Exploratory-Data-Analysis-Pt1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis Case Study - Pt. 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/taxis_bikes_nycity.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Data</th>\n",
       "      <th>Distancia</th>\n",
       "      <th>Tempo</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8/8/15</td>\n",
       "      <td>3.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8/9/15</td>\n",
       "      <td>2.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8/10/15</td>\n",
       "      <td>3.36</td>\n",
       "      <td>0:28:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8/11/15</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0:13:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8/12/15</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0:10:35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Data  Distancia    Tempo\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\n",
       "5   8/8/15       3.40      NaN\n",
       "6   8/9/15       2.50      NaN\n",
       "7  8/10/15       3.36  0:28:37\n",
       "8  8/11/15       1.67  0:13:07\n",
       "9  8/12/15       1.42  0:10:35"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data          object\n",
       "Distancia    float64\n",
       "Tempo         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Data', 'Distancia', 'Tempo'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=81, step=1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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: Data, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Data'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/taxis_bikes_nycity.csv', parse_dates = ['Data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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: Data, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Data'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('Data', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Distancia</th>\n",
       "      <th>Tempo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data</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": [
       "            Distancia    Tempo\n",
       "Data                          \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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a2e7eb8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "?df.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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(kind = 'bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = 'area')\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Distancia</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": [
       "       Distancia\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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distancia</th>\n",
       "      <th>Tempo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-03</th>\n",
       "      <td>5.30</td>\n",
       "      <td>0:39:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>3.05</td>\n",
       "      <td>0:22:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-07</th>\n",
       "      <td>5.63</td>\n",
       "      <td>0:49:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-07</th>\n",
       "      <td>1.26</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-09</th>\n",
       "      <td>3.05</td>\n",
       "      <td>0:22:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-10</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0:09:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-10</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0:11:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-10</th>\n",
       "      <td>4.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-27</th>\n",
       "      <td>1.00</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Distancia    Tempo\n",
       "Data                          \n",
       "2015-11-02       1.37  0:08:43\n",
       "2015-11-03       5.30  0:39:26\n",
       "2015-11-04       3.05  0:22:12\n",
       "2015-11-07       5.63  0:49:05\n",
       "2015-11-07       1.26      NaN\n",
       "2015-11-07       1.20      NaN\n",
       "2015-11-09       3.05  0:22:36\n",
       "2015-11-10       1.50  0:09:00\n",
       "2015-11-10       1.50  0:11:33\n",
       "2015-11-10       4.00      NaN\n",
       "2015-11-23       3.05  0:22:35\n",
       "2015-11-27       1.00      NaN\n",
       "2015-11-28       3.00      NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2015-11']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['2015-11'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data/dataframe_saved_v1.csv')"
   ]
  }
 ],
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