Snippets _2020.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Snippets\n",
    "<img style=\"float:left;\" src=\"../images/snippet.jpg\" width=\"500\" height=\"500\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"Sections\"></a></p>\n",
    "\n",
    "\n",
    "- <a href=\"#basic\">Basic Help and Information</a><br>\n",
    "- <a href=\"#explore\">Exploratory Data Analysis</a><br>\n",
    "- <a href=\"#finance1\">Fetch Financial Data</a><br>\n",
    "- <a href = \"#bqlobject\"> BQL Objects</a>  \n",
    "- <a href = \"#plot\"> Plotting</a>  <br> <br>\n",
    "- <a href=\"#finance5\">Bloomberg Data using pdblp</a><br>\n",
    "- <a href=\"#os\">Directory and Files</a><br>\n",
    "- <a href=\"#cleaning\">Data Cleaning</a><br>\n",
    "- <a href=\"#embed\">Embedding Objects</a><br>\n",
    "- <a href=\"#misc\">Miscellaneous</a><br>\n",
    "- <a href=\"#end\">End</a><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"basic\"></a></p>\n",
    "\n",
    "## Basic Help and Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function len in module builtins:\n",
      "\n",
      "len(obj, /)\n",
      "    Return the number of items in a container.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(len)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "filestring = 'titanic2.csv'\n",
    "titanic = pd.read_csv(filestring, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m\n",
       "\u001b[0mtitanic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mhow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'left'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mlsuffix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mrsuffix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "Join columns of another DataFrame.\n",
       "\n",
       "Join columns with `other` DataFrame either on index or on a key\n",
       "column. Efficiently join multiple DataFrame objects by index at once by\n",
       "passing a list.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "other : DataFrame, Series, or list of DataFrame\n",
       "    Index should be similar to one of the columns in this one. If a\n",
       "    Series is passed, its name attribute must be set, and that will be\n",
       "    used as the column name in the resulting joined DataFrame.\n",
       "on : str, list of str, or array-like, optional\n",
       "    Column or index level name(s) in the caller to join on the index\n",
       "    in `other`, otherwise joins index-on-index. If multiple\n",
       "    values given, the `other` DataFrame must have a MultiIndex. Can\n",
       "    pass an array as the join key if it is not already contained in\n",
       "    the calling DataFrame. Like an Excel VLOOKUP operation.\n",
       "how : {'left', 'right', 'outer', 'inner'}, default 'left'\n",
       "    How to handle the operation of the two objects.\n",
       "\n",
       "    * left: use calling frame's index (or column if on is specified)\n",
       "    * right: use `other`'s index.\n",
       "    * outer: form union of calling frame's index (or column if on is\n",
       "      specified) with `other`'s index, and sort it.\n",
       "      lexicographically.\n",
       "    * inner: form intersection of calling frame's index (or column if\n",
       "      on is specified) with `other`'s index, preserving the order\n",
       "      of the calling's one.\n",
       "lsuffix : str, default ''\n",
       "    Suffix to use from left frame's overlapping columns.\n",
       "rsuffix : str, default ''\n",
       "    Suffix to use from right frame's overlapping columns.\n",
       "sort : bool, default False\n",
       "    Order result DataFrame lexicographically by the join key. If False,\n",
       "    the order of the join key depends on the join type (how keyword).\n",
       "\n",
       "Returns\n",
       "-------\n",
       "DataFrame\n",
       "    A dataframe containing columns from both the caller and `other`.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "DataFrame.merge : For column(s)-on-columns(s) operations.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "Parameters `on`, `lsuffix`, and `rsuffix` are not supported when\n",
       "passing a list of `DataFrame` objects.\n",
       "\n",
       "Support for specifying index levels as the `on` parameter was added\n",
       "in version 0.23.0.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],\n",
       "...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})\n",
       "\n",
       ">>> df\n",
       "  key   A\n",
       "0  K0  A0\n",
       "1  K1  A1\n",
       "2  K2  A2\n",
       "3  K3  A3\n",
       "4  K4  A4\n",
       "5  K5  A5\n",
       "\n",
       ">>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],\n",
       "...                       'B': ['B0', 'B1', 'B2']})\n",
       "\n",
       ">>> other\n",
       "  key   B\n",
       "0  K0  B0\n",
       "1  K1  B1\n",
       "2  K2  B2\n",
       "\n",
       "Join DataFrames using their indexes.\n",
       "\n",
       ">>> df.join(other, lsuffix='_caller', rsuffix='_other')\n",
       "  key_caller   A key_other    B\n",
       "0         K0  A0        K0   B0\n",
       "1         K1  A1        K1   B1\n",
       "2         K2  A2        K2   B2\n",
       "3         K3  A3       NaN  NaN\n",
       "4         K4  A4       NaN  NaN\n",
       "5         K5  A5       NaN  NaN\n",
       "\n",
       "If we want to join using the key columns, we need to set key to be\n",
       "the index in both `df` and `other`. The joined DataFrame will have\n",
       "key as its index.\n",
       "\n",
       ">>> df.set_index('key').join(other.set_index('key'))\n",
       "      A    B\n",
       "key\n",
       "K0   A0   B0\n",
       "K1   A1   B1\n",
       "K2   A2   B2\n",
       "K3   A3  NaN\n",
       "K4   A4  NaN\n",
       "K5   A5  NaN\n",
       "\n",
       "Another option to join using the key columns is to use the `on`\n",
       "parameter. DataFrame.join always uses `other`'s index but we can use\n",
       "any column in `df`. This method preserves the original DataFrame's\n",
       "index in the result.\n",
       "\n",
       ">>> df.join(other.set_index('key'), on='key')\n",
       "  key   A    B\n",
       "0  K0  A0   B0\n",
       "1  K1  A1   B1\n",
       "2  K2  A2   B2\n",
       "3  K3  A3  NaN\n",
       "4  K4  A4  NaN\n",
       "5  K5  A5  NaN\n",
       "\u001b[1;31mFile:\u001b[0m      c:\\blp\\bquant\\environments\\bqnt-2.192.0\\lib\\site-packages\\pandas\\core\\frame.py\n",
       "\u001b[1;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#help(titanic.join)\n",
    "#dir(pd)\n",
    "?titanic.join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#len? # help on function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = [1,2,3]\n",
    "#help(L.sort)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__imul__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__reversed__',\n",
       " '__rmul__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " 'append',\n",
       " 'clear',\n",
       " 'copy',\n",
       " 'count',\n",
       " 'extend',\n",
       " 'index',\n",
       " 'insert',\n",
       " 'pop',\n",
       " 'remove',\n",
       " 'reverse',\n",
       " 'sort']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(L)   # list all the methods and attributes for the object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__doc__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " 'acos',\n",
       " 'acosh',\n",
       " 'asin',\n",
       " 'asinh',\n",
       " 'atan',\n",
       " 'atan2',\n",
       " 'atanh',\n",
       " 'ceil',\n",
       " 'copysign',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'degrees',\n",
       " 'e',\n",
       " 'erf',\n",
       " 'erfc',\n",
       " 'exp',\n",
       " 'expm1',\n",
       " 'fabs',\n",
       " 'factorial',\n",
       " 'floor',\n",
       " 'fmod',\n",
       " 'frexp',\n",
       " 'fsum',\n",
       " 'gamma',\n",
       " 'gcd',\n",
       " 'hypot',\n",
       " 'inf',\n",
       " 'isclose',\n",
       " 'isfinite',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'ldexp',\n",
       " 'lgamma',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'modf',\n",
       " 'nan',\n",
       " 'pi',\n",
       " 'pow',\n",
       " 'radians',\n",
       " 'remainder',\n",
       " 'sin',\n",
       " 'sinh',\n",
       " 'sqrt',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tau',\n",
       " 'trunc']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(math)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function factorial in module math:\n",
      "\n",
      "factorial(x, /)\n",
      "    Find x!.\n",
      "    \n",
      "    Raise a ValueError if x is negative or non-integral.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(math.factorial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfactorial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m/\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "Find x!.\n",
       "\n",
       "Raise a ValueError if x is negative or non-integral.\n",
       "\u001b[1;31mType:\u001b[0m      builtin_function_or_method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#same as above\n",
    "math.factorial?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfactorial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m/\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "Find x!.\n",
       "\n",
       "Raise a ValueError if x is negative or non-integral.\n",
       "\u001b[1;31mType:\u001b[0m      builtin_function_or_method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?math.factorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "cell": {
        "!": "OSMagics",
        "HTML": "Other",
        "SVG": "Other",
        "bash": "Other",
        "capture": "ExecutionMagics",
        "cmd": "Other",
        "debug": "ExecutionMagics",
        "file": "Other",
        "html": "DisplayMagics",
        "javascript": "DisplayMagics",
        "js": "DisplayMagics",
        "latex": "DisplayMagics",
        "markdown": "DisplayMagics",
        "perl": "Other",
        "prun": "ExecutionMagics",
        "pypy": "Other",
        "python": "Other",
        "python2": "Other",
        "python3": "Other",
        "ruby": "Other",
        "script": "ScriptMagics",
        "sh": "Other",
        "svg": "DisplayMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "writefile": "OSMagics"
       },
       "line": {
        "alias": "OSMagics",
        "alias_magic": "BasicMagics",
        "autoawait": "AsyncMagics",
        "autocall": "AutoMagics",
        "automagic": "AutoMagics",
        "autosave": "KernelMagics",
        "bookmark": "OSMagics",
        "cd": "OSMagics",
        "clear": "KernelMagics",
        "cls": "KernelMagics",
        "colors": "BasicMagics",
        "conda": "PackagingMagics",
        "config": "ConfigMagics",
        "connect_info": "KernelMagics",
        "copy": "Other",
        "ddir": "Other",
