{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#import all relevant packages\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import folium\n", "from folium import plugins\n", "import seaborn as sns\n", "import branca.colormap as cm\n", "import datetime\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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delivery_idcustomer_iddpartner_idvehicle_typepickup_placeplace_categoryitem_nameitem_quantityitem_category_namehow_long_it_took_to_orderpickup_latpickup_londropoff_latdropoff_lonwhen_the_delivery_startedwhen_the_dpartner_arrived_at_pickupwhen_the_dpartner_left_pickupwhen_the_dpartner_arrived_at_dropoff
01457973327168162381vanMelt ShopAmericanLemonade1.0Beverages19:58.640.744607-73.99074240.752073-73.98537051:59.9NaNNaN52:06.3
1137705664452104533bicyclePrince Street PizzaPizzaNeapolitan Rice Balls3.0Munchables25:09.140.723080-73.99461540.719722-73.99185858:58.726:02.148:23.159:22.9
2147654783095132725bicycleBareburgerBurgerBare Sodas1.0Drinks06:44.540.728478-73.99839240.728606-73.99514339:52.737:18.859:10.004:40.6
31485494271149157175bicycleJuice PressJuice BarOMG! My Favorite Juice!1.0Cold Pressed JuicesNaN40.738868-74.00274740.751257-74.00563454:11.504:17.816:37.932:38.1
41327707122609118095bicycleBlue Ribbon SushiJapaneseSpicy Tuna & Tempura Flakes2.0Maki (Special Rolls)03:45.040.726110-74.00249240.709323-74.01586707:18.514:42.725:19.448:27.2
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" ], "text/plain": [ " delivery_id customer_id dpartner_id vehicle_type pickup_place \\\n", "0 1457973 327168 162381 van Melt Shop \n", "1 1377056 64452 104533 bicycle Prince Street Pizza \n", "2 1476547 83095 132725 bicycle Bareburger \n", "3 1485494 271149 157175 bicycle Juice Press \n", "4 1327707 122609 118095 bicycle Blue Ribbon Sushi \n", "\n", " place_category item_name item_quantity \\\n", "0 American Lemonade 1.0 \n", "1 Pizza Neapolitan Rice Balls 3.0 \n", "2 Burger Bare Sodas 1.0 \n", "3 Juice Bar OMG! My Favorite Juice! 1.0 \n", "4 Japanese Spicy Tuna & Tempura Flakes 2.0 \n", "\n", " item_category_name how_long_it_took_to_order pickup_lat pickup_lon \\\n", "0 Beverages 19:58.6 40.744607 -73.990742 \n", "1 Munchables 25:09.1 40.723080 -73.994615 \n", "2 Drinks 06:44.5 40.728478 -73.998392 \n", "3 Cold Pressed Juices NaN 40.738868 -74.002747 \n", "4 Maki (Special Rolls) 03:45.0 40.726110 -74.002492 \n", "\n", " dropoff_lat dropoff_lon when_the_delivery_started \\\n", "0 40.752073 -73.985370 51:59.9 \n", "1 40.719722 -73.991858 58:58.7 \n", "2 40.728606 -73.995143 39:52.7 \n", "3 40.751257 -74.005634 54:11.5 \n", "4 40.709323 -74.015867 07:18.5 \n", "\n", " when_the_dpartner_arrived_at_pickup when_the_dpartner_left_pickup \\\n", "0 NaN NaN \n", "1 26:02.1 48:23.1 \n", "2 37:18.8 59:10.0 \n", "3 04:17.8 16:37.9 \n", "4 14:42.7 25:19.4 \n", "\n", " when_the_dpartner_arrived_at_dropoff \n", "0 52:06.3 \n", "1 59:22.9 \n", "2 04:40.6 \n", "3 32:38.1 \n", "4 48:27.2 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#read in csv file into dataframe\n", "df_raw = pd.read_csv('./analyze_me.csv')\n", "df_raw.head() #view first few rows" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing values for each column:\n", "delivery_id 0\n", "customer_id 0\n", "dpartner_id 0\n", "vehicle_type 0\n", "pickup_place 0\n", "place_category 883\n", "item_name 1230\n", "item_quantity 1230\n", "item_category_name 1230\n", "how_long_it_took_to_order 2945\n", "pickup_lat 0\n", "pickup_lon 0\n", "dropoff_lat 0\n", "dropoff_lon 0\n", "when_the_delivery_started 0\n", "when_the_dpartner_arrived_at_pickup 