Lab session 0725

pandas-worksheet-demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas worksheet\n",
    "\n",
    "Before you begin, run the cell below to import the appropriate libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('max_rows', 25)\n",
    "# plt.style.use('ggplot')\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this worksheet, you'll be using a simplified version of a month's worth of data from NYC's [Green Taxis](http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml). You can download the data [here](green_tripdata_simple_2016-12.csv.gz). Make sure you've decompressed the file and put it in the same directory as this worksheet. (The simplified version omits a number of fields and randomly samples 100k rows from several million rows, mainly to decrease the file size.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./green_tripdata_simple_2016-12.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.84</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-01 00:00:33</td>\n",
       "      <td>1</td>\n",
       "      <td>4.20</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12-01 00:00:47</td>\n",
       "      <td>1</td>\n",
       "      <td>2.34</td>\n",
       "      <td>14.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-12-01 00:01:23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.87</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-12-01 00:01:54</td>\n",
       "      <td>1</td>\n",
       "      <td>4.35</td>\n",
       "      <td>17.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-12-01 00:01:58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.72</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-12-01 00:04:51</td>\n",
       "      <td>1</td>\n",
       "      <td>2.86</td>\n",
       "      <td>18.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-12-01 00:05:33</td>\n",
       "      <td>1</td>\n",
       "      <td>1.70</td>\n",
       "      <td>8.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-12-01 00:05:38</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-12-01 00:07:17</td>\n",
       "      <td>1</td>\n",
       "      <td>1.34</td>\n",
       "      <td>10.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-12-01 00:08:05</td>\n",
       "      <td>1</td>\n",
       "      <td>25.17</td>\n",
       "      <td>125.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016-12-01 00:08:40</td>\n",
       "      <td>1</td>\n",
       "      <td>1.40</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99988</th>\n",
       "      <td>2016-12-31 23:56:07</td>\n",
       "      <td>3</td>\n",
       "      <td>0.65</td>\n",
       "      <td>6.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99989</th>\n",
       "      <td>2016-12-31 23:56:15</td>\n",
       "      <td>1</td>\n",
       "      <td>0.65</td>\n",
       "      <td>7.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99990</th>\n",
       "      <td>2016-12-31 23:56:26</td>\n",
       "      <td>1</td>\n",
       "      <td>1.88</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99991</th>\n",
       "      <td>2016-12-31 23:56:32</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99992</th>\n",
       "      <td>2016-12-31 23:57:37</td>\n",
       "      <td>1</td>\n",
       "      <td>0.42</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99993</th>\n",
       "      <td>2016-12-31 23:57:40</td>\n",
       "      <td>5</td>\n",
       "      <td>2.61</td>\n",
       "      <td>11.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99994</th>\n",
       "      <td>2016-12-31 23:57:56</td>\n",
       "      <td>1</td>\n",
       "      <td>2.50</td>\n",
       "      <td>13.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>2016-12-31 23:58:03</td>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>2016-12-31 23:58:54</td>\n",
       "      <td>1</td>\n",
       "      <td>3.77</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>2016-12-31 23:59:33</td>\n",
       "      <td>1</td>\n",
       "      <td>3.43</td>\n",
       "      <td>14.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>2016-12-31 23:59:57</td>\n",
       "      <td>1</td>\n",
       "      <td>1.44</td>\n",
       "      <td>10.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>2016-12-31 23:59:58</td>\n",
       "      <td>2</td>\n",
       "      <td>5.20</td>\n",
       "      <td>17.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      lpep_pickup_datetime  passenger_count  trip_distance  total_amount\n",
       "0      2016-12-01 00:00:00                1           1.84          9.80\n",
       "1      2016-12-01 00:00:33                1           4.20         16.30\n",
       "2      2016-12-01 00:00:47                1           2.34         14.16\n",
       "3      2016-12-01 00:01:23                1           0.87          6.80\n",
       "4      2016-12-01 00:01:54                1           4.35         17.30\n",
       "5      2016-12-01 00:01:58                1           1.72         10.30\n",
       "6      2016-12-01 00:04:51                1           2.86         18.96\n",
       "7      2016-12-01 00:05:33                1           1.70          8.80\n",
       "8      2016-12-01 00:05:38                1           1.00          8.30\n",
       "9      2016-12-01 00:07:17                1           1.34         10.14\n",
