Lyft Data Challenge

gistfile1.txt

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Lyft Data Challenge.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"Qj3MQb1MhoxI","colab_type":"code","outputId":"d43de904-2a52-4969-f13f-7050c64ee8d0","executionInfo":{"status":"ok","timestamp":1568616741811,"user_tz":-540,"elapsed":2779,"user":{"displayName":"Ba Thien Tran","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDZzr3dvT_qcyFXBLgb5tjA8PvIQYvRDEpL44O5=s64","userId":"05844970973101512611"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["from scipy import stats\n","import pandas as pd\n","from google.colab import drive\n","drive.mount('/content/gdrive')\n","import numpy as np\n","from matplotlib import pyplot as plt\n","from datetime import datetime, date, timedelta\n","\n","\n","ride_ids = pd.read_csv('/content/gdrive/Shared drives/Lyft Data Challenge/ride_ids.csv')\n","driver_ids = pd.read_csv('/content/gdrive/Shared drives/Lyft Data Challenge/driver_ids.csv')\n","ride_timestamps = pd.read_csv('/content/gdrive/Shared drives/Lyft Data Challenge/ride_timestamps.csv')"],"execution_count":213,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"AAWC8V25hsTZ","colab_type":"code","colab":{}},"source":["#merging ride_ids and driver_ids on driver_id and keeping all data\n","ride_driver = pd.merge(ride_ids, driver_ids, on='driver_id', how='inner')\n","\n","#merging ride_timestamps into merged ride_driver dataframe on ride_id keeping all data\n","full_data = pd.merge(ride_driver, ride_timestamps, on='ride_id', how='inner')\n","\n","#removing NAs\n","full_data = full_data[pd.notnull(full_data)]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QK2_biJ6qfaL","colab_type":"code","outputId":"920b9047-47db-465a-f7db-c74e4642a744","executionInfo":{"status":"ok","timestamp":1568616743455,"user_tz":-540,"elapsed":4398,"user":{"displayName":"Ba Thien Tran","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDZzr3dvT_qcyFXBLgb5tjA8PvIQYvRDEpL44O5=s64","userId":"05844970973101512611"}},"colab":{"base_uri":"https://localhost:8080/","height":124}},"source":["#remove all events that are not requested at --> we declare rides as rides that have been dropped off at\n","df_lifetime = full_data.loc[full_data['event'] == \"dropped_off_at\"]\n","\n","# Calculate the \"birth date\" of a driver's lifetime\n","# The date of their first ride\n","driver_first_ride = {}\n","\n","for aid, grp in df_lifetime.groupby('driver_id'):\n","  driver_first_ride[aid] = min(grp['timestamp'])\n","\n","df_lifetime[\"first_ride\"] = df_lifetime['driver_id'].map(driver_first_ride) "],"execution_count":215,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: 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","  # Remove the CWD from sys.path while we load stuff.