550\n", "when_the_dpartner_left_pickup 550\n", "when_the_dpartner_arrived_at_dropoff 0\n", "dtype: int64\n" ] } ], "source": [ "count_missing_values = len(df_raw) - df_raw.count()\n", "print('Missing values for each column:')\n", "print(count_missing_values)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5983 records\n", "5967 records after removing duplicates\n" ] } ], "source": [ "# filter data for only unique deliveries\n", "df = df_raw.drop_duplicates()\n", "print(len(df_raw), ' records')\n", "print(len(df), ' records after removing duplicates')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "delivery_id int64\n", "customer_id int64\n", "dpartner_id int64\n", "vehicle_type object\n", "pickup_place object\n", "place_category object\n", "item_name object\n", "item_quantity float64\n", "item_category_name object\n", "how_long_it_took_to_order object\n", "pickup_lat float64\n", "pickup_lon float64\n", "dropoff_lat float64\n", "dropoff_lon float64\n", "when_the_delivery_started object\n", "when_the_dpartner_arrived_at_pickup object\n", "when_the_dpartner_left_pickup object\n", "when_the_dpartner_arrived_at_dropoff object\n", "dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\ebuns\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\indexing.py:543: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self.obj[item] = s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "delivery_id int64\n", "customer_id int64\n", "dpartner_id int64\n", "vehicle_type object\n", "pickup_place object\n", "place_category object\n", "item_name object\n", "item_quantity float64\n", "item_category_name object\n", "how_long_it_took_to_order datetime64[ns]\n", "pickup_lat float64\n", "pickup_lon float64\n", "dropoff_lat float64\n", "dropoff_lon float64\n", "when_the_delivery_started datetime64[ns]\n", "when_the_dpartner_arrived_at_pickup datetime64[ns]\n", "when_the_dpartner_left_pickup datetime64[ns]\n", "when_the_dpartner_arrived_at_dropoff datetime64[ns]\n", "dtype: object\n" ] } ], "source": [ "# drop raw dataframe\n", "# del df_raw\n", "\n", "# apply date format to columns\n", "df.loc[:,14:] = df.iloc[:,14:].apply(pd.to_datetime, errors='coerce')\n", "df.iloc[:, 9] = df.iloc[:, 9].apply(pd.to_datetime, format=\"%M:%S.%f\")\n", "print(df.dtypes)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "In the month of October, 578 delivery partners made 5214 deliveries from 898 places to 3192 customers.\n" ] } ], "source": [ "# Count of unique deliveries\n", "number_customers = df['customer_id'].unique()\n", "number_jumpmen = df['dpartner_id'].unique()\n", "number_deliveries = df['delivery_id'].unique()\n", "number_partners = df['pickup_place'].unique()\n", "print('In the month of October,', len(number_jumpmen), ' delivery partners made ', len(number_deliveries), ' deliveries from ', \n", " len(number_partners), ' places to ', len(number_customers), ' customers.') " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Top 10 jumpmen completed 12.10% deliveries \n", " dpartner_id count_deliveries\n", "0 99219 78\n", "1 104533 76\n", "2 142394 73\n", "3 66416 62\n", "4 61900 61\n", "5 30743 58\n", "6 3296 57\n", "7 20962 56\n", "8 32580 56\n", "9 23359 54\n", "Top 10 cutsomers made 3.22% deliveries \n", " customer_id count_deliveries\n", "0 369272 28\n", "1 52832 23\n", "2 275689 17\n", "3 125123 16\n", "4 91817 16\n", "5 58898 16\n", "6 100889 14\n", "7 250494 13\n", "8 115610 13\n", "9 276192 12\n", "Top 10 places completed 28.98% deliveries \n", " pickup_place count_deliveries\n", "0 Shake Shack 310\n", "1 Momofuku Milk Bar 186\n", "2 The Meatball Shop 