        "debug": "ExecutionMagics",
        "dhist": "OSMagics",
        "dirs": "OSMagics",
        "doctest_mode": "BasicMagics",
        "echo": "Other",
        "ed": "Other",
        "edit": "KernelMagics",
        "env": "OSMagics",
        "gui": "BasicMagics",
        "hist": "Other",
        "history": "HistoryMagics",
        "install": "Other",
        "killbgscripts": "ScriptMagics",
        "ldir": "Other",
        "less": "KernelMagics",
        "load": "CodeMagics",
        "load_ext": "ExtensionMagics",
        "loadpy": "CodeMagics",
        "logoff": "LoggingMagics",
        "logon": "LoggingMagics",
        "logstart": "LoggingMagics",
        "logstate": "LoggingMagics",
        "logstop": "LoggingMagics",
        "ls": "Other",
        "lsmagic": "BasicMagics",
        "macro": "ExecutionMagics",
        "magic": "BasicMagics",
        "matplotlib": "PylabMagics",
        "mkdir": "Other",
        "more": "KernelMagics",
        "notebook": "BasicMagics",
        "page": "BasicMagics",
        "pastebin": "CodeMagics",
        "pdb": "ExecutionMagics",
        "pdef": "NamespaceMagics",
        "pdoc": "NamespaceMagics",
        "pfile": "NamespaceMagics",
        "pinfo": "NamespaceMagics",
        "pinfo2": "NamespaceMagics",
        "pip": "PackagingMagics",
        "popd": "OSMagics",
        "pprint": "BasicMagics",
        "precision": "BasicMagics",
        "prun": "ExecutionMagics",
        "psearch": "NamespaceMagics",
        "psource": "NamespaceMagics",
        "pushd": "OSMagics",
        "pwd": "OSMagics",
        "pycat": "OSMagics",
        "pylab": "PylabMagics",
        "qtconsole": "KernelMagics",
        "quickref": "BasicMagics",
        "recall": "HistoryMagics",
        "rehashx": "OSMagics",
        "reload_ext": "ExtensionMagics",
        "ren": "Other",
        "rep": "Other",
        "rerun": "HistoryMagics",
        "reset": "NamespaceMagics",
        "reset_selective": "NamespaceMagics",
        "rmdir": "Other",
        "run": "ExecutionMagics",
        "save": "CodeMagics",
        "sc": "OSMagics",
        "set_env": "OSMagics",
        "store": "StoreMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "tb": "ExecutionMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "unalias": "OSMagics",
        "unload_ext": "ExtensionMagics",
        "who": "NamespaceMagics",
        "who_ls": "NamespaceMagics",
        "whos": "NamespaceMagics",
        "xdel": "NamespaceMagics",
        "xmode": "BasicMagics"
       }
      },
      "text/plain": [
       "Available line magics:\n",
       "%alias  %alias_magic  %autoawait  %autocall  %automagic  %autosave  %bookmark  %cd  %clear  %cls  %colors  %conda  %config  %connect_info  %copy  %ddir  %debug  %dhist  %dirs  %doctest_mode  %echo  %ed  %edit  %env  %gui  %hist  %history  %install  %killbgscripts  %ldir  %less  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %macro  %magic  %matplotlib  %mkdir  %more  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pip  %popd  %pprint  %precision  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %ren  %rep  %rerun  %reset  %reset_selective  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
       "\n",
       "Available cell magics:\n",
       "%%!  %%HTML  %%SVG  %%bash  %%capture  %%cmd  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
       "\n",
       "Automagic is ON, % prefix IS NOT needed for line magics."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%lsmagic   #list of magic commands"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "!pip install https://github.com/ipython-contrib/jupyter_contrib_nbextensions/tarball/master\n",
    "!pip install jupyter_nbextensions_configurator\n",
    "!jupyter contrib nbextension install --user\n",
    "!jupyter nbextensions_configurator enable --user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\"><a href=\"#Sections\">Return to Top ↑</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Replace spaces in column headings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.columns = df.columns.str.replace(' ', '_')\n",
    "#ufo.columns = ufo.columns.str.lower().str.replace(' ', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"explore\"></a></p>\n",
    "\n",
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "#titanic = sns.load_dataset('titanic')\n",
    "titanic = pd.read_csv('titanic2.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class display_(object):\n",
    "    \"\"\"Display HTML representation of multiple objects\"\"\"\n",
    "    template = \"\"\"<div style=\"float: left; padding: 10px;\">\n",
    "    <p style='font-family:\"Courier New\", Courier, monospace'>{0}</p>{1}\n",
    "    </div>\"\"\"\n",
    "    def __init__(self, *args):\n",
    "        self.args = args\n",
    "        \n",
    "    def _repr_html_(self):\n",
    "        return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n",
    "                         for a in self.args)\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n",
    "                           for a in self.args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>titanic</p><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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>McMahon, Mr. Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370372</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Madsen, Mr. Fridtjof Arne</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C 17369</td>\n",
       "      <td>7.1417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Miss. Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>F E69</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ekstrom, Mr. Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347061</td>\n",
       "      <td>6.9750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Drazenoic, Mr. Jozef</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349241</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Coelho, Mr. Domingos Fernandeo</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101307</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>133</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Robins, Mrs. Alexander A (Grace Charity Laury)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 3337</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>134</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Weisz, Mrs. Leopold (Mathilde Francoise Pede)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>228414</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Sobey, Mr. Samuel James Hayden</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 29178</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Richard, Mr. Emile</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2133</td>\n",
       "      <td>15.0458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Newsom, Miss. Helen Monypeny</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11752</td>\n",
       "      <td>26.2833</td>\n",
       "      <td>D47</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mr. Jacques Heath</td>\n",
       "      <td>male</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Osen, Mr. Olaf Elon</td>\n",
       "      <td>male</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.2167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Giglio, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17593</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>B86</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Boulos, Mrs. Joseph (Sultana)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2678</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>142</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Nysten, Miss. Anna Sofia</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347081</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hakkarainen, Mrs. Pekka Pietari (Elin Matilda ...</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101279</td>\n",
       "      <td>15.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Burke, Mr. Jeremiah</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365222</td>\n",
       "      <td>6.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Andrew, Mr. Edgardo Samuel</td>\n",
       "      <td>male</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>231945</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Nicholls, Mr. Joseph Charles</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>C.A. 33112</td>\n",
       "      <td>36.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. August Edvard (\"Wennerstrom\")</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350043</td>\n",
       "      <td>7.7958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ford, Miss. Robina Maggie \"Ruby\"</td>\n",
       "      <td>female</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6608</td>\n",
       "      <td>34.3750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Navratil, Mr. Michel (\"Louis M Hoffman\")</td>\n",
       "      <td>male</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>230080</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>F2</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Byles, Rev. Thomas Roussel Davids</td>\n",
       "      <td>male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244310</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Bateman, Rev. Robert James</td>\n",
       "      <td>male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.O.P. 1166</td>\n",
       "      <td>12.5250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113776</td>\n",
       "      <td>66.6000</td>\n",
       "      <td>C2</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Meo, Mr. Alfonzo</td>\n",
       "      <td>male</td>\n",
       "      <td>55.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 11206</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Billiard, Mr. Austin Blyler</td>\n",
       "      <td>male</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>A/5. 851</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fa 265302</td>\n",
       "      <td>7.3125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Williams, Mr. Charles Duane</td>\n",
       "      <td>male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17597</td>\n",
       "      <td>61.3792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 12 columns</p>\n",
       "</div>\n",
       "    </div>"
      ],
      "text/plain": [
       "titanic\n",
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "151          152         1       1   \n",
       "152          153         0       3   \n",
       "153          154         0       3   \n",
       "154          155         0       3   \n",
       "155          156         0       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "151                  Pears, Mrs. Thomas (Edith Wearne)  female  22.0      1   \n",
       "152                                   Meo, Mr. Alfonzo    male  55.5      0   \n",
       "153                    van Billiard, Mr. Austin Blyler    male  40.5      0   \n",
