Refresher Exercises - Python Analytical Stack (Numpys, Pandas)_2.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "56200918",
   "metadata": {},
   "source": [
    "# Refresher Exercises - Python Analytical Stack (Numpys, Pandas)_2\n",
    "source: https://github.com/guiwitz/NumpyPandas_course"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3085698",
   "metadata": {},
   "source": [
    "## Pandas basics\n",
    "Exands based functionality of numpys to allow more robust operations.\n",
    "Equivalient to R loaded with packages such as data.table, tidyr,et al."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dcad2b4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# first we load the necessary packages and data\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "023bed1d",
   "metadata": {},
   "source": [
    "## Importing data\n",
    "This can be done from a local source or directly from a URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7f0750b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# local import\n",
    "projects = pd.read_csv('Data/P3_GrantExport.csv',sep = ';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fc567de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import from url\n",
    "#projects = pd.read_csv('http://p3.snf.ch/P3Export/P3_GrantExport.csv',sep = ';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd507f0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that the same pd.read_csv is valid for both sources (like R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a4221cf",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Project Number</th>\n",
       "      <th>Project Number String</th>\n",
       "      <th>Project Title</th>\n",
       "      <th>Project Title English</th>\n",
       "      <th>Responsible Applicant</th>\n",
       "      <th>Funding Instrument</th>\n",
       "      <th>Funding Instrument Hierarchy</th>\n",
       "      <th>Institution</th>\n",
       "      <th>Institution Country</th>\n",
       "      <th>University</th>\n",
       "      <th>Discipline Number</th>\n",
       "      <th>Discipline Name</th>\n",
       "      <th>Discipline Name Hierarchy</th>\n",
       "      <th>All disciplines</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Approved Amount</th>\n",
       "      <th>Keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1000-000001</td>\n",
       "      <td>Schlussband (Bd. VI) der Jacob Burckhardt-Biog...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kaegi Werner</td>\n",
       "      <td>Project funding (Div. I-III)</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unassignable - NA</td>\n",
       "      <td>10302</td>\n",
       "      <td>Swiss history</td>\n",
       "      <td>Humanities and Social Sciences;Theology &amp; reli...</td>\n",
       "      <td>10302</td>\n",
       "      <td>01.10.1975</td>\n",
       "      <td>30.09.1976</td>\n",
       "      <td>11619.00</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>1000-000004</td>\n",
       "      <td>Batterie de tests à l'usage des enseignants po...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Massarenti Léonard</td>\n",
       "      <td>Project funding (Div. I-III)</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>Faculté de Psychologie et des Sciences de l'Ed...</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>University of Geneva - GE</td>\n",
       "      <td>10104</td>\n",
       "      <td>Educational science and Pedagogy</td>\n",
       "      <td>Humanities and Social Sciences;Psychology, edu...</td>\n",
       "      <td>10104</td>\n",
       "      <td>01.10.1975</td>\n",
       "      <td>30.09.1976</td>\n",
       "      <td>41022.00</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>1000-000005</td>\n",
       "      <td>Kritische Erstausgabe der 'Evidentiae contra D...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kommission für das Corpus philosophorum medii ...</td>\n",
       "      <td>Project funding (Div. I-III)</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>Kommission für das Corpus philosophorum medii ...</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Non-profit organisations (libraries, museums, ...</td>\n",
       "      <td>10101</td>\n",
       "      <td>Philosophy</td>\n",
       "      <td>Humanities and Social Sciences;Linguistics and...</td>\n",
       "      <td>10101</td>\n",
       "      <td>01.03.1976</td>\n",
       "      <td>28.02.1985</td>\n",
       "      <td>79732.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1000-000006</td>\n",
       "      <td>Katalog der datierten Handschriften in der Sch...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Burckhardt Max</td>\n",
       "      <td>Project funding (Div. I-III)</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>Abteilung Handschriften und Alte Drucke Univer...</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>University of Basel - BS</td>\n",
       "      <td>10302</td>\n",
       "      <td>Swiss history</td>\n",
       "      <td>Humanities and Social Sciences;Theology &amp; reli...</td>\n",
       "      <td>10302</td>\n",
       "      <td>01.10.1975</td>\n",
       "      <td>30.09.1976</td>\n",
       "      <td>52627.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>1000-000007</td>\n",
       "      <td>Wissenschaftliche Mitarbeit am Thesaurus Lingu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Schweiz. Thesauruskommission</td>\n",
       "      <td>Project funding (Div. I-III)</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>Schweiz. Thesauruskommission</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Non-profit organisations (libraries, museums, ...</td>\n",
       "      <td>10303</td>\n",
       "      <td>Ancient history and Classical studies</td>\n",
       "      <td>Humanities and Social Sciences;Theology &amp; reli...</td>\n",
       "      <td>10303</td>\n",
       "      <td>01.01.1976</td>\n",
       "      <td>30.04.1978</td>\n",
       "      <td>120042.00</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   Project Number Project Number String  \\\n",
       "0               1           1000-000001   \n",
       "1               4           1000-000004   \n",
       "2               5           1000-000005   \n",
       "3               6           1000-000006   \n",
       "4               7           1000-000007   \n",
       "\n",
       "                                       Project Title Project Title English  \\\n",
       "0  Schlussband (Bd. VI) der Jacob Burckhardt-Biog...                   NaN   \n",
       "1  Batterie de tests à l'usage des enseignants po...                   NaN   \n",
       "2  Kritische Erstausgabe der 'Evidentiae contra D...                   NaN   \n",
       "3  Katalog der datierten Handschriften in der Sch...                   NaN   \n",
       "4  Wissenschaftliche Mitarbeit am Thesaurus Lingu...                   NaN   \n",
       "\n",
       "                               Responsible Applicant  \\\n",
       "0                                       Kaegi Werner   \n",
       "1                                 Massarenti Léonard   \n",
       "2  Kommission für das Corpus philosophorum medii ...   \n",
       "3                                     Burckhardt Max   \n",
       "4                       Schweiz. Thesauruskommission   \n",
       "\n",
       "             Funding Instrument Funding Instrument Hierarchy  \\\n",
       "0  Project funding (Div. I-III)              Project funding   \n",
       "1  Project funding (Div. I-III)              Project funding   \n",
       "2  Project funding (Div. I-III)              Project funding   \n",
       "3  Project funding (Div. I-III)              Project funding   \n",
       "4  Project funding (Div. I-III)              Project funding   \n",
       "\n",
       "                                         Institution Institution Country  \\\n",
       "0                                                NaN                 NaN   \n",
       "1  Faculté de Psychologie et des Sciences de l'Ed...         Switzerland   \n",
       "2  Kommission für das Corpus philosophorum medii ...         Switzerland   \n",
       "3  Abteilung Handschriften und Alte Drucke Univer...         Switzerland   \n",
       "4                       Schweiz. Thesauruskommission         Switzerland   \n",
       "\n",
       "                                          University  Discipline Number  \\\n",
       "0                                  Unassignable - NA              10302   \n",
       "1                          University of Geneva - GE              10104   \n",
       "2  Non-profit organisations (libraries, museums, ...              10101   \n",
       "3                           University of Basel - BS              10302   \n",
       "4  Non-profit organisations (libraries, museums, ...              10303   \n",
       "\n",
       "                         Discipline Name  \\\n",
       "0                          Swiss history   \n",
       "1       Educational science and Pedagogy   \n",
       "2                             Philosophy   \n",
       "3                          Swiss history   \n",
       "4  Ancient history and Classical studies   \n",
       "\n",
       "                           Discipline Name Hierarchy All disciplines  \\\n",
       "0  Humanities and Social Sciences;Theology & reli...           10302   \n",
       "1  Humanities and Social Sciences;Psychology, edu...           10104   \n",
       "2  Humanities and Social Sciences;Linguistics and...           10101   \n",
       "3  Humanities and Social Sciences;Theology & reli...           10302   \n",
       "4  Humanities and Social Sciences;Theology & reli...           10303   \n",
       "\n",
       "   Start Date    End Date Approved Amount Keywords  \n",
       "0  01.10.1975  30.09.1976        11619.00      NaN  \n",
       "1  01.10.1975  30.09.1976        41022.00      NaN  \n",
       "2  01.03.1976  28.02.1985        79732.00      NaN  \n",
       "3  01.10.1975  30.09.1976        52627.00      NaN  \n",
       "4  01.01.1976  30.04.1978       120042.00      NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And now for a brief look at the imported data using head (like R)\n",
    "projects.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "001f66c2",
   "metadata": {},
   "source": [
    "## Exploring data\n",
    "Pandas offers many of the same tools found in data.table and tidyR.\n",
    "As with prior examples, in Python, this is done using object methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c1feec7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Institution abroad - IACH      13348\n",
       "University of Zurich - ZH       8170\n",
       "University of Geneva - GE       7385\n",
       "ETH Zurich - ETHZ               7278\n",
       "University of Berne - BE        6445\n",
       "University of Basel - BS        5560\n",
       "EPF Lausanne - EPFL             5174\n",
       "University of Lausanne - LA     4944\n",
       "Unassignable - NA               2642\n",
       "University of Fribourg - FR     2535\n",
       "Name: University, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example, value counts are done using the value_counts() method\n",
    "projects['University'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b82123d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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SQkREVEkIEREBJCFERES1zglB0naSvizpQkkXSDqwHt9S0imSvlc/b9F5zcGSVkq6WNIeneO7SjqvPnaEJC3s24qIiNtqIVcINwJ/Z/uPgN2BAyTtBBwEnGp7CXBqvU99bBmwM7An8B5JG9avdSSwP7Ckfuy5gLgiImIdrHNCsH2l7bPq7euAC4FtgL2AY+vTjgX2rrf3Ao63fYPtS4CVwG6StgYW2T7NtoHjOq+JiIhG1ssYgqTtgQcCpwN3s30llKQB3LU+bRvg8s7LVtVj29Tb48cjIqKhBScESXcEPg68zPYv5nvqhGOe5/ikc+0vaYWkFddcc81tDzYiIua0oIQg6XaUZPAftj9RD19Vu4Gon6+ux1cB23Vevi1wRT2+7YTja7F9lO2ltpcuXrx4IaFHRMSYhVQZCTgauND22zoPLQf2rbf3BU7sHF8maWNJO1AGj8+o3UrXSdq9fs1nd14TERGNLGQto4cBzwLOk3ROPfYa4HDgBEn7AZcB+wDYvkDSCcB3KRVKB9i+qb7uRcAxwKbASfUjIiIaWueEYPsbTO7/B3jsHK85DDhswvEVwC7rGktERCxcZipHRASQhBAREVUSQkREAEkIERFRJSFERASQhBAREVUSQkREAEkIERFRJSFERASQhBAREVUSQkREAEkIERFRJSFERASQhBAREVUSQkREAEkIERFRJSFERASQhBAREVUSQkREAEkIERFRJSFERASQhBAREdVgEoKkPSVdLGmlpIP6jiciYtYMIiFI2hD4V+BPgZ2AZ0jaqd+oIiJmyyASArAbsNL2D2z/Bjge2KvnmCIiZspQEsI2wOWd+6vqsYiIaES2+44BSfsAe9h+fr3/LGA323879rz9gf3r3fsCFy/w1FsBP1ng11ioIcQAw4hjCDHAMOIYQgwwjDiGEAMMI471EcM9bS+e9MBGC/zC68sqYLvO/W2BK8afZPso4Kj1dVJJK2wvXV9f7/c1hqHEMYQYhhLHEGIYShxDiGEocUw7hqF0GX0bWCJpB0m3B5YBy3uOKSJipgziCsH2jZJeAnwB2BD4gO0Leg4rImKmDCIhANj+HPC5xqddb91PCzCEGGAYcQwhBhhGHEOIAYYRxxBigGHEMdUYBjGoHBER/RvKGEJERPQsCSEiIoAkhJkjadE8j92jZSxzxLBxw3M9pnN7h7HHntoqjnGStpCkvs4/TtJHBhDD7QYQwyZ1zlSfMdxZ0mun9fVnIiFIesV8H41iuE7SLyZ8XCfpFy1iqL7SienUscc+1SIASVdKeu4cD5/WIobqLZ3bHx977HUtApD0j5J2rLc3lvRl4PvAVZIe1yKGW+GhfZxUxWMkvZ8yV6mPGDaU9KeSjgN+CPxlo/NuJ+koSZ+R9HxJd5D0VuC/gbtO67wzkRCAO3U+Xjl2/04tArB9J9uLbC8Cvj+6PTreIoaq2/Lccp7Hpul64DmSPiZp855iGD/X+HlbxfGXrJ5xv2/9vBh4JPCGRjEMiqSHSHon5Q14OfB1YMfGMTxC0nuBS4HnA08AdrD9tEYhHEeZnPsuYGfgW8DdgfvZPnBaJx1M2ek02f6n0W1Je3fv96TP0i7PcXvS/Wn5OfAo4LXA2ZKeZ/srjWMYP1dfP4vfeHWp3x7A8bZvAi6U1Oz/U9KD5noIaNJdI+kw4OnAZcCHgX8GVtg+tsX5O3GsqjEcCbzK9nWSLrH9q4ZhbGn70Hr7C5KuAh5s+4ZpnnQmEsKYWa+zvWvtJlPnNvX+xPVNpqG+Cb5e0snAcZI+RaNumo57SVpO+d5Ht6n3d5j7ZevVDZJ2Aa4CHk25gh25Q6MYAN46z2MXNYphf8rV0pHAZ2xfL6mP/9ePA3tTrt5uknQiPbxvSNqC1VeqPwbuIGkzANs/m8o5Z20egqSzbM/VGprmebuDlG9hzX98bH+iURyHzPd4i6snSWfbfmDn/h2BI4D7AYtt33PaMdTzPnK+x21/tUEMuwPHUJLxO2z/Sz3+Z8CzbD9j2jEMRd0X5QnAM4DHAF8GHgdsZ/vGxrGIkqCfAfwZsAjYD/ic7f9tcP5Lgd8xuevStu81lfPOQkKQdB6rM/x9gJWjhyg/3Ps1iOGD8zxs28+bdgxDIekDk75fSU8DXm+7aX9xgKQ32H5Nvf1426f0HM8mwJMob8gPB061/cyeYrkdZfOuZcATbG/VRxwtzEpCmLfFafuHrWLpm6SdgXvbXl7vvx0YDey+2/ZZvQXXmKQlwGsoYxpvA94H/Amlymc/2ysaxLAJpWviZ8BngFd3YvgX202WW+5eOfd1FT2XWir9Etu9D7JL2tT2rxuc5yW2311v79xqbbdZSQj3Ae5m+7/Gjv8JcIXt7zeI4TTgtba/NOGxU20/dtox1HN9Gvh/tr9Z738X+AdKf/Vf2N67UQxz/uHZfvK0Y6hxfINSzbEIeDnwMuDTlDfk19t+SIMYTgB+C2wGbAGcX2N4OPAA20+adgw1jsEmBABJl9luMk+mlv7O9ffpFv+rff0+ZmVQ+R2UluC4X9fH/rxBDPcA3i3pc8DBtn/beWy8/HOath4lg+oXtj8OIOkFjWIY1f+L0ip/fqPzjrtj3WMDSS+0/dF6/BRJb24Uw062d6kVRatsj8Y1Pi/p3EYxwNzFBgDYflvDWCZpWY78ygnHdqdcvV3dMI6RZt/7rCSE7W1/Z/yg7RWStm8Uw1WUVt8RwOmSnmF7VH/e8jJtjXkXtnfv3J3ahJexc948WCvpf1sM3s7hd53b45MDf0cbv4Gbl4Af3xTqpkYxQEnMd5pweyia/Y/YPnN0uxYe/AOwMfBC2yc1CuPOkp5CmSu2aKwoZWpFKLOSEDaZ57FNWwVR65ifX3/Rp9SBvPfStvVzhaSH2D69e7BWu6y1S10DffZZ7ijpO5Sf/73rber9qVRxTLCtpCPqOUe3RzE021d8VF0maatW4xbjJL2LyX8PAu7cOJY9KIngeuAw219ueX7gq8Co6/RrrNmLYSAJYQG+LelvbL+ve1DSfsCZc7xmamx/UtIZwDG1vPCODU//98BHJB0DjAaQd6XMkm01Lb/bRbbhWL311GqsJ/ijRueZz6s6t8cHsac+qD0i6UnAB4HfSvod8PSxrsUW5vt+W/4svk0pA34zdSmV7sS9FoUXtuda2mWqZmVQ+W7AJymX56MEsBS4PfBU21c2iOHztveccPxVlGqS+a5i1ncsdwVeQpkSD3AB8K+2r2p0/ksorZymNdYxt3p19HTbF0l6CPCmznjGTJH0FVZfqYz/ndr2Y9Z60f8RM5EQRiQ9Gtil3r1gUsVPTJ+ke85Sqe/vg/FKliFWGsX0zVRC6JJ0b8qkl2W2d7ml58f6kzeb4anr93QriV7RvT+AKqNoYFZWOwVA0taSXlb77y8ANqQkhWhrMGv9j5tnkbf/60aVRaOP8fvRM0lT39N5Jq4QJP0N5Y1/W+CE+nGi7VYLmA2apD+w/eOG57saOH6ux22/tFUs44Zw9TKEGIYiP4vVWvwsZuUK4V8pVwPPtP26Oieh90wo6TN9x1B9rvH5fk0Z3J/ro09DuHppHkOdMT26/caxx05uHU/39D2e+2aSDu07BhpMipuVstO7A/sAb6sVRyfQaI33W9CszvwWtP6n+2nrNe5vg773ygD4bA/nXNK5/XhKefJIs2XRJ+jjZzHJk4FD+wxgUpXi+jYTVwi2f2L7SNuPAB4LXAtcLelCSX0umHV2j+fuet8tP2W9+k3j891qtj81gBha7wsB818x93Y13dPPYpJBXKlM20wkhC7bq2y/xfauwF7AVHcguoVYBrHkte33ND7lu0c3JD2s+4CklzSOJYo7SHqgpF2BTevtB43u9x3cAOzadwAtzMqg8lPne7zV5jRRzLeSYwYR+1FX+JyT7Ue3iiX6MytjCPOtZjq1dUFiTkPY3H7Nk0oPB5bY/qCkxZSVUC/pI5Y+DO0NX9KmwD06C0DOpM7aVl3XUvaaPnF9n28mEkJf64LEnIawuf3NVLYVXQrcl7Kez+2AfwceNt/r1tO5u7v5rfEQjXbzGxpJf05ZIv32wA6SHgD8c6t9MgZmE2BHYLQ0+19Q5lDtJ+nRtl+2Pk82EwlhSCT9IWVBs3vS+fm3Xh+lrm76LsoCb7enlOX+0vaiBqcfwiqjXU8BHkhd7M/2FZJaTcZqsgHO75lDgd2ArwDYPqfhMvU3qxWJbwDubvtPJe0EPNT20Q3DuA/wGNc9pSUdCZxMqQQ7b32fLAmhvY8C76VU9rRc737cuyl7xH6U0jp+NuWPr4UhrDLa9RvblmQASZu1OnF3TSeVrV6X2P5i7TKZ1f/PG21fK/Ve2HMM5YrxtfX+fwMfAVomhG0ou+ldW+9vRklQN0la7wUxs/oH16cbbR/ZdxAAtldK2tD2TcAHJTVZ7niAC9udIOnfKJuS/A3wPBqX4tbz7k/ZPe/elFn176WUSfdC0qG2D+3h1OdLeiZlafQlwEuB1ktxA2xl+wRJB8PNmxi1bsS9CTinrsAq4BHAG2qj5Yvr+2QzV3Y60mJdkLHzbVn3Afi0pBfXdZW27Bxv7VeSbk/5Y3uTpJdTWh8zx/ZbgI8BH6eMI/yj7Xc1DuMAypjFL2pM36PRDnbz6KvP/m8pS7PfAHyY8jN5WQ9x/FLSXahjPLWb9dr5X7L+SNoAuBD4Y+BT9ePhtt9v+5e2XzXPy9ftnLNQdjpJ6/LGoe0BULsnrqKMH7wc2Bx4j+2VLeOIQtLpth8i6WzbD1TZY/msPgeVR7H0df6+1YUO30VZMv98yoztp03ajneKMZxm+6GtzjfLXUZNN8se4EJ6DwI+Z/sXDGC5BkkParET1RznfirwRkqLXKyu8GkxwD7yVUmvoUwKezzwYuDTDc8/SS+TsWrhxSuB7emx8ML2WSp7Kt+X8jdxse3ftowBOFnSXwCfcIPW+8xeIfRF0gHAf9j+n3p/C+AZrWcLS/og8BjKfq3HA18YVTL0oc8JaZJWAn9u+8I+zl9j2ADYD3gC5c3nC8D7W7wJDI2kcynjJ2fSKbyw3WThwyFNZJV0HaUr90bK/s5TbawkITQm6RzbDxg71suluaTbAX9K2Uv54cAptp/fOo4aS2/dE5L+y/bU5xzcWnVMaduWXRNDIunMurRMX+f/4DwPeyhLzkzDLHcZ9WUDSRq1/CRtSOnHb872byWdRBnb2JSytlMvCYF+u61WSPoIZdDu5lK+xi3Br1AGcTcCzgGukfRV269oFcOAfFrSiyn7oHd/Hz9rcfIhTWSV9IhJx21/bSrnyxVCW5LeTOkbfS/ljfiFwOW2/65xHHtS5iE8mjIB6CPAyX12G/VljhZh05ZgZzD5+cB2tg+R9J3Wg8pDmIxVCzDG9VF4cRfgEMrVs4FvUGZM/7RhDN1xpE0oE/bOnNZ4ykxcIUh6F/MsieC2O3T9PfAC4EWU/sCTgfc3PP/IcyhjBy+w3duKr32rV2g/mUYJ3220kaStgaezeiJUH46h58lYAyrAOJ4yxvYX9f5fUX4Wj2sVgO011mGTtB1lbsJUzERCAFbUzw8DdqL8UqFsmtN0hy7bvwOOrB+9sb2slp7+CXDzzFjb1/UZV2t1xucQVlf9Z8pA8jdsf1vSvYDv9RDHECZjIemPWbvK6LjGYWxp+186918vae/GMYxbRSmDnYqZSAiuu3NJeg7w6FHpmKT3UlroUyfpBNtPn2sxsx66BgYxM7a20O/Gmv/4l7WMgTI5bzllGY9fduJoNoZg+6OsXsAM2z9gdcu0pV4nY9VzfojyN3kOq6uMDLROCF+WtIyywyLA02i8g9tY78YGwAOAc6d2vlkaQ5B0MaU/9Gf1/hbAt2zft8G5t7Z9ZW2Vr6X1cg6SzqH0R54+qu6RdJ7t/69hDH9L6aO9CvhdPewekuMQxhA2oZSd7kzpKx4F0bSiZSCTsS4Eduqr5LaWeo4mkW7G6r/NDYD/bTk/RdK+nbs3Apfa/q9pnW8mrhA6DgfO1urNQB5Jo31SazLYEDjadrM+yHncYPs3owXE6szY1v+ABwL3bTlIN8lAqko+BFwE7EHpPvoryrIFTQ1kMtb5wB8AVzY+LwC2W610e4tsH1uXmPnDemiq+0PMVEJw2fzkJOAh9dBBtn/c8Pw3SfqVpM1tN70Mn2AIM2Mvp3F3xCR1ZuyRwN1s7yLpfsCTbb++YRj3sb2PpL3qm8B/UsYU+rAbq/vvHySpdf/9VsB3JZ3BmmWnzddWqr0IS1jzqm0qJZ9znP9RwLHApZQEvZ2kfVN2up4M4Bd8ArA7cApr9le3rHTqdWaspFFt/c6UluhnWfMf/23TjmEsnq9S9qj4t0732fm2pzZ4NyGGM2zvJulrlOT8Y+CMHkotJ/bft/z7rFcoa7H91VYx1DieT7mK3Zby89gdOK3lEhqSzgSe6bpzXG28fHhaE/dm6gphrl8wZQmHVj5L44GpSWq10/tovMxzNbokv6x+3J6eJudVd7B9htZcf7/1fIyjamPlH4DlwB3r7daW0mP/PbR/45/HgcCDKeOMj5a0I+0nUN7OnW1Ebf93XWFgKmYqITCAX/Co4qlvkh5GGT8Z7dw2WiNl6i1S270vpjfmJ5LuzerKmqfRuP/a9mguylfpZ9e4kV7776H33fy6rrd9vSQkbWz7IklTL0AZs0LS0ZQxJihjS1MrlZ+1hND7L1irl8FeQ+uuAcpEo5cztoBYS5JOAfbxmgv9HW97j8ahHAAcRdna80fAJZR/vCZqF8nPbX9H0tMpm6CsBI7sYdLgEPrvJ+3mt6Th+UdWSbozZUmTUyT9HLiicQwvovx9vpTSaPsaMLWFMGctIQzhF7y0c3sTyuS4PjbIudb2ST2ct2vxKBkA2P65pOabwtSa/8ep7EK1QcvJeZL+FbgfsEkti74j8HnKpigfoGFiqg5tfL6J3NNufmMxPKXePLRWJm5O+d20jOEGSe8GTqWUv15s+zfTOt/MDSqP1FbZ5sDnp/kDvpWxfMP2wxuf83DKpfgnWLMl2GxPgjpg9pTRRLQ6R+OTbrtxUa+tc0nftb1TnYfwI+CutRpNwHdazgsZijqw/jjKki4/pnRfPcf2/XuIpdeJk5KeSJkw+n3KFcIOlOVmptKYm7UrBCTdn7JcA8DXWyeDsWUSNqBcMfRR9zwqve1esZi2A+yvBb5Rq3ygvBnv3+rkndb5xpL+m35a59cD1K7MH9YWMbYtqXX9/1A2C3oW5X/jJZRuze3oYdb2XBMnKX8zrbyVsrrCyhrTvSlFKVNJCDN1hSDpQOBvKK1igKcAR7nh/rmdSXFQKlkuAd7arSSYJZK2olR7iVLS95OG5+69dS5pFfA2yvf/8nqbev9ltrebdgxj8fS+WVBXHVfaruVM6c65VwIP6XPipKSv2X5E576Ar3aPrU+zdoWwH+UX/EsASW+klJ02Swi2H93qXPNRj8scS9qxDuiPrpZG4zj3kHSPht1WQ2idv4/VV4jd29DPKrhX9Z0MNJy9IXqbOKnVu7ZdIOlzlPWUTBlz/Pa0zjtrCUGsWVFzUz02/RNLpwGvtf2lCY+darvponL0u8zxKyhdQ2+d8FjLbqu71kly6tym3l/cIoABluD2vlkQsLntX9R5Qx903Rui4flHfgB8RVIfEye7y15fRVlmB+AaYItpnXTWEsIHgNMlfbLe35t267zfA3h3zfYHj60P00eVUW/LHNvev86Ufp2nuFDXrTC01vkQLAJ+RZnBPmJWd7O2MJS9IXqbOOme1teamYRQ34BOp0z8eTilFfhc22c3CuGqet4jKEnpGZ1xgz4Gcnpd5tj27yS9BXhoq3NOiGForfPe9fVGNGYQe0P0+fch6dW236Q5Nvea1lIiM5MQ6hvQW20/FGhWWjkWw6+A50t6CmUexBtsv5dG3VZjXkFZIuHekv6Lusxx4xhOlvQXwCf6XCqhT5IOtP1OSQ/r+WppFE/vy3B7IHtDqGxfOf53eS1lw61/s339FE8/GsdZMe+z1rNZqzL6J+A79PAGJOmsbn29pG0o/fi/Bv7IdvOZmCpLXve2zLHKuvObUaqtrqefEsdeSTrH9gPG/z56jOejlGW4n0lnGW7bBzaM4YNMbhW33hvinZSG0ofrob+kzIvYFFhk+1lTPv+GwOFuuL3rzFwhVK+gvAHdJGmU3Vu9AV3dvWP7R8DjJb2KNftrp0rSIsoyz9+r4wY7Uf7AHyTpC7avahWLe153fiCt8wslXQosHhs4HSXHppsFMYxluD/Tub0JpTy89YoCAA8cK+/89KgMVNIF0z55LYGeyqqmc5mpK4QASUcB37R9TL2/EvgccAfgRtsvbBjLxFpqN1qOfCitc0l/QHnTXWu9ILffSW8Qy3CPxbQB8EU3XHa6nvdCYI/OTPp7UFY22EnS2a5LpU85hrdS1nFqsr3rrF0hjOp7H065JP267U/1G1FzDwZe0Ll/3WiAStI3GsfSvRTehLIxy5m0KzsdROvcZZOm+2tsZ6zWXXjVaBnu17F6Ge5/7CGOriWUKr3W/o4yk767bMSLVda8arVq8ZbAT1nzf2JqVV8zdYUg6T3AfVizT/D7tg/oL6q2NLZvsqRdbJ9fbzfdFGZCbNsBb7L9jIbnHETrXGVNpePo7IwF7NvqamlItOaexqZcpRxs++M9xLIxsGON5aIpDyR3z/sG26+ptx9v+5Qm552xhHABsMtoQLleip5ne+cG5x5CfzWSzqVcBv947Pg2wEk99Fl3Y+htQbe+W+dqvDPWPHEcSJmweB1lbsaDKFvNntwyjj5JeoztL3VmC6+hxSS9bjdmyy7NWesyuphy6Tlq+W1HqTpq4bnAOynLZPRZTfJmyuDY3wGjORgPAt5SH2tmrMZ6A+ABwLktY6hxrNU61xT3rZ1D052x5vG82nDZg7LA3XMpCaJpQlC/W90+EvgSa84WvjkM2k7Sa2omEkKnnnhzSr/xGfX+Q4BW66wPpb/63yX9BHg9pdbcwAXAP7r9/gjdGusbKS3iPq6e3gY8Ybx1DrRsnTfdGWseozkxf0ZZNuLceuXWLoCet7q1fUj93OckvbmWVQGmt3zGTHQZaY5Nu0fcaA/XofRX903SfT3H6q59dKlJ+s54Qp50bMoxbEzZGWs0i/5rwHvceMe0OgdgG8oA6v0pe2Z8pWXXlaTzWL3V7QNUt7q1/ZetYqhx9NZ9JumQ+R73lGZRz0RCGJq++6v7Jul3lJbwi11Xnu081rwEVNIHKFdK3db5Rj23EHtRx9UeAPzA9v+oLG+yjRsuPy3p27YfLOkcyurEN4xKhFvFUOM41/b9a/fZAcA/UK6aep9AOC0z0WU0ogFs3j2Q/uq+XQCsAs6W9Gzb3+o81scyHk33rR240c5992vcU9Q1hK1uYQDdZ63N1BWCpBVM2Lx7VN7VKIZeq0mGUO00ugqoE9M+QEmQr3dZb2oQSzjMqjreNnLz3JDWk8I68Yy2uj2ph8qv3rvPWtug7wBac9mKbkPbN9n+IPCoxiGsVU0CtKwmGXWDNNsUaC71qmgppc7765K27zei/kjqbf5Hl+0/73w8HtiFslJvX/F81fZyyp7Cre0HHAQ82GVhytux+v9nqur4BZIe1uJ8IzPVZQT8qvbfnyPpTZTNuzdrHEPf1SRDqHa6+bLb9v8Az5S0L/B1yhIas+i99W/zGOA/689lCFZRkkLf+uiqeShwju1fSvpryqDyOxudu5cy9VnrMronpbVze8r+tZtTKjlWNoyh92qSvqudJL3Y9lp99Crr3r/aDddTque9ebZ2nyQtAZ5H2SbxDEq/dZMZqp0YJs0NudT2X7eMY5yky2w3Xb6iNpjuD9yP0oA7Gniq7XmrFtfTuT9MSUiLWfPqaKoNt5lKCLGmWa92GqlrOA2ida6y5PHelI2UfkF5A3hNi9mx9fz7du7eSEkGTcaaxmvtuw9Rtp9turNgZ6zrH4Ef2T665RhXHw23WesyiirVTqvZfnindb6iTlxs2jqXdD9KN8ETgVOAP7d9lqS7UyZlNUkItlst2jbJfMuht+qq6bpOZYvZvwYeUZN1s/E+97DoYa4QZlTf1U5D1GfrXGW56fcBH7P967HHnmX7Q5Nfud7jWAL8P2An1lw2orflr/tSW+jPBL5t++sqy18/yvZxDWNouuhhEkJjA+qv7n127lBMaJ0f3W2d275ngxheZvsdY8cOtN20ZVy7zw4B3k5Zy+e5lPeJeWfOxnS0brjNVEKoP8xXAfek013WssZ6KP3Vfc7OrUsTTPrD62WXsCG0zif1TavRJixj5zzT9q7qLJMu6eu2/6RlHEMwx0TW/7W9ecMYmjbcZm0M4aPAeyn//Df1EcAQ+qurPmfnPql+FvBZykzQPn1i/E1/1DqfdjKQ9AxKt8QOkpZ3HroTZWOU1q6vy1d8T9JLgB9RVj2duiFMmhzzbiZMZG0cQ9My9Vm7QjhzKH3kfVeTDMUQZib32TqvpdA7UPrtD+o8dB1lb4gbpx3DWDwPBi4E7gz8C7CIsmnR6Q3OPYgtTTvxrLC9tNsil/RN23/cMIamZeqzlhAOpWx2/0ng5h+o7Z81jKH3/uoh6fOfv9M6fzhlUtzInYCbbD+uj7iGRtJbbL+ywXl6qb2fJ56vAY8D3k/Zte1K4Dm2798yjpZmLSFcMuGwW1ZQDKG/um+SugngPyhvyt3Zy2c1iqP31rmkb9RuxNG2kTc/RPnbbLbw4lxaTgrre9LkWCy9T2RtbaYSwhAMqJqkt2onSV+e52G3HOSPWybpctvbNT5nJk32YKYSgsqWhC8CHlEPfQX4t5Z/bAOqJumt2knSQ22f1up888QxmNa5pHsDq1zW/n8UZbmE41r9XiTNNQtYwLm2t20RR42lae39PHE8DDiUtasSW/YoNG24zVpCeD9lpuFoNuazKH3Fz29w7sH1V/e1ds5QBg2HRGUzmKXA9pQuk+XAfW03qcCq3amGiYvIte5WHcSkSUkXUbqKzqRTlWi7WfVX64bbrJWdPnhsQOhLklpt6v5NyqDUVsBbO8evA5rtRtVl+3uSXkfZ2/gI4IGSWlQ7DWqTkb5b59XvbN8o6SnAO2y/S9LZrU5ue4dW57oV1loivl7dt3at2+8zvobWZeqzdoVwFrCP7e/X+/eiDO7OXGu1z2onSf9DKZ+byPZaA4rT1HfrvMZwOvAO4LWUdYwukXS+7SEsPd1Un5Mmx+I4nDIZ7ROsWZXYpOhhLJYmZeqzdoXwKuDLkn5A+YHek3YbXgymv7p6N6Xa6TXdaifbV9Srhmm6hjWvkvrWa+u8ei7wQuCwmgx2AP69cQxDMZQtTR9SPy/tHDPQcmWDposeztQVAtw80eO+lD+0i6Y1wWPo+qx26mMQfT59t85r6+9Y97znQAxP6zL1mbhCkPQY21+S9NSxh+4tiZazgwfSXw1lGv47xo49hzbLDE+aD9KnXlvntm+StFjS7W3/ptV5uyQ9dfR/IGkL2z/vI44h0Nr7Mhj4CfAN263/dpsuqzITVwiS/sn2ISqbZo+z7ec1jOUc+q0m6b3aSdKrbb+p3t7H9kc7j73B9mumHUPnfINonUv6N8pWicuBX46O235bo/PfXPk161Vgkiat7LolsAdwqO3jG8bStEx9Jq4QvHrp3n8ez/C1NdhS3/3VQ6h2Wga8qd4+mLJ42MieQLOEMITWeXVF/diA+TeKmRbNcbu5PidNAtj+p0nH61yNLwJTTwjqadHDmUgIHR9n7Q2rPwa0rG/+bf1l70tZbx7a7sL0Q+CHlDVj+jLfm08fb0aXAv9V//Gat87ruf4JQNJmtn95S8+fgk0lPZCSkDapt5svJ1K9t85UPoaetzTtsv2zWpbdQi8Nt5lICJJ2BHYGNh8bR1hEZ1eoRnrtrx5ItZPnuD3pfgt9t86R9FDKJu53BO4h6f7AC2y/uFEIPwbeNuE2NK6saV17f2tJegzQZGylr4bbrIwh7EWp4X0ypY925DrgeNvfbBTHIPqr+ybpJkpLXMCmwK9GDwGb2O5jElKfrfNRpdPTgOWj/uFZnYcw0qr2fsJ5J23gtCWl0fBs2xdN8/w1hl4abjNxhWD7RODEvtfQGVB/da/VTrY3nPY5bosBtM4BsH35WI9Es02caov8zcB9gPOAV9r+Uavzj8XStPZ+gieN3Tfw05aNBdsPr5+bXrHORELo2F/S34wfbFllxAD6q6uPA0sl3YfyZrgc+E/6372sD++gVJAsB7B9rqRHzPuK9e9ySX8MuPafv5SyUU0rH6AsKPc1ypX0u4DxMu1W+pw02XyZ7fm0brhtMI0vOmCfoWzZ+FngVMoYwv82juGKGseov3r00drvXNb7H1U7vRzYuoc4BsH25WOHWm+x+kLK7NxtgFXAA+r9Vu5k+322L7b9ZkpZdF8+YftD3WQg6UCAadTeD9zHgZs6DbcdKA23qZipKwTbH+/eV9mh6YuNY+i7mmSk12qngem7dQ5lPO+vGp+za7yyaNPu/cZVRn1OmhyapmXqM5UQJlgCNNkJamQo/dVk7ZyuF1LebEat85Np2zoH+KbKEtQfAT7eQ6nleGVR936TKqO+au8HrmnDbSaqjEYmjNj/GDh4/MphyjH0Xk2Saqc1SVps+5oBxLEbZdLe3sB3KRVwM5OkNYAtTYdG0k6UBstptj9cG25/afvwqZxvlhLCEEg63fZDutPPJZ3rxht3S/oCpXqj12qnIZD0Pcr6Sn21zsfj2YrSOv+rVhVZQ1pOJIo+Gm4zNags6dRbc2zK1uivlvRK2vdXw+pqp3+Q9IrRRw9x9M72EuB1lMmLZ0n6jKSmV0+SFknaV9JJrJ6lulvDEJZ1bh889tieLQJQ2R0MSddJ+kXn4zpJv2gRw5DYvglYXMe1mpiJMQRJmwB3ALaStAWrB84WAXdvHM4Q+qthALNzh8T2GcAZkt5AaZ0fS9sxlXOBT1HW2+pjrkzvy4n0VXs/cJfSsEx9JhIC8ALgZZQ3/261xC+Af20cS9/VJMCgqp16J2kRpfx2GXBv4JO0bZ0D3Mv99t8OZjmRPidNDlDThttMjSFI+lvb7+o5hkH0V3ernWz3We3Uu1rd8ynghNatc0nvsP0ySZ9mwhuvG20nOqTlRDSALU2HplXDbSYSgubeIAeg6QY5NZ7eq0mGUO00FJLUV+tc0q62z5T0yEmP2/5q65j6proHgKRXAdePau89oF32WmndcJuVLqNHAl9idR1vl5n+2ihrnrD//upRHL2tnTMEo9Y5sFxSL61z22fWzzP3xj+PTJpc7R00XFZlJhKCB7RBzkD6q2EYs3P7NloG4S29RgFIehhwKHBPyv/laFXLe/UZV08yabKjZcNtJrqMRjR5O7ozbTfbIKfP/uqxOLaiVDs9jvLmczJwoO1ZnRHaK0kXAS8HzqTzDz9rv49MmlyTpI9RehHeDexOabgttb1s3heuo5m4QtCwNsjpu5pkZBDVTkMwkNb5tbZPani+QfKAlogfiKZl6jOREID7UtY4vzNrjiNcB6y1HPY0DKG/ekzfa+cMydFMaJ039mVJb6aMZ90wOth4UbmhuJRhLBE/BE0bbrPWZdTbBjlDrCYZQrXTEIyWE+k5hi9POGzbzbauHApJh0w6Ppo7M0tal6nPWkJ4E/B64NfA54H7Ay+bxTfBrj7WzhkSSYcDG5LW+aBk0mTRsuE2awnhHNsPqGuL703pJvhyy4XlBtJfPVe10wmjMshZ0mfrfML6UQZ+AnxjvCJuVmTS5GQtGm6zMoYwMqpl/jPgw7Z/NlbO1cIQ+quh/7VzBsP2o3s8/aTlCLYHXivpUNvHN45nCN5B/1uaDkLrMvVZSwifruV9vwZeLGkxcH3jGIZSTTKUaqfeDKF1Ple/uKQtKbv5zWJCmPlJkx1NG24zlRBsHyTpjcAvannbL4G9GofRazXJAKud+jTY1nm9em1++ToQmTS5WtOG20yNIQDUP7Tt6SRD28c1PH+v1SRDrHYamlHrfHwSY+MYHgO8bkarjGZ+0mRfix7OVEKQ9CFKP9w5rL4Ete2X9hZUDFKrxdQkncfa//BbUpY8frbti6Ydw9BoIFua9qmvhtusJYQLgZ366DsfQn/1WDyDqHYaopatc5V9hLsM/HSWyy2HskT8LJqpMQTgfOAPKNsTtja0/uqhVDv15pZa5y1isP3DFuf5fWJ7Saf2/rWSZnnSZNOG26xdIXwZeABwBmsO6PY2kNpXf/UQZuf2La3z4cukybaLHs7aFcKhfQcwrsdqkplfOyet82Ea0BLxQ9C0TH2mrhCGqK9qkr6rnSLmMpQl4oeg9bIqM5EQJF3H5I3CR/1xixrEkGqSiFuhzy1Nh6Z1w20mEsIQDKW/emjVThEjfdXex2qzNobQmwH1Vw+t2iliZDBbmvatr4ZbrhACGMbs3Igo5tgTYkvKon9Ta7glIcTNWs3OjZhPJk3ObdoNt3QZBXBztdPP+44jgkyanNO0y9STEGbMEGbnRtyCoSwRPzjTbrily2jGDKXaKWIu2dK0vzL1JISIGJRMmuyv4ZaEEBERQMYQImIgMmmyfxv0HUBERHWnsY9FwFLgJEnL+gxsVqTLKCIGLZMm28kVQkQMmu2fUSanxZQlIUTEoGXSZDsZVI6IQcikyf5lDCEiBiGTJvuXhBAREUDGECIiokpCiIgIIAkhIiKqJISIiADg/wdimZD8/ybAxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This can be plotted using similar syntax. Note matplotlib is used.\n",
    "projects['University'].value_counts().head(10).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43fbdde8",
   "metadata": {},
   "source": [
    "## Handling different data types\n",
    "Unlike Numpy arrays, Pandas data.frames can have different data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a49b2c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For example, dates are presented in the data.\n",
    "# However, they must first be converted from strings (ala R).\n",
    "# Here, we extract data to a new column and then then the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cc4e7458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1975-01-10 00:00:00')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, we create a new column and apply the to_datetime method to it\n",
    "projects['start'] = pd.to_datetime(projects['Start Date'])\n",
    "projects.loc[0].start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03aaf02a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1975.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Next, we create another column and extract the year\n",
    "projects['year'] = projects.start.apply(lambda x: x.year)\n",
    "projects.loc[0].start\n",
    "projects.loc[0].year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f18dc6",
   "metadata": {},
   "source": [
    "## Data wrangling, aggregation and statistics\n",
    "Data wrangling is done in a fashion similar to R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "55603536",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Unable to parse string \"data not included in P3\" at position 9210",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.9/site-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Unable to parse string \"data not included in P3\"",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/l9/zf8fhrds2nd_sfhd808zjdd80000gn/T/ipykernel_45801/2851061094.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# For example, first we convert Approved Amount to a numeric value.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# We do this, because it was loaded as text dt \"NA\" strings in some rows.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprojects\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Approved Amount'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojects\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Approved Amount'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.9/site-packages/pandas/core/tools/numeric.py\u001b[0m in \u001b[0;36mto_numeric\u001b[0;34m(arg, errors, downcast)\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0mcoerce_numeric\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m             values, _ = lib.maybe_convert_numeric(\n\u001b[0m\u001b[1;32m    184\u001b[0m                 \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoerce_numeric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_numeric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m             )\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.9/site-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Unable to parse string \"data not included in P3\" at position 9210"
     ]
    }
   ],
   "source": [
    "# For example, first we convert Approved Amount to a numeric value.\n",
    "# We do this, because it was loaded as text dt \"NA\" strings in some rows.\n",
    "projects['Approved Amount'] = pd.to_numeric(projects['Approved Amount'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cfff7a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# However, we get an error for not coercing strings to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8ee37a35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We fix that by adding \"coerce\" as the error parameter\n",
    "projects['Approved Amount'] = pd.to_numeric(projects['Approved Amount'], errors = 'coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d2ca7b1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We then maybe wish to extract the fields from subfields hierarchy.\n",
    "# This is to allow for cleaner tabulation tabulation and visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "78d344e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, we specify the field names to be extracted and save.\n",
    "science_types = ['Humanities', 'Mathematics','Biology']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "937659ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We then write a function that adds a label according to the match.\n",
    "projects['Field'] = projects['Discipline Name Hierarchy'].apply(\n",
    "    lambda el: next((y for y in [x for x in science_types if x in el] if y is not None),None) if not pd.isna(el) else el)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4e8d5559",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We then group the data by the extracted fields and year\n",