       "10     2016-12-01 00:08:05                1          25.17        125.00\n",
       "11     2016-12-01 00:08:40                1           1.40          7.80\n",
       "...                    ...              ...            ...           ...\n",
       "99988  2016-12-31 23:56:07                3           0.65          6.96\n",
       "99989  2016-12-31 23:56:15                1           0.65          7.56\n",
       "99990  2016-12-31 23:56:26                1           1.88          9.80\n",
       "99991  2016-12-31 23:56:32                1           0.00          0.00\n",
       "99992  2016-12-31 23:57:37                1           0.42          4.80\n",
       "99993  2016-12-31 23:57:40                5           2.61         11.80\n",
       "99994  2016-12-31 23:57:56                1           2.50         13.30\n",
       "99995  2016-12-31 23:58:03                1           0.33          5.80\n",
       "99996  2016-12-31 23:58:54                1           3.77         15.30\n",
       "99997  2016-12-31 23:59:33                1           3.43         14.30\n",
       "99998  2016-12-31 23:59:57                1           1.44         10.80\n",
       "99999  2016-12-31 23:59:58                2           5.20         17.30\n",
       "\n",
       "[100000 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem set #1: Columns as series and series operations\n",
    "\n",
    "Write an expression in the cell below that evaluates to the *average trip distance* of all rows in the data frame.\n",
    "\n",
    "Expected output:\n",
    "\n",
    "    2.6186306999999607"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.84</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-01 00:00:33</td>\n",
       "      <td>1</td>\n",
       "      <td>4.20</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12-01 00:00:47</td>\n",
       "      <td>1</td>\n",
       "      <td>2.34</td>\n",
       "      <td>14.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lpep_pickup_datetime  passenger_count  trip_distance  total_amount\n",
       "0  2016-12-01 00:00:00                1           1.84          9.80\n",
       "1  2016-12-01 00:00:33                1           4.20         16.30\n",
       "2  2016-12-01 00:00:47                1           2.34         14.16"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6186306999999607"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['trip_distance'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write an expression in the cell below that evaluates to the sum of the `total_amount` field for rows 100 through 200 in the data set.\n",
    "\n",
    "Expected output:\n",
    "\n",
    "    1267.4999999999982"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.84</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-01 00:00:33</td>\n",
       "      <td>1</td>\n",
       "      <td>4.20</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12-01 00:00:47</td>\n",
       "      <td>1</td>\n",
       "      <td>2.34</td>\n",
       "      <td>14.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-12-01 00:01:23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.87</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-12-01 00:01:54</td>\n",
       "      <td>1</td>\n",
       "      <td>4.35</td>\n",
       "      <td>17.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-12-01 00:01:58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.72</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-12-01 00:04:51</td>\n",
       "      <td>1</td>\n",
       "      <td>2.86</td>\n",
       "      <td>18.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-12-01 00:05:33</td>\n",
       "      <td>1</td>\n",
       "      <td>1.70</td>\n",
       "      <td>8.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-12-01 00:05:38</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-12-01 00:07:17</td>\n",
       "      <td>1</td>\n",
       "      <td>1.34</td>\n",
       "      <td>10.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lpep_pickup_datetime  passenger_count  trip_distance  total_amount\n",
       "0  2016-12-01 00:00:00                1           1.84          9.80\n",
       "1  2016-12-01 00:00:33                1           4.20         16.30\n",
       "2  2016-12-01 00:00:47                1           2.34         14.16\n",
       "3  2016-12-01 00:01:23                1           0.87          6.80\n",
       "4  2016-12-01 00:01:54                1           4.35         17.30\n",
       "5  2016-12-01 00:01:58                1           1.72         10.30\n",
       "6  2016-12-01 00:04:51                1           2.86         18.96\n",
       "7  2016-12-01 00:05:33                1           1.70          8.80\n",
       "8  2016-12-01 00:05:38                1           1.00          8.30\n",
       "9  2016-12-01 00:07:17                1           1.34         10.14"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    16.30\n",
       "2    14.16\n",
       "3     6.80\n",
       "4    17.30\n",
       "Name: total_amount, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_amount'][1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1267.4999999999982"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_amount'][100:200].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, now this one is tricky. Write an expression (or a series of statements that ends in an expression) in the cell below that evaluates to the *total trip distance* (i.e., the sum of the trip distance field) for all single-occupant rides (i.e., where the passenger count is 1).\n",
