twoyears.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "trained-dance",
   "metadata": {},
   "source": [
    "## Analyzing Two Years of NYC Bike Share data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "outer-sullivan",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import to_datetime\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "level-oklahoma",
   "metadata": {},
   "source": [
    "### Monthly Ridership"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polyphonic-enlargement",
   "metadata": {},
   "source": [
    "Read all trips (2019 & 2020) selecting only columns needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "contemporary-personality",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_parquet('tripdata' , columns=['year','month','gender','usertype']) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "turkish-master",
   "metadata": {},
   "source": [
    "Aggregate ride counts by year and month.  Display in wide format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fixed-bridges",
   "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>month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2019</th>\n",
       "      <td>967287</td>\n",
       "      <td>943744</td>\n",
       "      <td>1327960</td>\n",
       "      <td>1766094</td>\n",
       "      <td>1924563</td>\n",
       "      <td>2125370</td>\n",
       "      <td>2181064</td>\n",
       "      <td>2344224</td>\n",
       "      <td>2444900</td>\n",
       "      <td>2092573</td>\n",
       "      <td>1478708</td>\n",
       "      <td>955210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1240596</td>\n",
       "      <td>1146830</td>\n",
       "      <td>1068457</td>\n",
       "      <td>682762</td>\n",
       "      <td>1487890</td>\n",
       "      <td>1882273</td>\n",
       "      <td>2105808</td>\n",
       "      <td>2329514</td>\n",
       "      <td>2488225</td>\n",
       "      <td>2248869</td>\n",
       "      <td>1736704</td>\n",
       "      <td>1088929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month       1        2        3        4        5        6        7        8   \\\n",
       "year                                                                            \n",
       "2019    967287   943744  1327960  1766094  1924563  2125370  2181064  2344224   \n",
       "2020   1240596  1146830  1068457   682762  1487890  1882273  2105808  2329514   \n",
       "\n",
       "month       9        10       11       12  \n",
       "year                                       \n",
       "2019   2444900  2092573  1478708   955210  \n",
       "2020   2488225  2248869  1736704  1088929  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = df.groupby([\"year\", \"month\"]).size()\n",
    "counts.unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "positive-houston",
   "metadata": {},
   "source": [
    "Convert year and month from indexes to columns, rename values columns as ridecount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "moved-mount",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 24 entries, 0 to 23\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype\n",
      "---  ------      --------------  -----\n",
      " 0   year        24 non-null     int64\n",
      " 1   month       24 non-null     int64\n",
      " 2   Ride Count  24 non-null     int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 704.0 bytes\n"
     ]
    }
   ],
   "source": [
    "counts=counts.reset_index( level = ['year','month'])\n",
    "counts.rename(columns = {0:'Ride Count'}, inplace=True)\n",
    "counts.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "binding-latest",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "months=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']\n",
    "ax=sns.barplot(data=counts, x=\"month\" , y='Ride Count',  hue='year', palette='Paired' )\n",
    "ax.set_xlabel(\"Months\") \n",
    "ax.set_ylabel(\"Ride Counts in Millions\") \n",
    "ax.set_xticklabels(months)\n",
    "ax.legend_.remove()\n",
    "y=0\n",
    "for p in  ax.patches: \n",
    "    ax.annotate(counts.year[y],   (p.get_x() + p.get_width()/ 2, 0), rotation=90,\\\n",
    "    ha='center', size=12, fontweight='bold',  xytext=(0, 10),  textcoords='offset points')  \n",
    "    y+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "french-manual",
   "metadata": {},
   "source": [
    "### Ridership by Gender and User Type \n",
    "Aggregrate by user type, year and month.  \n",
    "Show counts by gender (0=unspecified, 1=male, 2=female)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "funky-proposition",
   "metadata": {},
   "outputs": [],
   "source": [
    "countsg = df.groupby([\"usertype\",\"year\", \"month\", \"gender\"]).size().unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advance-channel",
   "metadata": {},
   "source": [
    "Create `total` by summing across gender value columns.  Then create `pct female` as percentage of total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "infinite-uruguay",
   "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></th>\n",
       "      <th>gender</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>total</th>\n",
       "      <th>pct female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usertype</th>\n",
       "      <th>year</th>\n",
       "      <th>month</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 rowspan=\"5\" valign=\"top\">Customer</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">2019</th>\n",
       "      <th>1</th>\n",
       "      <td>17918</td>\n",
       "      <td>16612</td>\n",
       "      <td>5643</td>\n",
       "      <td>40173</td>\n",
       "      <td>14.046748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19884</td>\n",
       "      <td>19329</td>\n",
       "      <td>7138</td>\n",
       "      <td>46351</td>\n",
       "      <td>15.399883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48623</td>\n",
