Notebooks/Python_Tasks/17_Pandas_EDAa.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Python  \n",
    "\n",
    "## Pandas - Exploratory Data Analysis (EDA) part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.4.2'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         object\n",
       "Distance    float64\n",
       "Time         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Distance', 'Time'], dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=81, step=1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../../Data/CSV/taxis_bikes_nycity.csv', parse_dates = ['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('Date', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
       "    </tr>\n",
       "  </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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(figsize=(10,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ?df.plot   #help on method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar', figsize=(18,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = 'area', figsize=(10,6))\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2015-11','Distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc['2015-11'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Seconds'] = df.Time.map(total_seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Minutes'] = df['Seconds'].map(lambda x: x / 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Distance', ylabel='Minutes'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='Distance', y='Minutes', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(method = 'spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(method = 'kendall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot('Minutes', return_type = 'axes', figsize=(12,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Min_Per_Km'] = df['Minutes'] / df['Distance']"
   ]
  },
  {
   "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>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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Min_Per_Km'}>]], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist('Min_Per_Km')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Min_Per_Km'}>]], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist('Min_Per_Km', bins = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yMyNvvO31m1DJJSsjlklWLdEopVTc1IRt1deTs1yBt0cgzs3y0OkP4u9RggnPyDPdrn43/Oj5xhEvGuCVUmkp0lmsjvxsT6hNMrwGH209Gl/4JGsMGbxOsiqlVBxVHm4DYNLoUb2uC2XwAdMt04628XavGrw/iDEm6nN7/SbuPfCgAV4plab21beR5XExNr93iabA3rbP6w/0mGSNvG1feIkmy/7qrGMTiZZolFIqjvYdbmPy6FHdTnJyOPuy+gKmW7dLbpZVoul5NqvV9tg9wPe1Ho3XHyRLM3illIoPJ8BHUpBtbdvXswbv7OrUc9s+X1hNvSvAR6/DWxm89sErpQYgEIxeFlBdjDHsO9wWsf4OVgbf0umn3RvoHuCzIm/b5wuY0CSrk/H3tWSwt8cbR7xogFcqRazcXMOim1bS1OFL9qEMe/WtXtq8AaaMiZLB2wuO1bd6e5RonBp8gO01zcz63lPsqmuxMnhP1yQr9J3B92y/jBcN8EqliAfeqaSl009jmwb4/uyzO2iilWic5QqOtHm798FnOjV4PxsPNOL1B9la3TzgGnxKTLKKyB4R2Sgi60SkIp7PpVSq23mwha/+c23EwNHY7uOVHXVJOKqR42BzB1/8+xpaOv2hFsm+avAAxtBtzZjwjberG9oBqG5s79bXHmuJJlUmWd9njFlsjFmegOdSKmW9uqOO/6yrYkdtS6/rnttS22dbnoK3dx/miY3V7DzYwt766D3wAIWjMkLfh9fKczLciFgnOlXZm4VUN3Z0m4zNsbP8qoaOqMfi0z54VbHnML9+9t1kH4YaJo7YpZf9R9p7XffExupEH86IcySsdLXvcBtlBVlkZ7gj3tbJ4IFuNXiXSxiVYa0JX91o/R1qGju6tVMunVzMxOIcfvv8jqiT3qnSRWOAlSKyWkSujXQDEblWRCpEpKKuTj9ihrv79d389vkdbKlqSvahqGGgsc0LwP4jbd0vb/fx6o46xuRmJuOwRgzn9wd9t0hCVw0eem+r52zbV21n6FWN7d1q8NkZbr59/hy2Vjfx0Or9ER/fmmSN/OYylOId4E8xxiwFLgC+KCKn9byBMeZOY8xyY8zy0tLSOB/OyGGMoWLPEQD+tboyyUczfHX0s7FCKnEyUKd+7HjWLs+cv2BcMg5rxAjP4CsPtzF5dG7U24Zv4dezlJJrb9tXFZbBe3tk5BcvLGfJ5CJ+sXJ75A1CUiGDN8YcsL8eBB4BVsTz+VLJ/iPtHGzuJCfDzaNrD/S7S3u6McZwwyMbWfGT50IbHqe6I6EMvnuJ5smN1UwoymHRpKIkHNXI4fz+On0Bapo6+szgczLcvfraHblZbg42ddDc4Scnw01tU0evM1NFhO9dNI+DzZ3c8cqubvc3xoz8SVYRyRWRfOd74FxgU7yeL9VU7D0MwFfPnsmRNh/Pbz2Y5CMaXn658l3+8da+0NmG6aDByeDDSjROeebCY8cR/3xwZGsMm8MwBiaPyYl6WxEJlWl6ZfCZHt6rsya6F00qxCmz97zdsinFXLywnDtfeS9UrwfrZDSrO2cEB3igDHhNRNYDbwNPGGOejuPzpZSKPUfIz/JwzSnTKC/M5sEKLdM47nl9N394cSej06zm3NBuZaCVh9tDKxU65ZmLFo5P5qGNCE4G39UDH71EA10TrT231svN8nCoxXqsZVOKQ5dH6mv/9vlzCBr4xTNdzRJOt9OI7oM3xuwyxiyy/803xvwkXs+VilbvPcLiyUVkuF18aNlEXnm3rlsWkK4eW1/FTY9v4dx5ZVx1whTA6lVOBw2tPjLdLtp9AQ63WgHmiQ1VVnlmYmGSj274cz4B9XeSk6PAzuB7l2i66vNLJ4cF+AgZ+aTRo/j0yVN5aM1+Nh1oBLr64/VM1jTV1OFje20zy6eMBuBDyyYSNPDwmgNJPrLYPLmxmvf/4TUOtXQO6eO+uqOObzy4juOmjOZ3Vy7h0XXW7+OC377KvyoqU3qewhcI0tzpZ055PgCVR9ppbPPx2s5DXLSwPOKKiI7Kw218+f613PbiTtbsO9JrN6J0EZ7B52S4Kcnr+xNgKMD3KtFY3S8isDhs3iPaHqtffN8xjM7N5OYntoTq7zDCM3h19Nbua8AYWD7Vyg6mjMnlhOmjebCikuAIWEzqiY3VbNjfyBf/vmbI6uPrKxv43H2rmVGax5+uXk52hpuWDqs7wRcIct2/N3DKz17kthd3cqTV28+jjTxO9rlggpWpVx5uY+WWGqs8c2x5n/d9e/dh/ru+iluf2c4H/vgGi25ayaf+8jZ3vPweG/Y3pMUCZcGgobG9K4OPtkxwuKg1eDuDH5ufxejczNAuT5nuyI9XkJ3B18+eyZu7DvPsltpQgI92+6GkAX4YWr3nMC6hW1fEFcsnsbe+jbf3HE7egcVo3b4GyguzeWv3Yf73ya2DfrxddS18+p53GJ2byV8/s6JbjzLAY186hb9+ZgVzxuVz6zPbOemWF/j+o5vYfah10M89XDTa9fdj7QC//0g7T2ysZmJxDgtjLM88/P9O4raPLeXypROoPNzGT5/axvv/8DqLf7SSz95bwV2v7WZLVdOISCIGqrnDH5oMrWvuZHKURcbCFWRbgbxnpu1k8OWFOYgI4wqtLf/6mjS9csVkjhmbx0+f2hZaajhaxj+UPP3fRCVaxd4jzC0vCG0PBnDBgnJu/M9mHqyo5ITpY5J4dH072NzBgYZ2vnfRXKoaOrj79d0snFjI5UsmHtXj1TZ1cNVdbyPAfdccz9gI+2eKwGmzSjltVinbapq469XdPPBOJX97ay9nzy3jf06dznFTi/vN2IYzp4d7YnEOxaMy2FTVyGs7DnHNKdNiHldJbhZLJxdz0UIr4z/Y1MGqXfWseq+eVbvqeW5rLQDFozI4ftoYTpxh/Zs5Nm9E/+6gqzzj6K/+Dl0ZfM9M28ngxxdZ/xfHF+awq661zwDvcbu44cI5fOaeCv7yxh4gMV00aRngt9U0sauulQvtj7YtnX4a231MKIreNpUo/kCQdZUNfHhZ94CYk+nmksXjeXjNfm56/3zyszOiPEJyrdvXAFi1yatPKmJzVSPfeWgjM8fmh8oLsWps93H13W/T0Obln9eeyLSSvrseAOaMK+DWDy/iuvNn89c39vK3t/by7JZaFk0s5JpTp3PhgnF4EvDCGmpO2akoJ5NJo0fx9KYaAkET+j98NMYWZHPp4glcungCAFUN7aFgv+q9ep7eXANASV4mJ0y3A/70MUwryR1xAb+hvfsKm7EE+P4mWcsLrXjhZPD9ZeTvmz2WU44p4f6391m310nW+Pj509v5yv1rQzW5Hz62mQ/88fU+N8lNlK3VzbR5AyybOrrXdVcsn0SHL8jjG4bvuiPrKhvwuIQFEwrJcLu47eNLGZ2byefuWx3q/IhFhy/AZ+99h/fqWrjjquUcO8AukbH52XzzvNms+s5Z/PiyBTR1+PnK/Ws5/daX+POru2geYWumOwGqaFQGk4pHEQiaAZVnYjG+KIcPLpvILz68iNe/cyavfut9/PyDCzl1ZikVe47w3Uc2ceYvX+aEnz7P1/65lgfe2ce++rZh8brpT68MPoYSTfQavFWiGW8nhOOdAN9PwBYRvnvR3NDPiZhkTbsM3hcI8tauevxBw4vbDnL+gnE8vamGlk4/dc2dEUsAieSc4LQ8rL/WsWhiIbPK8niwopIrV0yOeP+DTR0E+njBFWRndGvzCmeM4WBzJ8GjvD9YAX5OeX5oEaeSvCz+7xPL+PAdq/jK/Wu559PH9ZtB+wNBvvSPtVTsPcLvPrqEU2aW9Hn7vuRkurnqhCl8fMVknttay59f283NT2zlN8/t4KPHTeLTp0wbFp/c+tNgB6ji3EwmjraO96Jj++6egb7XJO/PpNGjmDR6FFccNwljDHvq20IZ/ms763l0XRUAE4pyOGH6GE6aMYaTjykJZbTDScNRlGi6+uB7n+gEXYF9nJ3Jx1JymVtewBXLJvFARWVCMvi0C/Ab9jfS6rX+0z+9qYZRme7Q9lvv1rYkJMC/tuMQr+08xHcumNPrutV7jzC+MDuUHYQTEa5YPombn9jKjtpmZpbld7v+1me2cduL7/X53GNyM3n9O2dGXEXvjy+9x63PbO/z/qNzM3nrhrMi/mcOBA3rKxv4wNLu5aVFk4q4+bIFfOvfG7h15Xauv2Bur/uGu//tfTy3tZab3j+fSxYNzQk8Lpdw7vxxnDt/HBv2N/CnV3fzlzf28EBFJS9f975hf9LUkTYfHpeQm+lm2hirVOXU0qN5ZnMNX/vnOr569sxBP7+IMK0kl2kluXzs+MkYY9h5sCVUznlhWy0PrdlPpsfFQ58/qc9PXJWH27jhkY34A4YMj4tMt+BxucjwuMhwCxkuFxkeIcPtsv/1/t7jtu7X8/uyguyIpcCGsHVoRKy5jP7MHpfP6NzMXksKHzM2j9G5maHncXaFysuOLZxed/5sMj2uIf30FU3aBfg3dh4CrMWAnttai8EwKtNtbcFV2zyobDFW/3xnH49vqOZLZx7TbSIVrAC/PEJ5xnHZkgnc8tQ2/rV6Pzdc2BUo99a3cucruzh77ljOnlsW8b77j7Tzhxd38tL2ul4LUxljeGj1fhZMKOATx0+JeP9NVY387c197KprZfa4/F7X7zzYQqs30K032HHF8kls2N/AHS/v4tgJhVzcx5mXFfab3NUnTY16m8FYOLGI31+5hE+dNIUP3r6KpzfV8LHjI38iGi4a2rwUjcpERLhsyQQmjxnFwolFfd7nkTUHaPcF2LC/YciPR0SYWZbPzLJ8PnniVIJBw9aaJj5519v8+IktPHDtCVE/Xbz0bh2v7jjEkslFtPkC+ANBfIEgvoCxv3b/3h8w+AfQ2fN/n1jK+Qu6v/mFLzRWXpAd2lavL8eMzWPN98/pdfn00u6XnzRjDPddsyLmk81K8rL48WULYrrtYKVdgH/9vUPMKy/gYysm8/iGap7ZXMtHlk/i2a217KhtTsgxvGs/z/aa5m6nOh9oaKe6sSNiecZRkpfFWXPH8vCa/Vx33uxQJn3rM9vxuFz85PJjKYvyKcQfCHL/2/v474aqXgF+c1UTuw618r+XH8tHo5R/ttc087c397G1uiligF9Xaa1+uXhyUcT7/+Di+Wytbua6f23gmLF5zBlXEPF2W6qamFse+bqhtHRyMdNLcvnv+qoREOB9FNubUGRnuDlpRt+JSIcvENrhKRHLTbtcwvzxhXz9nFl879FNrNxSy3nzI69uufdQK9kZVqbvcsU2WRsMGnxBK9j7AkG8ga7vw98Qvvmv9dzy1DbOnFPWbdIzvEQTbZOPoyUinDpzeK6Em1aTrO3eAGv2NnDyMWNYMW00RfYL5qKF5cwqy2N7AgK81x9kV53Vn729pvvzVdg97sv6CPBgZcOHWry8uM1agGxdZQOPb6jmf06dFjW4g9WqdcGx43hh60HavN2XMP3vhio8Lulzydnppblkul1sqY4cMNZVNlCQ7QmVEHrK9Li4/eNLyc/28Ln7VkfcO7TDF2DXoVbmjY9/gBcRLl40njd313OwKfruO8PBkTYvxaNiLyO9vvMQbd4ALoE99W3932GIfPS4Scwcm8dPn9wadcu6PfWtTBmdG3NwB+sNJMvjJjfLQ9GoTMbmW2XMKWNyOWZsPnPLC1g4sYjrL5jLnvo2/vHW3m73D8/gY6m/p4q0CPDrKxt4dO0Bnt5cjTcQ5KRjSqxgt6Cc0vwsTpwxhlll+eyobenWEdDhC3CweWhf+LsOtYQ+bm6v6R4oK/YcITfTzZwI2XG402eVUpqfxYMV+zHG8NMntzImN5NrT5/R7/NfsnA87b4Az4WtTmmM4fH11Zwys6TPWnSG28WscXlsjRLg1+5rYPHk4j5fuGMLsrn9E0upamjnqw+s7XUW5bu1zQSCJiEZPMAlC8sxxlpeYThraPN120auP89uqSU/y8OZc8bG8ah687hd3HCRFWTve3NvxNvsPtTK1JL4BNkzZpdy0owx/Pb5HaEuOeiewU+JoYMmVaR0gH9792GuuustLr3tdb72wDq+/sB6PC5hhV3jvvGSeTz11VOtwFWWT0unP7TPIsDNT2zhkt+/NqRtYE7WXpiTwdYeGfyqXfUsnzq63y4Tj9vFB5dO5MXtB3ngnUre2n2Yr509s1c9P5Ljpo6mrCCLx9dXhS5bW9nAgYb2PuvijrnjCthS1dTrd9La6efd2uaI9feelk0ZzY2XzOel7XX85rnuWxI6bx7zEhTgZ5blM2dcPv8dxq2n4GTwsQX4oDE8t7WWM+aMZVE/dfp4OGNWKafOLOF3z+/o1b0SCBoqD7czNYZzGo6GiHDDhXNpaPdx+0tdDQcNbT6cKYGhLtEMZykZ4HfUNvORO1ZxxR2r2FrdxPUXzOGxL53Mt8+fw82XLQi1+WVnuCnJywJglt2R4tTHA0HDUxtrqG3qjLgH5tHaXtOMxyWcM6+M7TXNoUB5sLmDnQdbOGlGbGepfnj5RAJBa9OLaSW5UevmPblcwkXHjuel7XU02b3gj6+vJtPt4tz5kSdnw80bX0B9q5e65u4LiW3Y30jQwJIYN534+PGT+cjySfz+hZ08Y59QA1a9ODfTndCP0ZcsGs/qvUc40DB8V+u0avCxlWjWVTZwqMXLOfPKEvZJKJzT793c4eN3z+/sdl1VQzveQDBqGW8oLJhQyOWLJ3D367tD2xseafMyxf4/FW3uJxWl5CTr9Q9vZGddCz+4eB5Xrpgc2uW8r66DWWV5ALxb08z7Zo9l7b4j1Nsn5mw80Dhk7/rv1jYzvTSXYycU8u/V+6lt6mRcYTar3qsH4MQYA/yM0jyOm1rMO3uO8O3zZw/otOeLF5Vz9+u7eXZzLZctmcDjG6o4fXZpt42Go3Ey683VTd1aStc6E6wxBngR4aZL57OtpolvPLieGV+0aqlbq5uZU14woPrsYF28sJxbn9nOExuquPa0/stcA1HX3Mnf39rLi9vrCASDBIPWRsXGWJs+BI3BYH0l7OeSvCx++9HFTCweRbs3QKc/GHOJZuXmWjLcwhmzS0MLsiXanHEFfOS4Sdz35h6uOnFK6CzkPfXW/FO8MnjHN86bzRMbq/nlynf59UcW09jm44PLJnLNKdM0gx+JnEx4W00TFXuP8MUzjuEzp0wLBff+WBM3Wbxba+3UsnKL9SLxuCS0jnOb10/VILO87bXNzCrLD3WhbLPr8G/uqic/28P88bH3xn7z3Nl84YwZUbsVolkyqYgJRTn8d0MV7+w5zMHmzpj7zefYAb5nHX7dvgamjhlF8QD6ybMz3Nz+iWVkZ7i49r7VNHX42FrdxNzyvucgHE4bnmuQp81PGZPLwomFQ3qG8JaqJq7713pOvuUFfvPcDrLcLsYVWBODk4pzmDJmFNNLc5lZlsfscfnMKy9gwYRCFk0qYsmkInbUNvPJu9/mSKs3dBZmrBn8yi01nDB9DAXZGZQXZvdanC1Rvn7OLDLdLm55qmvBuT32AnCxLDsxGBOKcvjMKdN4ZO0B1u47QnOnn+JRvXvaU11KZPAPvLOPVe/V88srFvO3N/eS6bE2yRio2ePy2Vpt1Zef3VLLCdPHcKjFy0Y7wP/48S2s3FzLWzecdVTrmbR0+qk83M4VyyaFJlK31zRzxuyxrHqvnuOnjcY9gMz1+OljOP4oFh4TES5ZNJ4/v7qL3CwPORluzp4b22RcYU4GE4tz2FrdNX9gjGFtZQMnx/jpI9z4ohxu+9hSPv7nt/jU3W/T3OlnXnlsb3J//PhSbnhk45CcpHTJwvH85Mmt7DnUetTZZTBoeGHbQe56bTerdtWTk+Hmoysm8amTpjK9NG9Aj/X27sN84q63uObed/juRfMAYq7B+wKGc+03fRFhXnkBq3bVD2wwQ2BsfjZfOGMGv1j5Lm/tquf46WPYfchai31sflbcn/8LZ8zggXcquf7hjQAU5w7P9ZviKSUy+CNtPh5dV8U3/7WeR9Yc4OKF5QPKJB0nTB/DluomvviPNew+1Mq588o4dkIBm6ua8AWCPLmxhvpWL5uPsq/Y6bOfPS6folGZjCvIZntNM9WN7eypb0voKpEXLyzHHzQ8saGaM+eOZVRm7O/1c8sL2Li/IVTDr2rsoK65M+byTE/HTx/Ddy+ayxp7obJYM/gV00bz3P93+lE9Z0/OWaFPROim8QeCVDW0s3rvYTbub+x1fUunn3te382Zv3yJz/61gr31rVx/wRzevP4sfnTpggEHd7DG9ruPLmFdZQNff2AdYH3KjNU5YSe7JaMO7/jsqdMZX5jNzU9sJRg0VovkmP7XYh8KBdkZfPWsmWyzmxkG8vtLFSmRwX/+9Bm0dfr53QvWhM4nToh8JmZ/vnD6DJrafaFd0M+eZ71IHqzYz0Or94farlbtqmfRpCI6/QHaOgOhN5Oaxg5e2HYQq4raXabbFeqgccozs8fl8/DaA5Ta2Uys9fehMH98AdNLctl1qJVLBrif5+JJRdYKjTetZNbY/NDxL5ncd/9+Xz510lQ2Hmjk6U01EU+iirfxRTksn1LMP9/ZR0Obl6rGDqrtE88ONnd2a+e84cI5oYn6XXWtPFhRSXOHn6WTi7juvDmcN79sSFasPH/BOH506QK+96i1V31RjBn8mNzMbuvBrJg2mvve3BPzqfRDKTvDzXXnz+brD6zn0XUH2FPfyuyyxP19P3b8ZO55Yw+7D7VSlKRSVTKlRIAHq97ndrnYW98acydHTy6XcP2Fc5kxNo8DR9opL8xhvr3exG+f30FuppuS/CxWvVfP50+fwU3/3cLKzTW8+q0zycl08/3/bOLZLbV9Pkd+todJxVYdcP74Al5+t447XtnF+MJs5iZwdl9E+Mhxk7jrtd2cMXtgZ+F97rTpLJpYxJp9R1i99whr9h2hJC9zUJmiiPCLDy3i+gvmDujTxFD68PKJfPuhjfx11V7GF+VQXpjNSTNKGF+UTXlhDhv2N/DPdyr53ye3he7jcQkXHlvOp0+eOqg3uGg+ccIU6po7+dOru0LL00Zz0O5s6tkNdd78Mt664eykrbdz6aIJ/OX1Pdz6zHYOtXQOeM5oMDLcLr530Vy+8Pc1ca/7D0cynJb6XL58uamoqEj2YXTT4Qsw/8ZnCAQN7180noIcDw+vOcDL172PU372Ap3+ID//4EJOm1XKyT97gatPnMrnT5/e63HafQEONndSlJMRWiSsucPHhv2NlORlMWl0TsIDmzHWGh+D3XggGLQeJxE71MRbS6ef3Ex31BLC4VZvtz1NczLdCVmb3+sP9vv7vfeNPdz42GYe+sKJLJsSfT2jZHh792GuuGMVAD//4EKuOG5SQp/fFwgmZIONZBGR1caY5T0vT5kMPl6yM9zMHJvHtppmLlgwjqCBv725jxsf20SnP0hpfhZ/fXMP1Y0dBIKGq