nikokozak · March 3, 2026 00:46
diff --git a/ProgDat_Ex2.ipynb b/ProgDat_Ex2.ipynb
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 2: Pandas\n",
    "\n",
    "Before you begin, run the cell below to import the appropriate libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\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](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page). You can download the data [here](green_tripdata_simple_2016-12.csv). 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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./green_tripdata_simple_2016-12.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "...                    ...              ...            ...           ...\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": [
    "## Task 1: Indexing columns\n",
    "\n",
    "Modify the expression in the cell below so that it evaluates to the *average trip distance* of all rows in the data frame.\n",
    "\n",
    "Expected output: `2.6186307`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(2.6186307)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"trip_distance\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2: Limiting rows by count\n",
    "\n",
    "Modify the expression in the cell below so that it evaluates to the sum of the `total_amount` field for rows 100 through 200 in the data set.\n",
    "\n",
    "Expected output: `1267.5`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1267.5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"total_amount\"][100:200].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3: Filtering the data frame\n",
    "\n",
    "Okay, now this one is tricky. Modify the expression in the cell below so that it 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: `219594.61`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(219594.61)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"passenger_count\"] == 1]['trip_distance'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4: Filtering the data frame, part two\n",
    "\n",
    "Write an expression in the cell below that shows the trip distance of the *longest ride* taken by two or more people.\n",
    "\n",
    "Expected output: `100.39`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(100.39)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"passenger_count\"] >= 2][\"trip_distance\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5: 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": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"passenger_count\"].value_counts().plot.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",
    "![histogram](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"trip_distance\"].plot.hist(bins=50)"
   ]
  },
  {
   "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 plot](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='trip_distance', ylabel='total_amount'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df[\"total_amount\"] > 0].plot.scatter(x=\"trip_distance\", y=\"total_amount\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent! You're done."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.14.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
 }
No results found