180\n", "3 Blue Ribbon Sushi 151\n", "4 sweetgreen 148\n", "5 Blue Ribbon Fried Chicken 132\n", "6 Whole Foods Market 119\n", "7 Parm 102\n", "8 RedFarm Broadway 93\n", "9 Mighty Quinn's BBQ 90\n" ] } ], "source": [ "# View customers, jumpmen and places by # of deliveries\n", "def partner_by_delivery(column = 'column_header'):\n", " return df.groupby(column).size().sort_values(ascending=False).reset_index(name='count_deliveries')\n", "\n", "#find the sum total of top n deliveries\n", "def sum_top_n(top_n, df):\n", " return df.iloc[:top_n , 1].sum(axis=0)\n", " \n", "dpartner_deliveries = partner_by_delivery('dpartner_id')\n", "customer_deliveries = partner_by_delivery('customer_id')\n", "place_deliveries = partner_by_delivery('pickup_place')\n", "\n", "top_n = 10\n", "print('Top ', top_n, ' Delivery partners completed ', \n", " '{:2.2%}'.format(sum_top_n(top_n, dpartner_deliveries)/ len(number_deliveries)), ' deliveries', \n", " '\\n', jumpmen_deliveries.head(top_n))\n", "print('Top ', top_n, ' cutsomers made ', \n", " '{:2.2%}'.format(sum_top_n(top_n, customer_deliveries)/ len(number_deliveries)), ' deliveries', \n", " '\\n',customer_deliveries.head(top_n))\n", "print('Top ', top_n, ' places completed ', \n", " '{:2.2%}'.format(sum_top_n(top_n, place_deliveries)/ len(number_deliveries)), ' deliveries', \n", " '\\n',place_deliveries.head(top_n))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '# of deliveries')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# of deliveries over time\n", "df_daily = df.delivery_id.groupby(by=df['when_the_delivery_started'].dt.date).count()\n", "df_daily.plot(x='when_the_delivery_started', y = 'delivery_id')\n", "plt.title('Number of deliveries by day in October')\n", "plt.xticks(rotation=90)\n", "plt.ylabel('# of deliveries')" ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:2657: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " return self._engine.get_loc(key)\n" ] }, { "data": { "text/plain": [ "Text(0.5, 0, 'Week of the year')" ] }, "execution_count": 148, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# of deliveries over time (week)\n", "df_weekly = df.delivery_id.groupby(by=df['when_the_delivery_started'].dt.strftime('%W')).count()\n", "df_weekly.plot(x='when_the_delivery_started', y = 'delivery_id')\n", "plt.title('Number of deliveries by weeks in October')\n", "plt.ylabel('# of deliveries')\n", "plt.xlabel('Week of the year')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '# of deliveries')" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#group deliveries by the hour the delivery started. \n", "df['delivery_hr'] = df['when_the_delivery_started'].apply(lambda x: x.hour)\n", "df.set_index('delivery_hr', drop=False, inplace=True)\n", "\n", "df.hist(column='delivery_hr', bins=24)\n", "plt.suptitle('Deliveries by time in the day.')\n", "plt.xlabel('Hour in the day', fontsize=10)\n", "plt.ylabel('# of deliveries', fontsize=12)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "scrolled": true }, "outputs": [], "source": [ "def createPlot(default=[40.74460714, -73.99074197], zm=12):\n", " mymap = folium.Map(location=default, control_scale=True, zoom_start=zm)\n", " return mymap\n", "# mymap = createPlot()\n", "# mymap" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# legend for heatmap\n", "gradient_map = {0.2: 'rgb(0,0,255)', 0.4: 'rgb(0,255,255)', 0.6: 'lime', 0.8: 'rgb(255,255,0)', 1: 'rgb(255,0,0)'}\n", "\n", "nyheatmap = createPlot()\n", "\n", "plugins.HeatMap(data=df[['dropoff_lat', 'dropoff_lon', 'delivery_id']].groupby(['dropoff_lat', 