    "\n",
    "Expected output:\n",
    "\n",
    "    219594.60999999827"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.84</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-01 00:00:33</td>\n",
       "      <td>1</td>\n",
       "      <td>4.20</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lpep_pickup_datetime  passenger_count  trip_distance  total_amount\n",
       "0  2016-12-01 00:00:00                1           1.84           9.8\n",
       "1  2016-12-01 00:00:33                1           4.20          16.3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "219594.60999999827"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one = df[df['passenger_count'] == 1]\n",
    "one['trip_distance'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem set #2: Plots\n",
    "\n",
    "In the cell below, write a statement or sequence of statements that draws a pie chart of passenger count frequency. (Hint: use `.value_counts()`.)\n",
    "\n",
    "Expected output:\n",
    "\n",
    "![pie chart](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.84</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-01 00:00:33</td>\n",
       "      <td>1</td>\n",
       "      <td>4.20</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lpep_pickup_datetime  passenger_count  trip_distance  total_amount\n",
       "0  2016-12-01 00:00:00                1           1.84           9.8\n",
       "1  2016-12-01 00:00:33                1           4.20          16.3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    84399\n",
       "2     7623\n",
       "5     3582\n",
       "3     1977\n",
       "6     1713\n",
       "4      688\n",
       "0       11\n",
       "7        4\n",
       "8        2\n",
       "9        1\n",
       "Name: passenger_count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['passenger_count'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1161d0748>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YtWoVPv/886hfSw8RB8t33nkH//znP3H48GFMmTIF3bp1AwDs2bMH8+bNQ1FR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1161b1358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['passenger_count'].value_counts().plot(kind = \"pie\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, in the cell below, draw a histogram of trip distances. Use 50 bins.\n",
    "\n",
    "Expected output:\n",
    "\n",
    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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.84</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-01 00:00:33</td>\n",
       "      <td>1</td>\n",
       "      <td>4.20</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lpep_pickup_datetime  passenger_count  trip_distance  total_amount\n",
       "0  2016-12-01 00:00:00                1           1.84           9.8\n",
       "1  2016-12-01 00:00:33                1           4.20          16.3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df4.plot.hist(stacked=True, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118f105c0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+n969e7N27VqGDBlCeXk5r732GitXrqRv374APPbYYwwfPpyKigrS0tJYvXo1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\nSZIEGIwlSZIkwGAsSZIkAfD/AYlJWoxGHLUmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117f8df60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['trip_distance'].plot.hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay! This last one is extremely tricky, but I know you can do it. In the cell below, write a statement or series of statements that draws a scatter plot with trip distance as the X axis and the total amount on the Y axis, but only including the rows where the total amount is greater than zero.\n",
    "\n",
    "Expected output:\n",
    "\n",
    "![scatter 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>1.84</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-01 00:00:33</td>\n",
       "      <td>1</td>\n",
       "      <td>4.20</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12-01 00:00:47</td>\n",
       "      <td>1</td>\n",
       "      <td>2.34</td>\n",
       "      <td>14.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lpep_pickup_datetime  passenger_count  trip_distance  total_amount\n",
       "0  2016-12-01 00:00:00                1           1.84          9.80\n",
       "1  2016-12-01 00:00:33                1           4.20         16.30\n",
       "2  2016-12-01 00:00:47                1           2.34         14.16"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "new = df[df['total_amount'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 4)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99533, 4)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118877908>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FQv/+/Vm0aBHdu3d3nCM/P585c+awfv168vLyGDx4MC+99BLt2rWrdXwiIiIi4h/crkyT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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d90278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new.plot.scatter(x='trip_distance', y='total_amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent! You're done."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}