Notebooks/Python_Tasks/01_EDA_Pandas_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Python  \n",
    "\n",
    "## Pandas - Exploratory Data Analysis (EDA) part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import zipfile\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.display import display, Image\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "#InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Basic EDA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.5.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# Importing csv file\n",
    "df = pd.read_csv('../../Data/csv/taxis_bikes_nycity.csv')\n",
    "print(type(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8/2/15</td>\n",
       "      <td>1.70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8/3/15</td>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8/4/15</td>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8/6/15</td>\n",
       "      <td>2.38</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8/7/15</td>\n",
       "      <td>2.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date  Distance Time\n",
       "0  8/2/15      1.70  NaN\n",
       "1  8/3/15      1.40  NaN\n",
       "2  8/4/15      2.10  NaN\n",
       "3  8/6/15      2.38  NaN\n",
       "4  8/7/15      2.30  NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>12/17/15</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1:39:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>12/19/15</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0:26:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>12/20/15</td>\n",
       "      <td>14.80</td>\n",
       "      <td>2:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>12/22/15</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0:28:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>12/23/15</td>\n",
       "      <td>5.63</td>\n",
       "      <td>0:51:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>12/24/15</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0:25:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>12/25/15</td>\n",
       "      <td>14.80</td>\n",
       "      <td>2:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>12/26/15</td>\n",
       "      <td>5.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>12/30/15</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0:22:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1/1/16</td>\n",
       "      <td>26.20</td>\n",
       "      <td>4:20:43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Distance     Time\n",
       "71  12/17/15     12.00  1:39:00\n",
       "72  12/19/15      3.10  0:26:15\n",
       "73  12/20/15     14.80  2:15:00\n",
       "74  12/22/15      3.10  0:28:00\n",
       "75  12/23/15      5.63  0:51:50\n",
       "76  12/24/15      3.05  0:25:08\n",
       "77  12/25/15     14.80  2:20:00\n",
       "78  12/26/15      5.20      NaN\n",
       "79  12/30/15      3.15  0:22:10\n",
       "80    1/1/16     26.20  4:20:43"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 81 entries, 0 to 80\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Date      81 non-null     object \n",
      " 1   Distance  81 non-null     float64\n",
      " 2   Time      52 non-null     object \n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 2.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         object\n",
       "Distance    float64\n",
       "Time         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Distance', 'Time'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=81, step=1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    8/2/15\n",
       "1    8/3/15\n",
       "2    8/4/15\n",
       "3    8/6/15\n",
       "4    8/7/15\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../../Data/csv/taxis_bikes_nycity.csv', parse_dates = ['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2015-08-02\n",
       "1   2015-08-03\n",
       "2   2015-08-04\n",
       "3   2015-08-06\n",
       "4   2015-08-07\n",
       "Name: Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 81 entries, 0 to 80\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   Date      81 non-null     datetime64[ns]\n",
      " 1   Distance  81 non-null     float64       \n",
      " 2   Time      52 non-null     object        \n",
      "dtypes: datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 2.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('Date', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-02</th>\n",
       "      <td>1.70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-03</th>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-04</th>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>2.38</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td>2.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-08</th>\n",
       "      <td>3.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-09</th>\n",
       "      <td>2.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-10</th>\n",
       "      <td>3.36</td>\n",
       "      <td>0:28:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-11</th>\n",
       "      <td>1.67</td>\n",
       "      <td>0:13:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-12</th>\n",
       "      <td>1.42</td>\n",
       "      <td>0:10:35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Distance     Time\n",
       "Date                         \n",
       "2015-08-02      1.70      NaN\n",
       "2015-08-03      1.40      NaN\n",
       "2015-08-04      2.10      NaN\n",
       "2015-08-06      2.38      NaN\n",
       "2015-08-07      2.30      NaN\n",
       "2015-08-08      3.40      NaN\n",
       "2015-08-09      2.50      NaN\n",
       "2015-08-10      3.36  0:28:37\n",
       "2015-08-11      1.67  0:13:07\n",
       "2015-08-12      1.42  0:10:35"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(figsize=(10,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ?df.plot   #help on method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar', figsize=(18,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = 'area', figsize=(10,6))\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.137531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.634519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>26.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Distance\n",
       "count  81.000000\n",
       "mean    3.137531\n",
       "std     3.634519\n",
       "min     0.650000\n",
       "25%     1.370000\n",
       "50%     2.100000\n",
       "75%     3.050000\n",
       "max    26.200000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2015-11-02    1.37\n",
       "2015-11-03    5.30\n",
       "2015-11-04    3.05\n",
       "2015-11-07    5.63\n",
       "2015-11-07    1.26\n",
       "2015-11-07    1.20\n",
       "2015-11-09    3.05\n",
       "2015-11-10    1.50\n",
       "2015-11-10    1.50\n",
       "2015-11-10    4.00\n",
       "2015-11-23    3.05\n",
       "2015-11-27    1.00\n",
       "2015-11-28    3.00\n",
       "Name: Distance, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2015-11','Distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc['2015-11'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def total_seconds(time):\n",
    "    if time is np.nan:\n",
    "        return np.nan \n",
    "    hrs, mins, seconds = str(time).split(':')\n",
    "    seconds = int(seconds) + 60 * int(mins) + 60 * 60 * int(hrs)\n",
    "    return seconds    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Seconds'] = df.Time.map(total_seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "      <th>Time</th>\n",
       "      <th>Seconds</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-02</th>\n",
       "      <td>1.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-03</th>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-04</th>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>2.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td>2.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-08</th>\n",
       "      <td>3.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-09</th>\n",
       "      <td>2.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-10</th>\n",
       "      <td>3.36</td>\n",
       "      <td>0:28:37</td>\n",
       "      <td>1717.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-11</th>\n",
       "      <td>1.67</td>\n",
       "      <td>0:13:07</td>\n",
       "      <td>787.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-12</th>\n",
       "      <td>1.42</td>\n",
       "      <td>0:10:35</td>\n",
       "      <td>635.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Distance     Time  Seconds\n",
       "Date                                  \n",
       "2015-08-02      1.70      NaN      NaN\n",
       "2015-08-03      1.40      NaN      NaN\n",
       "2015-08-04      2.10      NaN      NaN\n",
       "2015-08-06      2.38      NaN      NaN\n",
       "2015-08-07      2.30      NaN      NaN\n",
       "2015-08-08      3.40      NaN      NaN\n",
       "2015-08-09      2.50      NaN      NaN\n",
       "2015-08-10      3.36  0:28:37   1717.0\n",
       "2015-08-11      1.67  0:13:07    787.0\n",