0+aEnVFyik9en/zszM4+Zj4L24WjYiQMQT7QrpcQmYC2xrjqb+TxZKVBcfy5vnx4ydz6sySo6r5x9uKaaM5f/44nt5cE/cWyUhSObj3RQN8DBZPKmLf4TZOn11Ku73U8JMba1g+pZhLl0zg+49uYs+hNk45pqRXEFcqUTxu17AM7o4fXDKP8qJsFk2K/zK5ypKeb2sD9M3zZvPvz5/EqEwPY/KyQi2OV504hcuXTCAvy0NLpz/qJhxKKWsi+8ZL5se0VK8aGhrgY1CSl9VtdcNz5pUxsTiHCxaUk5fl4WPHT2ZCUQ7nzOv/VH+llEoUnWQ9Cs7a1E4m0vNnpZRKpGiTrHHN4EXkfBHZLiI7ReQ78XyuRHLWpo72s1JKDQdxC/Ai4gZuAy4A5gFXisi8eD2fUkqp7uKZwa8AdhpjdhljvMA/gUvj+HxKKaXCxDPATwAqw37eb1/WjYhcKyIVIlJRV1cXx8NRSqn0kvQuGmPMncaY5caY5aWlw3PjWqWUGoniGeAPAOHnI0+0L1NKKZUA8Qzw7wAzRWSaiGQCHwUei+PzKaWUChPXPngRuRD4DeAG7jbG/KSf29cBkbdij64EOHRUBziypdu40228jnQbd7qNN9xgxj7FGNOrxj2sTnQ6GiJSEanBP9Wl27jTbbyOdBt3uo03XDzGnvRJVqWUUvGhAV4ppVJUKgT4O5N9AEmSbuNOt/E60m3c6TbecEM+9hFfg1dKKRVZKmTwSimlItAAr5RSKUoD/DAm0XZ+TlHpNt50pX/nxBkRAV5EisK+T6f/HCPi7zOEQruVp9nfOd0kZ+fyNDSsA4iIXCAiLwO3icj1ACYNZoVF5CIReRz4sYicnOzjiTcROVdE3gD+ICIfh7T5O18mIr8XkdHJPpZEEJELReRp4LciclWyjydR7L/zj5Px3J5kPGksRGQF8EPgJ0Aj8CURWWCM2ZTUA4szEVkG3Ig19gLgahGZaYy5R0RcxphgUg9wiIlIKfAj4BagCfiaiEw2xvw0FccLoU8nl2P9384HXhKRR1JxrAAi4gG+hTXm7wNjgItFpMEY89+kHlyc2H9jF/Bp4DvAFBFZaYx5NZHHMWwDPHAy8Iox5jERmQ4EgPecF72ISIpmeWcDrxpjnhSRHKzdsL5iB4DGVBq3/SIoA9YbYx61L6sBXhORPxljDqXSeB3GGCMiu4BTgDOAT2AtzrcvmccVL8YYvz3ejxpj3hORfGApKVyqsf/PBkRkJ7AEa7HFH2P9vRNm2JRoROQrIvInEfkf+6LngI+JyO+BV4DxwO3ATck6xniIMO4XgUtEpNgY0w74sD7BfBtGfulCRK4WkXMgNJYW4CSnTGGM2QI8CPw+eUc59MLHbdtkjKk3xjyE9Tf+gL3qakqIMN6Hgd0ikmGMacZaPnxUco4ufsJez5+1L3rZGNNsjPkTkCsi19i3S0zsNcYk/R/wKeBN4HzgZayPcUVAMfAr4BL7dnOBTcD8ZB9znMb9XWAsVnB7HHgV+AtwHvBHIDfZxzyIsRYD/waqgQ2AO+y6vwL39bjtW8C0ZB93vMaNlVw5JxqeDDwPLO1xX0n28Q/leMNukw08CsxO9vEO8dh7vp6vB2aEXX8BsBkoTtQxDZcM/izgZ8aYp4FvABnAl40xR4BZdC0hvA1YBWQl5SiHXs9xZwOfNMZ8Gfh/wI+MMZ8GOoAcY0xr8g51cOy/5UqsN+nVwA/Crv4ScL6IHGf/3AqsB7wJPcg46Gvcxn7VG2NeB9YBF4jIHBG5Nvz6kaSfv7OjGMg2xmwXkUki8sFEHmMcRXo9f9y50hjzFLAVuFZE8kXkw/E+oKQG+LCPKWuBiwGMMRXAG1iTEvOAF4A/i8go4HvAAqz9XUesPsb9OjBLRE41xuwzxjxr3+4i4L3EH+nQCGt5/KsxpgHr08gHRGQKgDGmCav09n0RuRrr7zwfq3wzYvU1bmPNI7nD/i/8BivjexnrU9yIaxWNYbzOnN90IF9Evoa1CdCI3quzj9fzKmBCj064bwM/BXYA4+J9bAkN8CIyzv7qAjBdXQOvAy4ROc3+eRNWEJ9jjPkVsB3rY9884APGmIOJPO7BGuC4q7D/8CJymt0mOhP4v4Qe9CBEGK+TqXbYX98BnsLqIsG+7A9YQW4ZMAX4sDGmMaEHPkgDHbcxJmAHvjLgD1jJzGJjzM3h9x+ujmK8fvuuS4ETgWOAi4wxI+b/NoCIzBeR0Dkb/byeq7HmDxGRY7De9B7FKsfFf54pQbWpJVg1xj/1uNxlfx0NfBO4ja6a3e3A9fb3mUBRoupWw2Dc37K/n8wImm/oY7xCWA02bGxvYmXqZcAx9uXuRBzrMBl3KTANa8ezyckeR4L+zmOwPoWfmuxxHMW4FwKvAY8AE8Iu7+/1fJ39fSkwM5HHHNcMXiy/xppEu9cY8z9h14X3ODdjTShmAb8QkQysOl0tgDHGa6yPfCPCEIz7EICxyjSbE3v0AxfDeI2xMtUcEckDa2xYL5SNWGWJAvvyQOJHcHSGYNyvYk24BezLh7UhGO8rWFvLbTIJ7gcfIt8D/m2MudwYcwBARNwxvJ7rAYwxdcaYHYk84LgGeGO9beUDa40xfwUQkRnhQU6sM7z+gdUK+H2sX8ir9s/3xvP44iXdxh3jeG8C/o5Vf0VErsSaSP4FcKwxZk1SDn4Q0m3cQzDeBSNpvA4RcYl1Lk6LMeY39mXniLWEitg/38wwfD0P+XrwInICcNgY8679cwHWSRx/x2r3q8WaPPuN/fWHwA+MMTvt27uw2gGbh/TA4izdxj0E4z0BqDXG7E74wQ9Cuo073cbriDLu1cDXgc8COVhj3wrcD/wvw/H1PFS1Hqy+9SewPqZ8j7CebeArWG1vp2F9fPkZ1kxyadhtXEN1LIn8l27jHoLxjrgaezqOO93GG+O4bwDWAO+3fz4N+A9wYththtXreShLNLnAM8CX7e+dmWSMMb8DzjDGvGKM6cSaRV4OtEGvuvRIk27jHux4R0yNvYd0G3e6jdcRddxYJx9OxZpMBagAarDOUxmWr+dBBXgR+aSInC4iBcaadLgT6zTzDuB4ERnv3NZYJ0A4lgGVWOvLMNx+Kf1Jt3Gn23gd6TbudBuvI4ZxTwAwxmwArgO+KCIlWGsIHUvXJOqwG/eAA7w9k14uIi8CV2OdqXW7iJQYYzqMMW1Y68gUA2eG3S9LRM4QkQqs2t0txu6XHQnSbdzpNl5Huo073cbrONpxG2Puwqq5/xD4IPBZM4w7oAYU4MVqCXJm0g8YY84CvgAcJmxHcGOder0HmCMihSKSY3+U8wI3G2MuMfbkxUiQbuNOt/E60m3c6TZex1GMe7Y97nz78l8BXzfGnGesxfGGrZi6aETEjbXUpRt4Eqtn+UPGmKvt611YZ2B+xBjzsn1ZHnAz1kJKk4ElxpiqeAwiXtJt3Ok2Xke6jTvdxusY5LhPwjrDekSNu98MXkROx2oPKgZ2Yv2CfMD7xNqUw6k9/dD+57gIq/91HVa/74j5pUD6jTvdxutIt3Gn23gdQzDu9YzAccey4UcQ+KUx5j4AEVmCdXr1D7BOw11mv/M9CpwpIlONMXuwJijONsa8Eo8DT4B0G3e6jdeRbuNOt/E60nLcsdTgVwMP2h9vwFpQZ7Ix5h7ALSJftt/5JgIB+5eCMeY/I/WXYku3cafbeB3pNu50G68jLcfdb4A3xrQZYzpNV1/rOUCd/f2ngblibRB9P9ZJAIiMrGVOI0m3cafbeB3pNu50G68jXccd856s9jufwVoR7jH74mass7sWALuNvQCPiWXmdoRIt3Gn23gd6TbudBuvI93GPZA2ySDWTkuHgIX2u933gaAx5jXnl5KC0m3c6TZeR7qNO93G60ircQ9osTGxFuB5w/73F2M1/ae8dBt3uo3XkW7jTrfxOtJp3AMN8BOBq4BfGetEh7SQbuNOt/E60m3c6TZeRzqNe8iXC1ZKKTU8JHXTbaWUUvGjAV4ppVKUBnillEpRGuCVUipFaYBXSqkUpQFepS0RCYjIOhHZLCLrReQb9oJTfd1nqoh8LFHHqNRgaIBX6azdGLPYGDMfa22SC4Ab+7nPVEADvBoRtA9epS0RaTHG5IX9PB14ByjB2tzhPqyNlwG+ZIx5Q0TeBOYCu4F7gd8BtwBnAFnAbcaYOxI2CKX6oAFepa2eAd6+rAGYjbUAVdAY0yEiM4H7jTHLReQM4JvGmIvt218LjDXG3CwiWVjL0H7YGLM7gUNRKqKYV5NUKs1kAH8QkcVAAJgV5XbnYi1a9SH750JgJlaGr1RSaYBXymaXaALAQaxafC2wCGuuqiPa3YAvG2OeSchBKjUAOsmqFCAipcD/AX+w1wEvBKrtXX6uwtqoGazSTX7YXZ8BviAiGfbjzBKRXJQaBjSDV+ksR0TWYZVj/FiTqr+yr/sj8JCIfBJ4Gmi1L98ABERkPXAP8Fuszpo19g5AdcBliTl8pfqmk6xKKZWitESjlFIpSgO8UkqlKA3wSimVojTAK6VUitIAr5RSKUoDvFJKpSgN8EoplaL+fwT92C6SJujzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Distance'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Distance'].plot()\n",
    "pd.Series(df['Distance']).rolling(window = 2).mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Distance'].plot()\n",
    "pd.Series(df['Distance']).rolling(window = 2).sum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2015-11':'2015-12']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2015-11':'2016-1-1']['Distance'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2015-8-12']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "datapath = '../../Data/EDA/'\n",
    "transform = 'sales_transactions.xlsx'\n",
    "zipname = 'vehicles'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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": 74,
     "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": 75,
   "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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 700
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(os.path.join(datapath,'groupby-example.png'), width=700))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 78,
   "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": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dir(gb)\n",
    "\n",
    "len(gb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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": 55,
   "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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tf.groupby('order')[\"ext price\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 56,
     "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": 57,
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tf1 = df_tf.merge(order_total)\n",
    "df_tf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": 58,
     "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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 700
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(os.path.join(datapath,'transform-example.png'), width=700))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x7fcca657f2e0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tf.groupby('order')[\"ext price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 61,
     "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": 62,
   "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": 62,
     "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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