       "      <td>38816</td>\n",
       "      <td>19219</td>\n",
       "      <td>106658</td>\n",
       "      <td>18.019277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110290</td>\n",
       "      <td>76224</td>\n",
       "      <td>42919</td>\n",
       "      <td>229433</td>\n",
       "      <td>18.706550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>138218</td>\n",
       "      <td>97919</td>\n",
       "      <td>58429</td>\n",
       "      <td>294566</td>\n",
       "      <td>19.835623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                    0      1      2   total  pct female\n",
       "usertype year month                                          \n",
       "Customer 2019 1       17918  16612   5643   40173   14.046748\n",
       "              2       19884  19329   7138   46351   15.399883\n",
       "              3       48623  38816  19219  106658   18.019277\n",
       "              4      110290  76224  42919  229433   18.706550\n",
       "              5      138218  97919  58429  294566   19.835623"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countsg['total'] = countsg.sum(axis=1)\n",
    "countsg['pct female'] = countsg[2] / countsg.total * 100\n",
    "countsg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tutorial-dressing",
   "metadata": {},
   "source": [
    "Convert multi-index to columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "imported-ribbon",
   "metadata": {},
   "outputs": [],
   "source": [
    "countsg=countsg.reset_index( level = ['usertype','year','month'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "liable-plasma",
   "metadata": {},
   "source": [
    "Iterate over usertypes to create separate charts for Subscribers and Customers.  \n",
    "Use catplot to show separate charts for each year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "heard-baltimore",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1728x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usertypes=['Subscriber','Customer']\n",
    "sns.set(font_scale = 2)\n",
    "\n",
    "for u in usertypes:\n",
    "    ax=sns.catplot(data=countsg[countsg.usertype==u],\\\n",
    "           x=\"month\", y=\"pct female\", col=\"year\",\\\n",
    "           color='Green', kind='bar', height=5, aspect=12/5 ) \n",
    "    ax.set_axis_labels(\"\", u + \"\\n% Female\")\n",
    "    ax.set_xticklabels(months)\n",
    "    ax.set_titles('{col_name}')\n",
    "    ax.set(ylim=(0,40))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "attempted-chancellor",
   "metadata": {},
   "source": [
    "For all riders (Customers+Subscribers) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "wrapped-eclipse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale = 2)\n",
    "ax=sns.catplot(data=countsg,\\\n",
    "      x=\"month\", y=\"pct female\", col=\"year\",\\\n",
    "      color='Green', kind='bar', height=5, aspect=12/5 ) \n",
    "ax.set_axis_labels(\"\", \"\\n% Female\")\n",
    "ax.set_xticklabels(months)\n",
    "ax.set_titles('{col_name}')\n",
    "ax.set(ylim=(0,40)) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "permanent-tunisia",
   "metadata": {},
   "source": [
    "### Ridership by User Type\n",
    "Aggregate ride count by `year`, `month` and `usertype`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "raised-reunion",
   "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>usertype</th>\n",
       "      <th>Customer</th>\n",
       "      <th>Subscriber</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2019</th>\n",
       "      <th>1</th>\n",
       "      <td>40173</td>\n",
       "      <td>927114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46351</td>\n",
       "      <td>897393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>106658</td>\n",
       "      <td>1221302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>229433</td>\n",
       "      <td>1536661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>294566</td>\n",
       "      <td>1629997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "usertype    Customer  Subscriber\n",
       "year month                      \n",
       "2019 1         40173      927114\n",
       "     2         46351      897393\n",
       "     3        106658     1221302\n",
       "     4        229433     1536661\n",
       "     5        294566     1629997"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_type = df.groupby([\"year\", \"month\", \"usertype\"]).size().unstack()\n",
    "count_type.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hindu-swaziland",
   "metadata": {},
   "source": [
    "Create `custpct` as percentage of ride by Customers (vs Subscribers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bottom-spare",
   "metadata": {},
   "outputs": [],
   "source": [
    "custpct = count_type.Customer / (count_type.Customer + count_type.Subscriber) * 100\n",
    "custpct=custpct.reset_index( level = ['year','month'])\n",
    "custpct.rename(columns = {0:'Customer Percent'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "alert-italy",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>Customer Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4.153162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>4.911395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>8.031718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>12.990985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>15.305604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  Customer Percent\n",
       "0  2019      1          4.153162\n",
       "1  2019      2          4.911395\n",
       "2  2019      3          8.031718\n",
       "3  2019      4         12.990985\n",
       "4  2019      5         15.305604"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custpct.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "oriental-mississippi",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale = 3)\n",