'dropoff_lon'])\n", " .sum().reset_index().values.tolist(), radius=8, max_val=0.5, max_zoom=13, \n", " gradient = gradient_map).add_to(nyheatmap)\n", "\n", "nyheatmap.save('dropoff_location_deliveries.html')\n", "\n", "nyheatmap" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 147, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nyheatmap2 = createPlot()\n", "plugins.HeatMap(data=df[['pickup_lat', 'pickup_lon', 'delivery_id']].groupby(['pickup_lat', 'pickup_lon'])\n", " .sum().reset_index().values.tolist(), radius=8, max_zoom=13, \n", " gradient = gradient_map).add_to(nyheatmap2)\n", "nyheatmap2.save('pickup_locaion_deliveries.html')\n", "nyheatmap2" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "82.95% orders took 10 minutes or less\n" ] }, { "data": { "image/png": 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wP/OnF54HcGyNeucB5w1qcGZmVoqvRDczs1KcQMzMrBQnEDMzK8UJxMzMSnECMTOzUpxAzMysFCcQMzMrxQnEzMxKcQIxM7NSnEDMzKwUJxAzMyvFCcTMzEpxAjEzs1KcQMzMrBQnEDMzK8UJxMzMSnECMTOzUpxAzMysFCcQMzMrxQnEzMxKaUgCkXSepBWS7i2U/Zuk30i6R9IPJY0vzDtJ0mJJD0r6QKF871y2WNK8RsRuZmbVNWoP5Hxg74qyhcBbIuKtwG+BkwAk7QAcBrw5L/NNSaMkjQL+E9gH2AH4cK5rZmZN0JAEEhE3ASsryq6PiDV58hZgSn5+ELAgIv4YEb8DFgM758fiiHgkIl4GFuS6ZmbWBKObHUD2UeCy/HwyKaH0WpbLAJZWlO9SbWWS5gBzADo6Ouju7u43gJ6enqr15s5cs3blQVZPfGXValcra8c2QXu2y21qHWXa1fQEIumzwBrgkt6iKtWC6ntLUW2dETEfmA/Q2dkZXV1d/cbR3d1NtXqz513T77Lrasnha293sNRqVytrxzZBe7bLbWodZdrV1AQiaRawP7BXRPQmg2XA1EK1KcAT+XmtcjMza7CmncYraW/gRODAiHixMOsq4DBJG0raGpgB/C/wa2CGpK0lbUA60H5Vo+M2M7OkIXsgki4FuoCJkpYBp5DOutoQWCgJ4JaIOCYi7pN0OXA/qWvr2Ij4c17PJ4DrgFHAeRFxXyPiNzOztTUkgUTEh6sUn9tH/dOA06qUXwtcO4ihmZlZSb4S3czMSnECMTOzUpxAzMysFCcQMzMrxQnEzMxKcQIxM7NSnEDMzKwUJxAzMyvFCcTMzEpxAjEzs1KaPpy7wfQhHDJ+7sw1zJ53DUtO32/ItmFmI5P3QMzMrBQnEDMzK8UJxMzMSnECMTOzUpxAzMysFCcQMzMrxQnEzMxKcQIxM7NSnEDMzKyUhiQQSedJWiHp3kLZZpIWSnoo/52QyyXp65IWS7pH0k6FZWbl+g9JmtWI2M3MrLpG7YGcD+xdUTYPuCEiZgA35GmAfYAZ+TEHOAdSwgFOAXYBdgZO6U06ZmbWeA1JIBFxE7Cyovgg4IL8/ALg4EL5hZHcAoyXNAn4ALAwIlZGxCpgIWsnJTMza5BmHgPpiIjlAPnvFrl8MrC0UG9ZLqtVbmZmTTAcR+NVlbLoo3ztFUhzSN1fdHR00N3d3e9Ge3p6qtabO3NNv8sOZx1jUhvqeQ1aRa33qtW1Y7vcptZRpl3NTCBPSZoUEctzF9WKXL4MmFqoNwV4Ipd3VZR3V1txRMwH5gN0dnZGV1dXtWqv0d3dTbV6s4dwqPVGmDtzDWcuGs2Sw7uaHcqgqfVetbp2bJfb1DrKtKuZXVhXAb1nUs0CriyUH5nPxtoVeDZ3cV0HvF/ShHzw/P25zMzMmqAheyCSLiXtPUyUtIx0NtXpwOWSjgYeAw7J1a8F9gUWAy8CRwFExEpJXwZ+net9KSIqD8ybmVmDNCSBRMSHa8zaq0rdAI6tsZ7zgPMGMTQzMyupVBeWpG0kTRvsYMzMrHXUlUAkXSrpXfn5UcB9wP25+8nMzEagevdA9gJuy88/A7yXdDX4vJpLmJlZW6v3GMgGEfGypMnAZhHxCwBJHUMXmpmZDWf1JpC7JJ0ETAOuAcjJ5LmhCszMzIa3eruwjgZmAmOAz+WydwKXDEVQZmY2/PW7ByJpFOlCv49GxB96yyPiCuCKIYzNzMyGsX73QCLiz6TrMl4e+nDMzKxV1NuFdQFwzFAGYmZmraXeg+g7A5+UdAJpSPVXRsGNiN2GIjAzMxve6k0g384PMzMzoM4EEhEX9F/LzMxGknqHMpGkf5T0M0n35LLdJH1oaMMzM7Phqt6D6F8iXQsyH9gqly0DThyKoMzMbPirN4HMBvaPiAW8egD9d8A2QxGUmZkNf/UmkFFAT37em0DGFcrMzGyEqTeBXAt8VdKGkI6JAF8GfjxUgZmZ2fBWbwL5DLAl8CywKWnPYxo+BmJmNmLVexrvc8DBefj2rYClEfHkkEZmZmbDWs0EIqna3snT+fHK/Ij4y9CEZmZmw1lfeyBrKAxZ0odRgxSLmZm1kL6OgWxNOk13G+CTwI3A3sD2+e/PgU+sawCS/knSfZLuzfde30jS1pJulfSQpMskbZDrbpinF+f509d1+2ZmVk7NBBIRj/Y+SAfR/zYiFkbEbyNiIXAIcPy6bDzf1fBTQGdEvIW0N3MYcAZwVkTMAFaRLmIk/10VEW8Czsr1zMysCeo9C2tTYOOKso1z+boaDYyRNDqvczmwJ6/erOoC4OD8/KA8TZ6/Vz6l2MzMGkwR/R/mkPTvwL7A2aTh3KeS9hyui4i56xSAdBxwGvAScD1wHHBL3stA0lTgJxHxFkn3AntHxLI872Fgl4h4pmKdc4A5AB0dHe9YsGBBv3H09PQwbty4tcoXPf7sOrSu+TrGwFMvwczJg5Hrh4da71Wra8d2uU2to7Jde+yxx+0R0dnXMvUO534CsBg4lHQ9yHLgG6zjEO+SJpD2KrYGVgP/BexTpWpvlqu2t7FWBoyI+aRxu+js7Iyurq5+Y+nu7qZavdnzrul32eFs7sw1nLloNEsO72p2KIOm1nvV6tqxXW5T6yjTrnrviX4KcFpEfKtcaDW9F/hdRPSeGvwD4F3AeEmjI2INMAV4ItdfRtr7WZa7vDYFVg5yTGZmVoeB3BP9T0Ow/ceAXSVtnI9l7AXcTzrD64O5zizgyvz8qjxNnv+zqKcPzszMBl1T74keEbeSDobfASzK8cwnDZHyGUmLgc2Bc/Mi5wKb5/LPAPMGOyYzM6tP0++JHhGnkLrIih7J26ys+wfS6cNmZtZkvie6mZmV4nuim5lZKfUeA0HSUfme6A/mv0cNZWBmZja81bUHIumzwJHAmcCjpHuBnCBpy4g4bQjjMzOzYareYyD/AHTlcbEAkHQdcBPpKnIzMxth6u3CGku+D0jB74ExgxuOmZm1inoTyE+BSyRtK2mMpO1I14ZcN3ShmZnZcFZvAvkE8DxwN+l+6HcBL5DuE2JmZiPQQO6JfqSk2cBE4BnfytbMbGSr9yA68Mr9z1cMUSxmZtZC6r4OxMzMrMgJxMzMSqmZQCT9W+H5no0Jx8zMWkVfeyBzCs9/NNSBmJlZa+nrIPrdkq4g3eBpQ0lfqlYpIj4/JJGZmdmw1lcC+SBpL2Qa6V7kU6vU8d0AzcxGqJoJJCJWAKcC5PuTe/RdMzN7Rb0XEh4laQJwADAZeBy4OiJWDmVwZmY2fNV1Gq+kdwIPk+6L/lbgY8DiXG5mZiNQvVeinw18PCIW9BZIOhT4OvDXQxGYmZkNb/VeSPhXwOUVZVcAb1rXACSNl3SFpN9IekDSOyVtJmmhpIfy3wm5riR9XdJiSfdI2mldt29mZuXUm0AeAg6rKDuE1K21rr4G/DQitgPeBjwAzANuiIgZwA15GmAfYEZ+zAHOGYTtm5lZCfV2YX0auFrSp0i3tJ1O+hLff102Lul1wG7AbICIeBl4WdJBQFeudgHQDZwIHARcGBEB3JL3XiZFxPJ1icPMzAZO6bu4joqpG2k/YEvgCeDadT0LS9KOwHzSxYpvA24HjgMej4jxhXqrImKCpKuB0yPi5lx+A3BiRNxWsd455CvpOzo63rFgwQL609PTw7hx49YqX/T4syVbNzx0jIGnXoKZkzdtdiiDptZ71erasV1uU+uobNcee+xxe0R09rVM3cO5R8Qq4OLy4dXc/k7AJyPiVklf49XuqmpULbS1CiLmkxITnZ2d0dXV1W8g3d3dVKs3e941/S47nM2duYYzF41myeFdzQ5l0NR6r1pdO7bLbWodZdrV7NF4lwHLIuLWPH0FKaE8JWkSQP67olC/eEX8FNLekJmZNVhTE0hEPAkslbRtLtqL1J11FTArl80CrszPryLdGVGSdgWe9fEPM7PmGNAdCYfIJ4FLJG0APAIcRUpsl0s6GniMdMYXwLXAvsBi4MVc18zMmqDuBCJpWkQ8OtgBRMRdQLUDNXtVqRvAsYMdg5mZDdxAurDuBMin8pqZ2QjX5x6IpNtJp9beCYzKxV8gDWFiZmYjWH97IB8ErifdE2RjSXeQbi61h6T2ubDAzMwGrL8Esl5EXBER84DnSVeCi3Tg+y5JDw11gGZmNjz1dxD9e5K2Ip1auxEwAfhDRPwtgKTNhjg+MzMbpvpMIBGxi6TRwEzgZuAbwCaSzgHuyA/fVMrMbATq9yysiFgTEXcCL0fEbsALpMENZwBnDG14ZmY2XA3kQsJ/yn8jIi4DLhuCeMzMrEXUfR1IRJyfn24zNKGYmVkrGfBYWHlUXjMzG+GaPRqvmZm1KCcQMzMrxQnEzMxKcQIxM7NSnEDMzKwUJxAzMyvFCcTMzEpxAjEzs1KcQMzMrBQnEDMzK2VYJBBJoyTdKenqPL21pFslPSTpMkkb5PIN8/TiPH96M+M2MxvJhkUCAY4DHihMnwGcFREzgFXA0bn8aGBVRLwJOAsPJ29m1jRNTyCSpgD7Ad/J0wL2BK7IVS4ADs7PD8rT5Pl75fpmZtZgiojmBiBdAXwF2AQ4HpgN3JL3MpA0FfhJRLxF0r3A3hGxLM97GNglIp6pWOccYA5AR0fHOxYsWNBvHD09PYwbN26t8kWPP1u+ccNAxxh46iWYOXnTZocyaGq9V62uHdvlNrWOynbtsccet0dEZ1/LDOSGUoNO0v7Aioi4XVJXb3GVqlHHvFcLIuYD8wE6Ozujq6ursspauru7qVZv9rxr+l12OJs7cw1nLhrNksO7mh3KoKn1XrW6dmyX29Q6yrSrqQkEeDdwoKR9gY2A1wFnA+MljY6INcAU4IlcfxkwFViW79W+Kb4nu5lZUzT1GEhEnBQRUyJiOnAY8LOIOBz4OfDBXG0WcGV+flWeJs//WTS7D87MbIRq+kH0Gk4EPiNpMbA5cG4uPxfYPJd/BpjXpPjMzEa8ZndhvSIiuoHu/PwRYOcqdf4AHNLQwMzMrKrhugdiZmbDnBOImZmV4gRiZmalOIGYmVkpTiBmZlaKE4iZmZXiBGJmZqU4gZiZWSlOIGZmVooTiJmZlTJshjKxoTW9QcPSLzl9v4Zsx8yaz3sgZmZWihOImZmV4gRiZmalOIGYmVkpTiBmZlaKE4iZmZXiBGJmZqU4gZiZWSlOIGZmVkpTE4ikqZJ+LukBSfdJOi6XbyZpoaSH8t8JuVySvi5psaR7JO3UzPjNzEayZu+BrAHmRsT2wK7AsZJ2AOYBN0TEDOCGPA2wDzAjP+YA5zQ+ZDMzgyYnkIhYHhF35OfPAw8Ak4GDgAtytQuAg/Pzg4ALI7kFGC9pUoPDNjMzmr8H8gpJ04G3A7cCHRGxHFKSAbbI1SYDSwuLLctlZmbWYIqIZseApHHAjcBpEfEDSasjYnxh/qqImCDpGuArEXFzLr8BOCEibq9Y3xxSFxcdHR3vWLBgQb8x9PT0MG7cuLXKFz3+7Dq0rPk6xsBTLzVuezMnbzrk26j1XrW6dmyX29Q6Ktu1xx573B4RnX0t0/Th3CWtD3wfuCQifpCLn5I0KSKW5y6qFbl8GTC1sPgU4InKdUbEfGA+QGdnZ3R1dfUbR3d3N9XqzW7QMOhDZe7MNZy5qHFv85LDu4Z8G7Xeq1bXju1ym1pHmXY1+ywsAecCD0TEVwuzrgJm5eezgCsL5Ufms7F2BZ7t7eoyM7PGavYeyLuBI4BFku7KZScDpwOXSzoaeAw4JM+7FtgXWAy8CBzV2HDNzKxXUxNIPpahGrP3qlI/gGOHNCgzM6vLsDkLy8zMWosTiJmZleIEYmZmpTiBmJlZKU4gZmZWihOImZmV4gRiZmalOIGYmVkpzb4S3drM9AaMHXb+3mOHfBtm1j/vgZiZWSlOIGZmVooTiJmZleIEYmZmpTiBmJlZKU4gZmZWik/jtZaz6PFnh/xWw0tO329I12/WDrwHYmZmpTiBmJlZKU4gZmZWio+BmFXRiCFZwMdarLW15B6IpL0lPShpsaR5zY7HzGwkarkEImkU8J/APsAOwIcl7dDcqMzMRp5W7MLaGVgcEY8ASFoAHATc39SozEoodpXNnblmSE5PdjeZDameK0EAAAdsSURBVJVWTCCTgaWF6WXALk2KxWzYa6fjOY1oixNu/VoxgahKWbymgjQHmJMneyQ9WMd6JwLPrGNsw86n2rBd7dgmaP126YyqxS3XphrtKGq5NtWpsl3T+lugFRPIMmBqYXoK8ESxQkTMB+YPZKWSbouIznUPb3hpx3a1Y5ugPdvlNrWOMu1quYPowK+BGZK2lrQBcBhwVZNjMjMbcVpuDyQi1kj6BHAdMAo4LyLua3JYZmYjTsslEICIuBa4dpBXO6AurxbSju1qxzZBe7bLbWodA26XIqL/WmZmZhVa8RiImZkNA04gtM/QKJLOk7RC0r2Fss0kLZT0UP47oZkxDpSkqZJ+LukBSfdJOi6Xt2y7JG0k6X8l3Z3b9MVcvrWkW3ObLssnibQUSaMk3Snp6jzdDm1aImmRpLsk3ZbLWvbzByBpvKQrJP0m/2+9s0ybRnwCabOhUc4H9q4omwfcEBEzgBvydCtZA8yNiO2BXYFj8/vTyu36I7BnRLwN2BHYW9KuwBnAWblNq4CjmxhjWccBDxSm26FNAHtExI6F01xb+fMH8DXgpxGxHfA20ns28DZFxIh+AO8EritMnwSc1Oy41qE904F7C9MPApPy80nAg82OcR3bdyXwvnZpF7AxcAdpNIVngNG5/DWfy1Z4kK7JugHYE7iadNFvS7cpx70EmFhR1rKfP+B1wO/Ix8DXpU0jfg+E6kOjTG5SLEOhIyKWA+S/WzQ5ntIkTQfeDtxKi7crd/XcBawAFgIPA6sjYk2u0oqfw7OBE4C/5OnNaf02QRrp4npJt+dRLqC1P3/bAE8D383djd+RNJYSbXICqWNoFGs+SeOA7wOfjojnmh3PuoqIP0fEjqRf7TsD21er1tioypO0P7AiIm4vFlep2jJtKnh3ROxE6uY+VtJuzQ5oHY0GdgLOiYi3Ay9QsgvOCaSOoVFa3FOSJgHkvyuaHM+ASVqflDwuiYgf5OKWbxdARKwGuknHd8ZL6r02q9U+h+8GDpS0BFhA6sY6m9ZuEwAR8UT+uwL4ISnht/LnbxmwLCJuzdNXkBLKgNvkBNL+Q6NcBczKz2eRjiG0DEkCzgUeiIivFma1bLskvV7S+Px8DPBe0kHMnwMfzNVaqk0RcVJETImI6aT/oZ9FxOG0cJsAJI2VtEnvc+D9wL208OcvIp4ElkraNhftRbodxoDb5AsJAUn7kn4t9Q6NclqTQypF0qVAF2lUzaeAU4AfAZcDWwGPAYdExMpmxThQkt4D/A+wiFf71k8mHQdpyXZJeitwAenzth5weUR8SdI2pF/vmwF3Ah+JiD82L9JyJHUBx0fE/q3ephz/D/PkaOB7EXGapM1p0c8fgKQdge8AGwCPAEeRP4sMoE1OIGZmVoq7sMzMrBQnEDMzK8UJxMzMSnECMTOzUpxAzMysFCcQa1uSTpb0nQZsZytJPXlgzqHeVkPaZFYPn8ZrLUtST2FyY9Iot3/O0x+LiEsaH1XryFeN/0NE/HezY7HW1JK3tDUDiIhxvc/9ZWjWeO7CsrYl6QuSLs7Pp0sKSUdJWipplaRjJP21pHskrZb0jYrlP5pvtrNK0nWSptXYTu+6R+fpbklflvQLSc9Lul7SxBrLdklaJukEpZuBLZd0sKR9Jf1W0kpJJ/fTplmSHpP0jKTPFuqeL+nUym3l5xeRrjj+ce5+OyGX7yrpl/n1uDtfVd67/GxJj+Q2/U7S4QN8S6zNOIHYSLMLMAM4lDR8zWdJY1G9GfiQpN0BJB1MGjLlb4HXk4ZTuXQA2/l70vAQW5CGizi+j7pvADYiDXX+eeDbwEeAdwB/A3w+D6lRy3uAbUljGn1eUrWRfV8jIo4gDVdxQESMi4h/lTQZuAY4lTT0yPHA9/PYXWOBrwP7RMQmwLuAu/rbjrU3JxAbab4cEX+IiOtJw1hfGhErIuJxUpJ4e673MeArEfFAvp/FvwA71toLqeK7EfHbiHiJNL7Qjn3U/RNwWkT8iTRu1ETgaxHxfETcB9wHvLWP5b8YES9FxN3A3aQ7zJXxEeDaiLg2Iv4SEQuB24B98/y/AG+RNCYilufYbARzArGR5qnC85eqTPceV5kGfC135awGVpLub1HvDZGeLDx/sbDean4fEb0H/1+qEWdfyw9kW32ZBhzS2+bc7veQ7lL3Ammv7RhguaRrJG1XcjvWJpxAzKpbSjqTa3zhMSYiftnswAbgBdLZab3eUDG/8hTMpcBFFW0eGxGnA0TEdRHxPtLtTn9D6mqzEcwJxKy6bwEnSXozgKRNJR3S5JgG6i5gX0mbSXoD8OmK+U+Rbm/a62LgAEkfULrl7kb5wPsUSR2SDszHQv4I9PDqKdM2QjmBmFURET8EzgAWSHqOdBOhfZob1YBdRDomsgS4HrisYv5XgM/l7qrjI2IpcBDp5IGnSXsk/0z6nlgPmEu6o+BKYHfg4w1ogw1jvpDQzMxK8R6ImZmV4gRiZmalOIGYmVkpTiBmZlaKE4iZmZXiBGJmZqU4gZiZWSlOIGZmVooTiJmZlfL/AY4Sc+prQ+JSAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# df.iloc[:, 9] = df.iloc[:, 9].apply(pd.to_datetime, format=\"%H:%M:%S.%f\")\n", "\n", "#TDistribution of ime it takes to order by minute\n", "df['time_to_order'] = df['how_long_it_took_to_order'].apply(lambda x: x.minute)\n", "df.set_index('time_to_order', drop=False, inplace=True)\n", "\n", "df.hist(column='time_to_order', bins=12)\n", "plt.suptitle('How long does it take to place orders?')\n", "plt.xlabel('Time in minutes', fontsize=12)\n", "plt.ylabel('# of orders', fontsize=12)\n", "\n", "#missing data is skewing the results. Almost 50% of time to order missing\n", "print('{:2.2%}'.format(df['delivery_id'][df['time_to_order'] <= 10].count()/len(df['time_to_order'].dropna())), ' orders took 10 minutes or less')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#which methods have the fastest deliveries" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#deliveries by vehicle type\n", "count_vehicle_type = df.delivery_id.groupby(df.vehicle_type).sum()\n", "plt.pie(count_vehicle_type, autopct='%1.1f%%')\n", "plt.title('Deliveries by Vehicle Type')\n", "plt.axis('equal')\n", "plt.legend(labels=count_vehicle_type.index, loc=\"best\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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delivery_idcustomer_idjumpman_idvehicle_typepickup_placeplace_categoryitem_nameitem_quantityitem_category_namehow_long_it_took_to_order...when_the_Jumpman_arrived_at_pickupwhen_the_Jumpman_left_pickupwhen_the_Jumpman_arrived_at_dropoffdelivery_hrtime_to_ordertime_to_arrive_at_pickuptime_spent_waitingtime_to_arrive_at_dropofftotal_delivery_timevehicle_cat