       "2015-08-12      1.42  0:10:35    635.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "      <th>Seconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.137531</td>\n",
       "      <td>1901.788462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.634519</td>\n",
       "      <td>2560.424171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.650000</td>\n",
       "      <td>376.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.370000</td>\n",
       "      <td>583.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.100000</td>\n",
       "      <td>1343.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.050000</td>\n",
       "      <td>1743.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>26.200000</td>\n",
       "      <td>15643.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Distance       Seconds\n",
       "count  81.000000     52.000000\n",
       "mean    3.137531   1901.788462\n",
       "std     3.634519   2560.424171\n",
       "min     0.650000    376.000000\n",
       "25%     1.370000    583.750000\n",
       "50%     2.100000   1343.500000\n",
       "75%     3.050000   1743.250000\n",
       "max    26.200000  15643.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 81 entries, 2015-08-02 to 2016-01-01\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Distance  81 non-null     float64\n",
      " 1   Time      52 non-null     object \n",
      " 2   Seconds   52 non-null     float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 2.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "      <th>Seconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.137531</td>\n",
       "      <td>1220.901235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.634519</td>\n",
       "      <td>2240.756985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.370000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.100000</td>\n",
       "      <td>573.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.050000</td>\n",
       "      <td>1426.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>26.200000</td>\n",
       "      <td>15643.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Distance       Seconds\n",
       "count  81.000000     81.000000\n",
       "mean    3.137531   1220.901235\n",
       "std     3.634519   2240.756985\n",
       "min     0.650000      0.000000\n",
       "25%     1.370000      0.000000\n",
       "50%     2.100000    573.000000\n",
       "75%     3.050000   1426.000000\n",
       "max    26.200000  15643.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Minutes'] = df['Seconds'].map(lambda x: x / 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Distance</th>\n",
       "      <th>Seconds</th>\n",
       "      <th>Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.137531</td>\n",
       "      <td>1220.901235</td>\n",
       "      <td>20.348354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.634519</td>\n",
       "      <td>2240.756985</td>\n",
       "      <td>37.345950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.370000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.100000</td>\n",
       "      <td>573.000000</td>\n",
       "      <td>9.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.050000</td>\n",
       "      <td>1426.000000</td>\n",
       "      <td>23.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>26.200000</td>\n",
       "      <td>15643.000000</td>\n",
       "      <td>260.716667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Distance       Seconds     Minutes\n",
       "count  81.000000     81.000000   81.000000\n",
       "mean    3.137531   1220.901235   20.348354\n",
       "std     3.634519   2240.756985   37.345950\n",
       "min     0.650000      0.000000    0.000000\n",
       "25%     1.370000      0.000000    0.000000\n",
       "50%     2.100000    573.000000    9.550000\n",
       "75%     3.050000   1426.000000   23.766667\n",
       "max    26.200000  15643.000000  260.716667"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Distance', ylabel='Minutes'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='Distance', y='Minutes', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\WZHRERO\\AppData\\Local\\Temp\\1\\ipykernel_22372\\1134722465.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "      <th>Seconds</th>\n",
       "      <th>Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Distance</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997203</td>\n",
       "      <td>0.997203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seconds</th>\n",
       "      <td>0.997203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minutes</th>\n",
       "      <td>0.997203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Distance   Seconds   Minutes\n",
       "Distance  1.000000  0.997203  0.997203\n",
       "Seconds   0.997203  1.000000  1.000000\n",
       "Minutes   0.997203  1.000000  1.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\WZHRERO\\AppData\\Local\\Temp\\1\\ipykernel_22372\\875244283.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr(method = 'spearman')\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "      <th>Seconds</th>\n",
       "      <th>Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Distance</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.96482</td>\n",
       "      <td>0.96482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seconds</th>\n",
       "      <td>0.96482</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minutes</th>\n",
       "      <td>0.96482</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Distance  Seconds  Minutes\n",
       "Distance   1.00000  0.96482  0.96482\n",
       "Seconds    0.96482  1.00000  1.00000\n",
       "Minutes    0.96482  1.00000  1.00000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(method = 'spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\WZHRERO\\AppData\\Local\\Temp\\1\\ipykernel_22372\\3622761649.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr(method = 'kendall')\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "      <th>Seconds</th>\n",
       "      <th>Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Distance</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.88305</td>\n",
       "      <td>0.88305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seconds</th>\n",
       "      <td>0.88305</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minutes</th>\n",
       "      <td>0.88305</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Distance  Seconds  Minutes\n",
       "Distance   1.00000  0.88305  0.88305\n",
       "Seconds    0.88305  1.00000  1.00000\n",
       "Minutes    0.88305  1.00000  1.00000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(method = 'kendall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pp56a97///bnpppuydevWfPKTn8z8+fNf8eXhAAAAcDjpV2h/6UtfSpKcc845fdZvv/32fPCDH0ySfP7zn09tbW3mzp2bzs7OzJw5M7fddlvv3rq6uixfvjxXXXVVJk+enKFDh+YDH/hA/uEf/uHAngkAAAAcAg7oe7SrpaOjI8OHD9+n7y8DAP5bV1dX7r333syePduHoQFAP/SnQ/frw9AAAACAVya0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAK6ndor1mzJhdddFHGjh2bmpqa3H333X2Of/CDH0xNTU2fPxdccEGfPc8//3wuueSSNDY2ZsSIEbn88suzY8eOA3oiAAAAcCjod2jv3Lkzb3rTm3Lrrbf+yT0XXHBBtmzZ0vvnrrvu6nP8kksuyc9//vOsXLkyy5cvz5o1a3LllVf2f3oAAAA4xAzq7w/MmjUrs2bNetU9DQ0NaWpqesVjv/zlL3P//ffnxz/+cc4888wkyT/90z9l9uzZufnmmzN27Nj+jgQAAACHjH6H9r548MEHM2rUqLzmNa/JO9/5znz2s5/NsccemyRZu3ZtRowY0RvZSXL++eentrY2Dz/8cN797nfvdX+dnZ3p7Ozsvd3R0ZEk6erqSldX18F4CgAwIO05bzp/AkD/9OfcWTy0L7jggsyZMycTJkzIU089leuuuy6zZs3K2rVrU1dXl61bt2bUqFF9hxg0KCNHjszWrVtf8T6XLFmSG264Ya/1FStWZMiQIaWfAgAMeCtXrqz2CABwWNm1a9c+7y0e2hdffHHv31taWnLaaaflpJNOyoMPPpjzzjtvv+5z8eLFWbhwYe/tjo6OjBs3LjNmzEhjY+MBzwwAR4qurq6sXLky06dPT319fbXHAYDDxp5XVu+Lg/LS8T/2+te/Pscdd1yefPLJnHfeeWlqasq2bdv67Nm9e3eef/75P/m+7oaGhjQ0NOy1Xl9f75cEANgPzqEA0D/9OW8e9O/R/s1vfpPf/e53GTNmTJJk8uTJeeGFF7J+/frePT/4wQ/S3d2ds84662CPAwAAAAdVv69o79ixI08++WTv7U2bNuXRRx/NyJEjM3LkyNxwww2ZO3dumpqa8tRTT+UTn/hEmpubM3PmzCTJKaeckgsuuCBXXHFFvvzlL6erqysLFizIxRdf7BPHAQAAOOz1+4r2T37yk5x++uk5/fTTkyQLFy7M6aefnk996lOpq6vLz372s/zlX/5lTj755Fx++eU544wz0t7e3uel31/72tcyceLEnHfeeZk9e3bOPvvsfOUrXyn3rAAAAKBK+n1F+5xzzklPT8+fPP69733vz97HyJEjc+edd/b3oQEAAOCQd9Dfow0AAABHEqENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAHCEqlUpWr16dNWvWZPXq1alUKtUeCQAGJKENAEeAtra2NDc3Z/r06bnlllsyffr0NDc3p62trdqjAcCAI7QBYIBra2vLvHnz0tLSkvb29tx1111pb29PS0tL5s2bJ7YBoLCanp6enmoP0V8dHR0ZPnx4tm/fnsbGxmqPAwCHrEqlkubm5rS0tOTuu+9OpVLJvffem9mzZ6euri6tra3ZsGFDNm7cmLq6umqPCwCHrP50qCvaADCAtbe35+mnn851112X2tq+p/3a2tosXrw4mzZtSnt7e5UmBICBR2gDwAC2ZcuWJMmkSZNe8fie9T37AIADJ7QBYAAbM2ZMkmTDhg2veHzP+p59AMCBE9oAMIBNmTIl48ePz4033pju7u4+x7q7u7NkyZJMmDAhU6ZMqdKEADDwCG0AGMDq6uqydOnSLF++PK2trVm3bl1efPHFrFu3Lq2trVm+fHluvvlmH4QGAAUNqvYAAMDBNWfOnCxbtiyLFi3K1KlTe9cnTJiQZcuWZc6cOVWcDgAGHl/vBQBHiEqlklWrVuW+++7LrFmzcu6557qSDQD7qD8d6oo2ABwh6urqMm3atOzcuTPTpk0T2QBwkHiPNgAAABQktAEAAKAgoQ0AR4hKpZLVq1dnzZo1Wb16dSqVSrVHAoABSWgDwBGgra0tzc3NmT59em655ZZMnz49zc3NaWtrq/ZoADDgCG0AGODa2toyb968tLS0pL29PXfddVfa29vT0tKSefPmiW0AKMzXewHAAFapVNLc3JyWlpbcfffdqVQquffeezN79uzU1dWltbU1GzZsyMaNG30KOQC8iv50qCvaADCAtbe35+mnn851112X2tq+p/3a2tosXrw4mzZtSnt7e5UmBICBR2gDwAC2ZcuWJMmkSZNe8fie9T37AIADJ7QBYAAbM2ZMkmTDhg2veHzP+p59AMCBE9oAMIBNmTIl48ePz4033pju7u4+x7q7u7NkyZJMmDAhU6ZMqdKEADDwCG0AGMDq6uqydOnSLF++PK2trVm3bl1efPHFrFu3Lq2trVm+fHluvvlmH4QGAAUNqvYAAMDBNWfOnCxbtiyLFi3K1KlTe9cnTJiQZcuWZc6cOVWcDgAGHl/vBQBHiEqlklWrVuW+++7LrFmzcu6557qSDQD7qD8d6oo2ABwh6urqMm3atOzcuTPTpk0T2QBwkHiPNgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQUIbAAAAChLaAAAAUJDQBgAAgIKENgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQUIbAAAAChLaAAAAUJDQBgAAgIKENgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQUIbAAAAChLaAAAAUJDQBgAAgIKENgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQUIbAAAAChLaAAAAUJDQBgAAgIKENgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQUIbAAAAChLaAAAAUJDQBgAAgIKENgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQUIbAAAAChLaAAAAUJDQBgAAgIKENgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQUIbAAAAChLaAAAAUJDQBgAAgIKENgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQUIbAAAAChLaAAAAUJDQBgAAgIKENgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQUIbAAAAChLaAAAAUJDQBgAAgIKENgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQUIbAAAAChLaAAAAUJDQBgAAgIKENgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQUIbAAAACup3aK9ZsyYXXXRRxo4dm5qamtx99919jvf09ORTn/pUxowZk6OPPjrnn39+Nm7c2GfP888/n0suuSSNjY0ZMWJELr/88uzYseOAnggAAAAcCvod2jt37syb3vSm3Hrrra94/KabbsoXv/jFfPnLX87DDz+coUOHZubMmXnppZd691xyySX5+c9/npUrV2b58uVZs2ZNrrzyyv1/FgDAn1WpVLJ69eqsWbMmq1evTqVSqfZIADAg1fT09PTs9w/X1OTb3/52Wltbk/zhavbYsWOzaNGiXHPNNUmS7du3Z/To0bnjjjty8cUX55e//GVOPfXU/PjHP86ZZ56ZJLn//vsze/bs/OY3v8nYsWP3epzOzs50dnb23u7o6Mi4cePy3HPPpbGxcX/HB4Ajxre//e1ce+21efrpp3vXxo8fn8997nN597vfXb3BAOAw0dHRkeOOOy7bt2//sx06qOQDb9q0KVu3bs3555/fuzZ8+PCcddZZWbt2bS6++OKsXbs2I0aM6I3sJDn//PNTW1ubhx9++BVP9kuWLMkNN9yw1/qKFSsyZMiQkk8BAAactWvX5qabbsqZZ56Zq666KieccEI2b96cZcuW5eKLL84nPvGJTJ48udpjAsAhbdeuXfu8t2hob926NUkyevToPuujR4/uPbZ169aMGjWq7xCDBmXkyJG9e/6nxYsXZ+HChb2391zRnjFjhivaAPAqKpVKrr766syePTv//u//nkqlkpUrV2bBggX5u7/7u8ydOzff/OY3c/3116eurq7a4wLAIaujo2Of9xYN7YOloaEhDQ0Ne63X19envr6+ChMBwOHhoYceytNPP5277rorDQ0N6erqSvLf59C///u/z9vf/vasW7cu55xzTnWHBYBDWH/as+jXezU1NSVJnn322T7rzz77bO+xpqambNu2rc/x3bt35/nnn+/dAwCUsWXLliTJpEmTXvH4nvU9+wCAA1c0tCdMmJCmpqY88MADvWsdHR15+OGHe9/7NXny5LzwwgtZv359754f/OAH6e7uzllnnVVyHAA44o0ZMyZJsmHDhlc8vmd9zz4A4MD1O7R37NiRRx99NI8++miSP3wA2qOPPprNmzenpqYmV199dT772c/mP/7jP/LYY4/l0ksvzdixY3s/mfyUU07JBRdckCuuuCKPPPJIHnrooSxYsCAXX3zxK37iOACw/6ZMmZLx48fnxhtvTHd3d59j3d3dWbJkSSZMmJApU6ZUaUIAGHj6/R7tn/zkJzn33HN7b+/5kLIPfOADueOOO/KJT3wiO3fuzJVXXpkXXnghZ599du6///4cddRRvT/zta99LQsWLMh5552X2trazJ07N1/84hcLPB0A4I/V1dVl6dKlmTdvXlpbW/Pxj388L774YtatW5d//Md/zPLly7Ns2TIfhAYABR3Q92hXS0dHR4YPH75P318GACRtbW1ZtGhRn+/RnjBhQm6++ebMmTOneoMBwGGiPx0qtAHgCFGpVLJq1arcd999mTVrVs4991xXsgFgH/WnQw+Lr/cCAA5cXV1dpk2blp07d2batGkiGwAOkqKfOg4AAABHOqENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoA8ARolKpZPXq1VmzZk1Wr16dSqVS7ZEAYEAS2gBwBGhra0tzc3OmT5+eW265JdOnT09zc3Pa2tqqPRoADDhCGwAGuLa2tsybNy8tLS1pb2/PXXfdlfb29rS0tGTevHliGwAKq+np6emp9hD91dHRkeHDh2f79u1pbGys9jgAcMiqVCppbm5OS0tL7r777lQqldx7772ZPXt26urq0tramg0bNmTjxo2pq6ur9rgAcMjqT4e6og0AA1h7e3uefvrpXHfddamt7Xvar62tzeLFi7Np06a0t7dXaUIAGHiENgAMYFu2bEmSTJo06RWP71nfsw8AOHBCGwAGsDFjxiRJNmzY8IrH96zv2QcAHDihDQAD2JQpUzJ+/PjceOON6e7u7nOsu7s7S5YsyYQJEzJlypQqTQgAA4/QBoABrK6uLkuXLs3y5cvT2tqadevW5cUXX8y6devS2tqa5cuX5+abb/ZBaABQ0KBqDwAAHFxz5szJsmXLsmjRokydOrV3fcKECVm2bFnmzJlTxekAYODx9V4AcISoVCpZtWpV7rvvvsyaNSvnnnuuK9kAsI/606GuaAPAEaKuri7Tpk3Lzp07M23aNJENAAeJ92gDAABAQUIbAAAAChLaAAAAUJDQBgAAgIKENgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQUIbAAAAChLaAAAAUNCgag8AAAPdrl278vjjj1d7jCTJjhc786PHnsprjvtJjjm6odrjJEkmTpyYIUOGVHsMAChGaAPAQfb444/njDPOqPYYfdxU7QH+yPr16/OWt7yl2mMAQDHFQ/v666/PDTfc0GftDW94Q+//yX/ppZeyaNGifP3rX09nZ2dmzpyZ2267LaNHjy49CgAcEiZOnJj169dXe4wkyRNbXsjCbz2WW/6qJW8YM6La4yT5w78PAAwkB+WK9hvf+MZ8//vf/+8HGfTfD/Oxj30s99xzT771rW9l+PDhWbBgQebMmZOHHnroYIwCAFU3ZMiQQ+aKbe2vfpeG9hdzyqQ35c0nHlvtcQBgQDoooT1o0KA0NTXttb59+/b8y7/8S+688868853vTJLcfvvtOeWUU7Ju3bq87W1vOxjjAAAAwP8xByW0N27cmLFjx+aoo47K5MmTs2TJkpxwwglZv359urq6cv755/funThxYk444YSsXbv2T4Z2Z2dnOjs7e293dHQkSbq6utLV1XUwngIADEi7d+/u/a9zKADsu/6cN4uH9llnnZU77rgjb3jDG7Jly5bccMMNmTJlSjZs2JCtW7dm8ODBGTFiRJ+fGT16dLZu3fon73PJkiV7ve87SVasWOFTSgGgH369I0kGZd26dfmvDdWeBgAOH7t27drnvcVDe9asWb1/P+2003LWWWflxBNPzDe/+c0cffTR+3WfixcvzsKFC3tvd3R0ZNy4cZkxY0YaGxsPeGYAOFL8v5ufTx77Sd72trflTSeMrPY4AHDY2PPK6n1x0L/ea8SIETn55JPz5JNPZvr06Xn55Zfzwgsv9Lmq/eyzz77ie7r3aGhoSEPD3t/1WV9fn/r6+oMxNgAMSHs+oHTQoEHOoQDQD/05b9YexDmSJDt27MhTTz2VMWPG5Iwzzkh9fX0eeOCB3uNPPPFENm/enMmTJx/sUQAAAOCgK35F+5prrslFF12UE088Mc8880w+/elPp66uLu9973szfPjwXH755Vm4cGFGjhyZxsbGfPSjH83kyZN94jgAAAADQvHQ/s1vfpP3vve9+d3vfpfjjz8+Z599dtatW5fjjz8+SfL5z38+tbW1mTt3bjo7OzNz5szcdtttpccAAACAqige2l//+tdf9fhRRx2VW2+9NbfeemvphwYAAICqO+jv0QYAAIAjidAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAggZVewAAOFg2PbczOzt3V3uMQ8pTv93Z+99Bg/wa8MeGNgzKhOOGVnsMAAYAZ1gABqRNz+3MuTc/WO0xDlmLlj1W7REOSauuOUdsA3DAhDYAA9KeK9lfeM+b0zzqmCpPc+jY+WJnlj+4Nu86Z3KGHt1Q7XEOGU9u25Grv/GoV0AAUITQBmBAax51TCa9dni1xzhkdHV1ZevxyVtOfE3q6+urPQ4ADEg+DA0AAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUNqvYAAHAwdFZeSu1R/5VNHU+k9qhjqj3OIWP37t15Zvcz+eXzv8ygQX4N2GNTx47UHvVf6ay8lGR4tccB4DDnDAvAgPTMzl9l6IR/ynWPVHuSQ9Nt999W7REOOUMnJM/sfHPOyOhqjwLAYU5oAzAgjR16YnZu+mj+7/e8OSeNckV7j927d+ehHz6Ud5z9Dle0/8hT23bk777xaMaee2K1RwFgAHCGBWBAaqg7Kt0vvTYTGt+QU4/1UuA9urq6smnQppwy8pTU19dXe5xDRvdL29P90m/TUHdUtUcBYADwYWgAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBg6o9AAAcDC92VZIkG/5re5UnObTsfLEzP/lt0vSr/52hRzdUe5xDxpPbdlR7BAAGEKENwID01P8fTv9X22NVnuRQNCj/z5M/rvYQh6ShDX41AuDAOZsAMCDNeGNTkuSkUcfk6Pq6Kk9z6Hhiy/YsWvZYls5ryRvGDK/2OIeUoQ2DMuG4odUeA4ABQGgDMCCNHDo4F//FCdUe45Cze/fuJMlJxw/NpNcKbQA4GHwYGgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQT51HAAOsl27duXxxx+v9hhJkie2vJDOrU/mlxuOTvfvRlR7nCTJxIkTM2TIkGqPAQDFCG0AOMgef/zxnHHGGdUeo4+//tdqT/Df1q9fn7e85S3VHgMAihHaAHCQTZw4MevXr6/2GEmSHS925p5Va3PhuZNzzNEN1R4nyR/+fQBgIBHaAHCQDRky5JC5YtvV1ZX//dy2TP6LM1NfX1/tcQBgQPJhaAAAAFCQ0AYAAICChDYAAAAUJLQBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAAAAoS2gAAAFCQ0AYAAICChDYAAAAUVNXQvvXWWzN+/PgcddRROeuss/LII49UcxwAAAA4YFUL7W984xtZuHBhPv3pT+enP/1p3vSmN2XmzJnZtm1btUYCAACAA1a10L7llltyxRVX5LLLLsupp56aL3/5yxkyZEi++tWvVmskAAAAOGCDqvGgL7/8ctavX5/Fixf3rtXW1ub888/P2rVr99rf2dmZzs7O3tsdHR1Jkq6urnR1dR38gQFggNhz3nT+BID+6c+5syqh/dxzz6VSqWT06NF91kePHp3HH398r/1LlizJDTfcsNf6ihUrMmTIkIM2JwAMVCtXrqz2CABwWNm1a9c+761KaPfX4sWLs3Dhwt7bHR0dGTduXGbMmJHGxsYqTgYAh5eurq6sXLky06dPT319fbXHAYDDxp5XVu+LqoT2cccdl7q6ujz77LN91p999tk0NTXttb+hoSENDQ17rdfX1/slAQD2g3MoAPRPf86bVfkwtMGDB+eMM87IAw880LvW3d2dBx54IJMnT67GSAAAAFBE1V46vnDhwnzgAx/ImWeemb/4i7/IF77whezcuTOXXXZZtUYCAACAA1a10H7Pe96T3/72t/nUpz6VrVu35s1vfnPuv//+vT4gDQAAAA4nVf0wtAULFmTBggXVHAEAAACKqsp7tAEAAGCgEtoAAABQkNAGAACAgoQ2AAAAFCS0AQAAoCChDQAAAAUJbQAAAChIaAMAAEBBQhsAAAAKGlTtAfZHT09PkqSjo6PKkwDA4aWrqyu7du1KR0dH6uvrqz0OABw29vTnnh59NYdlaP/+979PkowbN67KkwAAAHAk+f3vf5/hw4e/6p6ann3J8UNMd3d3nnnmmQwbNiw1NTXVHgcADhsdHR0ZN25cfv3rX6exsbHa4wDAYaOnpye///3vM3bs2NTWvvq7sA/L0AYA9k9HR0eGDx+e7du3C20AOEh8GBoAAAAUJLQBAACgIKENAEeQhoaGfPrTn05DQ0O1RwGAAct7tAEAAKAgV7QBAACgIKENAAAABQltAAAAKEhoAwAAQEFCGwAOQ+ecc06uvvrqao8BALwCoQ0Ah4gPfvCDqampyYc//OG9js2fPz81NTX54Ac/mCRpa2vLZz7zmaKPf8cdd2TEiBFF7xMAjkRCGwAOIePGjcvXv/71vPjii71rL730Uu68886ccMIJvWsjR47MsGHDqjEiAPBnCG0AOIS85S1vybhx49LW1ta71tbWlhNOOCGnn35679r/fOn4+PHjc+ONN+ZDH/pQhg0blhNOOCFf+cpXeo8/+OCDqampyQsvvNC79uijj6ampiZPP/10HnzwwVx22WXZvn17ampqUlNTk+uvvz5J0tnZmWuuuSavfe1rM3To0Jx11ll58MEHe+/nV7/6VS666KK85jWvydChQ/PGN74x9957b/F/GwA4XAhtADjEfOhDH8rtt9/ee/urX/1qLrvssj/7c0uXLs2ZZ56Z//zP/8xHPvKRXHXVVXniiSf26THf/va35wtf+EIaGxuzZcuWbNmyJddcc02SZMGCBVm7dm2+/vWv52c/+1n+6q/+KhdccEE2btyY5A8va+/s7MyaNWvy2GOP5XOf+1yOOeaY/XjmADAwCG0AOMS8733vyw9/+MP86le/yq9+9as89NBDed/73vdnf2727Nn5yEc+kubm5lx77bU57rjjsmrVqn16zMGDB2f48OGpqalJU1NTmpqacswxx2Tz5s25/fbb861vfStTpkzJSSedlGuuuSZnn3127/8M2Lx5c97xjnekpaUlr3/96/Oud70rU6dOPaB/AwA4nA2q9gAAQF/HH398Lrzwwtxxxx3p6enJhRdemOOOO+7P/txpp53W+/c9wbxt27YDmuWxxx5LpVLJySef3Ge9s7Mzxx57bJLkb//2b3PVVVdlxYoVOf/88zN37tw+swDAkUZoA8Ah6EMf+lAWLFiQJLn11lv36Wfq6+v73K6pqUl3d3eSpLb2Dy9i6+np6T3e1dX1Z+9zx44dqaury/r161NXV9fn2J6Xh//N3/xNZs6cmXvuuScrVqzIkiVLsnTp0nz0ox/dp7kBYKDx0nEAOARdcMEFefnll9PV1ZWZM2ce8P0df/zxSZItW7b0rj366KN99gwePDiVSqXP2umnn55KpZJt27alubm5z5+mpqbefePGjcuHP/zhtLW1ZdGiRfnnf/7nA54ZAA5XQhsADkF1dXX55S9/mV/84hd7XUneH83NzRk3blyuv/76bNy4Mffcc0+WLl3aZ8/48eOzY8eOPPDAA3nuueeya9eunHzyybnkkkty6aWXpq2tLZs2bcojjzySJUuW5J577kmSXH311fne976XTZs25ac//WlWrVqVU0455YBnBoDDldAGgENUY2NjGhsbi9xXfX197rrrrjz++OM57bTT8rnPfS6f/exn++x5+9vfng9/+MN5z3vek+OPPz433XRTkuT222/PpZdemkWLFuUNb3hDWltb8+Mf/7j3e70rlUrmz5+fU045JRdccEFOPvnk3HbbbUXmBoDDUU3PH79ZCwAAADggrmgDAABAQUIbAAAAChLaAAAAUJDQBgAAgIKENgAAABQktAEAAKAgoQ0AAAAFCW0AAAAoSGgDAABAQUIbAAAAChLaAAAAUND/B74z/G72glTUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot('Minutes', return_type = 'axes', figsize=(12,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Min_Per_Km'] = df['Minutes'] / df['Distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Distance</th>\n",
       "      <th>Seconds</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Min_Per_Km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.137531</td>\n",
       "      <td>1220.901235</td>\n",
       "      <td>20.348354</td>\n",
       "      <td>4.959450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.634519</td>\n",
       "      <td>2240.756985</td>\n",
       "      <td>37.345950</td>\n",
       "      <td>3.803856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.370000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.100000</td>\n",
       "      <td>573.000000</td>\n",
       "      <td>9.550000</td>\n",
       "      <td>6.962963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.050000</td>\n",
       "      <td>1426.000000</td>\n",
       "      <td>23.766667</td>\n",
       "      <td>7.792350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>26.200000</td>\n",
       "      <td>15643.000000</td>\n",
       "      <td>260.716667</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Distance       Seconds     Minutes  Min_Per_Km\n",
       "count  81.000000     81.000000   81.000000   81.000000\n",
       "mean    3.137531   1220.901235   20.348354    4.959450\n",
       "std     3.634519   2240.756985   37.345950    3.803856\n",
       "min     0.650000      0.000000    0.000000    0.000000\n",
       "25%     1.370000      0.000000    0.000000    0.000000\n",
       "50%     2.100000    573.000000    9.550000    6.962963\n",
       "75%     3.050000   1426.000000   23.766667    7.792350\n",
       "max    26.200000  15643.000000  260.716667   10.000000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot: title={'center': 'Min_Per_Km'}>]], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist('Min_Per_Km')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot: title={'center': 'Min_Per_Km'}>]], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist('Min_Per_Km', bins = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist('Min_Per_Km', bins = 20, figsize = (10, 8))\n",
    "plt.xlim((5, 11))\n",
    "plt.ylim((0, 12))\n",
    "plt.title(\"Histogram Minutes/Km\")\n",
    "plt.grid(False)\n",
    "#plt.savefig('./hist_minutes_per_km.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Distance'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Distance</th>\n",
       "      <th>Time</th>\n",
       "      <th>Seconds</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Min_Per_Km</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-02</th>\n",
       "      <td>1.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-03</th>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-04</th>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>2.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td>2.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-08</th>\n",
       "      <td>3.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-09</th>\n",
       "      <td>2.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-10</th>\n",
       "      <td>3.36</td>\n",
       "      <td>0:28:37</td>\n",
       "      <td>1717.0</td>\n",
       "      <td>28.616667</td>\n",
       "      <td>8.516865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-11</th>\n",
       "      <td>1.67</td>\n",
       "      <td>0:13:07</td>\n",
       "      <td>787.0</td>\n",
       "      <td>13.116667</td>\n",
       "      <td>7.854291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-12</th>\n",
       "      <td>1.42</td>\n",
       "      <td>0:10:35</td>\n",
       "      <td>635.0</td>\n",
       "      <td>10.583333</td>\n",
       "      <td>7.453052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-13</th>\n",
       "      <td>2.35</td>\n",
       "      <td>0:17:25</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>17.416667</td>\n",
       "      <td>7.411348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-14</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0:06:16</td>\n",
       "      <td>376.0</td>\n",
       "      <td>6.266667</td>\n",
       "      <td>6.962963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-14</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0:06:16</td>\n",
       "      <td>376.0</td>\n",
       "      <td>6.266667</td>\n",
       "      <td>6.962963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-15</th>\n",
       "      <td>1.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-16</th>\n",
       "      <td>0.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Distance     Time  Seconds    Minutes  Min_Per_Km\n",
       "Date                                                         \n",
       "2015-08-02      1.70      NaN      NaN        NaN         NaN\n",
       "2015-08-03      1.40      NaN      NaN        NaN         NaN\n",
       "2015-08-04      2.10      NaN      NaN        NaN         NaN\n",
       "2015-08-06      2.38      NaN      NaN        NaN         NaN\n",
       "2015-08-07      2.30      NaN      NaN        NaN         NaN\n",
       "2015-08-08      3.40      NaN      NaN        NaN         NaN\n",
       "2015-08-09      2.50      NaN      NaN        NaN         NaN\n",
       "2015-08-10      3.36  0:28:37   1717.0  28.616667    8.516865\n",
       "2015-08-11      1.67  0:13:07    787.0  13.116667    7.854291\n",
       "2015-08-12      1.42  0:10:35    635.0  10.583333    7.453052\n",
       "2015-08-13      2.35  0:17:25   1045.0  17.416667    7.411348\n",
       "2015-08-14      0.90  0:06:16    376.0   6.266667    6.962963\n",
       "2015-08-14      0.90  0:06:16    376.0   6.266667    6.962963\n",
       "2015-08-15      1.78      NaN      NaN        NaN         NaN\n",
       "2015-08-16      0.65      NaN      NaN        NaN         NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Distance'].plot()\n",
    "pd.Series(df['Distance']).rolling(window = 2).mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Distance'].plot()\n",
    "pd.Series(df['Distance']).rolling(window = 2).sum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-08-02', '2015-08-03', '2015-08-04', '2015-08-06',\n",
       "               '2015-08-07', '2015-08-08', '2015-08-09', '2015-08-10',\n",
       "               '2015-08-11', '2015-08-12', '2015-08-13', '2015-08-14',\n",
       "               '2015-08-14', '2015-08-15', '2015-08-16', '2015-08-17',\n",
       "               '2015-08-18', '2015-08-19', '2015-08-19', '2015-08-20'],\n",
       "              dtype='datetime64[ns]', name='Date', freq=None)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Distance</th>\n",
       "      <th>Time</th>\n",
       "      <th>Seconds</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Min_Per_Km</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-02</th>\n",
       "      <td>1.37</td>\n",
       "      <td>0:08:43</td>\n",
       "      <td>523.0</td>\n",
       "      <td>8.716667</td>\n",
       "      <td>6.362530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-03</th>\n",
       "      <td>5.30</td>\n",
       "      <td>0:39:26</td>\n",
       "      <td>2366.0</td>\n",
       "      <td>39.433333</td>\n",
       "      <td>7.440252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>3.05</td>\n",
       "      <td>0:22:12</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>7.278689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-07</th>\n",
       "      <td>5.63</td>\n",
       "      <td>0:49:05</td>\n",
       "      <td>2945.0</td>\n",
       "      <td>49.083333</td>\n",
       "      <td>8.718176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-07</th>\n",
       "      <td>1.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-07</th>\n",
       "      <td>1.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-09</th>\n",
       "      <td>3.05</td>\n",
       "      <td>0:22:36</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>7.409836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-10</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0:09:00</td>\n",
       "      <td>540.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-10</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0:11:33</td>\n",
       "      <td>693.0</td>\n",
       "      <td>11.550000</td>\n",
       "      <td>7.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-10</th>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-23</th>\n",
       "      <td>3.05</td>\n",
       "      <td>0:22:35</td>\n",
       "      <td>1355.0</td>\n",
       "      <td>22.583333</td>\n",
       "      <td>7.404372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-27</th>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-28</th>\n",
       "      <td>3.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-09</th>\n",
       "      <td>2.93</td>\n",
       "      <td>0:25:00</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>8.532423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-12</th>\n",
       "      <td>1.37</td>\n",
       "      <td>0:09:15</td>\n",
       "      <td>555.0</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>6.751825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-13</th>\n",
       "      <td>5.30</td>\n",
       "      <td>0:43:36</td>\n",
       "      <td>2616.0</td>\n",
       "      <td>43.600000</td>\n",
       "      <td>8.226415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-13</th>\n",
       "      <td>2.21</td>\n",
       "      <td>0:18:59</td>\n",
       "      <td>1139.0</td>\n",
       "      <td>18.983333</td>\n",
       "      <td>8.589744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-15</th>\n",
       "      <td>6.50</td>\n",
       "      <td>0:58:43</td>\n",
       "      <td>3523.0</td>\n",
       "      <td>58.716667</td>\n",
       "      <td>9.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-17</th>\n",
       "      <td>12.00</td>\n",
       "      <td>1:39:00</td>\n",
       "      <td>5940.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>8.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-19</th>\n",
       "      <td>3.10</td>\n",
       "      <td>0:26:15</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>26.250000</td>\n",
       "      <td>8.467742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-20</th>\n",
       "      <td>14.80</td>\n",
       "      <td>2:15:00</td>\n",
       "      <td>8100.0</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>9.121622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22</th>\n",
       "      <td>3.10</td>\n",
       "      <td>0:28:00</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>9.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-23</th>\n",
       "      <td>5.63</td>\n",
       "      <td>0:51:50</td>\n",
       "      <td>3110.0</td>\n",
       "      <td>51.833333</td>\n",
       "      <td>9.206631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-24</th>\n",
       "      <td>3.05</td>\n",
       "      <td>0:25:08</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>25.133333</td>\n",
       "      <td>8.240437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-25</th>\n",
       "      <td>14.80</td>\n",
       "      <td>2:20:00</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>9.459459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-26</th>\n",
       "      <td>5.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>3.15</td>\n",
       "      <td>0:22:10</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>22.166667</td>\n",
       "      <td>7.037037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Distance     Time  Seconds     Minutes  Min_Per_Km\n",
       "Date                                                          \n",
       "2015-11-02      1.37  0:08:43    523.0    8.716667    6.362530\n",
       "2015-11-03      5.30  0:39:26   2366.0   39.433333    7.440252\n",
       "2015-11-04      3.05  0:22:12   1332.0   22.200000    7.278689\n",
       "2015-11-07      5.63  0:49:05   2945.0   49.083333    8.718176\n",
       "2015-11-07      1.26      NaN      NaN         NaN         NaN\n",
       "2015-11-07      1.20      NaN      NaN         NaN         NaN\n",
       "2015-11-09      3.05  0:22:36   1356.0   22.600000    7.409836\n",
       "2015-11-10      1.50  0:09:00    540.0    9.000000    6.000000\n",
       "2015-11-10      1.50  0:11:33    693.0   11.550000    7.700000\n",
       "2015-11-10      4.00      NaN      NaN         NaN         NaN\n",
       "2015-11-23      3.05  0:22:35   1355.0   22.583333    7.404372\n",
       "2015-11-27      1.00      NaN      NaN         NaN         NaN\n",
       "2015-11-28      3.00      NaN      NaN         NaN         NaN\n",
       "2015-12-09      2.93  0:25:00   1500.0   25.000000    8.532423\n",
       "2015-12-12      1.37  0:09:15    555.0    9.250000    6.751825\n",
       "2015-12-13      5.30  0:43:36   2616.0   43.600000    8.226415\n",
       "2015-12-13      2.21  0:18:59   1139.0   18.983333    8.589744\n",
       "2015-12-15      6.50  0:58:43   3523.0   58.716667    9.033333\n",
       "2015-12-17     12.00  1:39:00   5940.0   99.000000    8.250000\n",
       "2015-12-19      3.10  0:26:15   1575.0   26.250000    8.467742\n",
       "2015-12-20     14.80  2:15:00   8100.0  135.000000    9.121622\n",
       "2015-12-22      3.10  0:28:00   1680.0   28.000000    9.032258\n",
       "2015-12-23      5.63  0:51:50   3110.0   51.833333    9.206631\n",
       "2015-12-24      3.05  0:25:08   1508.0   25.133333    8.240437\n",
       "2015-12-25     14.80  2:20:00   8400.0  140.000000    9.459459\n",
       "2015-12-26      5.20      NaN      NaN         NaN         NaN\n",
       "2015-12-30      3.15  0:22:10   1330.0   22.166667    7.037037"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2015-11':'2015-12']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2015-11':'2016-1-1']['Distance'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Distance           1.42\n",
       "Time            0:10:35\n",
       "Seconds           635.0\n",
       "Minutes       10.583333\n",
       "Min_Per_Km     7.453052\n",
       "Name: 2015-08-12 00:00:00, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2015-8-12']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Time</th>\n",
       "      <th>Seconds</th>\n",
       "      <th>Minutes</th>\n",
       "      <th>Min_Per_Km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>1.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-08-03</td>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-08-04</td>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-08-06</td>\n",
       "      <td>2.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-08-07</td>\n",
       "      <td>2.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0:25:08</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>25.133333</td>\n",
       "      <td>8.240437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2015-12-25</td>\n",
       "      <td>14.80</td>\n",
       "      <td>2:20:00</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>9.459459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2015-12-26</td>\n",
       "      <td>5.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0:22:10</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>22.166667</td>\n",
       "      <td>7.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>26.20</td>\n",
       "      <td>4:20:43</td>\n",
       "      <td>15643.0</td>\n",
       "      <td>260.716667</td>\n",
       "      <td>9.951018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Distance     Time  Seconds     Minutes  Min_Per_Km\n",
       "0  2015-08-02      1.70      NaN      NaN         NaN         NaN\n",
       "1  2015-08-03      1.40      NaN      NaN         NaN         NaN\n",
       "2  2015-08-04      2.10      NaN      NaN         NaN         NaN\n",
       "3  2015-08-06      2.38      NaN      NaN         NaN         NaN\n",
       "4  2015-08-07      2.30      NaN      NaN         NaN         NaN\n",
       "..        ...       ...      ...      ...         ...         ...\n",
       "76 2015-12-24      3.05  0:25:08   1508.0   25.133333    8.240437\n",
       "77 2015-12-25     14.80  2:20:00   8400.0  140.000000    9.459459\n",
       "78 2015-12-26      5.20      NaN      NaN         NaN         NaN\n",
       "79 2015-12-30      3.15  0:22:10   1330.0   22.166667    7.037037\n",
       "80 2016-01-01     26.20  4:20:43  15643.0  260.716667    9.951018\n",
       "\n",
       "[81 rows x 6 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_csv('../../CSV/Data/dataframe_saved_v1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Group By and Pandas Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U -q openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath = '../../Data/'\n",
    "transform = 'xls/sales_transactions.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>account</th>\n",
       "      <th>name</th>\n",
       "      <th>order</th>\n",
       "      <th>sku</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit price</th>\n",
       "      <th>ext price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>B1-20000</td>\n",
       "      <td>7</td>\n",
       "      <td>33.69</td>\n",
       "      <td>235.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>11</td>\n",
       "      <td>21.12</td>\n",
       "      <td>232.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>B1-86481</td>\n",
       "      <td>3</td>\n",
       "      <td>35.99</td>\n",
       "      <td>107.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-06532</td>\n",
       "      <td>48</td>\n",
       "      <td>55.82</td>\n",
       "      <td>2679.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-82801</td>\n",
       "      <td>21</td>\n",
       "      <td>13.62</td>\n",
       "      <td>286.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-06532</td>\n",
       "      <td>9</td>\n",
       "      <td>92.55</td>\n",
       "      <td>832.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-47412</td>\n",
       "      <td>44</td>\n",
       "      <td>78.91</td>\n",
       "      <td>3472.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>36</td>\n",
       "      <td>25.42</td>\n",
       "      <td>915.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>32</td>\n",
       "      <td>95.66</td>\n",
       "      <td>3061.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-33087</td>\n",
       "      <td>23</td>\n",
       "      <td>22.55</td>\n",
       "      <td>518.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-33364</td>\n",
       "      <td>3</td>\n",
       "      <td>72.30</td>\n",
       "      <td>216.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-20000</td>\n",
       "      <td>-1</td>\n",
       "      <td>72.18</td>\n",
       "      <td>-72.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    account           name  order       sku  quantity  unit price  ext price\n",