       "154                              Olsen, Mr. Ole Martin    male   NaN      0   \n",
       "155                        Williams, Mr. Charles Duane    male  51.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "151      0            113776  66.6000    C2        S  \n",
       "152      0        A.5. 11206   8.0500   NaN        S  \n",
       "153      2          A/5. 851  14.5000   NaN        S  \n",
       "154      0         Fa 265302   7.3125   NaN        S  \n",
       "155      1          PC 17597  61.3792   NaN        C  \n",
       "\n",
       "[156 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display_('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Useful methods  \n",
    "dataframe level: `.info()`, `.describe()` , `.dtypes` </br>\n",
    "column level: `.value_counts()`, `.plot(kind='hist')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>31</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Salkjelsvik, Miss. Anna Kristine</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>349909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78.500000</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>2.423077</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.141508</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.397436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.109587</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45.177428</td>\n",
       "      <td>0.477275</td>\n",
       "      <td>0.795459</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.613880</td>\n",
       "      <td>1.056235</td>\n",
       "      <td>0.870146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.401047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.003150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>117.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.371850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>156.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId    Survived      Pclass                              Name  \\\n",
       "count    156.000000  156.000000  156.000000                               156   \n",
       "unique          NaN         NaN         NaN                               156   \n",
       "top             NaN         NaN         NaN  Salkjelsvik, Miss. Anna Kristine   \n",
       "freq            NaN         NaN         NaN                                 1   \n",
       "mean      78.500000    0.346154    2.423077                               NaN   \n",
       "std       45.177428    0.477275    0.795459                               NaN   \n",
       "min        1.000000    0.000000    1.000000                               NaN   \n",
       "25%       39.750000    0.000000    2.000000                               NaN   \n",
       "50%       78.500000    0.000000    3.000000                               NaN   \n",
       "75%      117.250000    1.000000    3.000000                               NaN   \n",
       "max      156.000000    1.000000    3.000000                               NaN   \n",
       "\n",
       "         Sex         Age       SibSp       Parch  Ticket        Fare Cabin  \\\n",
       "count    156  126.000000  156.000000  156.000000     156  156.000000    31   \n",
       "unique     2         NaN         NaN         NaN     145         NaN    28   \n",
       "top     male         NaN         NaN         NaN  349909         NaN   D26   \n",
       "freq     100         NaN         NaN         NaN       2         NaN     2   \n",
       "mean     NaN   28.141508    0.615385    0.397436     NaN   28.109587   NaN   \n",
       "std      NaN   14.613880    1.056235    0.870146     NaN   39.401047   NaN   \n",
       "min      NaN    0.830000    0.000000    0.000000     NaN    6.750000   NaN   \n",
       "25%      NaN   19.000000    0.000000    0.000000     NaN    8.003150   NaN   \n",
       "50%      NaN   26.000000    0.000000    0.000000     NaN   14.454200   NaN   \n",
       "75%      NaN   35.000000    1.000000    0.000000     NaN   30.371850   NaN   \n",
       "max      NaN   71.000000    5.000000    5.000000     NaN  263.000000   NaN   \n",
       "\n",
       "       Embarked  \n",
       "count       155  \n",
       "unique        3  \n",
       "top           S  \n",
       "freq        110  \n",
       "mean        NaN  \n",
       "std         NaN  \n",
       "min         NaN  \n",
       "25%         NaN  \n",
       "50%         NaN  \n",
       "75%         NaN  \n",
       "max         NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "titanic.describe(include ='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    102\n",
       "1     54\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Survived.value_counts()\n",
    "# .sum, .smean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering and subsetting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Useful Methods </br>\n",
    "* `.str.contains(string, case=FALSE)`\n",
    "* `.filter()` : select columns\n",
    "* `.query()`  : filter based on column contents\n",
    "\n",
    "https://www.w3resource.com/pandas/dataframe/dataframe-filter.php </br>\n",
    "http://scrapingauthority.com/pandas-dataframe-filtering/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: `Dataframe.query()` method only works if the column name doesn’t have any empty spaces. So before applying the method, spaces in column names are replaced with ‘_’  \n",
    "\n",
    "`data.columns =[column.replace(\" \", \"_\") for column in data.columns] `\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\"><a href=\"#Sections\">Return to Top ↑</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"finance1\"></a></p>\n",
    "\n",
    "## Fetching financial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bql\n",
    "import pandas as pd\n",
    "\n",
    "bq = bql.Service()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bdh style data fetch\n",
    "from bql.util import get_time_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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></th>\n",
       "      <th>CURRENCY</th>\n",
       "      <th>PX_LAST</th>\n",
       "      <th>PX_OPEN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">QCOM US Equity</th>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>USD</td>\n",
       "      <td>78.30</td>\n",
       "      <td>73.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>USD</td>\n",
       "      <td>80.56</td>\n",
       "      <td>79.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>USD</td>\n",
       "      <td>78.56</td>\n",
       "      <td>80.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>USD</td>\n",
       "      <td>81.80</td>\n",
       "      <td>79.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>USD</td>\n",
       "      <td>79.17</td>\n",
       "      <td>79.460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          CURRENCY  PX_LAST  PX_OPEN\n",
       "ID             DATE                                 \n",
       "QCOM US Equity 2020-02-28      USD    78.30   73.760\n",
       "               2020-03-02      USD    80.56   79.015\n",
       "               2020-03-03      USD    78.56   80.820\n",
       "               2020-03-04      USD    81.80   79.120\n",
       "               2020-03-05      USD    79.17   79.460"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickers = ['AAPL US Equity', 'QCOM US Equity']\n",
    "fields = ['PX_LAST', 'PX_OPEN']\n",
    "start = '2020-01-02'\n",
    "end = '2020-03-05'\n",
    "\n",
    "data_ = get_time_series(tickers, fields, start, end)\n",
    "data_.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>ID</th>\n",
       "      <th>AAPL US Equity</th>\n",
       "      <th>QCOM US Equity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>300.35</td>\n",
       "      <td>88.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>297.43</td>\n",
       "      <td>87.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>299.80</td>\n",
       "      <td>86.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>298.39</td>\n",
       "      <td>88.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>303.19</td>\n",
       "      <td>88.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ID          AAPL US Equity  QCOM US Equity\n",
       "DATE                                      \n",
       "2020-01-02          300.35           88.69\n",
       "2020-01-03          297.43           87.02\n",
       "2020-01-06          299.80           86.51\n",
       "2020-01-07          298.39           88.97\n",
       "2020-01-08          303.19           88.71"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_['PX_LAST'].swaplevel()\n",
    "#newdf = data.pivot(columns='ID')\n",
    "data = data_.reset_index().pivot(index = 'DATE', columns ='ID',values ='PX_LAST')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\"><a href=\"#Sections\">Return to Top ↑</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"bqlobject\"></a></p>\n",
    "\n",
    "## BQL Response Object   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "request = \"\"\"\n",
    "get(PX_LAST, PX_VOLUME)\n",
    "for(['VIXY US Equity', 'IEV US Equity'])\n",
    "with(start=-5d,end=0d,fill='prev')\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = bq.execute(request)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['_Response__from_response',\n",
       " '_Response__sirs_by_name',\n",
       " '_SUPPORTED_BQLASVC_SCHEMA_MAJOR_VERSION',\n",
       " '_SUPPORTED_BQLSVC_SCHEMA_MAJOR_VERSION',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_raw',\n",
       " '_sirs',\n",
       " '_text',\n",
       " 'from_execute_response',\n",
       " 'from_fetch_response',\n",
       " 'get',\n",
       " 'single']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on Response in module bql.request.response object:\n",
      "\n",
      "class Response(builtins.object)\n",
      " |  Response(sirs, raw)\n",
      " |  \n",
      " |  Represents the response to a BQL query request.\n",
      " |  \n",
      " |  An instance of this class is returned by :meth:`Service.execute`\n",
      " |  as the result of the request execution. The response object contains one\n",
      " |  :class:`SingleItemResponse` instance for each item requested in the GET\n",
      " |  clause of the BQL query.\n",
      " |  \n",
      " |  Note\n",
      " |  ----\n",
      " |  Responses use the `sequence` protocol so it can be used like a list to\n",
      " |  obtain the responses for the individual items.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  To print the names of all single item responses obtained::\n",
      " |  \n",
      " |      >>> bq = bql.Service()\n",
      " |      >>> response = bq.execute(\"get(px_low,px_high)for(['IBM US Equity'])\")\n",
      " |      >>> for res in response:\n",
      " |      >>>     print res.name\n",
      " |      px_low\n",
      " |      px_high\n",
      " |  |\n",
