</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1529</td>\n",
       "      <td>-0.1821</td>\n",
       "      <td>0.0396</td>\n",
       "      <td>-0.0620</td>\n",
       "      <td>-0.2740</td>\n",
       "      <td>0.2740</td>\n",
       "      <td>0.0491</td>\n",
       "      <td>-0.0269</td>\n",
       "      <td>-0.0195</td>\n",
       "      <td>-0.0567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 8</th>\n",
       "      <td>0.0268</td>\n",
       "      <td>0.0590</td>\n",
       "      <td>-0.0497</td>\n",
       "      <td>0.0373</td>\n",
       "      <td>0.0592</td>\n",
       "      <td>-0.0885</td>\n",
       "      <td>0.1227</td>\n",
       "      <td>-0.0045</td>\n",
       "      <td>-0.1105</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2153</td>\n",
       "      <td>0.1480</td>\n",
       "      <td>0.0749</td>\n",
       "      <td>0.0339</td>\n",
       "      <td>0.1779</td>\n",
       "      <td>-0.1779</td>\n",
       "      <td>0.1062</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0277</td>\n",
       "      <td>-0.0234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 9</th>\n",
       "      <td>0.0238</td>\n",
       "      <td>-0.1527</td>\n",
       "      <td>0.0420</td>\n",
       "      <td>0.0726</td>\n",
       "      <td>-0.1181</td>\n",
       "      <td>0.0946</td>\n",
       "      <td>-0.0646</td>\n",
       "      <td>-0.1567</td>\n",
       "      <td>0.0763</td>\n",
       "      <td>-0.0122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1753</td>\n",
       "      <td>0.2221</td>\n",
       "      <td>0.1392</td>\n",
       "      <td>0.0963</td>\n",
       "      <td>0.1171</td>\n",
       "      <td>-0.1171</td>\n",
       "      <td>-0.1489</td>\n",
       "      <td>-0.0119</td>\n",
       "      <td>-0.0579</td>\n",
       "      <td>0.1203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 10</th>\n",
       "      <td>0.0228</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.0862</td>\n",
       "      <td>0.0578</td>\n",
       "      <td>-0.0920</td>\n",
       "      <td>0.0818</td>\n",
       "      <td>-0.0515</td>\n",
       "      <td>-0.0401</td>\n",
       "      <td>-0.0309</td>\n",
       "      <td>-0.0186</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0130</td>\n",
       "      <td>-0.0170</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>-0.0608</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>-0.0937</td>\n",
       "      <td>-0.0164</td>\n",
       "      <td>0.0373</td>\n",
       "      <td>-0.0441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 11</th>\n",
       "      <td>0.0206</td>\n",
       "      <td>0.0614</td>\n",
       "      <td>0.0339</td>\n",
       "      <td>-0.2195</td>\n",
       "      <td>0.1537</td>\n",
       "      <td>-0.0805</td>\n",
       "      <td>0.1789</td>\n",
       "      <td>0.1183</td>\n",
       "      <td>0.2938</td>\n",
       "      <td>0.1802</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0454</td>\n",
       "      <td>0.0923</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.1051</td>\n",
       "      <td>-0.0163</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.0522</td>\n",
       "      <td>0.1909</td>\n",
       "      <td>0.0480</td>\n",
       "      <td>0.0633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 12</th>\n",
       "      <td>0.0203</td>\n",
       "      <td>-0.1151</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0560</td>\n",
       "      <td>-0.1201</td>\n",
       "      <td>0.1621</td>\n",
       "      <td>-0.0712</td>\n",
       "      <td>-0.2377</td>\n",
       "      <td>0.0724</td>\n",
       "      <td>-0.0586</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0238</td>\n",
       "      <td>-0.0353</td>\n",
       "      <td>-0.0828</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>-0.0010</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0991</td>\n",
       "      <td>-0.0664</td>\n",
       "      <td>0.0627</td>\n",
       "      <td>-0.0103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 13</th>\n",
       "      <td>0.0184</td>\n",
       "      <td>0.0133</td>\n",
       "      <td>-0.0180</td>\n",
       "      <td>0.1041</td>\n",
       "      <td>-0.0396</td>\n",
       "      <td>-0.0120</td>\n",
       "      <td>-0.0190</td>\n",
       "      <td>-0.0128</td>\n",
       "      <td>-0.1641</td>\n",
       "      <td>-0.0567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0195</td>\n",
       "      <td>0.0943</td>\n",
       "      <td>-0.0189</td>\n",
       "      <td>0.1211</td>\n",
       "      <td>0.0080</td>\n",
       "      <td>-0.0080</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>-0.0590</td>\n",
       "      <td>0.0407</td>\n",
       "      <td>-0.0412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 14</th>\n",
       "      <td>0.0176</td>\n",
       "      <td>-0.0316</td>\n",
       "      <td>-0.0076</td>\n",
       "      <td>-0.0933</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0542</td>\n",
       "      <td>-0.0444</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0704</td>\n",
       "      <td>0.0435</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0760</td>\n",
       "      <td>0.1333</td>\n",
       "      <td>0.0821</td>\n",
       "      <td>0.1320</td>\n",
       "      <td>-0.0629</td>\n",
       "      <td>0.0629</td>\n",
       "      <td>-0.0105</td>\n",
       "      <td>0.0421</td>\n",
       "      <td>-0.1464</td>\n",
       "      <td>0.3511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 15</th>\n",
       "      <td>0.0169</td>\n",
       "      <td>-0.0010</td>\n",
       "      <td>-0.0053</td>\n",
       "      <td>-0.0579</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>-0.0087</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>-0.0094</td>\n",
       "      <td>0.1118</td>\n",
       "      <td>-0.1743</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0363</td>\n",
       "      <td>-0.2906</td>\n",
       "      <td>0.0870</td>\n",
       "      <td>-0.4733</td>\n",
       "      <td>0.1028</td>\n",
       "      <td>-0.1028</td>\n",
       "      <td>-0.0099</td>\n",
       "      <td>-0.1744</td>\n",
       "      <td>-0.1809</td>\n",
       "      <td>0.1710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 16</th>\n",
       "      <td>0.0160</td>\n",
       "      <td>-0.0695</td>\n",
       "      <td>0.0405</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>-0.0086</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.0836</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>-0.0020</td>\n",
       "      <td>0.3509</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1224</td>\n",
       "      <td>-0.2545</td>\n",
       "      <td>-0.1087</td>\n",
       "      <td>-0.3079</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>-0.0471</td>\n",
       "      <td>-0.0323</td>\n",
       "      <td>0.3558</td>\n",
       "      <td>0.0201</td>\n",
       "      <td>-0.0763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 17</th>\n",
       "      <td>0.0154</td>\n",
       "      <td>-0.0069</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>-0.0042</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0080</td>\n",
       "      <td>-0.0089</td>\n",
       "      <td>-0.0059</td>\n",
       "      <td>-0.0078</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>-0.0020</td>\n",
       "      <td>-0.0039</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>-0.0025</td>\n",
       "      <td>0.0052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 18</th>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.1373</td>\n",
       "      <td>-0.0531</td>\n",
       "      <td>-0.0332</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>-0.2465</td>\n",
       "      <td>0.1078</td>\n",
       "      <td>0.1744</td>\n",
       "      <td>-0.1935</td>\n",
       "      <td>-0.0304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0799</td>\n",
       "      <td>0.0770</td>\n",
       "      <td>0.0455</td>\n",
       "      <td>0.0346</td>\n",
       "      <td>0.0303</td>\n",
       "      <td>-0.0303</td>\n",
       "      <td>0.1322</td>\n",
       "      <td>-0.0131</td>\n",
       "      <td>0.0255</td>\n",
       "      <td>-0.2128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 19</th>\n",
       "      <td>0.0131</td>\n",
       "      <td>-0.0030</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>-0.0065</td>\n",
       "      <td>0.0644</td>\n",
       "      <td>-0.0819</td>\n",
       "      <td>0.0502</td>\n",
       "      <td>0.1631</td>\n",
       "      <td>-0.0708</td>\n",
       "      <td>-0.2508</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1131</td>\n",
       "      <td>-0.0384</td>\n",
       "      <td>-0.0397</td>\n",
       "      <td>0.0699</td>\n",
       "      <td>-0.0750</td>\n",
       "      <td>0.0750</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>-0.2459</td>\n",
       "      <td>-0.2087</td>\n",
       "      <td>0.4978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 20</th>\n",
       "      <td>0.0129</td>\n",
       "      <td>-0.0056</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>-0.0160</td>\n",
       "      <td>-0.0134</td>\n",
       "      <td>0.0477</td>\n",
       "      <td>-0.0488</td>\n",
       "      <td>-0.0278</td>\n",
       "      <td>0.0718</td>\n",
       "      <td>-0.1266</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0373</td>\n",
       "      <td>0.0286</td>\n",
       "      <td>0.0417</td>\n",
       "      <td>0.0565</td>\n",
       "      <td>-0.0342</td>\n",
       "      <td>0.0342</td>\n",
       "      <td>-0.0131</td>\n",
       "      <td>-0.1325</td>\n",
       "      <td>0.0682</td>\n",
       "      <td>-0.3422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 21</th>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0820</td>\n",
       "      <td>-0.0234</td>\n",
       "      <td>0.0761</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>-0.0957</td>\n",
       "      <td>-0.0121</td>\n",
       "      <td>-0.0530</td>\n",
       "      <td>-0.1462</td>\n",
       "      <td>-0.0186</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0490</td>\n",
       "      <td>-0.0828</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>-0.0980</td>\n",
       "      <td>-0.0064</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>0.0590</td>\n",
       "      <td>-0.0315</td>\n",
       "      <td>0.0458</td>\n",
       "      <td>-0.1046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 22</th>\n",
       "      <td>0.0119</td>\n",
       "      <td>0.0296</td>\n",
       "      <td>-0.0131</td>\n",
       "      <td>-0.0361</td>\n",
       "      <td>0.0375</td>\n",
       "      <td>-0.0090</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>0.0482</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0564</td>\n",
       "      <td>-0.0998</td>\n",
       "      <td>-0.0073</td>\n",
       "      <td>-0.2099</td>\n",
       "      <td>0.0343</td>\n",
       "      <td>-0.0343</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.1202</td>\n",
       "      <td>-0.2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 23</th>\n",
       "      <td>0.0115</td>\n",
       "      <td>-0.1253</td>\n",
       "      <td>0.0572</td>\n",
       "      <td>-0.0236</td>\n",
       "      <td>-0.0306</td>\n",
       "      <td>0.0938</td>\n",
       "      <td>-0.0173</td>\n",
       "      <td>0.0313</td>\n",
       "      <td>0.0694</td>\n",
       "      <td>-0.0750</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0011</td>\n",
       "      <td>-0.0124</td>\n",
       "      <td>0.0486</td>\n",
       "      <td>-0.0071</td>\n",
       "      <td>-0.0070</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>-0.0373</td>\n",
       "      <td>-0.0852</td>\n",
       "      <td>0.0599</td>\n",
       "      <td>-0.3846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 24</th>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.0251</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>-0.0295</td>\n",
       "      <td>0.0559</td>\n",
       "      <td>-0.0357</td>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.0854</td>\n",
       "      <td>0.0204</td>\n",
       "      <td>-0.0253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0239</td>\n",
       "      <td>0.0301</td>\n",
       "      <td>-0.0606</td>\n",
       "      <td>0.0554</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>-0.0153</td>\n",
       "      <td>-0.0015</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>-0.0944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 25</th>\n",
       "      <td>0.0107</td>\n",
       "      <td>-0.0762</td>\n",
       "      <td>0.0452</td>\n",
       "      <td>-0.0961</td>\n",
       "      <td>0.0509</td>\n",
       "      <td>-0.0030</td>\n",
       "      <td>0.1368</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>0.0691</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0141</td>\n",
       "      <td>-0.0499</td>\n",
       "      <td>-0.0445</td>\n",
       "      <td>-0.0170</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>-0.0112</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.0772</td>\n",
       "      <td>0.0280</td>\n",
       "      <td>0.0628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 26</th>\n",
       "      <td>0.0090</td>\n",
       "      <td>-0.0552</td>\n",
       "      <td>0.0306</td>\n",
       "      <td>-0.1753</td>\n",
       "      <td>0.0464</td>\n",
       "      <td>0.1570</td>\n",
       "      <td>0.1077</td>\n",
       "      <td>0.0910</td>\n",
       "      <td>0.2976</td>\n",
       "      <td>-0.0558</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0058</td>\n",
       "      <td>0.0524</td>\n",
       "      <td>-0.0229</td>\n",
       "      <td>0.0959</td>\n",
       "      <td>-0.0326</td>\n",
       "      <td>0.0326</td>\n",
       "      <td>-0.1097</td>\n",
       "      <td>-0.0295</td>\n",
       "      <td>-0.1229</td>\n",
       "      <td>-0.0997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 27</th>\n",
       "      <td>0.0078</td>\n",
       "      <td>0.0375</td>\n",
       "      <td>-0.1007</td>\n",
       "      <td>-0.0083</td>\n",
       "      <td>0.0259</td>\n",
       "      <td>0.0386</td>\n",
       "      <td>0.0283</td>\n",
       "      <td>-0.0168</td>\n",
       "      <td>0.0221</td>\n",
       "      <td>0.0539</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0117</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.0246</td>\n",
       "      <td>-0.0414</td>\n",
       "      <td>-0.0189</td>\n",
       "      <td>0.0189</td>\n",
       "      <td>0.0142</td>\n",
       "      <td>0.0609</td>\n",
       "      <td>0.0462</td>\n",
       "      <td>0.0799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 28</th>\n",
       "      <td>0.0074</td>\n",
       "      <td>-0.0511</td>\n",
       "      <td>0.0154</td>\n",
       "      <td>0.0504</td>\n",
       "      <td>-0.0206</td>\n",
       "      <td>-0.0236</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>-0.0180</td>\n",
       "      <td>-0.0622</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1711</td>\n",
       "      <td>0.0606</td>\n",
       "      <td>-0.0513</td>\n",
       "      <td>-0.1142</td>\n",
       "      <td>0.0911</td>\n",
       "      <td>-0.0911</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.1773</td>\n",
       "      <td>-0.0007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 29</th>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0287</td>\n",
       "      <td>-0.0967</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0536</td>\n",
       "      <td>0.0380</td>\n",
       "      <td>-0.0446</td>\n",
       "      <td>0.0978</td>\n",
       "      <td>0.0668</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0429</td>\n",
       "      <td>0.0555</td>\n",
       "      <td>-0.0379</td>\n",
       "      <td>-0.0170</td>\n",
       "      <td>-0.0212</td>\n",
       "      <td>0.0212</td>\n",
       "      <td>-0.0598</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>0.1379</td>\n",
       "      <td>0.0783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 30</th>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0757</td>\n",
       "      <td>-0.0218</td>\n",
       "      <td>-0.1906</td>\n",
       "      <td>-0.0070</td>\n",
       "      <td>0.0662</td>\n",
       "      <td>-0.0108</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>0.2806</td>\n",
       "      <td>0.0552</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0165</td>\n",
       "      <td>-0.0733</td>\n",
       "      <td>0.0776</td>\n",
       "      <td>-0.0542</td>\n",
       "      <td>-0.0245</td>\n",
       "      <td>0.0245</td>\n",
       "      <td>-0.0718</td>\n",
       "      <td>0.0537</td>\n",
       "      <td>0.0659</td>\n",
       "      <td>0.0913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 31</th>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0656</td>\n",
       "      <td>-0.0477</td>\n",
       "      <td>-0.0761</td>\n",
       "      <td>0.0165</td>\n",
       "      <td>-0.0698</td>\n",
       "      <td>0.0238</td>\n",
       "      <td>0.1217</td>\n",
       "      <td>0.1341</td>\n",
       "      <td>-0.0174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0537</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>-0.0252</td>\n",
       "      <td>-0.0130</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>-0.0177</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>-0.0394</td>\n",
       "      <td>-0.1288</td>\n",
       "      <td>-0.0954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 32</th>\n",
       "      <td>0.0064</td>\n",
       "      <td>-0.0256</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>0.0550</td>\n",
       "      <td>0.0376</td>\n",
       "      <td>-0.0366</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>-0.0373</td>\n",
       "      <td>-0.1593</td>\n",
       "      <td>-0.0283</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>-0.0027</td>\n",
       "      <td>-0.0340</td>\n",
       "      <td>-0.0053</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>-0.0022</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>-0.0304</td>\n",
       "      <td>0.1763</td>\n",
       "      <td>0.0986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 33</th>\n",
       "      <td>0.0064</td>\n",
       "      <td>-0.0257</td>\n",
       "      <td>0.0234</td>\n",
       "      <td>0.1310</td>\n",
       "      <td>0.0392</td>\n",
       "      <td>-0.1430</td>\n",
       "      <td>0.0109</td>\n",
       "      <td>0.0416</td>\n",
       "      <td>-0.2772</td>\n",
       "      <td>-0.0550</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0237</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>-0.0060</td>\n",
       "      <td>0.0237</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>-0.0105</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>-0.0605</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>-0.0176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 34</th>\n",
