prepare.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import to_datetime\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('202003-citibike-tripdata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1068457 entries, 0 to 1068456\n",
      "Data columns (total 15 columns):\n",
      " #   Column                   Non-Null Count    Dtype  \n",
      "---  ------                   --------------    -----  \n",
      " 0   tripduration             1068457 non-null  int64  \n",
      " 1   starttime                1068457 non-null  object \n",
      " 2   stoptime                 1068457 non-null  object \n",
      " 3   start station id         1068457 non-null  int64  \n",
      " 4   start station name       1068457 non-null  object \n",
      " 5   start station latitude   1068457 non-null  float64\n",
      " 6   start station longitude  1068457 non-null  float64\n",
      " 7   end station id           1068457 non-null  int64  \n",
      " 8   end station name         1068457 non-null  object \n",
      " 9   end station latitude     1068457 non-null  float64\n",
      " 10  end station longitude    1068457 non-null  float64\n",
      " 11  bikeid                   1068457 non-null  int64  \n",
      " 12  usertype                 1068457 non-null  object \n",
      " 13  birth year               1068457 non-null  int64  \n",
      " 14  gender                   1068457 non-null  int64  \n",
      "dtypes: float64(4), int64(6), object(5)\n",
      "memory usage: 122.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1589</td>\n",
       "      <td>2020-03-01 00:00:03.6400</td>\n",
       "      <td>2020-03-01 00:26:32.9860</td>\n",
       "      <td>224</td>\n",
       "      <td>Spruce St &amp; Nassau St</td>\n",
       "      <td>40.711464</td>\n",
       "      <td>-74.005524</td>\n",
       "      <td>3574</td>\n",
       "      <td>Prospect Pl &amp; Underhill Ave</td>\n",
       "      <td>40.676969</td>\n",
       "      <td>-73.965790</td>\n",
       "      <td>16214</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>389</td>\n",
       "      <td>2020-03-01 00:00:16.7560</td>\n",
       "      <td>2020-03-01 00:06:46.0620</td>\n",
       "      <td>293</td>\n",
       "      <td>Lafayette St &amp; E 8 St</td>\n",
       "      <td>40.730207</td>\n",
       "      <td>-73.991026</td>\n",
       "      <td>223</td>\n",
       "      <td>W 13 St &amp; 7 Ave</td>\n",
       "      <td>40.737815</td>\n",
       "      <td>-73.999947</td>\n",
       "      <td>29994</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>614</td>\n",
       "      <td>2020-03-01 00:00:20.0580</td>\n",
       "      <td>2020-03-01 00:10:34.2200</td>\n",
       "      <td>379</td>\n",
       "      <td>W 31 St &amp; 7 Ave</td>\n",
       "      <td>40.749156</td>\n",
       "      <td>-73.991600</td>\n",
       "      <td>515</td>\n",
       "      <td>W 43 St &amp; 10 Ave</td>\n",
       "      <td>40.760094</td>\n",
       "      <td>-73.994618</td>\n",
       "      <td>39853</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>597</td>\n",
       "      <td>2020-03-01 00:00:24.3510</td>\n",
       "      <td>2020-03-01 00:10:22.3390</td>\n",
       "      <td>3739</td>\n",
       "      <td>Perry St &amp; Greenwich Ave</td>\n",
       "      <td>40.735918</td>\n",
       "      <td>-74.000939</td>\n",
       "      <td>325</td>\n",
       "      <td>E 19 St &amp; 3 Ave</td>\n",
       "      <td>40.736245</td>\n",
       "      <td>-73.984738</td>\n",
       "      <td>42608</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1920</td>\n",