      " |  \n",
      " |  To retrieve single-item responses by index:\n",
      " |  \n",
      " |      >>> print response[0].name\n",
      " |      px_low\n",
      " |      >>> print response[1].name\n",
      " |      px_high\n",
      " |      >>> len(response)\n",
      " |      2\n",
      " |  |\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __getitem__(self, index)\n",
      " |  \n",
      " |  __init__(self, sirs, raw)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __len__(self)\n",
      " |  \n",
      " |  get(self, name)\n",
      " |  \n",
      " |  single(self)\n",
      " |      Return the only item response in this response.\n",
      " |      \n",
      " |      Return a :class:`SingleItemResponse` instance that represents that\n",
      " |      response, or raise a :class:`UnexpectedResponseError` if the response\n",
      " |      has no item responses, or if it has more than one.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods defined here:\n",
      " |  \n",
      " |  from_execute_response(json_fragments, execution_context=None, raw=None) from builtins.type\n",
      " |  \n",
      " |  from_fetch_response(json_fragments, execution_context=None, raw=None) from builtins.type\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## the `.name` descriptor has the data items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PX_LAST\n",
      "PX_VOLUME\n"
     ]
    }
   ],
   "source": [
    "#list the 'names' (fields)\n",
    "for res in response:\n",
    "    print(res.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#newdf2 = pd.concat([x.df() for x in response], axis=1)\n",
    "#newdf2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See what's inside `response`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     DATE CURRENCY  PX_LAST\n",
      "ID                                         \n",
      "VIXY US Equity 2020-06-18      USD    29.76\n",
      "VIXY US Equity 2020-06-19      USD    30.24\n",
      "VIXY US Equity 2020-06-20      USD    30.24\n",
      "VIXY US Equity 2020-06-21      USD    30.24\n",
      "VIXY US Equity 2020-06-22      USD    28.75\n",
      "                     DATE  PX_VOLUME\n",
      "ID                                  \n",
      "VIXY US Equity 2020-06-18  3627981.0\n",
      "VIXY US Equity 2020-06-19  5663871.0\n",
      "VIXY US Equity 2020-06-20  5663871.0\n",
      "VIXY US Equity 2020-06-21  5663871.0\n",
      "VIXY US Equity 2020-06-22  3934775.0\n"
     ]
    }
   ],
   "source": [
    "for res in response:\n",
    "    print(res.df().head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### concatenate the `.name` columns together left on 'ID'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#display('response[0].df()','response[1].df()')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### method1 (lost the date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>PX_LAST</th>\n",
       "      <th>PX_VOLUME</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VIXY US Equity</th>\n",
       "      <td>29.76</td>\n",
       "      <td>3627981.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIXY US Equity</th>\n",
       "      <td>30.24</td>\n",
       "      <td>5663871.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIXY US Equity</th>\n",
       "      <td>30.24</td>\n",
       "      <td>5663871.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIXY US Equity</th>\n",
       "      <td>30.24</td>\n",
       "      <td>5663871.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIXY US Equity</th>\n",
       "      <td>28.75</td>\n",
       "      <td>3934775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIXY US Equity</th>\n",
       "      <td>28.75</td>\n",
       "      <td>68960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IEV US Equity</th>\n",
       "      <td>40.54</td>\n",
       "      <td>114184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IEV US Equity</th>\n",
       "      <td>40.39</td>\n",
       "      <td>176029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IEV US Equity</th>\n",
       "      <td>40.39</td>\n",
       "      <td>176029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IEV US Equity</th>\n",
       "      <td>40.39</td>\n",
       "      <td>176029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IEV US Equity</th>\n",
       "      <td>40.97</td>\n",
       "      <td>310347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IEV US Equity</th>\n",
       "      <td>40.97</td>\n",
       "      <td>310347.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                PX_LAST  PX_VOLUME\n",
       "ID                                \n",
       "VIXY US Equity    29.76  3627981.0\n",
       "VIXY US Equity    30.24  5663871.0\n",
       "VIXY US Equity    30.24  5663871.0\n",
       "VIXY US Equity    30.24  5663871.0\n",
       "VIXY US Equity    28.75  3934775.0\n",
       "VIXY US Equity    28.75    68960.0\n",
       "IEV US Equity     40.54   114184.0\n",
       "IEV US Equity     40.39   176029.0\n",
       "IEV US Equity     40.39   176029.0\n",
       "IEV US Equity     40.39   176029.0\n",
       "IEV US Equity     40.97   310347.0\n",
       "IEV US Equity     40.97   310347.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf5 = pd.DataFrame({r.name:r.df()[r.name] for r in response})\n",
    "newdf5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### method 2 (two dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>DATE</th>\n",
       "      <th>CURRENCY</th>\n",
       "      <th>PX_LAST</th>\n",
       "      <th>DATE</th>\n",
       "      <th>PX_VOLUME</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VIXY US Equity</th>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>USD</td>\n",
       "      <td>29.76</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>3627981.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIXY US Equity</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>USD</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>5663871.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIXY US Equity</th>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>USD</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>5663871.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIXY US Equity</th>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>USD</td>\n",
       "      <td>30.24</td>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>5663871.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIXY US Equity</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>USD</td>\n",
       "      <td>28.75</td>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>3934775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIXY US Equity</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>USD</td>\n",
       "      <td>28.75</td>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>68960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IEV US Equity</th>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>USD</td>\n",
       "      <td>40.54</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>114184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IEV US Equity</th>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>USD</td>\n",
       "      <td>40.39</td>\n",
       "      <td>2020-06-19</td>\n",
       "      <td>176029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IEV US Equity</th>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>USD</td>\n",
       "      <td>40.39</td>\n",
       "      <td>2020-06-20</td>\n",
       "      <td>176029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IEV US Equity</th>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>USD</td>\n",
       "      <td>40.39</td>\n",
       "      <td>2020-06-21</td>\n",
       "      <td>176029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IEV US Equity</th>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>USD</td>\n",
       "      <td>40.97</td>\n",
       "      <td>2020-06-22</td>\n",
       "      <td>310347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IEV US Equity</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>USD</td>\n",
       "      <td>40.97</td>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>310347.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DATE CURRENCY  PX_LAST       DATE  PX_VOLUME\n",
       "ID                                                               \n",
       "VIXY US Equity 2020-06-18      USD    29.76 2020-06-18  3627981.0\n",
       "VIXY US Equity 2020-06-19      USD    30.24 2020-06-19  5663871.0\n",
       "VIXY US Equity 2020-06-20      USD    30.24 2020-06-20  5663871.0\n",
       "VIXY US Equity 2020-06-21      USD    30.24 2020-06-21  5663871.0\n",
       "VIXY US Equity 2020-06-22      USD    28.75 2020-06-22  3934775.0\n",
       "VIXY US Equity 2020-06-23      USD    28.75 2020-06-23    68960.0\n",
       "IEV US Equity  2020-06-18      USD    40.54 2020-06-18   114184.0\n",
       "IEV US Equity  2020-06-19      USD    40.39 2020-06-19   176029.0\n",
       "IEV US Equity  2020-06-20      USD    40.39 2020-06-20   176029.0\n",
       "IEV US Equity  2020-06-21      USD    40.39 2020-06-21   176029.0\n",
       "IEV US Equity  2020-06-22      USD    40.97 2020-06-22   310347.0\n",
       "IEV US Equity  2020-06-23      USD    40.97 2020-06-23   310347.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf6 = pd.concat([x.df() for x in response], axis=1)\n",
    "newdf6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manual merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>ID</th>\n",
       "      <th>CURRENCY</th>\n",
       "      <th>PX_LAST</th>\n",
       "      <th>PX_VOLUME</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-06-18</th>\n",
       "      <td>VIXY US Equity</td>\n",
       "      <td>USD</td>\n",
       "      <td>29.76</td>\n",
       "      <td>3627981.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-19</th>\n",
       "      <td>VIXY US Equity</td>\n",
       "      <td>USD</td>\n",
       "      <td>30.24</td>\n",
       "      <td>5663871.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-20</th>\n",
       "      <td>VIXY US Equity</td>\n",
       "      <td>USD</td>\n",
       "      <td>30.24</td>\n",
       "      <td>5663871.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-21</th>\n",
       "      <td>VIXY US Equity</td>\n",
       "      <td>USD</td>\n",
       "      <td>30.24</td>\n",
       "      <td>5663871.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-22</th>\n",
       "      <td>VIXY US Equity</td>\n",
       "      <td>USD</td>\n",
       "      <td>28.75</td>\n",
       "      <td>3934775.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        ID CURRENCY  PX_LAST  PX_VOLUME\n",
       "DATE                                                   \n",
       "2020-06-18  VIXY US Equity      USD    29.76  3627981.0\n",
       "2020-06-19  VIXY US Equity      USD    30.24  5663871.0\n",
