Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is a demo Notebook that walks you through some basic data operations using [pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data into a variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('demo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to explore data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>get</th>\n",
       "      <th>patch</th>\n",
       "      <th>upsert</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1530.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>834.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6914.0</td>\n",
       "      <td>834.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>6429.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>6508.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>7055.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7055.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>7055.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       get  patch  upsert\n",
       "0   1530.0  140.0     2.0\n",
       "1   7181.0  140.0     2.0\n",
       "2   7181.0  836.0     2.0\n",
       "3   7181.0  834.0     2.0\n",
       "4   6914.0  834.0     2.0\n",
       "..     ...    ...     ...\n",
       "95  6429.0  722.0     1.0\n",
       "96  6508.0  722.0     1.0\n",
       "97  7055.0  722.0     1.0\n",
       "98  7055.0  886.0     1.0\n",
       "99  7055.0  886.0     1.0\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>get</th>\n",
       "      <th>patch</th>\n",
       "      <th>upsert</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1530.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>834.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6914.0</td>\n",
       "      <td>834.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      get  patch  upsert\n",
       "0  1530.0  140.0     2.0\n",
       "1  7181.0  140.0     2.0\n",
       "2  7181.0  836.0     2.0\n",
       "3  7181.0  834.0     2.0\n",
       "4  6914.0  834.0     2.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* select columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1530.0\n",
       "1     7181.0\n",
       "2     7181.0\n",
       "3     7181.0\n",
       "4     6914.0\n",
       "       ...  \n",
       "95    6429.0\n",
       "96    6508.0\n",
       "97    7055.0\n",
       "98    7055.0\n",
       "99    7055.0\n",
       "Name: get, Length: 100, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['get']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>get</th>\n",
       "      <th>patch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1530.0</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>836.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>834.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6914.0</td>\n",
       "      <td>834.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>6429.0</td>\n",
       "      <td>722.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>6508.0</td>\n",
       "      <td>722.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>7055.0</td>\n",
       "      <td>722.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7055.0</td>\n",
       "      <td>886.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>7055.0</td>\n",
       "      <td>886.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       get  patch\n",
       "0   1530.0  140.0\n",
       "1   7181.0  140.0\n",
       "2   7181.0  836.0\n",
       "3   7181.0  834.0\n",
       "4   6914.0  834.0\n",
       "..     ...    ...\n",
       "95  6429.0  722.0\n",
       "96  6508.0  722.0\n",
       "97  7055.0  722.0\n",
       "98  7055.0  886.0\n",
       "99  7055.0  886.0\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['get', 'patch']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* select rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "get       7181.0\n",
       "patch      140.0\n",
       "upsert       2.0\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>get</th>\n",
       "      <th>patch</th>\n",
       "      <th>upsert</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>834.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6914.0</td>\n",
       "      <td>834.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6914.0</td>\n",
       "      <td>834.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      get  patch  upsert\n",
       "2  7181.0  836.0     2.0\n",
       "3  7181.0  834.0     2.0\n",
       "4  6914.0  834.0     2.0\n",
       "5  6914.0  834.0     2.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[2:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* show statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>get</th>\n",
       "      <th>patch</th>\n",
       "      <th>upsert</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6544.220000</td>\n",
       "      <td>769.25000</td>\n",
       "      <td>1.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>627.287258</td>\n",
       "      <td>109.80804</td>\n",
       "      <td>0.386123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1530.000000</td>\n",
       "      <td>140.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6286.000000</td>\n",
       "      <td>727.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6596.000000</td>\n",
       "      <td>790.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6898.500000</td>\n",
       "      <td>834.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7337.000000</td>\n",
       "      <td>946.00000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               get      patch      upsert\n",
       "count   100.000000  100.00000  100.000000\n",
       "mean   6544.220000  769.25000    1.180000\n",
       "std     627.287258  109.80804    0.386123\n",
       "min    1530.000000  140.00000    1.000000\n",
       "25%    6286.000000  727.00000    1.000000\n",
       "50%    6596.000000  790.00000    1.000000\n",
       "75%    6898.500000  834.00000    1.000000\n",
       "max    7337.000000  946.00000    2.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "get       6544.22\n",
       "patch      769.25\n",
       "upsert       1.18\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6544.22"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['get'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>get</th>\n",
       "      <th>patch</th>\n",
       "      <th>upsert</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>834.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7265.0</td>\n",
       "      <td>837.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7265.0</td>\n",
       "      <td>837.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7068.0</td>\n",
       "      <td>802.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7068.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>7337.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>7337.0</td>\n",
       "      <td>705.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>7055.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7055.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>7055.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       get  patch  upsert\n",
       "1   7181.0  140.0     2.0\n",
       "2   7181.0  836.0     2.0\n",
       "3   7181.0  834.0     2.0\n",
       "8   7265.0  837.0     2.0\n",
       "9   7265.0  837.0     1.0\n",
       "22  7068.0  802.0     1.0\n",
       "23  7068.0  790.0     1.0\n",
       "52  7337.0  946.0     1.0\n",
       "53  7337.0  705.0     1.0\n",
       "97  7055.0  722.0     1.0\n",
       "98  7055.0  886.0     1.0\n",
       "99  7055.0  886.0     1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter out interested rows\n",
    "data[data['get'] > 7000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "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>get</th>\n",
       "      <th>patch</th>\n",
       "      <th>upsert</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1530.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7181.0</td>\n",
       "      <td>834.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6914.0</td>\n",
       "      <td>834.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>6429.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>6508.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>7055.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7778.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7055.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7942.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>7055.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7942.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       get  patch  upsert     sum\n",
       "0   1530.0  140.0     2.0  1672.0\n",
       "1   7181.0  140.0     2.0  7323.0\n",
       "2   7181.0  836.0     2.0  8019.0\n",
       "3   7181.0  834.0     2.0  8017.0\n",
       "4   6914.0  834.0     2.0  7750.0\n",
       "..     ...    ...     ...     ...\n",
       "95  6429.0  722.0     1.0  7152.0\n",
       "96  6508.0  722.0     1.0  7231.0\n",
       "97  7055.0  722.0     1.0  7778.0\n",
       "98  7055.0  886.0     1.0  7942.0\n",
       "99  7055.0  886.0     1.0  7942.0\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['sum'] = data.sum(axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* show the correlations between each columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>get</th>\n",
       "      <th>patch</th>\n",
       "      <th>upsert</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>get</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.531862</td>\n",
       "      <td>-0.073563</td>\n",
       "      <td>0.990938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patch</th>\n",
       "      <td>0.531862</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.152113</td>\n",
       "      <td>0.640786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upsert</th>\n",
       "      <td>-0.073563</td>\n",
       "      <td>-0.152113</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.090273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0.990938</td>\n",
       "      <td>0.640786</td>\n",
       "      <td>-0.090273</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             get     patch    upsert       sum\n",
       "get     1.000000  0.531862 -0.073563  0.990938\n",
       "patch   0.531862  1.000000 -0.152113  0.640786\n",
       "upsert -0.073563 -0.152113  1.000000 -0.090273\n",
       "sum     0.990938  0.640786 -0.090273  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "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": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* show kenerl density chart (distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 16,
     "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": [
    "data['patch'].plot.kde()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to install Jupyter Notebook on your local machine?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " * Follow instructions here to intall a local one: <https://www.intunewiki.com/wiki/Jupyter_Notebook>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to query Kusto? (only works on locally installed version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Basic: query intune/intune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from iquery import config, kusto\n",
    "query = \"\"\"\n",
    "IntuneEvent\n",
    "| where env_time > ago(1h)\n",
    "| take 10\n",
    "| project env_time, EventUniqueName, ServiceName, Col1\n",
    "\"\"\"\n",
    "\n",
    "result = kusto(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Query a different cluster/database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from iquery import KustoClient\n",
    "kc = KustoClient('IcmDataWarehouse', 'icmcluster')\n",
    "query = '''\n",
    "IncidentsSnapshot(true)\n",
    "| where CreateDate >= ago(7d)\n",
    "| where OwningTenantName == 'Microsoft Intune'\n",
    "| take 5\n",
    "'''\n",
    "data = kc.kusto(query, use_time_index=False)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* When dealing with kusto & mdm data, pivot is a very common command to put different types of data into their own columns, examples are below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "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>_time</th>\n",
       "      <th>roleinstance</th>\n",
       "      <th>httpVerb</th>\n",
       "      <th>requestCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-18 02:07:00+00:00</td>\n",
       "      <td>mtfabricu_IN_17</td>\n",
       "      <td>get</td>\n",
       "      <td>1530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-03-18 02:07:00+00:00</td>\n",
       "      <td>mtfabricu_IN_17</td>\n",
       "      <td>patch</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-18 02:08:00+00:00</td>\n",
       "      <td>mtfabricu_IN_17</td>\n",
       "      <td>patch</td>\n",
       "      <td>836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-03-18 02:08:00+00:00</td>\n",
       "      <td>mtfabricu_IN_17</td>\n",
       "      <td>get</td>\n",
       "      <td>7181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-03-18 02:08:00+00:00</td>\n",
       "      <td>mtfabricu_IN_17</td>\n",
       "      <td>delete</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       _time     roleinstance httpVerb  requestCount\n",
       "0  2021-03-18 02:07:00+00:00  mtfabricu_IN_17      get          1530\n",
       "1  2021-03-18 02:07:00+00:00  mtfabricu_IN_17    patch           140\n",
       "2  2021-03-18 02:08:00+00:00  mtfabricu_IN_17    patch           836\n",
       "3  2021-03-18 02:08:00+00:00  mtfabricu_IN_17      get          7181\n",
       "4  2021-03-18 02:08:00+00:00  mtfabricu_IN_17   delete             1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = pd.read_csv('demo2.csv')\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>httpVerb</th>\n",
       "      <th>delete</th>\n",
       "      <th>get</th>\n",
       "      <th>patch</th>\n",
       "      <th>upsert</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-03-18 02:07:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-18 02:08:00+00:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7181.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-18 02:09:00+00:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6914.0</td>\n",
       "      <td>834.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-18 02:10:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7265.0</td>\n",
       "      <td>837.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-18 02:11:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6286.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "httpVerb                   delete     get  patch  upsert\n",
       "_time                                                   \n",
       "2021-03-18 02:07:00+00:00     NaN  1530.0  140.0     NaN\n",
       "2021-03-18 02:08:00+00:00     1.0  7181.0  836.0     NaN\n",
       "2021-03-18 02:09:00+00:00     1.0  6914.0  834.0     2.0\n",
       "2021-03-18 02:10:00+00:00     NaN  7265.0  837.0     NaN\n",
       "2021-03-18 02:11:00+00:00     NaN  6286.0  687.0     NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_data = data2.pivot(index='_time', columns='httpVerb', values='requestCount')\n",
    "pivot_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# If you want to learn more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Read all the resources shared in day of learning email\n",
    "2. Look at other examples in the zip/repo\n",
    "3. Ask questions in this bridge or in our [Teams channel](https://teams.microsoft.com/l/channel/19%3a6ed0d93998104a83b77dc45cacb179e8%40thread.skype/Ask%2520Jupyter%2520Notebook?groupId=9f67747e-53cb-4d4f-8a60-e849ef90fac0&tenantId=72f988bf-86f1-41af-91ab-2d7cd011db47)\n",
    "4. You may want to work on VSCode instead of WebUI as it has **intellisense** and better **source control** supports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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