       "      <td>2020-03-01 00:00:26.1120</td>\n",
       "      <td>2020-03-01 00:32:26.2680</td>\n",
       "      <td>236</td>\n",
       "      <td>St Marks Pl &amp; 2 Ave</td>\n",
       "      <td>40.728419</td>\n",
       "      <td>-73.987140</td>\n",
       "      <td>3124</td>\n",
       "      <td>46 Ave &amp; 5 St</td>\n",
       "      <td>40.747310</td>\n",
       "      <td>-73.954510</td>\n",
       "      <td>36288</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1993</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration                 starttime                  stoptime  \\\n",
       "0          1589  2020-03-01 00:00:03.6400  2020-03-01 00:26:32.9860   \n",
       "1           389  2020-03-01 00:00:16.7560  2020-03-01 00:06:46.0620   \n",
       "2           614  2020-03-01 00:00:20.0580  2020-03-01 00:10:34.2200   \n",
       "3           597  2020-03-01 00:00:24.3510  2020-03-01 00:10:22.3390   \n",
       "4          1920  2020-03-01 00:00:26.1120  2020-03-01 00:32:26.2680   \n",
       "\n",
       "   start station id        start station name  start station latitude  \\\n",
       "0               224     Spruce St & Nassau St               40.711464   \n",
       "1               293     Lafayette St & E 8 St               40.730207   \n",
       "2               379           W 31 St & 7 Ave               40.749156   \n",
       "3              3739  Perry St & Greenwich Ave               40.735918   \n",
       "4               236       St Marks Pl & 2 Ave               40.728419   \n",
       "\n",
       "   start station longitude  end station id             end station name  \\\n",
       "0               -74.005524            3574  Prospect Pl & Underhill Ave   \n",
       "1               -73.991026             223              W 13 St & 7 Ave   \n",
       "2               -73.991600             515             W 43 St & 10 Ave   \n",
       "3               -74.000939             325              E 19 St & 3 Ave   \n",
       "4               -73.987140            3124                46 Ave & 5 St   \n",
       "\n",
       "   end station latitude  end station longitude  bikeid    usertype  \\\n",
       "0             40.676969             -73.965790   16214  Subscriber   \n",
       "1             40.737815             -73.999947   29994  Subscriber   \n",
       "2             40.760094             -73.994618   39853  Subscriber   \n",
       "3             40.736245             -73.984738   42608  Subscriber   \n",
       "4             40.747310             -73.954510   36288  Subscriber   \n",
       "\n",
       "   birth year  gender  \n",
       "0        1980       1  \n",
       "1        1991       2  \n",
       "2        1991       1  \n",
       "3        1989       1  \n",
       "4        1993       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1068457.00</td>\n",
       "      <td>1068457.00</td>\n",
       "      <td>1068457.00</td>\n",
       "      <td>1068457.00</td>\n",
       "      <td>1068457.00</td>\n",
       "      <td>1068457.00</td>\n",
       "      <td>1068457.00</td>\n",
       "      <td>1068457.00</td>\n",
       "      <td>1068457.00</td>\n",
       "      <td>1068457.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1160.83</td>\n",
       "      <td>1809.89</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>1804.10</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>33307.91</td>\n",
       "      <td>1979.90</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13946.59</td>\n",