time_to_order
19.01457973327168162381vanMelt ShopAmericanLemonade1.0Beverages1900-01-01 00:19:58.582052...NaTNaT2014-10-26 14:52:06.3130881970-01-01 00:00:00.0000000131970-01-01 00:00:00.000000019NaTNaTNaT01:00:06.414164other
25.0137705664452104533bicyclePrince Street PizzaPizzaNeapolitan Rice Balls3.0Munchables1900-01-01 00:25:09.107093...2014-10-16 22:26:02.1209312014-10-16 22:48:23.0912532014-10-16 22:59:22.9488731970-01-01 00:00:00.0000000211970-01-01 00:00:00.00000002500:27:03.46602100:22:20.97032200:10:59.85762001:00:24.293963bicycle
6.0147654783095132725bicycleBareburgerBurgerBare Sodas1.0Drinks1900-01-01 00:06:44.541717...2014-10-28 21:37:18.7934052014-10-28 21:59:09.9848102014-10-28 22:04:40.6349621970-01-01 00:00:00.0000000211970-01-01 00:00:00.000000006-1 days +23:57:26.13901100:21:51.19140500:05:30.65015200:24:47.980568bicycle
NaN1485494271149157175bicycleJuice PressJuice BarOMG! My Favorite Juice!1.0Cold Pressed JuicesNaT...2014-10-30 11:04:17.7595772014-10-30 11:16:37.8958162014-10-30 11:32:38.0900611970-01-01 00:00:00.000000010NaT00:10:06.22768300:12:20.13623900:16:00.19424500:38:26.558167bicycle
3.01327707122609118095bicycleBlue Ribbon SushiJapaneseSpicy Tuna & Tempura Flakes2.0Maki (Special Rolls)1900-01-01 00:03:45.035418...2014-10-10 00:14:42.7022232014-10-10 00:25:19.4002942014-10-10 00:48:27.1505951970-01-01 00:00:00.0000000001970-01-01 00:00:00.00000000300:07:24.25171800:10:36.69807100:23:07.75030100:41:08.700090bicycle
\n", "

5 rows × 25 columns

\n", "
" ], "text/plain": [ " delivery_id customer_id jumpman_id vehicle_type \\\n", "time_to_order \n", "19.0 1457973 327168 162381 van \n", "25.0 1377056 64452 104533 bicycle \n", "6.0 1476547 83095 132725 bicycle \n", "NaN 1485494 271149 157175 bicycle \n", "3.0 1327707 122609 118095 bicycle \n", "\n", " pickup_place place_category \\\n", "time_to_order \n", "19.0 Melt Shop American \n", "25.0 Prince Street Pizza Pizza \n", "6.0 Bareburger Burger \n", "NaN Juice Press Juice Bar \n", "3.0 Blue Ribbon Sushi Japanese \n", "\n", " item_name item_quantity \\\n", "time_to_order \n", "19.0 Lemonade 1.0 \n", "25.0 Neapolitan Rice Balls 3.0 \n", "6.0 Bare Sodas 1.0 \n", "NaN OMG! My Favorite Juice! 1.0 \n", "3.0 Spicy Tuna & Tempura Flakes 2.0 \n", "\n", " item_category_name how_long_it_took_to_order ... \\\n", "time_to_order ... \n", "19.0 Beverages 1900-01-01 00:19:58.582052 ... \n", "25.0 Munchables 1900-01-01 00:25:09.107093 ... \n", "6.0 Drinks 1900-01-01 00:06:44.541717 ... \n", "NaN Cold Pressed Juices NaT ... \n", "3.0 Maki (Special Rolls) 1900-01-01 00:03:45.035418 ... \n", "\n", " when_the_Jumpman_arrived_at_pickup \\\n", "time_to_order \n", "19.0 NaT \n", "25.0 2014-10-16 22:26:02.120931 \n", "6.0 2014-10-28 21:37:18.793405 \n", "NaN 2014-10-30 11:04:17.759577 \n", "3.0 2014-10-10 00:14:42.702223 \n", "\n", " when_the_Jumpman_left_pickup \\\n", "time_to_order \n", "19.0 NaT \n", "25.0 2014-10-16 22:48:23.091253 \n", "6.0 2014-10-28 21:59:09.984810 \n", "NaN 2014-10-30 11:16:37.895816 \n", "3.0 2014-10-10 00:25:19.400294 \n", "\n", " when_the_Jumpman_arrived_at_dropoff \\\n", "time_to_order \n", "19.0 2014-10-26 14:52:06.313088 \n", "25.0 2014-10-16 22:59:22.948873 \n", "6.0 2014-10-28 22:04:40.634962 \n", "NaN 2014-10-30 11:32:38.090061 \n", "3.0 2014-10-10 