       "0    383080       Will LLC  10001  B1-20000         7       33.69     235.83\n",
       "1    383080       Will LLC  10001  S1-27722        11       21.12     232.32\n",
       "2    383080       Will LLC  10001  B1-86481         3       35.99     107.97\n",
       "3    412290  Jerde-Hilpert  10005  S1-06532        48       55.82    2679.36\n",
       "4    412290  Jerde-Hilpert  10005  S1-82801        21       13.62     286.02\n",
       "5    412290  Jerde-Hilpert  10005  S1-06532         9       92.55     832.95\n",
       "6    412290  Jerde-Hilpert  10005  S1-47412        44       78.91    3472.04\n",
       "7    412290  Jerde-Hilpert  10005  S1-27722        36       25.42     915.12\n",
       "8    218895      Kulas Inc  10006  S1-27722        32       95.66    3061.12\n",
       "9    218895      Kulas Inc  10006  B1-33087        23       22.55     518.65\n",
       "10   218895      Kulas Inc  10006  B1-33364         3       72.30     216.90\n",
       "11   218895      Kulas Inc  10006  B1-20000        -1       72.18     -72.18"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tf = pd.read_excel(os.path.join(datapath, transform))\n",
    "df_tf.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12 entries, 0 to 11\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   account     12 non-null     int64  \n",
      " 1   name        12 non-null     object \n",
      " 2   order       12 non-null     int64  \n",
      " 3   sku         12 non-null     object \n",
      " 4   quantity    12 non-null     int64  \n",
      " 5   unit price  12 non-null     float64\n",
      " 6   ext price   12 non-null     float64\n",
      "dtypes: float64(2), int64(3), object(2)\n",
      "memory usage: 800.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df_tf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 700
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(os.path.join(datapath, \"figs\", 'groupby-example.png'), width=700))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
     ]
    }
   ],
   "source": [
    "gb = df_tf.groupby('order')\n",
    "print(type(gb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>account</th>\n",
       "      <th>name</th>\n",
       "      <th>order</th>\n",
       "      <th>sku</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit price</th>\n",
       "      <th>ext price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>B1-20000</td>\n",
       "      <td>7</td>\n",
       "      <td>33.69</td>\n",
       "      <td>235.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>11</td>\n",
       "      <td>21.12</td>\n",
       "      <td>232.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>B1-86481</td>\n",
       "      <td>3</td>\n",
       "      <td>35.99</td>\n",
       "      <td>107.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-06532</td>\n",
       "      <td>48</td>\n",
       "      <td>55.82</td>\n",
       "      <td>2679.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-82801</td>\n",
       "      <td>21</td>\n",
       "      <td>13.62</td>\n",
       "      <td>286.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-06532</td>\n",
       "      <td>9</td>\n",
       "      <td>92.55</td>\n",
       "      <td>832.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-47412</td>\n",
       "      <td>44</td>\n",
       "      <td>78.91</td>\n",
       "      <td>3472.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>36</td>\n",
       "      <td>25.42</td>\n",
       "      <td>915.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>32</td>\n",
       "      <td>95.66</td>\n",
       "      <td>3061.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-33087</td>\n",
       "      <td>23</td>\n",
       "      <td>22.55</td>\n",
       "      <td>518.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-33364</td>\n",
       "      <td>3</td>\n",
       "      <td>72.30</td>\n",
       "      <td>216.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-20000</td>\n",
       "      <td>-1</td>\n",
       "      <td>72.18</td>\n",
       "      <td>-72.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    account           name  order       sku  quantity  unit price  ext price\n",
       "0    383080       Will LLC  10001  B1-20000         7       33.69     235.83\n",
       "1    383080       Will LLC  10001  S1-27722        11       21.12     232.32\n",
       "2    383080       Will LLC  10001  B1-86481         3       35.99     107.97\n",
       "3    412290  Jerde-Hilpert  10005  S1-06532        48       55.82    2679.36\n",
       "4    412290  Jerde-Hilpert  10005  S1-82801        21       13.62     286.02\n",
       "5    412290  Jerde-Hilpert  10005  S1-06532         9       92.55     832.95\n",
       "6    412290  Jerde-Hilpert  10005  S1-47412        44       78.91    3472.04\n",
       "7    412290  Jerde-Hilpert  10005  S1-27722        36       25.42     915.12\n",
       "8    218895      Kulas Inc  10006  S1-27722        32       95.66    3061.12\n",
       "9    218895      Kulas Inc  10006  B1-33087        23       22.55     518.65\n",
       "10   218895      Kulas Inc  10006  B1-33364         3       72.30     216.90\n",
       "11   218895      Kulas Inc  10006  B1-20000        -1       72.18     -72.18"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dir(gb)\n",
    "\n",
    "len(gb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10001\n",
      "<class 'int'>\n",
      "   account      name  order       sku  quantity  unit price  ext price\n",
      "0   383080  Will LLC  10001  B1-20000         7       33.69     235.83\n",
      "1   383080  Will LLC  10001  S1-27722        11       21.12     232.32\n",
      "2   383080  Will LLC  10001  B1-86481         3       35.99     107.97\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "10005\n",
      "<class 'int'>\n",
      "   account           name  order       sku  quantity  unit price  ext price\n",
      "3   412290  Jerde-Hilpert  10005  S1-06532        48       55.82    2679.36\n",
      "4   412290  Jerde-Hilpert  10005  S1-82801        21       13.62     286.02\n",
      "5   412290  Jerde-Hilpert  10005  S1-06532         9       92.55     832.95\n",
      "6   412290  Jerde-Hilpert  10005  S1-47412        44       78.91    3472.04\n",
      "7   412290  Jerde-Hilpert  10005  S1-27722        36       25.42     915.12\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "10006\n",
      "<class 'int'>\n",
      "    account       name  order       sku  quantity  unit price  ext price\n",
      "8    218895  Kulas Inc  10006  S1-27722        32       95.66    3061.12\n",
      "9    218895  Kulas Inc  10006  B1-33087        23       22.55     518.65\n",
      "10   218895  Kulas Inc  10006  B1-33364         3       72.30     216.90\n",
      "11   218895  Kulas Inc  10006  B1-20000        -1       72.18     -72.18\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "for g in gb:\n",
    "    print(g[0])\n",
    "    print(type(g[0]))\n",
    "    print(g[1].head())\n",
    "    print(type(g[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order\n",
       "10001     576.12\n",
       "10005    8185.49\n",
       "10006    3724.49\n",
       "Name: ext price, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tf.groupby('order')[\"ext price\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>order</th>\n",
       "      <th>Order_Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>576.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10005</td>\n",
       "      <td>8185.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10006</td>\n",
       "      <td>3724.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order  Order_Total\n",
       "0  10001       576.12\n",
       "1  10005      8185.49\n",
       "2  10006      3724.49"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_total = df_tf.groupby('order')[\"ext price\"].sum().rename(\"Order_Total\").reset_index()\n",
    "order_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>account</th>\n",
       "      <th>name</th>\n",
       "      <th>order</th>\n",
       "      <th>sku</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit price</th>\n",
       "      <th>ext price</th>\n",
       "      <th>Order_Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>B1-20000</td>\n",
       "      <td>7</td>\n",
       "      <td>33.69</td>\n",
       "      <td>235.83</td>\n",
       "      <td>576.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>11</td>\n",
       "      <td>21.12</td>\n",
       "      <td>232.32</td>\n",
       "      <td>576.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>B1-86481</td>\n",
       "      <td>3</td>\n",
       "      <td>35.99</td>\n",
       "      <td>107.97</td>\n",
       "      <td>576.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-06532</td>\n",
       "      <td>48</td>\n",
       "      <td>55.82</td>\n",
       "      <td>2679.36</td>\n",
       "      <td>8185.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-82801</td>\n",
       "      <td>21</td>\n",
       "      <td>13.62</td>\n",
       "      <td>286.02</td>\n",
       "      <td>8185.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-06532</td>\n",
       "      <td>9</td>\n",
       "      <td>92.55</td>\n",
       "      <td>832.95</td>\n",
       "      <td>8185.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-47412</td>\n",
       "      <td>44</td>\n",
       "      <td>78.91</td>\n",
       "      <td>3472.04</td>\n",
       "      <td>8185.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>36</td>\n",
       "      <td>25.42</td>\n",
       "      <td>915.12</td>\n",
       "      <td>8185.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>32</td>\n",
       "      <td>95.66</td>\n",
       "      <td>3061.12</td>\n",
       "      <td>3724.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-33087</td>\n",
       "      <td>23</td>\n",
       "      <td>22.55</td>\n",
       "      <td>518.65</td>\n",
       "      <td>3724.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-33364</td>\n",
       "      <td>3</td>\n",
       "      <td>72.30</td>\n",
       "      <td>216.90</td>\n",
       "      <td>3724.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-20000</td>\n",
       "      <td>-1</td>\n",
       "      <td>72.18</td>\n",
       "      <td>-72.18</td>\n",
       "      <td>3724.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    account           name  order       sku  quantity  unit price  ext price  \\\n",
       "0    383080       Will LLC  10001  B1-20000         7       33.69     235.83   \n",
       "1    383080       Will LLC  10001  S1-27722        11       21.12     232.32   \n",
       "2    383080       Will LLC  10001  B1-86481         3       35.99     107.97   \n",
       "3    412290  Jerde-Hilpert  10005  S1-06532        48       55.82    2679.36   \n",
       "4    412290  Jerde-Hilpert  10005  S1-82801        21       13.62     286.02   \n",
       "5    412290  Jerde-Hilpert  10005  S1-06532         9       92.55     832.95   \n",
       "6    412290  Jerde-Hilpert  10005  S1-47412        44       78.91    3472.04   \n",
       "7    412290  Jerde-Hilpert  10005  S1-27722        36       25.42     915.12   \n",
       "8    218895      Kulas Inc  10006  S1-27722        32       95.66    3061.12   \n",
       "9    218895      Kulas Inc  10006  B1-33087        23       22.55     518.65   \n",
       "10   218895      Kulas Inc  10006  B1-33364         3       72.30     216.90   \n",
       "11   218895      Kulas Inc  10006  B1-20000        -1       72.18     -72.18   \n",
       "\n",
       "    Order_Total  \n",
       "0        576.12  \n",
       "1        576.12  \n",
       "2        576.12  \n",
       "3       8185.49  \n",
       "4       8185.49  \n",
       "5       8185.49  \n",
       "6       8185.49  \n",
       "7       8185.49  \n",
       "8       3724.49  \n",
       "9       3724.49  \n",
       "10      3724.49  \n",
       "11      3724.49  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tf1 = df_tf.merge(order_total)\n",
    "df_tf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>account</th>\n",
       "      <th>name</th>\n",
       "      <th>order</th>\n",
       "      <th>sku</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit price</th>\n",
       "      <th>ext price</th>\n",
       "      <th>Order_Total</th>\n",
       "      <th>Percent_of_Order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>B1-20000</td>\n",
       "      <td>7</td>\n",
       "      <td>33.69</td>\n",
       "      <td>235.83</td>\n",
       "      <td>576.12</td>\n",
       "      <td>0.409342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>11</td>\n",
       "      <td>21.12</td>\n",
       "      <td>232.32</td>\n",
       "      <td>576.12</td>\n",
       "      <td>0.403249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>B1-86481</td>\n",
       "      <td>3</td>\n",
       "      <td>35.99</td>\n",
       "      <td>107.97</td>\n",
       "      <td>576.12</td>\n",
       "      <td>0.187409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-06532</td>\n",
       "      <td>48</td>\n",
       "      <td>55.82</td>\n",
       "      <td>2679.36</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.327330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-82801</td>\n",
       "      <td>21</td>\n",
       "      <td>13.62</td>\n",
       "      <td>286.02</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.034942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-06532</td>\n",
       "      <td>9</td>\n",
       "      <td>92.55</td>\n",
       "      <td>832.95</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.101759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-47412</td>\n",
       "      <td>44</td>\n",
       "      <td>78.91</td>\n",
       "      <td>3472.04</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.424170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>36</td>\n",
       "      <td>25.42</td>\n",
       "      <td>915.12</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.111798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>32</td>\n",
       "      <td>95.66</td>\n",
       "      <td>3061.12</td>\n",
       "      <td>3724.49</td>\n",
       "      <td>0.821890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-33087</td>\n",
       "      <td>23</td>\n",
       "      <td>22.55</td>\n",
       "      <td>518.65</td>\n",
       "      <td>3724.49</td>\n",
       "      <td>0.139254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-33364</td>\n",
       "      <td>3</td>\n",
       "      <td>72.30</td>\n",
       "      <td>216.90</td>\n",
       "      <td>3724.49</td>\n",
       "      <td>0.058236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-20000</td>\n",
       "      <td>-1</td>\n",
       "      <td>72.18</td>\n",
       "      <td>-72.18</td>\n",
       "      <td>3724.49</td>\n",
       "      <td>-0.019380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    account           name  order       sku  quantity  unit price  ext price  \\\n",
       "0    383080       Will LLC  10001  B1-20000         7       33.69     235.83   \n",
       "1    383080       Will LLC  10001  S1-27722        11       21.12     232.32   \n",
       "2    383080       Will LLC  10001  B1-86481         3       35.99     107.97   \n",
       "3    412290  Jerde-Hilpert  10005  S1-06532        48       55.82    2679.36   \n",
       "4    412290  Jerde-Hilpert  10005  S1-82801        21       13.62     286.02   \n",
       "5    412290  Jerde-Hilpert  10005  S1-06532         9       92.55     832.95   \n",
       "6    412290  Jerde-Hilpert  10005  S1-47412        44       78.91    3472.04   \n",
       "7    412290  Jerde-Hilpert  10005  S1-27722        36       25.42     915.12   \n",
       "8    218895      Kulas Inc  10006  S1-27722        32       95.66    3061.12   \n",
       "9    218895      Kulas Inc  10006  B1-33087        23       22.55     518.65   \n",
       "10   218895      Kulas Inc  10006  B1-33364         3       72.30     216.90   \n",
       "11   218895      Kulas Inc  10006  B1-20000        -1       72.18     -72.18   \n",
       "\n",
       "    Order_Total  Percent_of_Order  \n",
       "0        576.12          0.409342  \n",
       "1        576.12          0.403249  \n",
       "2        576.12          0.187409  \n",
       "3       8185.49          0.327330  \n",
       "4       8185.49          0.034942  \n",
       "5       8185.49          0.101759  \n",
       "6       8185.49          0.424170  \n",
       "7       8185.49          0.111798  \n",
       "8       3724.49          0.821890  \n",
       "9       3724.49          0.139254  \n",
       "10      3724.49          0.058236  \n",
       "11      3724.49         -0.019380  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tf1[\"Percent_of_Order\"] = df_tf1[\"ext price\"] / df_tf1[\"Order_Total\"]\n",
    "df_tf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 700
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(os.path.join(datapath, \"figs\",'transform-example.png'), width=700))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x000002741EF8E320>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tf.groupby('order')[\"ext price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>account</th>\n",
       "      <th>name</th>\n",
       "      <th>order</th>\n",
       "      <th>sku</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit price</th>\n",
       "      <th>ext price</th>\n",
       "      <th>Order_Total</th>\n",
       "      <th>Percent_of_Order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>B1-20000</td>\n",
       "      <td>7</td>\n",
       "      <td>33.69</td>\n",
       "      <td>235.83</td>\n",
       "      <td>576.12</td>\n",
       "      <td>0.409342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>11</td>\n",
       "      <td>21.12</td>\n",
       "      <td>232.32</td>\n",
       "      <td>576.12</td>\n",
       "      <td>0.403249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>B1-86481</td>\n",
       "      <td>3</td>\n",
       "      <td>35.99</td>\n",
       "      <td>107.97</td>\n",
       "      <td>576.12</td>\n",
       "      <td>0.187409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-06532</td>\n",
       "      <td>48</td>\n",
       "      <td>55.82</td>\n",
       "      <td>2679.36</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.327330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-82801</td>\n",
       "      <td>21</td>\n",
       "      <td>13.62</td>\n",
       "      <td>286.02</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.034942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-06532</td>\n",
       "      <td>9</td>\n",
       "      <td>92.55</td>\n",
       "      <td>832.95</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.101759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-47412</td>\n",
       "      <td>44</td>\n",
       "      <td>78.91</td>\n",
       "      <td>3472.04</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.424170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>36</td>\n",
       "      <td>25.42</td>\n",
       "      <td>915.12</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.111798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>32</td>\n",
       "      <td>95.66</td>\n",
       "      <td>3061.12</td>\n",
       "      <td>3724.49</td>\n",
       "      <td>0.821890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-33087</td>\n",
       "      <td>23</td>\n",
       "      <td>22.55</td>\n",
       "      <td>518.65</td>\n",
       "      <td>3724.49</td>\n",
       "      <td>0.139254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-33364</td>\n",
       "      <td>3</td>\n",
       "      <td>72.30</td>\n",
       "      <td>216.90</td>\n",
       "      <td>3724.49</td>\n",
       "      <td>0.058236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-20000</td>\n",
       "      <td>-1</td>\n",
       "      <td>72.18</td>\n",
       "      <td>-72.18</td>\n",
       "      <td>3724.49</td>\n",
       "      <td>-0.019380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    account           name  order       sku  quantity  unit price  ext price  \\\n",
       "0    383080       Will LLC  10001  B1-20000         7       33.69     235.83   \n",
       "1    383080       Will LLC  10001  S1-27722        11       21.12     232.32   \n",
       "2    383080       Will LLC  10001  B1-86481         3       35.99     107.97   \n",
       "3    412290  Jerde-Hilpert  10005  S1-06532        48       55.82    2679.36   \n",
       "4    412290  Jerde-Hilpert  10005  S1-82801        21       13.62     286.02   \n",
       "5    412290  Jerde-Hilpert  10005  S1-06532         9       92.55     832.95   \n",
       "6    412290  Jerde-Hilpert  10005  S1-47412        44       78.91    3472.04   \n",
       "7    412290  Jerde-Hilpert  10005  S1-27722        36       25.42     915.12   \n",
       "8    218895      Kulas Inc  10006  S1-27722        32       95.66    3061.12   \n",
       "9    218895      Kulas Inc  10006  B1-33087        23       22.55     518.65   \n",
       "10   218895      Kulas Inc  10006  B1-33364         3       72.30     216.90   \n",
       "11   218895      Kulas Inc  10006  B1-20000        -1       72.18     -72.18   \n",
       "\n",
       "    Order_Total  Percent_of_Order  \n",
       "0        576.12          0.409342  \n",
       "1        576.12          0.403249  \n",
       "2        576.12          0.187409  \n",
       "3       8185.49          0.327330  \n",
       "4       8185.49          0.034942  \n",
       "5       8185.49          0.101759  \n",
       "6       8185.49          0.424170  \n",
       "7       8185.49          0.111798  \n",
       "8       3724.49          0.821890  \n",
       "9       3724.49          0.139254  \n",
       "10      3724.49          0.058236  \n",
       "11      3724.49         -0.019380  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tf[\"Order_Total\"] = df_tf.groupby('order')[\"ext price\"].transform('sum')\n",
    "df_tf[\"Percent_of_Order\"] = df_tf[\"ext price\"] / df_tf[\"Order_Total\"]\n",
    "df_tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account</th>\n",
       "      <th>name</th>\n",
       "      <th>order</th>\n",
       "      <th>sku</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit price</th>\n",
       "      <th>ext price</th>\n",
       "      <th>Order_Total</th>\n",
       "      <th>Percent_of_Order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>B1-20000</td>\n",
       "      <td>7</td>\n",
       "      <td>33.69</td>\n",
       "      <td>235.83</td>\n",
       "      <td>576.12</td>\n",
       "      <td>0.409342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>11</td>\n",
       "      <td>21.12</td>\n",
       "      <td>232.32</td>\n",
       "      <td>576.12</td>\n",
       "      <td>0.403249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>383080</td>\n",
       "      <td>Will LLC</td>\n",
       "      <td>10001</td>\n",
       "      <td>B1-86481</td>\n",
       "      <td>3</td>\n",
       "      <td>35.99</td>\n",
       "      <td>107.97</td>\n",
       "      <td>576.12</td>\n",
       "      <td>0.187409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-06532</td>\n",
       "      <td>48</td>\n",
       "      <td>55.82</td>\n",
       "      <td>2679.36</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.327330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-82801</td>\n",
       "      <td>21</td>\n",
       "      <td>13.62</td>\n",
       "      <td>286.02</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.034942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-06532</td>\n",
       "      <td>9</td>\n",
       "      <td>92.55</td>\n",
       "      <td>832.95</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.101759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-47412</td>\n",
       "      <td>44</td>\n",
       "      <td>78.91</td>\n",
       "      <td>3472.04</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.424170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>412290</td>\n",
       "      <td>Jerde-Hilpert</td>\n",
       "      <td>10005</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>36</td>\n",
       "      <td>25.42</td>\n",
       "      <td>915.12</td>\n",
       "      <td>8185.49</td>\n",
       "      <td>0.111798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>S1-27722</td>\n",
       "      <td>32</td>\n",
       "      <td>95.66</td>\n",
       "      <td>3061.12</td>\n",
       "      <td>3724.49</td>\n",
       "      <td>0.821890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-33087</td>\n",
       "      <td>23</td>\n",
       "      <td>22.55</td>\n",
       "      <td>518.65</td>\n",
       "      <td>3724.49</td>\n",
       "      <td>0.139254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-33364</td>\n",
       "      <td>3</td>\n",
       "      <td>72.30</td>\n",
       "      <td>216.90</td>\n",
       "      <td>3724.49</td>\n",
       "      <td>0.058236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>218895</td>\n",
       "      <td>Kulas Inc</td>\n",
       "      <td>10006</td>\n",
       "      <td>B1-20000</td>\n",
       "      <td>-1</td>\n",
       "      <td>72.18</td>\n",
       "      <td>-72.18</td>\n",
       "      <td>3724.49</td>\n",
       "      <td>-0.019380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    account           name  order       sku  quantity  unit price  ext price  \\\n",
       "0    383080       Will LLC  10001  B1-20000         7       33.69     235.83   \n",
       "1    383080       Will LLC  10001  S1-27722        11       21.12     232.32   \n",
       "2    383080       Will LLC  10001  B1-86481         3       35.99     107.97   \n",
       "3    412290  Jerde-Hilpert  10005  S1-06532        48       55.82    2679.36   \n",
       "4    412290  Jerde-Hilpert  10005  S1-82801        21       13.62     286.02   \n",
       "5    412290  Jerde-Hilpert  10005  S1-06532         9       92.55     832.95   \n",
       "6    412290  Jerde-Hilpert  10005  S1-47412        44       78.91    3472.04   \n",
       "7    412290  Jerde-Hilpert  10005  S1-27722        36       25.42     915.12   \n",
       "8    218895      Kulas Inc  10006  S1-27722        32       95.66    3061.12   \n",
       "9    218895      Kulas Inc  10006  B1-33087        23       22.55     518.65   \n",
       "10   218895      Kulas Inc  10006  B1-33364         3       72.30     216.90   \n",
       "11   218895      Kulas Inc  10006  B1-20000        -1       72.18     -72.18   \n",
       "\n",
       "    Order_Total  Percent_of_Order  \n",
       "0        576.12          0.409342  \n",
       "1        576.12          0.403249  \n",
       "2        576.12          0.187409  \n",
       "3       8185.49          0.327330  \n",
       "4       8185.49          0.034942  \n",
       "5       8185.49          0.101759  \n",
       "6       8185.49          0.424170  \n",
       "7       8185.49          0.111798  \n",
       "8       3724.49          0.821890  \n",
       "9       3724.49          0.139254  \n",
       "10      3724.49          0.058236  \n",
       "11      3724.49         -0.019380  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tf[\"Percent_of_Order\"] = df_tf[\"ext price\"] / df_tf.groupby('order')[\"ext price\"].transform('sum')\n",
    "df_tf"
   ]
  }
 ],
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