       "      <td>1511.14</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1511.58</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>8231.19</td>\n",
       "      <td>12.53</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>61.00</td>\n",
       "      <td>72.00</td>\n",
       "      <td>40.66</td>\n",
       "      <td>-74.02</td>\n",
       "      <td>72.00</td>\n",
       "      <td>40.66</td>\n",
       "      <td>-74.07</td>\n",
       "      <td>14530.00</td>\n",
       "      <td>1885.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>379.00</td>\n",
       "      <td>401.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>398.00</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>28841.00</td>\n",
       "      <td>1969.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>680.00</td>\n",
       "      <td>530.00</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>529.00</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>35221.00</td>\n",
       "      <td>1982.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1246.00</td>\n",
       "      <td>3360.00</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>3360.00</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-73.97</td>\n",
       "      <td>40136.00</td>\n",
       "      <td>1990.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3247190.00</td>\n",
       "      <td>3919.00</td>\n",
       "      <td>40.82</td>\n",
       "      <td>-73.90</td>\n",
       "      <td>3919.00</td>\n",
       "      <td>40.82</td>\n",
       "      <td>-73.90</td>\n",
       "      <td>43878.00</td>\n",
       "      <td>2004.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tripduration  start station id  start station latitude  \\\n",
       "count    1068457.00        1068457.00              1068457.00   \n",
       "mean        1160.83           1809.89                   40.74   \n",
       "std        13946.59           1511.14                    0.03   \n",
       "min           61.00             72.00                   40.66   \n",
       "25%          379.00            401.00                   40.72   \n",
       "50%          680.00            530.00                   40.74   \n",
       "75%         1246.00           3360.00                   40.76   \n",
       "max      3247190.00           3919.00                   40.82   \n",
       "\n",
       "       start station longitude  end station id  end station latitude  \\\n",
       "count               1068457.00      1068457.00            1068457.00   \n",
       "mean                    -73.98         1804.10                 40.74   \n",
       "std                       0.02         1511.58                  0.03   \n",
       "min                     -74.02           72.00                 40.66   \n",
       "25%                     -73.99          398.00                 40.72   \n",
       "50%                     -73.98          529.00                 40.74   \n",
       "75%                     -73.97         3360.00                 40.76   \n",
       "max                     -73.90         3919.00                 40.82   \n",
       "\n",
       "       end station longitude      bikeid  birth year      gender  \n",
       "count             1068457.00  1068457.00  1068457.00  1068457.00  \n",
       "mean                  -73.98    33307.91     1979.90        1.17  \n",
       "std                     0.02     8231.19       12.53        0.54  \n",
       "min                   -74.07    14530.00     1885.00        0.00  \n",
       "25%                   -73.99    28841.00     1969.00        1.00  \n",
       "50%                   -73.98    35221.00     1982.00        1.00  \n",
       "75%                   -73.97    40136.00     1990.00        1.00  \n",
       "max                   -73.90    43878.00     2004.00        2.00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column Name</th>\n",
       "      <th>Unique</th>\n",
       "      <th>Data Type</th>\n",
       "      <th>Memory Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tripduration</td>\n",
       "      <td>10252</td>\n",
       "      <td>int64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>starttime</td>\n",
       "      <td>1068008</td>\n",
       "      <td>object</td>\n",
       "      <td>86545145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>stoptime</td>\n",
       "      <td>1067964</td>\n",
       "      <td>object</td>\n",
       "      <td>86545145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>start station id</td>\n",
       "      <td>889</td>\n",
       "      <td>int64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>start station name</td>\n",
       "      <td>889</td>\n",
       "      <td>object</td>\n",
       "      <td>81856205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>start station latitude</td>\n",
       "      <td>923</td>\n",
       "      <td>float64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>start station longitude</td>\n",
       "      <td>912</td>\n",
       "      <td>float64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>end station id</td>\n",
       "      <td>899</td>\n",
       "      <td>int64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>end station name</td>\n",
       "      <td>899</td>\n",
       "      <td>object</td>\n",
       "      <td>81862917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>end station latitude</td>\n",
       "      <td>932</td>\n",
       "      <td>float64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>end station longitude</td>\n",
       "      <td>921</td>\n",
       "      <td>float64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bikeid</td>\n",
       "      <td>14882</td>\n",
       "      <td>int64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>usertype</td>\n",
       "      <td>2</td>\n",
       "      <td>object</td>\n",
       "      <td>71277969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>birth year</td>\n",
       "      <td>103</td>\n",
       "      <td>int64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>gender</td>\n",
       "      <td>3</td>\n",
       "      <td>int64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Column Name   Unique Data Type  Memory Usage\n",
       "0              tripduration    10252     int64       8547784\n",
       "1                 starttime  1068008    object      86545145\n",
       "2                  stoptime  1067964    object      86545145\n",
       "3          start station id      889     int64       8547784\n",
       "4        start station name      889    object      81856205\n",
       "5    start station latitude      923   float64       8547784\n",
       "6   start station longitude      912   float64       8547784\n",
       "7            end station id      899     int64       8547784\n",
       "8          end station name      899    object      81862917\n",
       "9      end station latitude      932   float64       8547784\n",
       "10    end station longitude      921   float64       8547784\n",
       "11                   bikeid    14882     int64       8547784\n",
       "12                 usertype        2    object      71277969\n",
       "13               birth year      103     int64       8547784\n",
       "14                   gender        3     int64       8547784"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_records([(col, df[col].nunique(), df[col].dtype, df[col].memory_usage(deep=True) )  for col in df.columns],\n",
    "    columns=['Column Name', 'Unique', 'Data Type','Memory Usage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['starttime'] = to_datetime(df['starttime'])\n",
    "df['stoptime'] = to_datetime(df['stoptime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['start station name', 'end station name', 'usertype', 'gender']\n",
    "for col in cols:\n",
    "    df[col] = df[col].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['bikeid'] = df['bikeid'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Column Name</th>\n",
       "      <th>Unique</th>\n",
       "      <th>Data Type</th>\n",
       "      <th>Memory Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tripduration</td>\n",
       "      <td>10252</td>\n",
       "      <td>int64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>starttime</td>\n",
       "      <td>1068008</td>\n",
       "      <td>datetime64[ns]</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>stoptime</td>\n",
       "      <td>1067964</td>\n",
       "      <td>datetime64[ns]</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>start station id</td>\n",
       "      <td>889</td>\n",
       "      <td>int64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>start station name</td>\n",
       "      <td>889</td>\n",
       "      <td>category</td>\n",
       "      <td>2239175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>start station latitude</td>\n",
       "      <td>923</td>\n",
       "      <td>float64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>start station longitude</td>\n",
       "      <td>912</td>\n",
       "      <td>float64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>end station id</td>\n",
       "      <td>899</td>\n",
       "      <td>int64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>end station name</td>\n",
       "      <td>899</td>\n",
       "      <td>category</td>\n",
       "      <td>2239883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>end station latitude</td>\n",
       "      <td>932</td>\n",
       "      <td>float64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>end station longitude</td>\n",
       "      <td>921</td>\n",
       "      <td>float64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bikeid</td>\n",
       "      <td>14882</td>\n",
       "      <td>category</td>\n",
       "      <td>2784522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>usertype</td>\n",
       "      <td>2</td>\n",
       "      <td>category</td>\n",
       "      <td>1068825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>birth year</td>\n",
       "      <td>103</td>\n",
       "      <td>int64</td>\n",
       "      <td>8547784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>gender</td>\n",
       "      <td>3</td>\n",
       "      <td>category</td>\n",
       "      <td>1068717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Column Name   Unique       Data Type  Memory Usage\n",
       "0              tripduration    10252           int64       8547784\n",
       "1                 starttime  1068008  datetime64[ns]       8547784\n",
       "2                  stoptime  1067964  datetime64[ns]       8547784\n",
       "3          start station id      889           int64       8547784\n",
       "4        start station name      889        category       2239175\n",
       "5    start station latitude      923         float64       8547784\n",
       "6   start station longitude      912         float64       8547784\n",
       "7            end station id      899           int64       8547784\n",
       "8          end station name      899        category       2239883\n",
       "9      end station latitude      932         float64       8547784\n",
       "10    end station longitude      921         float64       8547784\n",
       "11                   bikeid    14882        category       2784522\n",
       "12                 usertype        2        category       1068825\n",
       "13               birth year      103           int64       8547784\n",
       "14                   gender        3        category       1068717"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_records([(col, df[col].nunique(), df[col].dtype, df[col].memory_usage(deep=True) )  for col in df.columns],\n",
    "    columns=['Column Name', 'Unique', 'Data Type','Memory Usage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tripminutes'] = df['tripduration'] // 60 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df,x=\"tripminutes\" , bins=range(1,61) , aspect=10/5  ) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Date components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['start hour']=df['starttime'].dt.hour.astype('category')\n",
    "df['start day']=df['starttime'].dt.day.astype('category')\n",
    "df['weekday']=df['starttime'].dt.weekday.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rides per Hour of the Day "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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64BXAD4B1dFdAGfdJopIkSdI2bbZjzt8JrKiqOwGS7AK8hy60S5IkSRqD2R45f+pEMAeoqjuAp/ezSpIkSdK2abbh/GFJdp540Y6cz/aouyRJkqRZmG3Afi/wf5KspLvO+SuY4rrjkiRJkjbfbO8QenaSVcALgAAvr6rrel0zSZIkaRsz66EpLYwbyCVJkqSezHbMuSRJkqSeGc4lSZKkgTCcS5IkSQNhOJckSZIGwnAuSZIkDYThXJIkSRoIw7kkSZI0EIZzSZIkaSBmfRMiSdJDy0s+flov/X7ud4/ppV9JkkfOJUmSpMEwnEuSJEkDYTiXJEmSBsJwLkmSJA2E4VySJEkaCMO5JEmSNBCGc0mSJGkgDOeSJEnSQBjOJUmSpIEwnEuSJEkDsWC+V0DSsJ183iG99Pu2V17US7+SJD2UGc4laY685JPv7q3vz73sT3rrW5I0dxzWIkmSJA2E4VySJEkaCMO5JEmSNBCGc0mSJGkgDOeSJEnSQMxbOE+yXZKvJ/lse71LkkuS3Niedx6Z96Qka5LckOSQkfaDklzT3vtAkrT27ZOc19ovT7LvnG+gJEmS9CDN55Hz44HrR16fCFxaVfsDl7bXJDkAWA48BVgGfDDJdm2ZU4FjgP3bY1lrPxq4s6qeCLwPOKXfTZEkSZK23LyE8ySLgJcAHxppPhw4q02fBRwx0n5uVd1bVTcBa4AlSfYEdqyqy6qqgLMnLTPR10pg6cRRdUmSJGmo5uvI+d8Afwr8fKRtj6paB9Ced2/tewG3jsy3trXt1aYnt2+yTFVtBO4Cdp28EkmOSbIqyaoNGzZs4SZJkiRJW2bO7xCa5KXA+qq6MsnzZrPIFG01Q/tMy2zaUHUacBrA4sWL7/e+JEnS0N38Nz/ore99T3h8b31ranMezoFnAYclORR4JLBjko8AtyfZs6rWtSEr69v8a4G9R5ZfBNzW2hdN0T66zNokC4CdgDv62iBJkiRpHOZ8WEtVnVRVi6pqX7oTPb9YVa8GLgBWtNlWAJ9u0xcAy9sVWPajO/Hzijb05e4kB7fx5K+ZtMxEX0e2Gh4ZlyRJ0qDNx5Hz6bwLOD/J0cAtwFEAVXVtkvOB64CNwHFVdV9b5ljgTGAH4ML2ADgdOCfJGroj5svnaiMkSZKkzTWv4byqvgx8uU3/CFg6zXwnAydP0b4KOHCK9nto4V6SJEl6qPAOoZIkSdJAGM4lSZKkgTCcS5IkSQNhOJckSZIGwnAuSZIkDYThXJIkSRoIw7kkSZI0EIZzSZIkaSAM55IkSdJAGM4lSZKkgTCcS5IkSQNhOJckSZIGwnAuSZIkDYThXJIkSRoIw7kkSZI0EIZzSZIkaSAM55IkSdJAGM4lSZKkgTCcS5IkSQNhOJckSZIGwnAuSZIkDYThXJIkSRoIw7kkSZI0EIZzSZIkaSAM55IkSdJAGM4lSZKkgVgw3ysgSZKkh44fvPdbvfT7+D/+lV76fajxyLkkSZI0EIZzSZIkaSAM55IkSdJAGM4lSZKkgTCcS5IkSQNhOJckSZIGwnAuSZIkDYThXJIkSRoIw7kkSZI0EHMezpPsneRLSa5Pcm2S41v7LkkuSXJje955ZJmTkqxJckOSQ0baD0pyTXvvA0nS2rdPcl5rvzzJvnO9nZIkSdKDtWAeam4E/riqrkryWODKJJcArwUurap3JTkROBH4syQHAMuBpwBPAP45yZOq6j7gVOAY4KvA54FlwIXA0cCdVfXEJMuBU4BXzulWSj35x3MOeeCZNsMf/P5FvfQrSZJmb86PnFfVuqq6qk3fDVwP7AUcDpzVZjsLOKJNHw6cW1X3VtVNwBpgSZI9gR2r6rKqKuDsSctM9LUSWDpxVF2SJEkaqnkdc96GmzwduBzYo6rWQRfggd3bbHsBt44stra17dWmJ7dvskxVbQTuAnbtZSMkSZKkMZm3cJ7kMcDHgROq6sczzTpFW83QPtMyk9fhmCSrkqzasGHDA62yJEmS1Kt5CedJHk4XzD9aVZ9ozbe3oSq05/WtfS2w98jii4DbWvuiKdo3WSbJAmAn4I7J61FVp1XV4qpavHDhwnFsmiRJkrTZ5uNqLQFOB66vqr8eeesCYEWbXgF8eqR9ebsCy37A/sAVbejL3UkObn2+ZtIyE30dCXyxjUuXJEmSBms+rtbyLOD3gWuSXN3a/hvwLuD8JEcDtwBHAVTVtUnOB66ju9LLce1KLQDHAmcCO9BdpeXC1n46cE6SNXRHzJf3vE2SJEnSFpvzcF5V/8rUY8IBlk6zzMnAyVO0rwIOnKL9Hlq4lyRJkh4q5uPIuSRJkjQrt7//sl763eP4Z/bS75aa10spSpIkSfoFw7kkSZI0EIZzSZIkaSAM55IkSdJAGM4lSZKkgTCcS5IkSQNhOJckSZIGwnAuSZIkDYThXJIkSRoIw7kkSZI0EIZzSZIkaSAM55IkSdJALJjvFZAkSZKGYv3fXdhLv7u/8cWzms8j55IkSdJAGM4lSZKkgTCcS5IkSQNhOJckSZIGwnAuSZIkDYThXJIkSRoIw7kkSZI0EIZzSZIkaSAM55IkSdJAGM4lSZKkgVgw3yswRBtO/UhvfS889tW99S1JkqSHNsO5tIXOO2NZL/2+8nVf6KVfSZI0XA5rkSRJkgbCcC5JkiQNhOFckiRJGgjDuSRJkjQQhnNJkiRpILxaywDcfuq7e+l3j2P/pJd+JUmS1A+PnEuSJEkDYTiXJEmSBsJwLkmSJA2EY863Qbd84Mhe+t3nzSt76ffBuuj0Q3vp95CjP99Lv5IkSRO26nCeZBnwfmA74ENV9a55XqVt0tf+8Xd66fc3/uAzvfQrSZI0X7baYS1JtgP+HngxcADwe0kOmN+1kiRJkqa31YZzYAmwpqq+W1U/Bc4FDp/ndZIkSZKmtTWH872AW0der21tkiRJ0iClquZ7HXqR5CjgkKp6Q3v9+8CSqnrTyDzHAMe0l08GbtiMUrsBP9zC1bWe9axnvfmsZT3rWW/bqbc1b9tDqd5/qaqFU72xNZ8QuhbYe+T1IuC20Rmq6jTgtC0pkmRVVS3ekj6sZz3rWW8+a1nPetbbduptzdu2tdTbmoe1fA3YP8l+SR4BLAcumOd1kiRJkqa11R45r6qNSd4IXER3KcUPV9W187xakiRJ0rS22nAOUFWfB/q+c8wWDYuxnvWsZ70B1LKe9ay37dTbmrdtq6i31Z4QKkmSJD3UbM1jziVJkqSHFMP5FkiyLMkNSdYkObHnWh9Osj7J6j7rjNTbO8mXklyf5Nokx/dc75FJrkjyjVbvL/qs12pul+TrST7bd61W7+Yk1yS5Osmqnms9LsnKJN9q/4bP7LHWk9s2TTx+nOSEvuq1mm9pvyerk3wsySN7rnd8q3VtH9s21f/vJLskuSTJje15557rHdW27+dJxnrlgWnqvbv9fn4zySeTPK7neu9sta5OcnGSJ/RZb+S9tyapJLv1WS/Jnyf5/sj/w0P7rNfa39Q+A69N8ld91Upy3sh23Zzk6nHUmqHe05J8dWJfnWRJz/V+Pcll7fPhM0l2HGO9KT/L+9q/zFCvl/3LDPXGvn+Zodb49y1V5WMzHnQnmX4H+CXgEcA3gAN6rPdc4BnA6jnavj2BZ7TpxwLf7nn7AjymTT8cuBw4uOdt/CPgn4DPztHP9GZgtzmqdRbwhjb9COBxc1R3O+AHdNdv7avGXsBNwA7t9fnAa3usdyCwGngU3Xk6/wzsP+Ya9/v/DfwVcGKbPhE4ped6v0p3v4cvA4vnYPteBCxo06fMwfbtODL9ZuAf+qzX2vemuyjB98b5f3+a7ftz4K3j/Hd7gHrPb/8Xtm+vd+/zZzny/nuBt/e8bRcDL27ThwJf7rne14DfbtOvB945xnpTfpb3tX+ZoV4v+5cZ6o19/zJDrbHvWzxyvvmWAGuq6rtV9VPgXODwvopV1VeAO/rqf4p666rqqjZ9N3A9Pd5htTr/0V4+vD16OyEiySLgJcCH+qoxX9pRl+cCpwNU1U+r6t/nqPxS4DtV9b2e6ywAdkiygC403/YA82+JXwW+WlU/qaqNwL8ALxtngWn+fx9O9yWL9nxEn/Wq6vqq2pwbsW1uvYvbzxPgq3T3ouiz3o9HXj6aMe5fZtg/vw/403HWeoB6vZim3rHAu6rq3jbP+h5rAZAkwCuAj42j1gz1Cpg4er0TY9y/TFPvycBX2vQlwO+Osd50n+W97F+mq9fX/mWGemPfv8xQa+z7FsP55tsLuHXk9Vp6DK/zKcm+wNPpjmb3WWe79ufK9cAlVdVnvb+h+9D8eY81Jivg4iRXprs7bV9+CdgAnJFu2M6Hkjy6x3qjljPGD86pVNX3gfcAtwDrgLuq6uIeS64Gnptk1ySPojuStvcDLDMOe1TVOug+FIDd56DmfHk9cGHfRZKcnORW4FXA23uudRjw/ar6Rp91Jnlj+/P6h8c5DGoaTwKek+TyJP+S5Dd6rgfwHOD2qrqx5zonAO9uvyvvAU7qud5q4LA2fRQ97V8mfZb3vn+Zq+wwi3pj379MrjXufYvhfPNlirat7tI3SR4DfBw4YdK3w7Grqvuq6ml033CXJDmwjzpJXgqsr6or++h/Bs+qqmcALwaOS/LcnuosoPuz6alV9XTgP+n+bNmrdDf7Ogz4Xz3X2ZnuqM9+wBOARyd5dV/1qup6uj+LXgJ8gW4I28YZF9KsJXkb3c/zo33Xqqq3VdXerdYb+6rTvsS9jZ6/AExyKvDLwNPovrS+t+d6C4CdgYOBPwHOb0e2+/R79PzlvzkWeEv7XXkL7a+QPXo93WfClXTDJX467gJz+Vk+pHp97F+mqjXufYvhfPOtZdNvt4vo90/rcy7Jw+l+AT9aVZ+Yq7ptCMaXgWU9lXgWcFiSm+mGI70gyUd6qvX/VdVt7Xk98Em6oVF9WAusHfnLw0q6sN63FwNXVdXtPdd5IXBTVW2oqp8BnwB+q8+CVXV6VT2jqp5L9yfpvo/cAdyeZE+A9jyWYQNDkmQF8FLgVdUGbM6Rf2KMQwem8Mt0Xx6/0fYzi4Crkjy+r4JVdXs7wPFz4H/S3/5lwlrgE21I4hV0f4Uc20mvk7UhbC8HzuurxogVdPsV6A429PqzrKpvVdWLquogui8f3xln/9N8lve2f5nr7DBdvT72L7PYtrHsWwznm+9rwP5J9mtHDJcDF8zzOo1NOwJyOnB9Vf31HNRbOHE2dZId6ALYt/qoVVUnVdWiqtqX7t/ti1XV25FXgCSPTvLYiWm6k1V6ufJOVf0AuDXJk1vTUuC6PmpNMldHtW4BDk7yqPZ7upRu7F9vkuzenvehCwhzsZ0X0IUE2vOn56DmnEmyDPgz4LCq+skc1Nt/5OVh9LR/Aaiqa6pq96rat+1n1tKdSPaDvmpOBK3mZfS0fxnxKeAFrfaT6E48/2GP9V4IfKuq1vZYY8JtwG+36RfQ85fxkf3Lw4D/DvzDGPue7rO8l/3LPGSHKev1sX+Zodb49y2TzxD18aDO3D2U7mzd7wBv67nWx+j+VPkzuh390T3XezbdMJ1vAle3x6E91nsq8PVWbzVjPBv/Aeo+jzm4WgvdOPBvtMe1c/D78jRgVft5fgrYued6jwJ+BOw0R/9uf9F2gKuBc2hXjOix3v+m+4LzDWBpD/3f7/83sCtwKV0wuBTYped6L2vT9wK3Axf1XG8N3Xk7E/uXcV49Zap6H2+/L98EPkN3Ildv9Sa9fzPjvVrLVNt3DnBN274LgD17rvcI4CPtZ3oV8II+f5bAmcAfjmubHmDbng1c2f6/Xw4c1HO94+myxLeBd9FuEDmmelN+lve1f5mhXi/7lxnqjX3/MkOtse9bvEOoJEmSNBAOa5EkSZIGwnAuSZIkDYThXJIkSRoIw7kkSZI0EIZzSZIkaSAM55K0DUpyQruT5YNd7rVJnjDNe19OsnjL106Stl2Gc0naNp1Ad336WUuyHfBaYMpw3pdWV5K2CYZzSdqKtbvTfi7JN5KsTvLKJG+mC9hfSvKlNt+pSVYluTbJX4wsf3OStyf5V7q7wC4GPprk6nY338mOSnJFkm8neU7r45FJzkhyTZKvJ3l+a39tkr8bqfXZJM9r0/+R5C+TXA48s5cfjiQN0IL5XgFJUq+WAbdV1UsAkuxUVXcl+SPg+VU1ccv1t1XVHe0o9aVJnlpV32zv3VNVz27LvwF4a1WtmqbegqpakuRQ4B10t10/DqCqfi3JrwAXt1u+z+TRwOqqevtmbrckPSR55FyStm7XAC9MckqS51TVXdPM94okVwFfB54CHDDy3nkPot4n2vOVwL5t+tl0t5enqr4FfA94oHB+H91tsSVpm2I4l6StWFV9GziILqT/jyT3OxKdZD/grcDSqnoq8DngkSOz/OeDKHlve76PX/x1NtPMu5FNP4dGa95TVfc9iLqStFUwnEvSVqxdWeUnVfUR4D3AM9pbdwOPbdM70gXwu5LsAbx4hi5Hl5utrwCvauvzJGAf4AbgZuBpSR6WZG9gyYPsV5K2Oo45l6St268B707yc+BnwLGt/TTgwiTrqur5Sb4OXAt8F/i3Gfo7E/iHJP8XeGZV/d9ZrMMH2zLX0B0tf21V3Zvk34Cb6I7qrwauevCbJ0lbl1TVfK+DJEmSJBzWIkmSJA2G4VySJEkaCMO5JEmSNBCGc0mSJGkgDOeSJEnSQBjOJUmSpIEwnEuSJEkDYTiXJEmSBuL/AVSCv5HgccC0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.countplot(data=df, x=\"start hour\" ) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rides per Day of Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['day'] = [\"Weekday\" if d <= 4  else \"Weekend\" for d in df['weekday']] # weekday values start with 0=Monday\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.set_palette(\"Set2\")\n",
    "sns.countplot(data=df,x=\"start day\" , hue='day' , dodge=False  ) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Station Use\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# startat = df.groupby(\"start station name\")['start station name'].count().nlargest(20) \n",
    "startsstation = df['start station name'].value_counts()[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12,10))\n",
    "sns.barplot( x=startsstation.values , y=list(startsstation.index),  orient=\"h\"  ) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rider's Ages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df,x=\"birth year\" , bins=range(1946,2004),  aspect=10/5) ;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 809.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df,x=\"birth year\", bins=range(1946,2004), hue='usertype', multiple='stack',  aspect=10/5) ;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 763.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df,x=\"birth year\" , bins=range(1946,2004), hue='gender',  multiple='stack', aspect=10/5) ;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     981616\n",
       "unique        59\n",
       "top           30\n",
       "freq       42167\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skip = (df['gender'] == 0) | (df['birth year'] < 1946) \n",
    "df['age'] = (2020 - df['birth year']).mask(skip,None)\n",
    "df['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 763.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df, x='age', hue='gender', multiple='stack',  aspect=10/5) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write to Parquet "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_parquet('202003-citibike-tripdata.parquet') "
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}