       "2020-06-20  VIXY US Equity      USD    30.24  5663871.0\n",
       "2020-06-21  VIXY US Equity      USD    30.24  5663871.0\n",
       "2020-06-22  VIXY US Equity      USD    28.75  3934775.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf7 = pd.merge(response[0].df().reset_index(), response[1].df().reset_index(), how = 'left', on=['ID','DATE'])\n",
    "newdf7.set_index('DATE').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\"><a href=\"#Sections\">Return to Top ↑</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Style and Formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#temp.sort_values('count', ascending=False).head(15).style.format(\"{:.2f}\")\n",
    "newdf8 = newdf7.set_index('DATE').head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"plot\"></a></p>\n",
    "\n",
    "# Plotting\n",
    "**Note :** `.plot()` on a `pd.Series` will plot the data against the index. On a `pd.DataFrame`, the `.plot()` method allows plotting of one column against another.\n",
    "\n",
    "By default, `.plot()` renders a line graph, but you can specify which type of plot you'd like - bar, line, histogram, area, scatter, etc..\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b52a401d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#easy way to plot close prices\n",
    "data[['AAPL US Equity']].plot(kind = 'line', figsize=(20,10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data2.plot.line('Date','Close')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x23dfaf79ac8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23dfc571b38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# verbose way to plot\n",
    "data3 =data[['AAPL US Equity']]  #standalone dataframe\n",
    "my_plot = data3.plot(figsize=(9, 7))\n",
    "my_plot.set_xlabel(\"Date\")\n",
    "my_plot.set_ylabel(\"Price\")\n",
    "my_plot.set_title('Line Chart of Close Prices')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\"><a href=\"#Sections\">Return to Top ↑</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"finance5\"></a></p>\n",
    "\n",
    "## Bloomberg Interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pdblp   #pandas wrapper to Bloomberg API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Ninjitsu On Fetched Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slice by Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ID</th>\n",
       "      <th>AAPL US Equity</th>\n",
       "      <th>QCOM US Equity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>299.80</td>\n",
       "      <td>86.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>298.39</td>\n",
       "      <td>88.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>303.19</td>\n",
       "      <td>88.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>309.63</td>\n",
       "      <td>89.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>310.33</td>\n",
       "      <td>90.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-13</th>\n",
       "      <td>316.96</td>\n",
       "      <td>90.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-14</th>\n",
       "      <td>312.68</td>\n",
       "      <td>90.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-15</th>\n",
       "      <td>311.34</td>\n",
       "      <td>89.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-16</th>\n",
       "      <td>315.24</td>\n",
       "      <td>91.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-17</th>\n",
       "      <td>318.73</td>\n",
       "      <td>95.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-21</th>\n",
       "      <td>316.57</td>\n",
       "      <td>94.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>317.70</td>\n",
       "      <td>92.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>319.23</td>\n",
       "      <td>92.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>318.31</td>\n",
       "      <td>89.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>308.95</td>\n",
       "      <td>87.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-28</th>\n",
       "      <td>317.69</td>\n",
       "      <td>89.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-29</th>\n",
       "      <td>324.34</td>\n",
       "      <td>88.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>323.87</td>\n",
       "      <td>87.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>309.51</td>\n",
       "      <td>85.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-03</th>\n",
       "      <td>308.66</td>\n",
       "      <td>86.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-04</th>\n",
       "      <td>318.85</td>\n",
       "      <td>88.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05</th>\n",
       "      <td>321.45</td>\n",
       "      <td>90.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-06</th>\n",
       "      <td>325.21</td>\n",
       "      <td>90.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <td>320.03</td>\n",
       "      <td>87.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>321.55</td>\n",
       "      <td>88.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11</th>\n",
       "      <td>319.61</td>\n",
       "      <td>90.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12</th>\n",
       "      <td>327.20</td>\n",
       "      <td>91.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13</th>\n",
       "      <td>324.87</td>\n",
       "      <td>90.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-14</th>\n",
       "      <td>324.95</td>\n",
       "      <td>89.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ID          AAPL US Equity  QCOM US Equity\n",
       "DATE                                      \n",
       "2020-01-06          299.80           86.51\n",
       "2020-01-07          298.39           88.97\n",
       "2020-01-08          303.19           88.71\n",
       "2020-01-09          309.63           89.91\n",
       "2020-01-10          310.33           90.26\n",
       "2020-01-13          316.96           90.97\n",
       "2020-01-14          312.68           90.56\n",
       "2020-01-15          311.34           89.67\n",
       "2020-01-16          315.24           91.79\n",
       "2020-01-17          318.73           95.91\n",
       "2020-01-21          316.57           94.54\n",
       "2020-01-22          317.70           92.97\n",
       "2020-01-23          319.23           92.00\n",
       "2020-01-24          318.31           89.65\n",
       "2020-01-27          308.95           87.05\n",
       "2020-01-28          317.69           89.15\n",
       "2020-01-29          324.34           88.23\n",
       "2020-01-30          323.87           87.78\n",
       "2020-01-31          309.51           85.31\n",
       "2020-02-03          308.66           86.48\n",
       "2020-02-04          318.85           88.93\n",
       "2020-02-05          321.45           90.91\n",
       "2020-02-06          325.21           90.61\n",
       "2020-02-07          320.03           87.42\n",
       "2020-02-10          321.55           88.60\n",
       "2020-02-11          319.61           90.00\n",
       "2020-02-12          327.20           91.72\n",
       "2020-02-13          324.87           90.03\n",
       "2020-02-14          324.95           89.60"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View all observations that occured in Feb 5 thru 15\n",
    "response.loc['1/5/2020':'2/15/2020']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ID</th>\n",
       "      <th>AAPL US Equity</th>\n",
       "      <th>QCOM US Equity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-03</th>\n",
       "      <td>308.66</td>\n",
       "      <td>86.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-04</th>\n",
       "      <td>318.85</td>\n",
       "      <td>88.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05</th>\n",
       "      <td>321.45</td>\n",
       "      <td>90.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-06</th>\n",
       "      <td>325.21</td>\n",
       "      <td>90.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <td>320.03</td>\n",
       "      <td>87.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>321.55</td>\n",
       "      <td>88.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11</th>\n",
       "      <td>319.61</td>\n",
       "      <td>90.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12</th>\n",
       "      <td>327.20</td>\n",
       "      <td>91.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13</th>\n",
       "      <td>324.87</td>\n",
       "      <td>90.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-14</th>\n",
       "      <td>324.95</td>\n",
       "      <td>89.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-18</th>\n",
       "      <td>319.00</td>\n",
       "      <td>88.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-19</th>\n",
       "      <td>323.62</td>\n",
       "      <td>90.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-20</th>\n",
       "      <td>320.30</td>\n",
       "      <td>89.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>313.05</td>\n",
       "      <td>87.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>298.18</td>\n",
       "      <td>83.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>288.08</td>\n",
       "      <td>80.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>292.65</td>\n",
       "      <td>79.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27</th>\n",
       "      <td>273.52</td>\n",
       "      <td>75.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>273.36</td>\n",
       "      <td>78.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ID          AAPL US Equity  QCOM US Equity\n",
       "DATE                                      \n",
       "2020-02-03          308.66           86.48\n",
       "2020-02-04          318.85           88.93\n",
       "2020-02-05          321.45           90.91\n",
       "2020-02-06          325.21           90.61\n",
       "2020-02-07          320.03           87.42\n",
       "2020-02-10          321.55           88.60\n",
       "2020-02-11          319.61           90.00\n",
       "2020-02-12          327.20           91.72\n",
       "2020-02-13          324.87           90.03\n",
       "2020-02-14          324.95           89.60\n",
       "2020-02-18          319.00           88.03\n",
       "2020-02-19          323.62           90.56\n",
       "2020-02-20          320.30           89.47\n",
       "2020-02-21          313.05           87.03\n",
       "2020-02-24          298.18           83.32\n",
       "2020-02-25          288.08           80.22\n",
       "2020-02-26          292.65           79.66\n",
       "2020-02-27          273.52           75.94\n",
       "2020-02-28          273.36           78.30"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View all observations that occured in all of Feb 2018\n",