       "      <td>0.0062</td>\n",
       "      <td>-0.0445</td>\n",
       "      <td>0.0468</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>-0.0432</td>\n",
       "      <td>-0.0241</td>\n",
       "      <td>-0.0832</td>\n",
       "      <td>-0.0442</td>\n",
       "      <td>-0.0700</td>\n",
       "      <td>-0.0064</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0669</td>\n",
       "      <td>-0.0429</td>\n",
       "      <td>0.0360</td>\n",
       "      <td>-0.1287</td>\n",
       "      <td>-0.0026</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.0273</td>\n",
       "      <td>-0.0126</td>\n",
       "      <td>0.0934</td>\n",
       "      <td>0.0612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 35</th>\n",
       "      <td>0.0060</td>\n",
       "      <td>0.0458</td>\n",
       "      <td>-0.0125</td>\n",
       "      <td>-0.0458</td>\n",
       "      <td>-0.0199</td>\n",
       "      <td>0.0320</td>\n",
       "      <td>0.0544</td>\n",
       "      <td>-0.0429</td>\n",
       "      <td>0.1026</td>\n",
       "      <td>-0.0369</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0059</td>\n",
       "      <td>-0.0446</td>\n",
       "      <td>0.0402</td>\n",
       "      <td>0.0978</td>\n",
       "      <td>0.0642</td>\n",
       "      <td>-0.0642</td>\n",
       "      <td>-0.0275</td>\n",
       "      <td>-0.0316</td>\n",
       "      <td>-0.6051</td>\n",
       "      <td>-0.2890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Explained Variance  ALTERSKATEGORIE_GROB  ANREDE_KZ  \\\n",
       "Dimension 1               0.1661               -0.1135     0.0085   \n",
       "Dimension 2               0.1325                0.2623     0.0891   \n",
       "Dimension 3               0.0933                0.0786    -0.3688   \n",
       "Dimension 4               0.0530               -0.0395     0.0299   \n",
       "Dimension 5               0.0408               -0.0098     0.0237   \n",
       "Dimension 6               0.0325                0.0005     0.0243   \n",
       "Dimension 7               0.0292               -0.0018    -0.0236   \n",
       "Dimension 8               0.0268                0.0590    -0.0497   \n",
       "Dimension 9               0.0238               -0.1527     0.0420   \n",
       "Dimension 10              0.0228                0.0588     0.0862   \n",
       "Dimension 11              0.0206                0.0614     0.0339   \n",
       "Dimension 12              0.0203               -0.1151     0.0093   \n",
       "Dimension 13              0.0184                0.0133    -0.0180   \n",
       "Dimension 14              0.0176               -0.0316    -0.0076   \n",
       "Dimension 15              0.0169               -0.0010    -0.0053   \n",
       "Dimension 16              0.0160               -0.0695     0.0405   \n",
       "Dimension 17              0.0154               -0.0069     0.0022   \n",
       "Dimension 18              0.0143                0.1373    -0.0531   \n",
       "Dimension 19              0.0131               -0.0030     0.0011   \n",
       "Dimension 20              0.0129               -0.0056     0.0086   \n",
       "Dimension 21              0.0121                0.0820    -0.0234   \n",
       "Dimension 22              0.0119                0.0296    -0.0131   \n",
       "Dimension 23              0.0115               -0.1253     0.0572   \n",
       "Dimension 24              0.0109                0.0251     0.0051   \n",
       "Dimension 25              0.0107               -0.0762     0.0452   \n",
       "Dimension 26              0.0090               -0.0552     0.0306   \n",
       "Dimension 27              0.0078                0.0375    -0.1007   \n",
       "Dimension 28              0.0074               -0.0511     0.0154   \n",
       "Dimension 29              0.0074                0.0205     0.0287   \n",
       "Dimension 30              0.0071                0.0757    -0.0218   \n",
       "Dimension 31              0.0068                0.0656    -0.0477   \n",
       "Dimension 32              0.0064               -0.0256     0.0097   \n",
       "Dimension 33              0.0064               -0.0257     0.0234   \n",
       "Dimension 34              0.0062               -0.0445     0.0468   \n",
       "Dimension 35              0.0060                0.0458    -0.0125   \n",
       "\n",
       "              FINANZ_MINIMALIST  FINANZ_SPARER  FINANZ_VORSORGER  \\\n",
       "Dimension 1             -0.2171         0.1407           -0.1097   \n",
       "Dimension 2              0.0713        -0.2388            0.2346   \n",
       "Dimension 3              0.1564        -0.1061            0.1000   \n",
       "Dimension 4              0.0530        -0.0012            0.0055   \n",
       "Dimension 5              0.0847        -0.0123           -0.1044   \n",
       "Dimension 6             -0.0546        -0.0023           -0.0506   \n",
       "Dimension 7              0.0268         0.0250            0.0401   \n",
       "Dimension 8              0.0373         0.0592           -0.0885   \n",
       "Dimension 9              0.0726        -0.1181            0.0946   \n",
       "Dimension 10             0.0578        -0.0920            0.0818   \n",
       "Dimension 11            -0.2195         0.1537           -0.0805   \n",
       "Dimension 12             0.0560        -0.1201            0.1621   \n",
       "Dimension 13             0.1041        -0.0396           -0.0120   \n",
       "Dimension 14            -0.0933         0.0002            0.0542   \n",
       "Dimension 15            -0.0579         0.0103           -0.0087   \n",
       "Dimension 16             0.0244        -0.0086            0.0138   \n",
       "Dimension 17             0.0042        -0.0042            0.0035   \n",
       "Dimension 18            -0.0332         0.1791           -0.2465   \n",
       "Dimension 19            -0.0065         0.0644           -0.0819   \n",
       "Dimension 20            -0.0160        -0.0134            0.0477   \n",
       "Dimension 21             0.0761         0.0162           -0.0957   \n",
       "Dimension 22            -0.0361         0.0375           -0.0090   \n",
       "Dimension 23            -0.0236        -0.0306            0.0938   \n",
       "Dimension 24            -0.0295         0.0559           -0.0357   \n",
       "Dimension 25            -0.0961         0.0509           -0.0030   \n",
       "Dimension 26            -0.1753         0.0464            0.1570   \n",
       "Dimension 27            -0.0083         0.0259            0.0386   \n",
       "Dimension 28             0.0504        -0.0206           -0.0236   \n",
       "Dimension 29            -0.0967         0.0051            0.0536   \n",
       "Dimension 30            -0.1906        -0.0070            0.0662   \n",
       "Dimension 31            -0.0761         0.0165           -0.0698   \n",
       "Dimension 32             0.0550         0.0376           -0.0366   \n",
       "Dimension 33             0.1310         0.0392           -0.1430   \n",
       "Dimension 34             0.0162        -0.0432           -0.0241   \n",
       "Dimension 35            -0.0458        -0.0199            0.0320   \n",
       "\n",
       "              FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER  \\\n",
       "Dimension 1           0.0657                 0.0669            0.1647   \n",
       "Dimension 2          -0.2052                -0.2301            0.1144   \n",
       "Dimension 3          -0.1877                -0.1004           -0.0419   \n",
       "Dimension 4          -0.1366                 0.0981           -0.1062   \n",
       "Dimension 5           0.0398                -0.0716           -0.0481   \n",
       "Dimension 6           0.0207                -0.0420            0.1061   \n",
       "Dimension 7          -0.0007                 0.0528           -0.1365   \n",
       "Dimension 8           0.1227                -0.0045           -0.1105   \n",
       "Dimension 9          -0.0646                -0.1567            0.0763   \n",
       "Dimension 10         -0.0515                -0.0401           -0.0309   \n",
       "Dimension 11          0.1789                 0.1183            0.2938   \n",
       "Dimension 12         -0.0712                -0.2377            0.0724   \n",
       "Dimension 13         -0.0190                -0.0128           -0.1641   \n",
       "Dimension 14         -0.0444                 0.0363            0.0704   \n",
       "Dimension 15          0.0105                -0.0094            0.1118   \n",
       "Dimension 16          0.0836                 0.0050           -0.0020   \n",
       "Dimension 17         -0.0004                 0.0005            0.0014   \n",
       "Dimension 18          0.1078                 0.1744           -0.1935   \n",
       "Dimension 19          0.0502                 0.1631           -0.0708   \n",
       "Dimension 20         -0.0488                -0.0278            0.0718   \n",
       "Dimension 21         -0.0121                -0.0530           -0.1462   \n",
       "Dimension 22          0.0071                -0.0016            0.0482   \n",
       "Dimension 23         -0.0173                 0.0313            0.0694   \n",
       "Dimension 24          0.0453                 0.0854            0.0204   \n",
       "Dimension 25          0.1368                 0.0023            0.0391   \n",
       "Dimension 26          0.1077                 0.0910            0.2976   \n",
       "Dimension 27          0.0283                -0.0168            0.0221   \n",
       "Dimension 28          0.0009                -0.0180           -0.0622   \n",
       "Dimension 29          0.0380                -0.0446            0.0978   \n",
       "Dimension 30         -0.0108                 0.0800            0.2806   \n",
       "Dimension 31          0.0238                 0.1217            0.1341   \n",
       "Dimension 32          0.0331                -0.0373           -0.1593   \n",
       "Dimension 33          0.0109                 0.0416           -0.2772   \n",
       "Dimension 34         -0.0832                -0.0442           -0.0700   \n",
       "Dimension 35          0.0544                -0.0429            0.1026   \n",
       "\n",
       "              GREEN_AVANTGARDE          ...           PLZ8_GBZ  ARBEIT  \\\n",
       "Dimension 1            -0.1100          ...            -0.1700  0.1466   \n",
       "Dimension 2            -0.0137          ...            -0.0678  0.0651   \n",
       "Dimension 3             0.0465          ...            -0.0385  0.0354   \n",
       "Dimension 4             0.3776          ...             0.0660  0.0156   \n",
       "Dimension 5             0.1424          ...            -0.3162  0.2428   \n",
       "Dimension 6             0.0226          ...             0.1176 -0.1331   \n",
       "Dimension 7            -0.0284          ...            -0.1529 -0.1821   \n",
       "Dimension 8             0.0055          ...             0.2153  0.1480   \n",
       "Dimension 9            -0.0122          ...             0.1753  0.2221   \n",
       "Dimension 10           -0.0186          ...            -0.0130 -0.0170   \n",
       "Dimension 11            0.1802          ...             0.0454  0.0923   \n",
       "Dimension 12           -0.0586          ...            -0.0238 -0.0353   \n",
       "Dimension 13           -0.0567          ...             0.0195  0.0943   \n",
       "Dimension 14            0.0435          ...             0.0760  0.1333   \n",
       "Dimension 15           -0.1743          ...            -0.0363 -0.2906   \n",
       "Dimension 16            0.3509          ...            -0.1224 -0.2545   \n",
       "Dimension 17            0.0051          ...            -0.0080 -0.0089   \n",
       "Dimension 18           -0.0304          ...             0.0799  0.0770   \n",
       "Dimension 19           -0.2508          ...            -0.1131 -0.0384   \n",
       "Dimension 20           -0.1266          ...             0.0373  0.0286   \n",
       "Dimension 21           -0.0186          ...            -0.0490 -0.0828   \n",
       "Dimension 22            0.0026          ...            -0.0564 -0.0998   \n",
       "Dimension 23           -0.0750          ...            -0.0011 -0.0124   \n",
       "Dimension 24           -0.0253          ...             0.0239  0.0301   \n",
       "Dimension 25            0.0691          ...            -0.0141 -0.0499   \n",
       "Dimension 26           -0.0558          ...            -0.0058  0.0524   \n",
       "Dimension 27            0.0539          ...            -0.0117  0.0134   \n",
       "Dimension 28            0.0051          ...             0.1711  0.0606   \n",
       "Dimension 29            0.0668          ...             0.0429  0.0555   \n",
       "Dimension 30            0.0552          ...            -0.0165 -0.0733   \n",
       "Dimension 31           -0.0174          ...             0.0537  0.0126   \n",
       "Dimension 32           -0.0283          ...            -0.0100 -0.0027   \n",
       "Dimension 33           -0.0550          ...            -0.0237  0.0362   \n",
       "Dimension 34           -0.0064          ...             0.0669 -0.0429   \n",
       "Dimension 35           -0.0369          ...            -0.0059 -0.0446   \n",
       "\n",
       "              ORTSGR_KLS9  RELAT_AB  OST_WEST_KZ_O  OST_WEST_KZ_W  GENERATION  \\\n",
       "Dimension 1        0.1980    0.1359         0.0605        -0.0605      0.0955   \n",
       "Dimension 2        0.0846    0.0611         0.0281        -0.0281     -0.2533   \n",
       "Dimension 3        0.0447    0.0306         0.0191        -0.0191     -0.1074   \n",
       "Dimension 4        0.2435    0.0952        -0.1996         0.1996      0.0344   \n",
       "Dimension 5        0.0953    0.0911         0.3956        -0.3956      0.0302   \n",
       "Dimension 6       -0.1069   -0.2330         0.2460        -0.2460     -0.0076   \n",
       "Dimension 7        0.0396   -0.0620        -0.2740         0.2740      0.0491   \n",
       "Dimension 8        0.0749    0.0339         0.1779        -0.1779      0.1062   \n",
       "Dimension 9        0.1392    0.0963         0.1171        -0.1171     -0.1489   \n",
       "Dimension 10       0.0106    0.0030        -0.0608         0.0608     -0.0937   \n",
       "Dimension 11       0.0002    0.1051        -0.0163         0.0163      0.0522   \n",
       "Dimension 12      -0.0828    0.0004        -0.0010         0.0010     -0.0991   \n",
       "Dimension 13      -0.0189    0.1211         0.0080        -0.0080     -0.0002   \n",
       "Dimension 14       0.0821    0.1320        -0.0629         0.0629     -0.0105   \n",
       "Dimension 15       0.0870   -0.4733         0.1028        -0.1028     -0.0099   \n",
       "Dimension 16      -0.1087   -0.3079         0.0471        -0.0471     -0.0323   \n",
       "Dimension 17      -0.0059   -0.0078         0.0020        -0.0020     -0.0039   \n",
       "Dimension 18       0.0455    0.0346         0.0303        -0.0303      0.1322   \n",
       "Dimension 19      -0.0397    0.0699        -0.0750         0.0750      0.0369   \n",
       "Dimension 20       0.0417    0.0565        -0.0342         0.0342     -0.0131   \n",
       "Dimension 21       0.0146   -0.0980        -0.0064         0.0064      0.0590   \n",
       "Dimension 22      -0.0073   -0.2099         0.0343        -0.0343      0.0100   \n",
       "Dimension 23       0.0486   -0.0071        -0.0070         0.0070     -0.0373   \n",
       "Dimension 24      -0.0606    0.0554         0.0005        -0.0005     -0.0153   \n",
       "Dimension 25      -0.0445   -0.0170         0.0112        -0.0112      0.0146   \n",
       "Dimension 26      -0.0229    0.0959        -0.0326         0.0326     -0.1097   \n",
       "Dimension 27       0.0246   -0.0414        -0.0189         0.0189      0.0142   \n",
       "Dimension 28      -0.0513   -0.1142         0.0911        -0.0911      0.0099   \n",
       "Dimension 29      -0.0379   -0.0170        -0.0212         0.0212     -0.0598   \n",
       "Dimension 30       0.0776   -0.0542        -0.0245         0.0245     -0.0718   \n",
       "Dimension 31      -0.0252   -0.0130         0.0177        -0.0177      0.0395   \n",
       "Dimension 32      -0.0340   -0.0053         0.0022        -0.0022      0.0378   \n",
       "Dimension 33      -0.0060    0.0237         0.0105        -0.0105      0.0180   \n",
       "Dimension 34       0.0360   -0.1287        -0.0026         0.0026      0.0273   \n",
       "Dimension 35       0.0402    0.0978         0.0642        -0.0642     -0.0275   \n",
       "\n",
       "              MOVEMENT  CAMEO_INTL_2015_tens  CAMEO_INTL_2015_ones  \n",
       "Dimension 1    -0.1078                0.2084               -0.1245  \n",
       "Dimension 2    -0.0118                0.0698                0.0051  \n",
       "Dimension 3     0.0445                0.0270               -0.0080  \n",
       "Dimension 4     0.3775               -0.1106                0.0391  \n",
       "Dimension 5     0.1383               -0.0553                0.0815  \n",
       "Dimension 6     0.0258               -0.0712                0.0848  \n",
       "Dimension 7    -0.0269               -0.0195               -0.0567  \n",
       "Dimension 8     0.0008                0.0277               -0.0234  \n",
       "Dimension 9    -0.0119               -0.0579                0.1203  \n",
       "Dimension 10   -0.0164                0.0373               -0.0441  \n",
       "Dimension 11    0.1909                0.0480                0.0633  \n",
       "Dimension 12   -0.0664                0.0627               -0.0103  \n",
       "Dimension 13   -0.0590                0.0407               -0.0412  \n",
       "Dimension 14    0.0421               -0.1464                0.3511  \n",
       "Dimension 15   -0.1744               -0.1809                0.1710  \n",
       "Dimension 16    0.3558                0.0201               -0.0763  \n",
       "Dimension 17    0.0051               -0.0025                0.0052  \n",
       "Dimension 18   -0.0131                0.0255               -0.2128  \n",
       "Dimension 19   -0.2459               -0.2087                0.4978  \n",
       "Dimension 20   -0.1325                0.0682               -0.3422  \n",
       "Dimension 21   -0.0315                0.0458               -0.1046  \n",
       "Dimension 22    0.0093                0.1202               -0.2001  \n",
       "Dimension 23   -0.0852                0.0599               -0.3846  \n",
       "Dimension 24   -0.0015                0.0180               -0.0944  \n",
       "Dimension 25    0.0772                0.0280                0.0628  \n",
       "Dimension 26   -0.0295               -0.1229               -0.0997  \n",
       "Dimension 27    0.0609                0.0462                0.0799  \n",
       "Dimension 28    0.0035                0.1773               -0.0007  \n",
       "Dimension 29    0.0727                0.1379                0.0783  \n",