    "aggregated = projects.groupby(['Institution Country', 'year','Field'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2516ab66",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HX/7yl7n55pv5+teXbNpCQMAR4apaDwaOTTe/Pfwo7aNPwuqzj/prL5YgB3Oc8J73vIePf/zjnHHGGVXbKyX3v/GNb5S3zyenX8mVV17JLbfcwsSEN7k6kUgwMDDQ9LpSqRRr165F0zT++7//u5y/me/1r776av75n/+5bF9mZmaYnJxCSsmb3/xmPvOZz/D44483vYaAgOWmpENWia7r5J18U8cvZZVk25O3sOXnX6m7ppVGYGBWEPPNgtmwYQMf+chHarb/6Z/+Kddffz0vf/nLq0YQX3755Tz77LNVSf565z711FP5q7/6K66++mrOPPNMXvWqVzE6Otr0mj/wgQ9www03cNFFF/HCCy+URwbMJ+f/l3/5l8zMzHDWmWdw8QXn8uD99zE8PMxll13Gzp07efe7313XowoIOFZU6ZD5GMKgYC9cSVZwCuxN7F0SI2O7Nnoxie6aOPbK78MJ5Pp9VoJcv1SSvJ1HoWgNtS6ZXH/pnFE9Skg/ugnJ+XBcScH2jGI0pBPSF77fCeT6A44Fo9lRDqQO0BntLG+zXRspJeesOWfeY6cKUzw58SQXrL2A1nDrEa0ja2Vxv34NHWNPk//IbuJdWxc+aJkJ5PqPI5arV6WRBxMQELAwljvbA1Oi1Gy50E36THEGF5epwtQRr8N0TYxiGgA3f+TnW24CA/NLxEozMJWrCRzpgJVM0S2Wu/hLCOHdtC00F2a6ME1vrJfR3OgR503yTp6Q6eU2ZT5xROc6GgQGZgWhlELUK1VZshdYvlMfFqryy5W2uICAWSzXKjdZVqG8Dv9GFJ0ijnII62Ec6ZC20ke0jpyZxTADDyZgBbLSLuKl9Qjvm4CAFYvlWjUeDADCy8U0Imfnyu/tiBE54g78fHYMzfeCAg8mYFEoGleRHSkld34lMRsWEytsZQEB1VTqkM3FdM2GxyXNJIbutRvGjThThanD7p1xpINbkccJPJiAlcUKu4orvOIDRKCmHLBymatDVslCopczxRliRgzwbvKEECSKh+d5WK5Vzr8AqELysM5zNAkMzAqinoeh6zo7d+7krLPOqhKTHBkZ4S1vecu857vvvvt43eteN+/5jyVKKXz7ssJWFhAwSz0dshKGZjRstjRdk6JbxNBmBVPioTgj2ZHDWofpmoSKszkcUZg5rPMcTQKpmJVEnatsLBZj9+7dANxxxx1cf/313H///axbt45bbrml6VMva/HAYeJ5MP7XgYUJWKHU1SHzCWkh8nZ9A5O38zWeeUSPkCgkyNk5WkIti1pH0SkSKnoejBIaopialV1aoQQezApCMX8VWTqdpqurC/Bk8k8//XQAisViWf7+7LPP5t577605NpFI8PY3v50zzzyTiy66iCeffBKAyclJXvWqV3HOOefw/ve/n82bNzM1NcXHPvYxvvjFL5aP/4u/+Iu681yOhMCoBBwPNNIhA9CFTtEt1g3xps10Of9SdYymH1ZPTMbOEPGnaNptazCsrKcwsIIJPJgm+dtH/pa9ib1Les6Tu0/mzy74s/L39d6khUKBnTt3UiwWGR0d5Z577qnZ58tf/jIATz31FHv37uXqq6+uUTX+7Kc/y5k7z+TWH9zKPffcw7ve9S52797Npz71Ka644gquv/56br/9dr761a8C8N73vpc3velNfOQjH0FKyY033sgjjzyylD9+GQHIwNoErFDm610pibba0iash6uemy5OE9VrxV9bQi2MZkfZ2LaxfulzAzJmhnYzhxIaVvs6jGIGRzpHfVzAYggMzAqiXo6kMkT20EMP8a53vas8M6XET3/6Uz70oQ8B3nyYzZs31xiYn//s53zzxm8CcMUVVzA9PU0qleKnP/0p3/ve9wB49atfXfaQtmzZQk9PD0888QTj4+OcffbZ9PT0LO3PqxSaKCX5l/TUAQFLRj0dsiqEl4CvNDC2a5Ozc3THumt2NzTD64kx01XSM/MhlaTgFAiZGdxIG26sg3BmdMEmz2NNYGCapNLTWC4UXjy1UUXVy172MqamppicnKw+romrc0lIszJmO99rAfzWb/0W3/jGNxgbG+M973nPIn6S5lBQFXpY6fHkgF9ObNee/31ZZy5M3snPG+4O6SHG8mNNGxjTNREIjGIaN9qBG2nHMDMUV7iBCXIwK4kF7MTevXtxXbfGk7j00kv51re+BcALL7zA4OAgJ510UtU+r7jkFdx8482AV13W29tLe3s7r3jFK7j5Zm/7nXfeyczMbGXKG9/4Rm6//XYeffRRrrnmmiP96aoIypIDjhfq6ZBVIWp7YVJmCk3zLq+2Ixmcqi4EiIfiTBem523SnLsGhUIvJnFjHbjRNgwzi9Pk8ceKwINZQdRL8pdyMOBdlG+44QZ0vfrN/oEPfIDf+Z3f4YwzzsAwDL7xjW9UDRcD+IuP/wXve+/7OOuss4jH49xwww0AfOITn+Cd73wnN910E6985StZu3YtbW1tgDfR8vLLL6ezs7PmNZeDyqqygICVQj0dskpCWsjr2K9gujhN1PDyL7YrydvVnoYmNJRSJM0kffG+hdfgFEGAXkxjt63GjbQjlMQsTEPLqsP4qY4OgYFZQdQzMJUzXirZsmVLORcTjUarho2VuOyyy7j0lZeSt/N0d3dz43dvpCXUUpVY7Ojo4I477sAwDB566CHuvffesnGSUvLwww/z//7f/1uin3CWuv5LYGECViANdch85jZbOtIhZ+fojHT630PRlkgJWsVpYqEYw7nhpgxM1s4S0kIYxRTFvh240Xbv3Lkp6D28n+toEBiYFcSxyEEMDg7ytre9DSkl4XCYf//3fwfg2Wef5dprr+WNb3wjO3bsWPoXrmNhgqBZwEpkoRCZoRlVvTA5O4dQovxZdl2J5UgcKQlXWJioEWWmMEPezhMPxeddQ9pKE9bD6IUkbrQTN+JFGZz85LzHHWsCA7PCWO6GyFL3fIkdO3bwxBNP1Ox36qmncuDAgeVbR8OtgQsTsLKwpEWr0XhQmKEZZNwMUkk0oZG1slVvY9M3Lo4rCRvVnpAQguni9LwGRilF3s7ToYXRXAs32l72YGR++sh+uGVmwSS/EOJvm9n2UuVoJaPL45JX+PVVKoXjyiU4U+3vdaHfdFAYEHC0mU+HrAoxW0mWKCbK+RfwDIxSUO9jU+qJme+9bUkLpVRZh8yrIvM8GLXC5WKaqSJ7VZ1tr1nqhaxEotEo09PTR+XCdrR0wo70daRUOPLI11r3VzrPaZVSTE9PE43WNq4FBCwX8+mQzcVyLVzpkjJTRPTZIhvTcdGFqHtjFtJDmK5J1s42PO9siXISACfWWfZgNDN9xEPMlpOGITIhxO8CHwC2CSGerHiqDfhZMycXQnQCXwNOx7t8vAd4HrgJ2AL0A29TSs34+18PvBdwgQ8rpe7wt58LfAOIAT8GPqKUUkKICPBfwLnANPB2pVS/f8x1wF/6S/krpdQNzay5kg0bNjA0NFTTd7IcKKUwXRNd6LjKrboDOtLzlpKUrnIJ6+FFdQ/PRUqFqxQh/cgq3F2psF3pNVrieUYhXUPXGn+ao9EoGzZsOKLXDQhYDPPpkFWhKMvwK1GdSy1YFilnHNOprz1maAbThWnawm11ny+XKBf8QWPR9rIHYxQzOMpBZ/mrPA+H+XIw3wZuA/4v8NGK7RmlVLN6018EbldKvUUIEQbiwJ8Ddyul/kYI8VH/3H8mhDgVeAdwGrAO+IkQ4kSllAv8K/A+4GE8A/Nqf23vBWaUUtuFEO8A/hZ4uxCiG/gEcB7e2+MxIcQPSoasWUKhEFu3bl3MIYdNwSnw2NhjdMW6SBfS7Fy/c0kS/pZr8ejYo3RGO0kX0mzu3Ux3tLa7uFkmMybTOZOT17Qf0bomMybPjKToafHu9GZyJqeua2dVW+ChBKwcXOU2lRcVQmC5Fpa00CoDQwoen/oZd4x8hz+If4o1nSfWHNsSamEsN8am9k11b/6yVhZd19GLKW9NsU6UEUVqIUJWDkc6VR7TSqLhbahSKqWU6ldKvRMYAmy8i3WrEGLTQicWQrQDlwL/4Z/PUkolgTcAJW/iBuBX/a/fANyolDKVUgeBF4ELhBBrgXal1EPKi1X915xjSue6BbhSeFfla4C7lFIJ36jchWeUVixSLUVeY/lfx5VySXTD5Jzx0AKBXILQW0DAUuJKFyUWfl8amkHOzpEoJIgYsxd7RyomiyOA4oGRu+oeq2s6jnTIWJm6z2esDGEtjOEbGCfaAUJ4zZbF9IqWi2kmyf9BYBzvIv0j//HDJs69DZgE/lMI8YQQ4mtCiBZgtVJqFMD/t9QltB44VHH8kL9tvf/13O1VxyilHCAF9Mxzrrk/2/uEELuEELuORhhsPpq9UzoSlFBHbGAsZ2kMoeVIKh00IcB2AwMTsLJYUIfMx9AMck6OpJms8iYcKUnannLynumHSVvpuseH9BAT+Ym6z2XtrFeiXPJgIu3lfw0rWyNTs5JoJpD++8BJSqnTlFJn+I8zmzjOAM4B/lUpdTaQozrUNpd6V9dGdaulP/nhHDO7QamvKqXOU0qd19e3cLPTclKuIltGNLQjTghaS1JBBo6ryvkXAE0InMDABKwwFtQh8wlpIXJWrlyqXMJxFUlrilXRdTjK5p6BWjV08KRjJvITNd6I7dq4yqti0wtJ3HAr+CMApC8X06zczLGgGQNzCM8zWCxDwJBS6hf+97fgGZxxP+yF/+9Exf4bK47fAIz42zfU2V51jBDCADqAxDznWrG4avkrQYQQRzw/YqmMgCMlesUHVxcCRx6dMGFAQLMsqEPmo2u6Z1zmXFJt1yVlTXFi5xlsbjmZO/rvrBvS0oSGVLLGw6nUONOL6XL1GHgeTMjKUXSLi/2xjhrNGJgDwH1CiOuFEH9Yeix0kFJqDDgkhCipLl4JPAv8ALjO33Yd8H3/6x8A7xBCRIQQW4EdwCN+GC0jhLjIz6+8a84xpXO9BbjHz9PcAVwthOgSQnQBV/vbVixHIwejCQ15hBdxe4nyJLasEyILDEzACmMhHbK5+4aNOTNhCglc5dAd6eO83iuYMRM8Mlp/rlLUiDKeG6/aZrpmOfaiF1Ne/sXHjbRhFDOYTrXQ5kqimU7+Qf8R9h+L4UPAt/wKsgPAb+IZtZuFEO/1z/tWAKXUM0KIm/GMkAP8nl9BBvC7zJYp3+Y/wCsg+G8hxIt4nss7/HMlhBCfAR719/v0IirfjgmLqbc/XATiiOO1tivRtSMrUYb6IbIlir4FBCwZC+mQVdIX78PQqi+pI9kxALojffSFtrM6vobbDt7Gxesvrjk+ZsTKCssh3RsilrNzZQ/KKKZwYl3l/d2ol4NZyR7MggZGKfWpwz25Umo3XqnwXK5ssP9ngc/W2b4Lr5dm7vYivoGq89zXga8vYrnHFEc6R9Sf0gya0I644sRxJISWwMBIVSX85yX5AwsTsLJoNkQG1BgXgPG855F0R1aB0rhs/au4ad9/s29mHzu6qjX+SrmeSoXlrJ0tGxu9mMLs2lze3420oVs5LLvASqWZKrJ7hRD3zH0cjcUd75SaJ5vBkc6yC10KIZCsjBCZ48gqD0bXgjLlgJWHJZs3MPWYyE8g0OiIdKMhOK/vYmJGjNsO3lZ3/1goxmhutPx91sqWRyLrhRRuZYispEdWTKxYGaVmQmR/XPF1FHgzXggrYAEKToHR7CgndJ2w4L62tGsShEuNQBxRFZlSasm8DEcpwqKyDybwYAJWFk3rkM3DdHGCzkgPutDRdQdBhMs3Xs4d/XeQKCRqRipHjSiJQoKiU8TQDCxp0aK1IFwb3c7jxKpzMABGMYsjnbKns5JY8DenlHqs4vEzpdQfAhcehbW9JGg2PnrUQmRHUEUmFUvSZAnguJJKVRhNE7iBBxOwgnCkc0R5USUhYU7SHfHCXZoQFB3JNVuuQSrJXQP1Gy81NGbMmbL0DDDbA1PHgzHMzIrthWkmRNZd8egVQlwDrDkKa3tJYElr4Z3wGrqOhoE5Eg/GlYqlKPSSUiEVVSFBTQgcqVasqx/wy4cjm2uybHy8JGlN0eMbGEPXsB3J6pbVnLv6XH4y8JMqI1IiHo4zmh2tujmta2B8DyZkZo+4/WC5aOaK9hiwy//3IeCP8DTAAprAkU5TifWj4cEIxBEl+V2plkwmph5CeF5SQMBK4EjVNdJmlqKboyPSQ8pMoAtB0fZu8F6z9TVk7Aw/Hf5pzXFhPUzezjNdmC7fhOmF+TyY7IqVi2mmiuzoqD2+RJFS4ioXY4FftStdDH15578daZLfVUtjYFyl6kstKM+IzaeoHBBwtGhWh6wRY35PS0eoG4nEUTZSgesqTu05lU1tm7jt4G1cvvHymgIfTWgkzSRhzesM0St1yErrK+VgrMxsZCI3DZFWMFaG+GUzIbKQEOLDQohb/McHhRArL5u0QnFUcx6MLe1l1yIrdQsfbhjKXaIQVqMwmybEkuV4AgKOlGZ1yBpRMjCd4R5ajFaKbh6Bf4MlBK/Z+hoOZQ7xzMTummNbwi3MmDPlCjKjrKRcaWA8DyZk5Si4fqlybhKK9fXOjgXNxGT+FW/eyr/4j3P9bQFN4Cq3KQNT0htadtThqwZIqajveyzyPKq+6ppaIg8pIGApaFaHrBGlHpj2cCdxo83XG5ytlnz5+pfTZrRw9/4f1hgyQzPoinTN9sDUCZGhG7ihOGEzh+X4uRzpgllflflY0ExM5nyl1FkV398jhNizXAt6qeHKhQ1MyatY7j4YAARI5GENKHKXSJDTnSPVX0IR5GACVg6max5RD8x4fpy40UZID9MSasOSFtNWFld6oa2wHua0tk3szxwCMw0VOmPgCWCW0IsppBFFzQl9udE2jLl6ZP7ky5VAM7fMrhCi3MghhNiGN3EyoAmkkgsamKMhdFlGHf5seykV2lJ4MFJRL/YgIChVDlgxlCbMHi6ThQm6wr0AGMKgL7YGSxawXf/zLiWbw11M2Gnyk3vnvbvSi+mq/EsJN9JOyJyjR1Y8HG3i5aEZD+ZPgHuFEAfwrgGb8TTFAppkoV6Yo1maKxCHbdAst7r7/nCRqnFoOyhTDlgpLEaHrB7TxUk2tWwHBZqmE9NbMDSjXEmGU2RTzCthHswe4uT8NmjtrXsuo5isyr+UcH3JflNWGBi7CI65IhL9zTRa3o2nbPxh/3GSUure5V7YS4l6te6VHI1hYyWUOPxEveUsjYFxZYMqMgIPJmBpKTrFKumVxWC5Vo2+2D2D9/Dfz/73gsc60iFtJeiO9qEAXegYmkFPtJeZUhLeMdkc9QzMgJ2B5AC49aMderFaJqaEjLSjm5nqSIm0YIXokzVTRabjjSC+DE+k8veakesPmGUhPTKp5LIPG5v7eoeDIxVLUUEslaKRjQvsS8BSMpGfYN/MvsMaymXJWg/m3sF7uf3g7QveNI7nJlEoeqJ9CEDzQ229sVXkLf96YOXoiXTQasQYKEx6CfpM/amWc2fBlHAjbeh+Ur9sYJQCZ2UoLDfj/90KvBtvFHFbxSOgCTRNm63waIBUctk8GKUUOTtX/l5w+L0wtiPRlsDC2G6D8yyhFE1AgFSSkewIUkmmi9OLPnauDplUksHMIK5yOZg6OO/xo2WZ/lWAKudy2sJtSGl4xqCYQoSibI6vYSA/BpE2SB/ywltz0AtJnGhnzXY32o7ue0Tl0LcehkJyUT/vctGMgdmglHqTUuoTSqlPlR7LvrKXCLrQq+OjdVjOYWP3D93PRx/8aHlSnlKHHyKz5dKEyObOgimhaQInELwMWCLSZhrbtemIdDCSHVnU+76eDtlYbqwcjXhh5oV5jy8NDuuOzIbIAEK6TkdoDdliBuw8aCE2xVdzqDCBFICmQ2rO8F3popvZuiEyN9KG5lpotjnrwRjhFZPob8bA3CaEuHrZV/ISpaT/NZ8RcZW7bCGyF5MvYkub6cLsHdzhJvk9w3Dka7LnCF2W0ITAXqKRzAEBo7lRIqEIYT1Mzs6RtbNNH1tPh2wwPQh4PSoLGZix/DiGCNFitKEJrRwiA2gPdWBZOU8NUwg2x9dgSZvR4jSEWyE7DmYO2/GuGbqZQaBwY3VCZPUEL/UwWNnGHc1HkWYMzMPA94QQBSFEWgiREUKsnFbR4wHBvKXKy+nBTOS9mG7WypbXcrivt1RVZE4DT0gT3nMBAUeK6ZpMF6eJG14viaEb5c9CM9QrvOlP96MLnfNWn8e+mX3zekQT+XE6w70oFIZWLXwS1mNEMCj6eaHNcU87eCA/7gnyGRHUzCAHJ7M4rkL3w11uvRBZqZvfzs3mmYTwjNcKyMM0Y2A+D7wMiCul2pVSbUqpWlMaMC8LGphlKiKbKkwBkLH97t4j6INxGuVOFnseSV0DI4TACTyYgCUgUUgglCg3L7eGWhnLjTUta19Ph2wgPcC61nWc2nMqSTPJZGGy4fET+Qk6w31I5Zb1xEoIYBVxCr4C8oZYH7rQvDwMQDiOlUtQzM7guLKcY3EaJPkBwmZ+TjuEOG4MzD7gaRU0KBw2Qol55bSPVJJiPuZ6MEIcXh9MSWJ/qUJk9X5cz4MJ3mYBR4ZSiqHMEPHwbCe8JjSUUiSb7HKvp0M2kB5gc/tmTuo+CYAXEnPCZLkEOA5KKaaKE3RH+pBIQqLawCigzXZAD6GUIqQZrI/2MVgyMIClxxDJAWxHVuiQddassxQiC1u56mZLAVi5mv2PNs0YmFHgPiHE9UKIPyw9lnthLyUUat45LMsp1V8yMBnL82CEOLyplkt54Xfd+orJ3kyYIEQWcGRk7AwFt0BYr76wx0IxhjJDTZ3Ddm1ExXs0baVJFBNsbt/MxraNRPVobR4mPw1WhpSZwpaWZ2CUWxMi0wFRyNIb7yPnex2b4qsZ8AsDAHKOjnBMHNetOwumRNmDsfKzgpfgNVmuAMmYZq5qB4G7gTCzJcqty7molyLzhcgc5SzLuGRHOiSKCaDawBzO7AjZQD/s8NbVKAcThMgCjpyJ/ESNcQFvHHHWzlaV7TdirkzMQHoAgC3tW9CExvau7bUGRrmQny6LXPbEViGVJKxXd9SHlY3l2KyJ9mD5IbvNLWtI2GnS/tpypkPE0MhbToWBaZzkD1nZag9Gj0Dh2FeSNTMPpqokWQgRBV63bCt6CaJp2rzNlrZrL4sHM1WYKlenlSpoNA5vbLLbQD/scLBdSbz2848mwAwMTMARYEubsdwYHZHau30AXdOZzE/S0tEy73mKTrHKwJQqyDa3bwbgxK4T+d6+71F0ikSN6OyB+QTjwvMkeqOrUErWeDAhVcSyJW2hFkJCx1VuVaL/lLatWK4iZmgUbIleSCG1ELJC/LKEDMdRQvOmWkoHKaR3q6qHvFJl14FlnjM1H01d1YQQuhDiNUKI/wL6gbcv66peYuhCn9fALNe45Mn8bBKyKkR2GDkYVymWohLBk+QPkvwBy0OymPS0vxp8ntrCbYzkRhb04m1ZfdPXn+qnK9JFu1+1taNrBwrF/uT+6gNdk7HUIUDQFelBIGoEM0NuDtNV6EJjTbSHnFNkc3w1AIP5MSxHIgBdCEzbxSimPB2yeolLoc128wtwqypEBTjHVjJm3quaEOJSIcRX8IzKbwFXA9uUUm85Cmt7yaBr+rzSEss1C6aUf2kLtZWT/JrQkIeR52ikgLxYGumQgefBuEEOJuAIGMoMEQvFGj6vCQ1HOgsm++fqkJUS/CV2dO4A6jRcCo2x7AjtoS4MLYRCVfXAAETsNKYIoRS0GjGkknSEWukKtTGQH8O0XRAlj971dcgaF+66kTb0YhqBwK4ynMoTvjyGNLyqCSGGgL8BfgacqpR6M1BQSuWP1uJeKuhCn1dR2XGdZZGKmSh4BmZ92/pymbJg/oq2RrhyaVpB3QbDxsDzYBQlYxYQsDhydo6cnasOWdWhJdTCcG644fNKqSodMtu1Gc4Os7lj1sC0hlvZ0Lqh1sAYMSZyY3T6Mv0I0EV1iEq30jgigqMUhtDLn4dN8dUM5MfJmS6G5pdYK9Dyybo9MCXcaDtaSY+sMjqhGcd8+Nh8t83fBdbjhcNeJ4RoYamC8L9k6GJ+D2auO75UTOQn0IXO6vjqKg/mcJL87hJVqSu1cKBtqV4r4JeLyfwkmrbw5yhqRMmYGfJ29b2yUoqZ4gx7JvfguLNh66HsEK5y2dK+pWr/HV072Dezr7pxWTeYsJJ0Gl3eOaEqRCZcE03aCE3HcWWVl7Q5voahwiQp0ySk67PHF1N1e2BKlDwYpVS1gTHCx7ySrOFfQyn1EWAL8A/A5cALQJ8Q4m1CiKCKbBHomo4t7YYNjssZIuuN9dIWbiPv5HGk5ykdVpmyszTDxlyp6seSfQSB4GXA4nGly2hulNZQc5cmTdPKjZIlw/LExBM8PfU0rnLpjneX9y1VkFWGyMBL9GftbNU4gKJrkXbzdOueQahUUgYQFTkRR3oeTInN8TW4ymXSSlT1m3lKyvWLFsAXvDQzKKGqVaP1yDHXJJv3qqY87lFK/Taesfk/wK/i5WQCFkm90NRyjkueyE+wKr6KlpBXMZO1s4ed5LeWqIvfbUJsM0jDBCyWlJXCkU7TI45bQ62MZkeZLkyXDYtC0R3rJqqFYeZQed+B9AARPcKaljVV5zix+0SguuFy3EwCsEorlSZXJ/k1O4/yQ8GO6zVZligl+sesWUkbTUHITNUdNlZCRtrQzTSGMChWKhVoOkinrjrz0aLp22allK2UulUp9X+Ajcu4ppckQtXvP1nOccmT+Un6Yn3lu7qslfXENw/jNRsJVC6WhVRxFIEHE7B4RrOjC+ZeKtE1HUc6PDv97KxhKR1fSEJuqrzvQHqAjW0ba6IMa1vW0hJqYd/MvvK2CWsGgD7CuNJEF0bVzaNuJlF6GA1POVwXGroQSCVZG+vBEDrj9qyBico8QskmPJisV3Um54bixTEdPnZYcRml1MoYl3YcoUT9bv7lVOCZyE/QF+8rezAZK+NVkR2G2KUtZd3u+8XiLpCE8UY6BwYmoHmUUiTN5KIMDEBXrKvasIB3h5MZAT/aoJRiID1Qk38BL595YteJVYn+kgfTG+pAWFlCc7v4zSRSD6MJyqOTo1oEV0l0obMm3MeEM2vc4o7XeOnMZ2AibQglCdsm5txcr+D4MzABh4Gq382/XOOS83aejJ2p9mD8ZkuvF2VxRsZ26s9wWSyubDzN0kOxjOLSAS9BLGmhlFqaPKaVBXNW1n+6OE3OzrGpfVPd3U/sOpGh7FBZHWDcnCGmRWkJtSCKM4QqhS6ljeZaoIXQdYHly/FH9BCucnGloi/Ux5g5Ub7xjDv+MLEFPBiAiJ2v9WC00DHNwwQG5ijSMAezDMV5pQRmZQ6m1GxZet3F0GhI2GJRSs1fiyiCKrKAxWG65tLdpGUnvC54n/5UPwBbOrbU3X1Hl9cPUwqTTZhJuowORCiGKE4TqihR1ioS/IbQMEsGRgvjKBfLlawO9ZGTBdKuZ+QifnuB3UCZAGb1yEJmDmvuTaxxbBP9DTUEhBC3Ms+lQCn1+mVZ0UsUIUTdueDLNS651GTZF+srqwiUDcxhzISxXZeI0VwCdf7zzF8soFRJliYgoDks11qamzTHgtwkRDvK/SMD6QEEgk1t9T2Y7Z3bEQhemHmBnV1nMlacoddYBZqOq2xa3dkLvmbny6vUNEHe8kNkehhHSRxbsibUB8CoOUG30UvYn0RrhhpPqS/NhDHMLMoI4Sh39sKuh6Ew41XONFHCvdTM94qfw5sFcxAoAP/uP7LA08u/tJcWjbr5l8uDKRuYeB8RPUJIC80OHeMwDIxUS1JFZi/gCQmWNy8V8NIjZ+XQ9SO/+SE3DXPCbAPpAVa3rG6Y34kaUTa3b2bfzD5c5TJlpegyOgGQQLSik1430yhfhFPTPE/dlRDRQigpyVkO6yOrABgxvc9v2PQMTCE8Tx9MtG32/IBTmes9xsPH5uuDuV8pdT9wtlLq7X4FWamK7BVHb4kvDXShU6ijC7RcVWSVHowQgtZQa1U3/2IMjJQKVy5NiMxTUm78vEBgu0ESJqB5MnamZqjXopESMsMQrhbBnCsRU48Tu05k38w+Js0kLpLukoHRw0TMRHk/r4JsVllZ4I2nCGkGCEHOdGgLx+kyOmYNjJVGomHqtUKXJUoeTEmPzK65phy74WPN+Ex9QohtpW+EEFuBvuVb0kuTUrPlXJZrXPJEfoK4Eac17CX428KzemTK/69ZljInslAuR9NEECILWBTZOtVai6aYAul6vSM+eTvPeH68bgVZJSd2nUjRLfLYzPMAdBp+vkQLE3KKCKcI0kFz8qiK/A5C4UiJIXRsVyIl6JpgXWQVo2UPJoMVbqXoNP5MlJL8ejGNhkaiIlLhvQ7HbPhYMwbmD/AGjt0nhLgPuBf4/WZfwFdifkII8UP/+24hxF1CiH3+v10V+14vhHhRCPG8EOKaiu3nCiGe8p/7J+EXlgshIkKIm/ztvxBCbKk45jr/NfYJIa5rdr3LRSNF5eUal1xqsizRGq7wYNTiPJj5BCoXiy3VfI383mch8GACmsSRzrwNljPFGR4bf2zhE6WHYY5I5qGM12y5oAfjN1w+OP0kQNmDUSh0NHQrg+YUUHPe90qB44Ih9CoV8bWRVUzaCSzpELZSmOEOik7jSIcyIkg9jG5maNFjjJpT1arKx3D42IIGRil1O7AD+Ij/OEkpdcciXuMjwHMV338UuFsptQNvkNlHAYQQpwLvAE4DXg38ixDlFth/Bd7nr2OH/zzAe4EZpdR24AvA3/rn6gY+AVwIXAB8otKQHQs0oWHVNEEt37jkycJktYEJtZaT/IrFlSkvZeOj49YfNlZC0wSBAxPQLPNp/AH897P/zd8/+vccSh9qvJOZBTPnXYgr6E/3AwsbmL5YHx2RDvrzYxhCp9PwogYCEKEYen4CzSki6ryvXenpkRUst3zjtS6yCoVizE4QNjPYkTaK1vyfVzfajl5MY2g6tpKk7Aov5hgOH1vQwAgh4sCfAB9USu0BNgkhrm3m5EKIDcCvAF+r2PwG4Ab/6xvwpGdK229USplKqYPAi8AFQoi1QLtS6iHlZX//a84xpXPdAlzpezfXAHcppRJKqRngLmaN0jGh1OA4t9lyucYlz/Vg2sPts0n+RVaRefphS7MuV9Yfl1xCE2A7gQcT0BzzzVlKmSl+MfoLAG49cGvjk2Qn6g7lGkgP0BZqozvaXeegWYQQnNjleTFdRjtGRcGBMOIYxWl0cwY55zV0ITAdr5u/aCt0/zKwNuwn+u0pwlYaOzK/BwPMzoQBolqY4cLsLCj0kDcXxl28yO2R0syV7T8BC3iZ//0Q8FdNnv8fgT/FK6gosVopNQrg/1u6Cq4HKm8zhvxt6/2v526vOkYp5QApoGeecx1bVG0vzNxxyVJJdk/uPixByvLLKFXu4i/RGm4la2c93bNFJ/lZMh1t25Xz2ipNCJzAhQlokqJTRDS4Ybnv0H24yuWsvrP42fDPSBQStTuVSpPDtRMuB1IDbGrf1FSEoWRguvUOdH9/BWhaCKFc9MJUVYIfvHxL0XaRElxHKxe/dIc6iYgww9Y0YTONFWnHlWreYXxuxPNgAOJ6hKSVJV9lfI/N8LFmDMwJSqm/A2woy8Qs+Bv3vZwJpVQTAVDvkDrbGo1RLP2mD+eYyjW+TwixSwixa3Jyss4hS89cwzF3XPIzU8/wb0/+Gz8f+flhv0bSTGJLm9W+eB54SX6pJHknj1KLT/Iv1SXfWaDcWQiCqZYBTdOogkwqyU8GfsKpPafy3jPei1SS2/pvqz1BLuGVJs8xIq6SDGYGGzZYzqXswejtaJqGVJ76uC40lGZ4PTBz1qlrAruYpVjMERIhXOFdGzQhWBtZxYjleTBWuB2BF15uhBttK8+EAdCFxmRxpmKPYzN8rBkDYwkhYvgXaCHECUAz8pwvB14vhOgHbgSuEEJ8Exj3w174/5aU3YaoFtHcAIz42zfU2V51jBDCADqAxDznqkIp9VWl1HlKqfP6+o5CYZygppJsrlR/Ke57IHXgsF+mskS5REkuJmP5o1UX4SEtVVWXJ1FTXUUm/W0lPA8mCJEFNEfOytWtINszuYfJwiSv2vwqVsVXceHaC/nJwE+qZ8A0KE0GGDNnsKW9YP6lxNaOrayN9LAxsh5NgESW1+WGWlCaXmPEdCFY//OPY9z9CSJaqCqqsDayilF7Gk3aWOF2v7+lOQ8GoMWIMVqsSPYfo+FjzRiYTwK3AxuFEN/CS8z/6UIHKaWuV0ptUEptwUve36OU+nXgB0Cpqus64Pv+1z8A3uFXhm3FS+Y/4ofRMkKIi/z8yrvmHFM611v811DAHcDVQoguP7l/tb/t2KJq+14cVZ2DKVWulGZQHA4lAzO3igy8kk4h6is7N8Jxl0ZtoJ4O2b89cIBP3fpM+fsgRBbQLCWPvHJoV4mfDPyEjkgH5685H4DXnfA6Ck6Bew/dO7tTYQakXVWaXKLfnwbbrIEJ62H+/vTf4/TY9vLaQqUaJc3AjfXUHmMnaUk+T3h0F6vyk1WVX+siqygqm2FDx/L7XObLTbrR2RwMUJvsP0bDx5qpIrsTeBPwbuA7wHlKqfuO4DX/BniVEGIf8Cr/e5RSzwA3A8/iGbTfU6p8Nf5dvEKBF4H9QMnX/Q+gRwjxIvCH+BVpSqkE8BngUf/xaX/bUeWRsUf45yf+uewt1AwEonZc8mB6EJj1ZA6HegamLex1+2asDBqalwtyzKZmRSyVVH89u7FvPMPz47MfDE14lTUBAQtRkoiZmyOZKkzx+PjjXL7x8rLxOaHzBE7pPoUfH/gxjpWH6X6Y2gfh+gPKBgqT6EJnfWvzqVtHzsbhpZIYotbwVRKf3AOAQrB15G5URap6fcQLb9/e0oIVaUfXBHm7cdRBRtrR7bw3/8UnqoVmk/16+Jj0wjRTRfYDPA/gPqXUD5VSUwsdMxel1H1KqWv9r6eVUlcqpXb4/yYq9vusUuoEpdRJSqnbKrbvUkqd7j/3Qd9LQSlVVEq9VSm1XSl1gVLqQMUxX/e3b1dK/edi17wUPDj0IM9MP8NIzovOGcLAlNUX9Mpxya50Gc56s8KPyIMp1IbI2nwto4xdIdmfHoWZ/gXPZ1WUFtvSIm3NLHBEfUpx6UomsyY50yVreh8MIbwyZRl4MQELYLpmXVmhuwfvBuDKTVdWbX/dtmuZLk7z8N5bID8Fsc4qYctKBoqTbGzbWNc7aoSrZgtYJHJBdYH45G6cUBtTG15F78jPCVc0SG6IrOE8fRX/3NXBczKLoQnMeQxMudmywojEjWidZP/RpZkQ2eeBS4BnhRD/TwjxFiHE4gYv/JJSCncdTB0E/F4Yp7puXyLLBmYsN4YtbdbE1zBZmCxLgC+WifwE3dFuQhUfnpIHUwqRudL1XObkoQXLFx13trT4gRfG+OT3nz+sdbmyurjAciTJvOfRTaSrE5DB0LGAhTCdWhVlRzrcO3gvZ686e7aKUgGFFDsdjfWRbm6dfAwVaZ13dPdAYbKhRH8jpKQcAvZCZPMYJ6WITTxBuvsMxje/Dk3abB9+uPy0EILfMTazznH42tTtmDJHwZrHwPiKyvocL6U22X90aSZEdr9S6gPANuCrwNuYTcwHzMNgxgt3lQyMoRlVdftSSaSUZRe/tP85q84BDt+LmcxXN1kCxENxBIKMnfE1kBxfHsNeMDZrV+iQPT2SZu+IQyK3+IoUOacabSo7+7sYn2NgAsn+lzjZiSOuaso5OYw5vSW7xnaRNJNctfkqb4PrwuTzMPEcmmFw7bpLGCiM83S6cRFN0s6SdHILSsTMxXbdcoWkW5Hkr0coO0SoOE2mdyd220Yyq87hhOGHERVFQB2Oxecnpsi5Bb7X/x8UHadhu0A9DwbqJPuPMk11+PlVZG8Gfgc4n9nmxoAG2K7NaG4UmJ0poQmtqvN4bi/KYGYQTWic2Xdm1XGLZW6TZem1W0Ot5bHJjlME14JIG6Tm6XLGSy6WlL6nc55ReHEyueh1SVn9hpusMjCzX3uKyos+/YIcnMwGobeVQmoIKiu6DoN6GmR3DdxFb6yXnat2ehvsHBRnIN4FephX9J5JZ6iVW0d/Vveck2aSr7xwIwA7E8OLeiNajqp6f+vzNFDHJ54AQK47l9aowcwJbyBmZVg79nh5n5Cd5xTL5lc2vp0X08/w84nbGwrBlj2YimFpUCfZf5RpJgdzE57UyxXAl/H6Yj603As73jmUPYRUkpZQC/3pfi/pV8eDqUxQHkofYm3LWta1rEMgDtuDmchPVOVfSrSGPbkYIQSunQcEhOKQnZr3btKuyMEk8144bf9UuuH+jXCVquq4nczM/i5G09UXm6UWvLRdyUTWDHTOVgJKeXPvj1DhN2tnCeuzeY7h7DDPTD/DVZuvmq3MdEwQs1ViIc3g1asv5MnUfgZyY+XtUkluH3uYP37yy+zNDfPR6QRXPfSfbLr1jzFyzaWdbSnLTZ8CgU7jEQLxiSewWtbitHjJfHPVOaTjq9g8cHfZqIXsPFaolfNWXcZZPRfxs4kfsmfiybrna+TBQCnZv+jU+ZLQbCf/CUqp31FK3aNUMNC2GUrVYGevOpuCU2A8N+55DtIpey6ucqtc3sHMIBvbNhLSQ6xtXXtYlWS2tEkUE1VNliXawm3lKjLpFLw3shBe6dY8H6KSgXGlQ7rgLfjA1OJr6ufmVSYyRQTQ2w5jqdkuY1Vn3yPFciR508G0g7fvMce1vAu/dfgejO3aNT1kPxn4CbrQuWzjZbM7WjmvB6SCq1afT0QL88Mxr5l5KD/BJ579Ot8YuI2TWzfw3bEE17btYOzS36dl6DFO+PZv0P7iPfMvSEHelITKQ71UYw9GOsSnnya/6uzZbUJwcNPldGSG6EruByDk5LHCbQgheP3mX6cnspp/e+pfmKmTU2mUgwE/2W8fm2R/MwbmbuD3hBC3+I8PCSGOUBv7pU/J+zh39blARdlxRZNjZUd9wSkwkZ8oJxa3tG85LA9mujCNQlXJxJRoC7eRtf0kv5Wb1V8Kt0JyoG44QCmF4+uH5e0iBT/CNzC9eJdbzlFlHk/naY1CX5tgPDN7Nyvw5WkOg7SVrm6m87EcSc5yKdpHX48pYA6+fD1zZeUXwVwNMtM1eWDoAS5YewGdkc6KJzI1lWKtRowrVp3Dz6ef4luDd/LRp7/CaHGKD2x7E59tOZVNhQzJTReSOOtt7H/HN7Db17Lxtr9k/Z2frOqWr3p9R6LkbChZIRpqDEYTe9GcAvm+nVXbJ9a/DCsUZ/OAZ8zCdg7LH5Uc0aO8YeP7MN0i//T4P9U0SrsRr9y6noEBT8190lx81OFIacbA/CtwLvAv/uMcf1vAPAykB2gPt7O9czuGZpQT/ajZbn5XueUQ2VDGk1srjWbd0r6F/nT/oqc7jufHAWpyMDCrqKwJDaeY8pqvwFORtfNQ5w1YGaoaz8xeuA8lFh/esOc0bE5kinS2QHerzmTGLv+sR+LBjGRGGKsIfZQwfbHAdDEwMMccx/KaG48gB2O6ZpX3/9DIQ+TsHK/a/KrZjVJ5xqxOqfFr1lyEUnDr6M84v+sUPn/mB7m07yy6Bn6OHe0gu+okAKzuLRx4y1eZuOC9dLxwN9u//RvEh5+oOV/Bdqm+J1Loon6ILD65G4VGoffMqu2hUCsH1r2M1RN7iOWnvBBZeHZU8pqWdbxp23U8l3iOm1+4ufqkmoEbbkFvYLTjeoRJK1n3ueWkGQNzvlLqOj88do9S6jfxEv0B8zCQHmBT2yYMzWBj28aygRGIcje/VLJ8US2F1Da2eQo3m9s3k7NzTBenF/W6k3mvsaqugQl7SX6BwLUzXvNVCc2AzHjNMZXVXBNZ783bFhWMp9wFpdLnMleHbCpr0dki6GsLYTmKVGG2guZwq8jSVpqx3FhNAUXWdIiHDDJmYGCOOVaOobxNPp8+7GqOvJNHq5gxf9fAXaxvXc8p3afM7iQtb1xwnXLkVZEuPrj9zfzZSb/OR3a8lY5QK3oxTdvIU6S2vKx6dLJuMHnheznw1q8ijSgbf/RRhF0tHJkp2BgV6xGA3uDyGp94gmLXDuScJs+wCLFvw0UoIdh06H5Cdh47MjsqWRcap3VeyKUbLuWH+39Ies4NoRttR28Qdiz3vh1lmjEwrq8/BoA/3XJ55vy+hBhID5SNxdb2rfSnPG9ECVWWaamUjTmUOUREj5RDWyWJisVWks3nwbSF27CkhWXlUa6DrPwQRVq9oUtzXG9Z8fkcz3ju9wmrQ8xkFani4u5AKxUBlFJMZ226WnR627w7zOHU7N3X4XgwtmtjSQupZHn2TYmc6dIS0cmZzqK9woClxSkkeTgxzFQx6+VjDoNKDbKJ/AT7k/u5YtMV1V39jsl8urwX95zO2Z07yt93DDyMUC7JLS+vu39x9SkMX/UXGGaGzud+XPVcqmATNvwSZSXRhVFXhVmzskRn9lXnX3xCWoh8pJ2x1eeyYfhnhO0sZnjWwBi6oOg4XLvtWlzl1gjiupG2hh7MsaIZA/PHwL3+RMv7gXuAP1reZR3fFJwC4/nx2XxKxxYydsbzRir0yCrvKAYzg2xq21SO25Zq8Beb6J/IT2BoRnUc2qfczV+cBkT1RVwzvLh4oTqB6KpZ/bCJrHfXtn1NCAUcmEouam2OOyvrkS462C50t4ToafVCCSNJ78MhELiHoahccAsIJTB0g6k5VTNZ0yFi6LhSBZVkx5jxmTFGzAyJYv6wDUxlBVnJ+y8pGpdxFnfuzoM/o9C9BbOjsTxMYc3p5FefSs+emz3vCK+Mv+jIcjPyfE2WsaknEcia/AtQlpYZ2Hw5IaeILp1qD0YTWI5iY+smNrdv5sGhB6uOdyPtDXMwx4p5DYw/UfIsPOHJD/uPk5RS98533C87c8NdWzu2Al7DpdAEpq//JZVECYVSikPpQ2xsnxWAXtOyhrAWXnSifzI/yarYqroJxnI3fzHp9ZrM7doKRb3+hApK45KlcpnJ2RgabOr1Pgj7FtkL4zVsel+PpbwPQm+rQXert9Zhf5smaFjvPx8Fu4ASipZQCxP5iXIi1HEltjt7ATCDgWbHDik5MH2IqBFhspBZtBEAr0im6BTLMi4leaUa3TAr21AKZi6R5CFiycGG3ksZIZje+XYiyUO09j8EQNGunnHkKSk3zr9II0ax++Sa53ThCSmlOrYw07nN+xEqPBjwbr5sKblk/SXsT+1nJDsrEu9G244vA+OLTb7enzL5pFJqj1Lq2AnbHCeUjEIpYb+5fTOa0DiYOoghjPLo5NIsmBlzhoydKe8PXsx0U/umw/Jg6lWQwayicqYwjdLqxGRDLZCdrOqJkdIzQ5ZrkilAe0yjr8378BxcZKmyK2f7acb8goGuFp1oSCMegdGygTk8ReWUlSKsh70iBtcph8msOUPOglLlY0e+mKE/N82ali4SZgF1GL0wcyvIDmUO0R3tJh6KV++4CAPTefBnSE0ntfmiBfdNn3A5dusqenZ7DZk50ykPGYOSB1P/deMTT5DvOb1u4YEhdEohvf5NVwBQjM6d9K6wHMnL178cgajyYtxIO9rxZGB8fi6E+GchxCVCiHNKj2Vf2XFMSfKl5MGE9TDrW9fTn+pHE1rZgylJ9ZfmhZf2L3E4pcoThdou/hKlEFm2MA1aCDnXgxHCe1T0xJSS7Za0SBcU7XGN1qggFhIcShQXNVfGcWclZ8bSJQPjvQV7WnUmMp54oddzs3gDkzbTRPypgWEjzKSvJGtVeCyGppELEv3HjMGpIRQQ1gxMFLnc4nWy5haXDGWGaj47KMAu1L2Q1yAdOvsfIrvurHK577zoBokz30zr0GNEJveRLNiEjYqptNQ3MEZunHButG7+BbxS4lJUYXz1Tnaf9huMr62upyrNhemKdnFm35k8OPzgbF9dKQezgnKMzRiYi4HTgE/jCV9+Hvjcci7qeKc/1U9frK/qjmpL+xbPg9FmFZUd6Y1LLhmkueJ6Wzq2cChzaFGzWybyE3WbLKHCgzGToIfqV5VEqntiXKlAgekWfA9GIIRgTafBRFrOOxN9LpVJ/vF0npAO8Yi3oadVJ5FV2NJC07yu6MVgS7sqbBIPxZksTOJKF8uZNaUhXZAu2o1PFLCsPDexn1bD08qVQieVXVyVJHi5tpJLKpVkODvMhrYN1Tu5ZsMKsrm0jj6NYaZJbn1F02tInPYGpBGle/dN5C2HsD57KXWVJFTHsMUnvfLmRgZGExqGMHCVRKFxh3suDsacfWZVlS/ZcAlThSn2JvZ6rxttR5Mu4jDzWstBM2KXl9d5XHE0Fne8MpgZrBlUtLVjKzPmDGkrXfZgSiGyQ5lDdEW6yjmSEpvbN+NIpyrOOh85O0fOzjUMkZVzMHYehFa/UsuIeB3WfkOZ5Ug0TZCzM+UQGcCazhATKUlhEXO+3Yoy5clska4WUU7697TqpPJe+alAYDuLuwszHbOqYEgTGq50yVgZsqZTLiGNGDqZoBfmmJDIZ5hKj9MSjgEgNIP0YRiYuRVktrTZ0DrHwDgWTUx2B6Cz/2c4kTYya89oeg0y2k7ylNfS+cJdRIozVS+llKqb5I9P7MaO9mDPXWsFYRFCKpcDMzr/unsVTw5WGwtDFxR8A3P+mvOJ6tFymEzO081/rGhGi6xHCPFPQojHhRCPCSG+KISoHc8WUGYgPVDXwICnN2ZLr6mwJNU/mB6sSvCXWGwlWb1BY5UYmkFMj5JxCwhEbYishK6Xe2JKMjGJQg7LmTUwazsNJtOStNVcHqakCFAKkU35Jcoluls1XAmj6TSatvg+mKJbrFGaDethxvPj5CyXkO69rq4JbD/pH3B0OZAYJeyaZQWJsBFmKjO96JBOxs6UK8hKDco1Hox/E9d+6FFOuO0viY/vrXsu3czSNrzby70sYvYLwPRZb0OTNif3V5csC1FH6FK5xCf3UFh19rxeVVjzPJhDKe+zMZGufp8amqDoh3wjeoQL1l7Aw6MPY7kWTkmP7AhFRJeSZkJkNwKTeGrKb/G/vmk5F3U8k7bSJIqJGgNT7mvxu/Nd5WJLu+ziVyb4S5QNTJO9MOUmy1h9AwPQGmoh4xS8npxGjVeRNkgPgXRxpEIIyVTWS8Z2VBgY24WBRLKptVWmVBxpk8wpuisMTKlUeXAm6SX5F1nplTJTVfNvwAuTTRWmSBeLhPTqt3pQSXZ0caXLM5P9dGk6yvc+YkaUyWJmUaXKSiny9uyY5NLMpRoD4yf4W0efIpoaZsu9f8eqJ/+nps+rffARNOmQ3LpA9VgdrK5NjK27kBP7f4RW9TMItDld/JHkAXQ7U7c8uWo/LYyLZCjtHT+VqV6vrmkUK+bCXLrhUgpOgV3juyo8mOPLwHQrpT6jlDroP/4K6FzmdR23lEqU5+ZT4qE4a1rWeB39wsu/OK5TdvHrDTfqjHbSEeloOtE/X5NliTY9Stb1wknayBPw3K21mlCaAa4NhSS2I3GVRcaPhLXHvbuvtZ3eRWL/ZLqpxsXKpH3OLpAtQldrtQcDpeS/WrQHkzbTVcq64IfJXJdkIVNrYOaZDhiw9IznEuQKBWKaKqsbR3SDrGtRNJsM6bgOlp3zC0G8v+dQZojeWC8xI1a9r29gwtlJCl2bSG59OX3P3srWe/6GUEURS+fBn1Hs2ECxc3HDxcBL8Ty9+Q1ErRQbBirFMFVNF/9s/mXnvOeMaBFc5TLkezBT2eobIW+kuCr3iZ3acyrd0W4eHHqwQlG5voExj4HX3oyBuVcI8Q4hhOY/3gb8aLkXdrxSMgb1hhVt7dhaloxxlYurZkck11TB+Gxu39y0gSlVTc1nYFr1CBlpglLEH/4KPPh5+OZb4IG/h8mKMEI4BqlDWK7EURbpoveGns3BeHeQY0mnXHY9H1Kpsg7ZWNq7oJQqyMDTIwO8RL8yFxXCcqVLzs7VGBgATQuRsqqbLjUhyM8zHTBg6dk7OUhkTm+IEN47IpVLNXeSzChmYn+Vnt1wdri2/0XhldprBqHsOJnWVYxc+F4Ovez9RJJDnHD7J2g7tItwepR44oCX3G+iGGAupuMy0nMmqY5tbNv3vapQ39wQWXxiN2b7Vtw6DdCVeEl+GM7U92DAS/eUimA0ofGK9a9gz+Qepv0foV4OxnYVidzKVFN+P/BtwPIfNwJ/KITICCGOvjznCmcgPYBA1LrseEZnsjBJzsp5HoxyOJQ5hCa02g9JxTGLycG0hlpr+wFKKGjTImSdArqdx0iPwImvgROugBfvhu/9DvzP++C5HwIa5CZxrSKOMinpXJYMzKp2A12D8bSk2EQvg1dQ4H0AR3xJmEoDE9IFHTGNmZzCliYKmpZ0KbqNX18nSsqaxq2oxAvrGhkzqCQ7WhTsAvunx+g2QjVZPwlk8k0amMIMVnq4XMo7fwWZi5AOoXwSq3UVeadIevNF7H/1pzDb1rDpZ19m0wP/iBIayS0L977U/7lcEIL9J76JjtRBehLeKHEFVSEy4RSJJp5tWD1WiSF0Ejkd2xWsitukCwrTrv0cOBVKF5dsuASpJA/OPAvUz8HYrqo65mjRTBVZm1JKU0oZ/kPzt7UppdoXOv6XjYH0AOta15X7MSopJ/qzXqJfSslQZsjr2q9z9w2egRnPj9eVoJ/LfE2WALgWbbpnYNqSfsf+9ivhlX8Kv34LvPwjXmjswc/Bt94K0/tRuUkKbo5cUSMWFmW9JV0TrGo3mq4kc+WsbsBoumRgqu9ou1s1kjnI2xmUqs7bzEfBKTQsGJJSoIQk58wWI4QNjWwhqCRbCopOkf3J/TXab5UMpMaxXQgLp+bvFNZDTMyMNvliKXL5aXRfamk8N44t7Vrv368gC+WmEChau0+g4Ho9VnbrKg5edT2Tp7yWSHaC7NozcKMdi/iJZ0kXbMK6xvCmyyhGu9g8eC9SKTQEOgIjP0nL6C/ofeY/0aSzYHgMPM9nJOOFn89e5X3mp7PVXoyiWuliY9tGtnZs5YGxX6CETjhXW5lnu+6SD/FrhqbKJoQQbwJegfezPaiU+t/lXNTxTElFuR5bOrYAXmKyVKo8mBlkW8e2hucrFQcMZgbpC28lU7TZ0lu/GazeqOQqHItWI0rOLdKS8MNufZ4sOeFWOO2NcOqvwvgzcMefw95bCW3fhNkeJ1OYTfCXWNNhMJl2SFtp1rSsafy6+KKZeF5JadBYZ7z6fN2tOvsnXDJ2it7walx/Ds1CZKxMOek7l4LtEtGjJMwp2sNeV3RI10gX7dnzW3lPOr6ld8HXCpglWUzyXOK5cqh3S/sW1reuR68IhSml2DsxQIvRgnAmUXMaECOhKFPZyYVfzLXBKZKRFmHH8z6Hsn4FWU2JsvfZCvmVkO09O1gb7WLKStEeagHNYOKst5LafBHOYRoXgHTRJmzoSE3n4Amv45Rn/otTXvgu8fwkPdkxdMsL8CgExc4dFHpOW/CchtAZTYfQhOKsVQXu6O9gKuOyrmv2/a1rguIcNYpL1l/Cfz37X+zZuJOzDjxIdv1OMutnPaaCLWuloY4CzZQp/wvwO8BTwNPA7wghvrzcCzseUUoxmK7tgSnRHm6nN9bLUGaIglOg6BaZyE80zL9AdfXZ0Ey+aszwXCbyE/NWkOEUadW9ZKic6cduW+tVjFUiBKw5HU56LWrg5+jpQ9jWDOkCdMwxCGs7Q0yk3BrZ8HpI5b29bWmRzEnaYhqGXm08elo1UnlFxsoipWxaUTlpJut6jAAFy6HFiJEyE9UNq8qLoZuuyfD0XiYmnmnqtQK89/mhzCH2TO0hakTpinbRFe1iMD3Ik1NPkrNncwCpYpqxTIb2WMwL3czJw0RDcZL5BO5COTe7AAiySEL++61hibKVBc1AZDzPSGvfwKb4aqSSVQrmZufGcmK8+geUVWOWy6d1LZJmkrydx3IktjN7A9R/wrXYRpSNww8Ttgtk117ExJm/y6FL/p79197Mocu+gGrwHq1EFzqjmTBrWiXrWj1DOjcPY2iCnFXtgV+8/mI0oXHL+hMpdm1kw8+/QjTRX36+eIyKWprxYF4JnK78gLgQ4gY8YxMwh0QxQcbONDQw4En3D2YGKTgFRrPeB6BeBVmJ0rleTBwk2uIp9DiuxJhTFSWVZLIwOb8HY2VpC7V458gMU+w5hYZKTae+DvHkjXSOP4De8SpS+Sir14ZwpE3WzgGtrOk0yBQV0/kCjnQaehFQKTljksxV519K9LTqKAXpAljRYlMGRipJzsrRGe2s+3zBcgkZBrajyDkZOsJdSCXJu2memhjHEklI9BNxHfrUpXUl1n9ZKDpFnpp6ip5oD73xXtpCbTW/D9u1eTH5IpOFSbqj3eVqLk1odMW6yNt5Hp94nG3t21jbupb9ySEghCZAOAWk//4roWkhhFskVczR3TLnZqfqhQvY0sHRDfRiBqRn5HpjvUR9ZYAyVg6MMEZmHGVEEbEuIkKwpWUtB3OjdIXneR3wPKBoB0opCk4B0w+vxYwY61vWcyhzCINwlT9gRTu575K/oiBNIpEutsUXX5UG3u9xNB1me49DS0gSDwumMtXGN2zoNXJHnZFOzuo7iwcTT9P/st/hhPs+z6YHvsiBV/0lTrznmBmYZpL8zwOVv62NwJPLs5zjm5Lky3wGZkvHFsZz48yYM+UKskYhNYCoEWVty1r2zRwob8vXebPMFGdwpLOAgcnR6n+4cnaefE/j0ByhFuT681k1+jO07ATpgqQjrvHT8R/zj3s+jlSStX4l2URyYckY6cd/LdcklVf1DUybt20mqzDdIm4+tWATXqnAoK5hUF5ppiEEYS3MdGGcycIoe2d2M5DdS6KQpCvSRbfSsKwseXvldEAfC3J2zhs1URhnz8QeHh17lEOZQ+X8X87OsWdyD8likp5YT13F7ngoTke4gwOpAzw1+RQvTg3THo4jpINQsnqQF4AQKNVEJVkxhSXwjlcu2LkFNchiuSlU+9pyhdiaaI+ngbZA343jFEhogqSZJG7EObHzRM5bcx7nrTmPLR1biBtxErlcTfjWNaK4QiurDBwOaVORLOqsb/O8l542rcaD0YSX5Lfn9HJdsuESElaKJ50kg5f+PpprsvnBL+KaedQxyL9AcwamB3jOnwdzH/AssEoI8QMhxA+WdXXHGaWGyIUMjEIxmBpkODdcNWSsEZvbN3MwNUBbxHvj5uuINS5Youx/8Np9A5PUNfLdDdZp5bzRztuuJGLN0DuxD6m8HMz+9DPknCxjubFyL8x4yl0w0W+7Eg1PciaVr03ww2ypciojUdOPI4Z+UZasacR8r2u50vu5BUT0GElripH8IIYWoivSDSqKcF1wTQSQzDWRC3gJk7WyhPQQ7eF2umPdhPUwg+lBHh9/nCfGn+CJiSdAQHu9sFIFuqbTHesmbxdIFRTxcBjhWg0zAJoQJBaSjCkmMTUNUCA03GKK0dxobf7FtUC5OEoSzU0j2mef14XG9tYNZOd5zxRdk7RdYEffaVy09iJO6z2NVS2rqvps1rasZSKbJmrUvoddJOEGSsrNcDDpGZM1voHpa9MblirPbRY+b/V5xPQI908/hdm5gUMXf4BIaphND/2rZ5SPAc2EyD6+7Kt4iTCYGcQQButa1zXcp1RJ1p/uZyQzwsa2jXXvBCtZ17KRPRNPE9IFEUNnJm+zpqO6sWwhmZhS6WarH6KY0XVWtTfQRLJysPp0nGION9LF9okngF8lHjUZm/S8tMH0IGf1eon9yYynD9Uba5AkdyxUPoGuBJO5DLZbP0TWGVNoQpGfTlJonUKpLu9udJ4LWtbOViWVK7HdWTEcIQSdkdn1KV2SN11vZjuCuB5lLHOI9V1bG77WS52EmSCqz4abQnqITr0T8CTy28Jt84ZB5+I6EaJ6iyfSLe2GymAxw2AsPdXgWbwKETPDjFv0hnKFDMZnXsSWNuvb5pT3+1MsC07BMzBbX1n1dFeoje5wG1mnQOuc5syMnUcoyc6uHbR1bG3YG9MabidvO3THap+XSh2RB3Mg5RuYdi8i0NuqsXtA1i14MR2X1opLeFgP8/Kes7h/8nGucwqw9nRGz/0N1u26gXPE93l829WHva7DpZky5fsrH4ADvK3i+wCfgfQAG9o2zPsh7Ip0ed35mQGGc/UlYmqOCa2n4GZJ20mihsZMrtbFn8/AKKXKpZtt/odqKt6FXecOjGIaWldDx3rc+GpeXHUOmzL72CpGKbK/XIkymBkkFtbojGtMpT2JnCqkhHwCRp+C/gfQRh6nffQhEsMvANAddWa1wxToxSQtiWfpirpMW3EKuoGFBsX5QycpM9WwxNuep+4/pGkUbMerHgPCQqdgZZsqB38p4kq3SkRyLhE9sijjoiQMJwvEw957TDhFlH/BVkrxnbFbeTb7IgAxPcx0dh4D4xSxXZsxc4YWIwp6uJzgry1RNgGFyCfQpA3t1Td7Qgi2tqzDdO2ymrhSihkrTYsRZWfLetraN87beKncCFE9hl23wVjVNFkWbMWjo82VxR+YkbRHFPGwt39Pm45UkJjT0R/SNXJmrVdy5erzsZXDz6a8LMbM9ssY2Poqdoz9nBNHH25qDUtJMyEyhBA7hRB/J4ToB/4KeG5ZV3Wc0p/unzc8Bv4bvH0rz04/S87O1RW5rMRxJVE8T2E0P4iha1iuxHSq31wT+QkEgp5YtQ5p0Xbpn8r7d+oQEQYhpZiKd+DOlcRXyvN0ek4AwGzbwN6ek3HR+TX9J8zYz6Gh0RnuKUvirOkMMZmSZKwMyr/TZHo/HLwfhh+D4gzEujAj3RRj7cz40Yk1Tj/RqT2EMoeIJF8kknwRdIPeFsV0XkcBWaFqRjhXL1eRtbINDYxpu2VxzRqE12dj51PeUCpNIKRNymqy6W8FoJRiNFlouiF1PgpOAYVasiKHVMGi6MiyRI/mFMoaZFP2DI9nnuHxzNMA6EYYy0qTsxo0zDpFUnbe6zEpScQUvZBa3SmWWohYSQ6mvbaBucWIsTHeR8bO40iXhJViXbSPU9u3EpFywXL1dNGmO7yaolsv1CbQqb5x+/qTJn9+f56B1MJhqv1Jl80dAoT3N+3185Jzw2QhQyNTqG0W3tqynq2x1dwz+Xj5ffH0Ca9nuPdMdh68A5KHFlzDUtLQwAghThRCfFwI8Rzwz8AhQPhy/f981FZ4nCCV5FD60LwVYSW2dmwt3/Ev5MGkCjY9Ee8ubCzvvTkEXnVUJRP5Cbqj3TV3oJmiQ7JgUcx5F9JIdpJO1yURacFhjoEppqB9Q7l0Oa9FyMdX8UTsbN6q389ofi/r4lvY2Lq1XNCwttNgNOUgnSLm0CMw+LA3TybS6n1QI23eaACpcLCZMb0QTGd7HGVE0IvTCCePG+1EaWF64pKpvIZAIy8lWBnPG6pD0S1W6VLNpWDPqig3wimkvBEFwiCmFGPZsXn3X0kUbcnesfS8pevNkrfzVRIsR4SCkWSBeGj2Qqs5+bJa8b58PwDD5rj/pIEuLZKFBt6jmWHEShAzZst8h8wEfZGuuhVklgYdBf9Gob1+uHp9rA+BIO3kOKltM9ta1816HuH5h45N50x6490NxWIrPZiUKbntgOfpPDwyvxfjSkV/SrKtc/YvUZoeW69UuejIuh+NK3p3MpAf40BuxC/HVxzc+EoECqZemHcNS818Hsxe4ErgdUqpVyilvgQEAk4NmMhPUHSLdTXI5lJquAQW9GCGkwU2tK7HECFGSwZGiJqZJo2aLKdyRRxXkcukQQ8RSxygU0qSRhhZqSyrJEgHumbXlndM7Ohqbg+9krBWYMgaZ3N8C2viG5jIT1BwCqztNJhKu9ijezEL055RiXXVSJ87SmFJi1TRIKQrWsIKhIEKtaIqpG1645K0qSEwSNj+dL4GSdmiU0SJxnfvRUuW58DUQ0gbxzK93gxdJyJdsna2KemblUDOcnAkvDCRqZraeTikrFo16sMlXXTI23J2yqPyPRhRbWCm7BmK0kRpRrlUuR757Bgp5RDTqw3MhkhX9YgGv5AlrySdhYzXy9JaPycZ1kKc3LaZnR07WB3t9jZKF4QB4Za6x4BXDZnKO3RE48SMFqw61ZOVNzw/2GdjutATE/xiAQMzlJHYErZ16uUfqz0mCOn1NckALKd2+8u7TyGihbh74jFsqVAKijH/Z0wNzbuGpWY+A/NmYAxP7PLfhRBX0uwEn19CSoKUzXowAB2RDtrDjRPYRdtlOmvREg6zOr6e0bznNUQMjcScPEy9HhgpFdMZi46oQSaT9ko3EwfpkIq0UEjUbHilkITOLRCevdhnzDwy0s5D8lTuiK7DRbE1tJo1ca844FBqkLXhDAqYsuLkjfqhKgDXVRRlkVTRoDsmG4a4e2LehTJbjJC0coDwm+xqydrZxgUSCgq2M68SQFhasx3RWgjsIkKIphpHVwLJvEU8pOO6ikOJI8sdpcxUrTdwOCgYnskTq8jvCelXkAmBVJL9hUE6DM9LHjUnAJ04MJKqX0k2nRlGqzAurnIZKU6zIdJdzqF5T3gVZChFLD8NbavnnfHSFWmjrVK3z85Da++8+Ze87ZZDiT3R1Zhy7s2IQvebNAuO4n9fsLhoncGrt4Z4ZsolbTa+Edif9J7b3mmUL7RCCHra9JpemBLFOjcWcT3KRd2n8fPpp8iY3vqK4Q4kAlIrJESmlPqeUurtwMnAfcAfAKuFEP8qhDj65QgrnPlUlOfSF+ujJdSyYL4mkbW8ChwhWBvfyGhuEL0wRUxzSBftqth7PQ8mZzm4ShHWHGzXwXQVsemDtBkxMr5XIFGe5wLQVW0c01aGiBEhacf4QcsWNKU4J5dgTcwLOwyOPMoazYt1JwpxUk7ji5wrJXk3T7LgGZhG9MS959JmCFNa5JXtFR7UIWWmajr4v/zEl7n5+ZtxXIVU84vkhjFnc1maDtIipkUZyx8fYbJE1iIa0umMhxlM5MnWKV9vBtu1KbrF+ZP4uQQUFx4ulyk65C2HSGj20iKkVb5gDpvjFGSRV3SeV/4eIYjpBpOZ2kS/dExGcmO0VngVY8UEjnLZGO0Fs2LUhGN5c46E8GRi2hpXc9bFsSA+f/5leCZfvmlpC3XUhMkEoizVf8cBm7SlePspYS5aH0IqeGSeZP+BpIuhwZb26r9DX5vOVLbWU9E1URMqL3HFqnMpSouHEk95wrGaTiHcvqI8GACUUjml1LeUUtcCG4DdwEeXe2HHGwPpASJ6ZP5GRx8hBO89/b28acebGu6jlOLQTL7c+7I2vpHxwjCRsUdpG32IlonHKU4NoooZvrrn30gUEzWaZqmCjSYEwrURCAoFk+jMAC3hdrK+MZBKet5Lz3YvF1F5fDFNSAuRLAqejylOshxOHn2MXscmpoUZzA2zptf74E9lDTLzNCparsTCZqagz2tgelu85xJ5HR2DZwtjmLmJur+fjJWpSvBbrsXPR37Ojw78iFTlhacBYSdHQVZ8mJUiIgRpK411lOeaSyUbv6Zr1+ShbFeSt13ChoYmBNGQzv6J7GEl/PPz3BiUyY5DZgFRSgXDqQKROdWJmjt7UX0x792IndN2Gi16nOGil4cJawbp4gyWU30BzuQmsJVbZfyG/Z6vDS1rvErFEq5JUTl0htoQmREv/6IqbqAWRNVKJ1WQytsMJwt0RL3PZESPVoXJXCXRhYYQAkcqbtlrclqvzul9Bid2a3RF5w+THUhKNrVrhA0NQzPKsjY9bRrTGbdG2SKsa2SK9VXBT2zdyIZYHw9MP0EpUpmPdqw8A1OJUiqhlPo3pdQVy7Wg45WB9ACb2jct2NMCoPIz/Orj3+Xix25GazA/O2s6FCy3HMdeG9+EoxwOWkWcWA9IF3P0Gf7hwT/nS7v/mdetfyXv2Hpt1TkmMiaxkI5wLUKGjjsxgCYd4rEesq5XfSQdywsPzUmGSiXJOQWEMshZNlljjJP0DtZNPUXLzItsjK3ikJmgKwZRQzGR0zFdC3vuh1kp1NPf4/T73stp+28HszCvgWmPKAxNkSjoREQUB51np57BdqovvqZr4iq36vd9MHUQV3n6Yg+NPFS1vysVP3+hWFW6bDhZXGFUyJgLhPTKp49mmMyWNs9NP8eTk0/iyjp3pFMvwMSzVUYmP6dEtTVikMhZTGUXn/BfMMEvJZhpKCbBaXyBzJoO2aJDNDSn/N0toPxepX35ftaE+2g3WlkfWV1O9AshEMohZVaHQ8fShwjN0QU7VPAqJte3rAMzBSUDZuUwlUuv0rxqxvZ1XtgrM7bwWOYF8i+uVOwdS9MaDlVV2vVG12BKPxqgJCE/z/TAoMN43vNewGsmvXCtwaOjDk6DrvoDSZdtnf4wNhHC9b2j3lYd24V0obZUOW+5NaPCwft9XtF3LgPFESYdL/SYD3dAcnD+38MSsygDE9CYgfQAm9vmD3kB6DOH2HbL++kd3MWa5+9k27d/g/jwEzX7jafNKr2xNXGvGOAvHp/2wgBGhM8eupVvjNzHOzdcwV+texXGoUe8vpNiGtuVpAs2EUNDs/PoRpiWmX4AYq1rcJXElBaykITeHV6pbgWma+JIl6wJenQIicO6zjPQpc3q6efZ1LKGwfw4oFjTphjNCJSAlF1xF+1a8MDfI37+RRw9xumD9/Jg5CO8Pj93xOwsmvDCZImCjqsUbZFWCk6B5yZ3V4lVFp1izQfrxaTXV9Eb6+WBkXvRKi6azw5Z3PRwlkf3exdgIW00aaM0A6d84VbgOkSMSHk66HJTcAo8OfkkaTNN3skzVZgTJlIKclPenefUvvKFMmPa6HPif+3REC+MZxc1rA1gxpxpWOoNeGrTpbrueWT5R5IFokbtJUW3cihhYEuHg8Uhtse9z8n6yGrGrSnv7ypAOTbJwuwNl+3aTKYPEZ+jXzaUn6Qv0kmk5HGX8jBmBqWH6Cgmve/b13vvwVjXgooQOAVo6WkYUx2eyVOwXWLhamPnhcm8ryWegVFKcdNek83tGheum/W8LlpvkLPh6cnam4iUKZkuKLZ1er+/kPBGJwP0livJ5oTjhGf7rQZ/70t6zkJH57GM1xOTi3RAeqRmbPRyEhiYJcCRDkOZoQVzKtGRPWy75beJmhnEu/6X9K/djBSCLf/zQVb/9EsIp+RqK8ZSBVojs2/OFrEWgISc5kDK4itj/8XtUz/lfVtfz/WrL0P73vvhme9BYRoGHybf/xiGlUYIgebkQA/RnR3ADrcQ96VpsmYGFYpAW63UvumYuC5kChI97umg9a59JYm2TWzp/wk7LJucW2TaSrO2TTGWEbToUZ5N9/N0+gCZ1CDc+hF4/sfIs9/Fgxd8kv848U94TJ7Iqyb/h0sf/DgbB+/3PIY59MQk03mtrJ/UEWohU0jwfOL58h1+zs7V9Gzsm9lHb6yXa7ddy2DmIFPWbDjg+VEvlPDUId/AuF5DnkLNejVCA9ckZsSYMWew5fIOJUuZKXZP7MZVLu3RdjoiHRxMH6x+XafoXSRbV0GyH3yF3ETOmq3S8gkbGo6UDM00n/BXSi2c4Lez3tUsFIE64UqAbNHLC9Z4L4BwiyjdYKA4jKMcdsS2AJ6BkUjGrCmU0IniMJmd7UOaKc6AlUULVa9tqDDBhpgvr6TpUEh59wZWDl0PEy01bbav8y6mrWvKEv6Nf8YitNSXbMqZDvsnc3TGao1wWI8QN1qxXNPzYDSDXWMuB5KSt54crurDOme1QUirX668f8YzEieUPBgtXPZg+tobT7cE1bCCMKrHOCW+g8czT2NLm3ykE6QN2fp/w+Vg2QyMEGKjEOJeIcRzQohnhBAf8bd3CyHuEkLs8//tqjjmeiHEi0KI54UQ11RsP1cI8ZT/3D8J/8oihIgIIW7yt/9CCLGl4pjr/NfYJ4S4brl+ToDR7CiOcuY1MG0v3MXW//0wItqJ9lv3wJZX0H7CVbz4jm8weeq19D7xHbbd9B6ik8+TMz03urIC6pEXTZQbRY+O8IVDX+Xh9OO8vfcNfGDzGxD3ftartHr6u/4Hqo9MKkHn9G7aJp9GuBYuOj2ZQWbaNtPqV85kzSSye2uNhDpAwS3gSkW6oNDjB+kOryfSuom9W65BIbjo2TsBmB58kLWtLmMZQUgL0xPpQJ/YS/h/P4hMHKB4xV+izn0POWWyX2zjPfafcvsZf0w+vorT9t7Ey376KbrHd1e9dm9ckihos42gAjq1MDPmDPuS+5BKkrJqO