    "response.loc['2/2020']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slice Data to just get PX_Last for each stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ID</th>\n",
       "      <th>AAPL US Equity</th>\n",
       "      <th>QCOM US Equity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>300.35</td>\n",
       "      <td>88.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>297.43</td>\n",
       "      <td>87.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>299.80</td>\n",
       "      <td>86.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>298.39</td>\n",
       "      <td>88.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>303.19</td>\n",
       "      <td>88.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ID          AAPL US Equity  QCOM US Equity\n",
       "DATE                                      \n",
       "2020-01-02          300.35           88.69\n",
       "2020-01-03          297.43           87.02\n",
       "2020-01-06          299.80           86.51\n",
       "2020-01-07          298.39           88.97\n",
       "2020-01-08          303.19           88.71"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort list (required for IndexSlice)\n",
    "response = response[sorted(list(response.columns))]\n",
    "response.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'slice'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-53-cbdd98414fe2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#slice just the PX_Last columns for all securities\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIndexSlice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mallcloses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'PX_LAST'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mallcloses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtail\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\blp\\BQuant\\environments\\bqnt-1.281.1\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1309\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1310\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1311\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\blp\\BQuant\\environments\\bqnt-1.281.1\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    793\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    794\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 795\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    796\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    797\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\blp\\BQuant\\environments\\bqnt-1.281.1\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    900\u001b[0m         \u001b[0;31m# we may have a nested tuples indexer here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_nested_tuple_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 902\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_nested_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    903\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    904\u001b[0m         \u001b[0;31m# we maybe be using a tuple to represent multiple dimensions here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\blp\\BQuant\\environments\\bqnt-1.281.1\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_nested_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0mcurrent_ndim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 977\u001b[0;31m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    978\u001b[0m             \u001b[0maxis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    979\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\blp\\BQuant\\environments\\bqnt-1.281.1\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1469\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot index with multidimensional key'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1470\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1471\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1473\u001b[0m             \u001b[0;31m# nested tuple slicing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\blp\\BQuant\\environments\\bqnt-1.281.1\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1033\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_validate_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1035\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1036\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1037\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\blp\\BQuant\\environments\\bqnt-1.281.1\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1390\u001b[0m             \u001b[0;31m# TODO: don't check the entire key unless necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1391\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer_for\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1393\u001b[0m                 raise KeyError(\"None of [%s] are in the [%s]\" %\n",
      "\u001b[0;32mC:\\blp\\BQuant\\environments\\bqnt-1.281.1\\lib\\site-packages\\pandas\\indexes\\base.py\u001b[0m in \u001b[0;36mget_indexer_for\u001b[0;34m(self, target, **kwargs)\u001b[0m\n\u001b[1;32m   2354\u001b[0m         \u001b[0;34m\"\"\" guaranteed return of an indexer even when non-unique \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2355\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2356\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2357\u001b[0m         \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2358\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\blp\\BQuant\\environments\\bqnt-1.281.1\\lib\\site-packages\\pandas\\indexes\\base.py\u001b[0m in \u001b[0;36mget_indexer\u001b[0;34m(self, target, method, limit, tolerance)\u001b[0m\n\u001b[1;32m   2254\u001b[0m                                  'backfill or nearest reindexing')\n\u001b[1;32m   2255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2256\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2258\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_ensure_platform_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_indexer (pandas\\index.c:6081)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas\\src\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.lookup (pandas\\hashtable.c:14045)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'slice'"
     ]
    }
   ],
   "source": [
    "#slice just the PX_Last columns for all securities\n",
    "idx = pd.IndexSlice\n",
    "allcloses = data_.loc[:,idx[:,'PX_LAST']]\n",
    "\n",
    "allcloses.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#chart close prices of AAPL\n",
    "%matplotlib inline    \n",
    "allcloses.plot()\n",
    "\n",
    "#my_plot = df.plot(kind='bar',legend=True)\n",
    "#my_plot.set_xlabel(\"\")\n",
    "#my_plot.set_ylabel(\"Score Value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###Alternative way \n",
    "\n",
    "#Use List Comprehension\n",
    "selected = [col for col in list(response.columns) if col[1] == 'PX_LAST']\n",
    "allclosesb = response[selected]\n",
    "allclosesb.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">CURRENCY</th>\n",
       "      <th colspan=\"2\" halign=\"left\">PX_LAST</th>\n",
       "      <th colspan=\"2\" halign=\"left\">PX_OPEN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th>AAPL US Equity</th>\n",
       "      <th>QCOM US Equity</th>\n",
       "      <th>AAPL US Equity</th>\n",
       "      <th>QCOM US Equity</th>\n",
       "      <th>AAPL US Equity</th>\n",
       "      <th>QCOM US Equity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>USD</td>\n",
       "      <td>USD</td>\n",
       "      <td>300.35</td>\n",
       "      <td>88.69</td>\n",
       "      <td>296.24</td>\n",
       "      <td>89.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>USD</td>\n",
       "      <td>USD</td>\n",
       "      <td>297.43</td>\n",
       "      <td>87.02</td>\n",
       "      <td>297.15</td>\n",
       "      <td>87.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>USD</td>\n",
       "      <td>USD</td>\n",
       "      <td>299.80</td>\n",
       "      <td>86.51</td>\n",
       "      <td>293.79</td>\n",
       "      <td>85.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>USD</td>\n",
       "      <td>USD</td>\n",
       "      <td>298.39</td>\n",
       "      <td>88.97</td>\n",
       "      <td>299.84</td>\n",
       "      <td>87.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>USD</td>\n",
       "      <td>USD</td>\n",
       "      <td>303.19</td>\n",
       "      <td>88.71</td>\n",
       "      <td>297.16</td>\n",
       "      <td>88.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 CURRENCY                       PX_LAST                 \\\n",
       "ID         AAPL US Equity QCOM US Equity AAPL US Equity QCOM US Equity   \n",
       "DATE                                                                     \n",
       "2020-01-02            USD            USD         300.35          88.69   \n",
       "2020-01-03            USD            USD         297.43          87.02   \n",
       "2020-01-06            USD            USD         299.80          86.51   \n",
       "2020-01-07            USD            USD         298.39          88.97   \n",
       "2020-01-08            USD            USD         303.19          88.71   \n",
       "\n",
       "                  PX_OPEN                 \n",
       "ID         AAPL US Equity QCOM US Equity  \n",
       "DATE                                      \n",
       "2020-01-02         296.24          89.05  \n",
       "2020-01-03         297.15          87.26  \n",
       "2020-01-06         293.79          85.91  \n",
       "2020-01-07         299.84          87.04  \n",
       "2020-01-08         297.16          88.90  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#temp = newdf2.reset_index().pivot(index = 'DATE', columns ='ID',values ='PX_LAST')\n",
    "data2 = data_.reset_index().pivot(index = 'DATE', columns = 'ID')\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### extract one Series (column) from DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "2020-02-28    78.30\n",
       "2020-03-02    80.56\n",
       "2020-03-03    78.56\n",
       "2020-03-04    81.80\n",
       "2020-03-05    79.17\n",
       "Name: (PX_LAST, QCOM US Equity), dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spycloses = data2[('PX_LAST', 'QCOM US Equity')]  \n",
    "spycloses.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23dfc2ea908>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23dfb045860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "spycloses.plot()   #chart it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(spycloses)  #confirm dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "spx_df = pd.DataFrame(spycloses) #confirm to dataframe with dates as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>PX_LAST</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>QCOM US Equity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>78.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>80.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>78.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>81.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>79.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  PX_LAST\n",
       "           QCOM US Equity\n",
       "DATE                     \n",
       "2020-02-28          78.30\n",
       "2020-03-02          80.56\n",
       "2020-03-03          78.56\n",
       "2020-03-04          81.80\n",
       "2020-03-05          79.17"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spx_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## File I/O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\MitchellDavid\\\\AppData\\\\Local\\\\bipy\\\\6316781\\\\projects\\\\0000000000000000000000000000000006316781\\\\Pure Python'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "import os\n",
    "\n",
    "%pwd\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = os.getcwd()\n",
    "#path = \"C:\\\\Users\\\\traveler\\\\Documents\\\\Python Scripts\"\n",
    "#os.chdir(path)\n",
    "filename = \"ETFbi.csv\"\n",
    "etf = pandas.read_csv(filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "etf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "etf.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\"><a href=\"#Sections\">Return to Top ↑</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"cleaning\"></a></p>\n",
    "\n",
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace unwanted cell entries with 'null'  (do as a lambda function on an 'unwanted' list?)\n",
    "tmp = etf.replace('#N/A Real Time', '').replace('#N/A Field Not Applicable','').replace('#VALUE','')\\\n",
    ".replace('N.A.','').fillna('')\n",
    "\n",
    "tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create two dataframes for 'goodata' and 'missing data'\n",
    "#missingdata= etf.loc[(tmp ==\"\").sum(axis=1) >0]\n",
    "#cleandata= etf.loc[(tmp ==\"\").sum(axis=1) ==0]\n",
    "\n",
    "#columns with null cells\n",
    "(tmp ==\"\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#explore which a column with data missing\n",
    "tmp[tmp['Ret YTD Pct']==\"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rows with missing data\n",
    "(tmp ==\"\").sum()\n",
    "#cleandata= etf.loc[(tmp ==\"\").sum(axis=1) ==0]\n",
    "\n",
    "#missingdata.shape\n",
    "#tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#explore which a column with data missing\n",
    "tmp.iloc[421]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "missingdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleandata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#manual alternative\n",
    "etf_b = etf.replace('#N/A Real Time', '')\n",
    "etf_b = etf.replace('#N/A Field Not Applicable','')\n",
    "etf_b = etf.replace('#VALUE','')\n",
    "etf_b = etf.replace('N.A.','')\n",
    "etf_b = etf.fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleandata.describe()   #count should be the same now for all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "etf.isnull().sum()  #nulls are note necessarily bad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#etf_b.isnull().sum()\n",
    "cleandata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "etf_b.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "etf_b.to_csv('ETFbi_clean.csv', index = False)   #save back to new filename\n",
    "missingdata.to_csv('ETFbi_missing.csv', index = False)   #save back to new filename\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'ETFbi_clean.csv'\n",
    "newdf = pandas.read_csv(filename)\n",
    "newdf.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agglist = ['mean', 'count']\n",
    "newdf.pivot_table(index ='Fd Obj', aggfunc=agglist)\n",
    "#newdf.pivot_table(values='Ret YTD Pct', index ='Fd Obj', aggfunc=agglist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"pivot\"></a></p>\n",
    "\n",
    "## Pivot Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "wd = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#wd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "titanic = sns.load_dataset('titanic')\n",
    "\n",
    "#path = \"C:\\\\Users\\\\traveler\\\\Documents\\\\Python Scripts\"\n",
    "#os.chdir(path)\n",
    "#filestring = 'titanic2.csv'\n",
    "#titanic = pd.read_csv(filestring, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27d737387b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#chart using plt.scatter\n",
    "plt.scatter(titanic['age'], titanic['fare'])\n",
    "\n",
    "plt.xlabel(\"age\")\n",
    "plt.ylabel('fare');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#chart using plt.plot\n",
    "plt.plot(titanic['age'], titanic['fare'], 'o')\n",
    "\n",
    "plt.xlabel(\"age\")\n",
    "plt.ylabel('fare');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "sns.set()\n",
    "#grid = sns.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n",
    "#grid.map(plt.hist, \"tip_pct\", bins=np.linspace(0, 40, 15));\n",
    "\n",
    "\n",
    "grid = sns.FacetGrid(titanic, row=\"sex\", col=\"survived\", margin_titles=True)\n",
    "grid.map(plt.hist, \"age\", bins=np.linspace(0, 80, 5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "titanic.columns = titanic.columns.str.lower()\n",
    "titanic.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use <font color='blue'>pivot_table()</font> to sort/aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "titanic.pivot_table('survived', index='sex', columns='pclass', aggfunc= 'max')\n",
    "#titanic.pivot_table('survived', index='sex', columns='pclass')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>Alternative</font> to 'pivot_table'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf=titanic.groupby(['sex','pclass'])[['survived']].mean()\n",
    "newdf=newdf.unstack()\n",
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is the same as...\n",
    "#newdf=pd.DataFrame(titanic.groupby(['sex','pclass']).mean()['age'])\n",
    "\n",
    "#newdf=titanic.groupby(['sex','pclass']).mean()[['age']]   #double bracket\n",
    "newdf=titanic.groupby(['sex','pclass'])[['age']].mean()   #double bracket\n",
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#type(titanic.groupby(['sex','pclass'])['survived'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#titanic.groupby(['sex','pclass'])['survived'].mean().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = pd.cut(titanic['age'], [0, 18, 80])\n",
    "titanic.pivot_table('survived', ['sex', age], 'pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#alternative using groupby\n",
    "newdf=titanic.groupby(['sex', age, 'pclass'])[['survived']].mean()\n",
    "newdf=newdf.unstack()\n",
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fare = pd.qcut(titanic['fare'], 2)\n",
    "titanic.pivot_table('survived', ['sex', age], [fare, 'pclass'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Additional pivot table options\n",
    "\n",
    "The full call signature of the ``pivot_table`` method of ``DataFrame``s is as follows:\n",
    "\n",
    "```python\n",
    "# call signature as of Pandas 0.18\n",
    "DataFrame.pivot_table(data, values=None, index=None, columns=None,\n",
    "                      aggfunc='mean', fill_value=None, margins=False,\n",
    "                      dropna=True, margins_name='All')\n",
    "```\n",
    "\n",
    "We've already seen examples of the first three arguments; here we'll take a quick look at the remaining ones.\n",
    "Two of the options, ``fill_value`` and ``dropna``, have to do with missing data and are fairly straightforward; we will not show examples of them here.\n",
    "\n",
    "The ``aggfunc`` keyword controls what type of aggregation is applied, which is a mean by default.\n",
    "As in the GroupBy, the aggregation specification can be a string representing one of several common choices (e.g., ``'sum'``, ``'mean'``, ``'count'``, ``'min'``, ``'max'``, etc.) or a function that implements an aggregation (e.g., ``np.sum()``, ``min()``, ``sum()``, etc.).\n",
    "Additionally, it can be specified as a dictionary mapping a column to any of the above desired options:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group & Aggregate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped=titanic.groupby(['sex', 'pclass'])[['age']]   #retruns a 'groupby' object\n",
    "newdf= grouped.agg(['max', 'min', 'mean'])   #returns a df\n",
    "newdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\"><a href=\"#Sections\">Return to Top ↑</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"embed\"></a></p>\n",
    "\n",
    "## Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/Iwg6su4p3Z0\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1cfb31f0c50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Embed a Youtube Video\n",
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('Iwg6su4p3Z0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Embed HTML\n",
    "from IPython.core.display import HTML\n",
    "HTML(\"<iframe src=https://www.techatbloomberg.com/ width=800 height=350></iframe>\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display picture files in a directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from IPython.display import display, Image\n",
    "wd = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = os.listdir(os.getcwd())   #read the directory\n",
    "pictures = [f for f in filenames if f.endswith('.jpg')]\n",
    "pictures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#alternate way using magic commands\n",
    "pictures2= !ls *.jpg\n",
    "pictures2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for filename in pictures:\n",
    "    display(Image(wd + \"/\"+ filename, width=300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stats Stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(42)   #generate some random data\n",
    "\n",
    "#map random data to a dataframe\n",
    "df = pd.DataFrame(rng.randint(0, 10, (3, 4)),\n",
    "                  columns=['A', 'B', 'C', 'D'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Built-In Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#iris.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wd = os.getcwd()\n",
    "wd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filestring = 'titanic2.csv'\n",
    "mydata = pd.read_csv(filestring, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map & Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "func_lst = [math.sin, math.cos, math.exp]\n",
    "\n",
    "# execute each function in list with an argument of '0'\n",
    "list(map(lambda f: f(0), func_lst))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function using filter to return all the lists whose length is 3 from a nested list. \n",
    "\n",
    "def ex_4(L):\n",
    "    # Your code here\n",
    "    #map(len,L)\n",
    "    return filter(lambda x: len(x)==3, L)\n",
    "\n",
    "L = [[2, 5, 4, 9], [2, 2], [3, 4, 5]]\n",
    "list(ex_4(L))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For the list, Lst, below, square all its elements.\n",
    "\n",
    "Lst = [4,6,9]\n",
    "list(map(lambda x: x**2, Lst))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For the nested list, Lst, below, square all the elements in each inner list.\n",
    "\n",
    "Lst = [[1,2,3],[2,3,4],[3,4,5]]\n",
    "\n",
    "f = [[x**2 for x in lst] for lst in Lst]\n",
    "f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\"><a href=\"#Sections\">Return to Top ↑</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"misc\"></a></p>\n",
    "\n",
    "# Miscellaneous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selection and Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#help(np.sort)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2 = np.random.randint(10, size=(3, 4))  # Two-dimensional array\n",
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(x2[:2, :3])  # two rows, three columns\n",
    "print('---'*4)\n",
    "print(x2[::-1, ::-1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(x2[:, 0])  # first column of x2\n",
    "print(x2[0, :])  # first row of x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2_sub_copy = x2[:2, :2].copy()\n",
    "print(x2_sub_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "df1 = pd.DataFrame(np.arange(9).reshape((3, 3)), \n",
    "                   columns=['a', 'b', 'c'],\n",
    "                   index=['One', 'two', 'three'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#select a specific value  (loc only accepts labels)\n",
    "print(df1.loc['two', 'b'])\n",
    "print(df1['b'][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.loc['two'] # the row that has index two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.loc[['two']] # the row that has index two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fancy indexing (aka conditional selection)\n",
    "df1.loc[df1.a==0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The code below returns all rows for the columns in which row 'one' is zero.\n",
    "df1.loc[:, df1.loc['One']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#axis =1 means 'columns' not rows\n",
    "df1.drop('b', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionary Tricks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a dict\n",
    "mydict = {'name': 'david', 'ssn': '213-15-9999', 'rank': 'Colonel'}\n",
    "print(mydict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cycle thru dict using a list comprehension\n",
    "keys = sorted(mydict.keys())  #needs to be in a progressive order\n",
    "for key in keys:\n",
    "    print(key, \":\", mydict[key])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rint(type(kwargs))\n",
    "    keys = sorted(kwargs.keys())\n",
    "    for kw in keys:\n",
    "        print(kw, \":\", kwargs[kw])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BankAccount(object):\n",
    "    # Your code here\n",
    "    def __init__(self, name, balance):\n",
    "        self.__name = name\n",
    "        self.__balance = balance\n",
    "    \n",
    "    def deposit(self, n):\n",
    "        self.__balance += n\n",
    "        \n",
    "    def withdraw(self, n):\n",
    "        if n > self.__balance:\n",
    "            raise ValueError(\"You don't have enough money!\")\n",
    "        self.__balance -= n\n",
    "        \n",
    "    def check_balance(self):\n",
    "        print('Your current balance is {}'.format(self.__balance))\n",
    "\n",
    "account = BankAccount('Test', 2000)\n",
    "account.check_balance() # 2000        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><a name=\"end\"></a></p>\n",
    "------new code here------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### More cleaning tricks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_currency(val):\n",
    "    \"\"\"\n",
    "    Convert the string number value to a float\n",
    "     - Remove $\n",
    "     - Remove commas\n",
    "     - Convert to float type\n",
    "    \"\"\"\n",
    "    new_val = val.replace(',','').replace('$', '')\n",
    "    return float(new_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_percent(val):\n",
    "    \"\"\"\n",
    "    Convert the percentage string to an actual floating point percent\n",
    "    \"\"\"\n",
    "    new_val = val.replace('%', '')\n",
    "    return float(new_val) / 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df['2016'].apply(convert_currency)\n",
    "\n",
    "df['2017'].apply(convert_currency)\n",
    "\n",
    "#alternative with lambda function\n",
    "df['2016'].apply(lambda x: x.replace('$', '').replace(',', '')).astype('float')\n",
    "\n",
    "pd.to_numeric(df['Jan Units'], errors='coerce').fillna(0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List Comprehension\n",
    "\n",
    "- List comprehensions are another notation for defining lists. They mimic the mathematical notation of “set comprehensions” and have a concise syntax.  In one step, list comprehensions can perform the combined operation of any filter and map.\n",
    "- A list comprehension has the form: \n",
    "```\n",
    "[ <expresion> for <element> in <list> if <boolean> ]\n",
    "```\n",
    "- A list comprehension can also use if/else statements.  These types of list comprehensions have a slightly different syntax.\n",
    "\n",
    "- A list comprehension with an if/else statements has the form: \n",
    "```\n",
    "[ <expr1> if <boolean> else <expr2> for <element> in <list> ]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileExistsError",
     "evalue": "[WinError 183] Cannot create a file when that file already exists: 'C:\\\\Users\\\\MitchellDavid\\\\seaborn-data'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-37f3deae0173>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0miris\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_dataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"iris\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miris\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\blp\\BQuant\\environments\\bqnt-2.192.0\\lib\\site-packages\\seaborn\\utils.py\u001b[0m in \u001b[0;36mload_dataset\u001b[1;34m(name, cache, data_home, **kws)\u001b[0m\n\u001b[0;32m    423\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    424\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 425\u001b[1;33m         cache_path = os.path.join(get_data_home(data_home),\n\u001b[0m\u001b[0;32m    426\u001b[0m                                   os.path.basename(full_path))\n\u001b[0;32m    427\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcache_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\blp\\BQuant\\environments\\bqnt-2.192.0\\lib\\site-packages\\seaborn\\utils.py\u001b[0m in \u001b[0;36mget_data_home\u001b[1;34m(data_home)\u001b[0m\n\u001b[0;32m    397\u001b[0m     \u001b[0mdata_home\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpanduser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_home\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    398\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_home\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 399\u001b[1;33m         \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_home\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    400\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdata_home\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\blp\\BQuant\\environments\\bqnt-2.192.0\\lib\\os.py\u001b[0m in \u001b[0;36mmakedirs\u001b[1;34m(name, mode, exist_ok)\u001b[0m\n\u001b[0;32m    219\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 221\u001b[1;33m         \u001b[0mmkdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    222\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m         \u001b[1;31m# Cannot rely on checking for EEXIST, since the operating system\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileExistsError\u001b[0m: [WinError 183] Cannot create a file when that file already exists: 'C:\\\\Users\\\\MitchellDavid\\\\seaborn-data'"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "iris = sns.load_dataset(\"iris\")\n",
    "print(type(iris))\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you generalize joint plots to datasets of larger dimensions, you end up with *pair plots*. This is very useful for exploring correlations between multidimensional data, when you'd like to plot all pairs of values against each other.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cfaf29ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris, hue='species', size=2.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Facet Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Widgets, Callbacks, Interativity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20Events.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "import ipywidgets as widgets\n",
    "\n",
    "button = widgets.Button(description=\"Click Me!\")\n",
    "output = widgets.Output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80478d48f41c44cc8d145806c3bd52ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Click Me!', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "297be9cf480548e8b02b03d30b134187",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(button, output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_button_clicked(b):\n",
    "    with output:\n",
    "        print(\"Button clicked.\")\n",
    "\n",
    "button.on_click(on_button_clicked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "09c760911bff4832a2c8ff56c6d09e93",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32364529f36f4b37b304d163ed037886",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "int_range = widgets.IntSlider()\n",
    "output2 = widgets.Output()\n",
    "\n",
    "display(int_range, output2)\n",
    "\n",
    "def on_value_change(change):\n",
    "    with output2:\n",
    "        print(change['new'])\n",
    "\n",
    "int_range.observe(on_value_change, names='value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2eddb47e2f84373996795c4f8118edc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Label(value='The values of slider1 and slider2 are synchronized')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b08f7dc00924043a061adab2f4cb1a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=0, description='Slider 1')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "66c9a85376d848ebb7dd6afa8a2d6a62",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=0, description='Slider 2')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "caption = widgets.Label(value='The values of slider1 and slider2 are synchronized')\n",
    "sliders1, slider2 = widgets.IntSlider(description='Slider 1'),\\\n",
    "                    widgets.IntSlider(description='Slider 2')\n",
    "l = widgets.link((sliders1, 'value'), (slider2, 'value'))\n",
    "display(caption, sliders1, slider2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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