       "Dimension 30    0.0537                0.0659                0.0913  \n",
       "Dimension 31   -0.0394               -0.1288               -0.0954  \n",
       "Dimension 32   -0.0304                0.1763                0.0986  \n",
       "Dimension 33   -0.0605                0.0011               -0.0176  \n",
       "Dimension 34   -0.0126                0.0934                0.0612  \n",
       "Dimension 35   -0.0316               -0.6051               -0.2890  \n",
       "\n",
       "[35 rows x 66 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5eda2a9908>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate the variance accounted for by each principal component.\n",
    "pca_results(pca_df, pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ee6852940>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5eda5a2c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.16606398,  0.29859503,  0.39192887,  0.44490069,  0.48568195,\n",
       "        0.51820582,  0.54745133,  0.57424384,  0.59801833,  0.62083268,\n",
       "        0.64142926,  0.66169023,  0.6801087 ,  0.69767222,  0.71461784,\n",
       "        0.73062886,  0.74600808,  0.7603346 ,  0.77343828,  0.78633217,\n",
       "        0.79838462,  0.81028388,  0.8217487 ,  0.83265043,  0.84330304,\n",
       "        0.85225357,  0.86000523,  0.8674228 ,  0.87477566,  0.88185152,\n",
       "        0.88862102,  0.89505911,  0.90145269,  0.90762801,  0.91358561])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.16606398,  0.29859503,  0.39192887,  0.44490069,  0.48568195,\n",
       "        0.51820582,  0.54745132,  0.57424383,  0.59801832,  0.62083265,\n",
       "        0.64142921,  0.66169005,  0.6801085 ,  0.69767196,  0.71461737,\n",
       "        0.73062833,  0.74600718,  0.76033188,  0.77343291,  0.78632622,\n",
       "        0.79837764,  0.81027561,  0.82173752,  0.83263588,  0.84328231,\n",
       "        0.85222889,  0.85992119,  0.86730505])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-apply PCA to the data while selecting for number of components to retain.\n",
    "pca = PCA(n_components=28)\n",
    "pca_reduced = pca.fit(pca_df)\n",
    "pca_X = pca.transform(pca_df)\n",
    "np.cumsum(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_X_df = pd.DataFrame(data=pca_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>-0.551315</td>\n",
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       "      <td>-1.443685</td>\n",
       "      <td>-0.058472</td>\n",
       "      <td>0.198903</td>\n",
       "      <td>-0.913238</td>\n",
       "      <td>-0.217847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.543617</td>\n",
       "      <td>0.222373</td>\n",
       "      <td>3.247309</td>\n",
       "      <td>0.723963</td>\n",
       "      <td>0.377414</td>\n",
       "      <td>-3.484859</td>\n",
       "      <td>0.189444</td>\n",
       "      <td>1.799421</td>\n",
       "      <td>-1.486378</td>\n",
       "      <td>-0.372140</td>\n",
       "      <td>...</td>\n",
       "      <td>0.173369</td>\n",
       "      <td>-0.546954</td>\n",
       "      <td>-0.301758</td>\n",
       "      <td>1.466228</td>\n",
       "      <td>0.093624</td>\n",
       "      <td>-0.373412</td>\n",
       "      <td>0.477340</td>\n",
       "      <td>0.671596</td>\n",
       "      <td>-0.423247</td>\n",
       "      <td>0.066730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.760437</td>\n",
       "      <td>-1.359917</td>\n",
       "      <td>-1.057193</td>\n",
       "      <td>-1.485024</td>\n",
       "      <td>-3.306854</td>\n",
       "      <td>0.085124</td>\n",
       "      <td>-1.089942</td>\n",
       "      <td>-0.618175</td>\n",
       "      <td>3.720747</td>\n",
       "      <td>-0.375830</td>\n",
       "      <td>...</td>\n",
       "      <td>0.208029</td>\n",
       "      <td>-0.430898</td>\n",
       "      <td>-0.982991</td>\n",
       "      <td>0.416317</td>\n",
       "      <td>1.005108</td>\n",
       "      <td>-0.522322</td>\n",
       "      <td>0.217544</td>\n",
       "      <td>0.567126</td>\n",
       "      <td>1.290385</td>\n",
       "      <td>-1.050055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0  3.849416 -2.673648 -2.901628 -0.167448 -2.111233  2.563553  1.117129   \n",
       "1 -0.977058  0.221515 -3.101162  2.314944 -0.256577  0.523840 -1.003588   \n",
       "2 -3.896012  1.338427 -0.756223 -0.675024 -0.117563 -0.212431  0.436934   \n",
       "3  0.543617  0.222373  3.247309  0.723963  0.377414 -3.484859  0.189444   \n",
       "4 -0.760437 -1.359917 -1.057193 -1.485024 -3.306854  0.085124 -1.089942   \n",
       "\n",
       "         7         8         9     ...           18        19        20  \\\n",
       "0  0.822770 -0.715971 -1.110897    ...    -0.012240  1.048791 -1.222789   \n",
       "1 -0.486665 -0.792459 -1.243004    ...    -1.007077 -0.983289  0.903853   \n",
       "2 -0.623199  0.065773  0.250570    ...     1.057903  0.609497  0.843070   \n",
       "3  1.799421 -1.486378 -0.372140    ...     0.173369 -0.546954 -0.301758   \n",
       "4 -0.618175  3.720747 -0.375830    ...     0.208029 -0.430898 -0.982991   \n",
       "\n",
       "         21        22        23        24        25        26        27  \n",
       "0 -0.404697  0.494878 -0.950391 -0.236075  0.249665 -0.180778  1.099555  \n",
       "1  0.418974 -0.808904 -1.637613  0.432416  0.117937 -0.525329 -0.624111  \n",
       "2 -0.551315  1.714276 -1.443685 -0.058472  0.198903 -0.913238 -0.217847  \n",
       "3  1.466228  0.093624 -0.373412  0.477340  0.671596 -0.423247  0.066730  \n",
       "4  0.416317  1.005108 -0.522322  0.217544  0.567126  1.290385 -1.050055  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_X_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=28, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_reduced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding dimensionality reduction. How many principal components / transformed features are you retaining for the next step of the analysis?)\n",
    "\n",
    "28 features retained and we can explain over 85% of the variability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.11348233,  0.00848979, -0.21714473, ..., -0.1077523 ,\n",
       "         0.20835457, -0.12453028],\n",
       "       [ 0.26230616,  0.08907985,  0.07134538, ..., -0.01177249,\n",
       "         0.0697747 ,  0.00505927],\n",
       "       [ 0.07856533, -0.36883106,  0.15635788, ...,  0.04451003,\n",
       "         0.02695244, -0.00799224],\n",
       "       ..., \n",
       "       [-0.05420563,  0.03118991, -0.17417688, ..., -0.03004069,\n",
       "        -0.11260067, -0.09584223],\n",
       "       [ 0.03312097, -0.10343082,  0.00827392, ...,  0.05988631,\n",
       "         0.08343427,  0.08686132],\n",
       "       [-0.06095196,  0.03091219,  0.07889406, ..., -0.014099  ,\n",
       "         0.15682102, -0.01396233]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_reduced.components_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.3: Interpret Principal Components\n",
    "\n",
    "Now that we have our transformed principal components, it's a nice idea to check out the weight of each variable on the first few components to see if they can be interpreted in some fashion.\n",
    "\n",
    "As a reminder, each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.\n",
    "\n",
    "- To investigate the features, you should map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.\n",
    "- You should investigate and interpret feature associations from the first three principal components in this substep. To help facilitate this, you should write a function that you can call at any time to print the sorted list of feature weights, for the *i*-th principal component. This might come in handy in the next step of the project, when you interpret the tendencies of the discovered clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map weights for the first principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "# HINT: Try defining a function here or in a new cell that you can reuse in the\n",
    "# other cells.\n",
    "\n",
    "# I referenced my classmate: https://github.com/sallytxl/customerSeg/blob/master/Identify_Customer_Segments.ipynb\n",
    "def map_weight(df, component):\n",
    "    pca_df = pd.DataFrame(pca.components_, columns=list(df.columns))\n",
    "    pca_component = pd.DataFrame(pca_df.iloc[component])\n",
    "    print(pca_component.sort_values(component, ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              0\n",
      "PLZ8_ANTG3             0.227854\n",
      "PLZ8_ANTG4             0.221348\n",
      "CAMEO_INTL_2015_tens   0.208355\n",
      "HH_EINKOMMEN_SCORE     0.204412\n",
      "ORTSGR_KLS9            0.197998\n",
      "EWDICHTE               0.195587\n",
      "FINANZ_HAUSBAUER       0.164687\n",
      "KBA05_ANTG4            0.154187\n",
      "PLZ8_ANTG2             0.153626\n",
      "ARBEIT                 0.146645\n",
      "FINANZ_SPARER          0.140655\n",
      "ANZ_HAUSHALTE_AKTIV    0.136967\n",
      "KBA05_ANTG3            0.135993\n",
      "RELAT_AB               0.135931\n",
      "SEMIO_PFLICHT          0.109528\n",
      "SEMIO_REL              0.106855\n",
      "GENERATION             0.095498\n",
      "SEMIO_RAT              0.089852\n",
      "SEMIO_TRADV            0.083368\n",
      "SEMIO_MAT              0.074565\n",
      "SEMIO_FAM              0.073974\n",
      "REGIOTYP               0.072864\n",
      "SEMIO_KULT             0.068317\n",
      "FINANZ_UNAUFFAELLIGER  0.066902\n",
      "FINANZ_ANLEGER         0.065714\n",
      "OST_WEST_KZ_O          0.060535\n",
      "KKK                    0.044411\n",
      "SEMIO_SOZ              0.041717\n",
      "SEMIO_KAEM             0.040825\n",
      "PLZ8_HHZ               0.040676\n",
      "...                         ...\n",
      "ANREDE_KZ              0.008490\n",
      "KBA05_ANTG2            0.007926\n",
      "SEMIO_KRIT             0.005196\n",
      "SOHO_KZ               -0.002047\n",
      "ANZ_TITEL             -0.003974\n",
      "RETOURTYP_BK_S        -0.015160\n",
      "SEMIO_VERT            -0.040804\n",
      "ONLINE_AFFINITAET     -0.047740\n",
      "MIN_GEBAEUDEJAHR      -0.050569\n",
      "WOHNDAUER_2008        -0.056622\n",
      "OST_WEST_KZ_W         -0.060535\n",
      "SEMIO_LUST            -0.068032\n",
      "SEMIO_ERL             -0.071279\n",
      "ANZ_PERSONEN          -0.078966\n",
      "KBA13_ANZAHL_PKW      -0.079931\n",
      "MOVEMENT              -0.107752\n",
      "FINANZ_VORSORGER      -0.109717\n",
      "GREEN_AVANTGARDE      -0.109955\n",
      "ALTERSKATEGORIE_GROB  -0.113482\n",
      "GEBAEUDETYP_RASTER    -0.117630\n",
      "CAMEO_INTL_2015_ones  -0.124530\n",
      "BALLRAUM              -0.127457\n",
      "INNENSTADT            -0.165523\n",
      "KONSUMNAEHE           -0.167994\n",
      "PLZ8_GBZ              -0.169986\n",
      "FINANZ_MINIMALIST     -0.217145\n",
      "KBA05_GBZ             -0.217966\n",
      "KBA05_ANTG1           -0.226834\n",
      "PLZ8_ANTG1            -0.228129\n",
      "MOBI_REGIO            -0.242925\n",
      "\n",
      "[65 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "map_weight(pca_df, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              1\n",
      "ALTERSKATEGORIE_GROB   0.262306\n",
      "FINANZ_VORSORGER       0.234573\n",
      "SEMIO_ERL              0.231437\n",
      "SEMIO_LUST             0.184865\n",
      "RETOURTYP_BK_S         0.163730\n",
      "W_KEIT_KIND_HH         0.118941\n",
      "FINANZ_HAUSBAUER       0.114372\n",
      "SEMIO_KRIT             0.114216\n",
      "SEMIO_KAEM             0.110356\n",
      "ANREDE_KZ              0.089080\n",
      "PLZ8_ANTG3             0.086789\n",
      "EWDICHTE               0.085732\n",
      "ORTSGR_KLS9            0.084625\n",
      "PLZ8_ANTG4             0.083034\n",
      "FINANZ_MINIMALIST      0.071345\n",
      "CAMEO_INTL_2015_tens   0.069775\n",
      "SEMIO_DOM              0.069512\n",
      "ARBEIT                 0.065126\n",
      "KBA05_ANTG4            0.064680\n",
      "WOHNDAUER_2008         0.062304\n",
      "RELAT_AB               0.061124\n",
      "ANZ_HAUSHALTE_AKTIV    0.059822\n",
      "PLZ8_ANTG2             0.058975\n",
      "HH_EINKOMMEN_SCORE     0.052002\n",
      "KBA05_ANTG3            0.039219\n",
      "VERS_TYP               0.033300\n",
      "ANZ_HH_TITEL           0.030138\n",
      "OST_WEST_KZ_O          0.028122\n",
      "REGIOTYP               0.019894\n",
      "PLZ8_HHZ               0.010996\n",
      "...                         ...\n",
      "MOVEMENT              -0.011772\n",
      "GREEN_AVANTGARDE      -0.013738\n",
      "OST_WEST_KZ_W         -0.028122\n",
      "KBA13_ANZAHL_PKW      -0.034537\n",
      "GEBAEUDETYP_RASTER    -0.040623\n",
      "MIN_GEBAEUDEJAHR      -0.048065\n",
      "BALLRAUM              -0.056449\n",
      "HEALTH_TYP            -0.058408\n",
      "ANZ_PERSONEN          -0.059108\n",
      "KONSUMNAEHE           -0.064772\n",
      "PLZ8_GBZ              -0.067834\n",
      "SEMIO_VERT            -0.067961\n",
      "INNENSTADT            -0.069648\n",
      "KBA05_ANTG1           -0.072395\n",
      "KBA05_GBZ             -0.081566\n",
      "MOBI_REGIO            -0.081650\n",
      "PLZ8_ANTG1            -0.084675\n",
      "SEMIO_SOZ             -0.102584\n",
      "ONLINE_AFFINITAET     -0.164191\n",
      "SEMIO_MAT             -0.164668\n",
      "SEMIO_RAT             -0.172954\n",
      "SEMIO_FAM             -0.184946\n",
      "FINANZ_ANLEGER        -0.205241\n",
      "SEMIO_KULT            -0.219819\n",
      "FINANZ_UNAUFFAELLIGER -0.230093\n",
      "SEMIO_PFLICHT         -0.230141\n",
      "SEMIO_TRADV           -0.232963\n",
      "FINANZ_SPARER         -0.238796\n",
      "GENERATION            -0.253329\n",
      "SEMIO_REL             -0.256411\n",
      "\n",
      "[65 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "# Map weights for the second principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "map_weight(pca_df, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              2\n",
      "SEMIO_VERT             0.345504\n",
      "SEMIO_SOZ              0.262575\n",
      "SEMIO_FAM              0.250149\n",
      "SEMIO_KULT             0.235799\n",
      "FINANZ_MINIMALIST      0.156358\n",
      "RETOURTYP_BK_S         0.107736\n",
      "FINANZ_VORSORGER       0.100003\n",
      "W_KEIT_KIND_HH         0.090383\n",
      "ALTERSKATEGORIE_GROB   0.078565\n",
      "SEMIO_REL              0.069794\n",
      "SEMIO_LUST             0.064137\n",
      "SEMIO_MAT              0.055686\n",
      "GREEN_AVANTGARDE       0.046487\n",
      "ORTSGR_KLS9            0.044727\n",
      "MOVEMENT               0.044510\n",
      "PLZ8_ANTG4             0.044326\n",
      "EWDICHTE               0.043666\n",
      "PLZ8_ANTG3             0.043665\n",
      "ARBEIT                 0.035404\n",
      "WOHNDAUER_2008         0.033187\n",
      "RELAT_AB               0.030638\n",
      "PLZ8_ANTG2             0.028159\n",
      "CAMEO_INTL_2015_tens   0.026952\n",
      "KBA05_ANTG4            0.026322\n",
      "ANZ_HAUSHALTE_AKTIV    0.024943\n",
      "OST_WEST_KZ_O          0.019136\n",
      "ANZ_HH_TITEL           0.013048\n",
      "VERS_TYP               0.011364\n",
      "ANZ_TITEL              0.009555\n",
      "KBA05_ANTG3            0.007426\n",
      "...                         ...\n",
      "KBA05_ANTG2           -0.011352\n",
      "KKK                   -0.013165\n",
      "MIN_GEBAEUDEJAHR      -0.017487\n",
      "HH_EINKOMMEN_SCORE    -0.018878\n",
      "OST_WEST_KZ_W         -0.019136\n",
      "KBA05_ANTG1           -0.022368\n",
      "HEALTH_TYP            -0.023594\n",
      "KBA13_ANZAHL_PKW      -0.023867\n",
      "KBA05_GBZ             -0.025236\n",
      "MOBI_REGIO            -0.028014\n",
      "GEBAEUDETYP_RASTER    -0.029452\n",
      "BALLRAUM              -0.032996\n",
      "KONSUMNAEHE           -0.035885\n",
      "PLZ8_GBZ              -0.038499\n",
      "INNENSTADT            -0.041125\n",
      "FINANZ_HAUSBAUER      -0.041939\n",
      "PLZ8_ANTG1            -0.045069\n",
      "ONLINE_AFFINITAET     -0.054508\n",
      "SEMIO_PFLICHT         -0.078554\n",
      "SEMIO_TRADV           -0.078682\n",
      "FINANZ_UNAUFFAELLIGER -0.100397\n",
      "FINANZ_SPARER         -0.106102\n",
      "GENERATION            -0.107353\n",
      "SEMIO_ERL             -0.176686\n",
      "FINANZ_ANLEGER        -0.187746\n",
      "SEMIO_RAT             -0.217500\n",
      "SEMIO_KRIT            -0.276502\n",
      "SEMIO_DOM             -0.314708\n",
      "SEMIO_KAEM            -0.337515\n",
      "ANREDE_KZ             -0.368831\n",
      "\n",
      "[65 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "# Map weights for the third principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "map_weight(pca_df, 2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.3: Interpret Principal Components\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your observations from detailed investigation of the first few principal components generated. Can we interpret positive and negative values from them in a meaningful way?)\n",
    "\n",
    "positive values mean the feature is directly proportional to that principal component\n",
    "\n",
    "in the first principal components, we know that Number of family houses in the PLZ8 region are very important features PLZ8_ANTG3 & PLZ8_ANTG4 (positive), PLZ8_ANTG1 (negative) top 5 positive and top 5 negative\n",
    "\n",
    "in the second principal components, we know that Personality typology is a influent factor: event-oriented(+ve), sensual-minded(+ve), dutiful(-ve), religious(-ve) are all in top 5 +ve and top5 -ve\n",
    "\n",
    "in the third principal components, we know that other personality typology contributes a lot: +ve: dreamful, socially-minded, cultural-minded, family-minded\n",
    "-ve: event-oriented, critical-minded, dominant-minded, combative attitude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Clustering\n",
    "\n",
    "### Step 3.1: Apply Clustering to General Population\n",
    "\n",
    "You've assessed and cleaned the demographics data, then scaled and transformed them. Now, it's time to see how the data clusters in the principal components space. In this substep, you will apply k-means clustering to the dataset and use the average within-cluster distances from each point to their assigned cluster's centroid to decide on a number of clusters to keep.\n",
    "\n",
    "- Use sklearn's [KMeans](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans) class to perform k-means clustering on the PCA-transformed data.\n",
    "- Then, compute the average difference from each point to its assigned cluster's center. **Hint**: The KMeans object's `.score()` method might be useful here, but note that in sklearn, scores tend to be defined so that larger is better. Try applying it to a small, toy dataset, or use an internet search to help your understanding.\n",
    "- Perform the above two steps for a number of different cluster counts. You can then see how the average distance decreases with an increasing number of clusters. However, each additional cluster provides a smaller net benefit. Use this fact to select a final number of clusters in which to group the data. **Warning**: because of the large size of the dataset, it can take a long time for the algorithm to resolve. The more clusters to fit, the longer the algorithm will take. You should test for cluster counts through at least 10 clusters to get the full picture, but you shouldn't need to test for a number of clusters above about 30.\n",
    "- Once you've selected a final number of clusters to use, re-fit a KMeans instance to perform the clustering operation. Make sure that you also obtain the cluster assignments for the general demographics data, since you'll be using them in the final Step 3.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Over a number of different cluster counts...\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "def lets_kmeans(n, df):\n",
    "    kmeans = KMeans(n_clusters=n)\n",
    "    \n",
    "    # run k-means clustering on the data and...\n",
    "    preds = kmeans.fit_predict(df)\n",
    "    \n",
    "    # compute the average within-cluster distances.\n",
    "    pred_centroids = kmeans.cluster_centers_\n",
    "    \n",
    "    centers = pred_centroids[preds]\n",
    "    distance = np.sqrt(((train_trans - centers) ** 2).sum(axis = 1)).mean()\n",
    "    distances.append(distance)\n",
    "    \n",
    "    return model.score(df)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating for 1 clusters\n",
      "evaluating for 2 clusters\n",
      "evaluating for 3 clusters\n",
      "evaluating for 4 clusters\n",
      "evaluating for 5 clusters\n",
      "evaluating for 6 clusters\n",
      "evaluating for 7 clusters\n",
      "evaluating for 8 clusters\n",
      "evaluating for 9 clusters\n",
      "evaluating for 10 clusters\n",
      "evaluating for 11 clusters\n",
      "evaluating for 12 clusters\n",
      "evaluating for 13 clusters\n",
      "evaluating for 14 clusters\n",
      "evaluating for 15 clusters\n",
      "evaluating for 16 clusters\n",
      "evaluating for 17 clusters\n",
      "evaluating for 19 clusters\n",
      "evaluating for 20 clusters\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe11f3ecda0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate the change in within-cluster distance across number of clusters.\n",
    "# HINT: Use matplotlib's plot function to visualize this relationship.\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#cluster_nums = [1, 2, 3, 4]\n",
    "cluster_nums = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n",
    "distances = []\n",
    "\n",
    "for c in cluster_nums:\n",
    "    print('evaluating for {} clusters'.format(c))\n",
    "    \n",
    "    # run k-means clustering on the data and...\n",
    "    kmeans = KMeans(n_clusters=c)\n",
    "    \n",
    "    preds = kmeans.fit_predict(pca_X_df)\n",
    "    #print(preds)\n",
    "    # compute the average within-cluster distances.\n",
    "    pred_centroids = kmeans.cluster_centers_\n",
    "    #print(pred_centroids)\n",
    "    centers = pred_centroids[preds]\n",
    "    #print(centers)\n",
    "    distance = np.sqrt(((pca_X_df - centers) ** 2).sum(axis = 1)).mean()\n",
    "    distances.append(distance)\n",
    "    \n",
    "plt.plot(cluster_nums, distances, '-o')\n",
    "plt.ylabel('Avg. distance to centroid')\n",
    "plt.xlabel('# of clusters')\n",
    "plt.savefig('distance_to_centroid.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'scores' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-73-fa8cef49474b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'scores' is not defined"
     ]
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-fit the k-means model with the selected number of clusters and obtain\n",
    "# cluster predictions for the general population demographics data.\n",
    "kmeans = KMeans(n_clusters=8)\n",
    "model = kmeans.fit(pca_X_df)\n",
    "prediction_azdias = model.predict(pca_X_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_azdias = model.predict(pca_X_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.1: Apply Clustering to General Population\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding clustering. Into how many clusters have you decided to segment the population?)\n",
    "\n",
    "8, from the curve of average distance to centroid, we found that the elbow of the curve is around 8, which is also the turning point of the curve, \n",
    "so we decided to select 8 as the number of clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.2: Apply All Steps to the Customer Data\n",
    "\n",
    "Now that you have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters. Take care to not confuse this for re-fitting all of the models to the customer data. Instead, you're going to use the fits from the general population to clean, transform, and cluster the customer data. In the last step of the project, you will interpret how the general population fits apply to the customer data.\n",
    "\n",
    "- Don't forget when loading in the customers data, that it is semicolon (`;`) delimited.\n",
    "- Apply the same feature wrangling, selection, and engineering steps to the customer demographics using the `clean_data()` function you created earlier. (You can assume that the customer demographics data has similar meaning behind missing data patterns as the general demographics data.)\n",
    "- Use the sklearn objects from the general demographics data, and apply their transformations to the customers data. That is, you should not be using a `.fit()` or `.fit_transform()` method to re-fit the old objects, nor should you be creating new sklearn objects! Carry the data through the feature scaling, PCA, and clustering steps, obtaining cluster assignments for all of the data in the customer demographics data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the customer demographics data.\n",
    "customers = pd.read_csv('Udacity_CUSTOMERS_Subset.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AGER_TYP', 'ALTERSKATEGORIE_GROB', 'ANREDE_KZ', 'CJT_GESAMTTYP',\n",
       "       'FINANZ_MINIMALIST', 'FINANZ_SPARER', 'FINANZ_VORSORGER',\n",
       "       'FINANZ_ANLEGER', 'FINANZ_UNAUFFAELLIGER', 'FINANZ_HAUSBAUER',\n",
       "       'FINANZTYP', 'GEBURTSJAHR', 'GFK_URLAUBERTYP', 'GREEN_AVANTGARDE',\n",
       "       'HEALTH_TYP', 'LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB',\n",
       "       'LP_FAMILIE_FEIN', 'LP_FAMILIE_GROB', 'LP_STATUS_FEIN',\n",
       "       'LP_STATUS_GROB', 'NATIONALITAET_KZ', 'PRAEGENDE_JUGENDJAHRE',\n",
       "       'RETOURTYP_BK_S', 'SEMIO_SOZ', 'SEMIO_FAM', 'SEMIO_REL', 'SEMIO_MAT',\n",
       "       'SEMIO_VERT', 'SEMIO_LUST', 'SEMIO_ERL', 'SEMIO_KULT', 'SEMIO_RAT',\n",
       "       'SEMIO_KRIT', 'SEMIO_DOM', 'SEMIO_KAEM', 'SEMIO_PFLICHT', 'SEMIO_TRADV',\n",
       "       'SHOPPER_TYP', 'SOHO_KZ', 'TITEL_KZ', 'VERS_TYP', 'ZABEOTYP',\n",
       "       'ALTER_HH', 'ANZ_PERSONEN', 'ANZ_TITEL', 'HH_EINKOMMEN_SCORE',\n",
       "       'KK_KUNDENTYP', 'W_KEIT_KIND_HH', 'WOHNDAUER_2008',\n",
       "       'ANZ_HAUSHALTE_AKTIV', 'ANZ_HH_TITEL', 'GEBAEUDETYP', 'KONSUMNAEHE',\n",
       "       'MIN_GEBAEUDEJAHR', 'OST_WEST_KZ', 'WOHNLAGE', 'CAMEO_DEUG_2015',\n",
       "       'CAMEO_DEU_2015', 'CAMEO_INTL_2015', 'KBA05_ANTG1', 'KBA05_ANTG2',\n",
       "       'KBA05_ANTG3', 'KBA05_ANTG4', 'KBA05_BAUMAX', 'KBA05_GBZ', 'BALLRAUM',\n",
       "       'EWDICHTE', 'INNENSTADT', 'GEBAEUDETYP_RASTER', 'KKK', 'MOBI_REGIO',\n",
       "       'ONLINE_AFFINITAET', 'REGIOTYP', 'KBA13_ANZAHL_PKW', 'PLZ8_ANTG1',\n",
       "       'PLZ8_ANTG2', 'PLZ8_ANTG3', 'PLZ8_ANTG4', 'PLZ8_BAUMAX', 'PLZ8_HHZ',\n",
       "       'PLZ8_GBZ', 'ARBEIT', 'ORTSGR_KLS9', 'RELAT_AB'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply preprocessing, feature transformation, and clustering from the general\n",
    "# demographics onto the customer data, obtaining cluster predictions for the\n",
    "# customer demographics data.\n",
    "cleaned_customers = clean_data(customers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_customers = scaling(cleaned_customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>GREEN_AVANTGARDE</th>\n",
       "      <th>HEALTH_TYP</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "      <th>OST_WEST_KZ_O</th>\n",
       "      <th>OST_WEST_KZ_W</th>\n",
       "      <th>GENERATION</th>\n",
       "      <th>MOVEMENT</th>\n",
       "      <th>CAMEO_INTL_2015_tens</th>\n",
       "      <th>CAMEO_INTL_2015_ones</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-1.625594</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-2.174466</td>\n",
       "      <td>-1.433519</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>0.085847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.261033e+00</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-0.179668</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-2.174466</td>\n",
       "      <td>-0.998247</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.328600</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>0.786963</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>0.505832</td>\n",
       "      <td>-1.266430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>0.742840</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-0.179668</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.170875</td>\n",
       "      <td>-0.998247</td>\n",
       "      <td>-0.789309</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>0.085847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-1.625594</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>-0.562976</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>1.178112</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.328600</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>1.191332</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>0.307568</td>\n",
       "      <td>0.684613</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-1.625594</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.170875</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-2.174466</td>\n",
       "      <td>-1.868791</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>-0.590291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>0.836306</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>0.505832</td>\n",
       "      <td>0.085847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>1.178112</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.328600</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>1.191332</td>\n",
       "      <td>-1.266430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-1.625594</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>0.307568</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.930884</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-1.625594</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>-1.766962</td>\n",
       "      <td>0.786963</td>\n",
       "      <td>1.673133</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-2.174466</td>\n",
       "      <td>-0.998247</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>0.864553</td>\n",
       "      <td>-1.040677</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-1.625594</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.174106e+00</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>1.178112</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>1.165689</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>0.505832</td>\n",
       "      <td>-1.266430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-1.625594</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>0.836306</td>\n",
       "      <td>0.307568</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>0.466831</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-0.179668</td>\n",
       "      <td>0.085847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-2.174466</td>\n",
       "      <td>-0.998247</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.930884</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>0.505832</td>\n",
       "      <td>0.085847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-1.766074</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-2.174466</td>\n",
       "      <td>-1.433519</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.328600</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.261033e+00</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>1.613383</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>1.191332</td>\n",
       "      <td>-1.266430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.170875</td>\n",
       "      <td>0.307568</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-0.179668</td>\n",
       "      <td>0.085847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>0.786963</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-2.174466</td>\n",
       "      <td>-0.998247</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>0.466831</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>-0.590291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-2.174466</td>\n",
       "      <td>-1.433519</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-1.625594</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>0.742840</td>\n",
       "      <td>0.684613</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>0.505832</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>0.742840</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.328600</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>1.191332</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.261033e+00</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>1.178112</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.930884</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>1.191332</td>\n",
       "      <td>-1.266430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.170875</td>\n",
       "      <td>-0.998247</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.930884</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-0.179668</td>\n",
       "      <td>-0.590291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>0.107978</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.170875</td>\n",
       "      <td>-1.868791</td>\n",
       "      <td>-0.789309</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191622</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.170875</td>\n",
       "      <td>-1.433519</td>\n",
       "      <td>-0.789309</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.328600</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>0.505832</td>\n",
       "      <td>0.085847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191623</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-1.170875</td>\n",
       "      <td>-0.562976</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.930884</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191624</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-1.170875</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>1.191332</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191625</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.170875</td>\n",
       "      <td>-0.562976</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191626</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>0.836306</td>\n",
       "      <td>1.178112</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191627</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>0.742840</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>1.191332</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191628</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-1.625594</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.170875</td>\n",
       "      <td>-0.562976</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.930884</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>-0.590291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191629</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.261033e+00</td>\n",
       "      <td>-1.170875</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.328600</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>1.191332</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191630</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>0.836306</td>\n",
       "      <td>0.742840</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>1.191332</td>\n",
       "      <td>-1.266430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191631</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>0.836306</td>\n",
       "      <td>0.742840</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>0.505832</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191632</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>-1.868791</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191633</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>-0.789309</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.930884</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191634</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-2.174466</td>\n",
       "      <td>-1.433519</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.930884</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-0.179668</td>\n",
       "      <td>0.085847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191635</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.328600</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>0.505832</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191636</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>0.836306</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>0.684613</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-2.328600</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-0.179668</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191637</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-0.482167</td>\n",
       "      <td>0.411893</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>0.742840</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191638</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>-0.042763</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.174106e+00</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>0.742840</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.930884</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>1.191332</td>\n",
       "      <td>-0.590291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191639</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>0.191193</td>\n",
       "      <td>-0.314392</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>0.958614</td>\n",
       "      <td>-1.501369</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>0.836306</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>0.684613</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>0.466831</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>-0.590291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191640</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>0.683151</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>0.629152</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>0.742840</td>\n",
       "      <td>0.684613</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191641</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-1.625594</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.170875</td>\n",
       "      <td>-1.868791</td>\n",
       "      <td>-0.789309</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191642</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-1.625594</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.174106e+00</td>\n",
       "      <td>0.836306</td>\n",
       "      <td>1.613383</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>1.932634</td>\n",
       "      <td>-1.932634</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>1.191332</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191643</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-1.170875</td>\n",
       "      <td>-0.562976</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.930884</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>1.438124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191644</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>0.505832</td>\n",
       "      <td>-1.266430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191645</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>0.244095</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>-0.127704</td>\n",
       "      <td>-0.052348</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>0.505832</td>\n",
       "      <td>0.085847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191646</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.174106e+00</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>1.613383</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191647</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>1.085953</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651132e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>1.178112</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-1.550667</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191648</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-0.571007</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>-0.081022</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.054859e-16</td>\n",
       "      <td>-2.174466</td>\n",
       "      <td>-0.562976</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.930884</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-0.179668</td>\n",
       "      <td>-0.590291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191649</th>\n",
       "      <td>1.184901</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>1.885354</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>0.742840</td>\n",
       "      <td>1.421574</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>1.875412</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191650</th>\n",
       "      <td>0.201243</td>\n",
       "      <td>0.957905</td>\n",
       "      <td>-0.768677</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-0.470424</td>\n",
       "      <td>1.339325</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.479598e-01</td>\n",
       "      <td>-0.167284</td>\n",
       "      <td>-0.562976</td>\n",
       "      <td>0.684613</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-0.232027</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-0.865168</td>\n",
       "      <td>0.761985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191651</th>\n",
       "      <td>-0.782416</td>\n",
       "      <td>-1.043944</td>\n",
       "      <td>1.409065</td>\n",
       "      <td>-1.155526</td>\n",
       "      <td>1.138179</td>\n",
       "      <td>-1.249992</td>\n",
       "      <td>-1.184943</td>\n",
       "      <td>-0.791195</td>\n",
       "      <td>-0.530404</td>\n",
       "      <td>-0.269821</td>\n",
       "      <td>...</td>\n",
       "      <td>1.478186e+00</td>\n",
       "      <td>-2.174466</td>\n",
       "      <td>-0.998247</td>\n",
       "      <td>-1.526271</td>\n",
       "      <td>-0.517429</td>\n",
       "      <td>0.517429</td>\n",
       "      <td>-1.629742</td>\n",
       "      <td>-0.553153</td>\n",
       "      <td>-0.179668</td>\n",
       "      <td>0.085847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>141725 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ALTERSKATEGORIE_GROB  ANREDE_KZ  FINANZ_MINIMALIST  FINANZ_SPARER  \\\n",
       "0                   1.184901  -1.043944           1.409065      -1.155526   \n",
       "2                   1.184901   0.957905           1.409065      -1.155526   \n",
       "3                   1.184901  -1.043944           1.409065      -1.155526   \n",
       "4                   0.201243  -1.043944          -0.042763      -1.155526   \n",
       "5                   0.201243  -1.043944           1.409065      -1.155526   \n",
       "6                   1.184901  -1.043944           1.409065      -1.155526   \n",
       "7                   1.184901  -1.043944           1.409065      -1.155526   \n",
       "8                   1.184901   0.957905          -0.768677      -0.482167   \n",
       "9                   0.201243  -1.043944           1.409065      -0.482167   \n",
       "11                  1.184901  -1.043944           1.409065      -1.155526   \n",
       "12                  1.184901  -1.043944           1.409065      -0.482167   \n",
       "13                  0.201243  -1.043944           1.409065      -0.482167   \n",
       "14                  1.184901   0.957905          -0.042763      -1.155526   \n",
       "15                  0.201243  -1.043944           1.409065      -1.155526   \n",
       "16                  1.184901  -1.043944           1.409065      -1.155526   \n",
       "17                  1.184901  -1.043944           0.683151       0.191193   \n",
       "18                 -0.782416  -1.043944          -0.768677       0.864553   \n",
       "19                  1.184901   0.957905          -0.042763      -0.482167   \n",
       "20                  1.184901  -1.043944           1.409065       0.191193   \n",
       "21                  1.184901  -1.043944          -0.042763      -1.155526   \n",
       "22                 -1.766074   0.957905           1.409065      -1.155526   \n",
       "23                  0.201243  -1.043944          -0.042763       0.191193   \n",
       "24                  1.184901   0.957905           0.683151      -1.155526   \n",
       "25                  0.201243   0.957905           1.409065      -0.482167   \n",
       "26                  1.184901   0.957905           1.409065      -1.155526   \n",
       "27                  0.201243  -1.043944           1.409065      -0.482167   \n",
       "28                  1.184901   0.957905          -0.042763      -1.155526   \n",
       "29                  0.201243   0.957905          -0.042763      -1.155526   \n",
       "30                  0.201243   0.957905           0.683151      -1.155526   \n",
       "31                  0.201243  -1.043944           1.409065      -0.482167   \n",
       "...                      ...        ...                ...            ...   \n",
       "191622              1.184901  -1.043944           1.409065      -1.155526   \n",
       "191623              1.184901  -1.043944           1.409065      -1.155526   \n",
       "191624              1.184901  -1.043944           1.409065      -1.155526   \n",
       "191625              1.184901   0.957905           1.409065      -1.155526   \n",
       "191626             -0.782416  -1.043944          -0.042763       0.191193   \n",
       "191627              0.201243   0.957905          -0.768677      -1.155526   \n",
       "191628              1.184901  -1.043944           1.409065      -1.155526   \n",
       "191629              1.184901   0.957905          -0.768677      -1.155526   \n",
       "191630              0.201243  -1.043944           1.409065      -1.155526   \n",
       "191631              1.184901   0.957905          -0.768677      -1.155526   \n",
       "191632              0.201243  -1.043944           0.683151      -1.155526   \n",
       "191633              1.184901  -1.043944           1.409065      -1.155526   \n",
       "191634              0.201243  -1.043944           1.409065      -1.155526   \n",
       "191635              1.184901   0.957905           1.409065      -1.155526   \n",
       "191636              1.184901  -1.043944           1.409065      -1.155526   \n",
       "191637              0.201243   0.957905           1.409065      -0.482167   \n",
       "191638              1.184901  -1.043944          -0.042763      -1.155526   \n",
       "191639              0.201243  -1.043944           0.683151       0.191193   \n",
       "191640              0.201243  -1.043944           0.683151      -1.155526   \n",
       "191641              1.184901  -1.043944           1.409065      -1.155526   \n",
       "191642              1.184901   0.957905          -0.768677      -1.155526   \n",
       "191643              1.184901  -1.043944           1.409065      -1.155526   \n",
       "191644              1.184901   0.957905          -0.768677      -1.155526   \n",
       "191645              1.184901  -1.043944           1.409065      -1.155526   \n",
       "191646             -0.782416   0.957905          -0.768677      -1.155526   \n",
       "191647              0.201243  -1.043944           1.409065      -1.155526   \n",
       "191648              1.184901   0.957905           1.409065      -1.155526   \n",
       "191649              1.184901  -1.043944           1.409065      -1.155526   \n",
       "191650              0.201243   0.957905          -0.768677      -1.155526   \n",
       "191651             -0.782416  -1.043944           1.409065      -1.155526   \n",
       "\n",
       "        FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  \\\n",
       "0               1.138179       -1.249992              -0.470424   \n",
       "2               1.138179       -1.249992               0.958614   \n",
       "3               1.138179       -0.571007              -1.184943   \n",
       "4               0.411893        0.786963               1.673133   \n",
       "5               1.138179       -1.249992              -0.470424   \n",
       "6               1.138179       -1.249992              -1.184943   \n",
       "7               1.138179       -1.249992              -0.470424   \n",
       "8               1.138179       -1.249992              -1.184943   \n",
       "9               0.411893       -1.249992               0.244095   \n",
       "11              1.138179       -1.249992               0.244095   \n",
       "12              0.411893        0.107978              -0.470424   \n",
       "13              0.411893       -0.571007               0.958614   \n",
       "14              1.138179       -1.249992              -0.470424   \n",
       "15              1.138179       -1.249992              -1.184943   \n",
       "16              1.138179       -1.249992               0.244095   \n",
       "17             -1.766962        0.786963               1.673133   \n",
       "18             -1.040677       -0.571007               0.244095   \n",
       "19              0.411893        0.107978               0.244095   \n",
       "20              0.411893       -0.571007               0.244095   \n",
       "21              1.138179       -0.571007              -1.184943   \n",
       "22              1.138179       -1.249992              -1.184943   \n",
       "23              0.411893       -1.249992              -0.470424   \n",
       "24              1.138179       -0.571007              -0.470424   \n",
       "25              0.411893        0.786963              -0.470424   \n",
       "26              1.138179       -0.571007              -1.184943   \n",
       "27             -0.314392       -1.249992              -0.470424   \n",
       "28              1.138179       -1.249992              -1.184943   \n",
       "29              1.138179       -1.249992              -0.470424   \n",
       "30              1.138179       -1.249992              -0.470424   \n",
       "31             -0.314392        0.107978              -0.470424   \n",
       "...                  ...             ...                    ...   \n",
       "191622          1.138179       -1.249992              -1.184943   \n",
       "191623          1.138179       -1.249992               0.244095   \n",
       "191624          1.138179       -1.249992               0.958614   \n",
       "191625          1.138179       -0.571007              -1.184943   \n",
       "191626         -0.314392       -1.249992               0.244095   \n",
       "191627          1.138179       -1.249992              -0.470424   \n",
       "191628          1.138179       -1.249992              -1.184943   \n",
       "191629          1.138179       -1.249992              -0.470424   \n",
       "191630          1.138179       -1.249992              -1.184943   \n",
       "191631          1.138179       -1.249992              -0.470424   \n",
       "191632          1.138179       -1.249992              -1.184943   \n",
       "191633          1.138179       -1.249992               0.958614   \n",
       "191634          1.138179       -1.249992              -0.470424   \n",
       "191635          1.138179       -0.571007              -1.184943   \n",
       "191636          1.138179       -1.249992              -1.184943   \n",
       "191637          0.411893       -0.571007               0.244095   \n",
       "191638          1.138179       -1.249992              -1.184943   \n",
       "191639         -0.314392       -0.571007               0.958614   \n",
       "191640          1.138179       -1.249992              -0.470424   \n",
       "191641          1.138179       -1.249992              -0.470424   \n",
       "191642          1.138179       -0.571007              -1.184943   \n",
       "191643          1.138179       -1.249992               0.244095   \n",
       "191644          1.138179       -1.249992              -0.470424   \n",
       "191645          1.138179       -1.249992               0.244095   \n",
       "191646          1.138179       -1.249992              -0.470424   \n",
       "191647          1.138179       -1.249992              -1.184943   \n",
       "191648          1.138179       -0.571007              -0.470424   \n",
       "191649          1.138179       -1.249992              -1.184943   \n",
       "191650          1.138179       -1.249992              -0.470424   \n",
       "191651          1.138179       -1.249992              -1.184943   \n",
       "\n",
       "        FINANZ_HAUSBAUER  GREEN_AVANTGARDE  HEALTH_TYP          ...           \\\n",
       "0              -0.791195          1.885354   -1.625594          ...            \n",
       "2               0.629152          1.885354   -0.269821          ...            \n",
       "3              -0.791195         -0.530404   -0.269821          ...            \n",
       "4              -0.791195         -0.530404    1.085953          ...            \n",
       "5              -0.081022          1.885354    1.085953          ...            \n",
       "6              -0.791195          1.885354   -0.269821          ...            \n",
       "7              -0.791195          1.885354   -1.625594          ...            \n",
       "8               1.339325         -0.530404   -0.269821          ...            \n",
       "9              -1.501369          1.885354    1.085953          ...            \n",
       "11             -0.791195          1.885354   -1.625594          ...            \n",
       "12             -1.501369         -0.530404   -0.269821          ...            \n",
       "13             -1.501369         -0.530404    1.085953          ...            \n",
       "14              1.339325         -0.530404   -0.269821          ...            \n",
       "15             -0.791195          1.885354   -1.625594          ...            \n",
       "16             -0.791195          1.885354   -1.625594          ...            \n",
       "17             -1.501369          1.885354   -0.269821          ...            \n",
       "18             -0.081022         -0.530404   -1.625594          ...            \n",
       "19             -0.081022         -0.530404   -0.269821          ...            \n",
       "20             -1.501369          1.885354   -1.625594          ...            \n",
       "21              0.629152         -0.530404   -0.269821          ...            \n",
       "22             -0.791195         -0.530404    1.085953          ...            \n",
       "23             -0.791195         -0.530404    1.085953          ...            \n",
       "24             -0.081022         -0.530404   -0.269821          ...            \n",
       "25             -1.501369          1.885354    1.085953          ...            \n",
       "26             -0.081022         -0.530404   -0.269821          ...            \n",
       "27             -1.501369          1.885354   -1.625594          ...            \n",
       "28              1.339325         -0.530404   -0.269821          ...            \n",
       "29              1.339325         -0.530404    1.085953          ...            \n",
       "30             -0.791195          1.885354   -0.269821          ...            \n",
       "31             -1.501369          1.885354    1.085953          ...            \n",
       "...                  ...               ...         ...          ...            \n",
       "191622         -0.791195         -0.530404   -0.269821          ...            \n",
       "191623         -0.791195          1.885354   -0.269821          ...            \n",
       "191624         -0.791195          1.885354    1.085953          ...            \n",
       "191625         -0.791195          1.885354   -0.269821          ...            \n",
       "191626         -0.791195         -0.530404    1.085953          ...            \n",
       "191627          1.339325         -0.530404   -0.269821          ...            \n",
       "191628         -0.791195          1.885354   -1.625594          ...            \n",
       "191629          1.339325         -0.530404   -0.269821          ...            \n",
       "191630         -0.791195          1.885354    1.085953          ...            \n",
       "191631          1.339325         -0.530404   -0.269821          ...            \n",
       "191632          0.629152         -0.530404    1.085953          ...            \n",
       "191633         -0.791195          1.885354   -0.269821          ...            \n",
       "191634         -0.081022         -0.530404    1.085953          ...            \n",
       "191635         -0.081022         -0.530404   -0.269821          ...            \n",
       "191636         -0.791195          1.885354   -0.269821          ...            \n",
       "191637         -1.501369          1.885354    1.085953          ...            \n",
       "191638          0.629152         -0.530404   -0.269821          ...            \n",
       "191639         -1.501369         -0.530404    1.085953          ...            \n",
       "191640          0.629152          1.885354    1.085953          ...            \n",
       "191641         -0.791195          1.885354   -1.625594          ...            \n",
       "191642          1.339325         -0.530404   -1.625594          ...            \n",
       "191643         -0.791195          1.885354   -0.269821          ...            \n",
       "191644          1.339325          1.885354   -0.269821          ...            \n",
       "191645         -0.791195         -0.530404    1.085953          ...            \n",
       "191646          1.339325         -0.530404   -0.269821          ...            \n",
       "191647         -0.791195          1.885354    1.085953          ...            \n",
       "191648         -0.081022         -0.530404   -0.269821          ...            \n",
       "191649         -0.791195          1.885354   -0.269821          ...            \n",
       "191650          1.339325         -0.530404   -0.269821          ...            \n",
       "191651         -0.791195         -0.530404   -0.269821          ...            \n",
       "\n",
       "            PLZ8_GBZ    ARBEIT  ORTSGR_KLS9  RELAT_AB  OST_WEST_KZ_O  \\\n",
       "0       1.478186e+00 -2.174466    -1.433519 -1.526271      -0.517429   \n",
       "2      -1.261033e+00 -0.167284    -0.127704 -0.052348      -0.517429   \n",
       "3       5.651132e-01 -2.174466    -0.998247 -1.526271      -0.517429   \n",
       "4      -3.479598e-01 -0.167284    -0.127704 -1.526271      -0.517429   \n",
       "5       1.478186e+00 -0.167284     0.742840  1.421574      -0.517429   \n",
       "6       1.478186e+00 -1.170875    -0.998247 -0.789309      -0.517429   \n",
       "7      -3.479598e-01 -0.167284    -0.562976 -0.052348      -0.517429   \n",
       "8       5.651132e-01 -0.167284     1.178112 -0.052348      -0.517429   \n",
       "9       1.478186e+00 -0.167284     0.307568  0.684613      -0.517429   \n",
       "11     -3.479598e-01 -1.170875    -0.127704 -1.526271      -0.517429   \n",
       "12      1.478186e+00 -2.174466    -1.868791 -1.526271      -0.517429   \n",
       "13      5.651132e-01  0.836306    -0.127704  1.421574      -0.517429   \n",
       "14     -3.479598e-01 -0.167284     1.178112  1.421574      -0.517429   \n",
       "15      1.478186e+00 -0.167284     0.307568 -1.526271      -0.517429   \n",
       "16      5.651132e-01 -0.167284    -0.127704 -0.052348      -0.517429   \n",
       "17      1.478186e+00 -2.174466    -0.998247 -1.526271      -0.517429   \n",
       "18     -2.174106e+00 -0.167284     1.178112  1.421574      -0.517429   \n",
       "19      5.651132e-01 -0.167284    -0.127704  1.421574      -0.517429   \n",
       "20      5.651132e-01  0.836306     0.307568  1.421574      -0.517429   \n",
       "21     -3.479598e-01 -2.174466    -0.998247 -1.526271      -0.517429   \n",
       "22      1.478186e+00 -2.174466    -1.433519 -1.526271      -0.517429   \n",
       "23     -1.261033e+00 -0.167284     1.613383  1.421574      -0.517429   \n",
       "24     -3.479598e-01 -1.170875     0.307568 -0.052348      -0.517429   \n",
       "25      5.651132e-01 -2.174466    -0.998247 -1.526271      -0.517429   \n",
       "26      1.478186e+00 -2.174466    -1.433519 -1.526271      -0.517429   \n",
       "27     -3.479598e-01 -0.167284     0.742840  0.684613      -0.517429   \n",
       "28     -3.479598e-01 -0.167284     0.742840  1.421574      -0.517429   \n",
       "29     -1.261033e+00 -0.167284     1.178112  1.421574      -0.517429   \n",
       "30      1.478186e+00 -1.170875    -0.998247 -0.052348      -0.517429   \n",
       "31      1.478186e+00 -1.170875    -1.868791 -0.789309      -0.517429   \n",
       "...              ...       ...          ...       ...            ...   \n",
       "191622 -3.479598e-01 -1.170875    -1.433519 -0.789309      -0.517429   \n",
       "191623 -3.479598e-01 -1.170875    -0.562976 -0.052348      -0.517429   \n",
       "191624  5.651132e-01 -1.170875    -0.127704 -1.526271      -0.517429   \n",
       "191625  1.478186e+00 -1.170875    -0.562976 -0.052348      -0.517429   \n",
       "191626  5.651132e-01  0.836306     1.178112  1.421574      -0.517429   \n",
       "191627 -3.479598e-01 -0.167284     0.742840 -0.052348      -0.517429   \n",
       "191628  1.478186e+00 -1.170875    -0.562976 -0.052348      -0.517429   \n",
       "191629 -1.261033e+00 -1.170875    -0.127704 -1.526271      -0.517429   \n",
       "191630  1.478186e+00  0.836306     0.742840  1.421574      -0.517429   \n",
       "191631 -3.479598e-01  0.836306     0.742840  1.421574      -0.517429   \n",
       "191632  5.651132e-01 -0.167284    -1.868791 -0.052348      -0.517429   \n",
       "191633  5.651132e-01 -0.167284    -0.127704 -0.789309      -0.517429   \n",
       "191634  5.651132e-01 -2.174466    -1.433519 -1.526271      -0.517429   \n",
       "191635 -3.479598e-01 -0.167284    -0.127704 -0.052348      -0.517429   \n",
       "191636 -3.479598e-01  0.836306    -0.127704  0.684613      -0.517429   \n",
       "191637 -3.479598e-01 -0.167284     0.742840  1.421574      -0.517429   \n",
       "191638 -2.174106e+00 -0.167284     0.742840 -0.052348      -0.517429   \n",
       "191639 -3.479598e-01  0.836306    -0.127704  0.684613      -0.517429   \n",
       "191640  1.478186e+00 -0.167284     0.742840  0.684613      -0.517429   \n",
       "191641  1.478186e+00 -1.170875    -1.868791 -0.789309      -0.517429   \n",
       "191642 -2.174106e+00  0.836306     1.613383 -0.052348       1.932634   \n",
       "191643  1.478186e+00 -1.170875    -0.562976 -1.526271      -0.517429   \n",
       "191644 -3.479598e-01 -0.167284    -0.127704  1.421574      -0.517429   \n",
       "191645 -3.479598e-01 -0.167284    -0.127704 -0.052348      -0.517429   \n",
       "191646 -2.174106e+00 -0.167284     1.613383  1.421574      -0.517429   \n",
       "191647  5.651132e-01 -0.167284     1.178112  1.421574      -0.517429   \n",
       "191648 -4.054859e-16 -2.174466    -0.562976 -1.526271      -0.517429   \n",
       "191649  1.478186e+00 -0.167284     0.742840  1.421574      -0.517429   \n",
       "191650 -3.479598e-01 -0.167284    -0.562976  0.684613      -0.517429   \n",
       "191651  1.478186e+00 -2.174466    -0.998247 -1.526271      -0.517429   \n",
       "\n",
       "        OST_WEST_KZ_W  GENERATION  MOVEMENT  CAMEO_INTL_2015_tens  \\\n",
       "0            0.517429   -1.629742  1.875412             -1.550667   \n",
       "2            0.517429   -1.629742  1.875412             -0.179668   \n",
       "3            0.517429   -2.328600 -0.553153             -0.865168   \n",
       "4            0.517429   -0.232027 -0.553153              0.505832   \n",
       "5            0.517429   -1.629742  1.875412             -0.179668   \n",
       "6            0.517429   -1.629742  1.875412             -0.865168   \n",
       "7            0.517429   -1.629742  1.875412             -1.550667   \n",
       "8            0.517429   -2.328600 -0.553153              1.191332   \n",
       "9            0.517429   -0.232027  1.875412             -1.550667   \n",
       "11           0.517429   -1.629742  1.875412             -1.550667   \n",
       "12           0.517429   -0.232027 -0.553153             -0.865168   \n",
       "13           0.517429   -0.232027 -0.553153              0.505832   \n",
       "14           0.517429   -2.328600 -0.553153              1.191332   \n",
       "15           0.517429   -0.930884  1.875412             -1.550667   \n",
       "16           0.517429   -1.629742  1.875412             -1.550667   \n",
       "17           0.517429    1.165689  1.875412             -1.550667   \n",
       "18           0.517429    1.165689 -0.553153              0.505832   \n",
       "19           0.517429   -0.232027 -0.553153             -0.865168   \n",
       "20           0.517429    0.466831  1.875412             -0.179668   \n",
       "21           0.517429   -0.930884 -0.553153              0.505832   \n",
       "22           0.517429   -2.328600 -0.553153             -0.865168   \n",
       "23           0.517429   -0.232027 -0.553153              1.191332   \n",
       "24           0.517429   -1.629742 -0.553153             -0.179668   \n",
       "25           0.517429    0.466831  1.875412             -0.865168   \n",
       "26           0.517429   -1.629742 -0.553153             -0.865168   \n",
       "27           0.517429   -0.232027  1.875412              0.505832   \n",
       "28           0.517429   -2.328600 -0.553153              1.191332   \n",
       "29           0.517429   -0.930884 -0.553153              1.191332   \n",
       "30           0.517429   -0.930884  1.875412             -0.179668   \n",
       "31           0.517429   -0.232027  1.875412             -0.865168   \n",
       "...               ...         ...       ...                   ...   \n",
       "191622       0.517429   -2.328600 -0.553153              0.505832   \n",
       "191623       0.517429   -0.930884  1.875412             -1.550667   \n",
       "191624       0.517429   -0.232027  1.875412              1.191332   \n",
       "191625       0.517429   -1.629742  1.875412             -0.865168   \n",
       "191626       0.517429   -0.232027 -0.553153             -0.865168   \n",
       "191627       0.517429   -1.629742 -0.553153              1.191332   \n",
       "191628       0.517429   -0.930884  1.875412             -0.865168   \n",
       "191629       0.517429   -2.328600 -0.553153              1.191332   \n",
       "191630       0.517429   -1.629742  1.875412              1.191332   \n",
       "191631       0.517429   -1.629742 -0.553153              0.505832   \n",
       "191632       0.517429   -0.232027 -0.553153             -1.550667   \n",
       "191633       0.517429   -0.930884  1.875412             -1.550667   \n",
       "191634       0.517429   -0.930884 -0.553153             -0.179668   \n",
       "191635       0.517429   -2.328600 -0.553153              0.505832   \n",
       "191636       0.517429   -2.328600  1.875412             -0.179668   \n",
       "191637       0.517429   -0.232027  1.875412             -0.865168   \n",
       "191638       0.517429   -0.930884 -0.553153              1.191332   \n",
       "191639       0.517429    0.466831 -0.553153             -0.865168   \n",
       "191640       0.517429   -0.232027  1.875412             -1.550667   \n",
       "191641       0.517429   -1.629742  1.875412             -1.550667   \n",
       "191642      -1.932634   -1.629742 -0.553153              1.191332   \n",
       "191643       0.517429   -0.930884  1.875412             -1.550667   \n",
       "191644       0.517429   -0.232027  1.875412              0.505832   \n",
       "191645       0.517429   -0.232027 -0.553153              0.505832   \n",
       "191646       0.517429   -0.232027 -0.553153             -1.550667   \n",
       "191647       0.517429   -1.629742  1.875412             -1.550667   \n",
       "191648       0.517429   -0.930884 -0.553153             -0.179668   \n",
       "191649       0.517429   -1.629742  1.875412             -0.865168   \n",
       "191650       0.517429   -0.232027 -0.553153             -0.865168   \n",
       "191651       0.517429   -1.629742 -0.553153             -0.179668   \n",
       "\n",
       "        CAMEO_INTL_2015_ones  \n",
       "0                   0.085847  \n",
       "2                   0.761985  \n",
       "3                   0.761985  \n",
       "4                  -1.266430  \n",
       "5                   0.761985  \n",
       "6                   0.085847  \n",
       "7                   1.438124  \n",
       "8                   1.438124  \n",
       "9                   1.438124  \n",
       "11                  0.761985  \n",
       "12                 -0.590291  \n",
       "13                  0.085847  \n",
       "14                 -1.266430  \n",
       "15                  1.438124  \n",
       "16                  1.438124  \n",
       "17                  0.761985  \n",
       "18                 -1.266430  \n",
       "19                  0.761985  \n",
       "20                  0.085847  \n",
       "21                  0.085847  \n",
       "22                  0.761985  \n",
       "23                 -1.266430  \n",
       "24                  0.085847  \n",
       "25                 -0.590291  \n",
       "26                  1.438124  \n",
       "27                  0.761985  \n",
       "28                  0.761985  \n",
       "29                 -1.266430  \n",
       "30                 -0.590291  \n",
       "31                  0.761985  \n",
       "...                      ...  \n",
       "191622              0.085847  \n",
       "191623              0.761985  \n",
       "191624              0.761985  \n",
       "191625              1.438124  \n",
       "191626              0.761985  \n",
       "191627              1.438124  \n",
       "191628             -0.590291  \n",
       "191629              1.438124  \n",
       "191630             -1.266430  \n",
       "191631              0.761985  \n",
       "191632              0.761985  \n",
       "191633              0.761985  \n",
       "191634              0.085847  \n",
       "191635              1.438124  \n",
       "191636              0.761985  \n",
       "191637              1.438124  \n",
       "191638             -0.590291  \n",
       "191639             -0.590291  \n",
       "191640              1.438124  \n",
       "191641              0.761985  \n",
       "191642              1.438124  \n",
       "191643              1.438124  \n",
       "191644             -1.266430  \n",
       "191645              0.085847  \n",
       "191646              0.761985  \n",
       "191647              0.761985  \n",
       "191648             -0.590291  \n",
       "191649              0.761985  \n",
       "191650              0.761985  \n",
       "191651              0.085847  \n",
       "\n",
       "[141725 rows x 65 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141725, 65)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_customers.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_X_customers = pca.transform(scaled_customers)\n",
    "pca_X_customers_df = pd.DataFrame(data=pca_X_customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.89617722,  0.90102269,  4.25132427, ...,  0.30986512,\n",
       "        -0.3386009 , -0.02077607],\n",
       "       [-1.53201725,  4.92349142, -0.6639615 , ...,  0.17221706,\n",
       "        -0.02535044, -0.61606305],\n",
       "       [-4.3761865 ,  0.37014691,  2.69704582, ..., -0.84695825,\n",
       "        -0.18394769,  0.43612314],\n",
       "       ..., \n",
       "       [-4.45847029,  2.7202443 ,  2.40706778, ...,  0.05764122,\n",
       "         0.44285308, -1.23692741],\n",
       "       [-1.01287026,  2.76186265, -1.98855505, ...,  1.32775347,\n",
       "        -1.13247649, -0.023585  ],\n",
       "       [-3.72105162, -2.67188136,  2.31863576, ..., -0.5846864 ,\n",
       "        -0.83774814,  0.38338766]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_X_customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_of_customer = model.predict(pca_X_customers_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 3, ..., 3, 0, 3], dtype=int32)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_of_customer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "At this point, you have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, you will compare the two cluster distributions to see where the strongest customer base for the company is.\n",
    "\n",
    "Consider the proportion of persons in each cluster for the general population, and the proportions for the customers. If we think the company's customer base to be universal, then the cluster assignment proportions should be fairly similar between the two. If there are only particular segments of the population that are interested in the company's products, then we should see a mismatch from one to the other. If there is a higher proportion of persons in a cluster for the customer data compared to the general population (e.g. 5% of persons are assigned to a cluster for the general population, but 15% of the customer data is closest to that cluster's centroid) then that suggests the people in that cluster to be a target audience for the company. On the other hand, the proportion of the data in a cluster being larger in the general population than the customer data (e.g. only 2% of customers closest to a population centroid that captures 6% of the data) suggests that group of persons to be outside of the target demographics.\n",
    "\n",
    "Take a look at the following points in this step:\n",
    "\n",
    "- Compute the proportion of data points in each cluster for the general population and the customer data. Visualizations will be useful here: both for the individual dataset proportions, but also to visualize the ratios in cluster representation between groups. Seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) or [`barplot()`](https://seaborn.pydata.org/generated/seaborn.barplot.html) function could be handy.\n",
    "  - Recall the analysis you performed in step 1.1.3 of the project, where you separated out certain data points from the dataset if they had more than a specified threshold of missing values. If you found that this group was qualitatively different from the main bulk of the data, you should treat this as an additional data cluster in this analysis. Make sure that you account for the number of data points in this subset, for both the general population and customer datasets, when making your computations!\n",
    "- Which cluster or clusters are overrepresented in the customer dataset compared to the general population? Select at least one such cluster and infer what kind of people might be represented by that cluster. Use the principal component interpretations from step 2.3 or look at additional components to help you make this inference. Alternatively, you can use the `.inverse_transform()` method of the PCA and StandardScaler objects to transform centroids back to the original data space and interpret the retrieved values directly.\n",
    "- Perform a similar investigation for the underrepresented clusters. Which cluster or clusters are underrepresented in the customer dataset compared to the general population, and what kinds of people are typified by these clusters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ee54d2c50>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ee54bffd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the proportion of data in each cluster for the customer data to the\n",
    "# proportion of data in each cluster for the general population.\n",
    "sns.countplot(prediction_of_customer)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "141725"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_of_customer.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 3, ..., 3, 0, 3], dtype=int32)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_of_customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(798073,)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_azdias.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ee54bff60>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ee594dc88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(prediction_azdias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5e8d9b9d68>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ee54bca90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=prediction_of_customer, y=prediction_of_customer, estimator=lambda x: len(x)/len(prediction_of_customer)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ee54cbbe0>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ee6689f60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=prediction_azdias, y=prediction_azdias, estimator=lambda x: len(x)/len(prediction_azdias)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scaled_comparison(df_sample, kmeans, cluster):\n",
    "    X = pd.DataFrame.from_dict(dict(zip(df_sample.columns,\n",
    "pca_reduced.inverse_transform(kmeans.cluster_centers_[cluster]))), orient='index').rename(\n",
    "columns={0: 'feature_values'}).sort_values('feature_values', ascending=False)\n",
    "    X['feature_values_abs'] = abs(X['feature_values'])\n",
    "    pd.concat((X['feature_values'][:10], X['feature_values'][-10:]), axis=0).plot(kind='barh');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A27QQx9nh7tX2a0ARx9lBKY7zgGZxnFl5JahqhOj+pEZ0DikOdL+iCnXKcp7rvDupB+BxYFR+TtzhesDqwC6SitWn9mfeiled3nOta5V+Lndx71TavoOkScA/Sc+g+1SKWAghLEp6xSCxVvWGOM56JG1Buuu8JQeCLUUacHUu8BKdV5N6PzA9vy8D9wL3Srol13Fk5b3PyAPLtgfuIjXIq0k6MB+ypqQBefWqmZKWysErHa7VxPg8KG8j4E5JV9uu9hL0GTHoKITQl/WWO+h2jKaH4jibGA6MLKI3ba8JrCVpPdKgse0krQ4gaRCwNPCspDXzilOFgcDfq4VLWhLYGngyP4df3vZapajPk5kX9fn/gM/m85YFPgM0SjHrwPbjubxjmh0bQghh4ehzDXQPx3E2sn+NelwN7G/7X6Rc6xvz8+SfAsNzV/i7gdMlPZr3DaNjBnbxDHoSaSraVdR/z0U39zdJI9cnkkZ5/9Z2+XnyiMp77l/j/ZwPfKzG44QQQgjdIKI+Q5dF1GcIIbQvoj5DCCGEPqxPDRLrLs3iOPNgtANIS0LOAb5CWv1pDWBmPn6K7aF5RPYPgQG2p+TzRwBnAh/OSWJPk5K+pufgknOBTUhfoG4gjeQuBnxV67oj8xLAliNN2TrV9g2lYw4Fvp1fvgZ82/aded/twPrAenmwGpKuAXYtp5H1RZEk1rfEoL4QOooGuoYijrMWSdsCg4GtbL+dE8qK5RoPtF2rz/dB0jPqE/LroUCnUeN5VPlVwM9t751HqF8InAgc3aDK420PzmUMBK6RNNP2rZIGk75AbJ+/AGyV93+kNI3q36QvJHdKWoH0RSOEEEIPii7u9q0BTC/mTNuebvu5JudcA+wNIGl9UrZ2rRiunUmjyi/OZc8mJaB9QdJyrVQuT4v6EXB43nQM6Q68mNJ1P2lqWflLyFjmjQD/NOlLQgghhB4UDXT7/gCsI+lxSedJ+nhp32WlkdGnlba/RppStRlppPW4OmVvCtxX3pBDVp4BNmyjjvcD/1WvTGBC3l64lTRiewlSQ12vfhH1GUII3SQa6DbZngF8CDiUdBc8Lq8WBamLu0joqnZJF3ep+9B5ilRB1E4dq7e9HrWwv1zebNJCI8OAZessVgJE1GcIIXSXaKC7wPZs27fb/iGpK3nfFk67Hvgc8Ey+K65lMikTfC5J7wXWAZ5so4pbAo/knx8mfaEo24rOz8DHAucwLz40hBBCD4pBYm3KKV5zcqQmzEv+2qzRebZnSjqGlLddz63AKZIOsn1p7nI+Axht+80W67cF8L/Al/KmU4GfSNrd9kt5ENkhpFSysvGk9LAxrVynL4hRwSGEviwa6Pb1A87Jo51nAVNI3d1XkJ5BF9OsptvetXyi7bE0YNt5Za3zJP0vqYfjRmqv4FW2g6S/kaZZvQAcYfvWXOZ1eUGQuySZtLTlZ20/X702cHqT64QQQugmkSQWuiySxEIIoX2RJBZCCCH0YdHF3UdI2o2UVlY2tcn62SGEEPqo+W6gJc0oR0LmKUeDbB+eYy5n2D69tP/pvL/m+sSSZpOStwpjbZ+SIymPKkVj3md733zOUGCw7UMq138XaW3l2aRlJt9LGqlcxHj+GfiG7Vfzik5TgRNs/28ud2XgeeCC0vtpJbbz9XxNgDtsHyFpNPA/wPqlBLIJeanIWp/Du0irXu1MmhL1FjDE9lRJ/UiDx3aVNJm03vTRtu9pFBVaiQVdFrjB9lGlf7fTgH+UqnHA/K6T3RtE5GffEIP6QuioN3ZxzyzNJR5o+5Q6xw2StGmdfUVs5vmk5Ry/lAdB/Qp4yvYGtjcgNci/LJ32FCnGs7AfaepTWRHbWagV27lTqf5HlLbPBr5Qr84Vw4A1gS1sbw4MIUVykuv8MumLwqakUdkrl6JCr7E9ANiINKjtxFK5421vSZqKNVhSOXN8XOWz7/ONcwgh9FW9sYFu1ek0Ht18FrAScJDtOZI2JM0H/nHpmB+RGvoN8uuZwCOSiof3w+g8L7jV2M5afkpai7mVnos1gOfzmtHYnmb7lVzXrYHjSvuesv072ogKtT0TmAis1WLdQwghdKMF0UAvW4q3LHKgy0ZU9q/ZTnmShtU57nJgq9zwVh1Aaoz3tz0rb9sEmJgbLWBuAzaRjrGXY4H9c1fxbKCas91KbOdtpfqPKG1/hpTY9bk676n6/j6VyzhD0pZ5+6bV91HSclSopBWBAcAdpc3DKp/9stULRNRnCCF0jwUxSGym7YHFi+IZcGn/qBrPoFsur4HZpGem3wNuquwrsqg/QnrODK3HaP6edJf9L+pnUhexnbsBuwCfr+zfqd4zduAk4Dqg4YNR29NyKMrO+c+tkvZrdA6tvccdJE0CNgZOKa1oBamL+/BOZ3es14WkFbYYNGhQzNELIYSFpC93cQP8GvgYsG5l+6PAZ0g52cXd8WRgyzz4Cpg7EOu/mReLSV53+T7gSODKOtdtJbazpjy4bGKuX7Nj37Z9U871PomU4z0Z+O/y+yhpJSp0vO0tgM2Br+VksRBCCL1Mn55mZfs/kkYB3wX+VNl3l6SvAr+T9DHbU3La1nHM64Y/Drg/7+tfOv0M4P/laMxa120ltrORE2lyB53Xbf6n7edyY7wFMMn2k5ImAMdL+kFOHxtA6sK/jgZRoeX3YvtxSSeTlqMc3sX30SfE6OAQQl/UG++gq8+g643iLvyKOl80bN8AHA/8XtJKpKlWG0maIulJ0ijnL9Y4b7LtSxpd1PbYvLZyLeVn0JfWKp/UDd/IqsD1kh4CJpFiRX+W930JWB2YIulB4BfAc3mk+hBgP0lPkL5AvEX9wXTnk5aZ/EB+XX0G/dEmdQwhhLCQRNRn6LKI+gwhhPZF1GcIIYTQh/XIM+jc3XxrjV272H6pu+vTkyRtThrsVva27epykCGEEBYjPdJA50Z4gYwerhENug/QnxQLOjhP+7oIGGh7Uj7nIVI06NP59ZakZ8K72765VLaBM20fmV8fBfSzPVLSzcBqpeuuCjzbqGHNASX/BH5h+3sAth+U9O9c7qB83CBJt9veMcdzHmV7cKWs20lhJsXyllNsD837Pgt8B1iC9Oz6r7mMf9c7L8eYfpkUurIU8GPbi8za0GUR/dk7xWC+EDpaFLq4q9GgT9c4ZhpwbIMyhpMCRKqjmd8GPp1zszuwvVtxTVK292ukUeGNfAJ4DPiMOg8PX1XSHk3Orzqw9L6Lxnl3UnrYHjkGdCvgLjp+meh0XjYqv5+9gQskvbvN+oQQQlhAFoUGuhU3AJvm4I8OckM5lJRn/QlJy5R2zyKFcoyonldxFnCj7VuaHDc8H/sMsE1l32k0b+BbcSzpbvkfkNLSbF9k+7FWC7D9BPAmsOICqE8IIYQuWBQa6PK0rKvrHDMHOJXa0422Iy3b+CRwO7BnZf+5wIGS3lerYElDSOEg32tUyRybuQvpy8IYOt+t3w28LWmnRuVUXFZ676flbZvSfApXrfPKdd0KeML2CzX2RdRnCCF0g0WhgS53cTdaG/k3wDalOb+F4aToTvLfHRrOnBR2KXBE5TwkrQWcTVqW8e0m9RwM3Gb7TVJC2ZAcJFJ2Au3dRZe7qo+uUb/NcyP8ZCXTvN55IyQ9BtwDjKx1QdsX2h5ke9Aqq6zSRlVDCCG0Y1FooFuSF804g5ScBUBuIPcFfpAzws8B9pD0nsrpPyUFmixfOlfAJaQ861aWZRxOWr/5aVKU6EpAh7tl238ClqFz93c7JpOeO2P7wfxM+SbS+s/NjLK9MWkVr0sr3f0hhBC6UZ+O+uyC0aTRzUUDvCvwgO3digMkXUIaCT536pPtlyVdTmqkL8qbjyIt7Xhus4vmPOztgXWKO21Jnyc12n+sHH4iKeHrqXbfXHYycLqkvW1Py9taaZznsn2VpIOBg4ELuliPXitGC4cQ+oLF5g4a5i6EcTZpShSkBrL63PpK0nKVVWcA5dHcJwAfrERj3lbn0p8G/lTpBr8W2EvS0pU63kjn9aV3kTSt9GfbvL38LPmPpfPPBm6S9LCku0grf91cKq/TeTX8CPh2nUU5QgghLGQR9Rm6LKI+QwihfRH1GUIIIfRhi9sz6IVO0rmkqVtlZ9m+uCfqE0IIoW+KBroFpTjRJYFHgIPz+sozbPerHL5h5fWqwFeBiyUNJA0AW4YUgvJ12/c2ufa1wKq2ty1tG0ka7Na/mKssaQawHvMyzlcnPXsunmd/hBQ8Moo0SvwV4B3gVNtXS/oIKZQFQMBI2/XmlS9SIvqzd4jBeyF0FF3crSnmWm9GatS+Wu/AJhGgpwLH530/yK/rkrQCacrUCjXmb08Hjqxc+6XStc8nR3fm1/8BrgHusL2+7Q8B+wNr59MfAgblY3cnRX3GF7gQQugh0UC3bzyd75LrqUaAGnhv/vl9wHNNzt8XuJ4UoLJ/Zd9FwDBJ72+xLjsD79g+v9hg+++2z8k/v5nnikO6w4/RgyGE0IOigW5DvqPcg46rZ9U7tlYE6LeA0yQ9C5xOk3hQ0jSwMdSOBp1BaqS/2VLlW4gAlbS1pMmk9/fVUoNdPiaiPkMIoRtEA92aZSVNBCaQFrr4VaODG0SAfg0YYXsd0gIcdcuRtBrpTv1O248DsyRtVjnsbODgHITSFknnSnpA0l+LbbbvyStgfRj4Xq0ksYj6DCGE7hHPGFszMz+bbapJBOjBzLvj/S3wywZFDSMN6pqaV6Z8L6mbe25Wd17f+TfA11uo2mRSl3lx7mF5Gc1OE5ltPyLpDWCzWvsXNTE4KYTQG8Ud9ILXKAL0OeDj+eedgScalDMc2N12f9v9gWJQV9WZwFdo/mXrT8Aykr5W2rZc8YOkDxSDwiStB2wMPN2kzBBCCAtJ3EHPn+UkTSu9PpMUATotd4kXXrG9E/Bl4KzcEL4FHFqrUEn9gXWBvxTbbE+V9JqkrcvH2p6el9lsuGa1bUvaBxgl6Tuk6VdvMG/xkO2B70r6D2l5zq/bnt7w3YcQQlhoIuozdFlEfYYQQvsi6jOEEELow6KLu4flZSerU6X+bPuwnqhPCCGE3iEa6Ba0E/Up6WZgtdKmVYFnbW9dJ+rzYqBuTnd3RX2Wyl4XeJgU9Xl6a5/QoikiQLtXjKYPoaPo4m7N4hD1WRgF3NSoXiGEEBa+aKDbt0hGfQLkUd5PkeZMhxBC6EHRQLdhUY76lLQ8acrV8Y0KiajPEELoHtFAt2ZxiPo8ntQlPqPReRH1GUII3SMGibVmcYj63BoYKulUYAVgjqS3bP+shbIXSTFoKYTQk+IOesHrk1GftncoXeunwEmLc+McQgg9Le6g58+iFPUZQgihF4moz9BlEfUZQgjti6jPEEIIoQ+LLu4eFlGfIYQQaokGeiFpNR7U9sWS9qdjPOgQSYMaxIPe2+C6uwM/Io36fgt4DDja9jOSRpMGqb2ayxtj+3hJ65ACUcrWB35uO55Rh24R0apJzB4IhWigF565U7MkXUaKBz2z1oG2dyt+zoEh99E5HvQmSXvm1zvWKifPkz4H2Mv2I3nbXkB/0vxtSI31FZKWAR6WdKntqcDAUjmbAzeSRnOHEELoAdFAd4/xwBYtHjs/8aDHkKZHPVJssH1dnWOXyX+/Ud6YG+7LgMNsP99inUMIISxgMUhsIevmeNCGcZ7ZaXkK2DRgbLEaVsmppGfgNRv2iPoMIYTuEQ30wtPt8aCV8laSNFHS45KOKu06One9rw7sIumjpXP2AHalskpWWUR9hhBC94gu7oWnJ+JBJ5OWp3zA9kvAwNw496seaHuGpNuB7YG7JK0CXADsbfvNVuodwoIUg6NC6CjuoHuHBRUPeipwrKQPlrYtV+vA3PW+NfBk3nQRcI7tv7VT8RBCCAtH3EF3v4USDwpg+0FJ3wQulfQe4CVS9/oPS4edJuk4YCngVuAqSdsCg4F1JR1YOvYW20d3+Z2GEELosoj6DF0WUZ8hhNC+iPoMIYQQ+rDFqoGWNKP0856SnpC0bn59qKRH8597JW1fOvZ2SRNKrwflAVZIWk7SZZIelPSQpDsl9ZPUX9JDleuPLEZUSxot6c3cFV3sP0uS8zrN5J/PKO0/Kpfx+TxCe6KkmZJekXRu6bjRkqaWjrkrbz/VK7qMAAAgAElEQVREUoclJPN7G5R/fjq/j+K8s+frAw8hhNBli+UzaEm7kBK3PpEjMAeT1lTePi/fuBVwjaSP2P5nPm1VSXvYvqlS3DeBf9nePJe9MfCfFqsyBdgb+D9J7wJ2Av5R2v828GlJJ9ueXmy0fTFwcR4MdjnwfuA7lbKPtn1Fi/Uo26l8rRC6S0R9Nhcj3Rcvi9UdNICkHYBfAJ+0XYxgPobUoE0HsH0/adpTecGK05gXv1m2BqVG1fZjlXnMjYwBhuWfdwT+TMrbLswCLqT+Ws8HAL8G/gDs1eI1Qwgh9AGLWwO9NHAtsI/tR0vbNyXlX5dNyNsLdwNvS9qpctxFwDGS7pZ0gqQBbdTnCWAVSSsCw4GxNY45FzhQ0vtq7BsGjCM19MMr+04rdVVfVj6ntH0iKbms7LbS/k5fDCJJLIQQusfi1kD/B7gL+GILx4qUg112ApW7aNsTSSs/nUbqav5r7nquNzy+uv0qYH/SnOTxnQ62XwMuBY7oUDnpw8CLtv9Omi61VW7oC0fbHpj/lKdOjSttH0j6IlK2U2n/qBr1iSSxEELoBotbAz0H+AzwYUnfL21/GPhQ5dit8va5bP+JtMjENpXtM2xfZfvrwP8Be5LmIJcbTEgNePX57ljgx6Q5x3Pq1PunpC8Vy5e2DQf+S9LTpLCR9wL71jk/hBBCH7PYDRLLazIPBsZL+pftX5ESuH4iaXfbLymtwXwI6a626kTS+sxPAUjaDnjY9iuSlgI2AW7PUZrPS9rF9q2S3g/sTlqtqlyfZyQdC/yxQZ1flnQ5qZG+KA8o2w/YwvY/cj12It3dN4oCDaHXigFQIXS02DXQMLfB2x24Q9J029fmxSrukmTgdeCztZZbtH2jpPLD1w2An+c87XcBvwOuzPsOAs4tTZU6vjQwrVzmBS1U+wzg8Pzzx4B/FI1zdgewiaQ18usiMazwkRauAekZ9Oz88yTbB7V4XgghhAUoksRCl0WSWAghtC+SxEIIIYQ+LBroEEIIoReKBjqEEELohVoaJCZpCGm+7gdtPyqpP3CD7c1Kx5xPmn60FPAB4LG863jS9J/tgFfzttdt7yDpS8DJpCSuZYDzbJ+dy/sgabT0+0gBI7fb/pqkXYHDbe+TjzsZ2AIYYvsdSavl8r6SR2iTc7SXJE1zWpZ5yV+fIgWQvAIUA6Nusz0iD/o6EvgS8A5p/vItwDG2Z+c5x+fk9yzSHOYjbL8maUPgwfwZLA3cA3zJ9qxy/SvvvzDM9mPUkGNEzwT+C/h3/jx/YPvORp9lPvdrpFhS8nkjbBcZ3XcCqwAz87/fGcVnF0J3iajProsR8IumVkdxDwfuJAVqjKx1gO2vAuTG6YocgkHeti+pQbimxqmX2f6WpFWAxyT9No+e/hlwqu3f5cZys+qJkkYCHwYG234nbx5GanSHA7/KdSsWg/gSsJntb5XKANjB9r8rxR9Gysbe2varkpYmNdhLA28CFwMTbH82l3MCcAHzEr0esz1QaR3nW0lfUsbVe/81tlff63LADcC3bP8ub9sCGEj6t5lbVvWzlLQP8Hngo3kE+yDSOtAfsl2MSB9me2JeqOMJSZfYnkUIIYQe0bSLW1I/0t3vF0kN9EKRG4qnSNnW5L+n5X22/WClXscAOwN72X6rtGs48C1gfUmrz0eVvg981faruQ5v2z4pz6PemPSF4aTS8SOB7XLvQvl9zQL+Cqw1H3UB+BxwR9E457In2b60emCNz/IY4CjbL+f9E4DLgK/XuE4/4A3m9Sh0EFGfIYTQPVp5Br0P8HvbjwMv55WeumJUKeO5U6OSG7YlgGKJxjNJ85RvlPStShb1x4AvkBa8eLNSxoq27wOuIKWGtWJ8qW5H5O7rd9t+ts7xmwJ/Kyd/5Yb4AVJQSfl9LUu6y7+5TlkHlrOxc9hJvWve38qbqfFZbkLzrPFxkiYBjwAjXWf+XUR9hhBC92ilgS4v4jCWzosytGpEKeO5HH5xoKTJpKUXzyy6qm3/ktSwXAHsAtxdaryeAN6dt1frWnQjt1PXHUp1O5v0THkupbWjJ0r6u6SPUDunm8r2jfNiFC8BU2xPrnPty8rZ2KWu+oYkXSdpck4YK9T8LOsVUXkPw2xvAawHfFfS2q3UI4QQwsLR8Bm0pJVI3cib5YStJUj/Uz9vAdaheG66PXCdpJttvwCQk7IuIsVbPgp8MJ/zPHAw8EdJL9u+I28fDqwk6eD8ek1JH7A9tZ0K5ee0sySta/sZ2zcCN0r6PWkQ1WTS4hTvKu6iJS0BbE66A12Sec+g1yT1BOyZy+mqyZTSwGzvJWkb0gIehXqf5SOkrPE7Ssd2yhrP5b4g6YF8rWnzUd8Q2hIDnULoqNkd9FDgUtvr2e5vex1gKrDA765s30laNvEbAJJ2zwOsyI3cisBzpeMfJQ28GiNpC0mbAEvYXivXtT9phamuPjc/mRTh+b5cB5FGRxfXfhj4bun4HwJ/sf105X09B3wv/5kfvwZ2lFT+v9hytQ6sfpakrPFTc9c9+THFZ4GfV8+VtDzw36QFOEIIIfSQZqO4hwOnVLZdSRpAtbGk8h3WCNu/bVDWqDzqulBdPYp8rXslnQLsAZwl6S3SXfu3bL+YR10DYPuePDL7etI0p6tr1PUSUmPbyPhS/vTfbH+eNIVqWdLykW8BM/I1HsjHHQL8TFLRkP0ZOLRO+VcAIyVtW2PfgZJ2LL3+iu17qgflwWmfAs6QdA7wL+A1Og5UK5v7Wdq+Simj+y+5J+Q14ICipyIbJ2kmaZT6L2w/UKPMEEII3SSyuEOXRRZ3CCG0T5HFHUIIIfRdPb7cZO5aLs9x3gfoT5q3O1jSIaSBYgNtT8rnPEQKJ3k6v96SNAVpd9s3l8o2aTTzkfn1UUA/2yMl3QysVrruqsCztjutAZ3rsJvt4aVtK5MGX61N6oI/lZRMNof0fPow29Mq73FJ0jP8z9n+t9K6zj8lDcQz8BbpWfZJpC9PawPL532XA9/IoSn987UfIw1amwB80fZ/8vU+kuuzFmnpzOeB79p+MD9m+DJQnsS8Iynw5FrS/OllSUlxR1U/ixAWlkgS6z4xIK9v6A130DMr04yernHMNODYBmUUSWfVaVVvA5/OjWkHtncrrkkKYnkNOK56XHYV8D85zaswFLjO9tukBvU9wEa2BwDXkJK6igfmxXvcDHiZlFIGKfVsTWAL25sDQ4C7c50eB862vbTtZUgN+y9L138yH7c5qSH/DIBS1OnlwPdtD7C9FekZ/Aalc0dVPvMiRW287S2BLYHBkrar83mEEEJYyHpDA92KG4BNc4JXB7kRHEoatPUJScuUds8CLgRGNCn/LOBG27fU2mn7NdIUpU+VNu9PGkG+HClGc4Tt2fn4i0lfDnauUdzdzEsVWwN4vpiqZXua7VeU4lI/BPy4dN6PgEGSyg0t+Zr3lso8HLjEOWc7H3Ona8es1mR7JjCR+U8/CyGE0EW9oYFetpSiVR2FXZhD6rL9fo192wFTbT8J3A7sWdl/Lmmk9PuqJ8LchUAG0Xwa1BjylK087Wsj4DZgQ+CZ3IiXVZO6irnSuwDX5U2XA5/K7/2M3FUPKaBlYtHgw9yGeGKNMpcBtgZ+nze1kjg2ovSZ31bdmadjDaDjvOliX0R9hhBCN+gNDXS5i3tIg+N+A2wj6QOV7Q2TznLDeSlwRLVASWsBZ5OmHL3dpJ43ANtLei+pO/mK3Gi2kiq2bClV7P2kVbHIz6g3Jn05mAPcKmmXFsvcoFTmM8Xz+Rrv8R5Jj0g6q7S53MW9U2n7Djnu85+kZ9D/rJYXUZ8hhNA9ekMD3RKnrOszSAs/AHPvSPcFfiDpadLc5T0kvady+k9Ji30sXzpXpDnSp9julKhV4/ozSXepQ8jd23nXFGC9GtcsJ3XNzM+L1yMN6iqeQReLcNxk+2jSs+x9SKlhW+ZBZEV930UKEHkkbyqeQW9I+uKyV94+OV+7KH9r4H9Jy3Y2Mz7HfW4OfE3SwGYnhBBCWDh6fBR3m0YD3yENyALYFXjA9m7FAZIuITVyvy625ejOy0mN9EV581HAW7bPbeP6Y0gDrt4L/CWX/Ua+5pmSvuq0VvRBpJSvP5VPziOwjwCulfRzUkP4T9vP5QZ4C2CS7SmS/kYatPajfPpxwP15X/9Smc9L+i7pLvw6Upf+PTnms3gOXTNxrB7bjyuts30MXc9eD6EtMbI4hI76zB00gNPiD2eTpkRBajxqpYcdUOP0M4DyaO4TgA+q40pSnZ7HVvyBNOp6nDsmvHyPNEXqcUlPAPsBQyrHFO/hb6Q0sv3z+7g+TxubRBrU9rN86BeBjSRNyWllG+VttVwDLCdph9wtPQw4OZ97F2kQ3c9Kx4+ovO/+Nco8H/hYjUcKIYQQukEkiYUuiySxEEJoXySJhRBCCH1YX3sGvdBJOpc0davsrDy3OYQQQugW0UBX2D6suk3SbEnfJH1ejwAH59WlZtjuVzm2boRoHhV9PmnZylnA123f26g+kq4FVrW9bWnbSNJguf7FilSSZpBGid+aD1sdmM28SM+PkJbsHAVsA7wCvAOcavtqpbW/rwA+DIy2fXijeoWwoEXUZ8+JAXq9U3Rxt6Yc1fkO8NV6BzaJED0VOD7v+0F+XZekFUhTplaoMVhrOnBk5dovla59PqX5zsB/SIPJ7rC9vu0PkQaqFWt7v0WajhX52yGE0AtEA92+8aS5x62oRoiaNEUL0rzk55qcvy9preux5BSzkouAYZLe32JddgbesX1+scH2322fk39+w/adpIY6hBBCD4sGug2SlgT2oOPqW/WOrRUh+i3gNEnPAqfTPF50OGnu9Rg6z0eeQWqkv9lS5VuLAG0qoj5DCKF7RAPdmiKqcwLwDPCrRgc3iBD9GmlRjXVIC3jULSevSrUhcKftx4FZkjarHHY2cHCOH22LpHMlPSDpr+2cF1GfIYTQPaKBbk05L/wbOTClpiYRogeTlq4E+C1p4FY9w0iDuqbmGNP+VLq58zKRvwG+3sJ7qEaAHkZauCNa2RBC6IViFPeC1yhC9Dng46RVt3YGnmhQznBgd9t3A+RBYrfQec3qM4G/0vzf8k/ASZK+ZvvneVtbEaAhLEwxkjiEjqKBnj/LSZpWen0mKUJ0Wu4SL7ySV436MnBWfpb9FnBorUJz9Oa65LxvANtTJb0maevysban52U6G655bduS9gFGSfoOafrVG3RcfORp0iC2pfKxn2hlIZEQQggLXkR9hi6LqM8QQmhfRH2GEEIIfVh0cfcwSZ+n81SpP9dKNAshhLD4iAa6i8oxn5L2JIWS7AJ8gfSs+UVSpOdtwGG25+RjlwT+CfzC9vdyxvfFeRDYWOD9wMqSlrL9jqRDgNOAf+RL/8z2LxvUawApzvODwL9JSWY/tH1Hpax3k2JLD8qxpXUjSufncwqhVRH12fvFQL7uFV3c80nSLsA5pBHXz+TNo3K85ibA5qSR24VPAI8Bn8lTsgo/yecNIOVkl9d+Hlea5tWocV4G+B1woe0NcpznN4D1a5S1KSm2dBg0jSgNIYTQzaKBng+SdgB+AXzS9pM1DlmKdBf9SmnbcNLd9jOkRSuKudM7kxargDSPep8uVOlA4G7b1xUbbD9ke3SNui8JLF+pW6EaURpCCKGbRQPddUsD1wL72H60sm9Enmb1PPC47YkAkpYldYPfQMf4zpWAf9uelV9PA9YqlbevpEmSrpC0ToM6tRLnOSzX7R+k7vTryzvrRJSW90fUZwghdINooLvuP8BddOyKLhRd3KsCy0sqEsAGA7fZfhO4EhgiaQlANcoo5r9dT1pWcgvgj6S765ZIulrSQ5KuKm0el+u2OilT/OjS8fUiSudVKqI+QwihW0QD3XVzgM8AH5b0/VoH2P4P8HvgY3nTcGDXHAhyH+nOeSfS0pEr5G5nSEtAPpfLeKnUWP4C+FCDOlXjPIcAh5DulKt1M6nx/xg0jSgNIYTQzWIU93zIo58HA+Ml/ct2h8UvcqP3UWBiXtBie2CdosHNU6yG2/6jpNuAoaSR3AeTus+RtIbt53ORe5FGXtfzG+B7kvYqPYduFOe5PVA8O28UURrCQhcjhEPoKBro+WT7ZUm7A3dImp43j5D0WdJUpknAeaTR0n+qdB1fC5wqaWlS5OZYSScAf2PeSldHSNoLmAW8TLojrleXmfkLw5mSfgr8C3idFD9aGCZpe1LvybRSeY0iSkMIIXSziPoMXRZRnyGE0L6I+gwhhBD6sOji7oMkbQ78urL57Uj9CiGERUc00Cy42M5SeeXYzvuBz3UltjOXNQI4GVjN9qt580rAfwN72b4+H3eDpB1Jud4fAPoBqwBT8zlfB+4FfgTsR1pqEuC3tk/MZVxEmgr2gu3NWvrwQlhAIuqz74oBfgtHdHGX9KbYzpLhwF+BIZXt04BjqwfbHpLr+yVgfOlad5EGgq0JbJ6P2YE0kK0wGti9hTqFEEJYyKKBznphbCeSNiDdCR/HvNSxwgPAq5L+p8WyliP1BnzD9lsAtl+3PbI4xvYdpJHiIYQQelg00ElvjO0klzkGGA9sLGnVyv4TaH1Biw2BZ2y/3uLxNUXUZwghdI9ooJPeGtu5PzA2P/O+ivTseF6h9niYe/ffFkmflzRR0rMtfFEoXzOiPkMIoRvEILGkiO38o6Tv2z6peoDt/0gqYjvHku5ut8uxnTAvtvNWcmxnvovuENtZKvIXpGfVNUnaAhgA3JIfby8FPAVUk75OJD2LnkVjU4B1Jb0nd20X61A/BCzR5NwQFroYaBRCR3EHneU74cHAgZI63UmXYjufLMV2rmu7v+3+wGGk2E6TRnsPzad2iO0sFdkstnM4MLIo3/aawFqS1qvU+w/AiqRR3c3e36+An+V1o8l3/Es1Oi+EEELPiAa6xPbLpFHMx0naO28unkE/ROpxOA/4NLVjO/cqxXZ+W9IU0p11ObZzsqQHgCNoENtJ6t6+urLt6ry96kTSnXozx5KepT8k6W+kZ9uXkO/wJY0B7iY9755W64tKCCGE7hFRn6HLIuozhBDaF1GfIYQQQh8Wg8R6WMR2hhBCqCUa6DZIMvB/tj+XXy9JeqZ7j+3Beds+pDjNpUjTt/7X9jV532hSEtmrpNCTMbaPBwZKuh04ynbNPuM8Wvx10pStV4CDbP8975sNPFg6fKztU3L9GkV7liNONyWlqK1Nmip2KXCC4xlI6CYR9bloiVH58y+6uNvzBrBZDikB+B/m5Woj6b+B04G9bf8XaaT26XnKVOHoPK96IHBwzu1u1U55DvXtdAwomVmK9Bxo+5S8vVm0Z1HvZYHrgFNsb0QaEf5RUn53CCGEHhANdPtuAoqvhkXSV+Eo4CTbUwHy3ycDR9coZ5n89xs19jVzNx3TyTppJdqz5ADgz3nKVjEl63Dgu12oWwghhAUgGuj2jQX2z3OJtwDuKe3bFLivcvyEvL1wWp62NY3UFf1CF+qwO3BN6fWyORWs+DOM9qI9O9U755H3y3O+54qozxBC6B7RQLfJ9iSgP+nu+cbKbjEv1rPetqKLe3VgF0kfbePyt0l6AdgV+E1pe7WLe1z1xCbRnrXqXeiwPaI+Qwihe8Qgsa65jvSseUdSEElhMjAImFTathXwcLUA2zPywLDtSTngrdiJ1CU+mjT469sNjm0n2nMyKcJ0LknrAzPmd3GNEFoVg4pC6CjuoLvmIuBHth+sbD8d+J6k/gD57+8DZ1QLyCOstwZqLW1Zl+2ZwLeAgyS9v8Fx7UR7XgZsL2nXfNyywNnAqe3ULYQQwoITDXQX2J5m+6wa2yeSYj6vl/QoafWq7xRLVGbFM+hJpKlRV3Xh+s+TBqcdljdVn0EXo7gbRnuWypsJ7E2KOH0s1+uvwM/arVsIIYQFI6I+Q5dF1GcIIbQvoj5DCCGEPiwGifUyku4Blq5s/lyN590hhBAWYdFAt0HSsaRQj9nAHOArwE+ANYCZ+bAptodKGgn8EBhge0o+fwRwJvBh2xNyfOcg29MlrQ2cC7yf1LNxA2lK1js16rE88Aywvu1XS9uvIU2/Wg44jVLKWa73m6Q1qB8jDRabAHwR2Dm/D0jzp/+R388k2wd16cMKoU0R9RkKMaI/iS7uFknaFhgMbJXjNncFns27DyzNQR5aOu1BOq7fPJQaU64kiTRY7BrbA4CNgH6kdZ47sf0G8Adgn1IZ7yNN2bohbxpXmRtdXPfJPA97c1Lu9mds31wcR2q0i/cTjXMIIfSQaKBbtwYw3fbbALan236uyTnXkEZHF/OKXwVqxW/tDLyV5ypjezYwAvhCjuysZQwdG/8hwO/z9Kqm8jXupUlkaFUkiYUQQveIBrp1fwDWkfS4pPMkfby077LSFKfTSttfA56VtBkpeaxTwldWK2rzNVI39oZ1zvk98CFJRVDK/nTMBR9WmXq1bPnkPDd661xOyyJJLIQQukc00C2yPQP4EHAo6S54nKRD8u5yF3d1YYyxpMZzH+DqOsXXi9qsG8GZn01fBwyVtDJpdaw/lA6pdnEXz8g3yPOwXyJldU8ihBBCrxODxNqQu4VvB26X9CBwcAunXU8asDXB9mvpcXMnk4F9yxvyIhXr0DhpbAxp2UkB19r+Twv1edL2QElrkN7HXrava+G8EBaqGBgUQkdxB90iSRtLGlDaNBD4e7Pz8p3rMdQZ8JXdCiwn6aB8rSVI8aCjmzxTvg0YQEoUG9PguFr1ep60nOT32jkvhBBC94gGunX9gEskPSxpErAJMDLvKz+D/mP1RNtjbd9fr2CnOLchwH6SngAeB94i5XjXZXsOcCVpwY47Krurz6BrrZp1DemLwQ6NrhNCCKH7RdRn6LKI+gwhhPZF1GcIIYTQh8UgsV5O0m7MS/kqTLU9pCfqE0IIoXv0ygZa0mxSCldhH6A/cJTtwXl600XAwGKakKSHgMG2n86vtwTuB3a3fXOpbANn2j4yvz4K6Gd7pKSbgdVK110VeNb21nXqORr4DLCa7dfztrOAI4BVcoTnDNv98trQU4EjbJ+Tj/0ZaXT36FzWDbavkHQ7sD6wXq77zTnGc1fb/UrXHwGcnK//at62Y/E5Vep6e94+QdIXSEEoJvWiHAt8AtiOFAH6AVIcKMAJtq+o9f5DWJAi6jP0Jd0x66C3dnHPrMzhfbrGMdNIDUs9w4E7899lbwOfznOHO7C9WynycjtS0MhxTeo6hXlpYe8CdqJjBnbZC8A3JS3VpEyAf+c6IGkFUpJZ1XDSus0t303nzO9jge1zZOk2pMztw/L73pM8FSv/icY5hBB6QG9toFtxA7CppI2rO3K29VDgEOATOTWrMAu4kHQH2chZwI22b2ly3BhgWP55R+DP+Rq1vEiaUtXK/Oki4ATg06Ss7rkkbUAaWX4cnb+ENLIq8DowA1IAi+2prZ4cUZ8hhNA9emsDvWxpelC99K05wKnUnoq0Hek57ZOkYJE9K/vPBQ7MC0x0ImkIMIjW5gg/AawiaUVSQzm2yfGnAEfmuc6N3Ap8LB+3P51jQoeTvhyMBzaWtGoLdQV4APgXMFXSxZI+1eJ5QER9hhBCd+mtDXS5i7tR9+1vgG0kfaCyvdxQjqVyh5lzri8lPSvuQNJawNnAAcXCGC24itSIbk1qMOvKd6v3kpZ/bGQ2qYt+GLBsjW7+/YGxeS70VcB+rVQ0p6HtTupheBwYlZfGDCGE0Iv0ykFirbI9S9IZpKQuYG4K177AXnn9ZgErSXpPMZAr+ylpENnFpXMFXAKcUlqesRVjc1mX2J5TJ86z7CTgCjqHi9Qq92rmBaIU9dyClCB2S77WUsBTpJ6BpnIwyr3AvZJuIX0GIxueFMJCFlGfIXTUW++g2zGatDZz0d+6K/CA7XVs97e9Hilta5/ySbZfBi4HvljafBRp2ceWGrpSWc+QBl6d1+Lxj5LWhR7c5NDxpFHa1RjP4cDI/P76214TWEvSes2uLWlNSVuVNrUUWRpCCKF79fkGOq/qdDZp8BOkxqv63PpKancpnwGUR3OfAHywEpF5W4v1uCA/827VicDaTcq07dNtT6/s2p/O7/Fq5g0q20XStNKfbUvHvRs4XdKjeVWrYcA326h3CCGEbhBRn6HLJL0IvAFUv0CEzlYmPqdm4jNqLj6j1vT2z2k9201H2UYDHeaLpAmtZMou7uJzai4+o+biM2rNovI59elBYt1F0rnk0JCSs2xfXOv4EEIIYX5FA90C24f1dB1CCCEsXvr8ILHQ4y7s6Qr0EfE5NRefUXPxGbVmkfic4hl0CCGE0AvFHXQIIYTQC0UDHdoiaT9JkyXNkVR3lKSk3SU9JmmKpO92Zx17A0nvl3SLpCfy3yvWOW52ac79dd1dz57Q7HdD0tKSxuX99+SlWhcrLXxGh0h6sfS786WeqGdPknSRpBfyUsO19kvS2fkznFQJaOoTooEO7XqItLpW3ZjSHLd6LrAHsAkwXNIm3VO9XuO7wK22B5AWPqn3JaWcO79X91WvZ7T4u/FF4BXbGwKjgJ90by17Vhv//Ywr/e78slsr2TuMJq0rUM8epEjkAcChwM+7oU4LVDTQoS22H7H9WJPDPgJMsf1UTnobS14zezGyNynXnfz3Pg2OXZy08rtR/uyuICXjNQ24X4TEfz8tsH0H8HKDQ/YGLs2JjH8BVpC0RvfUbsGIBjosDGsBz5ZeT8vbFier2X4eIP9dbznQZfL62n+RtDg04q38bsw9xvYs4FVgpW6pXe/Q6n8/++au2yskrdM9VetT+vz/h2IedOhE0h+B1WvsOtb2ta0UUWPbIjddoNHn1EYx69p+TtL6wJ8kPdhmpntf08rvxmLx+9NAK+//emCM7bclfZXU47DzQq9Z39Lnf4+igQ6d2N51PouYBpS/0a8NPDefZfY6jT4nSf+StIbt53O32gt1yngu//2UpNuBLYFFuYFu5XFrnA8AAAFYSURBVHejOGaapCWB99G4K3NR0/Qzsv1S6eUvWMye07eoz/9/KLq4w8LwV2CApA9IWoq0ytZiMUK55Drg4PzzwUCnngdJK0paOv+8MilOtp11yPuiVn43yp/dUOBPXrwCG5p+RpVnqXsBj3Rj/fqK64CD8mjubYBXi8dOfUXcQYe2SBoCnENaf/t3kiba3k3SmsAvbe9pe5akw4GbgSWAi2xP7sFq94RTgMslfRF4BtgPIE9N+6rtLwEfBC6QNIf0ZfkU24t0A13vd0PSj4AJtq8DfgX8WtIU0p3z/vVLXPS0+BkdIWkvYBbpMzqkxyrcQySNAXYEVpY0DfghaTldbJ8P3AjsCUwB3gQ+3zM17bpIEgshhBB6oejiDiGEEHqhaKBDCCGEXiga6BBCCKEXigY6hBBC6IWigQ4hhBB6oWigQwghhF4oGugQQgihF4oGOoQQQuiF/j8G0saYbDatkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ee5598be0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scaled_comparison(scaled_customers, model, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What kinds of people are part of a cluster that is overrepresented in the\n",
    "# customer data compared to the general population?\n",
    "\n",
    "# 3 is overrepresented\n",
    "# FINANZ_MINIMALIST (financial typology) are most related feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5edfd80668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What kinds of people are part of a cluster that is underrepresented in the\n",
    "# customer data compared to the general population?\n",
    "\n",
    "# 5,6 is underrepresented\n",
    "plot_scaled_comparison(scaled_customers, model, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Txb4LvBHYKEd1XglcrkVzqor3uBnwDGnUNcBo4O3AFrY3Jw0Uezbv+xnwN9sb2t6Q1LD/tHT9bpGdAJLWBH4JfN32UNtbkdbD3rB07oTKZ15cc4rtLYEtgRGStiOEEEK/6IQGuhXXAptK2ri6IzeCo4BDgA/mhKzCAuDHpNjMRiYC19m+sdbOnOp1G+kOubA/MCk32ocCY/M8aXJk58ukO+OqO1kUr7kW8GQejY3t2bb/I+mdpFCU75TO+zYwXFK5oa0V2flF4ELbd5SOud32lU0+g3KZ84Fp1IgBjSSxEELoG53QQK9UmgpUL2nrNVKXba1pR9uRnsk+QgoR+XBl/9nAgbnLuBtJI4HhNJ8PvDD7WtLbSQta3EKK4nwsN+JlU0kRnuVrLQfsSkoAg3Sn+9H83k/PXfWQpnBNKxp8WNgQT6tRZjWyc1NSd38jY0uf+S3VnZJWJc2vrk7diiSxEELoI53QQJe7uBsNfLoY2FbS+pXtY0hxmuR/u3Rz54bzIuCIaoGS1gbOBA4oFsFo4Fpg+5zw9THg0txothLTuVIpXvOtwI25brOBjUlfDl4DbpK0a4tlthTZKemPkv4iaWJpc7mLe+fS9h3yHO9/kp5B/7PRBxJCCKH39PsgsVbZXiDpdFIqF7DwjnRfYM+8VrOA1SS90fbzpdO/T7qrPL90roALgZNLSzE2uv58Sb8lPSfen0Xd5jOB9WpccytSzCfkLyH5Lv5a0jPoM3O5LwO/AX4j6V+kQXITgS0lva7o/s4Dyv6HNDAN6kd2zsjXviqXv42kUaSlMpuZkgfmbQTcLukK29NaOC80EIOUQgg90Ql30O24gLQOc9G3uhtwr+11bQ+xvR4pWWvv8km2nyF1Jx9W2jyOtMTj2W1cfxLwVdLo7z/ksl8gNfRn5C8M5MjOlYGbK/V4jnQnP07S6yVtlbvLiwZ4C+DvtmcC99B10NrxwJ/zvnKZ1cjOs4FDKslh9ZasrMn2Q6SBZUc3OzaEEELvGFANdF7B6UzSlChI3dnV59aXAQfUOP10oDya+wRgk0ocZrfnsRU3kEZdX+KuEWzHkqZIPZSjOvcDRlaOKd7DPcC9pLvwwcA1edrYfaRBbT/Ihx4GbCRppqRHSM+8D6uWly2M7Mzd0qOBk/K5d5AG0f2gdPzYyvseUqPMc4AdazxSCCGE0Aci6jP0WER9hhBC+yLqM4QQQhjABswgsb4i6WwWpXcVJua5zSGEEEKfiAa6wvYX6u3L0aH/1/Yn8uvlSTGaf7Q9Im/bmxQqsgLwX+AbRUiIpAuADwDPASsCk2x/K++7lRRvWrPPWNKjpNhOA/8BDrL997yvGpc62fbJuX7fJj0TfyHv+5XtE/N582wPyr9vSooWXYc0Gv4i4IRaz9FD+yLus//EKPowUEUXd3teADaTtFJ+/X+AfxQ7Jf0PcBqwl+13AXsCp0naolTGUTmicxhwcJuDsHa2vQUpkKU8wrsal3py3n4CaVDb5vmaOwCvrxaa38/VpClnG5Gmc70f+HwbdQshhLAERQPdvt8AxVfyMaSpV4VxwHdtzwLI/54EHFWjnCKS9IUa+5opx4XWlCNIP01aYOOlXJ/nbY+vcfgBwO9t35CPe5EUGXpMjXIj6jOEEPpANNDtmwzsnyM2twD+WNq3KXB35fhq5OepOQFsNqkr+qke1GEP0tSqwkqVaVOjWRRB+nztIrroVu8cnTooJ6eVt0fUZwgh9IFooNuUIzWHkO6er6vsrhXRWd1WdHG/Ddi1EijSzC2SniIFtFxc2l7t4r6keqKkQ3Pj/bikdVuodyGeQYcQQj+IQWI9czXpWfNOwGql7TNIC2+Uc7G3Iq0P3YXteXlg2PbAHdX9dexM6hK/gDT466sNjp0JvKOIIM2j0M/PoSjLVY6dAexY3iBpA2Bei3fgoYkYqBRCaFfcQffMecC3bU+vbD8NOLZI5sr/fp2UYtZFHmG9DfBIOxfOS0F+BThI0lsbHPciaU3pHxRLcOYo0hVqHP4L0kIgu+XjViIltp3STt1CCCEsOdFA90Bet3lije3TSPnV10j6K2mxjK9VFpwonkHfR5oadXkPrv8kaXBaMSWs+gy6GMV9HGka2P2S7gGmkHLDn6iUNx/YCzhe0oO5Xn+iazxoCCGEPhRRn6HHIuozhBDaF1GfIYQQwgAWg8RaUErqWp60HvPBtl8sJ3GVjr2etBxlYTDweF6XeRhplagVSStXfd72XZXz/wi8obRpfeBR2/9TOmY88DVgSDFNS9I8YD3gpnzY24BXgWKy8nuBVYEJwLakNLJXgFNsXyFpNeBS4D3ABba/2N6nFNoRyWJ9JwbohYEq7qBbU0xj2ozUqH223oG2dy+mO5EyveeyKPXrFOBbed//UmMQlu1tSufvlM9foUbi2BzgyMq5/y6dew4wofT6v6S507fZ3sD21qQlL9fJp78EfIMUthJCCKGfRQPdvimkEJBWTASus31jfm2gCP54M5XBWjXsSxpoNpnUmJadB4xuNJK7YhfgFdvnFBts/932Wfn3F2zfTmqoQwgh9LNooNuQp0Z9iK4LU9Q7diRpTvSxpc1fIY3ifpw8JatJMUWU6KT8e9k8UiP95ZYqn9LC/tzisXVF1GcIIfSNaKBbs1KeGjUVeIw0v7guSWuT5hEfYPvl0q7PAWNtrwuMbVSOpDVJd+q3234IWCBps8phZ5IW3HhTtwKakHS2pHsl/amd8yLqM4QQ+kY00K0pR2l+yfYr9Q6UJNJc45NtVxPEDmbRvOdfkQZu1TOaNKhrVl5qcgiVbm7bz5IiP1tZdWoGKdWsOPcLwK5AtLIhhNCBYhT3kjcOeMn22TX2PUFaD/pW0jPhhxuUMwbYw/adAHmQ2I10XWYS4AxSqEizv+XNwHclfc72j/K2lZucE3pJjCwOITQTDfTiWVnS7NLrM0hrMM/OXeKF/9jembT848T8LPsl4PBaheaI0HcAfyi22Z4laa6kbcrH2p4j6QpSl3ldti1pb2CCpK+Rpl+9QEo+K677KGkQ2wr52A/W6AUIIYTQByJJLPRYJImFEEL7IkkshBBCGMCii7ufSTqU7lOlfp8HcYUQQlhGRQNNisksIjslfZgUMLIr8EnSc+OnSfGctwBfsP1aPnZ54J/AT2wfWypvfVK4yFtJc48/YfsVSYcApwL/yIf+wPZPgfMb1G0scBKwpu3n8radcl32tH1N3nYtaW71l0nxoINII7Rn5aI+D9xFWkd6P9LzZ4Bf2T4xl3EeMAJ4KqemhT4QsZ+9KwbkhYEqurhLJO0KnEUaPf1Y3jwhR2W+G9icNAq78EHgQeBjeXpV4Xv5vKGkzOvDSvsuKU3Z+mkL1RpDGqU9srJ9Nmk5yS5sj8z1/RQwpXStO0gD2N4ObJ6P2QF4fen0C4A9WqhTCCGEXhYNdCZpB+AnwEdsP1LjkBVId9H/KW0bQ7rbfoy0AEUxD3oX0sITkOZE793DOm1IuhM+nu5JYvcCz0n6Py2WtTKpN+BLtl8CsP287fHFMbZvA57pSV1DCCEsWdFAJ28ArgL2tv3Xyr6xecrUk8BDtqcBSFqJ1A1+LV2jOFcDnrW9IL+eDaxdKm9fSfdJulTSuk3qVUR9TgE2ljS4sv8Eus+LruedwGO2n2/x+Joi6jOEEPpGNNDJf4E76NoVXSi6uAcDq0gq0rxGALfYfhG4DBgpaTlANcoo5rJdQ1oicgvgd6S760b2BybnZ96Xk54dLyrUngIL7/7bIulQSdMkPd7CF4XyNSPqM4QQ+kA00MlrwMeA90j6eq0DbP8X+C2wY940Btgth3vcTbpz3pm0DORb8gAySMs5PpHL+Hcpm/snwNb1KiRpC2AocGO+xv507+YGOJEaz6JrmAm8Q9Ibc13Oz188ngOWa+H8EEIIfShGcWe2X5Q0Apgi6V+2uyxkkZ8tvx+Ylhen2B5Yt2hw83SpMbZ/J+kWYBRpJPfBpO5zJK1l+8lc5J7AXxpUaQww3vZJpTrMkrRepd43SPoOafBXs/f3M+AHkj5j+6V8x79Cww8m9LoYZRxCqCXuoEtsP0MaxXy8pL3y5uIZ9P2kLzQ/BPYBbq6sVHUVsKekN5DiM78qaSbpzrpo7I+QNEPSvcARwCENqrM/cEVl2xV0Xxca0l30Oi28xeNIz9Lvl3QP6dn2heQ7fEmTgDtJz7tnS6rV5R9CCKEPRNRn6LGI+gwhhPZF1GcIIYQwgMUz6H4maXPg55XNL9veptbxIYQQlg3RQPfQkooHtT0dGFaJB31Q0gpN4kHr1WsoMAHYBHgWmAt80/ZtlbJeTxqkdlAeQHY9sGapqMHA4/FFoe9F9OeSFYPwwkAVXdyLqZPiQSWtCPwa+LHtDW1vDXwJ2KBGWZsCrwCjAWzvXlwD2I7UsLcaghJCCGEJiwZ6MXRgPOiBwJ22ry422L7f9gU16r48sEqlboWJwHW2b+xBHUIIISwB0UD3XCfGg25KWj2rkdG5bv8gdadfU94paSQwHDi2xrkR9RlCCH0kGuie69R40IUkXSHpfkmXlzZfkuv2NmA6cFTp+LWBM4EDKnO8F1Uqoj5DCKFPRAPdcx0XDwrMALYqXX8kKQzlrTXqZlLjvyMs7Ga/EDjZ9gMNrhFCCKEPxCjuxdCB8aAXA8dK2rP0HHrlBsdvDxTPzscBL9k+u6U3H3pNjDoOIUA00IvN9jOS9gBukzQnbx4r6eOkqUz3keJBR1M7HvSUUjzoZEknAPfQNR50T2ABaa3mQxrUZX7+wnCGpO8D/wKeJy1LWRgtaXtS78nsUnknALPz8+nCf2zv3PqnEUIIYUmJqM/QYxH1GUII7YuozxBCCGEAiy7uASjiQUMIYekXDXQvkfQqaRrT8qSBXQfnQWULI0JLx9aN2ZQ0DDiHFHiyAPi87buAYXWuuwfwbeBNwEuk1LKjbD8m6QJSqtlzubxJtr+V51ZfUylqA+BHto/u8YcQ+kVEhXYVg+7CQBUNdO+Zn+cbI+kXwGeBM2odaHv34ndJq5CmYBUxm6cA37L9m5z5fQqwU61yJG1Gih3d0/Zf8rY9gSGk5DJIjfWlORb0AUkX2Z5FqcHPd+jXAd/vwfsOIYSwBEQD3TemAFu0eGw1ZtOku2GAN5PnR9dxNPDdonEGKMd+VqyY/32hvDE33L8gLfDxZLezQggh9IkYJNbLcvjIh0jd3c2OrRWz+RXgVEmPA6dRJ4IzayXq89Q8lWo2MNn2U5X9pwC/r9ewR9RnCCH0jWige89KuSGcSupe/lmjgxvEbH4OGGt7XWBss3JK5a0maZqkhySNK+06qhT1uauk95fO+RCwG3BkvXIj6jOEEPpGdHH3noXPoJtpErN5MPDl/PuvgLrLTbIo6vNe2/8mrTM9DhhUPdD2PEm3ktLE7pC0BnAusFfOCg8DVAyKCmHpEHfQnaFRzOYTLFpPehfg4QblnAIcJ2mT0raaUZ+5630bFkV9ngecZfuedioeQgihd8QddN9bWdLs0uszaByz+WlgYm5QXwIOr1ew7emSvgxcJOmNwL9J3evfLB12qqTjSWtV3wRcLul9pJW23iHpwNKxN9o+ihBCCH0uoj5Dj0XUZwghtC+iPkMIIYQBLBroFkial/8dIsmSvlTa9wNJh+TfL5D0j7w6FZJWz2s/F+fOzyOri5+D8r5HJV1WKnNUTv1C0pqSrpV0r6QHJF0n6fhc1nxJCyS9LOlpSb8rlTFW0kuS3lzatpOk5yTdI+lBSbfl1a+QdFypXq+Wfj+i1z7YEEIIdcUz6PY9BXxZ0rm2X6mx/1Xgk8CPaux7pMHI7uGSNrU9o7L926RnwRMBJG1h+z7yEpK5Ib/W9qWV88YAfwJGAheUtk+xXTTKw4ArJc23fSJwYt4+r9UR6KHzRNRnVzGqPQxUcQfdvqdJg6sOrrP/+6T1oNv98nMa8PUa29cihYoAkBvnhiRtSJpadTypoa7J9jTSF4AvtlnXEEIIvSwa6J45GThS0nI19j0G3A58osa+DStd3DuU9v0S2ErSOyvnnA38TNItuRv67S3UbwwwiRQxurGkwQ2O/TPwrhbKDCGE0Ieige6BvLjEXcABdQ75LnAU3T/fR2wPK/1MKe17FTiVSpRrneG8AAAgAElEQVSn7etJK0v9hNSQ3pNDRRrZnxTj+RpwObBfg2PVpKyuB0fUZwgh9IlooHvuu6TFKbp9hrZnAtOAj7VZ5s+BHYF3VMp7xvbFtj9Beq68Y70CJG0BDAVuzAPU9qdBNzewJWk5zJZE1GcIIfSNGCTWQ7b/KukBUsDHXTUOORFoa7SO7f9KmgAcA9wMIGkX4A95Lek3AhuyaOnIWsYA422fVGyQNEvSetUDc2P+DeBT7dQzdLYYFBXC0iHuoBfPicA6tXbk0djVlaWqz6BrTWH6GV2/OG0NTJV0H3An8FPbf2pQp/2BKyrbrsjbAXYoplmRnm8fYfumBuWFEELoB5EkFnosksRCCKF9kSQWQgghDGDRQIcQQggdKBroJnLU5sWS/ibpbkl3ShpZis0sP1PeLZ9jSaeXyhgnaXz+fXyOAy2f95ZKDOdfJZ1Woy5XSbqz9LpuPGe+zrh8nHI86MOSHspzqjctlVM3ajSEEEL/iFHcDUgScCVwoe0D8rb1gD2B/1CKzax4GdhH0km259TYP8F2lwY4XSqVJ2kl0nznK2z/Pu9/C7AVME/S+rZnNYrnLL4QZF8A3g/8Tx4N/kHg6hwt+lI+pl7UaAgDWkSf9o+YTbD44g66sV2AV2yfU2yw/XfbZzU5bwHwY2BsTy5qez5pHvXapc37AtcAk1k0IrtVRwNfsv1iLv8G4A6gvPZzvajREEII/SAa6MY2pftUqbIdKl3VG5b2nQ0cWF5NqmRs6ZxbqjslrUoKG7mttLmI75xE4+CRallvAlax/Uhl11TS+yvUixoNIYTQD6KBboOks5WWfSzmIU+pRHcubARtzwUuAmrNdZ5QOmfn0vYd8nznf5JWqPpnvu6awDuB220/BCyQtNnivh2gPMeuZtRot5Mi6jOEEPpENNCNzSA99wXA9heAXYFWMy6/DxwGrNLi8VNsbwFsDnwuLwcJMBpYFZiV4zuH0GI3d/6i8IKkDSq7tgIeqGyrGTVaKS+iPkMIoQ/EILHGbga+K+lztov1nVdu9WTbz0j6JamRPq+N8x6SdBLp2fGY/LOH7TsBJK0P3EhaTrIVpwJnStrP9vw82nx74DOV63aLGg1hoIvBSmGgijvoBpxi1vYGPpDzrO8CLiQ1nND9GfSoGsWcDqxe2Ta2ct6QGuedA+yYG+N3AH8o1WsWMFfSNi2+lbNIi2xMzxGf3wD2yoPRqqpRoyGEEPpBRH2GHouozxBCaF9EfYYQQggDWDTQIYQQQgeKBjqEEELoQDEYqAWSXgWmkz6vvwAH58jMebYHVY69HliztGkw8LjtbfK0qXOAFUlpY5+3fVeTa18FDLb9vtK28cDXgCG2n8rb5gHrAcXazm8jzW0uJiu/lzRVawKwLSmq9BXgFNtXSHovKf0M0hzp8bar60qHMOBE1Gf/iNHziy/uoFszP4eKbEZq1D5b70DbuxchJMB2wFwWTYc6BfhW3ve/+XVdpfztt+TR3GVzgCMr1/536drnUApEAf5LyhW/zfYGtrcmzaVeJ59+PzA8H7sHcK6k+AIXQgj9JBro9k0hpXq1YiJwne0b82sDb8q/vxl4osn5jfK3zwNGS3pri3VpmCtu+0XbC/KuFemaMrZQJImFEELfiAa6DfmO8kOk7u5mx44EhtM1OvMrwKmSHictTtEwVpPG+dvzSI30l1uqfPNccSRtI2kG6f19ttRgLxRJYiGE0DeigW7NSpKmkRaYeIwU5lGXpLWBM4EDbL9c2vU5YKztdUkrXdUtp8X87TOBg/OCGG2pkSuO7T/a3hR4D3CspBXbLTeEEMKSEc8YWzO/vNZyI3kN6QuBk21Xs64PZtEd76+AnzYoqpy/DalrfH9K8Z62n5V0MfD5Fqo2g9RlXpz7BUmrk750dGH7L5JeADartT+EgSQGK4WBKu6gl7xxwEu2z66x7wngA/n3XYCHG5RT5G8PsT0EKAZ1VZ1BytRu9mXrZmBFSZ8rbVuYKy5p/WJQmKT1gI2BR5uUGUIIoZfEHfTiWVnS7NLrM4ATgNm5S7zwn7ys5KeBibkhfAk4vFahOZu7W/62pG7527bnSLqC1GVel21L2huYIOlrpOlXL7AoV3x74BhJ/wVeI00Bm9Pw3YcQQug1kcUdeiyyuEMIoX2RxR1CCCEMYNHF3c8kHUr3qVK/t/2F/qhPCCGEzhANdA9IOg44gBSl+RppkNb3gLWAYo3lmbZH5VjObwJDbc/M548lPa9+j+3zJX2TlOI1R9I6wNmSHib1cFwLHGX7lTp12Qm4CvgbadDXv0jxndeWjjkc+Gp+ORf4qu3b875bgQ2A9fL610i6EtitGmMawkAUUZ/9L0bS90x0cbdJ0vuAEcBWtrcAdgMez7sPLKI1bY8qnTadriOwRwHVKVjFFK3LgSttDwU2AgYBJzap1hTbW9reGDgC+IGkXXOZI0hfILa3/S5STOnFkt5WOv9ZUixpES+6VrPPIYQQQu+KBrp9awFzigAS23NsN4vsvBLYC0DSBsBzLFrEomwX0hSt83PZr5JGZ39S0so1ju/G9jTg28AX86ajSXfgc/L+P5PmaZe70MtRovuQviTUFFGfIYTQN6KBbt8NwLqSHpL0Q0kfKO37haRp+efU0va5wOM5CWwMcEmdsjcF7i5vsD2XlF7Wav43pEjPd9UrkxQ+smnp9U3AjpKWIzXU9eoXUZ8hhNBH4hl0m2zPk7Q1sAOwM3CJpGPy7gNt15t3VNyl7g7sChxa4xhRe5GKetvrUQv7y+W9CtxOSi9byfajOb0shBBCP4kGugdy1/OtwK2SppMiPJu5BjgVmGp7bp0GsEscJ0DO2V4XeKSNKm5JWrca0rPurUlJYoWt6P4MfDJwBTC+jeuE0PFigFIYqKKLu02SNpY0tLRpGPD3ZufZnk96HtxowNdNpHSyg/K1lgNOBy6w/WKL9dsC+AZQRI2eAnxP0mp5/zDgEOCHlVOnACeRVs4KIYTQz+IOun2DgLPyaOcFwExSZOelpGfQxTSrObZ3K59oe3KjgnMc50jgh5K+QfoCdR3w9SZ12kHSPaRpVk8BR9i+KZd5dV5d6w5JBp4HPm77yeq1SUtghhBC6AAR9Rl6LKI+QwihfRH1GUIIIQxgA76LW9KrpCCQwt7AEGCc7RGSDgHOA4bZvi+fcz8wwvaj+fWWpKlJe9i+vlS2gTNsH5lfjwMG2R4v6XpgzdJ1BwOP2+6y2lSlrssD/wR+YvvY0vZbc7nD8+vhwGm2d8pJYeOAs0hpZYV3kkZiF4PHZhbhKJI+DnwNWI7UDf+n/Hk8m69VL/Hs06T52SsA37Edz6PDgBdJYp0hBuu1b2m4g55fSu8aVjS6FbOB4xqUMYY0zWhMZfvLwD6SVq+eYHv34pqkFK65wPFN6vpB4EHgY+o+jHuwpA/VO9H29eX3SZrLvHM1uUzSHqRwkw/Z3pQ0YvsOun6ZqJd4NiGXvRdwrqTXN3k/IYQQesnS0EC34lpgU0kbV3fkhnIUaWTzByWtWNq9APgxTdZaBiYC19m+sclxY/KxjwHbVvadSvMGvhXHke6W/wFpSpjt82w/2GoBth8GXgRWXQL1CSGE0ANLQwO9Uim964o6x7xGmm5UazT0dsAs24+Q5jZ/uLL/bOBASW+uVXAedT0cOLbW/tJxK5ECSq4lTWWq3q3fCbwsaedG5VTUSi7blNRd3+555bpuBTxs+6ka+yLqM4QQ+sDS0ECXu7hHNjjuYmBbSetXto8hhXSQ/+3ScOaozYtIi1B0kacvnQkcUGRzNzACuCXPZ74MGJnnOZedQHt30eWu6qNq1G/z3Ag/Iml0C+eNlfQg8EfqBJZE1GcIIfSNpaGBbontBaTQj6OLbbmB3Bf4X0mPkgZifUjSGyunfx84DFildK5Ii06cbLvbylQ1jAF2y9e5G1iNFBVaruPNwIp07/5uxwzSc2dsT8/PlH8DrNTCuRPyilijgYsq3f0hhBD60IAfxd2mC0ijm4sGeDfgXtu7FwdIupA0EvznxTbbz0j6JamRPi9vHkdaeapI7Korx3VuD6xb3GlLOpTUaP+ucviJwDmk9Z174iTgNEl72Z6dt7XSOC9k+3JJB5MiTM/tYT1C6AgxejgMVMvMHTSA7VdIXdKD86YxpPzpssuAA2qcfjpQHs19ArBJ6VnuNEm31Ln0PsDNlW7wq4A9Jb2hUsfr6L4U5a6SZpd+3pe3l58l/650/pnAbyQ9IOkO0mIY15fK63ZeDd8GvippmfpvJIQQOkUkiYUeiySxEEJoXySJhRBCCAPYsvYMutdJOps0datsou3z+6M+IYQQBqZooBdDngN9ObCJ7b9KGgJ8nrSa1Fn5mB+QIjmRdAHwAeA5QMBXi1WnWozgXIUUa3p8MXK83nl16jsemGf7tDxC+xpSgtp9wDcrh28BfMT2b9r/ZELoHBH1OXDEgL6uooFePEVE6P4smjf8FPBlSefmQWlVR9m+NAeS/Bgory19oO1aD3Un2D4NIM9nvlnS5rafbnJeTZJWIA2Gu9v2t/LmK0r7DwcOpOvAshBCCH0onkH3kKRBpK7sw0gNdOFp4CbSFKVG7gTWbve6ti8BbqD2SPNWLE8KZHnY9jHVnZI2Av4X+ITt13p4jRBCCIspGuie2xv4re2HgGdyPGbhZODIGklhZXsAV1a2NYzgLPkz8K4enAdpHvgC21+p7siLY1xMyvJ+rNbJEfUZQgh9I7q4e24MKWEMFkWEng1ge5aku6h9l3uqpFNIc7GriWGtdlVXV8Jqp4v7duB9kjbKXy7KvgPMsD25xnlAivokdc0zfPjwmKMXQgi9JBroHpC0GrALsFleM3o50kCwH5YO+y5wKXBb5fSjSAPLjiBFhW7dgypsSVpusiduy9f9jaQdbD8BkNed3pccExpCCKF/RQPdM6OAi2x/ptgg6f8B6xSv86juB0iLZNxVPtn2a5ImAgdL2t12y4OxJO1LWlf6yJ5W3vZlktYAfitpR9Id+fmkRT+e72m5IXSiGBkcBqpooHtmDOk5c9lldF/O8kTgnloF2LakE0jPhIsG+heSiulSc2zvln8fK+njpGlW9wO7lEZwNzqvLtvnSHobcDVpMY3BwI/SGiALnZQHpYUQQuhjEfUZeiyiPkMIoX0R9RlCCCEMYNHFvRSSdBywX2Xzr2yf2B/1CSGE0L5ooFuQG7wDSMs2vgZ8Bvge9aM5vwkMtT0znz8WOAN4j+2pkh4FhtueI2kd0vSsd5N6NK4lpY3VSiErRluPsz0ivz4BeA+wp+2X8+Cv8cAXbZ9bOu9RSfvn9wBwm+0jcvzox4A1iwFieQDbEcAatuf0/JMLof9F1OfAEwP7kujibiKvvTwC2Mr2FsBuwON594G2h+Wfcv71dLqmi40CHqhRtkhTrq60PRTYCBhEGlzWSt2OI6WZ7V1aa3o/4A+kgWxVO5fqe0Rp+0xgr1zm64CdgX+0UocQQgi9Ixro5tYijYx+GcD2nGLucANXsqjB24C0OEat2K1dgJeKla5svwqMBT4paeVGF5B0JPBh4KO255d2jSFNwVpHUqtRopOA0fn3nYDfAwtaPDeEEEIviAa6uRuAdSU9JOmHkj5Q2lcvYnMu8LikzUgNZr2pSpsCd5c32J4LPAa8s0GdtgM+C3zI9rxio6R1gbfZvgv4JYsa3cItpfqOLW1/GFhD0qq5vnWTxCLqM4QQ+kY00E3kBnBr4HDSXfAlkg7Ju8td3EdVTp1M6ubem9JKURUiL0XZ4vbCzHzMByvb9yc1zMX1q93c5S7uCZV9l+fztwGm1Luw7R/bHm57+BprrNGgiiGEEBZHDBJrQe56vhW4VdJ0mq9UBWmt5VOBqbbnVgJACjNI8ZoLSXoTsC7wSIOy/0VaDvImSf+2fUvePgZYU9KB+fXbJQ21/XAL9Z1MWoTjwpx01sIpIYQQeks00E1I2hh4rdTIDQP+DmzW6Dzb8yUdDVQXpCi7CThZ0kG2L8qrX50OXGD7xSblPyRpH+BKSR8hjSZfxfbC586SvkW6K/5O43cJth/Lg85+1+zYEAaSGBEcBqpooJsbBJwl6S2kgVMzSd3dl9IkYrPRqlB5vyWNBH4o6RukRw7X0T0ytN75f5J0KCmu8//RvSv9MtKdcdFA3yKpmGZ1n+2DKuWdSwghhI4QUZ+hxyLqM4QQ2hdRnyGEEMIAFl3cHUrS7qS0srJZtkf2R31CCCH0rWigSzol0rPSOL+TlOo1H3g+R31eBfwNWAm41va4yvlXAYNtv6+0bTzwadJUsVVIaWfH234g73uD7WNLxw8DJtnepJ3PMIROE1GfA9uyPMgvurizTor0tH19cT1gaun6xaCuKba3BLYERkjarnSttwBbAW+RtH6l6Am5nKGk8JSbc3Z3OUmssD9wca36hRBC6H3RQC/SkZGejeSIz2lAOdJzX9Ic7CIopd65l5BS0g6w/SDwrKRtSod8jAaJYiGEEHpXNNCLdGKkZ0M5mnMocFtp8xjSHfEkai+YUfZn4F3590nkBl3StsC/awWcRNRnCCH0jWigsw6N9KxnB0n3Af8kPYP+J4CkNUkN/u22HwIW5C8P9ZTjwiYDo/JqVvuTGuxuIuozhBD6RgwSK+nASM96ptgeIWkj4HZJV9ieRnqOvCowK9fjTaTG9vg65WxJesaN7cfzoLYP5Lq+r845IQwoy/IgozCwxR10JmljSUNLm4pIz4byc+CjabyG803AypIOytdqOdKzybUfAk7K14fUpb2H7SG2h5B6BGo+h5a0L2mxjfKd8iRgAvCI7dk9rVcIIYTFFw30IoOACyU9kLuP3w2Mz/vKz6C7ZVXbnmz7z/UKdoprGwnsJ+lhUj73S7QY6dnEOcCOecT2O4A/lK47C5hbGvw1Nr+Hh4GPA7vYLj9I/hXpeXkMDgshhH4WUZ+hxyLqM4QQ2hdRnyGEEMIAFoPE+llEeoYQQqhlqW+g8/KK00ub9gaGAOPySOhDgPOAYbbvy+fcD4yw/Wh+vSVpzvAetq8vlW3gDNtH5tfjgEG2x0u6HlizdN3BwOO2y2Eg5PKuz3OPJwJvAN4laXwu5xDSKPF/ACuQ0sB+UqpDs1jPFYDv2J6U911AGqn9XD78Rdvvr1xnReBc2xOafsAhdLiI+ly6LEuj8peFLu75pTnMw4pGt2I2cFyDMsYAt9M9+ONlYB9Jq1dPsL17Ka5zO1KoSb3pTgAXAofn4zcDflnad0nevhPw3TzfuaVYT1LS2bmSXl/ad1Tp83h/jetsBxwnad0G9Q0hhNCLloUGuhXXAptK2ri6I+dojwIOAT4oacXS7gXAj0mxnY1MBK6zfWODYwYDT0Kaj227W6a37adI86bXy5uaxnrmNLAXSfOjW2L738BMUvxpCCGEfrAsNNArlaZI1Uv6eg04hdrTnrYjPRN+hBRi8uHK/rOBAyW9uVbBkkYCw4Fja+0vmQA8KOkKSZ+pfBEoytoA2IDUeEILsZ6StgIezo174dTSZ/KLGue8g9TNfV+NfRH1GUIIfWBZaKDLXdyNBl5dDGxbo6t4DIvmBU+m0hDmTO2LgCOqBUpaGziTtCDFy40qafvbpIb8BtKSl78t7R4taRqpIf6M7WdaiPUcK+lB4I8sms9dKHdxH1i5zgzSUpYTbb9Uo54R9RlCCH1gqR8k1irbCySdzqJUriLxa19gz7xWtIDVJL3R9vOl079PGkR2fulckZ4rn1yru7pOHR4BfiTpJ8DTklbLuy6x/cXK4c1iPSfYPk3SPsBFkjas1eBWXGL7i3npzV9L+k2R8x3CQLUsDSoKS5dl4Q66HReQ1oEubg13A+61vW6Oz1wPuIw0Enwh28+QBnUdVto8jrTE5NmtXFjSR7QoyHso8CrwbINTWor1tH05KW+7lVzx4pw7gZ8DX271nBBCCEtWNNAltl8hdUkPzpvG0H2FqstIXdBVpwPl0dwnAJuUnvVOk3RLg8t/gvQMehqpcTwwL97RjaQhNI/1LPs28NW8UhV0fQY9TdIKNc75HnCopDc2qHMIIYReElGfocci6jOEENoXUZ8hhBDCABaDxPqYpLNJU7fKJto+v9bxIYQQlk3LZAOdR2QfQBqI9RrwGdIz17WA+fmwmbZH5djMbwJDbc/M548FzgDeY3uqpEeB4bbnSFqHNDf63aQeimtJ05peAbD9hUpddgKuknQEae7xZNvfKrYDs/Khc2zvluszz/ZplXLm2R6Uf9+INLJ8I+C/pKjTLwGbkCNOS+ddkOt4ILA+adnNNUrX/bztO1r+cEPoMBH1uXRbmkfpL3MNdJ5CNALYyvbLOaazGCR1oO1aD1Wnk0ZIn5BfjwK6TZ3Ko7AvB35ke688TevHwInAUQ2qNSXngq8CTJN0bXl7m+9vReDXwFdtX5O37cyikek1FXPE8xeDce1eN4QQwpK1LD6DXot0N/oygO05tp9ocs6VpEzrIs3rOdJCFFW7kKZWnZ/LfpUUA/pJSSs3q5jtF4C7gQ1bfC+1HADcWTTOudxbbN+/GGUuFEliIYTQN5bFBvoGYF1JD0n6oaQPlPb9ojT16NTS9rnA4zmpawxwSZ2yNyU1sAvlpLHHSKlfDeVgkm2BGXnTDqX6NFrMo2yzah0qymVOA/ZssVwgksRCCKGvLHNd3LbnSdoa2AHYGbhE0jF5d70ubli0IMXuwK7AoTWOEVBr3lq97YUdJN1Deh5+su0Zuau57S7uFkyp8Qw6hBBCh1nmGmhY2PV8K3CrpOm0lrJ1DWm95Km25y4K/epiBikadCFJbwLWJa1CVc+SbIhnkNZ7DiGwdA8iCku3Za6LW9LGkoaWNg0D/t7sPNvzSTndJzY47CZgZUkH5WstR0oYu8D2iz2vdVsuBt4vaeH/K0naQ9LmfXT9EEIIS8Ay10CTphFdKOkBSfeRpkONz/vKz6B/Vz3R9mTbf65XsFMs20hgP0kPAw8BL1F7GcvFcbyk2cVPpQ7zSaPUvyTpYUkPkNayfqpGOSGEEDpURH2GHouozxBCaF9EfYYQQggD2DI5SKw/SNqdlFZWNqsICAkhhBDKooFeAtqIDh22pKNDa9RlJ1JE6N+AlYBrbY+rHHMVMNj2+0r13y/v3pyUnAZwnu0ze/q5hNAJIuoz9Ia+mB0QXdyLqRIdugWwG/B43n2g7WH5Z1TptCI6tNAsOvRK20NJ2dqDaDySHNK0rS2BLYERkhYuziHpLcBWwFskrQ9g+8SinsD8Up2jcQ4hhH4SDfTi6+To0PnANGDt0uZ9SXO6i+CVtkTUZwgh9I1ooBdfJ0eHrgoMBW4rbR4DTMo/Y5qVURVRnyGE0DeigV5MtucBWwOHk+6CL5F0SN5d7uKurmZV3MHuDVxRp/jFiQ69D/gn6Rn0PwEkrUlq2G+3/RCwIH9JCCGE0GFikNgS0KnRoXld6NslXWF7GjAaWBWYla/3JtKXhONbqG8IA1JEfYaBKu6gF1MnR4fmu+ST8nUgdWnvYXuI7SGkO/+2n0OHEELofdFAL75Ojw49B9gxj9h+B/CHUvmzgLmStmmjvBBCCH0goj5Dj0l6mtRbsDowp5+r05vi/Q1s8f4GtqXx/a1nu+ko22igw2KTNLWVXNmBKt7fwBbvb2Bb2t9fIzFIbICK6NAQQli6RQM9QNm+Hri+v+sRQgihd8QgsbAk/Li/K9DL4v0NbPH+Bral/f3VFc+gQwghhA4Ud9AhhBBCB4oGOrRN0n6SZkh6TVLd0ZWSHpU0Pc8Dn9qXdVwcbby/PSQ9KGmmpGP6so6LQ9JbJd0o6eH876p1jnu1NI//6r6uZ7ua/T0kvUHSJXn/HyUN6fta9lwL7+8QSU+X/maf6o969pSk8yQ9Jen+Ovsl6cz8/u+TtFVf17GvRQMdeuJ+YB+6LsJRz845i3wgTZNo+v5yqtvZwIdI4TRjJL27b6q32I4BbspLmN6UX9dSXnp0z76rXvta/HscBvzH9juBCXSfBdGx2vjv7ZLS3+ynfVrJxXcBsEeD/R8iLf4zlLT2wY/6oE79Khro0Dbbf7H9YH/Xo7e0+P7eC8y0/Tfbr5AWP9mr92u3ROwFXJh/v5C0YMtA18rfo/y+LwV2VZ0Q/A40kP97a4nt24BnGhyyF3CRkz+Q1rRfq29q1z+igQ69ycANku6WdHh/V2YJWxt4vPR6Nl3X3e5ka9p+EiD/O7jOcSvmtb//IKnTG/FW/h4Lj7G9gLQO+2p9UrvF1+p/b/vm7t9LJa3bN1XrMwP5f3M9EvOgQ005O/xtNXYdZ/uqFovZzvYTkgYDN0r6a/6W3O+WwPurdefVMVMiGr2/Nop5R/77bQDcLGm67UarqPWnVv4eHf03a6KVul8DTLL9sqTPknoLdun1mvWdgfz365FooENNtndbAmU8kf99StIVpG66jmigl8D7m01a9rOwDvDEYpa5xDR6f5L+JWkt20/mLsKn6pRR/P3+JulWYEsaL3Pan1r5exTHzJa0PPBmGnepdpKm78/2v0svf8IAesbeoo7+31xviC7u0CskrSLpjcXvwAdJg6+WFn8ChkpaX9IKpGU7O36kc3Y1i9YsPxjo1mMgaVVJb8i/rw5sBzzQZzVsXyt/j/L7HgXc7IETBNH0/VWex+4J/KUP69cXrgYOyqO5twWeKx7VLLVsx0/8tPVDWgJzNvAy8C/g+rz97cB1+fcNgHvzzwxS13G/131Jvb/8+sOkJUAfGWDvbzXS6O2H879vzduHAz/Nv78fmJ7/ftOBw/q73i28r25/D+DbwJ759xWBXwEzgbuADfq7zkv4/Z2U/7d2L3AL8K7+rnOb728S8CTw3/y/v8OAzwKfzftFGsn+SP5vcnh/17m3fyJJLIQQQuhA0cUdQgghdKBooEMIIYQOFA10CCGE0IGigQ4hhBA6UDTQIYQQQgeKBjqEEELoQNFAhxBCCB0oGugQQsOQ3YQAAAAJSURBVAihA/1/yzjawPTuoRcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5edfd25c88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scaled_comparison(scaled_customers, model, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting findings and conclusions from the clustering analysis. Can we describe segments of the population that are relatively popular with the mail-order company, or relatively unpopular with the company?)\n",
    "\n",
    "These segments are relatively popular with company:\n",
    "3 are much more than the public\n",
    "3 - related high affinity of combative attitude, male, low financial interest\n",
    "\n",
    "\n",
    "These segments are relatively unpopular with company:\n",
    "5,6 are much less than the public\n",
    "5 is mostly related with Personality typology especially low affinity of rational, and relative young age (ALTERSKATEGORIE_GROB)\n",
    "6 is mostly related with Personality typology especially rational, and high financial interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Congratulations on making it this far in the project! Before you finish, make sure to check through the entire notebook from top to bottom to make sure that your analysis follows a logical flow and all of your findings are documented in **Discussion** cells. Once you've checked over all of your work, you should export the notebook as an HTML document to submit for evaluation. You can do this from the menu, navigating to **File -> Download as -> HTML (.html)**. You will submit both that document and this notebook for your project submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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