/j3zezjxK4TecX6V2CIEHsSPy0/98KYd9F+YdTGtBWaU0QJDV0IzHIlmQF2oWy45huidaSM58bZM7mHiBGhxb9DNzQDRzqM5yq8p5L3IATEe2DqBVRqiFS+/sW8Ixqmfypfo7bbiHqhxhryM2CEwYh6+Tqn1vCOpYo1uRcAlPSMuTDYl+9HQ7At5nnj6yKrARgxx0EziCMZzc7mVIYzQ8SkrKoEc6RfQVYaS2HEID8NrkXBKdAd6SzL9NO+FhDQ2gfRDv93OQ918i9KKV6cyBI19IYVib1+NZlEYmBw03MmfTHBFZurowKxkGDnap2Hhp2aXNkBX4Os5MFEtBDS92C6WjQ00cjACApW/b+17UjOaT2TgjR5Or+PfMSffXMUK8mW04NxgD9SSp0CXAT8nhDiVLwCgbuVUjuAu/3v8Z97B3Aa8GrgX4QoB1//FXgfsMN/vNrf/l5gRim1HfgC8Lf+ubqBTwAXAhcAn6g0ZEtNaaRxXQOjFL2P3sCmOz6Bufo0jN++F3q346+Tzb2nsvfi99H/us+jmxm23fxbrHvsO0Qr/jJSKe54dpyIWk2o8zFG3L387ob/w6u7r0Du+jpM74Odv+bdnTx5IwAzbgS9bRWaNInrEZSbpy03ymTLJmKaJxeTdYvIBknNVDGNJkIk8zZ6fIAtbTtQRpTJVWfz85f9GS1nvA2AxIt38RfD13OtepCZrEPn/vs5+f5/QBgRnr78j3mko48XMoPk3DzpoveBs1dt45Hz/4BHz/4Af9Yd488SP+KFmcfLr90Tl+RsjZxdGjIehmKarmgXk/lJDqYOkjbTVRVkiUKC6eI02zu30xpu5aSOc3h65hdYrkky7zKecjl1fQhHwt4RC83KoLSQV4lTUqfWDU+HDU/XaaLB3fqRIJWkP9XP84nn6Yh01FTBtYfbGUgPzCb8i8nZGwBNh5ZuzOEn0fNTdVUKdE0QMTT2TzaX8F9wGmkp/6JHZsNHc/JTedMhWbCJ1fNeKjTIXiwMsDG6jqj/M/eEuohoYYbNcZRm0IJkOp9ESknezpMtJohqelXYatxM4Cp31sDohqc+UUxjK5eesB8GinVBKI6nQxTx1CkayQCV8i91Ro1PZkymcxat0cYVdq2hdpRSuEoymIqwZ8LlTSeF6zb5XrQuxEhWMjQn3HUgKemOCjr9D76hGShmp8d2tdSqKgOEDUG2kYFxJVsiG+kJdbIr8xS50kiLl4KBUUqNKqUe97/O4MnLrAfeANzg73YD8Kv+128AblRKmUqpg8CLwAVCiLVAu1LqIX8mzX/NOaZ0rluAK33v5hrgLr8oYQa4i1mjtOSUutpremBcix0P/COrH/43UideTeQ3b0fMkaFoD7ezOr6a0XWnsf//fJPprZex87mbOOkXHy3HSp8cSjGaKrK1dTNCaTij7+CVnS+nNfEMxlM3womvhrPeAVsugWe+j5mZwrQlhi6QoRbCkdW0pAYQKJLtmwgp784/p1zkHC9AKknKTJEozvjNnYMIzWJHhzf50o6vAiTOCZfTG+5g97pT0HSdfwh/hQvu/EPWP/oN8qtO5sDVH0fv2U53uI2ElcZWFjMFnfaIJKQDQnB/LMxPIxpSwL9P3cm3Rn9Azs2XK8mmcpqXZ9HD5Qt/V7SL4ewwpmtWiVzuS+4DYEfXDhxXcUbXyym6BZ6e2cU+Pzz26rPixMOCp4d8A6OHMDQN0/Y/oEIvh1JiRozp4nT1kLIlYN/MPgYzg3TFuuqWBZd+puGMN8qB3BSEKkQZNYOC3k7LzJNoZrLua7RFQ0xnTaabSPinrXS5b6MulR4UeF7MnAmUY+ki4QaKCZrr/e4LbpFDxVF2xGdvwjQhWBde5Sf6dULKwXEdMlaRycIkep029UO+5t7GuQrkxSQK5QlYpke8/IvypbSNCMS6vbXXC5U5BYh3w5ymXNNxeX48Q2ds/gbUsB6hNdSGqSx+tD9Mawhee0L9nFYpJzM3TLY/6Za9F/AmW1YWJvQ26IUxdI1csX5OpaRkcUH7WRwsDvFowV/TUawkOyo5GD90dTbwC2C1UmoUPCMElOp61+PJ0ZQY8ret97+eu73qGKWUA6Twxgs0Otfcdb1PCLFLCLFrcvLwpdr7U/20hlrpiVbogClF+Ed/xOoXfsL4+b9Jy9u/jVZ5oahgU/smHOlQ0Fu468w/4rGT3k73/v9h4/1/ANLhtqdHaYsafOjU9/Lulg9SSJ3FvrE02576Ik7LGjj716F9I5z+FpA2avdN5ZbYBw7Z/N6PYmgT3q8j07kZ1/INjKaV3fC8nedQ5hCPjD7Ck5NPIhWEtDCTtnfh3tJ+IgCu0YIT7QUry6b4ag4Il12v/BS/Zf0Rk5H1TJ56LQOX/iFupLX0O6Y91EKH0cFMQStXkNnS4dbJu1kd7uVf9NP5wEySp7LP8bmBr5HUnwUgUfCE/tB07y7VcRBC0BXtoj1S3aC6b2YfhmawpX0LjivZGN9Ob3QNuyYf5IUxm5aIYGOPwSnrwzwzZKEcC4SBrgls6VUJeb0wNrheyEgquaRhMlvaTOarB3XVoz3SzlBuiIKZ8ZLTc24CMrZARDqITe5Bs+qXY8fDBsPJBbwTvGmg8yb4S/mXEkbEkxTyq/osRzKTs4mH6t/hC9dEAfsLgygU2/38S4n10dWMmBOUBvoK5ZDIZxjNjtKiqJkfM+RXkK2LVtyoGRHsfIKYESOqh30Ds877W4ZbvPVrGnRvq5/sd8y6Hf8Hp7ybmpC+8GWyO7KKiazBL0Y0XrcjTDxU3+CubtHY1qnx8PCsgXGkYjAtyxVkQI2eWW+bznQdD8bQBKYjKyohZyk6El3TOK/9DASCb41M4oY7jqoe2bIbGCFEK/Bd4PeVUvPVftb7i6h5th/uMbMblPqqUuo8pdR5fX3zz2SZj8HMIJvaN1UnnX/xbxhP3cLEBb9F96v/HmMeGY6YEWND6waenxxHuvDcKW9m9Lw/o3P//7L6Jx9m18FJrjplNX2xbl61ejW6gE3P/hshc5qBs/7AuzNrWwWrToZtlxN+4QfEHO9XffsBC9MVOKNjFKLdEOtCOgYCQU45ZKwMT00+xWPjjzGYHiQWitEd6yZmeOGFtHoR3e2jLdRRXq/Tug6UZGOsj5HiFF0tknvUOXx53Z8zceaba+4ESyQqDMwDyUdIOCne0HcVI1uv4TcLgq8lFR1GG7env0t0/bcZKxSQlX82X8JFE1pt/iW5j63tWwnpIS9pLwTn9V3CYPZF9k4dYseaEJoQnLExTM5U7E/OJo4FVH9A/SR7WA/XVnUdAXk735SgpCY0DGEwmHgBb/5J9f6pgk0oEkcaUaJTTyLqVOTFQjqJvF0jilqJVJKMmWk4btpb9Ex1f5QQ3i/Mb35N5v3XbqQp6hRQmsGL+QFCwmBztLocfl1kNbaymbI9UVNDSQ6lR7FdG8PO11Q3DhUmWRXpIlL59zeiFArT9EZ7vIKI3KRnYFzL08Qr0brK81JrSump3s//uSp7XhaiNdTOIwO9GBq88cR5DDZw0TqDp6dcMpb3njuUljhyNsEP3ujkyt9pb5tG3lLkGigB1EjGKE/s1dA02o1W2p2TMDoeYya25qXjwQghQnjG5VtKqf/xN4/7YS/8f0uB7iG8aZklNgAj/vYNdbZXHSOEMIAOIDHPuZaFgfRAdf6l/6dwx5/DSa9l1av/vimNp7DqZSZnU/LGJ8/6AKMX/DmrBn/IP+hf4jWneN5RSwje3/4w5+QeIHHSO0jGN2NrMe9OrW0t8tRfRbg2a/u/T8qUPDbmsr5NcILdT394ixftAFr0CAXlcihzCFOadMe66Yx2lsM2UnoxZcs4SIs6oWqtyghD5yY2hzpwlWTCnKKvBUYzDa4yThGjOE2iIOiOSZJ2mrsTD3FG60nsiG/BNaK8sOMNnDczyF/rJ/Hq7ksxWp/hYfFvHMyNVp2n7umlw4HkAXZ07QDAcj0V5bN7LkZDx4z8gpPWeh/6U9aHMDTYPTkbb1eALSvuWfywTjwUZ6owVb835TBImsmGs2vm0hZuYyJziOyckI7tKkzHC38qI4aQDuHUgZrjhfA6W1L5xpVwRafoR78a/N1ct64HRSjqNV4qmEibtIQb5yc0Jweawb5CP1tjG2vCguv9RP+wOY4A4rrGcHqKSCjihUXnvPZQYXK2gmz2h8WNttMZ6/WbQZVvYGyoHI+shzytvUplCOl6XlJFKbTX85KhPRJa8GaghOPqPDbcwlWbBV3R+S+rF64zkAoe9cNk+0sJ/q45ITIo59F6G4helpg7fKykQVZavpPeiWbkeDTc8dLIwfi5kP8AnlNK/UPFUz8ASlVd1wHfr9j+Dr8ybCteMv8RP4yWEUJc5J/zXXOOKZ3rLcA9fp7mDuBqIUSXn9y/2t+25FiuxUh2ZFYiJjUEN1/nueNv/ErDu/lKCpbL/okC27u2kXdnQx7jp7+fL2jXca3+Cy58/E/AtdEL03zI/g+ekNs5uPmthJw8hZhfZhzrpBDrI7H2Ejr7f8Sug1NIBZ8632GzNsHt6RMpOhDBJqbFKLhFuqJdVdP6SkgFk4Uh0Ip0G9trF92yik1xXzKmMM5avxemBtcBp8BU/CQsV6MnnOOHU/egUFzbe3l5t5G1F5Bs38KpL97K1R3n0Drxe0gl+d9DP/XCV3rYk2Kvw2B6EFvaZQOTN110DVpCbawyziTU8QTbvOsY0ZDGib0Oeybi5RC3I21myjIos53fmtBwfQHMpSBRSNT9XddDCEHEKtBvJ6u2z52t7kY6CGWH0Yu1Yw1iIZ3RVOMw2YId/HaBeh4URhSsLLl8jqJv7Bqh2QWSssiENc2O+Jaa51eHe9GF7iX6BbRoOpO5FHFCXli0wiA70mW0OF1jYJTy3h8tIT//Al4ORrpV2nre9rWesGtJ4sUp1uRfhmfyFG23bpVeI54eTuFIwZUbF56Zc3KPTmdE8PCIjWZlOZCwCGmwsa1CXkcIoiKMq7z3YsnATNfLw2iipmqwcpyy7cLUjJcheFJEXhoGBng58BvAFUKI3f7jtcDfAK8SQuwDXuV/j1LqGeBmvJHMtwO/p1R5zufvAl/DS/zvB27zt/8H0COEeBH4Q/yKNKVUAvgM8Kj/+LS/bclJW2nOXnU2J3Wf5NXS3/QbXkz3Hd/2SiMXQErF8+MZDE1jVXwVIT1cDgo9PjjDF/PX8LPtf0xH/21sePBPWPX0vxHC5fftD/DEJGhIcrr/OuFWcrZgbNubEa5N34H/ZVO7xslyPwCPWCfwo+ejhLEI661kGlywwbuLO5j18i/rojtqd9A01qw9B0PoDObHy70wVSjl3QH3bGfK9kIQZssUe7J7uaLzArpDnbP7Co29J7+FqJli28E7WRXpQy+cytPZffQnsjgiVFO9VKIywQ9ecrOsopy5EGHkGbd3l/ff2ZtlIm8wmtUYKo7xL2Pf4NP7voolbe+CZs96Srqme/0YR4jt2mTtxrNralDQ4tokpEmqwsDlLKe6ekwI3HAbkcRz5dBeiYXCZCkzNf/0RStbkwOpeGFSM1MY84iJohyEcnmx4F3QdsRqqyx1obM23MdIcRwlDMLKpjO8mlyxwNy421hx2qsgmyPHZEmb1lCckGZUGJh1lBP8lYRi3nOl95JTrMq/5K3GPS/zsftQkqgOp/Ys7PFoQnDBOq+r33UcDs44bOnQasqgW7RouSeqxx8nPlm3kkwjOyfRXznNdTit41jdKKmzF7wcWgN9v6VmOavIfqqUEkqpM5VSO/3Hj5VS00qpK5VSO/x/ExXHfFYpdYJS6iSl1G0V23cppU73n/ug76WglCoqpd6qlNqulLpAKXWg4piv+9u3K6X+c7l+zt5YLze85gau3HgF/OiPYORxz3PpO7Gp44eTBZJ5i7ZoCE3orI9vKX/ob396jK54iPglH2TkZZ+mfeheYonnmDzjt5k21vDYqIUejpKw/YuEEEyqTvSWHqbWXsKrzTt47fo8seQ+FILQmtXcfSDCeC5EzGgjPU93s1SKgcw+pNXN6tbeiu2yrL1ktHSzIdbHYHaEte2KtCmoEnkupLwPc7yXVF4CLk84d9IZ6uKK1tMRc8pGk53bGFlzPlsGfsJJ4QkKM2diKotn0vs5lLSwzbwXtpnDvpl9dEW6ykUWBdvF0L0ZNEMjmwmpbnZNPej9iqTNzr4soPjx2G7+eei/KUqTjJvj4aln/V6Y2W7yeCjOWG7siId65ebRaauLUwTpEA/F2Z8dwZI2eafIcGaGnMwwXBznQH6Q/flBLE1HSJuwL7haYqEwWbKYnD//UpipvUD7uEaU7NQI8XnDYzZKePIwcS3GWj8cNpd1kdUMm2MooaE5BXRNkM54f6NKhkoaZLFqA1N0Lfoind436RHPiEQ7KZcoz6Vj46y8jJrVHyv1vESM2ov9Quw+lOSMPr1hNd1cLlqnk7XhCXsj+9OCbR21l+IWPYrjT82MhATtsfqlyt50VpdK3U3vJsv7uj+pAzoh2c0Afsj1KOVhgk7+pWLXf8Dub8KlfwKnXLvw/niT8V6cyFbdLbWHu1jXspnJrMmugQRXnbIaQ9eYPu3dDF/8V8xs+1WyW67m7NUGj485qNZ1zBS8xi3blSRFB2Fs/jf2JmJYvNP9IZGZFyi2ruOyU0yihuLbe3uIhzpIz1Mh5biSseJ+3PxWOmKzb5O8myHvzF4sN3ZuY7AwwZoW741fDpOZGYh1eB9mIJWXhLoeIWEN85pN78DtPdMbNGWmqsoxXzjxDYDg13M3UcxsJyqiPF/chyMlw4kCxWJtWGffzD62d21HCIHrKhxXoQkYSjgULMGOlldwMPM8U8VxhGsSCxfo2fJtnufH7Iht5k83v48eo4s7xh/xDExFrqfU/NiUIOQ8pKzUokYO40vhxPQIOafAI4nn2DXzAk+lDjJijzBtz1CQJhknx/7CIcxwK+H0ITQzVXWaWEhnNF2bu7KlTcEpNM4PNsq/+BRcHZwcuqxfCi2kTTgzgELjxcIA2+ObqjwvId3y3319ZDV5WWRGFhCuSTxskEomyvNjSpQ1yGLVpf4SRZvhh8LSwxW6enU8GIBo+6x8jNDL+Zcpv7S7rcnEfonprMmhmQJnr24+pHZBV4GQBrcdsJgxBSe01d4ERLXq332vL3pZg/BuCM2Km69SBRlA/4xBW1jSpXeSDPnv48DAHEcMPgy3fRR2XA2XXd/UIY4reW4kTTxc3SEshKAr0sudz46jFFxz2qyMS2rb65jZ8WYQgnPXGEwUBIfsVhxXYjqSbNHBDbUihMb/jK/hPuNlbBz+MbHEcxS7TqQl4vL6HWn2TkcpFOPk7WyjYZEMZ4exVBYnv5WO+OzbRCqJLvTyHf2mjq3MODnaQ1611VhGeCEmYUD3CZ64GDCVTxPpu5NtbSdzatc5KCNKsftknFgvuj3rrhej3RzYejVn5XZxgdjHlvCJPJvbh2F453lxZJp8Rbw5baYZz4+zo9MLj9lSlgMrJXmYyza+Ag2NxyYfZCxzgH8YuwUr+jTmxDW8uftttBpxzm/byf78EP2FyaoQWelvkizpWx0m04Xpxc1bKWZA9y5Y3ZF2usJttGottOmtdITaaNHjRLUIbUYLRdfkYGEYKxQhmthbpTUVC+nM5KyaMNmCDZaN8i8+M3mbsG6g17lJEU6RSOJ5cIuMY5FyMmyvyL8I6XDpgx9ja/9dwGxH/7A5heaaaCiElaUg54hc5sdZHekiXBHWk/5te0spt5Uegbb1KNcmI0PsGU7xaH+iJndF9zZPlcDPv9iu5IXxLO3RxYXGAPYMJQE4p0kDI1ybuKE4fX07D7zgeWXb2+qEvkSoyodr1AtTwnL8vSsqyMDzYDZ32vQZHdihDBYctTxMYGCOlPQo3Pwu6NwIb/r3urIr9eif9hKJ9UIMjiu565lxzt3cxer2+helc1Z5H/zHRh2EgLzlkshZaKEYQ3ac56Zdnt/4doRrottZrM4T0dC4bH2SDZ3w/9t77zBZrvrO+3MqdlfH6Z4cb85BNygHEEISSSCiMdHGC7bXCWP79fq1H79re72E9dp4l3UG29gEGzAsQqAEEkIoSzfnNDdMzp2rK5z3j+rJ4d4r3ZGQqM/z9DM91dU11We66nfOL3x/J3t1HFllKL9wHOb4+FGAYAVTMzC2VyGmJTDVad/wZHGpowcXSu+EDGbfDetAm74RnPe+h1Bs3tz1s9OZOUKhZNRjz7n4z6y4nYJRxx/q/0Kj3EDZtzlR6kYzdCJ+maP9BcZLDlXX58TY7PjLzHTj4/0OLWmV1mSG9entPD30Q/7q9F9RlS7vqns/1ZFbOTAY3FCuSW5DFxoPDj0bNK2aITFv6RaDpRde1V/1qpTc0qXHXyCo4J9jkGzPW0g4l4QWo+iVOFMdRTpF9Pz0zUMIgZTz3WRlp7x0+8Al4i+OJ8lXXDTTQhT62Dv8BAUnWDkpTpHI6BFAIvX4lDz/2hn1L9mRo0TtcVr7ngag1WxAAL3VgaDGwK9ieBVyM055wimwZ+woV9tzVLV9hzo9EbQplj4y30c52sTRnhGOjvqUbQ/XlTx/bmx2IDxaF3RfrcVfzo6UcDx/nr7bpbDn/DjpqM7KOqOmcbc0qj2OndnANSvrmUxeXFWnzhOhNISGgjJlROsTKhNlf9qQzEAR05Ixju9PZZCVHRgoKHSlXNoiSRCSM0YkNDCvGAwrqKD/mS9BNH1Jb8lVHM6NFElbC99wnu4eZbRU5Y1bZotQlqtBMyeAtkiZ1oTK3gvjKEKQLzsMFWwsQ+UHfYG7YMualRTabgbAzqwnokTw8HjXNRblSjDjOzw4wKnBApU5jYtOTRxDkykiIotRWz3YXpl6s5mYlpiSMulMBAZmQPdojbt8/aDGwyMrkDOKSrtz3RT0J7GqN9IUnV3vWpQaCIXyjIvfVw2OrrmbLUo3mwcmiCgm+/NHQdGJ+EWiusKpwQIHL+R47OwBBAq618xw3qZQcYO0Y09yZtBhXUtg5K5pfC22V2aV2cJvdnyYa+rbyVoe+/uD1xNGlM3WBh4b3k/Jq06lKkNQD/NiWikXneJ0DMedX7MyD88LZE3muK9Ktoe+SGwgocWZ8PKcpog+fhJlxsrCMlT657jJxipjF4m/jC4afylWgkmNVA2ez+3j62c+z1/s/30eO/9N1JGDSNVA1lYUJ0rdpLUk2RkJHc0DgSxQotBLtDyCoRg06Nkp6X7VKWJoChNld2qF/UDf47jS56MXjmPkphvC2V6VejOF40mG+nsQnsOAyCJ8h0QqS8zUiEc0VCF4/uwYucn+KUJA81aw6slXHM6PFqlb5HpcCikl+86Ps609jZNejVRNtMroou0BVHsCJ9aMG23g6hVBG+P6uEG0rhl1TraiEIKoZuHWJnNLpSrrqiBfu4ZmBvjPTQSSM51ph45oEGs6HMmGLrJXDJEUvOvz0LTpknb3fcnx/jwxU1tQSwqC4H593GBXV2Zqm5SSkuPh1fLbFc9hR3uKAz0TqIpgIF+hUvXQVYVHzlXYVOfTHFcY3vIRhjd+ADu9mrjQqSoqK5stVtcHmWcTlTKFisuh3hwXRss4ro+UktO5YxjuKlLR4EvtSx+BQkxPYWnxqRVM2kyTMBKcqwzzx7d7dGYU/uyhMp/89hATJQ8pJf965F/Aj9KhzFbr8WWQpdXS0ICKM2slM9y2mzGRomvsIButtRwqHscRKsIpoSuCVFQnGdUYqJylOdpO2VY4P1quScardA85OB6sq9W/rE1t5jc2/SEfa3gTcT2JELC92eXIkMakbbs6fhW27/CjsfkZWUKIF1zVP2FPoKla0Etl4DBULpL27MzPoIJAsVhboqo8pSUYdfOc8/MYI0emUnGjusposTpLdXfcXiLA73lBp8iFVlwShos2UU3Flz4/yO2hyWxiVWwl9/Xfy6f7/p19pTNBryHpc6p8lrXWiqlVq/Bdmgb3MZYOmuM1DAVS8pMV/QJQnBJCgOcHLYorns39/U9za6nMSsclc/L706cjIapEOTaQY7S/GwAt3Y6pgNSmU5QtQyOiq+w5N87YZCaKmcBXdI4P5Inqi1+PS3FutMRYyeGqjhRStyg37sSxWtDKw/MKOoXngJRUU6tBCJqSEdY2xtnQnMSNNi6oLB7T4rhy0sAE//uF4jCGpk6t0NwZK5zuseD67Uq51GvBNX9Ij4UG5tVK30SFfMVdNPvmSF+OPefHuWNT86zYTK7i0pSMUBfTKds2Uqhs72qg4vicGS4Ggo0Czo+WODNS4XXtgPRwo/WMrX8vCJUEYNcyZm5eF2Rcff/wMDFTIxnRGcrbHOrJcc+Jh8g7E8jyqin3WMUrkTEb0BQNU4tMzciFEHQmOjmXP0fLqrV8+v1dfPiWNE+dLPEr/9TLV/Y9zuGRQ1SGbqchNltYs+K41Fk6kXgdHUkVDzkdKxCCvvQGruUQcWcTZd/mZDnQUxO1zBpf+vQUz9CZWE3UUElENJJRHVNXONbnoAhY0zQ9zs16ZtZNZFuTg+sLDg8FK4WuaAsdkWYeHNkbSMnMwFTNF+wmGy4PE1WjUBoO0mNzF7m4ndK82IfrSZw5CtsLkVITDPhl+ordqPkgZXfSTTZZdW97No7vLF70uUT8pex6OK5EVQUHC8cZcie4M7Gdj6Vv4aOt78ZQDP6l/1v8Tc+XeSZ3gLJvz0pPzo4cQ3fLnF5xB4VYE42DB4AgDjPu5sj7ZYQbyNMYqiBXrvLw0B4K0uFDJZeJzmtJn34MxSkH8UBFUCpDueqTdgI3rRMLVv5zdfYiukrc0Nh7fpyhfLCiG8xVyJVdYuZlJGDMYO/5cQC2d6SDDYqGnVlPJbsZzZ6YlSmp2hPYmQ3IGa7PP3nbFn7jtrX4RgKp6POMkqXF59XCDBfmx2EUUfuOSJ+yOzuDrN7yiBkSQ9ExyXJM1V4yuZjQwLyEVByPk4OLi+ftvzDOH377IM3JCG/a2jK13a9liHXUWdRZBn61hGs1sK2jDkXAvgsT2I5PRFP50YkhBHDjmiyKU0ZKyJVr3TE9BU8L6lEarEDL69xYjjNDDkJAIqKxb/wRvnz883TGNmCP75wyMI7vkIkEBW6GMnvm25ns5EL+Ar4AVRW859o0f/6BFhJRn2+d+gqmbMIZu4a62Oyvm+NJMjETzBiGIunKxPB8OdUwy2ldT73IIc/FAzdZ4SgSUGp+7sFyL7ZfoT02u1U0BLL8XfUakZm94b0yckZcYW3WxdJ9DvYJVLeMrqnsjG3ngj3K0ZFDs473QnvE2J5NxaugowSGxcoGweWlWjpXxuc3gHP9JUMmkwghSGoJeqjQM/Q4E5Uhqp49y01WdhYO8I+WR3nw7IP4dm7R+Euu5KIowYr64bEnqdfr2BJbg2umWBdbzcc7f553NNzJQHWYrw8GlQarZwhcNg8+j6NFGK7fyGDDVjJjJ1Dd8nRFvzMWtFJQDAxNZaxs852+H7PDdljVsJWR9bejuhXSZ35M2bNJqkn6JmwSpoZe6kMKFTfaEHT6WWCFZmgKqajOgZ4c50dLnBgskLqImOVS7D0/Tls6SmNidrzMjTVTatqNANTKeOAasxpx5xSJxsxgZYVQcGIt89xkhmoipajtqxA1BEO5heuaBOC4cnYG2bjGirrp/evNDi6oPjLfOyvOuFyEBuYl5NRgAU1VFnRzPNM9yh/dc5iGRIRPvWPrrC/9RNmhM2MRNVRihoaJQ9lsIGZqrG9OsufcGI2JCJah8uiJYba2pUhlmlC8KkXbpc7SSJgqvifIeQrjZQf84IKIRMp8b28JKSWP9H6H753/KhvTO/jQ2l8nX1JJWQqu72AqQWMlCEQwVaFOBR87E53Ynj1rhr+6yeTNNx9CMYcZP/9GQJ1lYAL3mAhmjrWGUhFdoTNr1cT7fE4mg5tO+8Rx1phrOVg4gSfk1KzwfDEoe+qIzzYw5arPuRF3Kv4yiWrnkDNSP1UFtjS63DX0FW544lMoSDZHN2ApJg/0/GjWe4NVgKSwRHHqQkzVv5RHcV2XnpxDyZ9REDgXSVAEN+fmWHa8xRK65qEIQVqvo+zmOTvyHIfH9nC+eJgTI2cZLefIV/MoCyhMfOHgF/j8gc/z4NkHF4y/+D6Ml6tE9KC3ywW7n9fWXYswklMGSRUK16d38Ltdv8hr6q7lpvQukrXvjfA9Ggf3M9iwjaL06K/fgiI96oePzDAwI4GBUXWEgEPF44xUc/z8+Di5jl2Us6spZVaROfF9bM/Gd0x8Gayo9GJ/oPYtFAQCuUhSha4qZCyDE4P5oI/aJYhZLoTj+RzsnZhevcwdLyNBqWlX0OIcQTW9ZtGsPADXapgf6FdMZtYDLSZ6OYnteFQdH01RmKgIxsoKK9LT+3clVjJm2EjpQ37Z1LOmCA3MS8RwvsJgvkJygRz7x04O86ffPUJHJson376VbHz64vb8oCtEeyYImipCUh83ycng9x0daU4OFijaLmeGi/SMl7l5bQO+HkdKH9eXtKYtVqYVtq9Zxea2JlbWR2lN1gGwosnmWF+Vfzv27zzU8y2uyl7Pe9f8ElU30EtKRRXKXomGaMu0H10IYkZsXibZudx0v+9CtcA3T32DrfVb+ZM3v4bdqwxWNEx/9nLVJRMzAoUO1QzqT3yfqK7SlbEYt8u4kUbGIo3cqByC4hbKfoXjlX7U2k3+fOE0lhYna84uvDvZ7yDldPwFguwmpZpDzlkZ7Gos8BZ+TKw8RKw0iKnqXJvewtNjhxmfI4f/QsQvxyvjaCg4o+e4UFDIV1y6J3zGhvsXXsW4lSCLbY4BKNjuZd0IhRCYRpr6apGknkYiGaxc4PELz3J64jQRdfaM+9T4KZ4deJaoFuUrFx5iaIG6n1LVxZeBO+bhsSdJqnF2JbYs+PejaoS31N/K2xpun9qWHT2G4RQZaNqB7dv0JFqo6jEah/ZjqVHSWpKe6nDw+Wup8E/kn6FDatxUlRRrcc7Rdbdh5vtJ9B0lVxDEzeB/qhf7cGItCN/F16wlVAiCyU1DPELqMiv2Z3J8IE/F8blqEQMDgKJjZzZRato9yzW2EL6eCCZANTeZ5/toio6m6Hg1UZP6uMJwYWEDo6sKedvFlxIh4Ox44FJbkZ5eqWysX42nePRr6ksShwkNzEuA4/kcHywsWMD10OEB/sf9R1nflOBP7946b7k+Xq6yoj423S2wWiJR34JH4DPe0ZFGAvt7Jnj0xDCqIrhhdRapRcjLCA2WDNxE1TIi0Ux9LIUVkaxsSBJRI9QlSyTavsXB/INc23gr71j586hCrVXeQzKqIKVPysjMOq+kkZzKJGtPtCMQnJ1RSf6NE9+g5JT44KYPsqUjylt3Raey0SAQ48vEahe3AMxkoD0FWKZKNqlg+jEGs2u4Tj3C6XNrMBWTfaVTKE4BJFwonKYjtmqeIOHxfgdDgxX1GkgPrdBDZPRI4OOe0/vkdTxLXASuo7qxE+iqws7oVXjS5+FzD8/a19ItRsojUyu3S2GkPIJaLtMzMo6DQszUiEUMBoo+F86dnhWQDQZmfqaa7xMkcFyCrt1MpGqiOGUUt4ChmmQidXhOjEw0M68m52vHvkZcj/NHu38XCfx99z3z1AvGSg66IjhX6eVk+Sy31F19WcWjTQN7cFWT3rq1mIqJLyRD9ZtpGD6E8D3azCZ67EFcK1jNnCh101cd5OfGJii0bJuaHOQ6rsaJJGk++SgxzZyyxYGBaUb4Dv4CzcOuNHvPj6MI2Np2EUkoIS5qXCb3c+KtgZtMwnCxiuP5QaB/MpMsqTJa8AN9vjnomkJ5RjZo97iGIiQdqeltu1s3AHBG10MD82rh/GiQYz+3pew9+3r5yx+cYHt7mj966+Z5gUbH89FVhZbU7C9nNNNBytIpVV3WNiWIGSrPnxvjRyeG2NGRJlkzUiWjgUZz8sslIZIiYUynGCeMBAfGH4PkU9jDr2W98a6pPiUTpeB9VsQhoddh1Fw2k/echJHArflwTdWkOdbM+Vr9RW+hlwe6H+DWzlvnN2EjcI9pipitwhtJTaUGe9KjLmqyrq6RntRqIlRZaZ+jXV3LoeIJPL9KpTrOYKWX9vjC8ZdVjTqmVyAyfAij1I9nJBe8yFcOPkW/qGeINJnR4+iqQkytY3Osg++ffWiWMVGEgiOdKfHLecV7c6i4FfJ2iYFzp3EVC1NTOVA4xlO5Pez3TvNo3yN8ad83uPfkfdzffT8Hhg4ESQBzbtrVybTTy09ywlcNtFoL5oimMlF28L05BnnsOHuH9nLX6rvoNNL8bMvN7J84xQ+H907t43iSgu1iaio/GH2CqBLh2tRVl3wewvdoGtxLT2YrTw8YPHKynr6cyUDDVgynSHr8NG1mE8POKHYtieORsadIKRHunhhhqGn6b0lVp6frBpqHj5CuBp9NqRZQnSKOFRgYz0gsdBpXlL3nx1nbmCD+AhMEFsKL1iN8j6rnEzPUoFZOi+PUxqQ+HvRIGisuLHrp+LMzyFoTPjNPb2Otm+5pXYfxc3MPccW5ciPzU8xE2Vk0UJivOHSPFMnGZvu0v/H8Bf7p8W6uX5Xld+5cv6D7Y6JSZXNLanbMxoxDJEVbWnK4L4dlaGxrT/PDY0NUPZ/3XxsEVAu2SyLTgOWPTbeENeLEcKfk5xNGgqHyEDc1vo3Hu2/kvv1l1jYbQeV6bQUTMas0RKc1pCquR1fUwlSVWTe8zmQnZyeCFcyXj3wZXdF5z/r3LDgmpapLfdyY7cHQo1NptQW3QlukngQJ9iRX4QuF12oH+ObEVkrWQU5WenALgbHumBPgnyh59E943NBewhztw9ejeOrCs1mzMk525CiPZN9MbnCU148cAhm4JG9JbeWve7/LnsE97GraNfUeVQTil54TYd+FoMCuIxMkXyhzMrxGSnnO9vbR4FYw4xnOlM/zxb5vzj6JHDCjI8EnVr2TazIbgWA4PCmpLNIS91KQWhStMobjVqYMbMF2Sc8ogv3asa+RNJLcueJOGDnNHU3X8kTuJP9y9j62p9ZQZySYKAetjwecYQ4VT/D6zA1ElCXqaICqCz15ldOjGonBI9zpFPnvPTdz//lA6qWlt56V16/hKqHSOHSAtratSKDXHkQXgcT/z/lZVEWjO7F+VsfA/fW76Dz+IKkz9zK87WPotbYOgYvMm5WivBwUbZfjA3nevavj4jtfBr4ex1dNqlWbpKEy4vlETSuImTC7Fmby+Uw8zyNSyxo8O65yVctkUkpgeDKRDKqMccywX5IVTGhgXiTdw0Xe9TeP8zt3rudnrp49W/d9yfGBPNacHPsnT4/wT493c8vaej5x+/oFU08rjkfM0KiPz7mIYw2gmWRiPooQeL5kR2eaJ06PoKuC61Zlpt6/qaMZ+k4GVdnxBlAUImoEUbMMd6+5m7xdIc1WYltdvv50keP9DutbDHJlHyEgGVWJaTNmgxLSlhHUdcygM9HJ031P8/zA8zw78Czv3fBe0pMChHPwfDm/yHSGjL3nezRE6lCljm7UMZ7s5PbSAf7swttJrTfYWz5NSlQRCNrjK0F6KG4V4Vc5dqYAmGxKjeGZqSWDqi39zyCQjK3cxHMDI7zNeRyrNEjFyLLO6KDOTPNA9wOzDIylWfTlB+irxIgZGrbrs//CBKau0FFn0Zg0MTWVUtXlie5uIqVR9KiFBJ7NHcQQOp/o+gU0oSIkaNU8xfQG8p7g2+f/lv9z5tv4doq0lpmu1ZNg6pfnbHC8YKVoagKpqGjlYZxEO6amMFywSccCA3Nk5AgHhg/wwU0fJCJ0sAsokSS/uPJt/O6Bv+YL3ffy863vYGCiQjyic8/Ak+hC46b07qm/lbMFvTmV/oLCQEFloKDQX1AZLU+f8/+MPEcZk/S6Nfxq3SBp0cl/e7zCA+dS3JhZR+PQflpXvR6AXnuA7vIFTGHwocGzjGQ2MOHpNHoSXRWUbI9hLPKtN5A89xAjGz8wy8DA/BTlK83B3gl8yaIB/heMEDjxNpTBU6TrDC4QBPpriWRTtTCL9YXxtAoVt8R4MU7RUaYyyBTfxdeiCCFIae0c14rIiQsvZFF8WYQG5kXSXhdlY0uS3//mQTozMa5fPd02ueJ65MvurKB973iZv3joOGsa4/zGbesWrWsoVh22tdfNnhWbiUBDiaAXd3MqwmDOZkdHELDf3ZXBMjRKVZdEVCNpGYFBGjsDjUGAdNL3LqXkmpZr8DzJvgvjXLcmwkMHg4yydc06EyWfeASarGbUmsum4njEIxpRI1Bn1YSG53uoikpnshOJ5K/3/jX10XretPJNC36uqeyxuXVAmgaqSblaJG3EsWouuYwZpze9hk0T38fyqtSzjoOlk7R7JZqMetKjJ1D8QLVXSDjWm8LSNVqzkaVdSlLS1vsU46mVOMkMo3UpyEN65ASFjiaKBcnrmq/jP87ez0BxgKZYsIrTVZ0Tw0MkKdKaTANBEZ/j+ZwaKnBqqEBzKsJw3qZYvECTcJFqCsd32F84ytb4+llV7QqCqFcmmeji3e0f4u9OfoavDt3Dr7V/8PKkZYKPxLkJlR+fM3jmgkHM8Pm9WwrEdAutPIgbayaiaUyUHSpVD1NX+Pdj/07aTPP6ztcHLhMhQAhao/W8u/1Wvnz+QTqVfXxg5DR9msoe9zA3pHcQq60Kjw+r/K8n47h+MNimKmlOeKzJuDTFPVqTPqvSNnc9+Qwj2S1cu9omocVYETW474zNgyeSfGTDNnaN/ButdomYGuVg4Tiny+e4zdpAQ/kkB1a9OZBDsl2SEZ0L4wWSRpTc6reR6vkRyfM/QKllFjqxZhSntGCK8pVk77lxTE1hQ/OVd8V5kQzSP0bCtFCFgio0kME1m7QUdHVh2X5f+kRUnVatkXvOBaveyQwy4dm4tWSY1lgn3fYJvLFzy24AwhjMi0RTFT73vp2sqI/xy196bqqP9yQzJ9AVx+O/f/cIqiL4vTdsWFT3qGi7pKIGddYct5uqz2rt2pSM4Hg+zakIP3fDCn72mmAFVXI8VtXHg+B3ojnI0qoVWCpCIaZPZ4CpalAV70mfO7ZFOTPkcrTXYaLkk4hA2pxWri1WXVpT0yuNpJmk6s+WjMk7ed634X2zbo6e7zFWHQhUiasu9XFz4QSfaJqyU6B1Rq1AazzF+eRKFCR3xQ8yPrydkl/hePksXUYTUjPwzBS+keKp4Sx7BlJsaLCRLB2IT+QvkCj00t2yi7gaY93KNgZlGn3gJIoATyjcVLcDIQQPnH1g6n3lqsdgzkbVZgfjdVUhGzOpswxG8lUkDtFqH6IWbD5UPEnFt9mdnJ115WsWWnkI4ds0G1E+UP96BqpDfGPo/ou2CPCkz9lyD6OVKg+fNvjTHyb45KMJnjhnsLHRYays8MW9FhIVISWqPQYCTE3hUG+Ox8/v4cjoEe5eczdmYTjoUmlO3zBvSl1Ni97E/aMPkj37AD8eeRyB5Jb0NQBUPfjXfRbpiM/Hry/wqTsm+OybJvi9Wwp8ZFeJN6+32dHisLJ0HMMp0N+0E0c6pLWgButXd1r4UvD3I8FqqHH4AG1mc62gVvCekodEMNiwFVNTGCs55G2HiWqZxkgdlbr1VNJrSZ/+DnqxF9esQ6omAhZNUb5S7L0wzpa21AtOcV4KW7HQIxaGApm4TtWFiBbFk0EvoGxcXbDxWMUrkdaTNBtZiqUGdEUSj9bUtaXENwLV6LV1qyloPrl8z6KSNleK0MBcAVJRnS98+GoE8Av/9MyC/TeklHzu4ZOcGy3x23esp3EREUvPDyRhVjXEL9quNVlbTVRdn3fubGdlfQzb9bB0dbqY00wG3f1maIPNDPQDZCwD2/W5dnWETEzhe/tKjJc80jGN6Mz4hYS6mLHgcRqsBizNYm16Lde3Xj/rPMtumaSeoejkau6xheNVnh5D8z3S+rQRbbAsBuLtuKrJW2IHGBjagC6Cc+i0OoLYEvDIGYPPPxejK+Xy0R0qRW9pef22vqfwhcqZhs00GvXc0h7jGbmBpvHjwUWnaEQchetaruMH535AySkhfTg7UiRhxMhVF05XVoQgGdVRvGEUpzClyfVc7iBpLcmquU23hIIUKlppELUywbrYGu7I3szz+UM8MbFn0fPvt4f43Jl/4HMX/oVPnv1Lvj3xVaqxJ7l7Sx+fvjPHx3aXeMemCvv6dX5w2sTXY2iFPpCSiK5iGSrfOPl1UkYdN6e3BJXd0fTUjChXdugft3lP05so+zZ/0NjI15MJ3lQo0VEJJlHfOx5hsKjy/u1lNjS4pCNyQY9k88AeXMVgqH4zAFYtRbotofKGNVXu629jKNoexGFq9TA7EpvZPHiI0bq1OEYcXVUoVT36chVMTRBXLRCC8VVvwShcIN77+CWnKL9Yhgs2F8bKXNWeXpbjV1yfaH0XIMhaBhXXw1Kn5ZkCVeX5KxjXd6nTg4y202MKazMqWTPBuD2Kp2pTRveq5kAY9qyoBkW9y0hoYK4QnVmLv/vQbi6MlfnlLz03VY0+yb0H+vjh8SHef10XOzvrFjyG6/mMlmzWNMQuqbpYCEF7XZS8PW3QCrbLyvrYtGtNj0DL1lnvSxgJnBlijrFIcJPWVMEd2yzODrv0jfs0xWNTRs52Z7rHau/TY/g1NUJFKPzetb/Hb+7+zXmGserb1EfaSOpZbL+waA/3gnRpNTKBMm6NVCQKqsFo3Vq2VA+hC424sx6AzkgrUsI9RyN89YDFxsYKf3AzdMRS+HNmZp4vGS8HaZ/C92jpe4bBhi24RoyEZpE0ovSl1pKR42j5ITTdpFDMc9fquyi7Zb5/7vuMFG0KtksqYlF0C1OZPQtRGjuKqgTGJecWOF46zc7E5gX1rnw9hl4cQHHySFXndXU3sNFazbeHHuJcZXYxnCd9fjD6BH957h8pVIf5/eFRfiZfpjHZTzl9D9/3/pK/7fs89488ysa282xvdviPwxFOT5govoNaDWa0ZwqH6S2d5vq61zJw+ihFJTZlXPIVhwtjZSxTY4td4KPjEzwaNagK+GDRYce+v2VotML9J02u66iysWGJJATp0zS4l6GGLdhCYCmRWXL7791oUhd1+Y69i7qxU6zTG9CExhsja0gU+xiYkT2mKOB7QVFltGakCm234JppVLd0xVKUTw0V+J2v7+NHJ4YWfH2ePMwVpur5JLMtYFhYtRSwmJ6Yaj7WllHpn/A4MzR9DctacoqlRvF8yckxjw0ZjRWRdlZqaSb06Qnt7pYgieSMri+7ZExoYK4gV6/I8Ml3bOXxUyP84f89NOXiONKX4x8eO8M1KzK8e1f7gu+tuj5j5SqbmpN0ZmOX/Dfr4yay5p+dTGvOzk0MmENUj04F+iGQz4jqQU+Ma1abZOLB16I5Ob2SKNqz3WNAUKw34365tm4tmcjsehkAgSCqxkhqbWRiJo5c+MbsazoNZnLWst1QFbKRBL3p1STKg9zR1MdQz63ckNxNvV7Pl/dHufd4hBs6bT64c5g6I4qpGKS0OJVJ6XQZZK41JyJUHJ/s8BHMap5TTVdRr2dQa7Ux6RWB+6p89hSGplOplOmIdbI5u5nvnv4e3cM5ErWiPgGU5sh6TH1eO0+xeA7DCGaTe/OH8ZHsTG5ecH+EglQUFM8BoaIIwXub7yKpJfhi3zcp1Ioe++0hPnf+i3xv5IfcWijwmfMqVvNH+H/yBe6/cIE/bHwHb66/FVMYfH/0Cf7i/BeoNv01iexz/P3zOnkZQS/0IX3J93u+RVrP8DqjAWEm6B6tMDBh15SFK1iGhqoI1pz8Dh8u+XSZLexObmVi8y8QLY+w+vkvEtM93rVp6b4ymbETmNU8/U07Kfs2aX12zUjajHDXxnG+Vd6FQHJzscAfr/o428e6ARho3D61b8zQMAxJTLXQav8zqepMdN0JTGaQOXhGnBdKqery6fuOcqw/z2fuP8Zn7j9KrjzbI7GvJs+/Irt8mWrRWOB5sEwDRQj0GRX9r9sUJW0pfOmx/JR0f9W3iekpNKHRnQPbg/VZFSEEjVqCtdnryNRWh22JVhSpctpY/lqY0MBcYd65q51fuXU1X3n6HF984iwTZYdP3XeUxoTJb96+bsEZbMXxyNsO29vTNKejCxx1cSK6SjZuUHY8chWHldnYRQURI2oEKWbP8DMxE9sJxANfsyl4rTE+fQFJmMo8mmRJufcaFbdC3IijKTqK0NnduolcNTcvvlBxKyQiaSyrAbzZBqg9nuJccgUAdyf2Uy610lh5E59/LsaPzprcuabCB7aVUJWgAyRAo5nFloGBKTou6ZhOQ9KkIWHQ2PMkVT1GT3YtmRk3vPVdaxgiRXzo5JThLFcqvGX1WxizRzmSe25qbHXFYNgeJO9MUHZLVD0bXwZdGuX4URxFnRKTfC5/kA6zhSZjdifGmfRXUzw62MBknZylRvhQy9speiW+3P9tfjD6BJ89909MVIb4s4Eh7hrs4PDOT2Cs3MDTuz+O8F3u2vuPvNno4j93fID/b9Wv8db626hKG6/h61TbPs2fn3iS4WIPx0efoqfYzR3Jq1B0C003SUR0RktVzo6UsfSgCV7d6HHqR49xfuUd/OeOD/HuxjcxXreab9e/lxv8vfyvpq8TN5f24TcN7MFTdIbrNyORJLTZkydTMdjR4uFm2xmUaer6D6ArGk0DexlPrsCOzF7tV/zqVAxnkomVb8KJNlLObkZID6ld+gRtJlJK/uqRUwzkKvzp3Vv4wHVdPHFqhF/9yvM8fWZ0ap+9F8bZ3pG+qAv7heBLiSoEUUODzEpUVSEV1cCfbj4WMRTef2OcobzPPc8H7sqqXyFTi5ceHQv2XJ9Rgx45QsWMNtAQDTp9qopKTDTVii2XdwUTZpEtA791+3pODxX5zP3HaElFKNgu//Wu7QsWZBVtF9f32dlVt6CMzKXQlo6y9/w4hqbQkLz4Td9Qp7XEJgsrE1GNnnFJ2S1y49oUrbEsN6wJMuJs1yNmqPMUoFVFJaJGcDxn0da7FbdCZ2IFE2MSQ1PpTGVxKdBf7CcdSU/tV3JKbMpuAjEGufOztLAaYwn2RhqoGEk2Vw/TYN3Ov+6LIhG8e3OZ21bbVH0XS41OrUbiqoUmNMqugyrElBhhg+7RNnKAM83XENESWOq0QY9oJt3RdWwsHeXRCkRQyBfKrEpuod5s5dnhB7mu6cZAhkWNUnRynK65nKSQIMGwc1j58/hGYJx77UF67UHurkmmxAu9FK2GQFWgxqlRlb96KkbRUfjWUZ8719jcvMKmPdLM2xvu4GuD3+NEuZsbqwb/ve803/Nuo3Lt22mrDV8h0cozV3+cq5/9S6555rM8vfs3IN7CzXVXc1N6N6fL5/hWz376lKf41MATmIpBVkuxK74BWXM1CcHs76eUrD35HSpmivPtN09NjEbLgj/qfRPx6FluG7qH54faGGqY7YKdPoZP88Behuq34Kg6iucSXaB2pk5PcteWCR7+8VXcNfIk0dIQ6dxZjq29e/4hkcTm1Lh40Szdd34BAK08+oJTlB86MsAPjw/xgeu62NqeZmt7mqu76viLh47zJ/ce5vaNTbx+UxPjJWfZ4i+24wcxvBmTxEzMZLzooApt6ppd22xwy4YIjx6tsLXDoDkbuNFghGOjkDCgNS5Q3DKeWRcU784Qt8xGVnDKOYe7zJlk4QpmGVAUwf98z3Y2tSTpHa/wq7euYWX9/FlVruKA4EUZFwjqUkxNobPOuuSslrmBfkvXEMKn6jl0JVfzhs0tUwalaLu0LLKyShiJqUyyhfClT9IIZpyt6Ugg75/sRFf0qQZenu+hKRopMxUEmr3ZAcy0YSEUhZHsBrIjR3ntigpCwM/vLHLb6mCVYvtVUjPqdRSh0Kg3MG6XaEtbaLULNt3zLKrvcKRhG1l1vjtPa9tKsxiju3sMQxOMFwqcHS1yY/MdDJR7OJELVJaFEMT1JAkjRcJIkdTTpFSLVGkQzBQJNfh/P5c7iIrCVYmNtPQ9w02P/zdueeyPaO19EqTP3j6dzz4eJ2ZIPrq7SEvC42uHovzBQ0keOmVyVWw776q7hf86ofBXF07xD977sK9/x5RxmaQQb+Xp3R9HAtc8+1nihWmp/tVWF59Ycxed479NdegO4iR4a90NKMbslcBMsqNHyYyf5PTKN0zdsKWEr+638BEM7f4Zcol2th34J6xF2hgE7rEc/c07sP0qaT05NaGZSVKLkY5WGW7ahiUrdB74BjDbPQbB7F5BLFngKWEqRfnUUIG//eEpPn3f0XkN1+ZybrTE3zx6mm3tKd61c9qNvaohzp+/5yrevaud7x8d4Pe/GbQXWK74S8XxpiWUaiQjOlIwq/kYwF07YzQmVb78eB7fMzFrk4WjY5L1mcA9priVQERzDiuSK+nVVMYHzyzL55gkXMEsE5ah8fcf2sW39vRy89r5/+DxUhXLVNncmgrkul8EqiLY1Jq6LMmKhJEgV8hN1cUIBTS9TFy0EVFnGxMJ8770kySNJEPlIWL6fAPq+R66ohPTY0SN6lTRqK7orM+sZ9/QPgzVoOgUaYm1BLpWusVM9VgAS9OJGzr9qbW09T3N2xrPcH1XB5Y+vZ/En6rNmBoXL0KdpRE1pm9q6e7HqSSa0VrWoLhm8KdmeDqSnbvg5BcQvadQVq2jWrXxNcmO+uv4Qe+3eKz/ftalglhN1ZXT+moS9Px5FEVDr13onvTZkz/EhthqmkrjbD70JcaTXQjps+3gF2k6/hDfKLyXtsR2fuW6EglTsqvV4cSIynePGvQcOU3l5BP8lvo0qlflt8Wvs/2GjTTFF07BLsZbeObq3+TqZz/L1c98lmd2/waFRFD/LgT8wlUqf/rD15I//Rq6Ogrzxnl6MCVrT95DOVLH+fYbpjY/36ezf0DnHZvK1CU19l71Ma5/8lPs2PO3DDVsJmJPYNo5THsC055Ad8t4is5Q/RZs6dCuLVwzElEiCAFdm1dR+aHOyon9TFgtlGJNs/ar+lUSWnxWEsjc887ZcO/BYR46OsSZ4SK6KtAUhT3nxvj129Zyw+r5bkrb9fjMfUeJ6iq/tUDhs64qfOj6FVyzMsNnHzqBZag0JJanzkYi5002LVNFAjEtyXClf0q2ydAE778xzmfvm+CBvYKdd0LFlZzJwXVttRgVBAXHc9jSsJZHBgXduW4Wd9y+eEIDs4ykLYPNrfNniePlKjFTZWt7+orl0S9mABYjYSSmMsAgUD9uT2ZxyrO/blXXx9Lnu8cmiWrRRes1ym6ZbDRbU641ZhnAlJmiM9HJhcIFpJQ0WjVFZD0aCFJKfyrVVAhBazzF2eRKdgH1o0cprJidLCFhKrMIAnHItBklFWuk6BWJaVH0whCxoWOc23wXq9ON6NUYYyWHRGT6vPxEJzk1yRr7GP2lrdSlHfyIDgJuaLqN+y98g55CN88da+Dhw2VuWh/hjVdZJP0JtMoY3gy334lSN3mvyDXWOnbs+ztcLcKeHb9ERU9wYe8hbhv8Jn9v/Dmj5kpOFO9mzFhNevwMbx16jo86zxMxclQweNDZyVe1N/PGGxqojy1d31OMNfH07o9zzbOf5dpn/icHN3+QgaYdAMQMyX/aVeTPfhznTx5J8LPbSmxvnp8B1jB8kPRENwc3vW/KlVesCv7tQJSOlMttq4JVYzmaZd+2j7Bj79/Rde4RbCOFbSYpxJoZyazHNlOMp1fhaRGE48xyR85EVzRiahRfkZxPbGRtfj9fLlxL9xmDW7qqTF4iFd+mxZw/WfN8ybP9LveftnmiR8OV3axpjPPLr1nNLWsbKNgun7n/KJ/83lHesrWFn79x5awatH/40RnOjpb4o7s2L3kdbWhO8lfv37mg0OSVwodZmZoQGLiEqTFRjUypKk+yokHn5o2CRw8XeHrdCJma4vX6rIrwHKQWCVLl55zzNe0b4BBccIbZzfIRGpiXmFzFwTJUtrRdOePyQpiZAeb5Ho7vsK1pM3vOBr1hJgOYxaq7oHtvElMzURbxtFa9KtloNnDTNM6fvXYkOhipjKAqKtZkaqkQgZvMLddWMwFN0RTHjRiFWBPZkaN0r3j91GuO72ApkanMIt8Pagk2tyZxhMKB8XFiWpR09xMA9HXsZGM0QzRmka9MUHX96RuOEJSym7lu4DB/2mvxlpSNXRunqxtewyO99/JvR77L2SPvoate40fHKjx/xuada0e4ris+SzzgudxBLCXCu7p/TLQ8zDO7P05JT/GlfVEev3AzP+rYyW/XP8y60/dy7bN/QVWPYThFPEVjqH4z/c27GarfQl/B4u6oT8K8NBXnUqyJJ6/5ba7a93l27Pt7ujtv5di6tyMVjVUZj9+5qcC/7LX466fj7Gyp8jNby6QitRuQlKw5+R1K0Xp6avVMFyYU/u/RKHlb8CvXlpn5tR3JbuT7t/5Z0MhtkaC34ztEVHNWevJc0lqKXnuASud2OLSffbFd3HfA4tFuk3dtLrMqWyCmWaT16Qlbb97nvjNVHjjjMFKWpE142zqTW3duomtGJmY8ovHpd27ji0908629vRzuz/G7d26gNR3lsZPD3Heon3fubGNn18LlAzNRhEBRl0dgxfF8Yrq6YAF2Nm4wNqTPyv6EwAX9+i065wY0/vfDJ3njyuCWvj6jorg5qvGFtdI21q8GCX2iDK69YP+fK0FoYF5CchUHU1XY0pZatIr/pcJUzSkPybg9zprUGlKROGnLoeL4U7Mof6as/mLHEcwySpMEcYrFU0ZVRWVjZuN8+XsrCyMnZxmYbDSGQDKc2UhHz48RvjM1u7b9Ko0zFAdylSqdGQvLUJEyhqHqOJ5DqvvH5BvXo8ZbSGgWQghWNsQ52pdDU4zpFizNV9Ey+AQ9PTlYM/OzRqnzr6NPPsrV697C+67toHfU4xtPjPDPBzM82uPy3q1lutJBH/mDxePcRprOwed5ZsW7uK+4kSeOGBwd1nnzugpvWe/QK26iv/UaOs/9kETuHMMNWxhs3IY3Q5utM720avNCVKJZnrrmE6w//k1WnHuY9MQZ9m77T1SiGVbWefy/r8nz4EmTe49HODIUuL1u7KrSPLiPVP48z234EI+ei/LYOYOz4xqaIrl7Y4WuBc5FLtZ6efJc/CrN5tKOmLgWRdqSntZrGU+v5G1WlhX9Bb5xKMr/fjLOhgaVX9+VxPEEj11wuO90lb2DHoqAq1s0fnWXzo3ZEn6qASc5f0Kkqwq/cNMqtral+OxDJ/j4v+3l/dd28uWnz7G+KcEHru1a4KxeWspVj8ZFknRSUQMFPdBDmkHFK1EfrecTtzfxiX/fy1cPOTRGIRNVEGUfL7Kw0YxoEZJ+jNN6EXI9UxJUV5rQwLxE5CsOuirY0p6aJ9v/cqAqKlEtynhlnLSZpjke9DFvSkQ4NpCfUgiIGuqS/coVoWDpFo7vzJKHsT2bmB67qJ6WtVBRnJmYJ2ERVQ0sU2UgvY4V5x+hbvwMo5l1AHj4QWU3UKgEMjuTWWNCCFYWc6jPfxGzMMjZda+nLdowZQwTpkZ7nUXPeHmquLXSsA2AtdXjnBvbQFODj0TwnT0lTp64jviaR6lreRxFvJeuWIH/sus8jw818B9Honzq0Tg3dFaxModwpcsHeg/xPf9afvno2wGBqfq8Y8sot3Q5SHQECr5qcLD1VkRbUPdzJVa2Vb+KIz0Orn8HY+nVbD30r9zw5Cc5sOVDDDVsRVPgjetsdrY6fGlflC/tt3jmgsa/evfSqzbzgQOvp+xptCY83r2lxLXtDnHjhbmGfPx56clziSiBCKuPoBhrRgBXtbhsbszzvVPwg5Mpfu1+h6jmUHCgOSb4ua0md67Uqa+19dbKHpWLqChfszLLX743zv944Bj/8NgZYobK79y5fsEusy81ju/PUsqYiWWoqIqGLgw8353SB3R9l5RZRzIR433XdPLFJ8+yPkMtPVnB1xfXSsuojZzWx5Dj5xGhgXnlUqi4KIpgW3v6RQf0ryQJM0GhUGBNes1Udk/K0qdWNhdzj02SNJIMl4dnGZOKU6Er+QJnhUaMuQFoUzWoi0bpTazEFwrZkaNTBgYkEcWcat7VlbUCb83gEXjun2k4/ySObtG/9R0MdF5N15xeIU2JCOOlKuWqR9RQceLtVI0UN3iHuadvF3d35bjnsMpDB21uWNeMyF7Nc8M/4raWN1CX70aaMa7rdNje4nDv8Qg/OG2SYC8dukfKzvB/mj7MBzNlGpNFViV16swkObdA3i3i4+F6EgWVFekUF0ZtFCEuWsu0GJ70KHhFDEySSpy8LHC8fi29V/8aNx78Mrv2/DWnV9zBSGYdEXuCVfY4t9aNUxR5RH6URtHDb7n/mV3tHjd2llmRvvRWzQvhS7+W+bV0wy1VKCRrxbGRGfVVvqhyxxrJB9fH+LfDLkVHcvtKne2N6sKqCJdQm9WQMPnk27dyz/5eVjfEaVpEtulyGStViZvaC58gyMCQLEREVzE1BdOxsGUZFa3mNQCrljzxjp3tDA31c3OzE6QnRzKwxOqy2VrJHv8UuYHTpFa95oWd80UIDcwyU/V8dE3hqva6nyjjApCNZEnqyVmriKiuEtFVHM+/qHtskoSeoL/QP2ubJ70g7fiFoJmg1lrHzmi+1RhJMKDYTKRWUD9ymHMdt1BWNKKqiSY0xqtV1jUmMEePwnP/DOefCrTYrv4o3V1Xc97N0xLJYM5ZVSkKrMjGONibw9RUFEVg12/lpr5D/EG/hfHMMA+cSXNzR4GfXTdGj9zF/tGneK7nXl4f2zjlqovq8K7NFXa09vE3w+d523iJCzf8Ku+Mgy8r5L0KXbEOTMWgwcggpcT2qwwUC9TXCfJyjMakzsCEQyKiX9aNXUpJwSshkbRHWtFcC0vTSLuNoLnISDvPXtvByiNfYnX3A6zqnhbwrOoxbDNFIZPmx/pbuHnzJiL60hX6l8pk+viimV8zSGsJzrkFIgRGwpeSsl9hQ2wVMVXjV3YtfbuamaJ8MVRFcPdVbRff8RIpVz10VTBRdua32LgEPL8mgbPEPSIbNxkux3D9CVCjQfW+lprqKqoqgt+8NoFqj6G4NtXk0quSlQ1beLL0A070HVq2QH9oYJYRRQgyMYONLcl5mSE/CWSj2XnbhBA0pUzODJWwzKXdY5NE9NkzQF/6aIq2sPvrUrEyUB6bpe7bEE0gxQBDmQ2sO/1dbn30/516zVMjoEVRDRPy/YFhueajsOntYFg0OUV6x4/TsoCUDQSZOx2ZKOdHy6QtnXLDNhp7HyPjjPC9M03c1GXzs1ttVOnQ5Susi7Tz6OgTjFXH0BUNXehoQsMQCrnBp0CFVW1vohgP+pPkvSKtZiOmMm3chBC4jsqKZJY1CYXRiuCgHCUT1xgrurOy25ai4tlUpE2DnqHZbABfxdclm1pSlKou/bkKEyWNrB6juuv3ObpyP/3lHqqRDEQb8ecUyV6Z+XxAVTq0682XtK+lRpEzVq4Fr0iTUR+kn0sfrTIGgBvJzE8okEG++XKrKC9GseqwvaOO86MlChWX+CX+7yaxXY+0ZSypDlBnGQhMav1NqfoVmqILG0mJXDA9eSY727bwlbNwevxUaGBeiUR0las66l6wu+PlImOZHHHzrGm8NMmNudIzJadEJpJZsKjukrGykB+AGZNBS4sSjygcab2VUrwZ3SlTrebISpMYHnWGD04JNt0Nm946K0kgoVmsjXeQXKBeZ5LGeISxYuAq0+uD6vQ7ovs50XgLP7O1jCJUJCpShdvrX8u/DdzLweIJHOng+C7+ZIsAFXYQx2u9EQiyqDSh0WDMNm6+D75XpcOwQUmRMTOsFypH5CAxN+jrs1h6OASrxLxbJKZZbIisJlZLAx6vOKxtjKMqkIhoJCJxyo7HcL7KUKGCjG8mnd7AGfscvnSJ8cKLfC+KnJ0+vhQRxUQTKp708aWHKtSawXTQKhPYqVUovoOeP4cbzc5WTJYuUjOXVUV5MUpVl7QVtNcwtThPnxkl6quXdd3brk973dL/h5ipYigGwg2OK+Vk9f5shFdFataCLcJncnX7BgAuVPqX3O/FEBqYZeaVZlwgSOtsiJtkLnGpP1d6xvEc6qMvsnzLmG8IoqpB1NApVFX6m4M511g1xypjNdvbsihLdH0UtSZaSzHTVWZb7bhmmg/XHeSZzVtQxOwajhXRdn53xS9O/W4VB9m+569QKqPs2/AOJtpunnqt6JdZHe2ckrEBQPpUcsO0pyzMtm2QaIHKOE0XnqUaa+GE30vVVbFdb8GkkLJXoSoduqKtZPW6qZlvxfGImeo8Ne6oHqzQmlMRRoqB3HyH2kmf30PeLV40CL8YtuuRr1YwNDEv9uDjE1GNWau2pQi6LSbIuQVsv8q62Ep0z0F1ClTqt+BajUFfE0XDnDiNG80ENVOA8Bz8JTIWl5Ni1WVDcwYhBDFTY0W9xbmREpnYpbvKpJTEL6LmEdVVTC2Cb8taUs109f5MFLdE5RLin9loHXFPoV/mL/k8L5eXP3Ui5CcOVRFsabt8ZYBJ6RkpJPEXoWgLgD4/0G8oOknDwKkVmzmei+9prGtMEbnMlsKLMekqy9ku5fqtNE2cQUUh7xYXfU9m5CjXPfUZok6Jwzt/jUL7a6eMScmrkFTjs2VsqgX8wjAy2UZ2862Qagusm5WBZCvtwmBFvIl4PEgA8LzpcfClZMLNowqVjdEVNLsS1ckHhalAxfHpqLMWjd/ouDTrZTY3RYibJvW0EhURJtz5AqRL4UvIV1zKfpn2OguTKFFhkdTiU4+0lqSl1kVxLko1h1LNw5zCwZSWJO8VqTfqSPsSxatSatwVGBcIWgqnVmKn16CVx8CfbAn84lSUXyiFStBALzWjx1F7nYWuKdjupaeX+ywe4J9ECEE2FkHBpODkyCxQdAogfB9vkXblc2n0Y/RqzrI1HgtXMCELcrkxo4SRIF/IowiFuBa/JKXlJVG1II5i52fFYeoiCXrUIVzPZ8wusTrVQJ11Zf3uk66yXHoTbT0/YrOwOKX6jDkTJLUECoKK6+L4kg0Dj7P52NcoWk08v+OXKVvTqyRfBjPNNVZnsMLwXbTKOF6kjv7oOrau6kAz5px7dg2iOMiKSJaq7+J5I+QKEFM0POlS8Eo0m/W0GPWYlXHs9BoUt4xW6qdSdamzEvNjN74LdiH4qUUg1UF0oof1SY9hK4E2qtBXHWBC5oLPd5HsAtvxcHyJZXk0xdNsT6/CzsKJ/gKaKpZOZpESrTKGG8kgVROtPIDwPXxFw9fjWGqEjJ6m0zeRukG5fuuCrh4n2YUUKpGxY7iROoTvIS+SonylkVJScT221M9W69BVhXWNcfZfmKAhcfHrqOr6JC4x+ywbM1CxqMgJEvrCMRZfjy2ZnjyTJq2Bw8o41dwARurSYmWXQ2hgQq4IcT2O53uUnTIdyYWrhy+b5i3QuxdKo8HsHkhrMQxjkELRRVMl67ILz+JeDJOuspMjm2kDWg78HXWJLsaBAd9G0VNEYkmaRo/S0v0IfZnNHNz+ETx9thut4BVpjtQTVSMo1QKKV6WS3cSIyJCN6Qtn6OkRqF+PMnCINfE2HOngynGGc0Wihs5aq4uUnkArj2CnVuAkg1bVldRKciMD7EhOQHE4SE8VKng2KHrQ1TTRFBhtISDdhRg9RcP4eRKNUVL5Do5N9DJaHSWtx6Yyk2biy0D41DIU6pIKlm6wNbUiSHIwYH1zgmMDeSpVj8hCExTpo5VHceKt2Om1oKjY/hrUah61MoRe7MfyXTYRw4s1Uc6sD859EdxEOxVFIzJyKHCdvcQB/lzFpTkVIbGAaysbN6lPmOQrzoKvz6TseDSnLm1CFotoRNUYvrSILuLWdKP1S6Ynz6Q93sUTxdMcP7OXLVe94ZLeczmEBibkijApmunhkb7E5flFMWLQcU1Qz5LvAyuLpUWI6iquppBKRknqVzLnaZqoodLQsZbx7A5iYyeJDu0n6zusnrPf4Lo3cGTVWxkvORh4mLXZu+u7qEKhUc+glUfwjCTFhqtw1ShuucrqhiVmmIkWyPWgOSU2JLpwfJeE6qOWMyTUGGplDMdqwkmunHpLrirINHcSa62t+nL9QV+dZAtEUvNvOJoBjRsh0UJk8DBrYwUykQ4Oj0Y4U+5FIILsLQHIaWdlc8okYkg8PDanVs5K+bYMlQ3NCY4P5CnbHlFzxt/0XfTKGJX0GpxE53QWmKLiRdJ4kTTV1GqUan5aAXhGwN7zJbbroSpiVkzKjTVTFiqRseNTrQdeCnwpcX2frkWajgkhWNMY56nTo1i+XDIW6/r+Ja/CY4aGqUaxtOaFk2iEghu99EnXusYtcOZhDp1/LjQwIT+5TErGqKgLKiu/YFQdmrcGM++hY0TNGBFdIVlnoqpyXk3LlaQxEeX4jX8MQHMyQkKXqG6JYnmE02MncIWCX9dFVqikYgb9ExXyFRfLUCn4JVbrjUQqE5QTKynH2nGloFissqYxtrQLUlGCm//ZJ9CNGNtSa1DTCsOFKuf7+onF4lTr1k3dpCdvdivqazc7MwFLGbCZRNPQcS0i10P90DFuaU5xtdbEsUIPI5UcCT2GkAqelIG8kXCpeA7b02ummrvNOpyusr4pyYnBPMWKSyyiIbwqqp2jnN2MG1vCDSMUfDOFrSWwqz5VN5CmlwRup2RUp2S7jBQrRDRtKoXesxooGYmLZk1dSXIVh7Z0dMksP8vQWFUf48xIkexFAv5LHWcmqiJoStTheekFX3diLfjapTct3LnqejjzvzkzdvyS33M5hAYm5IqgKRqmYpIwEi8uPXkhhIDMCjDj6H37iEqfimLTcBF9qxeLosC6xsS0RhmAbhCLplmf7uRCeYiiU6bs21SlSyoBiu3SP5Ej6XtoUqMnswkiaSKKQkwPZN5bL6VrqZmA7GoYPYMWC+qVGkwPNRtjv7KepFSmkosv5Wa39AdVId0JVj2MnSGa62W7kaLPiHG6PIihaiS0KFXfoejabEutJqZFA5FEe1L2vzZDV3UimsG6xhinhkrk8zmi2BQbtqPEFv5/eb6kXPWouIG2mKmppC2ddNTCMjQihjK1apFSMlF2ODtaYrhgB0rDEQ3lJTQuni/xfElH5uIxn7a6KL0TZSqOt2BsyvMlmiIuK0mlPmbQPVJCV5Wpc/H8YJLhSQMcD4E3K0VGShYs4lzTuomIL+lzei/5718Or2oDI4R4A/CXgAr8g5TyUy/zKb2qSUVSZBYpZLwixOqh8zqSx0cYyXeTjjTMq/afh1uBamkqywoIbqi6tbSCrJTg2ShuJShYmYOBZJUwQTcAgS99HOlSNVzKURct2UmseQu6bs7qTnhZpLsg1xvcyIUAp0xm3XVssnUO9kyQtoJ+7Zd6s7sohgVNmyGzGlEYoHXsDGkjw3F7jFEvhwS2JTpIOjZUa2rXDesgWhf8H5wyVHJQyWE6OdbGfSZ0g+H0dspelHLRntIC1hQFtzaumqJQHzeoT8SJm9qSSQJCCNKWQdoyKNouPeNlescD1YGIpqKrCpoqLpqoMBMpJY4nqXo+jutPFTJOHjOiz65pyVUcOjPWJSlzaKrC+uYk+86PU6y6KEJgqAqGFmjOVRwvKKC8jPNNRQ0kRQq2Q0RXiZoqEU3B1BQiujrrWDMPu1ASgaKotLmCATl+yX//cnjVGhghhAr8H+B24ALwjBDi21LKwy/vmb16WZFcsWBw+IpixEh13kikVyUWawqMh1thagatGUHq62R7WDMJmdWBK0i3wClCaSyI6RSHg330SBBMdivgOcGxRO29yY7gmDD9NyYRSuDCUzQURcNUNExVJ6Foi0rXXxaqBo2b4MKzwfHadoKZoNGEbQIO9OTwpWRVQ+zKyhDpEajrglQ7VmmUrSMn6Z3oxlJN0sKETAfEsmDOSQuO1kEy6PuOlOiuTb1QqK+Nn+P5VByPsuNRqLkSExEdy1Av6wY7SczUWNeUoCtrMZKvMlGpUrQ9JsrO7PYnMxZYk39GECQtiNovMUOjztJJRDSihoamCsq2y0ixykixiu8HGgOGqgCStrpLd0NlYgZXr8xgOx6lqkfediiUXfIVd8k4zmKkLJ2b1zS88InLHK4XSWxxZaSB5iIuJ/f9lYQQ4nrgv0op76z9/nsAUspPLrT/7t275bPPPvsSnmHICyVfzXN64jTbG2otdd1qUMHvlALjoRrBDdCIzzAOC+CUoTIBhcHgebRu2hDpFrN9Yy8j/YcgkoT07Oy8kYLNqaECV3XULX/7h0ouuDsb8StjPJcRKYPVSNUNHq4fiEIqQtQewUpIEcGs3tSUJQ2clJJS1aNYMzipqH5pbs6L4PlyqhfRy1qQPdYNWjTIMnwBCCGek1IuqDbzql3BAG3A+Rm/XwCunbmDEOJjwMcAOjs7X7ozC3lRxPU4a9NrpzdoRvCIpqdn0JeCHg0eiSuf/39Fad684OZs3KTOMq7YTHZJIvM7s/6kIkSQaXal2mJMVujHTI3GK6S8DEHA/idCo7BuxbId+idkirYsLHTVzVquSSn/Tkq5W0q5u6HhytdThCwPQogXJ6T5KuIlMS4hIS+QV7OBuQDM9Cm0A8uTKhESEhISMo9Xs4F5BlgrhFgphDCA9wLffpnPKSQkJOSnhldtDEZK6QohfhW4nyBN+QtSykMv82mFhISE/NTwqjUwAFLK7wLffbnPIyQkJOSnkVeziywkJCQk5GUkNDAhISEhIctCaGBCQkJCQpaF0MCEhISEhCwLr1qpmMtFCDEEnH25z+NFUg8Mv9wn8RNAOA4B4TgEhOMQsFzj0CWlXLBSPTQwryKEEM8upgn000Q4DgHhOASE4xDwcoxD6CILCQkJCVkWQgMTEhISErIshAbm1cXfvdwn8BNCOA4B4TgEhOMQ8JKPQxiDCQkJCQlZFsIVTEhISEjIshAamJCQkJCQZSE0MD/BCCG+IIQYFEIcnLFtuxDiCSHEASHEPUKIZG27LoT459r2I5Mtomuv7aptPymE+F/ihTRAfxm5zHEwhBD/WNu+Twjx2hnveaWPQ4cQ4uHa//eQEOI3atszQogHhRAnaj/rZrzn92qf95gQ4s4Z21+xY3G54yCEyNb2LwghPjfnWD9N43C7EOK52ud9TgjxuhnHWp5xkFKGj5/QB3ALsBM4OGPbM8Bras8/AvxJ7fn7gK/WnltAN7Ci9vvTwPUEXT6/B7zx5f5syzgOvwL8Y+15I/AcoLxKxqEF2Fl7ngCOA5uAzwD/pbb9vwCfrj3fBOwDTGAlcApQX+lj8QLGIQbcBPwS8Lk5x/ppGocdQGvt+RagZ7nHIVzB/AQjpXwUGJ2zeT3waO35g8A7J3cHYkIIDYgCVSAnhGgBklLKJ2TwTfoicPdyn/uV5DLHYRPw/dr7BoFxYPerZBz6pJTP157ngSNAG/A24J9ru/0z05/rbQSTDltKeQY4CVzzSh+Lyx0HKWVRSvkYUJl5nJ/CcdgjpZzs6nsIiAghzOUch9DAvPI4CLy19vzdTLeF/jpQBPqAc8CfSSlHCb5wF2a8/0Jt2yudxcZhH/A2IYQmhFgJ7Kq99qoaByHECoIZ6VNAk5SyD4KbDsHKDYLPd37G2yY/86tmLC5xHBbjp3kc3gnskVLaLOM4hAbmlcdHgF8RQjxHsCyu1rZfA3hAK4E75LeEEKsIlrxzeTXkpi82Dl8guECeBT4LPA64vIrGQQgRB74BfFxKmVtq1wW2ySW2v6K4jHFY9BALbHvVj4MQYjPwaeAXJzctsNsVGYdXdUfLVyNSyqPAHQBCiHXAm2svvQ+4T0rpAINCiB8Du4EfAe0zDtEO9PIKZ7FxkFK6wG9O7ieEeBw4AYzxKhgHIYROcDP5kpTyP2qbB4QQLVLKvpq7Y7C2/QLTKzuY/swXeIWPxWWOw2L81I2DEKId+CbwISnlqdrmZRuHcAXzCkMI0Vj7qQB/APxN7aVzwOtEQAy4DjhaWyLnhRDX1TJDPgT835fh1K8oi42DEMKqfX6EELcDrpTy8KthHGrn/XngiJTyz2e89G3gw7XnH2b6c30beG/Nz74SWAs8/UofixcwDgvy0zYOQog0cC/we1LKH0/uvKzj8HJnQoSPJbNEvkIQU3EIZhm/APwGQbbIceBTTKsxxIGvEQTvDgO/M+M4uwliFqeAz02+55XyuMxxWAEcIwh4PkQgJf5qGYebCFwX+4G9tcebgCxBYsOJ2s/MjPf8fu3zHmNGZtAreSxe4Dh0EySKFGrfoU0/beNAMBErzth3L9C4nOMQSsWEhISEhCwLoYssJCQkJGRZCA1MSEhISMiyEBqYkJCQkJBlITQwISEhISHLQmhgQkJCQkKWhdDAhISEhIQsC6GBCQl5FSGEUF/ucwgJmSQ0MCEhLxNCiD+Z7OFR+/1PhRC/LoT4HSHEM0KI/UKIP5rx+rdqfTwOCSE+NmN7QQjxx0KIpwgk10NCfiIIDUxIyMvH56lJetQkb94LDBBIulwDXAXsEkLcUtv/I1LKXQRV178uhMjWtscIeuVcKwNZ+pCQnwhCscuQkJcJKWW3EGJECLEDaAL2AFcTiHjuqe0WJzA4jxIYlbfXtnfUto8QqGh/46U895CQSyE0MCEhLy//APwc0EzQauA24JNSyr+duZMIWj+/HrheSlkSQjwCRGovV6SU3kt0viEhl0zoIgsJeXn5JvAGgpXL/bXHR2o9PhBCtNWUo1PAWM24bCBQyw4J+YkmXMGEhLyMSCmrQoiHgfHaKuQBIcRG4IlAOZ0C8AHgPuCXhBD7CZSRn3y5zjkk5FIJ1ZRDQl5GasH954F3SylPvNznExJyJQldZCEhLxNCiE3ASeD7oXEJeTUSrmBCQkJCQpaFcAUTEhISErIshAYmJCQkJGRZCA1MSEhISMiyEBqYkJCQkJBlITQwISEhISHLwv8PvwL9wR21MbEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finally, we plot the means and variances (done via Seaborn library)\n",
    "sns.lineplot(data = aggregated, x = 'year', y='Approved Amount', hue='Field');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4089d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that Seaborn adds all the labels and data (means/error) automat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da67fb33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In terms of analysis, we not a large increase after 2010.\n",
    "# In order to understand this better, will look at funding targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "24b0d185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>Funding Instrument Hierarchy</th>\n",
       "      <th>total_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1975.0</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>32124534.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975.0</td>\n",
       "      <td>Science communication</td>\n",
       "      <td>44600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1976.0</td>\n",
       "      <td>Programmes;National Research Programmes (NRPs)</td>\n",
       "      <td>268812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1976.0</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>96620284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1976.0</td>\n",
       "      <td>Science communication</td>\n",
       "      <td>126939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>Programmes;r4d (Swiss Programme for Research o...</td>\n",
       "      <td>195910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>Project funding</td>\n",
       "      <td>193568294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>Project funding;Project funding (special)</td>\n",
       "      <td>19239681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>Science communication</td>\n",
       "      <td>3451740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>Science communication</td>\n",
       "      <td>55200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>383 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year                       Funding Instrument Hierarchy    total_sum\n",
       "0    1975.0                                    Project funding   32124534.0\n",
       "1    1975.0                              Science communication      44600.0\n",
       "2    1976.0     Programmes;National Research Programmes (NRPs)     268812.0\n",
       "3    1976.0                                    Project funding   96620284.0\n",
       "4    1976.0                              Science communication     126939.0\n",
       "..      ...                                                ...          ...\n",
       "378  2020.0  Programmes;r4d (Swiss Programme for Research o...     195910.0\n",
       "379  2020.0                                    Project funding  193568294.0\n",
       "380  2020.0          Project funding;Project funding (special)   19239681.0\n",
       "381  2020.0                              Science communication    3451740.0\n",
       "382  2021.0                              Science communication      55200.0\n",
       "\n",
       "[383 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, lets group by year and funding type and calculate the total:\n",
    "grouped = projects.groupby(['year','Funding Instrument Hierarchy']).agg(\n",
    "    total_sum=pd.NamedAgg(column='Approved Amount', aggfunc='sum')).reset_index()\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a72a01f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, for each year we keep only the 5 largest funding types and plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "464af8e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, we create the groups:\n",
    "group_sorted = grouped.groupby('year',as_index=False).apply(lambda x: (x.groupby('Funding Instrument Hierarchy')\n",
    "                                      .sum()\n",
    "                                      .sort_values('total_sum', ascending=False))\n",
    "                                     .head(5)).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "92573504",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next, we filter years < 2000 for analytical clarity\n",
    "instruments_by_year = group_sorted[(group_sorted.year > 2005) & (group_sorted.year < 2012)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6b33efb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, we plot using matplotlib:\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(data=instruments_by_year,\n",
    "            x='year', y='total_sum', hue='Funding Instrument Hierarchy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e2196b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# From this, we see that projecting funding has increased dramatically."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e202422e",
   "metadata": {},
   "source": [
    "## Pandas structures\n",
    "Pandas offers a variety of data structures & operations for large data\n",
    "Here, we will focus on the two main structures: series and dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03f88568",
   "metadata": {},
   "source": [
    "### Series\n",
    "Series are the Pandas version of 1D Numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2c2042f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4\n",
       "1     8\n",
       "2    38\n",
       "3     1\n",
       "4     6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas structures are usually created from simply structures, eg:\n",
    "# create Numpys arrays\n",
    "numpy_array = np.array([4,8,38,1,6])\n",
    "# now, convert to Pandas series using pd.Series()\n",
    "pd_series = pd.Series(numpy_array)\n",
    "pd_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ccc38884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  8, 38,  1,  6])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The underlying structure can be recovered with the .values attribute:\n",
    "pd_series.values\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "849b4e1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Otherwise, indexing works identical to Numpy regular arrays\n",
    "pd_series[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bea7f2f2",
   "metadata": {},
   "source": [
    "### Indexing\n",
    "Similar to R, Pandas series can be assigned and accessed by name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ca95ae76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     4\n",
       "b     8\n",
       "c    38\n",
       "d     1\n",
       "e     6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here, we define and index for our Pandas series\n",
    "pd_series2 = pd.Series(numpy_array, index=['a', 'b', 'c', 'd','e'])\n",
    "pd_series2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4e7c37c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, an element can be access via index:\n",
    "pd_series2[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e1275955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or, it can be access by its assigned namme:\n",
    "pd_series2['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30dc9f43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A more direct way to create named series is to transform a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "df929e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here, we specify a dictionary array\n",
    "composer_birth = {'Mahler': 1860, 'Beethoven': 1770, 'Puccini': 1858, 'Shostakovich': 1906}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3cf0f6f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mahler          1860\n",
       "Beethoven       1770\n",
       "Puccini         1858\n",
       "Shostakovich    1906\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then, we convert it to a series:\n",
    "pd_composer_birth = pd.Series(composer_birth)\n",
    "pd_composer_birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8f90306e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1858"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And as before, we can access it directly via its name\n",
    "pd_composer_birth['Puccini']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae95497f",
   "metadata": {},
   "source": [
    "## Dataframes\n",
    "In Pandas, dataframes are a collection of Series with a common index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d98ceb0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To create such a dataframe, lets first define a few more Series\n",
    "composer_death = pd.Series({'Mahler': 1911, 'Beethoven': 1827, 'Puccini': 1924, 'Shostakovich': 1975})\n",
    "composer_city_birth = pd.Series({'Mahler': 'Kaliste', 'Beethoven': 'Bonn', 'Puccini': 'Lucques', 'Shostakovich': 'Saint-Petersburg'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11ba77c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that each Series has common data name-indices (composer name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c2d914c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth  death              city\n",
       "Mahler         1860   1911           Kaliste\n",
       "Beethoven      1770   1827              Bonn\n",
       "Puccini        1858   1924           Lucques\n",
       "Shostakovich   1906   1975  Saint-Petersburg"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, we combine into a single Dataframe and assing names for each\n",
    "composers_df = pd.DataFrame({'birth': pd_composer_birth, 'death': composer_death, 'city': composer_city_birth})\n",
    "composers_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "428cd27f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that the pd.DataFrame syntax specifies a series of series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "05ea2e34",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A more common way of creating a dataframe is to directly specify\n",
    "dict_of_list = {'birth': [1860, 1770, 1858, 1906], 'death':[1911, 1827, 1924, 1975], \n",
    " 'city':['Kaliste', 'Bonn', 'Lucques', 'Saint-Petersburg']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5669cbfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   birth  death              city\n",
       "0   1860   1911           Kaliste\n",
       "1   1770   1827              Bonn\n",
       "2   1858   1924           Lucques\n",
       "3   1906   1975  Saint-Petersburg"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We then convert the dictionary of lists into a dataframe.\n",
    "pd.DataFrame(dict_of_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85b76699",
   "metadata": {},
   "outputs": [],
   "source": [
    "# However, note that composer names have yet to be added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "075a69f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth  death              city\n",
       "Mahler         1860   1911           Kaliste\n",
       "Beethoven      1770   1827              Bonn\n",
       "Puccini        1858   1924           Lucques\n",
       "Shostakovich   1906   1975  Saint-Petersburg"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can do this as was done before with series, via an list of names\n",
    "pd.DataFrame(dict_of_list, index=['Mahler', 'Beethoven', 'Puccini', 'Shostakovich'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f6cac41",
   "metadata": {},
   "source": [
    "### Accessing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "07c5d297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mahler                   Kaliste\n",
       "Beethoven                   Bonn\n",
       "Puccini                  Lucques\n",
       "Shostakovich    Saint-Petersburg\n",
       "Name: city, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unlike Series, a bracket gives access to a column (not an index), eg:\n",
    "composers_df['city']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ac071dbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mahler                   Kaliste\n",
       "Beethoven                   Bonn\n",
       "Puccini                  Lucques\n",
       "Shostakovich    Saint-Petersburg\n",
       "Name: city, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alternatively, one can use the object-attributes syntax to access\n",
    "composers_df.city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f30be0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: the attributes syntax has limitations wrt characters.\n",
    "# So, its recommended to use the brackets notation by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "61ae668f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>Kaliste</td>\n",
       "      <td>1860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>Bonn</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>Lucques</td>\n",
       "      <td>1858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>Saint-Petersburg</td>\n",
       "      <td>1906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          city  birth\n",
       "Mahler                 Kaliste   1860\n",
       "Beethoven                 Bonn   1770\n",
       "Puccini                Lucques   1858\n",
       "Shostakovich  Saint-Petersburg   1906"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And when specifying multiple columns, a dataframe is returned.\n",
    "composers_df[['city', 'birth']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c4e4f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One important difference wrt Numpys is indexing requires a method\n",
    "# ...either .iloc[] (for the index) or the .loc[] (for the name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3ec837c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1911"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example,,,\n",
    "composers_df.iloc[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9ad2bb13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1911"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or via the explicit index (name):\n",
    "composers_df.loc['Mahler','death']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f2597e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: loc and ``ìloc``` use brackets [] and not parenthesis ()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8cc1d2cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           birth  death     city\n",
       "Beethoven   1770   1827     Bonn\n",
       "Puccini     1858   1924  Lucques"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And numpys-style indexing works here too:\n",
    "composers_df.iloc[1:3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "08879068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['birth', 'death', 'city'], dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can also retrieve a list of all the columns:\n",
    "composers_df.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9caf801d",
   "metadata": {},
   "source": [
    "### Adding columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d6108617",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Columns can be added simply by create a default of dummy value:\n",
    "composers_df['country'] = 'default'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f2b8367d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>country2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "      <td>default</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "      <td>default</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "      <td>default</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "      <td>default</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth  death              city  country country2\n",
       "Mahler         1860   1911           Kaliste  default  Austria\n",
       "Beethoven      1770   1827              Bonn  default  Germany\n",
       "Puccini        1858   1924           Lucques  default    Italy\n",
       "Shostakovich   1906   1975  Saint-Petersburg  default   Russia"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or an existing list can be created and added direction to the df:\n",
    "country = ['Austria','Germany','Italy','Russia']\n",
    "composers_df['country2'] = country\n",
    "composers_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b2021f2",
   "metadata": {},
   "source": [
    "## Importing/export, basic plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00fce00d",
   "metadata": {},
   "source": [
    "### Importing data to Pandas\n",
    "Another ways of creating dataframes is to import from CSV or Excel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0d85f521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beethoven</td>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Puccini</td>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       composer  birth  death              city\n",
       "0        Mahler   1860   1911           Kaliste\n",
       "1     Beethoven   1770   1827              Bonn\n",
       "2       Puccini   1858   1924           Lucques\n",
       "3  Shostakovich   1906   1975  Saint-Petersburg"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Excel files can be read in using the read_excel function (ala R)\n",
    "pd.read_excel('Data/composers.xlsx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d4c8bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that read_excel automatically recognizes the excel headers\n",
    "# However, numbered row indices were added automatically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "66048537",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To specify row indices, we can make explicit the column to use:\n",
    "specific_sheet = pd.read_excel('Data/composers.xlsx', index_col = 'composer')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "be59cf19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth  death              city\n",
       "composer                                    \n",
       "Mahler         1860   1911           Kaliste\n",
       "Beethoven      1770   1827              Bonn\n",
       "Puccini        1858   1924           Lucques\n",
       "Shostakovich   1906   1975  Saint-Petersburg"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specific_sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb2519a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Handling unknown values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c9f1a00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In Pandas, NaN is recognized as missing\n",
    "# If anything else is used, we need to be handle it explicitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4a6f8aa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860.0</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770.0</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858.0</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
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       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906.0</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sibelius</th>\n",
       "      <td>10.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haydn</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Röhrau</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               birth    death              city\n",
       "composer                                       \n",
       "Mahler        1860.0     1911           Kaliste\n",
       "Beethoven     1770.0     1827              Bonn\n",
       "Puccini       1858.0     1924           Lucques\n",
       "Shostakovich  1906.0     1975  Saint-Petersburg\n",
       "Sibelius        10.0  unknown           unknown\n",
       "Haydn            NaN      NaN            Röhrau"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, let's do a regular import\n",
    "import1 = pd.read_excel('Data/composers.xlsx', index_col = 'composer',sheet_name='Sheet2')\n",
    "import1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2921d2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can look at the columns and note data types have been assigned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2a6e333b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          1860.0\n",
       "Beethoven       1770.0\n",
       "Puccini         1858.0\n",
       "Shostakovich    1906.0\n",
       "Sibelius          10.0\n",
       "Haydn              NaN\n",
       "Name: birth, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example...\n",
    "import1.birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e6e34195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler             1911\n",
       "Beethoven          1827\n",
       "Puccini            1924\n",
       "Shostakovich       1975\n",
       "Sibelius        unknown\n",
       "Haydn               NaN\n",
       "Name: death, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or...\n",
    "import1.death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fccae46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that the death column has been imported as a string\n",
    "# This is problematic from the perspective of conducting analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3bcf53c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1480.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>823.674207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1770.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1858.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1860.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1906.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             birth\n",
       "count     5.000000\n",
       "mean   1480.800000\n",
       "std     823.674207\n",
       "min      10.000000\n",
       "25%    1770.000000\n",
       "50%    1858.000000\n",
       "75%    1860.000000\n",
       "max    1906.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example, the describe function simply skips non-numerics:\n",
    "import1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a26d72f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that the death column is missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ea67f1bf",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sibelius</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haydn</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Röhrau</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               birth   death              city\n",
       "composer                                      \n",
       "Mahler        1860.0  1911.0           Kaliste\n",
       "Beethoven     1770.0  1827.0              Bonn\n",
       "Puccini       1858.0  1924.0           Lucques\n",
       "Shostakovich  1906.0  1975.0  Saint-Petersburg\n",
       "Sibelius        10.0     NaN               NaN\n",
       "Haydn            NaN     NaN            Röhrau"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here, we specify that 'unknown' should be NaN (missing):\n",
    "import2 = pd.read_excel('Data/composers.xlsx', index_col = 'composer',\n",
    "                        sheet_name='Sheet2', na_values=['unknown'])\n",
    "import2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "47d1f209",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1480.800000</td>\n",
       "      <td>1909.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>823.674207</td>\n",
       "      <td>61.396933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1827.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1770.000000</td>\n",
       "      <td>1890.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1858.000000</td>\n",
       "      <td>1917.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1860.000000</td>\n",
       "      <td>1936.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1906.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             birth        death\n",
       "count     5.000000     4.000000\n",
       "mean   1480.800000  1909.250000\n",
       "std     823.674207    61.396933\n",
       "min      10.000000  1827.000000\n",
       "25%    1770.000000  1890.000000\n",
       "50%    1858.000000  1917.500000\n",
       "75%    1860.000000  1936.750000\n",
       "max    1906.000000  1975.000000"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, computations using the death column are possible:\n",
    "import2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8fdb16",
   "metadata": {},
   "source": [
    "### Columns types\n",
    "Column types can be specified using the dtype option and a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4049bd43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For example, we know classified is an integer not a float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3b154fa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# And here, we can make all data types explicit on import:\n",
    "import2 = pd.read_excel('Data/composers.xlsx', index_col = 'composer',sheet_name='Sheet1', na_values=['unknown'],\n",
    "                       dtype={'composer':str,'birth':np.int32,'death':np.int32,'city':str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3d213bfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          1860\n",
       "Beethoven       1770\n",
       "Puccini         1858\n",
       "Shostakovich    1906\n",
       "Name: birth, dtype: int32"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example, lets check birth\n",
    "import2.birth"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa94a68",
   "metadata": {},
   "source": [
    "### Modifications after import\n",
    "We can also do a default import and correct what needs to be after."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6b749114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          1860.0\n",
       "Beethoven       1770.0\n",
       "Puccini         1858.0\n",
       "Shostakovich    1906.0\n",
       "Sibelius          10.0\n",
       "Haydn              NaN\n",
       "Name: birth, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To replace values, we can use the replace() function; for example:\n",
    "import_nans = import1.replace('unknown', np.nan)\n",
    "import_nans.birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f125895f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that when we replace the values, Pandas adjusts column the type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "76f27ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example,,,\n",
    "import1.death.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "06ab1275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And...\n",
    "import_nans.death.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ac6993e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can change also change column type using same syntax from Numpys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "937486d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          1860\n",
       "Beethoven       1770\n",
       "Puccini         1858\n",
       "Shostakovich    1906\n",
       "Name: birth, dtype: int32"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example,,,\n",
    "import2.birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7003e834",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          1860.0\n",
       "Beethoven       1770.0\n",
       "Puccini         1858.0\n",
       "Shostakovich    1906.0\n",
       "Name: birth, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And now...\n",
    "import2.birth.astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d398bc58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# But if we look at import2 again, we note the type did not change\n",
    "# For dataframes, changes are only effective if we reassign the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8ae028cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          1860.0\n",
       "Beethoven       1770.0\n",
       "Puccini         1858.0\n",
       "Shostakovich    1906.0\n",
       "Name: birth, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reassignment is done using the same logic from R\n",
    "import2.birth = import2.birth.astype('float')\n",
    "import2.birth"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e101c5d",
   "metadata": {},
   "source": [
    "### Exporting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "db4bb7ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To export a dataframe to csv, the following command is used:\n",
    "import2.to_csv('mydataframe.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2624c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets confirms its been saved..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "cc7b320e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mydataframe.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls 'mydataframe.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "202a60b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# More complex data objects can be saved to a \"pickle\" format\n",
    "# Pickle is a Panda native format that is better wrt nested lists, etc\n",
    "import2.to_pickle('my_dataframe.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8d394b8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860.0</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770.0</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858.0</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906.0</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               birth  death              city\n",
       "composer                                     \n",
       "Mahler        1860.0   1911           Kaliste\n",
       "Beethoven     1770.0   1827              Bonn\n",
       "Puccini       1858.0   1924           Lucques\n",
       "Shostakovich  1906.0   1975  Saint-Petersburg"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And it can be releaded using the Pandas function pd.read_pickle()\n",
    "import3 = pd.read_pickle('Data/my_dataframe.pkl')\n",
    "import3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2893a19",
   "metadata": {},
   "source": [
    "### Plotting\n",
    "Pandas improves the output of native plots by using the df information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "33c63f23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets explore via a simple dataset\n",
    "composers = pd.read_excel('Data/composers.xlsx', sheet_name='Sheet5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "76967fe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Series can be used directly by Matplotlib; e.g. default scatter plot\n",
    "plt.plot(composers.birth, composers.death, 'o')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "023f9efa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that different plots can be specified using the \"kind\" option\n",
    "# The variables to plot are column names rather than whole series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "01ac6d7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For example...\n",
    "composers.plot(x = 'birth', y = 'death', kind = 'scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cee18b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We note that the plot automatically gets axis labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "bd503790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LiHvSfY8AvwVOAf4xLXoqMBE4NiI2AHemCWixpKURsUHSBJIEc0lEXJle6yFgJXA6cEGhkZpZ3RnM44efXvMmLW0d3faVSxQ7TBpX9vyxjWLCmMZtTWsjrXkMCk4wEdFznF5384CtwH2Zc16XtBz4OF0J5kjgjjS5lFxHUlM5GFgGHARMA67PXGujpGXp+U4wZrbNYGbar2tp4+s/ebbH/gs/sXePRDFvzoyy17j2iwcwdkxj3Q09HohqD1OeAGyNiI7c/jbgvZntvYAV2QIR8QqwKT1WKtMBvJi71nOZMmZmFfWflBtGXNr3zNoNPSZSTh7XyL47T+/xXnNnT+VzB+7Wbd/nDtyN+XvMqsuhxwNR7VFkLwETJL0vIn4FIGkisC8wNVNuJrC+zPnN6bFSmZYyyaoZmCRpXERsGcqbN7P6VG6Nr0aJe1f8jkP22okHXnqjR+0mYNu+LR2ddOQmUnZE9NqPctEx7+NzH9qdJ1etZ96cGcydPbVsuZFGEdF/qaF4o7QPJiIWZvaNI6mZvAacDGwALgVOAtojYmJarh34SkT8U+6aa4DvRsTfSvrbtMzMXJk/B64BxkVEe+7YKSR9PcyePfuD11133aBibGlpYcqUKYO6Rj0YDXGOhhhh9MbZ0RmseO0tOnO//xolAogIskca0scaZ8tLQoBIRh/tOnMi0yeOLSyG/lTrszzkkEMej4j55Y5VtQYTEVskfQq4lq4msAeA/wQOzRRtBmaUucR0umo2zcBUSY25WswMYFM+uaTvfw1J8mH+/PmxcOHC7Q0FgKamJgZ7jXowGuIcDTHC6I5z45NrOOem5TRKbNySb/jobtK4RgjY1N5Vbur4MfzzSe9n+sRxNdGPUoufZbWbyIiIR9N5L+8m6Y/5taRbgez01xXk+lEkzQEm05WYVgCNwFzg+UzRHv03ZmalNb7uXfE7Fi97pseIsKyOzmBrR/cmsdb2reyz8/SqJ5ZaVu1OfgAi8XyaXN4FfAz4TqbI7cARkrINlycCrXSNQHuQpInthFIBSZNI5sPcXuT9m1n92mnaeNpzySM3vYVj5u1MQ34n8MzaN+vm4V/VUGgNJv0Fvyjd3AWYJun4dPu2iNgk6UKSGsYbwPtIJkpeFxF3Zi51FfAl4GZJS4A9gcXAZaWhyxGxWdKlwIWSmtNrnkWSRL9VYJhmVoeyw5Q7A8Y0wMSxY9jS0UFnQGdHV3/Lj55cy7jGBto7umo5Wzvh1O/9ks6IEbuW2GAV3US2E3BDbl9pew+SSZCzgCtIJmGuIpn78s3sCRHRLOkw4EqSOS/rgctJkkzWpSQJ5bz0uo8Bh0fE60MQi5mNENlhyqWRZOPHNPDPJ30ACE77/hO0d2zdVn5so2jf2nNa36a072akriU2WEVPtFxJMsiirzJnAmdWcK1n6d7xX65MABenLzOzssoNUx7X2MD0iWPLPsulozP4+6P24es/eZYG1K2zH+rrMcbDqSb6YMzMhlNfjyLu7SmSJ33oHfzi3EO56rMfZHzuOS8jdS2xwar6KDIzs6J1dAZPrVq/LYGUksg5ucmUpRpIb0+RnDVlPB9999v4h+N7P9e6OMGY2Yh2y5NrWP3aW1x13yPd1hzr71HEpURUTj0/xng4uYnMzEasUmd+Z0TZNccG8yjien2M8XBygjGzEavUmZ81Uh/uVYucYMxsxOqrM9+K5wRjZiNWqTO/Qeo2IgzosRS/DT138pvZiHb0vF24+w8v8L2PvJ9dZ07kgZfeYMGSe7brQWM2MK7BmNmI19gg9k+fLNnfg8Zs6DjBmNmIVO6JlO70H15uIjOzESe7kGV7ZydLDkp+1bnTf3i5BmNmI0p2IctSM9jq5lbWtbT1ugyM57IUwzUYMxtRyi1kqXT/rCnjPQt/GDnBmNmIUq4ZrBOYPK5x23Zfy8DY0HETmZmNKNlmsPGNydNCBHziygf48ZNrqntzo4wTjJmNOEfP24VbT/8woSTBdEZ4SHIVOMGYWU0qN8x4IDZu6WB8o4ckV5P7YMys5uSHGW/PbHsPSa4+12DMrKaUG2a8PU1b2b6YRslDkqvANRgzqynlhhlv7zPvS0OSH33oAX5x9IedXIaZazBmVlOGumlr1pTxTBzb6ORSBU4wZlZTPNt+5HATmZnVHM+2HxmcYMxs0Na1tA15MvBs+/pXaBOZpLmSrpb0lKQOSU1lyvyRpH+XtEZSi6QnJJ1Uptzeku6WtEnSWkkXSWrMlZGk8yWtktQq6X5J84qL0MxueXINC5bcw2e+/QgLltxT0Wz5wc5xsfpQdA1mH2AR8DAwLn9QUgPwY2AWcA7wGnA88D1JmyLiv9NyM4G7gGeBY4B3At8kSZAXZC75VeBC4GxgBXAWcJekfSPitSICNBvNskOKS6O+zrlpOQvm7thr7WMo5rhYfSi6k39ZRMyJiBOAZ8ocfzcwHzgjIr4fEXdHxF8CTwCfypQ7FZgIHBsRd0bEVcDXgLMkTQOQNIEkwVwSEVdGxF3ACUAApxcVoNlotrq5lcZ0OZaS3mbLr2tp4/4Xfs85N/qJkqNFoQkmIjr7KTI2/ffN3P71JOvTlRwJ3BERGzL7riNJOgen2wcB04DrM++/EViWnm9mQ+zpNW+ycUtHt33lhhSXmtFO/a/Hadva/deCl28Zuao9TPlp4BHgIknvkjRN0ueBBcBVmXJ7kTR5bRMRrwCb0mOlMh3Ai7n3eC5TxsyGyLqWNr7+k2d77L/w43t3ax7LNqNtau/oUd7Lt4xcVR1FFhEh6UjgFuCFdHc7cHJE3JMpOpOkVpPXnB4rlWmJiPxPcDMwSdK4iNiSPSDpFOAUgNmzZ9PU1DSIaKClpWXQ16gHoyHO0RAjDC7O1vYOvvTedjoitu1rkNjhrZdoavptn+VKZQPYefpYfvXYQ9t1D5UaDZ9nLcZY1QSTdvL/F0kn/4nA70gGBXxH0rqI+GmmeJS7RG5/b2XKHouIa4BrAObPnx8LFy4caAjdNDU1Mdhr1IPREOdoiBEGF+e6ljb+Zsk9bG7vavKaMLahx5Is5cqNaQAJxjU20hEdLD3uXYV29I+Gz7MWY6x2E9kngI8Dn4yI6yOiKSLOAf4bWJop1wzMKHP+dLpqNs3A1PzQ5fS8TRHRPoT3bTbqVTrjPl9u/Jjk1057R7Kkvjv6R65qT7Tci+SXf77f5Ang6Mz2CnL9KJLmAJPp6ptZATQCc4Hnc+/Rrf/GzIZGpTPus+XebG3ntO//krfatm47vr2LWVptq3YN5mWS/pH35PZ/EFiZ2b4dOELS1My+E4FW4L50+0FgA8nQZAAkTQKOSs83swLMmjKe/efM6Dc5lMrts/M0P6dllCh6Jv8kScdLOh7YBXhbaTv95X8b8ArwI0mflvQxSZcDfwr8c+ZSVwFtwM1pmVOAxcBlpaHLEbEZuBQ4X9Jpkg4Dbkhj/FaRcZpZ5byY5ehRdBPZTiS/5LNK23tExMo0EVxCMjN/GvBrkomV15ROiIjmtNyVJPNa1gOXkySZrEtJEsp5JAMHHgMOj4jXhy4kMxssL2Y5OhSaYCJiJd0nTJYr8xKZZq0+yj0LHNpPmQAuTl9mVsO8mOXIV+0+GDMzG6EqSjCSjpX0oqQ3JW2Q9JakDf2faWZmo1WlTWRLgaMi4rkib8bMBq+IZ7OYbY9KE8zrTi5mtc9L4Vst6TPBSDo2/fIxST8EfkQyXBiAiLi5uFszs4Eo92yWs2/s+9ksZkXqrwZzVObrTcAfZ7YDcIIxqxGrm1sZ29CwLbkAtG3t5AePvMJfH/auKt6ZjVZ9JpiIOBlA0oKI+EX2mKQFRd6YmQ3MrjMnsqWj5yOYrrjrBf7sgN1ci7FhV+kw5XIz4T073qyGzJoyni8s2L3H/o6AZ9Z60KcNv/76YA4keVLk2ySdlTk0jWRhSTOrIQe+cxb/et9vyhwp9yQLs2L11wczDpiSlssuNLkBOL6omzKz7bPz9PILRva236xI/fXB3AfcJ+m7EfHyMN2TmW2njVs6mDC2ocdDwDZu6fmoYrOiVToPZpOkfwD2ASaUdkZEn2uDmdnw6m3Jey+Fb9VQaSf/90ke2rUH8DWSZ7X8T0H3ZGb9WNfSxlOr1vd4CuRQLIXf27XNBqrSGsysiPiOpDMyzWb39XuWmQ25/mbrD2YpfK8EYEOp0hpM6Xn2r0r6uKT3A7sWdE9m1ovsbP232rb2+jz7Sp8yuT3XNqtUpQnmG5KmA18GvgJ8G/ibwu7KzMoqzdbPKj3PvpavbaNTRU1kEXFr+uWbwCHF3Y6Z9WXXmRMLe559kde20anS58G8W9Ldkp5Ot/eTdEGxt2ZmeUU+z77Ia9voVGkn/78BZwNXA0TEckk/AL5R1I2ZWXlFPs++yGvb6FNpgpkUEY9Kyu7bWsD9mFkFinyefZHXttGl0k7+NyS9k3RBI0nHA68WdldmZlb3Kq3BnAZcA+wlaQ3wW+Ckwu7KzMzqXn+rKWdXUL4NuJek1rMROA64rLhbMzOzetZfE9nU9DUf+EtgJjADOBXYu7+LS5or6WpJT0nqkNSUO75QUvTyuiNXdu90JNsmSWslXSSpMVdGks6XtEpSq6T7Jc3r7z7NzGzo9bea8tcAJP0M+EBEvJVuLwZuqOD6+wCLgIdJlv7P+yVwYG7fbsAPgdtLOyTNBO4CngWOAd4JfJMkQWaHS38VuJBkxNsK4CzgLkn7RsRrFdyv2bBY19LmkVo24lXaB7MbsCWzvQXYvYLzlkXELQCSbgR2zB6MiA0kyWcbSR8BOoHrM7tPBSYCx6bn3ClpGrBY0tKI2CBpAkmCuSQirkyv9RDJwpyn0z0RmVWN1/uy0aLSUWT/BTwqabGkvwceAf6jv5MioucDwvv3aeC+iFib2XckcEeaXEquI0k6B6fbB5E8aXNbYoqIjcCy9HyzqvN6XzaaVJRgIuJi4GSgGVgPnBwRlwz1zUh6F/B+4Nrcob1Imryy9/QKsCk9VirTAbyYO/e5TBmzqhrIel9eNt/qXaVNZETEL0n6TIr0aZKVm2/K7Z9JktjymtNjpTItEZF/dF8zMEnSuIjINvMh6RTgFIDZs2fT1NQ0qJtvaWkZ9DXqwWiIs6gYOzqDU9/TRmfEtn0N6uCNF5+g6dddE5nfbG1ndXMrIpl8tuvMiUyfOHbI72c0fJYwOuKsxRgrTjDD5FPAzyLiD2WORZl9yu3vrUzZYxFxDcn8HubPnx8LFy4c0M3mNTU1Mdhr1IPREGeRMW58cg3n5PpgDsv0waxraWPBknvY3N41SHLC2K384tyPDvmAgNHwWcLoiLMWY6yZBCNpf+C9wMVlDjeTDI/Om05XzaYZmCqpMVeLmQFsioh2zGpAf+t9lZrRNtPVhVlqRvOIM6snNZNgSGovrcAtZY6tINePImkOMJmuvpkVQCMwF3g+U7RH/41ZtfW13peXzbeRotJRZMPhRJJhzS1ljt0OHCFpaq58K1B6dPODwAbghFIBSZOAo8jMqTGrdV4230aKQmsw6S/4RenmLsC0dKFMgNsiYlNa7kPAHiQTI8u5CvgScLOkJcCewGLgstLQ5YjYLOlS4EJJzXRNtGwAvjXUsZkVycvm20hQdBPZTvSc8V/a3oNkEiQkzWNv0ktNIyKaJR0GXEkyr2U9cDlJksm6lCShnAfMAh4DDo+I1wcRg1lVeNl8q3eFJpiIWEnXKK6+yp0JnNlPmWeBQ/spEySDBMoNFDAzs2FUS30wZmY2gjjBmJlZIZxgzMysEE4wZmZWCCcYsyHkBSrNutTSTH6zuubnvJh15xqM2RDwc17MenKCMRsCA3nOi9lo4QRjNgS8QKVZT04wZkPAC1Sa9eROfhsR1rW0VX1hSC9QadadE4zVve0ZvVVUQvIClWZdnGCsrmVHb5WeAHnOTctZMHfHXn/Rezix2fBwH4zVtYGO3vJwYrPh4wRjdW2go7c8nNhs+DjBWF0b6OgtDyc2Gz7ug7G6tq6ljXfMmsytp3+YjVs6+u20LyWkc3J9MO6YNxt6TjBWt8p11u8/Z0a/53k4sdnwcBOZ1YyBrEQ82M76WVPGs/+cGU4uZgVyDcZqwkCHDpc660tDk6Grs95Jw6w2uAZjVbc9tRF31pvVPicYq7rtGTqcHz02fkwDpy2cW/StmtkAOMFY1W1vbeToebvwi3MP5c8/uicQXHP/b1iw5B5+/OSaAu/WzCrlBGNVN9iViP+l6SXatoZn5pvVmEITjKS5kq6W9JSkDklNvZR7n6RbJb0p6S1Jj0r6YK7M3pLulrRJ0lpJF0lqzJWRpPMlrZLUKul+SfOKi9CGSqk28r0vHsAvzj204rXBPDPfrHYVPYpsH2AR8DAwrlyBNAH8HLgFODHd/f8AEzNlZgJ3Ac8CxwDvBL5JkiAvyFzuq8CFwNnACuAs4C5J+0bEa0MVlBVje1Yidme/We0qOsEsi4hbACTdCOxYpsxVabnPZPb9NFfmVJKEc2xEbADulDQNWCxpaURskDSBJMFcEhFXpu/5ELASOJ3uichGCM/MN6tdhSaYiOjs67ikvYEDgL/p51JHAnekyaXkOmAJcDCwDDgImAZcn3n/jZKWpec7wYxQnplvVpuq3cl/QPrvzLSfZqukX0v637lye5E0eW0TEa8Am9JjpTIdwIu5c5/LlLEqyc/SH8is/Up4Zr5Z7an2TP63p//+J7AU+B/geODbkl6NiNvS4zOB9WXOb06Plcq0RERHmTKTJI2LiC3ZA5JOAU4BmD17Nk1NTYMKpqWlZdDXqAcDjfPN1nZWN7ciIICZk8bSvKl92/auMycyfeLYYm52O/mzHFlGQ5y1GGO1E0ypBvXtiFiafn2vpPcC5wG3ZcpGmfOV299bmbLHIuIa4BqA+fPnx8KFCyu/8zKampoY7DXqwUDiXNfSxoIl97C5PTvgrxPo2p4wdiu/OPejNVX78Gc5soyGOGsxxmo3kf0h/ffe3P57gL0z283AjDLnT6erZtMMTM0PXU7P2xQR7YO5Uds+5YYR53lYsdnIVO0E81wv+wVkBwisINePImkOMJmuvpkVJH8W59cL6dF/Y8On3DDiPA8rNhuZqp1gHiSpeRyW238Y8FRm+3bgCElTM/tOBFqB+zLX2gCcUCogaRJwVHq+VUG5WfqfO3C37Z61b2b1o9A+mPQX/KJ0cxdgmqTj0+3bImKTpIuApZLWk3TyHwd8lGT4cclVwJeAmyUtAfYEFgOXlYYuR8RmSZcCF0pqpmuiZQPwreKitP6UG0Z8xmHv9rBisxGu6E7+nYAbcvtK23sAKyPiCkkNwF+TJI3ngeMj4uelEyKiWdJhwJUkc17WA5en5bMuJUko5wGzgMeAwyPi9aELybZHfpb+9szaN7P6UvREy5V0jeLqq9xlwGX9lHkWOLSfMgFcnL7MzKyKqt0HY2ZmI5QTjJmZFcIJxszMCuEEY2ZmhXCCMTOzQjjBmJlZIZxgzMysEE4wZmZWCCcYMzMrhBOMmZkVwgnGzMwK4QRjZmaFcIIxM7NCOMGYmVkhnGDMzKwQTjBmZlYIJxgzMyuEE4yZmRXCCcbMzArhBGNmZoVwgjEzs0I4wZiZWSGcYMzMrBCFJhhJcyVdLekpSR2SmsqUWSkpcq/XypTbW9LdkjZJWivpIkmNuTKSdL6kVZJaJd0vaV5xEZqZWW/GFHz9fYBFwMPAuD7K/QD4VmZ7S/agpJnAXcCzwDHAO4FvkiTICzJFvwpcCJwNrADOAu6StG9E9EhaZmZWnKITzLKIuAVA0o3Ajr2UezUiHu7jOqcCE4FjI2IDcKekacBiSUsjYoOkCSQJ5pKIuDJ9z4eAlcDpdE9EZmZWsEKbyCKic4gudSRwR5pcSq4jSToHp9sHAdOA6zPvvxFYlp5vZmbDqFY6+b8gaYukNyXdKOkdueN7kTR5bRMRrwCb0mOlMh3Ai7lzn8uUMTOzYVJ0E1klbiHpo1kNvBf4e+Dnkt4XEW+mZWYC68uc25weK5VpiYiOMmUmSRoXEfm+nVOAUwBmz55NU1PToAJpaWkZ9DXqwWiIczTECI5zJKnFGKueYCLijMzmzyU9CDwJnAxckS1a5nTl9vdWpuyxiLgGuAZg/vz5sXDhwkpvu6ympiYGe416MBriHA0xguMcSWoxxlppItsmIp4Gngc+kNndDMwoU3w6XTWbZmBqfuhyet6miGgf0hs1M7M+1VyCycjWOFaQ60eRNAeYTFffzAqgEZibu06P/hszMytezSUYSfsC7wEez+y+HThC0tTMvhOBVuC+dPtBYANwQuZak4Cj0vPNzGwYFdoHk/6CX5Ru7gJMk3R8un0bcAjwGeBWYC1JbeMC4BXgu5lLXQV8CbhZ0hJgT2AxcFlp6HJEbJZ0KXChpGa6Jlo20H0Sp5mZDYOiO/l3Am7I7Stt7wGsSstcQdJXsg74KXB+ds5LRDRLOgy4kmRey3rgcpIkk3UpSUI5D5gFPAYcHhGvD1E8ZmZWoUITTESspGsUV28Oq/BazwKH9lMmgIvT17BY19LG6uZWdp05cbje0sysLlR9mHI9u+XJNZx703LGNjTQ3tnJkoP87TQzK6m5Tv56sa6ljXNvWs7m9k7eatvK5vZOVje3sq6lrdq3ZmZWE5xgttPq5lbGNnT/9indb2ZmTjDbbdeZE2nv7L6WZ6T7zczMCWa7zZoynqXH7ceEsQ1MHT+GCWMb2HXmRGZNGV/tWzMzqwnulR6Eo+ftwoK5O24bRfarxx6q9i2ZmdUMJ5hBmjVlvGstZmZluInMzMwK4QRjZmaFcIIxM7NCOMGYmVkhnGDMzKwQStaHNEm/B14e5GV2BN4YgtupdaMhztEQIzjOkaRaMb4jIt5W7oATzBCS9FhEzK/2fRRtNMQ5GmIExzmS1GKMbiIzM7NCOMGYmVkhnGCG1jXVvoFhMhriHA0xguMcSWouRvfBmJlZIVyDMTOzQjjBmJlZIZxgUpLmSrpa0lOSOiQ19VP+Ckkh6R/LHNtb0t2SNklaK+kiSY25MpJ0vqRVklol3S9p3tBG1eO++o1R0so0ruzrtTLlajLG9H0r+iwlvU/SrZLelPSWpEclfTBXpm7jlLSwzGdZet1RD3FW+DP7R5L+XdIaSS2SnpB0UplyNRlj+r6VxDlD0v+V9Ic0ztslzS1TrnbijAi/kn6oY4BVwA3Ac0BTH2X3BjYAbwL/mDs2E1gL3AUcDpwKbAS+kSt3HtAKnA58DLiNZJLU26sZI7AS+D7woczrA/US4wDinAe8BXwP+JP0dSHw4ZESJzAt9zl+CPhTkoevnlkPcVYQYwPwP8BvgJOAw4B/TWP8f+shxgH8zN4BrAE+CxwNPEoyOXxarcZZ2Des3l5AQ+brG8t9wJnjdwFfJ/llnE8w5wHNuQ/9HGBTaR8wgSQ5/V2mzGTg9/kfhOGOsVxMZcrUbIwDiPNh4AcjPc4y55wNdAA710Oc/cUI7EWSTI7K7f8l8MN6iLHCOA9M4zw0s292ev9fqdU43USWiojOSspJOh54L3BpL0WOBO6IiA2ZfdcBE4GD0+2DSP66vD7z/huBZen5hag0xgrUbIzp+/QZp6S9gQOAb/VzqbqOsxefBu6LiLWZfTUbZwUxjk3/fTO3fz2gzHbNxpi+T39xzgO2AvdlznkdWA58PFOupuJ0ghkASROBbwJfTT+QcvYCVmR3RMQrJH9B7JUp0wG8mDv3uUyZavqCpC1p38SNkt6RO17vMR6Q/jszbfPeKunXkv53rly9x9mNpHcB7weuzR2q5zifBh4BLpL0LknTJH0eWABclSlXzzFCUuvYGhEduf1tJH/wltRUnE4wA3Me8CpJu31vZpL89ZTXnB4rlWkp88PSDEySNG6Q9zkYtwCnkbRln01SNf+5pOmZMvUe49vTf/+TpL/pcOCnwLclLcqUq/c48z4NtAM35fbXbZyRtO8cSfK77AWSmsw1wBci4p5M0bqNMfUSMEHS+0o70j949wV2yJSrqTjHDNWFRjpJewBfIWkD7W92arnjyu3vrUxvx4ZFRJyR2fy5pAeBJ4GTgSuyRcucXhcx0vWH1bcjYmn69b2S3kvyR8RtmbL1HGfep4CfRcQfyhyryzglNQD/BcwCTgR+BywCviNpXUT8NFO8LmNM3QH8Frha0skkg4wuBaaT/NGQVTNxugZTuUuB24EV6XDBGSTfv/HpdunDaQZmlDl/Ol1/WTQDU/NDB9PzNkVE/gemaiLiaeB54AOZ3fUeY+kX7L25/feQjBAsqfc4t5G0P0lTSr55DOo7zk+Q9EF8MiKuj4imiDgH+G9gaaZcPcdIRGwh+QNhNkkT2FpgT5Ja+OuZojUVpxNM5d4DHEvy4ZRec0iG+TUDu6TlVpBrx5Q0h2SUxopMmUYgP4a9R/tpDcn+VVPvMT7Xy34B2c7Weo8z61Mkw1JvKXOsnuPci+SXYr4/4QngnZnteo4RgIh4lOTe9gLmRsRHgJ1IRkSW1FScTjCV+yJwSO71OslIjENIhvhBUss5QtLUzLknkvznLo0AeZCkintCqYCkScBR6fk1Q9K+JMn18czueo/xQZI/Cg7L7T8MeCqzXe9xZp0ILIuIljLH6jnOl0n6Dd6T2/9BkiH3JfUc4zaReD4ifp0O2vgY8J1MkdqKs8ix3fX0AiYBx6evh4BnMtuTejlnJeUnWr4K3Eny4Z8CtFB+otMmujrUf0Iy0Wl2tWIkaWq4lmTC2iHAX5JM7PoNPSdz1WSMlX6WwJnAFuB8kk7+q0hqLx8ZSXGm5T5EUgP9ZC/Xqdk4K/iZnUqSZJ4jGcTwMeDyNN6/qocYB/AzeyFJUjgE+BLJH7XfreXPsrBvWL29gN3TH8pyr917OWclZSYlkrTj30PyV8OrJJMyG3NlBPwtsDot93Pg/dWMEdgPuDv9wW0HXgO+S2ZSXq3HOJDPEjiLpON0C/Ar4NgRGucVJO3v4/u4Vk3GWUmMJE09N5D0S7SQ1EL/gnS1+FqPcQBxXkHyB18byaiyc4ExtfxZerl+MzMrhPtgzMysEE4wZmZWCCcYMzMrhBOMmZkVwgnGzMwK4QRjZmaFcIIxqxJJu0t6usz+b6fPrCl3zpnprOvSdrmZ+WY1wQnGrMZExBcj4tn8/nRxwjNJZn2b1TwnGLPqGiPpPyQtTx/uNklSk6T5kNRQJF0k6RGSmdc7kzxaYNtK0JIuTh+c9rCk2VWKw6wHJxiz6noPcE1E7EeyAOFf5Y5PBp6OiAMi4iKS5VAOiYhDMscfjoj9gfuBPx+m+zbrlxOMWXWtiohfpF9/D/hw7ngHPZ9AmbUFuDX9+nGSNa3MaoITjFl15RcDzG9vjp6Pts1qj64FBTvwU2qthjjBmFXXbpIOTL/+NPBAP+XfIlmi3qzmOcGYVddzwP+StBzYAfjXfspfA9ye7eQ3q1Vert/MzArhGoyZmRXCCcbMzArhBGNmZoVwgjEzs0I4wZiZWSGcYMzMrBBOMGZmVoj/H9DazLmzWdMkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can also directly specify labels and axis titles\n",
    "composers.plot(x = 'birth', y = 'death', kind = 'scatter',\n",
    "               title = 'Composer birth and death', grid = True, fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "dfd93d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can also add additional formatting, for example:\n",
    "composers.plot(x = 'birth', y = 'death',kind = 'scatter',\n",
    "               c = composers.period.astype('category').cat.codes, colormap = 'Reds', title = 'Composer birth and death', grid = True, fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ec403a0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn provides for a smoother plotting experience; for example:\n",
    "sns.scatterplot(data = composers, x = 'birth', y = 'death', hue = 'period')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bd074f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some additional options are available in the plot module..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a4248218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For example, histograms...\n",
    "composers.plot.hist(alpha = 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "838f71a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can also use styles with plots; for example..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8dca1ee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, specify style\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e5145887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Then...\n",
    "composers.plot.hist(alpha = 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb2d114",
   "metadata": {},
   "source": [
    "## Operations with Pandas objects\n",
    "Here we practice analysis of tabular data using Pandas with Numpys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "bb1fabb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, load our libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "eec9e258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beethoven</td>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Puccini</td>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       composer  birth  death              city\n",
       "0        Mahler   1860   1911           Kaliste\n",
       "1     Beethoven   1770   1827              Bonn\n",
       "2       Puccini   1858   1924           Lucques\n",
       "3  Shostakovich   1906   1975  Saint-Petersburg"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Next, we load our data\n",
    "compo_pd = pd.read_excel('Data/composers.xlsx')\n",
    "compo_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "eefc5dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, mathematical operations work against entire columns as in R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "3b26becd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3720\n",
       "1    3540\n",
       "2    3716\n",
       "3    3812\n",
       "Name: birth, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example, multiplication\n",
    "compo_pd['birth']*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "76b1af1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.528332\n",
       "1    7.478735\n",
       "2    7.527256\n",
       "3    7.552762\n",
       "Name: birth, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or logarithms\n",
    "np.log(compo_pd['birth'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "764fd1e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3720</td>\n",
       "      <td>3822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3540</td>\n",
       "      <td>3654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3716</td>\n",
       "      <td>3848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3812</td>\n",
       "      <td>3950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   birth  death\n",
       "0   3720   3822\n",
       "1   3540   3654\n",
       "2   3716   3848\n",
       "3   3812   3950"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also apply operations to multiple columns\n",
    "compo_pd[['birth','death']]*2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3263a7ff",
   "metadata": {},
   "source": [
    "### Column operations\n",
    "Summary functions are avialable, that work on entire dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a2b5f2cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3417: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "birth    1848.50\n",
       "death    1909.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example, the mean\n",
    "np.mean(compo_pd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18ac07f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Or, standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "bff4be3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l9/zf8fhrds2nd_sfhd808zjdd80000gn/T/ipykernel_45801/237597691.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  compo_pd.std()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "birth    56.836021\n",
       "death    61.396933\n",
       "dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compo_pd.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1cf80b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that these function complain about non-numerics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d4d00f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1848.500000</td>\n",
       "      <td>1909.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>56.836021</td>\n",
       "      <td>61.396933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1770.000000</td>\n",
       "      <td>1827.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1836.000000</td>\n",
       "      <td>1890.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1859.000000</td>\n",
       "      <td>1917.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1871.500000</td>\n",
       "      <td>1936.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1906.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             birth        death\n",
       "count     4.000000     4.000000\n",
       "mean   1848.500000  1909.250000\n",
       "std      56.836021    61.396933\n",
       "min    1770.000000  1827.000000\n",
       "25%    1836.000000  1890.000000\n",
       "50%    1859.000000  1917.500000\n",
       "75%    1871.500000  1936.750000\n",
       "max    1906.000000  1975.000000"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A function to provide common discriptics is also available:\n",
    "compo_pd.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ab5bf2ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1848.5"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also call summary stats directly from a single column\n",
    "compo_pd.birth.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d03677",
   "metadata": {},
   "source": [
    "### Operations between Series\n",
    "We can also do computations with multiple series, as with Numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c685b586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    51\n",
       "1    57\n",
       "2    66\n",
       "3    69\n",
       "dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example, subtraction\n",
    "compo_pd['death']-compo_pd['birth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ca2b28d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can also add results to new dataframe columns; for example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5bd89a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beethoven</td>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Puccini</td>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       composer  birth  death              city  age\n",
       "0        Mahler   1860   1911           Kaliste   51\n",
       "1     Beethoven   1770   1827              Bonn   57\n",
       "2       Puccini   1858   1924           Lucques   66\n",
       "3  Shostakovich   1906   1975  Saint-Petersburg   69"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here, we create a derived age column:\n",
    "compo_pd['age'] = compo_pd['death']-compo_pd['birth']\n",
    "compo_pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5a46ab3",
   "metadata": {},
   "source": [
    "### Other functions\n",
    "Pandas offers apply methods to apply custom functions to dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "544b53e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For example, to define the the age >60 as 'old' and <60 as 'young'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "586f01bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, we create the function:\n",
    "def define_age(x):\n",
    "    if x>60:\n",
    "        return 'old'\n",
    "    else:\n",
    "        return 'young'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "008d9684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'young'"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here, we test it:\n",
    "define_age(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "0e507a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'old'"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And again\n",
    "define_age(70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2e25f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Good. Seems ok."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "8566a036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    young\n",
       "1    young\n",
       "2      old\n",
       "3      old\n",
       "Name: age, dtype: object"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can now apply to an entire Series.\n",
    "compo_pd.age.apply(define_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "53dee845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "      <th>age</th>\n",
       "      <th>age_def</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "      <td>51</td>\n",
       "      <td>young</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beethoven</td>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "      <td>57</td>\n",
       "      <td>young</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Puccini</td>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "      <td>66</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "      <td>69</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       composer  birth  death              city  age age_def\n",
       "0        Mahler   1860   1911           Kaliste   51   young\n",
       "1     Beethoven   1770   1827              Bonn   57   young\n",
       "2       Puccini   1858   1924           Lucques   66     old\n",
       "3  Shostakovich   1906   1975  Saint-Petersburg   69     old"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And again, we can directly add this output to a new column:\n",
    "compo_pd['age_def'] = compo_pd.age.apply(define_age)\n",
    "compo_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bc6c561",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can also apply a function to an entire DataFrame. \n",
    "# For example, how many composers have birth and death dates within the XIXth century:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "2d61ddfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here, we define the function:\n",
    "def nineteen_century_count(x):\n",
    "    return np.sum((x>=1800)&(x<1900))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "c5b955d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "birth    2\n",
       "death    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then we execute...\n",
    "compo_pd[['birth','death']].apply(nineteen_century_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ada803f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The function is applied column-wise and returns a number for each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "da30a199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   birth  death\n",
       "0   True  False\n",
       "1  False   True\n",
       "2   True  False\n",
       "3  False  False"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or, we can just directly return the boolean...\n",
    "# define function\n",
    "def nineteen_century_true(x):\n",
    "    return (x>=1800)&(x<1900)\n",
    "# execute\n",
    "compo_pd[['birth','death']].apply(nineteen_century_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c446820",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Like Numpys, dataframes can also be selected via logical indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "9dd0a39b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "Name: birth, dtype: bool"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So, if we use a logical comparison it yields a logical series\n",
    "compo_pd['birth'] > 1859"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33d3a2cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# And we can use this series directly to index a dataframe (like R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "639e84fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "Name: birth, dtype: bool"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, we create the index\n",
    "log_indexer = compo_pd['birth'] > 1859\n",
    "log_indexer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "9cc930b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "      <th>age</th>\n",
       "      <th>age_def</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "      <td>51</td>\n",
       "      <td>young</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "      <td>69</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       composer  birth  death              city  age age_def\n",
       "0        Mahler   1860   1911           Kaliste   51   young\n",
       "3  Shostakovich   1906   1975  Saint-Petersburg   69     old"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then we use in direclty in our data frame\n",
    "compo_pd[log_indexer]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70721422",
   "metadata": {},
   "outputs": [],
   "source": [
    "# And we can also create more complex logical indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "142af5bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First we test\n",
    "(compo_pd['birth'] > 1859)&(compo_pd['age']>60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10085e66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note the parentheses..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "06128450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "      <th>age</th>\n",
       "      <th>age_def</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "      <td>69</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       composer  birth  death              city  age age_def\n",
       "3  Shostakovich   1906   1975  Saint-Petersburg   69     old"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we use in our dataframe\n",
    "compo_pd[(compo_pd['birth'] > 1859)&(compo_pd['age']>60)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "de2d956d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "      <th>age</th>\n",
       "      <th>age_def</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "      <td>51</td>\n",
       "      <td>young</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "      <td>69</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       composer  birth  death              city  age age_def\n",
       "0        Mahler   1860   1911           Kaliste   51   young\n",
       "3  Shostakovich   1906   1975  Saint-Petersburg   69     old"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And we can create new arrays containing these subselections\n",
    "compos_sub = compo_pd[compo_pd['birth'] > 1859]\n",
    "compos_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "8b32daa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1817: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n"
     ]
    }
   ],
   "source": [
    "# We can then directly modify the new array\n",
    "compos_sub.loc[0,'birth'] = 3000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b46bb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note Pandas provides a warning referring to the set via copy issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "683de2ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "      <th>age</th>\n",
       "      <th>age_def</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>3000</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "      <td>51</td>\n",
       "      <td>young</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "      <td>69</td>\n",
       "      <td>old</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       composer  birth  death              city  age age_def\n",
       "0        Mahler   3000   1911           Kaliste   51   young\n",
       "3  Shostakovich   1906   1975  Saint-Petersburg   69     old"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We avoid this by using the copy() method, as with Numpys\n",
    "compos_sub2 = compo_pd[compo_pd['birth'] > 1859].copy()\n",
    "compos_sub2.loc[0,'birth'] = 3000\n",
    "compos_sub2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad40a772",
   "metadata": {},
   "source": [
    "## Combining information in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57936295",
   "metadata": {},
   "source": [
    "### Concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "351e9e85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can paste dataframes together via concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "0581d77b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth  death              city\n",
       "composer                                    \n",
       "Mahler         1860   1911           Kaliste\n",
       "Beethoven      1770   1827              Bonn\n",
       "Puccini        1858   1924           Lucques\n",
       "Shostakovich   1906   1975  Saint-Petersburg"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's create our first dataframe\n",
    "composers1 = pd.read_excel('Data/composers.xlsx', index_col='composer',sheet_name='Sheet1')\n",
    "composers1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "d698686c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Verdi</th>\n",
       "      <td>1813</td>\n",
       "      <td>1901</td>\n",
       "      <td>Roncole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dvorak</th>\n",
       "      <td>1841</td>\n",
       "      <td>1904</td>\n",
       "      <td>Nelahozeves</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schumann</th>\n",
       "      <td>1810</td>\n",
       "      <td>1856</td>\n",
       "      <td>Zwickau</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stravinsky</th>\n",
       "      <td>1882</td>\n",
       "      <td>1971</td>\n",
       "      <td>Oranienbaum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            birth  death         city\n",
       "composer                             \n",
       "Verdi        1813   1901      Roncole\n",
       "Dvorak       1841   1904  Nelahozeves\n",
       "Schumann     1810   1856      Zwickau\n",
       "Stravinsky   1882   1971  Oranienbaum\n",
       "Mahler       1860   1911      Kaliste"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And then now our second\n",
    "composers2 = pd.read_excel('Data/composers.xlsx', index_col='composer',sheet_name='Sheet3')\n",
    "composers2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "40d46d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>death</th>\n",
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       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Verdi</th>\n",
       "      <td>1813</td>\n",
       "      <td>1901</td>\n",
       "      <td>Roncole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dvorak</th>\n",
       "      <td>1841</td>\n",
       "      <td>1904</td>\n",
       "      <td>Nelahozeves</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schumann</th>\n",
       "      <td>1810</td>\n",
       "      <td>1856</td>\n",
       "      <td>Zwickau</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stravinsky</th>\n",
       "      <td>1882</td>\n",
       "      <td>1971</td>\n",
       "      <td>Oranienbaum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth  death              city\n",
       "composer                                    \n",
       "Mahler         1860   1911           Kaliste\n",
       "Beethoven      1770   1827              Bonn\n",
       "Puccini        1858   1924           Lucques\n",
       "Shostakovich   1906   1975  Saint-Petersburg\n",
       "Verdi          1813   1901           Roncole\n",
       "Dvorak         1841   1904       Nelahozeves\n",
       "Schumann       1810   1856           Zwickau\n",
       "Stravinsky     1882   1971       Oranienbaum\n",
       "Mahler         1860   1911           Kaliste"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We concatenate by providing the dataframes as a list to pd.concat\n",
    "all_composers = pd.concat([composers1,composers2])\n",
    "all_composers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23a12928",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If tables have overlapping info, we can use duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "81241ce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          False\n",
       "Beethoven       False\n",
       "Puccini         False\n",
       "Shostakovich    False\n",
       "Verdi           False\n",
       "Dvorak          False\n",
       "Schumann        False\n",
       "Stravinsky      False\n",
       "Mahler           True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, we get indices for duplicates\n",
    "all_composers.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "aabd7320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
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       "      <td>1813</td>\n",
       "      <td>1901</td>\n",
       "      <td>Roncole</td>\n",
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       "    <tr>\n",
       "      <th>Dvorak</th>\n",
       "      <td>1841</td>\n",
       "      <td>1904</td>\n",
       "      <td>Nelahozeves</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schumann</th>\n",
       "      <td>1810</td>\n",
       "      <td>1856</td>\n",
       "      <td>Zwickau</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stravinsky</th>\n",
       "      <td>1882</td>\n",
       "      <td>1971</td>\n",
       "      <td>Oranienbaum</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth  death              city\n",
       "composer                                    \n",
       "Mahler         1860   1911           Kaliste\n",
       "Beethoven      1770   1827              Bonn\n",
       "Puccini        1858   1924           Lucques\n",
       "Shostakovich   1906   1975  Saint-Petersburg\n",
       "Verdi          1813   1901           Roncole\n",
       "Dvorak         1841   1904       Nelahozeves\n",
       "Schumann       1810   1856           Zwickau\n",
       "Stravinsky     1882   1971       Oranienbaum"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then we apply directly to the data.frame -- inverted by ~\n",
    "all_composers[~all_composers.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edfd6a6b",
   "metadata": {},
   "source": [
    "### Joining two tables\n",
    "Other classical case is joining two dataframes via a common index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "66d50432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth  death              city\n",
       "composer                                    \n",
       "Mahler         1860   1911           Kaliste\n",
       "Beethoven      1770   1827              Bonn\n",
       "Puccini        1858   1924           Lucques\n",
       "Shostakovich   1906   1975  Saint-Petersburg"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's create our first dataframes\n",
    "composers1 = pd.read_excel('Data/composers.xlsx', index_col='composer',sheet_name='Sheet1')\n",
    "composers1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "ec8a0a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>Gustav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>Ludwig van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>Giacomo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brahms</th>\n",
       "      <td>Johannes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           first name\n",
       "composer             \n",
       "Mahler         Gustav\n",
       "Beethoven  Ludwig van\n",
       "Puccini       Giacomo\n",
       "Brahms       Johannes"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And then our second dataframe\n",
    "composers2 = pd.read_excel('Data/composers.xlsx', index_col='composer',sheet_name='Sheet4')\n",
    "composers2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "881cf7bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860.0</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>Kaliste</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770.0</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>Bonn</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>Lucques</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gustav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ludwig van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Giacomo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brahms</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johannes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               birth   death              city  first name\n",
       "composer                                                  \n",
       "Mahler        1860.0  1911.0           Kaliste         NaN\n",
       "Beethoven     1770.0  1827.0              Bonn         NaN\n",
       "Puccini       1858.0  1924.0           Lucques         NaN\n",
       "Shostakovich  1906.0  1975.0  Saint-Petersburg         NaN\n",
       "Mahler           NaN     NaN               NaN      Gustav\n",
       "Beethoven        NaN     NaN               NaN  Ludwig van\n",
       "Puccini          NaN     NaN               NaN     Giacomo\n",
       "Brahms           NaN     NaN               NaN    Johannes"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And now, what if we just use concatenation?\n",
    "pd.concat([composers1, composers2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1aee185b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note we have many missing values in the areas of overlapping index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "d6d17b01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "      <td>Gustav</td>\n",
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       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "      <td>Ludwig van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "      <td>Giacomo</td>\n",
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       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth  death              city  first name\n",
       "composer                                                \n",
       "Mahler         1860   1911           Kaliste      Gustav\n",
       "Beethoven      1770   1827              Bonn  Ludwig van\n",
       "Puccini        1858   1924           Lucques     Giacomo\n",
       "Shostakovich   1906   1975  Saint-Petersburg         NaN"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is resolved by using the join method; for example:\n",
    "composers1.join(composers2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "9236ab56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: Pandas automatically recognized and used the index column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e08a0af4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# However, also note that one of the columns (\"Brahms\") was dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33848ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is remedied by using either a left or right join"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2893b1e",
   "metadata": {},
   "source": [
    "### Join left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "0aaca142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
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       "      <th>Puccini</th>\n",
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       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth  death              city  first name\n",
       "composer                                                \n",
       "Mahler         1860   1911           Kaliste      Gustav\n",
       "Beethoven      1770   1827              Bonn  Ludwig van\n",
       "Puccini        1858   1924           Lucques     Giacomo\n",
       "Shostakovich   1906   1975  Saint-Petersburg         NaN"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here, we specify left joing using the \"how\" option\n",
    "composers1.join(composers2, how = 'left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c094212",
   "metadata": {},
   "outputs": [],
   "source": [
    "# But \"Brahms\" is omitted due to not being in the first/left table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61601ec3",
   "metadata": {},
   "source": [
    "### Join right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "5b79a71c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Bonn</td>\n",
       "      <td>Ludwig van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
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       "      <td>1924.0</td>\n",
       "      <td>Lucques</td>\n",
       "      <td>Giacomo</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johannes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            birth   death     city  first name\n",
       "composer                                      \n",
       "Mahler     1860.0  1911.0  Kaliste      Gustav\n",
       "Beethoven  1770.0  1827.0     Bonn  Ludwig van\n",
       "Puccini    1858.0  1924.0  Lucques     Giacomo\n",
       "Brahms        NaN     NaN      NaN    Johannes"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can try the opposite, by using the right indices...\n",
    "composers1.join(composers2, how = 'right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4601d58a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# But noww \"Shostakovich\" is dropped"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4552ee3",
   "metadata": {},
   "source": [
    "### Inner, outer joins specify we want the intersection or union joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "4593bc01",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "      <td>Gustav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "      <td>Ludwig van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "      <td>Giacomo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           birth  death     city  first name\n",
       "composer                                    \n",
       "Mahler      1860   1911  Kaliste      Gustav\n",
       "Beethoven   1770   1827     Bonn  Ludwig van\n",
       "Puccini     1858   1924  Lucques     Giacomo"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inner join specifies the intersection:\n",
    "composers1.join(composers2, how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "a4de8d73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>first name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770.0</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>Bonn</td>\n",
       "      <td>Ludwig van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brahms</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johannes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860.0</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>Kaliste</td>\n",
       "      <td>Gustav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>Lucques</td>\n",
       "      <td>Giacomo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               birth   death              city  first name\n",
       "composer                                                  \n",
       "Beethoven     1770.0  1827.0              Bonn  Ludwig van\n",
       "Brahms           NaN     NaN               NaN    Johannes\n",
       "Mahler        1860.0  1911.0           Kaliste      Gustav\n",
       "Puccini       1858.0  1924.0           Lucques     Giacomo\n",
       "Shostakovich  1906.0  1975.0  Saint-Petersburg         NaN"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outer join specifies the union:\n",
    "composers1.join(composers2, how = 'outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c736b134",
   "metadata": {},
   "source": [
    "### Joining on specified columns : merge\n",
    "Sometimes tables do not have common indices. Merge is used for such."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "5ec182d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, lets get our data:\n",
    "composers1 = pd.read_excel('Data/composers.xlsx', sheet_name='Sheet1')\n",
    "composers2 = pd.read_excel('Data/composers.xlsx', sheet_name='Sheet6')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "3c296830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beethoven</td>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Puccini</td>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       composer  birth  death              city\n",
       "0        Mahler   1860   1911           Kaliste\n",
       "1     Beethoven   1770   1827              Bonn\n",
       "2       Puccini   1858   1924           Lucques\n",
       "3  Shostakovich   1906   1975  Saint-Petersburg"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We check the first\n",
    "composers1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "0fc2c172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last name</th>\n",
       "      <th>first name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Puccini</td>\n",
       "      <td>Giacomo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beethoven</td>\n",
       "      <td>Ludwig van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brahms</td>\n",
       "      <td>Johannes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>Gustav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   last name  first name\n",
       "0    Puccini     Giacomo\n",
       "1  Beethoven  Ludwig van\n",
       "2     Brahms    Johannes\n",
       "3     Mahler      Gustav"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then we check the second\n",
    "composers2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b5cf5c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that indices do not match as neither do the column names 大変"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "58f1c041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "      <th>last name</th>\n",
       "      <th>first name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "      <td>Mahler</td>\n",
       "      <td>Gustav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beethoven</td>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "      <td>Beethoven</td>\n",
       "      <td>Ludwig van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Puccini</td>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "      <td>Puccini</td>\n",
       "      <td>Giacomo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    composer  birth  death     city  last name  first name\n",
       "0     Mahler   1860   1911  Kaliste     Mahler      Gustav\n",
       "1  Beethoven   1770   1827     Bonn  Beethoven  Ludwig van\n",
       "2    Puccini   1858   1924  Lucques    Puccini     Giacomo"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here, we can use merge to match the columns and select join type per\n",
    "pd.merge(composers1, composers2, left_on='composer', right_on='last name')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "a5626251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "      <th>last name</th>\n",
       "      <th>first name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>Kaliste</td>\n",
       "      <td>Mahler</td>\n",
       "      <td>Gustav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beethoven</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>Bonn</td>\n",
       "      <td>Beethoven</td>\n",
       "      <td>Ludwig van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Puccini</td>\n",
       "      <td>1858.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>Lucques</td>\n",
       "      <td>Puccini</td>\n",
       "      <td>Giacomo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>1906.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brahms</td>\n",
       "      <td>Johannes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       composer   birth   death              city  last name  first name\n",
       "0        Mahler  1860.0  1911.0           Kaliste     Mahler      Gustav\n",
       "1     Beethoven  1770.0  1827.0              Bonn  Beethoven  Ludwig van\n",
       "2       Puccini  1858.0  1924.0           Lucques    Puccini     Giacomo\n",
       "3  Shostakovich  1906.0  1975.0  Saint-Petersburg        NaN         NaN\n",
       "4           NaN     NaN     NaN               NaN     Brahms    Johannes"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or we can use alternatives to the default inner join; here, outer\n",
    "pd.merge(composers1, composers2, left_on='composer', right_on='last name',how = 'outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72712fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that outer join creates sparsity here; this is ok in some apps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "446a05fd",
   "metadata": {},
   "source": [
    "## Analyzing Pandas dataframes\n",
    "Split dataframes into groups (groupy) for category-based analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55fb2aec",
   "metadata": {},
   "source": [
    "### splitting data by groups\n",
    "One often needs to split data according to some categorical value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "f271c169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>period</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>1860</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>post-romantic</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beethoven</td>\n",
       "      <td>1770</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Puccini</td>\n",
       "      <td>1858</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>post-romantic</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>1906</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>modern</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Verdi</td>\n",
       "      <td>1813</td>\n",
       "      <td>1901.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       composer  birth   death         period  country\n",
       "0        Mahler   1860  1911.0  post-romantic  Austria\n",
       "1     Beethoven   1770  1827.0       romantic  Germany\n",
       "2       Puccini   1858  1924.0  post-romantic    Italy\n",
       "3  Shostakovich   1906  1975.0         modern   Russia\n",
       "4         Verdi   1813  1901.0       romantic    Italy"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, we load our data\n",
    "composers = pd.read_excel('Data/composers.xlsx', sheet_name='Sheet5')\n",
    "composers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90cf1c62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We now also add a column here to calculate the composers' age:\n",
    "composers['age'] = composers.death - composers.birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1598e0e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas has a groupby function that groups by a certain criteria; eg:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "fb8d6929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fc610e1cf40>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can group composers by period\n",
    "composer_grouped = composers.groupby('period')\n",
    "composer_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "dede2596",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'baroque': [14, 16, 17, 20, 21, 28, 29, 30, 31, 47], 'classic': [9, 10, 32, 40, 51], 'modern': [3, 7, 11, 12, 19, 25, 45, 46, 50, 53, 54, 55, 56], 'post-romantic': [0, 2, 8, 18, 49], 'renaissance': [13, 26, 27, 36, 37, 43, 44], 'romantic': [1, 4, 5, 6, 15, 22, 23, 24, 33, 34, 35, 38, 39, 41, 42, 48, 52]}"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What we have is a new group object added to the dataframe\n",
    "# Lets explore\n",
    "composer_grouped.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af14cf12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# What we find is list of indices and keys; doubt they'll be used ever"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "35eb30bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">birth</th>\n",
       "      <th colspan=\"8\" halign=\"left\">death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
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       "      <th>min</th>\n",
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       "      <th>50%</th>\n",
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       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>baroque</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1663.300000</td>\n",
       "      <td>36.009412</td>\n",
       "      <td>1587.0</td>\n",
       "      <td>1647.0</td>\n",
       "      <td>1676.5</td>\n",
       "      <td>1685.0</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1720.200000</td>\n",
       "      <td>43.460838</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>1697.25</td>\n",
       "      <td>1736.0</td>\n",
       "      <td>1755.25</td>\n",
       "      <td>1764.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classic</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1744.400000</td>\n",
       "      <td>12.054045</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>1732.0</td>\n",
       "      <td>1749.0</td>\n",
       "      <td>1754.0</td>\n",
       "      <td>1756.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1801.200000</td>\n",
       "      <td>6.942622</td>\n",
       "      <td>1791.0</td>\n",
       "      <td>1799.00</td>\n",
       "      <td>1801.0</td>\n",
       "      <td>1806.00</td>\n",
       "      <td>1809.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modern</th>\n",
       "      <td>13.0</td>\n",
       "      <td>1905.692308</td>\n",
       "      <td>28.595992</td>\n",
       "      <td>1854.0</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1974.090909</td>\n",
       "      <td>26.139834</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>1962.00</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1990.00</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>post-romantic</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1854.200000</td>\n",
       "      <td>17.123084</td>\n",
       "      <td>1824.0</td>\n",
       "      <td>1858.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>1864.0</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1927.400000</td>\n",
       "      <td>25.540164</td>\n",
       "      <td>1896.0</td>\n",
       "      <td>1911.00</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1949.00</td>\n",
       "      <td>1957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renaissance</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1527.142857</td>\n",
       "      <td>59.881629</td>\n",
       "      <td>1397.0</td>\n",
       "      <td>1528.5</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>1564.5</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1595.285714</td>\n",
       "      <td>56.295986</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>1594.00</td>\n",
       "      <td>1613.0</td>\n",
       "      <td>1624.50</td>\n",
       "      <td>1643.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>romantic</th>\n",
       "      <td>17.0</td>\n",
       "      <td>1824.823529</td>\n",
       "      <td>25.468695</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>1824.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>1867.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1883.588235</td>\n",
       "      <td>28.026904</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>1869.00</td>\n",
       "      <td>1887.0</td>\n",
       "      <td>1904.00</td>\n",
       "      <td>1919.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth                                                          \\\n",
       "              count         mean        std     min     25%     50%     75%   \n",
       "period                                                                        \n",
       "baroque        10.0  1663.300000  36.009412  1587.0  1647.0  1676.5  1685.0   \n",
       "classic         5.0  1744.400000  12.054045  1731.0  1732.0  1749.0  1754.0   \n",
       "modern         13.0  1905.692308  28.595992  1854.0  1891.0  1902.0  1918.0   \n",
       "post-romantic   5.0  1854.200000  17.123084  1824.0  1858.0  1860.0  1864.0   \n",
       "renaissance     7.0  1527.142857  59.881629  1397.0  1528.5  1540.0  1564.5   \n",
       "romantic       17.0  1824.823529  25.468695  1770.0  1810.0  1824.0  1841.0   \n",
       "\n",
       "                      death                                                   \\\n",
       "                  max count         mean        std     min      25%     50%   \n",
       "period                                                                         \n",
       "baroque        1710.0  10.0  1720.200000  43.460838  1640.0  1697.25  1736.0   \n",
       "classic        1756.0   5.0  1801.200000   6.942622  1791.0  1799.00  1801.0   \n",
       "modern         1971.0  11.0  1974.090909  26.139834  1928.0  1962.00  1982.0   \n",
       "post-romantic  1865.0   5.0  1927.400000  25.540164  1896.0  1911.00  1924.0   \n",
       "renaissance    1567.0   7.0  1595.285714  56.295986  1474.0  1594.00  1613.0   \n",
       "romantic       1867.0  17.0  1883.588235  28.026904  1827.0  1869.00  1887.0   \n",
       "\n",
       "                                \n",
       "                   75%     max  \n",
       "period                          \n",
       "baroque        1755.25  1764.0  \n",
       "classic        1806.00  1809.0  \n",
       "modern         1990.00  2016.0  \n",
       "post-romantic  1949.00  1957.0  \n",
       "renaissance    1624.50  1643.0  \n",
       "romantic       1904.00  1919.0  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# More interesting is application of the describe method on the groups\n",
    "composer_grouped.describe()#.loc['Austria','birth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98926663",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we now have group-wise statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "c3d8be47",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>9</th>\n",
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       "      <th>10</th>\n",
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       "      <td>classic</td>\n",
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       "      <td>classic</td>\n",
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       "      <th>40</th>\n",
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       "      <td>classic</td>\n",
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       "      <th>51</th>\n",
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       "      <td>classic</td>\n",
       "      <td>Czechia</td>\n",
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      ],
      "text/plain": [
       "    composer  birth   death   period  country\n",
       "9      Haydn   1732  1809.0  classic  Austria\n",
       "10    Mozart   1756  1791.0  classic  Austria\n",
       "32  Cimarosa   1749  1801.0  classic    Italy\n",
       "40     Soler   1754  1806.0  classic    Spain\n",
       "51     Dusek   1731  1799.0  classic  Czechia"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If we are interested in only a single group, we can use \"get_group\"\n",
    "composer_grouped.get_group('classic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "05cfb913",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    composer  birth   death   period  country\n",
       "9      Haydn   1732  1809.0  classic  Austria\n",
       "10    Mozart   1756  1791.0  classic  Austria\n",
       "32  Cimarosa   1749  1801.0  classic    Italy\n",
       "40     Soler   1754  1806.0  classic    Spain\n",
       "51     Dusek   1731  1799.0  classic  Czechia"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This returns a subgroup from the original table, equivalent to:\n",
    "composers[composers.period == 'classic']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6950588e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that the syntax above is identical to data.table usage in R"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc6dabdd",
   "metadata": {},
   "source": [
    "### Multi-level \n",
    "With multiple groups, we can also do a grouping on several levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "ca3eb211",
   "metadata": {},
   "outputs": [
    {
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       "      <th rowspan=\"4\" valign=\"top\">baroque</th>\n",
       "      <th>England</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1659.000000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1695.0</td>\n",
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       "      <td>1695.0</td>\n",
       "      <td>1695.00</td>\n",
       "      <td>1695.0</td>\n",
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       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1650.666667</td>\n",
       "      <td>29.263174</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>1634.50</td>\n",
       "      <td>1643.0</td>\n",
       "      <td>1663.00</td>\n",
       "      <td>1683.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1709.666667</td>\n",
       "      <td>51.733290</td>\n",
       "      <td>1661.0</td>\n",
       "      <td>1682.50</td>\n",
       "      <td>1704.0</td>\n",
       "      <td>1734.00</td>\n",
       "      <td>1764.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1685.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1685.0</td>\n",
       "      <td>1685.00</td>\n",
       "      <td>1685.0</td>\n",
       "      <td>1685.00</td>\n",
       "      <td>1685.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1754.500000</td>\n",
       "      <td>6.363961</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>1752.25</td>\n",
       "      <td>1754.5</td>\n",
       "      <td>1756.75</td>\n",
       "      <td>1759.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1663.000000</td>\n",
       "      <td>53.285395</td>\n",
       "      <td>1587.0</td>\n",
       "      <td>1649.25</td>\n",
       "      <td>1677.5</td>\n",
       "      <td>1691.25</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1717.250000</td>\n",
       "      <td>52.442826</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>1712.00</td>\n",
       "      <td>1736.0</td>\n",
       "      <td>1741.25</td>\n",
       "      <td>1757.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">classic</th>\n",
       "      <th>Austria</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1744.000000</td>\n",
       "      <td>16.970563</td>\n",
       "      <td>1732.0</td>\n",
       "      <td>1738.00</td>\n",
       "      <td>1744.0</td>\n",
       "      <td>1750.00</td>\n",
       "      <td>1756.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>12.727922</td>\n",
       "      <td>1791.0</td>\n",
       "      <td>1795.50</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1804.50</td>\n",
       "      <td>1809.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1731.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>1731.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>1731.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1799.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1799.0</td>\n",
       "      <td>1799.00</td>\n",
       "      <td>1799.0</td>\n",
       "      <td>1799.00</td>\n",
       "      <td>1799.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1749.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1749.0</td>\n",
       "      <td>1749.00</td>\n",
       "      <td>1749.0</td>\n",
       "      <td>1749.00</td>\n",
       "      <td>1749.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1801.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1801.0</td>\n",
       "      <td>1801.00</td>\n",
       "      <td>1801.0</td>\n",
       "      <td>1801.00</td>\n",
       "      <td>1801.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1754.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1754.0</td>\n",
       "      <td>1754.00</td>\n",
       "      <td>1754.0</td>\n",
       "      <td>1754.00</td>\n",
       "      <td>1754.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1806.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>1806.00</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>1806.00</td>\n",
       "      <td>1806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">modern</th>\n",
       "      <th>Austria</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1885.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>1885.00</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>1885.00</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1935.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>1935.00</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>1935.00</td>\n",
       "      <td>1935.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1854.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1854.0</td>\n",
       "      <td>1854.00</td>\n",
       "      <td>1854.0</td>\n",
       "      <td>1854.00</td>\n",
       "      <td>1854.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1928.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>1928.00</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>1928.00</td>\n",
       "      <td>1928.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1936.500000</td>\n",
       "      <td>48.790368</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>1919.25</td>\n",
       "      <td>1936.5</td>\n",
       "      <td>1953.75</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1983.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1983.00</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1983.00</td>\n",
       "      <td>1983.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1916.500000</td>\n",
       "      <td>12.020815</td>\n",
       "      <td>1908.0</td>\n",
       "      <td>1912.25</td>\n",
       "      <td>1916.5</td>\n",
       "      <td>1920.75</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>16.970563</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>1998.00</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2010.00</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1895.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1895.0</td>\n",
       "      <td>1895.00</td>\n",
       "      <td>1895.0</td>\n",
       "      <td>1895.00</td>\n",
       "      <td>1895.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1982.00</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1982.00</td>\n",
       "      <td>1982.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUssia</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1891.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>1891.00</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>1891.00</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1953.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1953.0</td>\n",
       "      <td>1953.00</td>\n",
       "      <td>1953.0</td>\n",
       "      <td>1953.00</td>\n",
       "      <td>1953.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1894.000000</td>\n",
       "      <td>16.970563</td>\n",
       "      <td>1882.0</td>\n",
       "      <td>1888.00</td>\n",
       "      <td>1894.0</td>\n",
       "      <td>1900.00</td>\n",
       "      <td>1906.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>2.828427</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>1972.00</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>1974.00</td>\n",
       "      <td>1975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1918.333333</td>\n",
       "      <td>18.502252</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1909.00</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>1927.50</td>\n",
       "      <td>1937.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1990.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>1990.00</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>1990.00</td>\n",
       "      <td>1990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">post-romantic</th>\n",
       "      <th>Austria</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1842.000000</td>\n",
       "      <td>25.455844</td>\n",
       "      <td>1824.0</td>\n",
       "      <td>1833.00</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>1851.00</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1903.500000</td>\n",
       "      <td>10.606602</td>\n",
       "      <td>1896.0</td>\n",
       "      <td>1899.75</td>\n",
       "      <td>1903.5</td>\n",
       "      <td>1907.25</td>\n",
       "      <td>1911.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1865.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>1865.00</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>1865.00</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1957.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>1957.00</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>1957.00</td>\n",
       "      <td>1957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1864.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1864.0</td>\n",
       "      <td>1864.00</td>\n",
       "      <td>1864.0</td>\n",
       "      <td>1864.00</td>\n",
       "      <td>1864.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1949.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>1949.00</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>1949.00</td>\n",
       "      <td>1949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1858.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1858.0</td>\n",
       "      <td>1858.00</td>\n",
       "      <td>1858.0</td>\n",
       "      <td>1858.00</td>\n",
       "      <td>1858.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1924.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1924.00</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>1924.00</td>\n",
       "      <td>1924.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">renaissance</th>\n",
       "      <th>Belgium</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1464.500000</td>\n",
       "      <td>95.459415</td>\n",
       "      <td>1397.0</td>\n",
       "      <td>1430.75</td>\n",
       "      <td>1464.5</td>\n",
       "      <td>1498.25</td>\n",
       "      <td>1532.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1534.000000</td>\n",
       "      <td>84.852814</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>1504.00</td>\n",
       "      <td>1534.0</td>\n",
       "      <td>1564.00</td>\n",
       "      <td>1594.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1551.500000</td>\n",
       "      <td>16.263456</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>1545.75</td>\n",
       "      <td>1551.5</td>\n",
       "      <td>1557.25</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1624.500000</td>\n",
       "      <td>2.121320</td>\n",
       "      <td>1623.0</td>\n",
       "      <td>1623.75</td>\n",
       "      <td>1624.5</td>\n",
       "      <td>1625.25</td>\n",
       "      <td>1626.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1552.666667</td>\n",
       "      <td>23.965253</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>1545.50</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>1566.50</td>\n",
       "      <td>1567.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1616.666667</td>\n",
       "      <td>24.704925</td>\n",
       "      <td>1594.0</td>\n",
       "      <td>1603.50</td>\n",
       "      <td>1613.0</td>\n",
       "      <td>1628.00</td>\n",
       "      <td>1643.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">romantic</th>\n",
       "      <th>Czechia</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1832.500000</td>\n",
       "      <td>12.020815</td>\n",
       "      <td>1824.0</td>\n",
       "      <td>1828.25</td>\n",
       "      <td>1832.5</td>\n",
       "      <td>1836.75</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1894.000000</td>\n",
       "      <td>14.142136</td>\n",
       "      <td>1884.0</td>\n",
       "      <td>1889.00</td>\n",
       "      <td>1894.0</td>\n",
       "      <td>1899.00</td>\n",
       "      <td>1904.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1821.000000</td>\n",
       "      <td>19.672316</td>\n",
       "      <td>1803.0</td>\n",
       "      <td>1810.50</td>\n",
       "      <td>1818.0</td>\n",
       "      <td>1830.00</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1891.333333</td>\n",
       "      <td>21.548395</td>\n",
       "      <td>1869.0</td>\n",
       "      <td>1881.00</td>\n",
       "      <td>1893.0</td>\n",
       "      <td>1902.50</td>\n",
       "      <td>1912.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1806.500000</td>\n",
       "      <td>26.388129</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>1811.5</td>\n",
       "      <td>1818.00</td>\n",
       "      <td>1833.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1865.750000</td>\n",
       "      <td>30.934070</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>1848.75</td>\n",
       "      <td>1869.5</td>\n",
       "      <td>1886.50</td>\n",
       "      <td>1897.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1817.250000</td>\n",
       "      <td>28.004464</td>\n",
       "      <td>1797.0</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>1807.0</td>\n",
       "      <td>1824.25</td>\n",
       "      <td>1858.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1875.750000</td>\n",
       "      <td>40.574006</td>\n",
       "      <td>1835.0</td>\n",
       "      <td>1844.75</td>\n",
       "      <td>1874.5</td>\n",
       "      <td>1905.50</td>\n",
       "      <td>1919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1836.000000</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>1833.0</td>\n",
       "      <td>1834.50</td>\n",
       "      <td>1836.0</td>\n",
       "      <td>1837.50</td>\n",
       "      <td>1839.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1884.000000</td>\n",
       "      <td>4.242641</td>\n",
       "      <td>1881.0</td>\n",
       "      <td>1882.50</td>\n",
       "      <td>1884.0</td>\n",
       "      <td>1885.50</td>\n",
       "      <td>1887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1863.500000</td>\n",
       "      <td>4.949747</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>1861.75</td>\n",
       "      <td>1863.5</td>\n",
       "      <td>1865.25</td>\n",
       "      <td>1867.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1912.500000</td>\n",
       "      <td>4.949747</td>\n",
       "      <td>1909.0</td>\n",
       "      <td>1910.75</td>\n",
       "      <td>1912.5</td>\n",
       "      <td>1914.25</td>\n",
       "      <td>1916.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      birth                                                   \\\n",
       "                      count         mean        std     min      25%     50%   \n",
       "period        country                                                          \n",
       "baroque       England   1.0  1659.000000        NaN  1659.0  1659.00  1659.0   \n",
       "              France    3.0  1650.666667  29.263174  1626.0  1634.50  1643.0   \n",
       "              Germany   2.0  1685.000000   0.000000  1685.0  1685.00  1685.0   \n",
       "              Italy     4.0  1663.000000  53.285395  1587.0  1649.25  1677.5   \n",
       "classic       Austria   2.0  1744.000000  16.970563  1732.0  1738.00  1744.0   \n",
       "              Czechia   1.0  1731.000000        NaN  1731.0  1731.00  1731.0   \n",
       "              Italy     1.0  1749.000000        NaN  1749.0  1749.00  1749.0   \n",
       "              Spain     1.0  1754.000000        NaN  1754.0  1754.00  1754.0   \n",
       "modern        Austria   1.0  1885.000000        NaN  1885.0  1885.00  1885.0   \n",
       "              Czechia   1.0  1854.000000        NaN  1854.0  1854.00  1854.0   \n",
       "              England   2.0  1936.500000  48.790368  1902.0  1919.25  1936.5   \n",
       "              France    2.0  1916.500000  12.020815  1908.0  1912.25  1916.5   \n",
       "              Germany   1.0  1895.000000        NaN  1895.0  1895.00  1895.0   \n",
       "              RUssia    1.0  1891.000000        NaN  1891.0  1891.00  1891.0   \n",
       "              Russia    2.0  1894.000000  16.970563  1882.0  1888.00  1894.0   \n",
       "              USA       3.0  1918.333333  18.502252  1900.0  1909.00  1918.0   \n",
       "post-romantic Austria   2.0  1842.000000  25.455844  1824.0  1833.00  1842.0   \n",
       "              Finland   1.0  1865.000000        NaN  1865.0  1865.00  1865.0   \n",
       "              Germany   1.0  1864.000000        NaN  1864.0  1864.00  1864.0   \n",
       "              Italy     1.0  1858.000000        NaN  1858.0  1858.00  1858.0   \n",
       "renaissance   Belgium   2.0  1464.500000  95.459415  1397.0  1430.75  1464.5   \n",
       "              England   2.0  1551.500000  16.263456  1540.0  1545.75  1551.5   \n",
       "              Italy     3.0  1552.666667  23.965253  1525.0  1545.50  1566.0   \n",
       "romantic      Czechia   2.0  1832.500000  12.020815  1824.0  1828.25  1832.5   \n",
       "              France    3.0  1821.000000  19.672316  1803.0  1810.50  1818.0   \n",
       "              Germany   4.0  1806.500000  26.388129  1770.0  1800.00  1811.5   \n",
       "              Italy     4.0  1817.250000  28.004464  1797.0  1800.00  1807.0   \n",
       "              Russia    2.0  1836.000000   4.242641  1833.0  1834.50  1836.0   \n",
       "              Spain     2.0  1863.500000   4.949747  1860.0  1861.75  1863.5   \n",
       "\n",
       "                                       death                                  \\\n",
       "                           75%     max count         mean        std     min   \n",
       "period        country                                                          \n",
       "baroque       England  1659.00  1659.0   1.0  1695.000000        NaN  1695.0   \n",
       "              France   1663.00  1683.0   3.0  1709.666667  51.733290  1661.0   \n",
       "              Germany  1685.00  1685.0   2.0  1754.500000   6.363961  1750.0   \n",
       "              Italy    1691.25  1710.0   4.0  1717.250000  52.442826  1640.0   \n",
       "classic       Austria  1750.00  1756.0   2.0  1800.000000  12.727922  1791.0   \n",
       "              Czechia  1731.00  1731.0   1.0  1799.000000        NaN  1799.0   \n",
       "              Italy    1749.00  1749.0   1.0  1801.000000        NaN  1801.0   \n",
       "              Spain    1754.00  1754.0   1.0  1806.000000        NaN  1806.0   \n",
       "modern        Austria  1885.00  1885.0   1.0  1935.000000        NaN  1935.0   \n",
       "              Czechia  1854.00  1854.0   1.0  1928.000000        NaN  1928.0   \n",
       "              England  1953.75  1971.0   1.0  1983.000000        NaN  1983.0   \n",
       "              France   1920.75  1925.0   2.0  2004.000000  16.970563  1992.0   \n",
       "              Germany  1895.00  1895.0   1.0  1982.000000        NaN  1982.0   \n",
       "              RUssia   1891.00  1891.0   1.0  1953.000000        NaN  1953.0   \n",
       "              Russia   1900.00  1906.0   2.0  1973.000000   2.828427  1971.0   \n",
       "              USA      1927.50  1937.0   2.0  1990.000000   0.000000  1990.0   \n",
       "post-romantic Austria  1851.00  1860.0   2.0  1903.500000  10.606602  1896.0   \n",
       "              Finland  1865.00  1865.0   1.0  1957.000000        NaN  1957.0   \n",
       "              Germany  1864.00  1864.0   1.0  1949.000000        NaN  1949.0   \n",
       "              Italy    1858.00  1858.0   1.0  1924.000000        NaN  1924.0   \n",
       "renaissance   Belgium  1498.25  1532.0   2.0  1534.000000  84.852814  1474.0   \n",
       "              England  1557.25  1563.0   2.0  1624.500000   2.121320  1623.0   \n",
       "              Italy    1566.50  1567.0   3.0  1616.666667  24.704925  1594.0   \n",
       "romantic      Czechia  1836.75  1841.0   2.0  1894.000000  14.142136  1884.0   \n",
       "              France   1830.00  1842.0   3.0  1891.333333  21.548395  1869.0   \n",
       "              Germany  1818.00  1833.0   4.0  1865.750000  30.934070  1827.0   \n",
       "              Italy    1824.25  1858.0   4.0  1875.750000  40.574006  1835.0   \n",
       "              Russia   1837.50  1839.0   2.0  1884.000000   4.242641  1881.0   \n",
       "              Spain    1865.25  1867.0   2.0  1912.500000   4.949747  1909.0   \n",
       "\n",
       "                                                         \n",
       "                           25%     50%      75%     max  \n",
       "period        country                                    \n",
       "baroque       England  1695.00  1695.0  1695.00  1695.0  \n",
       "              France   1682.50  1704.0  1734.00  1764.0  \n",
       "              Germany  1752.25  1754.5  1756.75  1759.0  \n",
       "              Italy    1712.00  1736.0  1741.25  1757.0  \n",
       "classic       Austria  1795.50  1800.0  1804.50  1809.0  \n",
       "              Czechia  1799.00  1799.0  1799.00  1799.0  \n",
       "              Italy    1801.00  1801.0  1801.00  1801.0  \n",
       "              Spain    1806.00  1806.0  1806.00  1806.0  \n",
       "modern        Austria  1935.00  1935.0  1935.00  1935.0  \n",
       "              Czechia  1928.00  1928.0  1928.00  1928.0  \n",
       "              England  1983.00  1983.0  1983.00  1983.0  \n",
       "              France   1998.00  2004.0  2010.00  2016.0  \n",
       "              Germany  1982.00  1982.0  1982.00  1982.0  \n",
       "              RUssia   1953.00  1953.0  1953.00  1953.0  \n",
       "              Russia   1972.00  1973.0  1974.00  1975.0  \n",
       "              USA      1990.00  1990.0  1990.00  1990.0  \n",
       "post-romantic Austria  1899.75  1903.5  1907.25  1911.0  \n",
       "              Finland  1957.00  1957.0  1957.00  1957.0  \n",
       "              Germany  1949.00  1949.0  1949.00  1949.0  \n",
       "              Italy    1924.00  1924.0  1924.00  1924.0  \n",
       "renaissance   Belgium  1504.00  1534.0  1564.00  1594.0  \n",
       "              England  1623.75  1624.5  1625.25  1626.0  \n",
       "              Italy    1603.50  1613.0  1628.00  1643.0  \n",
       "romantic      Czechia  1889.00  1894.0  1899.00  1904.0  \n",
       "              France   1881.00  1893.0  1902.50  1912.0  \n",
       "              Germany  1848.75  1869.5  1886.50  1897.0  \n",
       "              Italy    1844.75  1874.5  1905.50  1919.0  \n",
       "              Russia   1882.50  1884.0  1885.50  1887.0  \n",
       "              Spain    1910.75  1912.5  1914.25  1916.0  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For this, we just pass the two columns names to groupby() as a list\n",
    "composer_grouped = composers.groupby(['period','country'])\n",
    "composer_grouped.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "30942ac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>period</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Haendel</td>\n",
       "      <td>1685</td>\n",
       "      <td>1759.0</td>\n",
       "      <td>baroque</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Bach</td>\n",
       "      <td>1685</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>baroque</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   composer  birth   death   period  country\n",
       "14  Haendel   1685  1759.0  baroque  Germany\n",
       "47     Bach   1685  1750.0  baroque  Germany"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And we can do the same with get_group()\n",
    "composer_grouped.get_group(('baroque','Germany'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77ef8702",
   "metadata": {},
   "source": [
    "### Operations on groups\n",
    "Grouping allows us to do computations and plotting without loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "8a15ba90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">baroque</th>\n",
       "      <th>England</th>\n",
       "      <td>1659.000000</td>\n",
       "      <td>1695.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1650.666667</td>\n",
       "      <td>1709.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1685.000000</td>\n",
       "      <td>1754.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1663.000000</td>\n",
       "      <td>1717.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">classic</th>\n",
       "      <th>Austria</th>\n",
       "      <td>1744.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>1731.000000</td>\n",
       "      <td>1799.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1749.000000</td>\n",
       "      <td>1801.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1754.000000</td>\n",
       "      <td>1806.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">modern</th>\n",
       "      <th>Austria</th>\n",
       "      <td>1885.000000</td>\n",
       "      <td>1935.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>1854.000000</td>\n",
       "      <td>1928.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>1936.500000</td>\n",
       "      <td>1983.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1916.500000</td>\n",
       "      <td>2004.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1895.000000</td>\n",
       "      <td>1982.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUssia</th>\n",
       "      <td>1891.000000</td>\n",
       "      <td>1953.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>1894.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>1918.333333</td>\n",
       "      <td>1990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">post-romantic</th>\n",
       "      <th>Austria</th>\n",
       "      <td>1842.000000</td>\n",
       "      <td>1903.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>1865.000000</td>\n",
       "      <td>1957.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1864.000000</td>\n",
       "      <td>1949.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1858.000000</td>\n",
       "      <td>1924.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">renaissance</th>\n",
       "      <th>Belgium</th>\n",
       "      <td>1464.500000</td>\n",
       "      <td>1534.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>1551.500000</td>\n",
       "      <td>1624.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1552.666667</td>\n",
       "      <td>1616.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">romantic</th>\n",
       "      <th>Czechia</th>\n",
       "      <td>1832.500000</td>\n",
       "      <td>1894.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1821.000000</td>\n",
       "      <td>1891.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1806.500000</td>\n",
       "      <td>1865.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1817.250000</td>\n",
       "      <td>1875.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>1836.000000</td>\n",
       "      <td>1884.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1863.500000</td>\n",
       "      <td>1912.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             birth        death\n",
       "period        country                          \n",
       "baroque       England  1659.000000  1695.000000\n",
       "              France   1650.666667  1709.666667\n",
       "              Germany  1685.000000  1754.500000\n",
       "              Italy    1663.000000  1717.250000\n",
       "classic       Austria  1744.000000  1800.000000\n",
       "              Czechia  1731.000000  1799.000000\n",
       "              Italy    1749.000000  1801.000000\n",
       "              Spain    1754.000000  1806.000000\n",
       "modern        Austria  1885.000000  1935.000000\n",
       "              Czechia  1854.000000  1928.000000\n",
       "              England  1936.500000  1983.000000\n",
       "              France   1916.500000  2004.000000\n",
       "              Germany  1895.000000  1982.000000\n",
       "              RUssia   1891.000000  1953.000000\n",
       "              Russia   1894.000000  1973.000000\n",
       "              USA      1918.333333  1990.000000\n",
       "post-romantic Austria  1842.000000  1903.500000\n",
       "              Finland  1865.000000  1957.000000\n",
       "              Germany  1864.000000  1949.000000\n",
       "              Italy    1858.000000  1924.000000\n",
       "renaissance   Belgium  1464.500000  1534.000000\n",
       "              England  1551.500000  1624.500000\n",
       "              Italy    1552.666667  1616.666667\n",
       "romantic      Czechia  1832.500000  1894.000000\n",
       "              France   1821.000000  1891.333333\n",
       "              Germany  1806.500000  1865.750000\n",
       "              Italy    1817.250000  1875.750000\n",
       "              Russia   1836.000000  1884.000000\n",
       "              Spain    1863.500000  1912.500000"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example, we can apply most dataframe functions to groups; eg:\n",
    "composer_grouped.mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce4183ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here, we see that Pandas inteligently nests the outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "e7fcb70b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">baroque</th>\n",
       "      <th>England</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">classic</th>\n",
       "      <th>Austria</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">modern</th>\n",
       "      <th>Austria</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUssia</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">post-romantic</th>\n",
       "      <th>Austria</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">renaissance</th>\n",
       "      <th>Belgium</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">romantic</th>\n",
       "      <th>Czechia</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       composer  birth  death\n",
       "period        country                        \n",
       "baroque       England         1      1      1\n",
       "              France          3      3      3\n",
       "              Germany         2      2      2\n",
       "              Italy           4      4      4\n",
       "classic       Austria         2      2      2\n",
       "              Czechia         1      1      1\n",
       "              Italy           1      1      1\n",
       "              Spain           1      1      1\n",
       "modern        Austria         1      1      1\n",
       "              Czechia         1      1      1\n",
       "              England         2      2      1\n",
       "              France          2      2      2\n",
       "              Germany         1      1      1\n",
       "              RUssia          1      1      1\n",
       "              Russia          2      2      2\n",
       "              USA             3      3      2\n",
       "post-romantic Austria         2      2      2\n",
       "              Finland         1      1      1\n",
       "              Germany         1      1      1\n",
       "              Italy           1      1      1\n",
       "renaissance   Belgium         2      2      2\n",
       "              England         2      2      2\n",
       "              Italy           3      3      3\n",
       "romantic      Czechia         2      2      2\n",
       "              France          3      3      3\n",
       "              Germany         4      4      4\n",
       "              Italy           4      4      4\n",
       "              Russia          2      2      2\n",
       "              Spain           2      2      2"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And another example with counts\n",
    "composer_grouped.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3731b137",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Functions can also be applied on a group-wise basis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "3d336b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For example, lets do simple function to multiply max values * 3\n",
    "def mult(myseries):\n",
    "    return myseries.max() * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "fee5ed35",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>period</th>\n",
       "      <th>country</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">baroque</th>\n",
       "      <th>England</th>\n",
       "      <td>PurcellPurcellPurcell</td>\n",
       "      <td>4977</td>\n",
       "      <td>5085.0</td>\n",
       "      <td>baroquebaroquebaroque</td>\n",
       "      <td>EnglandEnglandEngland</td>\n",
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       "      <th>France</th>\n",
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       "      <th>Germany</th>\n",
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       "      <td>5055</td>\n",
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       "      <td>baroquebaroquebaroque</td>\n",
       "      <td>GermanyGermanyGermany</td>\n",
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       "      <th>Italy</th>\n",
       "      <td>ScarlattiScarlattiScarlatti</td>\n",
       "      <td>5130</td>\n",
       "      <td>5271.0</td>\n",
       "      <td>baroquebaroquebaroque</td>\n",
       "      <td>ItalyItalyItaly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">classic</th>\n",
       "      <th>Austria</th>\n",
       "      <td>MozartMozartMozart</td>\n",
       "      <td>5268</td>\n",
       "      <td>5427.0</td>\n",
       "      <td>classicclassicclassic</td>\n",
       "      <td>AustriaAustriaAustria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>DusekDusekDusek</td>\n",
       "      <td>5193</td>\n",
       "      <td>5397.0</td>\n",
       "      <td>classicclassicclassic</td>\n",
       "      <td>CzechiaCzechiaCzechia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>CimarosaCimarosaCimarosa</td>\n",
       "      <td>5247</td>\n",
       "      <td>5403.0</td>\n",
       "      <td>classicclassicclassic</td>\n",
       "      <td>ItalyItalyItaly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>SolerSolerSoler</td>\n",
       "      <td>5262</td>\n",
       "      <td>5418.0</td>\n",
       "      <td>classicclassicclassic</td>\n",
       "      <td>SpainSpainSpain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">modern</th>\n",
       "      <th>Austria</th>\n",
       "      <td>BergBergBerg</td>\n",
       "      <td>5655</td>\n",
       "      <td>5805.0</td>\n",
       "      <td>modernmodernmodern</td>\n",
       "      <td>AustriaAustriaAustria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>JanacekJanacekJanacek</td>\n",
       "      <td>5562</td>\n",
       "      <td>5784.0</td>\n",
       "      <td>modernmodernmodern</td>\n",
       "      <td>CzechiaCzechiaCzechia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>WaltonWaltonWalton</td>\n",
       "      <td>5913</td>\n",
       "      <td>5949.0</td>\n",
       "      <td>modernmodernmodern</td>\n",
       "      <td>EnglandEnglandEngland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>MessiaenMessiaenMessiaen</td>\n",
       "      <td>5775</td>\n",
       "      <td>6048.0</td>\n",
       "      <td>modernmodernmodern</td>\n",
       "      <td>FranceFranceFrance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>OrffOrffOrff</td>\n",
       "      <td>5685</td>\n",
       "      <td>5946.0</td>\n",
       "      <td>modernmodernmodern</td>\n",
       "      <td>GermanyGermanyGermany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUssia</th>\n",
       "      <td>ProkofievProkofievProkofiev</td>\n",
       "      <td>5673</td>\n",
       "      <td>5859.0</td>\n",
       "      <td>modernmodernmodern</td>\n",
       "      <td>RUssiaRUssiaRUssia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>StravinskyStravinskyStravinsky</td>\n",
       "      <td>5718</td>\n",
       "      <td>5925.0</td>\n",
       "      <td>modernmodernmodern</td>\n",
       "      <td>RussiaRussiaRussia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>GlassGlassGlass</td>\n",
       "      <td>5811</td>\n",
       "      <td>5970.0</td>\n",
       "      <td>modernmodernmodern</td>\n",
       "      <td>USAUSAUSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">post-romantic</th>\n",
       "      <th>Austria</th>\n",
       "      <td>MahlerMahlerMahler</td>\n",
       "      <td>5580</td>\n",
       "      <td>5733.0</td>\n",
       "      <td>post-romanticpost-romanticpost-romantic</td>\n",
       "      <td>AustriaAustriaAustria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>SibeliusSibeliusSibelius</td>\n",
       "      <td>5595</td>\n",
       "      <td>5871.0</td>\n",
       "      <td>post-romanticpost-romanticpost-romantic</td>\n",
       "      <td>FinlandFinlandFinland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>StraussStraussStrauss</td>\n",
       "      <td>5592</td>\n",
       "      <td>5847.0</td>\n",
       "      <td>post-romanticpost-romanticpost-romantic</td>\n",
       "      <td>GermanyGermanyGermany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>PucciniPucciniPuccini</td>\n",
       "      <td>5574</td>\n",
       "      <td>5772.0</td>\n",
       "      <td>post-romanticpost-romanticpost-romantic</td>\n",
       "      <td>ItalyItalyItaly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">renaissance</th>\n",
       "      <th>Belgium</th>\n",
       "      <td>LassusLassusLassus</td>\n",
       "      <td>4596</td>\n",
       "      <td>4782.0</td>\n",
       "      <td>renaissancerenaissancerenaissance</td>\n",
       "      <td>BelgiumBelgiumBelgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>DowlandDowlandDowland</td>\n",
       "      <td>4689</td>\n",
       "      <td>4878.0</td>\n",
       "      <td>renaissancerenaissancerenaissance</td>\n",
       "      <td>EnglandEnglandEngland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>PalestrinaPalestrinaPalestrina</td>\n",
       "      <td>4701</td>\n",
       "      <td>4929.0</td>\n",
       "      <td>renaissancerenaissancerenaissance</td>\n",
       "      <td>ItalyItalyItaly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">romantic</th>\n",
       "      <th>Czechia</th>\n",
       "      <td>SmetanaSmetanaSmetana</td>\n",
       "      <td>5523</td>\n",
       "      <td>5712.0</td>\n",
       "      <td>romanticromanticromantic</td>\n",
       "      <td>CzechiaCzechiaCzechia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>MassenetMassenetMassenet</td>\n",
       "      <td>5526</td>\n",
       "      <td>5736.0</td>\n",
       "      <td>romanticromanticromantic</td>\n",
       "      <td>FranceFranceFrance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>WagnerWagnerWagner</td>\n",
       "      <td>5499</td>\n",
       "      <td>5691.0</td>\n",
       "      <td>romanticromanticromantic</td>\n",
       "      <td>GermanyGermanyGermany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>VerdiVerdiVerdi</td>\n",
       "      <td>5574</td>\n",
       "      <td>5757.0</td>\n",
       "      <td>romanticromanticromantic</td>\n",
       "      <td>ItalyItalyItaly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>MussorsgskyMussorsgskyMussorsgsky</td>\n",
       "      <td>5517</td>\n",
       "      <td>5661.0</td>\n",
       "      <td>romanticromanticromantic</td>\n",
       "      <td>RussiaRussiaRussia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>GranadosGranadosGranados</td>\n",
       "      <td>5601</td>\n",
       "      <td>5748.0</td>\n",
       "      <td>romanticromanticromantic</td>\n",
       "      <td>SpainSpainSpain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                composer  birth   death  \\\n",
       "period        country                                                     \n",
       "baroque       England              PurcellPurcellPurcell   4977  5085.0   \n",
       "              France                  RameauRameauRameau   5049  5292.0   \n",
       "              Germany              HaendelHaendelHaendel   5055  5277.0   \n",
       "              Italy          ScarlattiScarlattiScarlatti   5130  5271.0   \n",
       "classic       Austria                 MozartMozartMozart   5268  5427.0   \n",
       "              Czechia                    DusekDusekDusek   5193  5397.0   \n",
       "              Italy             CimarosaCimarosaCimarosa   5247  5403.0   \n",
       "              Spain                      SolerSolerSoler   5262  5418.0   \n",
       "modern        Austria                       BergBergBerg   5655  5805.0   \n",
       "              Czechia              JanacekJanacekJanacek   5562  5784.0   \n",
       "              England                 WaltonWaltonWalton   5913  5949.0   \n",
       "              France            MessiaenMessiaenMessiaen   5775  6048.0   \n",
       "              Germany                       OrffOrffOrff   5685  5946.0   \n",
       "              RUssia         ProkofievProkofievProkofiev   5673  5859.0   \n",
       "              Russia      StravinskyStravinskyStravinsky   5718  5925.0   \n",
       "              USA                        GlassGlassGlass   5811  5970.0   \n",
       "post-romantic Austria                 MahlerMahlerMahler   5580  5733.0   \n",
       "              Finland           SibeliusSibeliusSibelius   5595  5871.0   \n",
       "              Germany              StraussStraussStrauss   5592  5847.0   \n",
       "              Italy                PucciniPucciniPuccini   5574  5772.0   \n",
       "renaissance   Belgium                 LassusLassusLassus   4596  4782.0   \n",
       "              England              DowlandDowlandDowland   4689  4878.0   \n",
       "              Italy       PalestrinaPalestrinaPalestrina   4701  4929.0   \n",
       "romantic      Czechia              SmetanaSmetanaSmetana   5523  5712.0   \n",
       "              France            MassenetMassenetMassenet   5526  5736.0   \n",
       "              Germany                 WagnerWagnerWagner   5499  5691.0   \n",
       "              Italy                      VerdiVerdiVerdi   5574  5757.0   \n",
       "              Russia   MussorsgskyMussorsgskyMussorsgsky   5517  5661.0   \n",
       "              Spain             GranadosGranadosGranados   5601  5748.0   \n",
       "\n",
       "                                                        period  \\\n",
       "period        country                                            \n",
       "baroque       England                    baroquebaroquebaroque   \n",
       "              France                     baroquebaroquebaroque   \n",
       "              Germany                    baroquebaroquebaroque   \n",
       "              Italy                      baroquebaroquebaroque   \n",
       "classic       Austria                    classicclassicclassic   \n",
       "              Czechia                    classicclassicclassic   \n",
       "              Italy                      classicclassicclassic   \n",
       "              Spain                      classicclassicclassic   \n",
       "modern        Austria                       modernmodernmodern   \n",
       "              Czechia                       modernmodernmodern   \n",
       "              England                       modernmodernmodern   \n",
       "              France                        modernmodernmodern   \n",
       "              Germany                       modernmodernmodern   \n",
       "              RUssia                        modernmodernmodern   \n",
       "              Russia                        modernmodernmodern   \n",
       "              USA                           modernmodernmodern   \n",
       "post-romantic Austria  post-romanticpost-romanticpost-romantic   \n",
       "              Finland  post-romanticpost-romanticpost-romantic   \n",
       "              Germany  post-romanticpost-romanticpost-romantic   \n",
       "              Italy    post-romanticpost-romanticpost-romantic   \n",
       "renaissance   Belgium        renaissancerenaissancerenaissance   \n",
       "              England        renaissancerenaissancerenaissance   \n",
       "              Italy          renaissancerenaissancerenaissance   \n",
       "romantic      Czechia                 romanticromanticromantic   \n",
       "              France                  romanticromanticromantic   \n",
       "              Germany                 romanticromanticromantic   \n",
       "              Italy                   romanticromanticromantic   \n",
       "              Russia                  romanticromanticromantic   \n",
       "              Spain                   romanticromanticromantic   \n",
       "\n",
       "                                     country  \n",
       "period        country                         \n",
       "baroque       England  EnglandEnglandEngland  \n",
       "              France      FranceFranceFrance  \n",
       "              Germany  GermanyGermanyGermany  \n",
       "              Italy          ItalyItalyItaly  \n",
       "classic       Austria  AustriaAustriaAustria  \n",
       "              Czechia  CzechiaCzechiaCzechia  \n",
       "              Italy          ItalyItalyItaly  \n",
       "              Spain          SpainSpainSpain  \n",
       "modern        Austria  AustriaAustriaAustria  \n",
       "              Czechia  CzechiaCzechiaCzechia  \n",
       "              England  EnglandEnglandEngland  \n",
       "              France      FranceFranceFrance  \n",
       "              Germany  GermanyGermanyGermany  \n",
       "              RUssia      RUssiaRUssiaRUssia  \n",
       "              Russia      RussiaRussiaRussia  \n",
       "              USA                  USAUSAUSA  \n",
       "post-romantic Austria  AustriaAustriaAustria  \n",
       "              Finland  FinlandFinlandFinland  \n",
       "              Germany  GermanyGermanyGermany  \n",
       "              Italy          ItalyItalyItaly  \n",
       "renaissance   Belgium  BelgiumBelgiumBelgium  \n",
       "              England  EnglandEnglandEngland  \n",
       "              Italy          ItalyItalyItaly  \n",
       "romantic      Czechia  CzechiaCzechiaCzechia  \n",
       "              France      FranceFranceFrance  \n",
       "              Germany  GermanyGermanyGermany  \n",
       "              Italy          ItalyItalyItaly  \n",
       "              Russia      RussiaRussiaRussia  \n",
       "              Spain          SpainSpainSpain  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And lets look at the results\n",
    "composer_grouped.apply(mult)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e0db99",
   "metadata": {},
   "source": [
    "## Reshaping dataframes\n",
    "We often want to reshape data to make more useable, eg, in plotting\n",
    "Here, we look at a few use case and the methods provided by Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4489988e",
   "metadata": {},
   "source": [
    "### re-indexing, unstacking\n",
    "One common use case of reshaping is making one row one observating.\n",
    "In cases of groups with multiple indices, each is recasts as a column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "92a9f27d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">baroque</th>\n",
       "      <th>England</th>\n",
       "      <td>1659.000000</td>\n",
       "      <td>1695.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1650.666667</td>\n",
       "      <td>1709.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1685.000000</td>\n",
       "      <td>1754.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1663.000000</td>\n",
       "      <td>1717.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">classic</th>\n",
       "      <th>Austria</th>\n",
       "      <td>1744.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>1731.000000</td>\n",
       "      <td>1799.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1749.000000</td>\n",
       "      <td>1801.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1754.000000</td>\n",
       "      <td>1806.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">modern</th>\n",
       "      <th>Austria</th>\n",
       "      <td>1885.000000</td>\n",
       "      <td>1935.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>1854.000000</td>\n",
       "      <td>1928.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       birth        death\n",
       "period  country                          \n",
       "baroque England  1659.000000  1695.000000\n",
       "        France   1650.666667  1709.666667\n",
       "        Germany  1685.000000  1754.500000\n",
       "        Italy    1663.000000  1717.250000\n",
       "classic Austria  1744.000000  1800.000000\n",
       "        Czechia  1731.000000  1799.000000\n",
       "        Italy    1749.000000  1801.000000\n",
       "        Spain    1754.000000  1806.000000\n",
       "modern  Austria  1885.000000  1935.000000\n",
       "        Czechia  1854.000000  1928.000000"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example, lets look at grouped means\n",
    "composer_grouped = composers.groupby(['period','country']).mean()\n",
    "composer_grouped.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "c72d2686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>country</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>baroque</td>\n",
       "      <td>England</td>\n",
       "      <td>1659.000000</td>\n",
       "      <td>1695.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>baroque</td>\n",
       "      <td>France</td>\n",
       "      <td>1650.666667</td>\n",
       "      <td>1709.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>baroque</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1685.000000</td>\n",
       "      <td>1754.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>baroque</td>\n",
       "      <td>Italy</td>\n",
       "      <td>1663.000000</td>\n",
       "      <td>1717.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>classic</td>\n",
       "      <td>Austria</td>\n",
       "      <td>1744.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    period  country        birth        death\n",
       "0  baroque  England  1659.000000  1695.000000\n",
       "1  baroque   France  1650.666667  1709.666667\n",
       "2  baroque  Germany  1685.000000  1754.500000\n",
       "3  baroque    Italy  1663.000000  1717.250000\n",
       "4  classic  Austria  1744.000000  1800.000000"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And now lets call the method reset_index()\n",
    "composer_grouped.reset_index().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f9f7558",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Once can be more specific and limit the number of indices reset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "214673c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>baroque</th>\n",
       "      <td>England</td>\n",
       "      <td>1659.000000</td>\n",
       "      <td>1695.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baroque</th>\n",
       "      <td>France</td>\n",
       "      <td>1650.666667</td>\n",
       "      <td>1709.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baroque</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1685.000000</td>\n",
       "      <td>1754.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baroque</th>\n",
       "      <td>Italy</td>\n",
       "      <td>1663.000000</td>\n",
       "      <td>1717.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classic</th>\n",
       "      <td>Austria</td>\n",
       "      <td>1744.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country        birth        death\n",
       "period                                    \n",
       "baroque  England  1659.000000  1695.000000\n",
       "baroque   France  1650.666667  1709.666667\n",
       "baroque  Germany  1685.000000  1754.500000\n",
       "baroque    Italy  1663.000000  1717.250000\n",
       "classic  Austria  1744.000000  1800.000000"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here, we do this by specifying a level one index\n",
    "composer_grouped.reset_index(level=1).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feaa7157",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note, prior example created a new index column; here we use level 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de380018",
   "metadata": {},
   "source": [
    "### unstacking\n",
    "Another way to move indices to columns is to pivot some to columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "6c046467",
   "metadata": {},
   "outputs": [
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       "      <th>baroque</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1659.0</td>\n",
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       "      <td>1744.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1731.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1749.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1799.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1801.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modern</th>\n",
       "      <td>1885.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1854.0</td>\n",
       "      <td>1936.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1916.500000</td>\n",
       "      <td>1895.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>1894.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>1982.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1953.0</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>post-romantic</th>\n",
       "      <td>1842.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1864.0</td>\n",
       "      <td>1858.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1949.00</td>\n",
       "      <td>1924.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renaissance</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1464.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1551.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1552.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1624.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1616.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>romantic</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1832.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1821.000000</td>\n",
       "      <td>1806.5</td>\n",
       "      <td>1817.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1836.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1894.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1891.333333</td>\n",
       "      <td>1865.75</td>\n",
       "      <td>1875.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1884.0</td>\n",
       "      <td>1912.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                birth                                                       \\\n",
       "country       Austria Belgium Czechia England Finland       France Germany   \n",
       "period                                                                       \n",
       "baroque           NaN     NaN     NaN  1659.0     NaN  1650.666667  1685.0   \n",
       "classic        1744.0     NaN  1731.0     NaN     NaN          NaN     NaN   \n",
       "modern         1885.0     NaN  1854.0  1936.5     NaN  1916.500000  1895.0   \n",
       "post-romantic  1842.0     NaN     NaN     NaN  1865.0          NaN  1864.0   \n",
       "renaissance       NaN  1464.5     NaN  1551.5     NaN          NaN     NaN   \n",
       "romantic          NaN     NaN  1832.5     NaN     NaN  1821.000000  1806.5   \n",
       "\n",
       "                                            ...   death                  \\\n",
       "country              Italy  RUssia  Russia  ... Czechia England Finland   \n",
       "period                                      ...                           \n",
       "baroque        1663.000000     NaN     NaN  ...     NaN  1695.0     NaN   \n",
       "classic        1749.000000     NaN     NaN  ...  1799.0     NaN     NaN   \n",
       "modern                 NaN  1891.0  1894.0  ...  1928.0  1983.0     NaN   \n",
       "post-romantic  1858.000000     NaN     NaN  ...     NaN     NaN  1957.0   \n",
       "renaissance    1552.666667     NaN     NaN  ...     NaN  1624.5     NaN   \n",
       "romantic       1817.250000     NaN  1836.0  ...  1894.0     NaN     NaN   \n",
       "\n",
       "                                                                          \\\n",
       "country             France  Germany        Italy  RUssia  Russia   Spain   \n",
       "period                                                                     \n",
       "baroque        1709.666667  1754.50  1717.250000     NaN     NaN     NaN   \n",
       "classic                NaN      NaN  1801.000000     NaN     NaN  1806.0   \n",
       "modern         2004.000000  1982.00          NaN  1953.0  1973.0     NaN   \n",
       "post-romantic          NaN  1949.00  1924.000000     NaN     NaN     NaN   \n",
       "renaissance            NaN      NaN  1616.666667     NaN     NaN     NaN   \n",
       "romantic       1891.333333  1865.75  1875.750000     NaN  1884.0  1912.5   \n",
       "\n",
       "                       \n",
       "country           USA  \n",
       "period                 \n",
       "baroque           NaN  \n",
       "classic           NaN  \n",
       "modern         1990.0  \n",
       "post-romantic     NaN  \n",
       "renaissance       NaN  \n",
       "romantic          NaN  \n",
       "\n",
       "[6 rows x 24 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For example,\n",
    "composer_grouped.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bd2f404",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This creates a multi-level column indexing, unstacking the grouping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c278fba4",
   "metadata": {},
   "source": [
    "### Wide to long: melt\n",
    "Used to consolodate date-types indexed by categorical values.\n",
    "For that, we need to specify id_vars (categories) and value_vars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "4617cbf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>period</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>1860</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>post-romantic</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beethoven</td>\n",
       "      <td>1770</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Puccini</td>\n",
       "      <td>1858</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>post-romantic</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>1906</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>modern</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Verdi</td>\n",
       "      <td>1813</td>\n",
       "      <td>1901.0</td>\n",
       "      <td>romantic</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       composer  birth   death         period  country\n",
       "0        Mahler   1860  1911.0  post-romantic  Austria\n",
       "1     Beethoven   1770  1827.0       romantic  Germany\n",
       "2       Puccini   1858  1924.0  post-romantic    Italy\n",
       "3  Shostakovich   1906  1975.0         modern   Russia\n",
       "4         Verdi   1813  1901.0       romantic    Italy"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recall the composer data\n",
    "composers.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "1c91a3b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>birth</td>\n",
       "      <td>1860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beethoven</td>\n",
       "      <td>birth</td>\n",
       "      <td>1770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Puccini</td>\n",
       "      <td>birth</td>\n",
       "      <td>1858.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>birth</td>\n",
       "      <td>1906.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Verdi</td>\n",
       "      <td>birth</td>\n",
       "      <td>1813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Smetana</td>\n",
       "      <td>death</td>\n",
       "      <td>1884.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Janacek</td>\n",
       "      <td>death</td>\n",
       "      <td>1928.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Copland</td>\n",
       "      <td>death</td>\n",
       "      <td>1990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Bernstein</td>\n",
       "      <td>death</td>\n",
       "      <td>1990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Glass</td>\n",
       "      <td>death</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         composer variable   value\n",
       "0          Mahler    birth  1860.0\n",
       "1       Beethoven    birth  1770.0\n",
       "2         Puccini    birth  1858.0\n",
       "3    Shostakovich    birth  1906.0\n",
       "4           Verdi    birth  1813.0\n",
       "..            ...      ...     ...\n",
       "109       Smetana    death  1884.0\n",
       "110       Janacek    death  1928.0\n",
       "111       Copland    death  1990.0\n",
       "112     Bernstein    death  1990.0\n",
       "113         Glass    death     NaN\n",
       "\n",
       "[114 rows x 3 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's call the melt method, specifying id_vars and value_vars.\n",
    "pd.melt(composers, id_vars=['composer'], value_vars=['birth', 'death'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d06d0e2",
   "metadata": {},
   "source": [
    "## A real-world example\n",
    "Here, we are going to perform an analysis with all our Pandas tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf65c166",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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