00:48:27.150595 \n", "\n", " delivery_hr time_to_order \\\n", "time_to_order \n", "19.0 1970-01-01 00:00:00.000000013 1970-01-01 00:00:00.000000019 \n", "25.0 1970-01-01 00:00:00.000000021 1970-01-01 00:00:00.000000025 \n", "6.0 1970-01-01 00:00:00.000000021 1970-01-01 00:00:00.000000006 \n", "NaN 1970-01-01 00:00:00.000000010 NaT \n", "3.0 1970-01-01 00:00:00.000000000 1970-01-01 00:00:00.000000003 \n", "\n", " time_to_arrive_at_pickup time_spent_waiting \\\n", "time_to_order \n", "19.0 NaT NaT \n", "25.0 00:27:03.466021 00:22:20.970322 \n", "6.0 -1 days +23:57:26.139011 00:21:51.191405 \n", "NaN 00:10:06.227683 00:12:20.136239 \n", "3.0 00:07:24.251718 00:10:36.698071 \n", "\n", " time_to_arrive_at_dropoff total_delivery_time vehicle_cat \n", "time_to_order \n", "19.0 NaT 01:00:06.414164 other \n", "25.0 00:10:59.857620 01:00:24.293963 bicycle \n", "6.0 00:05:30.650152 00:24:47.980568 bicycle \n", "NaN 00:16:00.194245 00:38:26.558167 bicycle \n", "3.0 00:23:07.750301 00:41:08.700090 bicycle \n", "\n", "[5 rows x 25 columns]" ] }, "execution_count": 142, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#average time to arrive at pick up by vehicle type\n", "# add new columns to dataframe \n", "df['time_to_arrive_at_pickup'] = df['when_the_Jumpman_arrived_at_pickup'] - df['when_the_delivery_started'] \n", "df['time_spent_waiting'] = df['when_the_Jumpman_left_pickup'] - df['when_the_Jumpman_arrived_at_pickup'] \n", "df['time_to_arrive_at_dropoff'] = df['when_the_Jumpman_arrived_at_dropoff'] - df['when_the_Jumpman_left_pickup'] \n", "df['total_delivery_time'] = df['when_the_Jumpman_arrived_at_dropoff'] - df['when_the_delivery_started']\n", "\n", "df['vehicle_cat'] = df['vehicle_type'].map(lambda vehicle: vehicle if vehicle in ['car', 'bicycle'] else 'other')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#box plot of time to arrive by vehicle type\n", "boxplot = df.boxplot(column=['time_to_arrive_at_pickup', 'time_to_arrive_at_dropoff'], by=['vehicle_cat'])" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vehicle_cat\n", "bicycle 11.664398\n", "car 15.455547\n", "other 14.743632\n", "Name: time_to_arrive_at_pickup, dtype: float64\n" ] } ], "source": [ "#average by vehicle category\n", "df['time_to_arrive_at_pickup'] = df['time_to_arrive_at_pickup'].dt.total_seconds()\n", "average_time_to_pick = df.groupby('vehicle_cat').mean()\n", "print(average_time_to_pick['time_to_arrive_at_pickup']/60)" ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vehicle_cat\n", "bicycle 13.167639\n", "car 16.974994\n", "other 15.532482\n", "Name: time_to_arrive_at_dropoff, dtype: float64\n" ] } ], "source": [ "df['time_to_arrive_at_dropoff'] = df['time_to_arrive_at_dropoff'].dt.total_seconds()\n", "average_time_to_pick = df.groupby('vehicle_cat').mean()\n", "print(average_time_to_pick['time_to_arrive_at_dropoff']/60)" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vehicle_cat\n", "bicycle 44.023133\n", "car 52.040923\n", "other 49.356794\n", "Name: total_delivery_time, dtype: float64\n" ] } ], "source": [ "df['total_delivery_time'] = df['total_delivery_time'].dt.total_seconds()\n", "average_time_to_pick = df.groupby('vehicle_cat').mean()\n", "print(average_time_to_pick['total_delivery_time']/60)" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#overall delivery time by vehicle type\n", "boxplot = df.boxplot(column=['total_delivery_time'], by=['vehicle_cat'])\n", "#what's the y-axis showing me?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "boxplot = df.boxplot(by=['time_spent_waiting'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }