18 - Data Analysis and Visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis and Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three popular modules used for data analysis in Python.\n",
    "\n",
    "[Pandas](https://pandas.pydata.org/)\n",
    "\n",
    "Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.\n",
    "\n",
    "[NumPy](https://numpy.org/)\n",
    "\n",
    "NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object sophisticated (broadcasting) functions tools for integrating C/C++ and Fortran code useful linear algebra, Fourier transform, and random number capabilities\n",
    "\n",
    "[Matplotlib](https://matplotlib.org/)\n",
    "\n",
    "Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.\n",
    "\n",
    "We will focus on `pandas` which uses `numpy` and `matplotlib` internally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on package pandas:\n",
      "\n",
      "NAME\n",
      "    pandas\n",
      "\n",
      "DESCRIPTION\n",
      "    pandas - a powerful data analysis and manipulation library for Python\n",
      "    =====================================================================\n",
      "    \n",
      "    **pandas** is a Python package providing fast, flexible, and expressive data\n",
      "    structures designed to make working with \"relational\" or \"labeled\" data both\n",
      "    easy and intuitive. It aims to be the fundamental high-level building block for\n",
      "    doing practical, **real world** data analysis in Python. Additionally, it has\n",
      "    the broader goal of becoming **the most powerful and flexible open source data\n",
      "    analysis / manipulation tool available in any language**. It is already well on\n",
      "    its way toward this goal.\n",
      "    \n",
      "    Main Features\n",
      "    -------------\n",
      "    Here are just a few of the things that pandas does well:\n",
      "    \n",
      "      - Easy handling of missing data in floating point as well as non-floating\n",
      "        point data.\n",
      "      - Size mutability: columns can be inserted and deleted from DataFrame and\n",
      "        higher dimensional objects\n",
      "      - Automatic and explicit data alignment: objects can be explicitly aligned\n",
      "        to a set of labels, or the user can simply ignore the labels and let\n",
      "        `Series`, `DataFrame`, etc. automatically align the data for you in\n",
      "        computations.\n",
      "      - Powerful, flexible group by functionality to perform split-apply-combine\n",
      "        operations on data sets, for both aggregating and transforming data.\n",
      "      - Make it easy to convert ragged, differently-indexed data in other Python\n",
      "        and NumPy data structures into DataFrame objects.\n",
      "      - Intelligent label-based slicing, fancy indexing, and subsetting of large\n",
      "        data sets.\n",
      "      - Intuitive merging and joining data sets.\n",
      "      - Flexible reshaping and pivoting of data sets.\n",
      "      - Hierarchical labeling of axes (possible to have multiple labels per tick).\n",
      "      - Robust IO tools for loading data from flat files (CSV and delimited),\n",
      "        Excel files, databases, and saving/loading data from the ultrafast HDF5\n",
      "        format.\n",
      "      - Time series-specific functionality: date range generation and frequency\n",
      "        conversion, moving window statistics, moving window linear regressions,\n",
      "        date shifting and lagging, etc.\n",
      "\n",
      "PACKAGE CONTENTS\n",
      "    _libs (package)\n",
      "    _version\n",
      "    api (package)\n",
      "    compat (package)\n",
      "    computation (package)\n",
      "    conftest\n",
      "    core (package)\n",
      "    errors (package)\n",
      "    formats (package)\n",
      "    io (package)\n",
      "    json\n",
      "    lib\n",
      "    parser\n",
      "    plotting (package)\n",
      "    testing\n",
      "    tests (package)\n",
      "    tools (package)\n",
      "    tseries (package)\n",
      "    tslib\n",
      "    types (package)\n",
      "    util (package)\n",
      "\n",
      "SUBMODULES\n",
      "    _hashtable\n",
      "    _lib\n",
      "    _tslib\n",
      "    offsets\n",
      "\n",
      "DATA\n",
      "    IndexSlice = <pandas.core.indexing._IndexSlice object>\n",
      "    NaT = NaT\n",
      "    __docformat__ = 'restructuredtext'\n",
      "    datetools = <module 'pandas.core.datetools' from 'C:\\\\Users\\...\\lib\\\\s...\n",
      "    describe_option = <pandas.core.config.CallableDynamicDoc object>\n",
      "    get_option = <pandas.core.config.CallableDynamicDoc object>\n",
      "    json = <module 'pandas.json' from 'C:\\\\Users\\\\marelp\\\\A...\\anaconda3\\\\...\n",
      "    lib = <module 'pandas.lib' from 'C:\\\\Users\\\\marelp\\\\Ap...\\\\anaconda3\\\\...\n",
      "    options = <pandas.core.config.DictWrapper object>\n",
      "    parser = <module 'pandas.parser' from 'C:\\\\Users\\\\marelp\\...naconda3\\\\...\n",
      "    plot_params = {'xaxis.compat': False}\n",
      "    reset_option = <pandas.core.config.CallableDynamicDoc object>\n",
      "    set_option = <pandas.core.config.CallableDynamicDoc object>\n",
      "    tslib = <module 'pandas.tslib' from 'C:\\\\Users\\\\marelp\\\\...anaconda3\\\\...\n",
      "\n",
      "VERSION\n",
      "    0.23.4\n",
      "\n",
      "FILE\n",
      "    c:\\users\\marelp\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages\\pandas\\__init__.py\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `Series` is a list of values. Like a row in a spreadsheet. However, a `Series` has an index (first row)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    3\n",
       "c    5\n",
       "d    7\n",
       "e    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1, 3, 5, 7, 9], index=['a', 'b', 'c', 'd', 'e'])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lookup value by index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[['a']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lookup multiple values by index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[['a', 'b']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `DataFrame` is a 2-dimension data structure with rows and columns. It is like a spreadsheet. A `DataFrame` also has an index."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The easiest way to create a `DataFrame` is to read in a `csv` or `spreadsheet`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv', index_col=0, dtype='int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>999994</th>\n",
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       "      <td>11774211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999995</th>\n",
       "      <td>112042212</td>\n",
       "      <td>88071527</td>\n",
       "      <td>11792997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999996</th>\n",
       "      <td>112066632</td>\n",
       "      <td>87905122</td>\n",
       "      <td>11609987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999997</th>\n",
       "      <td>112065997</td>\n",
       "      <td>87811625</td>\n",
       "      <td>11669146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999998</th>\n",
       "      <td>112029211</td>\n",
       "      <td>87921187</td>\n",
       "      <td>11703126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999999</th>\n",
       "      <td>112035222</td>\n",
       "      <td>87916473</td>\n",
       "      <td>11697991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                A         B          C\n",
       "0        47829970  20953780  184672856\n",
       "1        47625896  20859757  184642981\n",
       "2        47571412  20895762  184538889\n",
       "3        47696741  20926387  184410832\n",
       "4        47797495  20996450  184386167\n",
       "5        47811195  20913572  184518256\n",
       "6        47843300  21128588  184447763\n",
       "7        47848552  21066345  184247625\n",
       "8        47734700  21205326  184257233\n",
       "9        47768689  21413063  184510302\n",
       "10       47765593  21436380  184654497\n",
       "11       47832144  21542905  184616931\n",
       "12       47705997  21580247  184626157\n",
       "13       47910577  21360223  184670455\n",
       "14       47674433  21371023  184654111\n",
       "15       47646640  21432833  184434579\n",
       "16       47535745  21402139  184386595\n",
       "17       47575482  21336879  184449624\n",
       "18       47573675  21219845  184624817\n",
       "19       47592861  21174299  184649873\n",
       "20       47489233  21064130  184644078\n",
       "21       47449259  20945714  184471518\n",
       "22       47340849  20895540  184330560\n",
       "23       47498937  20822315  184357738\n",
       "24       47424559  20765185  184302775\n",
       "25       47375085  21039468  184345803\n",
       "26       47379301  21061511  184415267\n",
       "27       47196485  21003141  184357207\n",
       "28       47159361  20911675  184438389\n",
       "29       47318821  20791637  184564858\n",
       "...           ...       ...        ...\n",
       "999970  112342444  87868724   12023942\n",
       "999971  112296176  87871838   11929285\n",
       "999972  112164819  87834938   11948621\n",
       "999973  112264215  87817860   11927421\n",
       "999974  112197987  87881157   11941719\n",
       "999975  112348305  87812833   11875675\n",
       "999976  112453177  87819813   11828708\n",
       "999977  112358274  88069098   11850405\n",
       "999978  112323763  87987213   11917509\n",
       "999979  112509080  88023767   11726285\n",
       "999980  112503645  88190191   11674854\n",
       "999981  112518317  88202179   11656797\n",
       "999982  112471730  88231127   11676764\n",
       "999983  112400982  88139220   11558814\n",
       "999984  112363808  88022957   11555161\n",
       "999985  112253323  87965536   11523752\n",
       "999986  112098776  87812837   11543436\n",
       "999987  112121796  87812841   11466124\n",
       "999988  111895686  87713065   11519019\n",
       "999989  111961682  87744789   11516454\n",
       "999990  111836133  87834954   11542828\n",
       "999991  111977263  87948141   11664010\n",
       "999992  111995837  87952273   11681890\n",
       "999993  111938165  87865569   11780387\n",
       "999994  111936578  88004616   11774211\n",
       "999995  112042212  88071527   11792997\n",
       "999996  112066632  87905122   11609987\n",
       "999997  112065997  87811625   11669146\n",
       "999998  112029211  87921187   11703126\n",
       "999999  112035222  87916473   11697991\n",
       "\n",
       "[1000000 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the shape of a `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information about the `DataFrame` such as memory usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000000 entries, 0 to 999999\n",
      "Data columns (total 3 columns):\n",
      "A    1000000 non-null int64\n",
      "B    1000000 non-null int64\n",
      "C    1000000 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 30.5 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lookup first 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>47829970</td>\n",
       "      <td>20953780</td>\n",
       "      <td>184672856</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47625896</td>\n",
       "      <td>20859757</td>\n",
       "      <td>184642981</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47571412</td>\n",
       "      <td>20895762</td>\n",
       "      <td>184538889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47696741</td>\n",
       "      <td>20926387</td>\n",
       "      <td>184410832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47797495</td>\n",
       "      <td>20996450</td>\n",
       "      <td>184386167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47811195</td>\n",
       "      <td>20913572</td>\n",
       "      <td>184518256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47843300</td>\n",
       "      <td>21128588</td>\n",
       "      <td>184447763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>47848552</td>\n",
       "      <td>21066345</td>\n",
       "      <td>184247625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>47734700</td>\n",
       "      <td>21205326</td>\n",
       "      <td>184257233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>47768689</td>\n",
       "      <td>21413063</td>\n",
       "      <td>184510302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "          A         B          C\n",
       "0  47829970  20953780  184672856\n",
       "1  47625896  20859757  184642981\n",
       "2  47571412  20895762  184538889\n",
       "3  47696741  20926387  184410832\n",
       "4  47797495  20996450  184386167\n",
       "5  47811195  20913572  184518256\n",
       "6  47843300  21128588  184447763\n",
       "7  47848552  21066345  184247625\n",
       "8  47734700  21205326  184257233\n",
       "9  47768689  21413063  184510302"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lookup last 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>999990</th>\n",
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       "      <th>999991</th>\n",
       "      <td>111977263</td>\n",
       "      <td>87948141</td>\n",
       "      <td>11664010</td>\n",
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       "    <tr>\n",
       "      <th>999992</th>\n",
       "      <td>111995837</td>\n",
       "      <td>87952273</td>\n",
       "      <td>11681890</td>\n",
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       "    <tr>\n",
       "      <th>999993</th>\n",
       "      <td>111938165</td>\n",
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       "      <th>999994</th>\n",
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       "      <td>88004616</td>\n",
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       "    <tr>\n",
       "      <th>999995</th>\n",
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       "      <td>88071527</td>\n",
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       "    <tr>\n",
       "      <th>999996</th>\n",
       "      <td>112066632</td>\n",
       "      <td>87905122</td>\n",
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       "    <tr>\n",
       "      <th>999997</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>999999</th>\n",
       "      <td>112035222</td>\n",
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       "      <td>11697991</td>\n",
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       "                A         B         C\n",
       "999990  111836133  87834954  11542828\n",
       "999991  111977263  87948141  11664010\n",
       "999992  111995837  87952273  11681890\n",
       "999993  111938165  87865569  11780387\n",
       "999994  111936578  88004616  11774211\n",
       "999995  112042212  88071527  11792997\n",
       "999996  112066632  87905122  11609987\n",
       "999997  112065997  87811625  11669146\n",
       "999998  112029211  87921187  11703126\n",
       "999999  112035222  87916473  11697991"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lookup by index values where index is of type integer. If index were string use `df.loc` instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                A         B         C\n",
       "999993  111938165  87865569  11780387\n",
       "999996  112066632  87905122  11609987"
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     },
     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "df.iloc[[999993,999996]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lookup one column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          47829970\n",
       "1          47625896\n",
       "2          47571412\n",
       "3          47696741\n",
       "4          47797495\n",
       "5          47811195\n",
       "6          47843300\n",
       "7          47848552\n",
       "8          47734700\n",
       "9          47768689\n",
       "10         47765593\n",
       "11         47832144\n",
       "12         47705997\n",
       "13         47910577\n",
       "14         47674433\n",
       "15         47646640\n",
       "16         47535745\n",
       "17         47575482\n",
       "18         47573675\n",
       "19         47592861\n",
       "20         47489233\n",
       "21         47449259\n",
       "22         47340849\n",
       "23         47498937\n",
       "24         47424559\n",
       "25         47375085\n",
       "26         47379301\n",
       "27         47196485\n",
       "28         47159361\n",
       "29         47318821\n",
       "            ...    \n",
       "999970    112342444\n",
       "999971    112296176\n",
       "999972    112164819\n",
       "999973    112264215\n",
       "999974    112197987\n",
       "999975    112348305\n",
       "999976    112453177\n",
       "999977    112358274\n",
       "999978    112323763\n",
       "999979    112509080\n",
       "999980    112503645\n",
       "999981    112518317\n",
       "999982    112471730\n",
       "999983    112400982\n",
       "999984    112363808\n",
       "999985    112253323\n",
       "999986    112098776\n",
       "999987    112121796\n",
       "999988    111895686\n",
       "999989    111961682\n",
       "999990    111836133\n",
       "999991    111977263\n",
       "999992    111995837\n",
       "999993    111938165\n",
       "999994    111936578\n",
       "999995    112042212\n",
       "999996    112066632\n",
       "999997    112065997\n",
       "999998    112029211\n",
       "999999    112035222\n",
       "Name: A, Length: 1000000, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lookup multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>47734700</td>\n",
       "      <td>184257233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>47768689</td>\n",
       "      <td>184510302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>47765593</td>\n",
       "      <td>184654497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>47832144</td>\n",
       "      <td>184616931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>47705997</td>\n",
       "      <td>184626157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>47910577</td>\n",
       "      <td>184670455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>47674433</td>\n",
       "      <td>184654111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>47646640</td>\n",
       "      <td>184434579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>47535745</td>\n",
       "      <td>184386595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>47575482</td>\n",
       "      <td>184449624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>47573675</td>\n",
       "      <td>184624817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>47592861</td>\n",
       "      <td>184649873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>47489233</td>\n",
       "      <td>184644078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>47449259</td>\n",
       "      <td>184471518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>47340849</td>\n",
       "      <td>184330560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>47498937</td>\n",
       "      <td>184357738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>47424559</td>\n",
       "      <td>184302775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>47375085</td>\n",
       "      <td>184345803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>47379301</td>\n",
       "      <td>184415267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>47196485</td>\n",
       "      <td>184357207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>47159361</td>\n",
       "      <td>184438389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>47318821</td>\n",
       "      <td>184564858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999970</th>\n",
       "      <td>112342444</td>\n",
       "      <td>12023942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999971</th>\n",
       "      <td>112296176</td>\n",
       "      <td>11929285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999972</th>\n",
       "      <td>112164819</td>\n",
       "      <td>11948621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999973</th>\n",
       "      <td>112264215</td>\n",
       "      <td>11927421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999974</th>\n",
       "      <td>112197987</td>\n",
       "      <td>11941719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999975</th>\n",
       "      <td>112348305</td>\n",
       "      <td>11875675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999976</th>\n",
       "      <td>112453177</td>\n",
       "      <td>11828708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999977</th>\n",
       "      <td>112358274</td>\n",
       "      <td>11850405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999978</th>\n",
       "      <td>112323763</td>\n",
       "      <td>11917509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999979</th>\n",
       "      <td>112509080</td>\n",
       "      <td>11726285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999980</th>\n",
       "      <td>112503645</td>\n",
       "      <td>11674854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999981</th>\n",
       "      <td>112518317</td>\n",
       "      <td>11656797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999982</th>\n",
       "      <td>112471730</td>\n",
       "      <td>11676764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999983</th>\n",
       "      <td>112400982</td>\n",
       "      <td>11558814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999984</th>\n",
       "      <td>112363808</td>\n",
       "      <td>11555161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999985</th>\n",
       "      <td>112253323</td>\n",
       "      <td>11523752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999986</th>\n",
       "      <td>112098776</td>\n",
       "      <td>11543436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999987</th>\n",
       "      <td>112121796</td>\n",
       "      <td>11466124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999988</th>\n",
       "      <td>111895686</td>\n",
       "      <td>11519019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999989</th>\n",
       "      <td>111961682</td>\n",
       "      <td>11516454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999990</th>\n",
       "      <td>111836133</td>\n",
       "      <td>11542828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999991</th>\n",
       "      <td>111977263</td>\n",
       "      <td>11664010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999992</th>\n",
       "      <td>111995837</td>\n",
       "      <td>11681890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999993</th>\n",
       "      <td>111938165</td>\n",
       "      <td>11780387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999994</th>\n",
       "      <td>111936578</td>\n",
       "      <td>11774211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999995</th>\n",
       "      <td>112042212</td>\n",
       "      <td>11792997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999996</th>\n",
       "      <td>112066632</td>\n",
       "      <td>11609987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999997</th>\n",
       "      <td>112065997</td>\n",
       "      <td>11669146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999998</th>\n",
       "      <td>112029211</td>\n",
       "      <td>11703126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999999</th>\n",
       "      <td>112035222</td>\n",
       "      <td>11697991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                A          C\n",
       "0        47829970  184672856\n",
       "1        47625896  184642981\n",
       "2        47571412  184538889\n",
       "3        47696741  184410832\n",
       "4        47797495  184386167\n",
       "5        47811195  184518256\n",
       "6        47843300  184447763\n",
       "7        47848552  184247625\n",
       "8        47734700  184257233\n",
       "9        47768689  184510302\n",
       "10       47765593  184654497\n",
       "11       47832144  184616931\n",
       "12       47705997  184626157\n",
       "13       47910577  184670455\n",
       "14       47674433  184654111\n",
       "15       47646640  184434579\n",
       "16       47535745  184386595\n",
       "17       47575482  184449624\n",
       "18       47573675  184624817\n",
       "19       47592861  184649873\n",
       "20       47489233  184644078\n",
       "21       47449259  184471518\n",
       "22       47340849  184330560\n",
       "23       47498937  184357738\n",
       "24       47424559  184302775\n",
       "25       47375085  184345803\n",
       "26       47379301  184415267\n",
       "27       47196485  184357207\n",
       "28       47159361  184438389\n",
       "29       47318821  184564858\n",
       "...           ...        ...\n",
       "999970  112342444   12023942\n",
       "999971  112296176   11929285\n",
       "999972  112164819   11948621\n",
       "999973  112264215   11927421\n",
       "999974  112197987   11941719\n",
       "999975  112348305   11875675\n",
       "999976  112453177   11828708\n",
       "999977  112358274   11850405\n",
       "999978  112323763   11917509\n",
       "999979  112509080   11726285\n",
       "999980  112503645   11674854\n",
       "999981  112518317   11656797\n",
       "999982  112471730   11676764\n",
       "999983  112400982   11558814\n",
       "999984  112363808   11555161\n",
       "999985  112253323   11523752\n",
       "999986  112098776   11543436\n",
       "999987  112121796   11466124\n",
       "999988  111895686   11519019\n",
       "999989  111961682   11516454\n",
       "999990  111836133   11542828\n",
       "999991  111977263   11664010\n",
       "999992  111995837   11681890\n",
       "999993  111938165   11780387\n",
       "999994  111936578   11774211\n",
       "999995  112042212   11792997\n",
       "999996  112066632   11609987\n",
       "999997  112065997   11669146\n",
       "999998  112029211   11703126\n",
       "999999  112035222   11697991\n",
       "\n",
       "[1000000 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['A', 'C']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter rows by `query` expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>986405</th>\n",
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       "      <td>98670482</td>\n",
       "      <td>11087090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986532</th>\n",
       "      <td>111897297</td>\n",
       "      <td>99526879</td>\n",
       "      <td>10376624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988029</th>\n",
       "      <td>111895764</td>\n",
       "      <td>99031508</td>\n",
       "      <td>5801541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988085</th>\n",
       "      <td>111899493</td>\n",
       "      <td>100133378</td>\n",
       "      <td>6256199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A          B          C\n",
       "566746  111896904   46153156  164422671\n",
       "567018  111897069   45261456  166027477\n",
       "570147  111898786   46748703  158263890\n",
       "570490  111896836   45041160  155481762\n",
       "570523  111898012   45178903  155749464\n",
       "570596  111898024   46277311  155807897\n",
       "570598  111896038   46154413  155753459\n",
       "571541  111897559   51373568  152531769\n",
       "571883  111899212   51612195  153738697\n",
       "572762  111899771   54278431  153774027\n",
       "572838  111899028   53441746  153280799\n",
       "580069  111897916   49695748  152729114\n",
       "580151  111896563   51009891  152910868\n",
       "580192  111896032   50423543  152717562\n",
       "581288  111899955   48211873  153477844\n",
       "589945  111897233   49427524  152810695\n",
       "590745  111896005   54615247  153994837\n",
       "590868  111899233   55044349  155502971\n",
       "590870  111897185   55133802  155594125\n",
       "592103  111899005   50308556  154284773\n",
       "592123  111896710   50558392  154693038\n",
       "592603  111899839   52508213  152617604\n",
       "594568  111899895   55657925  157693650\n",
       "594900  111898099   55672915  159401472\n",
       "594929  111896129   55780019  158517938\n",
       "933555  111897456   74264981   45481768\n",
       "933809  111898370   72742266   48762166\n",
       "933840  111899617   73089660   49004729\n",
       "967264  111899873   92187513   26861628\n",
       "972605  111896671   75336774   22034006\n",
       "975699  111896294   86974212   20203973\n",
       "976957  111898272   82897775   20572153\n",
       "977107  111897870   84504383   19802712\n",
       "986405  111897156   98670482   11087090\n",
       "986532  111897297   99526879   10376624\n",
       "988029  111895764   99031508    5801541\n",
       "988085  111899493  100133378    6256199"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('A > 111895686 and A < 111899999')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cumulative sum first 1000 rows"
   ]
  },
  {
   "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></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47829970</td>\n",
       "      <td>20953780</td>\n",
       "      <td>184672856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95455866</td>\n",
       "      <td>41813537</td>\n",
       "      <td>369315837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>143027278</td>\n",
       "      <td>62709299</td>\n",
       "      <td>553854726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190724019</td>\n",
       "      <td>83635686</td>\n",
       "      <td>738265558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>238521514</td>\n",
       "      <td>104632136</td>\n",
       "      <td>922651725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>286332709</td>\n",
       "      <td>125545708</td>\n",
       "      <td>1107169981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>334176009</td>\n",
       "      <td>146674296</td>\n",
       "      <td>1291617744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>382024561</td>\n",
       "      <td>167740641</td>\n",
       "      <td>1475865369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>429759261</td>\n",
       "      <td>188945967</td>\n",
       "      <td>1660122602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>477527950</td>\n",
       "      <td>210359030</td>\n",
       "      <td>1844632904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>525293543</td>\n",
       "      <td>231795410</td>\n",
       "      <td>2029287401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>573125687</td>\n",
       "      <td>253338315</td>\n",
       "      <td>2213904332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>620831684</td>\n",
       "      <td>274918562</td>\n",
       "      <td>2398530489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>668742261</td>\n",
       "      <td>296278785</td>\n",
       "      <td>2583200944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>716416694</td>\n",
       "      <td>317649808</td>\n",
       "      <td>2767855055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>764063334</td>\n",
       "      <td>339082641</td>\n",
       "      <td>2952289634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>811599079</td>\n",
       "      <td>360484780</td>\n",
       "      <td>3136676229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>859174561</td>\n",
       "      <td>381821659</td>\n",
       "      <td>3321125853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>906748236</td>\n",
       "      <td>403041504</td>\n",
       "      <td>3505750670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>954341097</td>\n",
       "      <td>424215803</td>\n",
       "      <td>3690400543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1001830330</td>\n",
       "      <td>445279933</td>\n",
       "      <td>3875044621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1049279589</td>\n",
       "      <td>466225647</td>\n",
       "      <td>4059516139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1096620438</td>\n",
       "      <td>487121187</td>\n",
       "      <td>4243846699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1144119375</td>\n",
       "      <td>507943502</td>\n",
       "      <td>4428204437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1191543934</td>\n",
       "      <td>528708687</td>\n",
       "      <td>4612507212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1238919019</td>\n",
       "      <td>549748155</td>\n",
       "      <td>4796853015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1286298320</td>\n",
       "      <td>570809666</td>\n",
       "      <td>4981268282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1333494805</td>\n",
       "      <td>591812807</td>\n",
       "      <td>5165625489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1380654166</td>\n",
       "      <td>612724482</td>\n",
       "      <td>5350063878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1427972987</td>\n",
       "      <td>633516119</td>\n",
       "      <td>5534628736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>48503582763</td>\n",
       "      <td>17924893723</td>\n",
       "      <td>180114288455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>48550146908</td>\n",
       "      <td>17944660715</td>\n",
       "      <td>180300966641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>48596670980</td>\n",
       "      <td>17964493928</td>\n",
       "      <td>180487615912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>48643142306</td>\n",
       "      <td>17984443918</td>\n",
       "      <td>180674108281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>48689532839</td>\n",
       "      <td>18004281104</td>\n",
       "      <td>180860658799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>48736027905</td>\n",
       "      <td>18024145836</td>\n",
       "      <td>181047163235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>48782675546</td>\n",
       "      <td>18044052364</td>\n",
       "      <td>181233778587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>48829277368</td>\n",
       "      <td>18064015954</td>\n",
       "      <td>181420300511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>48875939575</td>\n",
       "      <td>18083896034</td>\n",
       "      <td>181606744082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>48922783884</td>\n",
       "      <td>18103698119</td>\n",
       "      <td>181793149700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>48969688729</td>\n",
       "      <td>18123623066</td>\n",
       "      <td>181979431450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>49016710554</td>\n",
       "      <td>18143655082</td>\n",
       "      <td>182165763631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>49063767519</td>\n",
       "      <td>18163592621</td>\n",
       "      <td>182351975260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>49110807449</td>\n",
       "      <td>18183516671</td>\n",
       "      <td>182538221700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>49157951925</td>\n",
       "      <td>18203476747</td>\n",
       "      <td>182724351873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>49204973440</td>\n",
       "      <td>18223469093</td>\n",
       "      <td>182910246933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>49251904549</td>\n",
       "      <td>18243426527</td>\n",
       "      <td>183096178629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>49298821737</td>\n",
       "      <td>18263408570</td>\n",
       "      <td>183282192833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>49345806869</td>\n",
       "      <td>18283416932</td>\n",
       "      <td>183468293993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>49392843973</td>\n",
       "      <td>18303333760</td>\n",
       "      <td>183654627651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>49440054145</td>\n",
       "      <td>18323207891</td>\n",
       "      <td>183840957543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>49487059521</td>\n",
       "      <td>18343090264</td>\n",
       "      <td>184027357057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>49534048143</td>\n",
       "      <td>18363016422</td>\n",
       "      <td>184213626562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>49581028302</td>\n",
       "      <td>18383051193</td>\n",
       "      <td>184399974740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>49628095866</td>\n",
       "      <td>18403161462</td>\n",
       "      <td>184586358470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>49675080451</td>\n",
       "      <td>18423280485</td>\n",
       "      <td>184772701598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>49722063796</td>\n",
       "      <td>18443297450</td>\n",
       "      <td>184959154242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>49769088587</td>\n",
       "      <td>18463156958</td>\n",
       "      <td>185145661371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>49816078058</td>\n",
       "      <td>18483037821</td>\n",
       "      <td>185332072155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>49863154782</td>\n",
       "      <td>18502888026</td>\n",
       "      <td>185518424787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               A            B             C\n",
       "0       47829970     20953780     184672856\n",
       "1       95455866     41813537     369315837\n",
       "2      143027278     62709299     553854726\n",
       "3      190724019     83635686     738265558\n",
       "4      238521514    104632136     922651725\n",
       "5      286332709    125545708    1107169981\n",
       "6      334176009    146674296    1291617744\n",
       "7      382024561    167740641    1475865369\n",
       "8      429759261    188945967    1660122602\n",
       "9      477527950    210359030    1844632904\n",
       "10     525293543    231795410    2029287401\n",
       "11     573125687    253338315    2213904332\n",
       "12     620831684    274918562    2398530489\n",
       "13     668742261    296278785    2583200944\n",
       "14     716416694    317649808    2767855055\n",
       "15     764063334    339082641    2952289634\n",
       "16     811599079    360484780    3136676229\n",
       "17     859174561    381821659    3321125853\n",
       "18     906748236    403041504    3505750670\n",
       "19     954341097    424215803    3690400543\n",
       "20    1001830330    445279933    3875044621\n",
       "21    1049279589    466225647    4059516139\n",
       "22    1096620438    487121187    4243846699\n",
       "23    1144119375    507943502    4428204437\n",
       "24    1191543934    528708687    4612507212\n",
       "25    1238919019    549748155    4796853015\n",
       "26    1286298320    570809666    4981268282\n",
       "27    1333494805    591812807    5165625489\n",
       "28    1380654166    612724482    5350063878\n",
       "29    1427972987    633516119    5534628736\n",
       "..           ...          ...           ...\n",
       "970  48503582763  17924893723  180114288455\n",
       "971  48550146908  17944660715  180300966641\n",
       "972  48596670980  17964493928  180487615912\n",
       "973  48643142306  17984443918  180674108281\n",
       "974  48689532839  18004281104  180860658799\n",
       "975  48736027905  18024145836  181047163235\n",
       "976  48782675546  18044052364  181233778587\n",
       "977  48829277368  18064015954  181420300511\n",
       "978  48875939575  18083896034  181606744082\n",
       "979  48922783884  18103698119  181793149700\n",
       "980  48969688729  18123623066  181979431450\n",
       "981  49016710554  18143655082  182165763631\n",
       "982  49063767519  18163592621  182351975260\n",
       "983  49110807449  18183516671  182538221700\n",
       "984  49157951925  18203476747  182724351873\n",
       "985  49204973440  18223469093  182910246933\n",
       "986  49251904549  18243426527  183096178629\n",
       "987  49298821737  18263408570  183282192833\n",
       "988  49345806869  18283416932  183468293993\n",
       "989  49392843973  18303333760  183654627651\n",
       "990  49440054145  18323207891  183840957543\n",
       "991  49487059521  18343090264  184027357057\n",
       "992  49534048143  18363016422  184213626562\n",
       "993  49581028302  18383051193  184399974740\n",
       "994  49628095866  18403161462  184586358470\n",
       "995  49675080451  18423280485  184772701598\n",
       "996  49722063796  18443297450  184959154242\n",
       "997  49769088587  18463156958  185145661371\n",
       "998  49816078058  18483037821  185332072155\n",
       "999  49863154782  18502888026  185518424787\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:1000].cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate percentage change first 1000 rows "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.004267</td>\n",
       "      <td>-0.004487</td>\n",
       "      <td>-0.000162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.001144</td>\n",
       "      <td>0.001726</td>\n",
       "      <td>-0.000564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002635</td>\n",
       "      <td>0.001466</td>\n",
       "      <td>-0.000694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.002112</td>\n",
       "      <td>0.003348</td>\n",
       "      <td>-0.000134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000287</td>\n",
       "      <td>-0.003947</td>\n",
       "      <td>0.000716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000671</td>\n",
       "      <td>0.010281</td>\n",
       "      <td>-0.000382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000110</td>\n",
       "      <td>-0.002946</td>\n",
       "      <td>-0.001085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.002379</td>\n",
       "      <td>0.006597</td>\n",
       "      <td>0.000052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000712</td>\n",
       "      <td>0.009796</td>\n",
       "      <td>0.001373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.000065</td>\n",
       "      <td>0.001089</td>\n",
       "      <td>0.000782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.001393</td>\n",
       "      <td>0.004969</td>\n",
       "      <td>-0.000203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.002637</td>\n",
       "      <td>0.001733</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.004288</td>\n",
       "      <td>-0.010196</td>\n",
       "      <td>0.000240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.004929</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>-0.000089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.000583</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>-0.001189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.002327</td>\n",
       "      <td>-0.001432</td>\n",
       "      <td>-0.000260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000836</td>\n",
       "      <td>-0.003049</td>\n",
       "      <td>0.000342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.005485</td>\n",
       "      <td>0.000950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000403</td>\n",
       "      <td>-0.002146</td>\n",
       "      <td>0.000136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.002177</td>\n",
       "      <td>-0.005203</td>\n",
       "      <td>-0.000031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.000842</td>\n",
       "      <td>-0.005622</td>\n",
       "      <td>-0.000935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.002285</td>\n",
       "      <td>-0.002395</td>\n",
       "      <td>-0.000764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.003339</td>\n",
       "      <td>-0.003504</td>\n",
       "      <td>0.000147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.001566</td>\n",
       "      <td>-0.002744</td>\n",
       "      <td>-0.000298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.001043</td>\n",
       "      <td>0.013209</td>\n",
       "      <td>0.000233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.001048</td>\n",
       "      <td>0.000377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.003859</td>\n",
       "      <td>-0.002771</td>\n",
       "      <td>-0.000315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.000787</td>\n",
       "      <td>-0.004355</td>\n",
       "      <td>0.000440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.003381</td>\n",
       "      <td>-0.005740</td>\n",
       "      <td>0.000686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>-0.003943</td>\n",
       "      <td>-0.001035</td>\n",
       "      <td>0.000647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>-0.001387</td>\n",
       "      <td>-0.007480</td>\n",
       "      <td>0.000696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>-0.000861</td>\n",
       "      <td>0.003350</td>\n",
       "      <td>-0.000155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>-0.001134</td>\n",
       "      <td>0.005888</td>\n",
       "      <td>-0.000841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>-0.001739</td>\n",
       "      <td>-0.005654</td>\n",
       "      <td>0.000312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>0.002253</td>\n",
       "      <td>0.001389</td>\n",
       "      <td>-0.000247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>0.003282</td>\n",
       "      <td>0.002104</td>\n",
       "      <td>0.000595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>-0.000982</td>\n",
       "      <td>0.002866</td>\n",
       "      <td>-0.000501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>0.001296</td>\n",
       "      <td>-0.004183</td>\n",
       "      <td>-0.000420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>0.003903</td>\n",
       "      <td>-0.003923</td>\n",
       "      <td>-0.000204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>0.001292</td>\n",
       "      <td>0.006204</td>\n",
       "      <td>-0.000665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>0.002494</td>\n",
       "      <td>0.005374</td>\n",
       "      <td>0.000271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>0.000747</td>\n",
       "      <td>-0.004716</td>\n",
       "      <td>-0.000647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>-0.000362</td>\n",
       "      <td>-0.000677</td>\n",
       "      <td>0.000187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>0.002222</td>\n",
       "      <td>0.001808</td>\n",
       "      <td>-0.000624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>-0.002608</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>-0.001263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>-0.001923</td>\n",
       "      <td>-0.001746</td>\n",
       "      <td>0.000197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>-0.000297</td>\n",
       "      <td>0.001233</td>\n",
       "      <td>0.000444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>0.001448</td>\n",
       "      <td>0.001317</td>\n",
       "      <td>0.000467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>0.001106</td>\n",
       "      <td>-0.004575</td>\n",
       "      <td>0.001249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>0.003679</td>\n",
       "      <td>-0.002144</td>\n",
       "      <td>-0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>-0.004338</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>-0.000356</td>\n",
       "      <td>0.002202</td>\n",
       "      <td>-0.000697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>-0.000180</td>\n",
       "      <td>0.005451</td>\n",
       "      <td>0.000422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>0.001860</td>\n",
       "      <td>0.003768</td>\n",
       "      <td>0.000191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>-0.001763</td>\n",
       "      <td>0.000435</td>\n",
       "      <td>-0.000218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>-0.000026</td>\n",
       "      <td>-0.005073</td>\n",
       "      <td>0.000588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.000882</td>\n",
       "      <td>-0.007866</td>\n",
       "      <td>0.000292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>-0.000751</td>\n",
       "      <td>0.001075</td>\n",
       "      <td>-0.000517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.001857</td>\n",
       "      <td>-0.001542</td>\n",
       "      <td>-0.000312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B         C\n",
       "0         NaN       NaN       NaN\n",
       "1   -0.004267 -0.004487 -0.000162\n",
       "2   -0.001144  0.001726 -0.000564\n",
       "3    0.002635  0.001466 -0.000694\n",
       "4    0.002112  0.003348 -0.000134\n",
       "5    0.000287 -0.003947  0.000716\n",
       "6    0.000671  0.010281 -0.000382\n",
       "7    0.000110 -0.002946 -0.001085\n",
       "8   -0.002379  0.006597  0.000052\n",
       "9    0.000712  0.009796  0.001373\n",
       "10  -0.000065  0.001089  0.000782\n",
       "11   0.001393  0.004969 -0.000203\n",
       "12  -0.002637  0.001733  0.000050\n",
       "13   0.004288 -0.010196  0.000240\n",
       "14  -0.004929  0.000506 -0.000089\n",
       "15  -0.000583  0.002892 -0.001189\n",
       "16  -0.002327 -0.001432 -0.000260\n",
       "17   0.000836 -0.003049  0.000342\n",
       "18  -0.000038 -0.005485  0.000950\n",
       "19   0.000403 -0.002146  0.000136\n",
       "20  -0.002177 -0.005203 -0.000031\n",
       "21  -0.000842 -0.005622 -0.000935\n",
       "22  -0.002285 -0.002395 -0.000764\n",
       "23   0.003339 -0.003504  0.000147\n",
       "24  -0.001566 -0.002744 -0.000298\n",
       "25  -0.001043  0.013209  0.000233\n",
       "26   0.000089  0.001048  0.000377\n",
       "27  -0.003859 -0.002771 -0.000315\n",
       "28  -0.000787 -0.004355  0.000440\n",
       "29   0.003381 -0.005740  0.000686\n",
       "..        ...       ...       ...\n",
       "970 -0.003943 -0.001035  0.000647\n",
       "971 -0.001387 -0.007480  0.000696\n",
       "972 -0.000861  0.003350 -0.000155\n",
       "973 -0.001134  0.005888 -0.000841\n",
       "974 -0.001739 -0.005654  0.000312\n",
       "975  0.002253  0.001389 -0.000247\n",
       "976  0.003282  0.002104  0.000595\n",
       "977 -0.000982  0.002866 -0.000501\n",
       "978  0.001296 -0.004183 -0.000420\n",
       "979  0.003903 -0.003923 -0.000204\n",
       "980  0.001292  0.006204 -0.000665\n",
       "981  0.002494  0.005374  0.000271\n",
       "982  0.000747 -0.004716 -0.000647\n",
       "983 -0.000362 -0.000677  0.000187\n",
       "984  0.002222  0.001808 -0.000624\n",
       "985 -0.002608  0.001617 -0.001263\n",
       "986 -0.001923 -0.001746  0.000197\n",
       "987 -0.000297  0.001233  0.000444\n",
       "988  0.001448  0.001317  0.000467\n",
       "989  0.001106 -0.004575  0.001249\n",
       "990  0.003679 -0.002144 -0.000020\n",
       "991 -0.004338  0.000415  0.000374\n",
       "992 -0.000356  0.002202 -0.000697\n",
       "993 -0.000180  0.005451  0.000422\n",
       "994  0.001860  0.003768  0.000191\n",
       "995 -0.001763  0.000435 -0.000218\n",
       "996 -0.000026 -0.005073  0.000588\n",
       "997  0.000882 -0.007866  0.000292\n",
       "998 -0.000751  0.001075 -0.000517\n",
       "999  0.001857 -0.001542 -0.000312\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:1000].pct_change()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Divide everything by 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df / 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiply everything by 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df * 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add 10 to everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df + 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subtract 10 from everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df - 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>1.000000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.724935e+07</td>\n",
       "      <td>5.875061e+07</td>\n",
       "      <td>1.662904e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.955716e+07</td>\n",
       "      <td>2.228546e+07</td>\n",
       "      <td>6.699266e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.190862e+07</td>\n",
       "      <td>4.499686e+07</td>\n",
       "      <td>1.326592e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.065915e+07</td>\n",
       "      <td>5.889322e+07</td>\n",
       "      <td>1.785114e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.364815e+08</td>\n",
       "      <td>7.885652e+07</td>\n",
       "      <td>2.238314e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.719223e+08</td>\n",
       "      <td>1.023920e+08</td>\n",
       "      <td>2.567824e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  A             B             C\n",
       "count  1.000000e+06  1.000000e+06  1.000000e+06\n",
       "mean   8.724935e+07  5.875061e+07  1.662904e+08\n",
       "std    4.955716e+07  2.228546e+07  6.699266e+07\n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00\n",
       "25%    4.190862e+07  4.499686e+07  1.326592e+08\n",
       "50%    7.065915e+07  5.889322e+07  1.785114e+08\n",
       "75%    1.364815e+08  7.885652e+07  2.238314e+08\n",
       "max    1.719223e+08  1.023920e+08  2.567824e+08"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot one column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1925c5bf978>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['A'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot two columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1925c61e550>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eYZmjtv69puNNX0qf6XJmq3yFg9GqMUUf29M44lDHMD6ETUVPRFWJaA0RHSKig0T0ikYbIqLJRJRIRPuJyAneQEWgVAWPDu8WSL51iSud09/nDc3PLROxXDGYw/XwEtttvA1L7+TcbM/IwTAuwsiMPhfAG0KI+gDaABhKRA0s2vQEUEf+GQzAQLomF+LpeCruoIKFYk6IBH5/ynn9h1ls+E5urt2uycPm58f+cZ4M7kS5L3Ez1XNyMIwLsKnohRDnhRC75eN0AIcAVLFo1hfAT0JiK4AyROTGAOoA8vNtt/EnLmgkMDm4wP1yRFnMhnf9aDr2Jm9YWzRWJHI/+rfn5GAYF2DXGj0RxQFoDsDSM6kKgDOK82SoHwYgosFEtJOIdjotFvTcQdJs9reBzunP18lwc4ztteNNx6nHzevu/dK9shQFZcjnE+vc/3tkGBdiWNETUSkA8wG8KoS4blmtcYkqfYsQYpoQIl4IER8TE6NxiQP8t1D65FmYxP7f7L9GaSL5yj7Tcb1e6rbWOKNIAzhsj/MCwrmL7vJD67+FwCe1vSPXLcM4AUOKnohCISn5X4UQWusDyQCqKs5jAZwruniM3Zxcb/81pxTJQZSOZgNmGbs++4b0qYwrFFXTfjk8zdqPzc8tg+QxjI9ixOqGAMwAcEgI8ZlOs8UAnpCtb9oASBNCnHeinIwlA+VwBUM2mpef329/X3Me0y43Gt55fGUgaSMw34EEL95ExUbm5/+MBNIvcLx6xucxMqNvD+BxAJ2JaK/804uIhhDRELnNUgAnACQCmA7gRdeIqyD1uJQfNlCp201y9qnYWMpS1UV2amozxPp1SvLz7A/k1f0j7fIfe9vXjzdSu6v5+f45wKf1gKVvekYehnES5KlM6PHx8WLnziJ4duo5CQ0/Y57OL1A4tRn4oafp/NH5QJ2u+u0BIOUwMLW16bzJAOD+78zb5OcBYxThihPSpKBlE6rp9/v4QqCWD3qbZt8ExusYiyVoBGpjGA9ARLuEEHbFP/FNz9gFz+vXBaKSB4CsdPPzXw3kfr1iYSXTTyPLkpZPQnikpPhaPqvdbxkrDwFvplgJT0vAMC7BNxW9lmVJ6xeAB704fZ0nyEixXn90mfm5rTX5kHDz887va7crXdF6P97MqCv2J4YHgBupplARDONl+J6i11pqqtYW6DkBaHif++XxFqq3U5cdsZEKcPdPxvp++Ffp82mLB0PxMtrtlTbpvkZQMDBosf3XTaoJrBhl/HfKMG7E9xT9Vo3oCoP+dL8c3oZlyAIA+FMVlsicCJVPmzb17wHeuwhU1gmDwJjgxCWMF+J7ij5dwzzfm/LCehvWNtuvK3K7DpxrvZ/QcO3ye74wHQeFAn2+Ni6bN/PcGvNzoxZeBT4FDONF+J6i3/yV6bjK7ZIHJqOP0aUER0M7K5eMRl0GWjzuWD/ehmVaSsuNaz1SDjpflqKSehxYI5vF5uUCy94NvNhQAU6IpwWwm0YPAAfmScfPrbbeNtDoNk5y8lFybrc6kYglD//i+JgF6fjq9rTeztcIs7DeunrKM3I4g6/kqOGXjwAH/5COU48Bj2rkCGb8Et9T9PdPA2p2AOp087Qk3ke7l6Wf8/uA7+6SyshAyObIWMfHJPJPG/OwCCC8DJB5TTrfMR1op5NP1ldmxwVKHvDdcNKMQ/je0k1QMNDiCd824XM1ZRSJuJPkEAlZGUDOLe32Jcq5XiZfIyhIys076C/p/GqSftvcTO3ypE3AniK8LTGMk/A9Rc/YpngZoLO8hHP5CPBJPeCjKsCHOg/HQMjI5Si5Wbbb5Nw0P0+IBDKvAz/2AhbZGWLCqEwJkeqw0PZA/K8fSPDd9leUa+YZF8zrhAA2K6xjQsLcI5MvUrWV+fm+OcCB+eZlZ3epr5ugCObq7NSE48pLn18VIWOnyHdeGkrG6/G9NXrGGJaRGAvY84trZpn+itI/QRlfqZEcYkIIYNZD1vuY+wQw0IE8AVpkWqaCKAK/9PfP/RVGBc/oAw1W8vahFxYiIRJIOwucWGu7j6N/A9edlJ7hmp3WP5Yx9pmAhBU9wzjK5w2AdRONtV07wTljWj4wUg7pt711zTzVIxOwsKL3Z967YLtN4wddL4c/c3qz6biBlVhLu4secC/tZg7wn0Ucnm/v0L/g+Koij8n4B6zo/ZnQ4rbbtBrsejkChY4j9OvqFS0xy74z19B0zD/AXgtzzXwr2a+2facuG7rD/NzZG8WMV8KKPlDo8I66rFQFtVUJ4xgtnwXK36ZffzO1SN33nbJJvzLTfEO1/vvL0Gz4b8CZbabCyKrIeGQRNlwra35tSgBnaQsgWNH7O0N3AC/tBDq9a17+2n/Am0c9I5OvUeMu2216fyp9thpsSsjyxhFgiKygz2x1eHjNLHCxige0RbavWzl52Bw2zLz9awfQ6IcbeHzGdozKUYTESLLIOWyFYxfTETd8Cf7a76SNZcZtsKL3d2LqAtF1pOP3LwPBYcAjvwGRBkMUM8Dji0zHtswRe02SlH5CmuS9LfKKPPxb80wJ39fkNZUOnlyi2TYvX3oolCCTo1dCs/WIG25q/1OeIoCdVthvHT5bIU0MXprFgQR9DVb0gURwKPB+ClDPzwKQuZqgICkWf8EafEKayY7eFtH1ijz8vF3JAABCPjoF75MKQ4qZN5I9eP89m4ZYMs8s9uPWZFWfaUJOm3g9GV+sPIptJ2wvLWXmFP2hxXgGVvQMY4TBa4GOw03n/WeYjt+/rH+dZRz//xYDaWrFa4SPQ6br1h2Z/Q5w6Sh+W7sHG8NetdnXSWFKgj5r5XY8N822hc6eM9eMCcp4HazoGcYRlI5URhPfCAHMfRz4vKGh5vn5onDJJSl8IB4KWafbtt7xH4ApLTHhuLF0ml/m3l94vD18KPaHP4fJc/7Sbnz0H+DGZVy7mVNY1G+qtPcwZ8dpdJy0Rvs6xmvgEAgM4yjlGwCx8cbbT7YvFeORi+l2CmScY0K9RzPs0KMALPYgprYDUg4ip3wTAKY3mj2nr2Fn0hW8M/9fAMDp1JuoVq6Ey+RligYreoZxlBe32Nf+6knt8tws4IdewNPLgWD5XzLlMP5Yr23dcj24LCI0a9TUy/xRszxZlLd9cV5uYcaskEvqzFkzNpq+T3pWjqqe8R546YZhPEF+nslZaVx54OxOYLxp3RxTW+PdQ/0wOuQHNKNEs0unVP4Y649eQpvMr2CLLJhv2n7YTyfYnRYLni08JA3roX2KNfvek42baTLuhxU9w7iaCI0MXt/eCYyLMS/LU3upDgpZgYVho8zKfjgWhidmbscFlEO+0Am6pkObmtaTzIhZD5tOlBmpZCrjMiKQAQA4l2aecOWt3/fZJQvjPljRM4yrua5hZVOQRDwjRV2Xb92MMRumzd+aWb/aJUqtmFI49mFPVCmjHR6Dji6TInP+O09VVxK3sDl8GPaHa4fN+H1XMmqM0LbvZzwLK3qGcTUtntCv+6SOumzxMHVZEend2LQsFBochI/7N7F+wfxnVEVPBi+3OY6WEy/jeVjRM4yruedL421zbqkDl9kguUKnwuMZuSZnuMvPmGLdTHm0BZImmAKr3VEnGleDo+0a563QuYbanbly03Yjxq3YVPRENJOIUojogE59RyJKI6K98s8orXYME7AE2TGfOrXZanVc5iyz89+HtEXsCwvx1Z07EZc5C2NzH8f2B3cBbyYiuuptKFeyGBLubaDZV9l3DxuXy4JeQfqxe65n6lvgZOVKy1IzN54sDKngKAt2Jxe5j0DBiHnljwC+BvCTlTYbhBD3OEUihglkbljxstWgZVwUAODlLnUwtFNtBAWZb87uev9u/YuDQ4ES5YBanZGVk4uww+rNVz0+C/0GS7PaaNb9sCkJnzzYVFU+c+NJjPnLPFrm63fXNTymJa/PlTZ/+zarjFoxpRzuJxCwOdUQQqwHcMUNsjCM//K6nAmqfh/r7S7st1p9eGwP9GpcUbPOUskb4u0TQP/vEdbpLbsuCyf9Wfu8Xck4ejEd/50zz297/FKG/fIZoMun+h7D1vhpSxKSLt9wrjBeirPW6NsS0T4i+puIdP27iWgwEe0kop2XLl1y0tAM4wNEVJaCoT38s/V2W74uPOyaNRH3R8031TUdiPDQYEx6oCle7VoHxz50YnC6Cg1Vy0IA0DJzKlbktdC8pGv9Crrddft8PXpN3mBW9uu200WT0Qr2BlzLzMnDqEUH0fGTta4RyMtwhqLfDaC6EKIpgK8ALNRrKISYJoSIF0LEx8TE6DVjGP+mRDTQRpGkvXp7zWaJIhYLhnU1FfSTQgqXDAvBq13rIjTY9bYU11ECz+W8oa4ILYHvB8WbbfBGFjcY88cF3Pb+MuTm5VttEzd8Ce6XY/T8tt11Dx1vpMh/KUKI60KIDPl4KYBQIrJvO59hAom3jwM9FEm7n1oKPLVM1eyf1+SEJ8+tAZ5f73KxvhzQTFWWjRAEKzeTR8pv4jkmy5rH21RH5chwrHy9g+p6d1rgdPlsHeKGL8GTP2xX1RU8BHaflrx5E/407RVoJnbxM4oc64aIKgK4KIQQRNQK0sOjaHnTGCYQGDgXiJCDi1Vvq6quW6G0dFBFe+nE2dxVJwatM79GOGVjXdjrAIBn76yF1++uhyV7t6J3dIo6Dj6Asfc1wtj7tEMrXLyeiapR9gU7G7/0EJpXLYOeCtt/S7QeIKdSpbK1R9TLwrXf+1u3r2UHLlgdyx8wYl45G8AWAPWIKJmIniGiIUQ0RG7yAIADRLQPwGQAA0QgPCIZpqjU7Q5U1FaQJ/K1N1xdSdmSxTDk3jtwSlREv6zRGJL9Kh5rUx3FiwWjd6v6QE31jN0WY/+ynpM2I8s8ubkQAtPWn8ALv+7G2Wu3dK974dddVvtdtPcsrt2UQkpsSjS3ZFJm2wKA3aevWu3LHzBidfOIEKKSECJUCBErhJghhPhWCPGtXP+1EKKhEKKpEKKNEMK6ITDDMDapGXTBI+PWqyi9RewRdfDt+NGoXq6k7Yt2zgQSVwJQB00LCQ7CQ99u0Y1ZHz9uhdl5Vq5pnf31OXt1hzxw9rpuHQC88tteNBsj9f3o99ustp2+QSeqqB/BnrEM44V8nv+IR8ZNvqo/i9blr9eAX6TUio+2ro75L5iWoW5k5WJ70hUkpWqv1WfmmG+g3so2Wc9sO3lFNfvW4tWuGmEkZNYfZes+gBU9w3gl9708ySPjXkrPst1IydUkVdHt1aOw7NU7AQCHL6iTp3zzqP6eQ76dq76fPtgUr3TRV/TvL9J06A84WNEzjJeQ9WYSzoko1M+ciahS4bYvcAFta1kPY2yGEMC2aZpV6Zm5muVlSoSiZ+NKODpO2wfgZrbaHj5u+BLczNbur2nVSBDpO4qd0nmTCDRY0TOMl3DkGqFd1te4hXCP2aQ3rCzlrppoK7olAHzRGKig7R8ZFqKtWn55pjUAoJiiPj/fNIv/bv1xzesajDJFzsxR2MvbE/pgRM/bDLf1N1jRM4yXkHLdzmUTFxAWEoykCb3xUMuqthunnTGzp0eqSUmXLaE2wwSAhqd/keLdK5ZormfmIG74EkxZk4hD57Xz5JYON1mCT1xmCsZmbTZvyfMdahluq8V9UzaZZdXyJVjRM4yXMHaJdVNEr6HpQNPx0jdNx+dNVjJ6tvO0/F3pIP18YdkD30q5dyctP4Jdp7RNHZWzeC0rmUZVjGbRNfHbYO2gbFos2X8ee89cQ98pm9Dzyw0Y86eP3CsZVvQM4yX4zHpymyHa5fOeBs6ovVI1+ax+4WFiiu1gZwNaVrNa/+NTrQqPkyb0xq6RXTXb7XivKza+0wlHxvUwS6toy/Vn6KzdhceHzl/HzE0nETd8CQ5fsG7m6S2womcYL+POOl4eQaSSOgRxITPuBvJygPSLOFTve6x/uXlh1VPt4xwe8sfNSVbrQywid0aVNF86ql5OesOIKR2G2LIlEBYSbFZ/Pi3T7K3BKD2+2GC7kRdQ5BAIDMM4h1JhIcjIysUH9+oGgPUNxkoPquIAqv37NZImjEfazRxEFg8BFA6tT7Stjp+2nNLsYuuILmjz0Sqrw7zcuXbhcZkSxfD1wOZoV0sa23Ltft1bnWCNdhOX8fgxAAAgAElEQVRWA4BZkDZ/gmf0DOMFTFt/vDAcQHQp7Y1Mn2TrFABAZIlQYHQZs6omsWW0rgAgWeUUs4jOabnEY5m05J4mlc1m8o6E59fCWigGX4EVPcN4AeOXmixJyuhYrHgVbx6zr/0NdZzDalaCnZUtEWqK3inT9bN1GLHAlJjFlsXNG93q2ScjgI3H1Bm+jvjIOrw1WNEzDGM/pcobbzuhGjD/aVVxjWj9ODpEhLjokqqllNnbzxgedlC7OJttFg41zwXw2Ax1XJwbWdaTmtib9MQTsKJnGC+gty+HyX1svvX6zDQgaZOqOKY4YeXrd6nK65Q3d4IKdnANplSY7S1Irdn6s//baXaudO6a/0I7Vfvb3l+GuOFLkHI90wEp3QMreobxAHn5AumZpryrS/6V7Mrb2ROCwNP0nQLU6Q7U7go8s9J623yNHLPjYlC7fGlUjjSFe/iwXyOssEhgsu+Dbg6LmDSht9UN1turl1WVrTx0EXHDl6DFWCn6ZUH4hQ/7NUKLamXwQkdtx6tW41fhz33ndOMFZWTlmnkBuxNW9AzjAV6fuxeNE/4pfO1vUU3amJz1nHEnHo/T/DHg0bnScdWWUk7c/jOk88rNta+5/SlV0eYRXTDzyXgAQJMq6g3aUmEhZhY2ALDhbetWNEapXb60bt2VG1I8+9fm7AMAHLuYASLCOz30Qym8PHsPWn6ofuilpGei0QfLUfPdpUWU2DFY0TOMB1i09xwAFHqCFqS483kaPwC8mQgMXqtd3+QhoNs4VXHn2yrg2Ic90Tg2UvMyy41Ve7NWWeO7x2/HqHsa2GxXUfHmkTSht6FrCuj15cbCY0/kZWJFzzAe5MtVdlqv+AKlYqTPTu+p60qU05zVA3BLsnMtujesiKfvqKFZp1TKyng7AHSv0eJyhmk5J2HxQTslLDqs6BnGg2w/ecXTIriODm8DwyyyRF09BRQzkLXKAzzfoaaqTBl7p3S4YxFFL6SZb9L+T8dJzJWwomcYxnWUqW5xXhVQ2r/bsYwRXSoMAHDsQ+1Y9kVlRM/6qrIfNiUVHlvO6K2hfBPQ8vDNzrU/3EJRYEXPMG7mlkVyjXf/+NdDkriBIAsVU95CmY4uA+TnSzN9G+wc2RVJE3p7bImntAFzzQJybVjXnHOzty0reoZxM1tPmHuJztp22kOSuIkqt1uvH1MW+LIJcGKde+SxgwKzV0B76WZga+2omttPXsGp1Bu6/Xb8ZG2RZbMHDmrGMC6m7UercF5ep40uFWa2MRcQdHgHmPWQ7XY/9QFGXVW/BXgJsWWLq8pG92mIxJQM1V7Lo99LHrbeEiTNO3+jDONHnFdsxgWckgeA2ndLn80es912amvXymKDApPJqlFqpV6iWLCqLDQ4CHOfb6vbX64DoY9dASt6hmFcS1CQ5Ex13xTbbS8fdb08Vnj6jhrY9m4XrHmjo6rOWhC12TqObv3l7FkAsG9UN5z8qFeRZXQEVvQM42Ry8/ILrSqszeiaVzP3An2ta12dlow7qRARjhA7N3xbxqlDKQAwyzEbWSIURIRWcVH4/GEryVtcACt6hnEytd/7G3VH/g0A2H82TbfdHy+2R70KJhf8Z+807oDj8ww/A1S/w9NSOI2Q4CAkTeiN93qpTTQtmTukLfo1j3WDVCZY0TOMk7h6Ixvx41YUnl+7mY37p262es1yRcz1knaY7/k84RHAwz9r16VfdK8sBlgyzNhD6fIN7T2YNjWjnCmO3bCiZxgn0XzsClzOyC48n7wqUbft4pfa69YFDCV0lN+ndYHr59wriw3qVtAPfqbk7voVNMs9vSzHip5hXMTMTScLj78eaIrm2KJaGatp9BgA89SJSjyJUSet+LgoHBnXQ2VWWa2c84KwOQIreoYpIqkZWYgbvsRqG2ViEctIlQdGd8fB0d1dIpvPcnqL7TYupqvO7NwWYSFqM8wYOXyDp7Cp6IloJhGlENEBnXoioslElEhE+4mohfPFZBjv5e8DF2y2UcYxV87uASneekCtzyupK8et6fWJuu6WZ0M3P9FWitMzsrftDVYtDozujkkPNMFH9ze224rH2Rj56/oRwNcAftKp7wmgjvzTGsA38ifDBART1+ivxSt5oWMtPBgfWxiciwFwx2vA0b+BOner6ybVAkapk4q7i7vqxmD+C+3QvKpjy2ylwkLwYHxVJ0vlGDYVvRBiPRHFWWnSF8BPQgrXtpWIyhBRJSHEeSvXMIzfcC7NeK5QVvIWVGstOVMBAAiAIhhYfi6QlwsEe+5tRyvVoC/ijPeJKgCUqdmT5TIVRDSYiHYS0c5Lly45YWiG8T62jujiaRF8k3YvqcvSeb7oDJyh6LX8gjVjdAohpgkh4oUQ8TExMU4YmmG8D2XKOcYO4jUsbVjROwVnvBMlA1AuRMUC8C4jWIZxMhlZuZi07LBZtqAut5XHuH6NzNp5S/RCnyCqJvDsKqBiE2CcPBFc+CLw8k7PyuUHOEPRLwbwEhH9BmkTNo3X5xl/55u1iaqUcDOebFl4vObNjihXqpi7xfJ9YuPNz1P9MKeuBzBiXjkbwBYA9YgomYieIaIhRDREbrIUwAkAiQCmA3jRZdIyjAfpPXkDPlgkWRlvPHbZatsa0SUR4WCOUQZAtXaelsCvMGJ184iNegFgqNMkYhgv5eC56zh47jqaxJbBvmT9YGWME+j3rZR1qmycpyXxC9gzlmGscPVGNhp9sBz/nbteWPbG7/s8KFGAUFZOKh4eCWRlAAmRXhf/xpdgRc8wVpi6NhEZWbnoNXmDp0UJTM7vAz6SrbU/c8xDlWFFzzBWmb7hpO1GAGpGl3SxJAzjOAEaYINhnMPhsT2w/OAF9Gla2dOiMIwurOgZpgiEhwajbzNNR3CG8Rp46YZhHMRf4qAw/g8reoZxkHlD2npaBP+m92fqsvw898vhB/DSDcPokHYrR1X2/j0NUDosBJ3rlweRVpgnxmm0fAZY8rp52bk9au9ZxiY8o2cYHZbsV0fy6FA3Gg+1rMrhht1Fhcbm5+f3ekYOH4cVPcPokJGlntHXLm8sSTTjJAavAd44ajo/s91zsvgwrOgZRoezV295WgQmOBQoXQGIriudE6ssR+DfGhPwZOfmIy9fnUKhWjl2gvIa2sixEq+d9qwcPgpvxgYQlzOycO1mDmqXL+VpUbyKuiP/BqCOHT/2r/8AAE+1j8ODt1dFZAmORulxTm3ytAQ+CSv6AKLN+FXIzRf448V2aF4tsG3Ar97IRvFiwUjPzLXZ9sSlG2hQOcINUjG6RNX0tAQ+DSv6ACJXXp7oN3UzTozvhaCgwDUPbD52hars4Lk0NKwcqSrv15w9Xz1Ojbs8LYFPw2v0AUqPL9d7WgSvY/mBC4XHyjX7trXKeUIcRonSZ0FopqRmrMCKPkBITEk3Oz96McNDkngWIQTihi/RrJu8OrHwuNa7SwuPK0Rwsm+vYgw/eO2FFX2A8NHSw54WwStYvM968or7pmyC4BmjdyPygKx02+2YQljRBwirDqd4WgSv4EJaptX6vWeuYeis3W6ShnGYHPZxsAdW9AxjwdJ/L9huxHiWnFtAbhZww3qSdkaCFX0As+HYpYBbprh6Ux3WgPERHptvOv6yCTCuPDCplnP6FgI4tQVY/4lz+vMyWNFDep3PyvWv8Ke3svPw+py9uJVt/r3a1IwqPH58xnY0Gf2Pu0XzKPuTr9nV/u4GFVwkCWM3tbsC9e91Xn9ndgAph4DElcDoMsAPPYDVY4EvmjhvDC8h4BV9bl4+2ny0CvVGLnPruHHDl+hafziD+qOWYcGes6g/ahk+WX6ksPy3weYx1I04DPkTm4+nAgC6NaiAYsHSn/+LHWvh+PheuN/CXn7iA00w/QkOietVXDyoLkt3YKnt1jVgRldgahvgl/7mdddOAXm5wI7vgTz/eAP0G0Xf44v1ePHXXXZfl3jJZGaYrxHvxB/4ek2i7UYBxl11Y7BxeCc82S4Or99dF8FBhEHt4szaPBRf1TPCMfpUbqEu2/Cp/f1ssLFEM/8ZYMkbwNho+/v2QvxG0R++kG73JtqivWfR44sNhecp6VnOFssraV6tTOFxdKliHpTEc7SpWQ7lS4cjoU9DhMgz+6ZVy9i4ivE4D8xQl22fZn8/m7+yXl8iynq9j+EXiv7IBcdsal/5zTyJwcXr1k3vnMVbv+8rPN5+8orT+79yI9tqfc9GFQuPb6toHsMlPTMHG4/5vyWDrcBu4+5r5CZJGK9k50xPS+BU/ELRd//COe78GxPdo+B+35VcePzDppNO77+FRhwXJc/daQoQZfmdbx+7Eo/N2IZLfvh2Y2Rp7pdnWqNKmeJ44PZYN0jEOMTA3+2/JiESWDXG+bL4CH6h6JX8vPWUw9fm5OU7URJjnLvmfscPIioMyVuwIQlIljrZ8u+g5YcrceVGNuKGL8HcHWfcLqMruHLT+psOANxRJxqbhndGeGiwGyRiHKJuN/va58v/19bW8is2Bso31K5bMcq+8bwQn1X0W46nIis3D2ctFOX7Cw843KetJQ9XsC85rUjX5+ULjP7zIDKyJOuZoxftW8bKVjzc2k1YZVZX8Gbw9vz9RZLRW7DlFcv4EH2+AvprrNdrYZlgXIvn1gIvbtauSzlkWCxvxZCiJ6IeRHSEiBKJaLhG/ZNEdImI9so/zzpfVBOTVx3DI9O3ot7IZWg/YbXT+v1pyymcuOT8YF+JKekQQuCXrafMTB2dwcTlh/HDpiS0GLMCF9Iy0e1z0zLWlhGd7erL352Jjrvg3jIeosUTQOMHTOe5ViZpu36w3V+wlYjtfhBXx6aiJ6JgAFMA9ATQAMAjRNRAo+kcIUQz+ed7J8tZyOnUm/hsxVHbDR2k86frnNZXyvVMxA1fgq6frccn/xzByIUHNE0dD56zf1YvhMCsbacxff0JANLMvM1H5jPySpHFsfil9oXne96/W7Ov7SevIDXD/9bkLVnN8X78l/WTtMuL4vldQo6SWamZ4314CUZm9K0AJAohTgghsgH8BqCva8XS565Ja2y2sdcRSWmF4kxajTcp3ilrjuu26z15o6H+rt3Mxpg//0NWbh7WHrmEd//4F3r7iwUKvkmsZDLYtGoZlC2pbUr50HdbcPu4lVbHPnzhuiEZvZlFe6XIlRHhnG/H71g/UV128wqwZrz16576G3j3vHbdE4ukz2uO7/t5C0YUfRUAyt24ZLnMkv5EtJ+I5hGRpqcJEQ0mop1EtPPSpUsOiAvEli1uqN03a/UVKwAcOGuaRU/ob+7yPHD6VsMxYLQ2K7/fcAIr/7to6HqjnLlyE83GrMDMTScxYNpWJKZYX4aoGWMyH0ya0BuLhra30to2Sn8DX+PoxXRsVlgXaWWRYvyMvFxgYg31A2D/75IFTgHV2wHFSqivv3sMkLxTOj6yFLjwr+tkdQNGFL1WvjlLLfgngDghRBMAKwH8T6sjIcQ0IUS8ECI+JibGPkllGhn8J/14mfX4638q4pJHFjdP+rz5eCr2nrEeE+WfgxcK3xzenr/f7MEwbskhPPvTTtsy9m9ss00Bd040vcnsOX0NHy61vkEUTIGbJtCSbp+vx8DvtxWej2Ubef8hUsd7eVoH7fIFNrYPR10FRqYA7V+RLHEK+PYO6SFhbS/AizGi6JMBKH+bsQDMsjcIIVKFEAWLvNMB3O4c8dTczNEOPla9nMZT2Qr/nrW+Lr7sgHUv28E/m4db6PjJWrw+dy/WHDG2DlylTHG0jDPmfedIhMnixWybB/43prvd/SoZ8vMuxA1fgrSbOcjJy8ep1BtF6s9dBKo3sF/yoGJOmavYZ7rooPVdUBAQEiYdl7QIf7DgWWCcYxNUT2NE0e8AUIeIahBRMQADACxWNiCiSorTPgBcZo/0yYPakeWeu7MmHmtTzXA/t+QHxjN31NCs79bQvqiFp1JvYsHus3jqhx2G2g/pUBPVoow9nE5cdo0CLVHMvrXqKWsSC4OxCSGw7KD0MGw65h/0/2YzOkxaizNXbrpCVKcSHMBJ0f2OCgrb93UTgTmPmy/NFBCpoRvIhvor4R9xbgADil4IkQvgJQDLISnwuUKIg0Q0hoj6yM2GEdFBItoHYBiAJ10lcPnS4Uia0FtlQdKxXgxG9zF/JY8bvgTzFF6oBSSmpGPPaWlpxnLZpoDgINe6GNSvFFEYYwWwPmt3V2gGW0xSmIb+b3OSWd1+2R9AucTkabJz8zU35kNcfG8ZNxKqyOd7dhdwaLF2uzZD1GU1O1rvu1hJR6XyOgz9xQshlgoh6gohagkhPpTLRgkhFsvHI4QQDYUQTYUQnYQQLk9QamlBUjEiHMFBhC8HmJtCvamIK1NA189MtuYF+nX7u13wSCvTU//3nfreoI7OWpXxU+Itlm3Ss/TDBQ+cvk23zhXMf6Fd4XFM6TDNNtY8kP/af07lyOZqBs3cjrk7z2Dz8cuFD80Go7RDT4eHsqL3S4J1luSe/gfYMkVdfs8X1vvzo30un/6Lv61iaZQrWQxJE3oXzo77NtMyCNKnT7PKAIDyEeFI6GNyD/h122nda+ydtS5+qT1WvHYXujWogMjiofj7lTtVbW5mOZ74ZOM7nfDNoy3Quob08Hi+Q00bV5hImtAb7/S4zazs9uplAQCNq0Ri83Btp6vjl/SXk16atQftJ6zGDSsPL2ez7uglvD1vPwZO31boZ5GrY3tKfvQPzACo2lr6vKUTILBaa6DDO+rystVdJ5OX4dOKftmrd2GXjhOQEmvLIjWiTa9nYSHBmDO4jVNkK6B97XJoElsGdSqURvmIcOz7oBvqV4pQtRv4/Vaz89WHLyJu+BKkKJZtFrzYzqxN/xaxqFuhFCpFFkfPxpUw5/m2ODi6O97pbq64bbEx0WTq2rV+eQDSA+DPl+9AaLDjfyINP1jukbADs7frP6S/eVQjnjnj20RIkzVcOaGui39G+mw20HnjHbPuc+KN+LSiN8othaWOLQuWVjWsW8LM2WGuRHaO7Gq1vd5mryUnLGbIT/8omWcqna4aVIpA7yamfe9PH2qKf17rYLa5WDIsBEF2bjYqs0y90NFJOThlLC1xhBCYuOwwTqc6b9PWMhjd5Qy1CVyxkCA80qoqunJqQP/j7G7p82aqefnAuUAvOcFIcKh0XsCdb9g3RkWFEciv/fXbeSkBoeizc02KQBnv5A+LGTJg+7X+nfkmx4nq5UogulQYhvfUn0E3jbWezOLzh5tarVcSHhqMoR1rAwDe6l7P8HW2+HJAc8WZc5c11h8zd4xbsPsspq49bsjDWY9hs/fg739N3ozD56udWdZYhDuYM7gNPrq/SZHeUBgvpccE7fK63SVzSeV5rS5AnW5AF4MRKYNky7Sn3Ztq1Nn45V/9hrc7mZ2nKqJSKjdim1cra7WfrSfMZwiHzpuHAZj0gKSkh3TQnwWXK6W9mVnAvU0qFx5fMxBGt0HlCOxP6IYXnTjzVq6lt6imfjAVhDQGgAEt7Uuvt/WE+brpG4rNcUfy5l5Kz8Lifefwwq+7sU92apu/W21Z9dSPJjPXI+N62LzXjA8TXUddVkfHR+TxBcCjdsSzf/+y5ERlaYGTat3z3tvwS0VvGSZhy/FUVZs2NW07Kw2YZr5ubhlMzdoyz7Z3u+DIuB42x1CaWMZbiTfzSCuTgo0ID3XqhmLDyhHo26wyFr/U3ma/A1ubLJN2v383Fg5tj1H3NDB7GCjZdeqq2XkdG5mdbLH+qOkNoe+UTYauCQvh2PJ+TZTGpOfRueoyRyAyvRUMU2Sk+8q39nr8UtFbKquRGjHqzzuwSdgyzjQrXPm6jou1TIWIcLsVjJ6VCADM3u665B9EhC8HNC8MgKY5/nNt8HLn2oVt+jStjKiSxdCsahk8Le9DWFrvaKEVq2jP6avIyjVmdfSGhbmsI17DjJ/hLr+IKIv9toRI8+iYW6YC07u4RxY7CagwfrmKTbtyOpEcjWKZc7TzbeWx+nAKlg67E3kGUtb5Gm1rlUPbWlLYVr3Z+5AONTGoXXWEBAWh7si/C8uXH7yA7g2lCKFajmj9pm7GQ/GxmPiAer9CCIHPVhzFV6sTNWPU1Bix1Krcr3TReK1nGGfxdUvgZTmu1fIR0ueNVKBkOc/JpIFfzugB4IenWqJdLdMv+8SlDOTkmRTwt48ZD8fz6T9H0O6jVdhgJWn2zCdbImlCbzSoHIHGsUWLjmjvurW3QEQoUSwExUKCMKavyTX9eUVcoJWHtKN6zt2pXmcHgG/XncBXq6UY/o5kD7unSSXbjRjGUVKPqcsmGfdjcRd+q+g71SuPmU+2LDzv/Ok6XFDYpJePCNe6DABwfHwvs/OvVifiXFpmoaK/v4V9Tlm2UIYQ9lUlb0kPnRj/yjV+S3afvqoqsxWFVIvQYNPSXYVI/fvM+CnD9rhvrPu+lT5TXB4MoEj4raIHoErwrBUOQQtbQa9G9tZKsOU4TataN8EEgLAQ37pV5UtrK9iU6/qZrPp/o5Oz0w5qRJdEl9tMtvKl7AzcxvgBUW6cUefJlnJTW5uX5+er23qQgPovsLQAMYLWDDuqiOv7jvBe7/puH9OZCCFsrqcXZV+1d5NK6N24Eno0rIia75rGsdd5jPFRanYCTrghoN4Ti4CfFAn2/hwGhGlYkmVdB4rbnsC5C9+aJjpA3Qrqm1BeJ1CXN9O+tu+FTFWal36w+KBT+7ZM2vJUuzj0alyJFXugUhD+4L5vXDtOzY7ABxZJieY9rW73Ux91mQfxe0U/+zl17Brl2r29GA1pYC9a+un4+F6FMetrlPO9kKlK89KfthQt76alpU9USdPDemL/JoWB2JgApSCv677Zrh+LSIqIaY3z+4BFQ4GNNiJkugm/V/RanqmNqjhuFTNj48miiKPLc3ep1xWDgwjr3+6EpAm9A2am2uiD5YbatYqLQoWIMCwc2h4Ptaxq5jvRr7m0WW7EKY7xM8hNznGlyttus+cXYOUHrpfFAH6v6J1NiIsUrqVdv7WwCv7AlwOaYd8H3VTlGVm5uJSuvWH7Xi9pn2LNmx0RWSIU297timYaG9mfP9wM84a0xU9Pt1bVMX7K4HXSp6uXbgqwTDNojVv27w06m4BQ9LYiUmqhTGDyrGK5xpr3alF4vE0cnmwXh4OjuyPxw55WA6X5Eotfaq8q2/hOJ/RtVgWRxUNxcHR3HBxtHpek5YdSKIj0zByz8ufuqomkCb3NQkvrER8XhWI+ZqnEFIHKzYCENCDCTX4TYaWBGhre8U0fUZd5gQVOQPwnfPaQ8QiRBSgzQI3o5XqLl+LFgpHQpyFKhoWYxb/xdbTCKsSWNeXKLRkWgpJh2sZfV2/kaJYzjFcwyCJtYUKaKQmKkmXD3SOPFfxHo1iheKj963ZVyhTH1wObY+fIrmZ29a3ieN3XHdzIysXWk+pgdAzjlbR8TvqM0XgT/9dJAdaKQEAo+jAHFD0A3NOkMqItNnM/7KeOt8IY5/BY7Yiej7cxT+t25UY23p633x0iMYzjvHEUGLwW6C0nOKne1nr7jBRg/SdFcxpxgIBQ9MWcuBTCIW/tp2C/Y/UbHVTeygWM6dsQq94wrXmeUmSgqhXje6alTIBQugJQubl52fupQNuXgJIaljmf1QdWjwVOrlfXuZCAUPTK2Ce3VSxdpL6qlSthuxFjRt9mVZA0oTdqxujHoici1FLUL1FkkBrTl9+iGB8iOATo/iHQ71vz8qx0IF9O8rPgObeKFBAhEJQ21stevcuhPnaO7Ipz1245SyTGBsoE30Xxe2AYj1FbEZv+yglgsmLmn6EdxdVVBISiB4D1b3VCThHMnKJLhanW6xnnc1fdGLMsUoBjm+kM41V8e6dHhw8YRc9LLr7BlRtqZym2h2d8nuwM8/PYVm4dnv+DGK/iiTZxnhaBYZxHu5fVZY0eAJ5d4VYxWNEzXkWfZpU9LQLDOI+IWHVZv+/cLgYresar0DO/ZBifpPXz6rJg96+Ys6JnvJqpj7bwtAgM4zjkHVFnWdEzXk2vxpzcm2GKiiFFT0Q9iOgIESUSkSpCDxGFEdEcuX4bEcU5W1CGYRif5s43gZd2emRom4qeiIIBTAHQE0ADAI8QkWV27GcAXBVC1AbwOYCPnS0owzCMT9PlfSC6jkeGNjKjbwUgUQhxQgiRDeA3AH0t2vQF8D/5eB6ALkResjjF+BwvdaoNAHili2f+KRjG3zCy/VsFwBnFeTIAy6DLhW2EELlElAagHIDLykZENBjAYACoVq2agyIz/s6LnWohOy8fL3T07yxbTIDw0M9AcDHb7VyIEUWvNTO3jLFppA2EENMATAOA+Ph498bpZHyGEsVC8K4bkr0wjFto0MfTEhhaukkGUFVxHgvgnF4bIgoBEAngijMEZBiGYYqGEUW/A0AdIqpBRMUADABgkUMLiwEMko8fALBaCDdH1mcYhmE0sbl0I6+5vwRgOYBgADOFEAeJaAyAnUKIxQBmAPiZiBIhzeQHuFJohmEYxjiGfHGFEEsBLLUoG6U4zgTwoHNFYxiGYZwBe8YyDMP4OazoGYZh/BxW9AzDMH4OK3qGYRg/hzxlBUlElwCccvDyaFh43QYA/J0DA/7OgUFRvnN1IUSMPRd4TNEXBSLaKYSI97Qc7oS/c2DA3zkwcPd35qUbhmEYP4cVPcMwjJ/jq4p+mqcF8AD8nQMD/s6BgVu/s0+u0TMMwzDG8dUZPcMwDGMQVvQMwzB+js8peluJyr0NIqpKRGuI6BARHSSiV+TyKCJaQUTH5M+ycjkR0WT5++0nohaKvgbJ7Y8R0SBF+e1E9K98zeSCNI56Y7jxuwcT0R4i+ks+ryEnjz8mJ5MvJpfrJpcnohFy+REi6q4o1/w70BvDTd+3DBHNI6LD8v1u6+/3mYhek/+uDxDRbCIK97f7TEQziSiFiA4oyjx2X62NoYsQwmd+IIVJPg6gJoBiAPYBaK6Ef0gAAAPcSURBVOBpuWzIXAlAC/m4NICjkJKsTwQwXC4fDuBj+bgXgL8hZe1qA2CbXB4F4IT8WVY+LivXbQfQVr7mbwA95XLNMdz43V8HMAvAX/L5XAAD5ONvAbwgH78I4Fv5eACAOfJxA/kehwGoId/7YGt/B3pjuOn7/g/As/JxMQBl/Pk+Q0ohehJAccXv/kl/u88A7gLQAsABRZnH7qveGFa/g7v+CZz0C28LYLnifASAEZ6Wy87vsAjA3QCOAKgkl1UCcEQ+/g7AI4r2R+T6RwB8pyj/Ti6rBOCworywnd4YbvqesQBWAegM4C/5j/IygBDLewkp10Fb+ThEbkeW97egnd7fgbUx3PB9IyApPbIo99v7DFOu6Cj5vv0FoLs/3mcAcTBX9B67r3pjWJPf15ZutBKVV/GQLHYjv6o2B7ANQAUhxHkAkD/Ly830vqO18mSNclgZwx18AeBtAPnyeTkA14QQuRpymiWXB1CQXN7e34W1MVxNTQCXAPxA0nLV90RUEn58n4UQZwF8AuA0gPOQ7tsu+Pd9LsCT99VuPehrit5QEnJvhIhKAZgP4FUhxHVrTTXKhAPlHoOI7gGQIoTYpSzWaCps1PnS7yIE0uv9N0KI5gBuQHrd1sOXvpsm8ppxX0jLLZUBlATQU6OpP91nW7jju9h9ja8peiOJyr0OIgqFpOR/FUIskIsvElElub4SgBS5XO87WiuP1Si3NoaraQ+gDxElAfgN0vLNFwDKkJQ83lJOveTy9v4uLlsZw9UkA0gWQmyTz+dBUvz+fJ+7AjgphLgkhMgBsABAO/j3fS7Ak/fVbj3oa4reSKJyr0LeQZ8B4JAQ4jNFlTKh+iBIa/cF5U/IO+ttAKTJr23LAXQjorLyTKobpHXJ8wDSiaiNPNYTFn1pjeFShBAjhBCxQog4SPdotRDiUQBrICWPt5RHL7n8YgADZGuNGgDqQNq40vw7kK/RG8OlCCEuADhDRPXkoi4A/oMf32dISzZtiKiELFPBd/bb+6zAk/dVbwx93LFp4+RNkV6QLFeOA3jP0/IYkPcOSK9V+wHslX96QVpnXAXgmPwZJbcnAFPk7/cvgHhFX08DSJR/nlKUxwM4IF/zNUwez5pjuPn7d4TJ6qYmpH/gRAC/AwiTy8Pl80S5vqbi+vfk73UEsjWCtb8DvTHc9F2bAdgp3+uFkKwr/Po+AxgN4LAs18+QLGf86j4DmA1pDyIH0mz6GU/eV2tj6P1wCASGYRg/x9eWbhiGYRg7YUXPMAzj57CiZxiG8XNY0TMMw/g5rOgZhmH8HFb0DMMwfg4reoZhGD/n/6UZsYpimDZ4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['A', 'C']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot percentage change of two columns for first 100000 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1925c689048>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['A', 'C']][0:100000].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot distribution of one column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1925c6680f0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['A'].hist(bins=1000)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}