    "ax = sns.catplot(data=custpct,\\\n",
    "           x=\"month\", y=\"Customer Percent\", col=\"year\", dodge=False, \\\n",
    "           color='blue', kind='bar', height=10, aspect=20/10 ) \n",
    "ax.set_axis_labels(\"\", \"Customer\\n% Rides\")\n",
    "ax.set_xticklabels(months)\n",
    "ax.set_titles('{col_name}')\n",
    "ax.set(ylim=(0,40)) ;\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "authorized-interest",
   "metadata": {},
   "source": [
    "### Ridership by Hour of Day"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exact-aberdeen",
   "metadata": {},
   "source": [
    "Create new dataframe with `start hour`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "written-customer",
   "metadata": {},
   "outputs": [],
   "source": [
    " df = pd.read_parquet('tripdata' , columns=['year','weekday','start hour']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "excited-juvenile",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_hr = df.groupby([\"year\", \"weekday\",\"start hour\"]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "accompanied-subcommittee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "count_hr=count_hr.reset_index( level = ['year','weekday','start hour'])\n",
    "count_hr.rename(columns = {0:'Ride Count'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "unsigned-money",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_hr['Ride Count'] /= 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "conventional-consultancy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale = 1)\n",
    "fig, ax = plt.subplots(1,7, figsize=(20,5)) \n",
    "\n",
    "days=['Mon','Tue','Wed','Thu','Fri','Sat','Sun']\n",
    "\n",
    "for d in range(len(days)):\n",
    "    sns.lineplot(ax=ax[d], data=count_hr[count_hr.weekday==d], linewidth=4,\\\n",
    "             x=\"start hour\", y=\"Ride Count\", hue='year',  palette='Paired')\\\n",
    "            .set_title(days[d]) ;\n",
    "    if ~d:\n",
    "        ax[d].set_ylabel('Ride Count in Thousands')\n",
    "    if d:\n",
    "        ax[d].set_ylabel('')\n",
    "        ax[d].set_yticklabels('')\n",
    "    if d != 3:\n",
    "        ax[d].set_xlabel('')\n",
    "    if d < 6:\n",
    "        ax[d].legend_.remove()   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cooked-holiday",
   "metadata": {},
   "source": [
    "### Trip Duration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prime-douglas",
   "metadata": {},
   "source": [
    "Create new data frame with trip duration (in seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "limiting-banks",
   "metadata": {},
   "outputs": [],
   "source": [
    " df = pd.read_parquet('tripdata' , columns=['year','month','tripduration']) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "functioning-obligation",
   "metadata": {},
   "source": [
    "Convert seconds to minutes (keeping decimal portion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "theoretical-curve",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.tripduration /= 60"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distant-banana",
   "metadata": {},
   "source": [
    "Calculate median trip duration by year/month.  \n",
    "Display in wide format to 100ths of a minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "corporate-place",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"12\" halign=\"left\">tripduration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2019</th>\n",
       "      <td>8.40</td>\n",
       "      <td>8.63</td>\n",
       "      <td>8.98</td>\n",
       "      <td>10.08</td>\n",
       "      <td>10.88</td>\n",
       "      <td>11.38</td>\n",
       "      <td>10.97</td>\n",
       "      <td>11.23</td>\n",
       "      <td>11.13</td>\n",
       "      <td>10.28</td>\n",
       "      <td>9.37</td>\n",
       "      <td>8.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>8.82</td>\n",
       "      <td>8.68</td>\n",
       "      <td>11.33</td>\n",
       "      <td>16.17</td>\n",
       "      <td>18.28</td>\n",
       "      <td>17.13</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.40</td>\n",
       "      <td>13.75</td>\n",
       "      <td>12.55</td>\n",
       "      <td>11.77</td>\n",
       "      <td>10.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tripduration                                                         \\\n",
       "month           1     2      3      4      5      6      7      8      9    \n",
       "year                                                                        \n",
       "2019          8.40  8.63   8.98  10.08  10.88  11.38  10.97  11.23  11.13   \n",
       "2020          8.82  8.68  11.33  16.17  18.28  17.13  14.63  14.40  13.75   \n",
       "\n",
       "                            \n",
       "month     10     11     12  \n",
       "year                        \n",
       "2019   10.28   9.37   8.70  \n",
       "2020   12.55  11.77  10.27  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dur = df.groupby([\"year\", \"month\"]).agg('median')\n",
    "dur.unstack().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "professional-watershed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dur.reset_index( level = ['year','month'], inplace=True)\n",
    "sns.set(font_scale = 1)\n",
    "plt.figure(figsize=(12,5))\n",
    "\n",
    "ax=sns.barplot(data=dur, x=\"month\" , y='tripduration',  hue='year', palette='Paired' )\n",
    "ax.set_xlabel(\"Months\") \n",
    "ax.set_ylabel(\"Median Trip Duration \\nin Minutes\") \n",
    "ax.set_xticklabels(months)\n",
    "ax.legend_.remove()\n",
    "y=0\n",
    "for p in  ax.patches: \n",
    "    ax.annotate(counts.year[y],   (p.get_x() + p.get_width()/ 2, 0), rotation=90,\\\n",
    "    ha='center', size=12, fontweight='bold',  xytext=(0, 10),  textcoords='offset points')  \n",
    "    y+=1"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}