\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"BUOLs8YTmcrz","colab_type":"code","outputId":"42bfd215-2b38-4f3e-9585-f72c8fcf073b","executionInfo":{"status":"ok","timestamp":1568616745496,"user_tz":-540,"elapsed":6425,"user":{"displayName":"Ba Thien Tran","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDZzr3dvT_qcyFXBLgb5tjA8PvIQYvRDEpL44O5=s64","userId":"05844970973101512611"}},"colab":{"base_uri":"https://localhost:8080/","height":880}},"source":["#transforming UTC strings to datetime objects\n","df_lifetime[\"driver_onboard_date\"] = pd.to_datetime(df_lifetime[\"driver_onboard_date\"], format=\"%Y-%m-%d %H:%M:%S\")\n","df_lifetime[\"first_ride\"] = pd.to_datetime(df_lifetime[\"first_ride\"], format=\"%Y-%m-%d %H:%M:%S\")\n","df_lifetime[\"timestamp\"] = pd.to_datetime(df_lifetime[\"timestamp\"], format=\"%Y-%m-%d %H:%M:%S\")\n","\n","#calculating differences\n","df_lifetime[\"onboard_first_difference\"] = df_lifetime[\"first_ride\"] - df_lifetime[\"driver_onboard_date\"]\n","df_lifetime[\"difference\"] = (df_lifetime[\"timestamp\"] - df_lifetime[\"first_ride\"]).dt.days\n","\n","#calculating revenues\n","df_lifetime[\"base_revenue\"] = (df_lifetime[\"ride_prime_time\"]/100 + 1) * (df_lifetime[\"ride_distance\"] *  0.00062137 * 1.15 + df_lifetime[\"ride_duration\"]/60 * 0.22 + 2)\n","df_lifetime[\"end_revenue\"] = np.select([df_lifetime[\"base_revenue\"] < 5, df_lifetime[\"base_revenue\"] > 400, (df_lifetime[\"base_revenue\"] > 5) & (df_lifetime[\"base_revenue\"] < 400)], [5, 400, df_lifetime[\"base_revenue\"]])\n","df_lifetime[\"lyft_revenue\"] = df_lifetime[\"end_revenue\"] /0.6 * 0.4 + 1.75"],"execution_count":216,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: 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","  \"\"\"Entry point for launching an IPython kernel.\n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: 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","  \n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: 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","  This is separate from the ipykernel package so we can avoid doing imports until\n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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","  \n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: 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","  import sys\n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: 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","  # Remove the CWD from sys.path while we load stuff.\n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: 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","  # This is added back by InteractiveShellApp.init_path()\n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: 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","  if sys.path[0] == '':\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"pnyeHajRnUFb","colab_type":"code","colab":{}},"source":["#calculating maximum days between latest onboarding and latest timestamp (maximum range to be observed)\n","dod = df_lifetime['driver_onboard_date'].max()\n","tmstp = df_lifetime['timestamp'].max()\n","interval = tmstp - dod"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"egt0AfcwSSFI","colab_type":"code","outputId":"5ed67642-158f-4c98-f95e-4f92aee2b107","executionInfo":{"status":"ok","timestamp":1568616746638,"user_tz":-540,"elapsed":7543,"user":{"displayName":"Ba Thien Tran","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDZzr3dvT_qcyFXBLgb5tjA8PvIQYvRDEpL44O5=s64","userId":"05844970973101512611"}},"colab":{"base_uri":"https://localhost:8080/","height":124}},"source":["#adding bins\n","bins = [i for i in range(-1,92,1)]\n","df_lifetime['bucket'] = pd.cut(df_lifetime.difference, bins)\n","\n","driver_count = len(df_lifetime.groupby('driver_id'))\n","\n","plot_data_all = []\n","for aid, grp in df_lifetime.groupby('bucket'):\n","    plot_data_all.append(len(grp)/driver_count)"],"execution_count":218,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: 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","  \n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"PX55_zIcg-2b","colab_type":"code","outputId":"14d1cc16-ca5f-43a4-a6c9-3ea371f302a9","executionInfo":{"status":"ok","timestamp":1568616749043,"user_tz":-540,"elapsed":9932,"user":{"displayName":"Ba Thien Tran","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDZzr3dvT_qcyFXBLgb5tjA8PvIQYvRDEpL44O5=s64","userId":"05844970973101512611"}},"colab":{"base_uri":"https://localhost:8080/","height":331}},"source":["#plotting driver's lifetime\n","# Average Driver's Lifetime\n","last_ride_data = []\n","for aid, grp in df_lifetime.groupby('driver_id'):\n","  last_ride_data.append(sorted(grp['bucket'].unique())[-1].right)\n","  \n","lifetime_mean = np.mean(last_ride_data)\n","lifetime_median = np.median(sorted(last_ride_data))\n","lifetime_mode = stats.mode(sorted(last_ride_data))\n","\n","print(\"Median Lifetime value of all drivers: \",lifetime_median)\n","  \n","plt.hist(sorted(last_ride_data),edgecolor='black', linewidth=1.2,bins = [i for i in range(1,92,3)])\n","plt.title(\"Distribution of Driver's Lifetime\")\n","plt.xlabel(\"Driver's Lifetime (days)\")\n","plt.ylabel(\"Frequency\")\n","plt.show"],"execution_count":219,"outputs":[{"output_type":"stream","text":["Median Lifetime value of all drivers:  57.0\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<function matplotlib.pyplot.show>"]},"metadata":{"tags":[]},"execution_count":219},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nexVNBP2SDqjNSJoDPD5EeYD1wCERsR/wQuBwSQcB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emrz+b1tBJ5P+vK1DPgmINLAZz8Y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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"FKWoyeaGr-G0","colab_type":"code","outputId":"85b19d3b-2f66-4478-f3fb-d19bcb39243f","executionInfo":{"status":"ok","timestamp":1568616749428,"user_tz":-540,"elapsed":10301,"user":{"displayName":"Ba Thien Tran","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDZzr3dvT_qcyFXBLgb5tjA8PvIQYvRDEpL44O5=s64","userId":"05844970973101512611"}},"colab":{"base_uri":"https://localhost:8080/","height":310}},"source":["# Plotting Distribution of Driver's Lifetime \"Birthdate\"\n","df_lifetime[\"first_ride\"].groupby(df_lifetime[\"first_ride\"].dt.month).count().plot(kind=\"bar\", legend=False)\n","plt.xlabel(\"Month (March, April, May)\")\n","plt.ylabel(\"Frequency\")\n","plt.title(\"Distribution of Driver's Lifetime Birthdate\")\n","plt.show"],"execution_count":220,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<function matplotlib.pyplot.show>"]},"metadata":{"tags":[]},"execution_count":220},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZsAAAETCAYAAADge6tNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBunpczaDIuKJPP0kMChPDwYWFOotzGXtlS+sUd7e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Iml4Y6MyM1t9NWWyAXYGWiJiXkS8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\nWen+P4x5NRsQuC5/AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"aJy71SErbVdR","colab_type":"code","outputId":"8cfcbb1f-d224-4082-851f-f672b00cd0d7","executionInfo":{"status":"ok","timestamp":1568616751222,"user_tz":-540,"elapsed":12081,"user":{"displayName":"Ba Thien Tran","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDZzr3dvT_qcyFXBLgb5tjA8PvIQYvRDEpL44O5=s64","userId":"05844970973101512611"}},"colab":{"base_uri":"https://localhost:8080/","height":808}},"source":["#plotting last rides based onboard month\n","last_ride_data = []\n","for aid, grp in df_lifetime.loc[(df_lifetime[\"first_ride\"] < date(year=2016, month=5, day=1))].groupby('driver_id'):\n","  last_ride_data.append(sorted(grp['bucket'].unique())[-1].right)\n","\n","print(len(last_ride_data))\n","lifetime_mean = np.median(last_ride_data)\n","plt.hist(sorted(last_ride_data),edgecolor='black', linewidth=1.2,bins = [i for i in range(1,92,3)])\n","plt.show()\n","print(lifetime_mean)\n","\n","last_ride_data = []\n","for aid, grp in df_lifetime.loc[(df_lifetime[\"first_ride\"] < date(year=2016, month=6, day=1)) & (df_lifetime[\"first_ride\"] > date(year=2016, month=4, day=30))].groupby('driver_id'):\n","  last_ride_data.append(sorted(grp['bucket'].unique())[-1].right)\n","\n","print(len(last_ride_data))\n","lifetime_mean = np.median(last_ride_data)\n","plt.hist(sorted(last_ride_data),edgecolor='black', linewidth=1.2,bins = [i for i in range(1,92,3)])\n","plt.show()\n","lifetime_mean"],"execution_count":221,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: FutureWarning: Comparing Series of datetimes with 'datetime.date'.  Currently, the\n","'datetime.date' is coerced to a datetime. In the future pandas will\n","not coerce, and a TypeError will be raised. To retain the current\n","behavior, convert the 'datetime.date' to a datetime with\n","'pd.Timestamp'.\n","  \n"],"name":"stderr"},{"output_type":"stream","text":["626\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8d1qe0tEPFMdIw9UF+YbISLOjYiHI+InE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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["65.0\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: FutureWarning: Comparing Series of datetimes with 'datetime.date'.  Currently, the\n","'datetime.date' is coerced to a datetime. In the future pandas will\n","not coerce, and a TypeError will be raised. To retain the current\n","behavior, convert the 'datetime.date' to a datetime with\n","'pd.Timestamp'.\n","  if sys.path[0] == '':\n"],"name":"stderr"},{"output_type":"stream","text":["226\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxcJBUUkqxKK5Qq87iLlYppyty6lpJd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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"execute_result","data":{"text/plain":["44.0"]},"metadata":{"tags":[]},"execution_count":221}]},{"cell_type":"code","metadata":{"id":"FHgD3y-yMIX3","colab_type":"code","outputId":"9436b3e6-253d-48e5-f5c9-dd4a6d532e9e","executionInfo":{"status":"ok","timestamp":1568616754173,"user_tz":-540,"elapsed":15019,"user":{"displayName":"Ba Thien Tran","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDZzr3dvT_qcyFXBLgb5tjA8PvIQYvRDEpL44O5=s64","userId":"05844970973101512611"}},"colab":{"base_uri":"https://localhost:8080/","height":331}},"source":["# Plotting driver's average ride per day\n","# Average Driver's ride per day\n","last_ride_data = []\n","ride_count_data = []\n","\n","for aid, grp in df_lifetime.groupby('driver_id'):\n","  ride_count_data.append(len(grp))\n","  last_ride_data.append(sorted(grp['bucket'].unique())[-1].right)\n","\n","avg_ride_per_time = []\n","for i in range(len(last_ride_data)):\n","  avg_ride_per_time.append(ride_count_data[i]/(last_ride_data[i]+1))\n","\n","median_ride_per_time = np.median(avg_ride_per_time)\n","print(\"Median value Lyft gains per ride: \",median_ride_per_time)\n","\n","plt.xlim(0, 20)\n","plt.hist(avg_ride_per_time,edgecolor='black', linewidth=1.2,bins = [i for i in range(1,91,1)])\n","plt.title(\"Distribution of Driver's Average Ride per Day\")\n","plt.xlabel(\"Driver's Average Ride per Day\")\n","plt.ylabel(\"Frequency\")"],"execution_count":222,"outputs":[{"output_type":"stream","text":["Median value Lyft gains per ride:  3.5686274509803924\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Frequency')"]},"metadata":{"tags":[]},"execution_count":222},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZAAAAEWCAYAAABIVsEJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npf2L6Z19LP39/U4gZrZKWaUTSEVX93jGjF2/3WGYmY0qvohuZma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qwoqIJZKOAX5Nuo33\nrIi4Z5BZzmxNZCPmOBvLcTbOaIgRHGejNSTOjrqIbmZmo0enNWGZmdko4QRiZmaljIoEImlPSQ9I\nekjScTWmrynpgjz9JklT2xDjppKulXSvpHskfapGmd0k9Um6I/99qdVx5jjmSborx/Ca2/mUnJa3\n552SdmpDjFsVttMdkp6TdGxVmbZsT0lnSXpK0t2FcRMkXS3pwfy/5s+2Jc3MZR6UNLPFMX5T0v35\nPb1M0rgB5h10/2hBnLMkLSy8r3sPMO+gx4UWxHlBIcZ5ku4YYN5Wbs+ax6Gm7Z8R0dF/pIvpDwPT\ngDWAPwLbVJX5OHB6Hj4IuKANcW4E7JSHxwJ/qhHnbsDPO2CbzgMmDjJ9b+BKQMCuwE0dsA/0AlM6\nYXsCbwV2Au4ujPs34Lg8fBzwjRrzTQDm5v/j8/D4Fsb4TmBMHv5GrRjr2T9aEOcs4HN17BODHhea\nHWfV9G8BX+qA7VnzONSs/XM0nIG82r1JRLwCVLo3KdoXmJ2HLwZ2l6QWxkhEPBERt+Xh54H7gMmt\njKGB9gXOieRGYJykjdoYz+7AwxExv40xvCoirgeqO0Qq7oOzgffUmPUfgasj4pmIWAxcDezZqhgj\n4qqIWJJf3kj6nVVbDbAt61HPcaFhBoszH2sOBM5r1vLrNchxqCn752hIIJOBxwqvF/DaA/OrZfIH\npA9o2zM8cxPajsBNNSb/raQ/SrpS0htbGthyAVwl6dbcNUy1erZ5Kx3EwB/OTtieABtExBN5uBfY\noEaZTtquh5POMmsZav9ohWNyU9tZAzS3dNK2fAvwZEQ8OMD0tmzPquNQU/bP0ZBARhVJ6wKXAMdG\nxHNVk28jNcNsD/wH8NNWx5f9fUTsBOwFHC3prW2KY0hKPyjdB7ioxuRO2Z4riNQe0LH3x0s6AVgC\n/GSAIu3eP74HvB7YAXiC1DzUyQ5m8LOPlm/PwY5Djdw/R0MCqad7k1fLSBoD9AB/bkl0BZJWJ71p\nP4mIS6unR8RzEfFCHv4lsLqkiS0Ok4hYmP8/BVxGag4o6qQuZfYCbouIJ6sndMr2zJ6sNPPl/0/V\nKNP27SrpUODdwAfzgeQ16tg/mioinoyIpRGxDPj+AMtv+7aEV483+wMXDFSm1dtzgONQU/bP0ZBA\n6une5AqgcsfAAcBvB/pwNEtuB/0hcF9EfHuAMhtWrs1I2pm0/Vua6CR1SxpbGSZdWK3u0fgK4BAl\nuwJ9hdPfVhvw210nbM+C4j44E7i8RplfA++UND43y7wzj2sJpYe1fR7YJyJeHKBMPftHU1Vdb9tv\ngOV3SrdHewD3R8SCWhNbvT0HOQ41Z/9sxZ0BDbizYG/S3QQPAyfkcf9C+iAArEVq4ngIuBmY1oYY\n/550WngncEf+2xs4CjgqlzkGuId0x8iNwN+1Ic5pefl/zLFUtmcxTpEe7PUwcBcwo03vezcpIfQU\nxrV9e5IS2hPAX0ntxEeQrrkMy900AAAGbklEQVRdAzwI/AaYkMvOID1ZszLv4Xk/fQg4rMUxPkRq\n467sn5U7FzcGfjnY/tHiOH+U97s7SQe+jarjzK9fc1xoZZx5/NmV/bFQtp3bc6DjUFP2T3dlYmZm\npYyGJiwzM+tATiBmZlaKE4iZmZXiBGJmZqU4gZiZWSlOIDYskpbmXkXvyV2IfFZSzf1I0saSLm7Q\ncqdKum6Y8+wgKfLvHzpaXr+X8ra9V9I5+QdhSJoh6bQB5pvXrh9PFmK+XdJ9km7OP1S0VURHPdLW\nRoWXImIHAEmvA84F1gNOKhaSNCYiHif9sHNE8q99yzgY+F3+/6sGxCHSUzyXjbSuATwcETtI6iJ1\nZHcg6dfEc4CmdgNej/yeLqka/XBE7JinTwMulaSI+O/WR2it5jMQKy1S1wxHkjq+k6RDJV0h6bfA\nNfkb6t0Akm4sdnYo6br8zbo7d5h3c/4mu2+evkJdwFJyb6iS3pjL35E73NuyOrZ8sH8fcCjwDklr\n5fFfl3R0odwsSZ/Lw/8s6ZZc55fzuKlKz5w4h/QL4k0lfU/SnHwW9uVCXXsrPW/jVqXnqfw8j6+5\njoNs16WkH8ROzvPvVqhrfUlX5WX/gPSjz8ryP1TYLmfkRFS9XeZJ+jel51PcLGmLPH6SpEvy+t8i\n6c2F7fMjSb8n/cBvsLjnAp8BPpnn3VnSDXmd/yBpqzz+ekk7FGL6naTtB6vbOlQzfxXpv5XvD3ih\nxrhnSb17Hkr6lW7lV65Tyc9PAD4NfDkPbwQ8kIf/FfhQHh5H+mVxd3VdVcv7D1JfTpCeBbF2jTJv\nBq7Jw+cC783DOwL/Uyh3L6n/n3cCZ5IOyKsBPyc9A2IqsAzYtTBPZf26gOuA7Ui9ITwGbJ6nnUd+\nVslA61gVb3FbrQVcC2yXX+9WqOs08nMngHeRfnU8Edga+Bmwep72XeCQGttlHst7HzikUO+5pE7/\nADYjdYUB6dkctw6wjV+NuTBuHOksFdKZaeX5I3sAl+ThmcApefj/AHPavV/7r9yfm7Cs0a6OiFrP\nTbgQuIrU1HUg6bktkA7c+1TOAkgHz82GqOsG4ARJmwCXRu1utA8mPSOC/P8Q0gHsdkmvk7QxMAlY\nHBGPKT257Z3A7XmedYEtgUeB+ZGei1JxoFK33GNIyXAbUtKZGxGP5DLnkc7OBlvH+6pifr3SU+02\nB34REXfWWK+3kjrvIyJ+IWlxHr87MB24JZ18sTa1O8yrxFb5/508vAewjZY/Rmc9pR5dAa6IiJcG\nqKta8Tk8PcDsfIYYwOp5/EXA/5P0z6SuM86us27rME4gNiK53Xspyw9W/bXKRcRCSX+WtB3wflKf\nVpAOOO+NiAeq6t1lkLrOlXQT6Rv4LyV9LCJ+W5i3C3gvsK9S1+UC1pc0NtJDdi4iXZvZkOW9qAr4\nWkScURXH1GIckjYHPge8KSIWSzqblBAGU3Mda6hcA5kI/F7SPhFRbweBAmZHxPF1lI0aw6uRzrL+\nskKlKaHUfB8GsCPLE+NXgGsjYr+8Ha8DiIgXJV1NesjRgaTEZ6OQr4FYaZImAacD/xkR9XSqdgGp\nN9iewrfrXwOfyNcskLRjHcudRvq2fxqpV9HtqorsDtwZEZtGxNSImELq3nq/QhwHkZJI5TkjvwYO\nr3zrljRZ6SaBauuRDqh9kjYgdTcP8AAwLR8oISXJimGtY0Q8TXrsaK1kcD3wgVzPXqRHj0K6TnRA\nJWalZ2BPGWAR7y/8vyEPXwV8olKgeI2iXnnd/53UxAjpDKTSHfihVcV/QGqOuyXS0+9sFHICseFa\nO1+kvYfUq+dVwJeHmKfiYtKB+8LCuK+QmjbuzHV+pY56DgTuzs092wLnVE0/mPTchaJL8ngi4h7S\n86IXRu6mPiKuIl0HuEHSXTnWsdULjog/kpq57s/lf5/HvwR8HPiVpFuB50lPxiy7jj8F1pH0lqrx\nXwbemuvZn9TERkTcC5xIevLdnaS7uAZ6DPH4XOZTpGtTkC58z8g3ENzL8jPEobw+XyS/j/S+nhbL\n78D6N+Brkm6nqrUjIm4FngN8t9Yo5t54zRpE0roR8UI+0/gv4MGI+M5Q87WSpHmk7vmfbnMcG5Oa\ntN4Qzbst2prMZyBmjfPRfFZ0D6n55owhyq+SJB1Cek73CU4eo5vPQMzMrBSfgZiZWSlOIGZmVooT\niJmZleIEYmZmpTiBmJlZKf8fuRb6odn1HvkAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Rk0Vyd7BvduO","colab_type":"code","outputId":"11b7997e-2cba-4afb-a82d-51df7d6f3007","executionInfo":{"status":"ok","timestamp":1568616755367,"user_tz":-540,"elapsed":16196,"user":{"displayName":"Ba Thien Tran","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDZzr3dvT_qcyFXBLgb5tjA8PvIQYvRDEpL44O5=s64","userId":"05844970973101512611"}},"colab":{"base_uri":"https://localhost:8080/","height":313}},"source":["# Plotting Lyft's value for each ride\n","# Average Lyft's value per ride\n","plt.hist(df_lifetime[\"lyft_revenue\"],edgecolor='black', linewidth=1.2,bins = [i for i in range(1,91,1)])\n","plt.xlabel(\"Lyft's Revenue from Each Ride (USD\")\n","plt.ylabel(\"Frequency\")\n","plt.title(\"Distribution of Each Ride's Value to Lyft\")\n","\n","median_price_per_ride = df_lifetime[\"lyft_revenue\"].median()\n","print(\"Median value Lyft gains per ride: \",median_price_per_ride)"],"execution_count":223,"outputs":[{"output_type":"stream","text":["Median value Lyft gains per ride:  7.628442359027778\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZUAAAEWCAYAAACufwpNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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BdgFmBMRcwEkXQRMBZoSVJYuXcrSpUvrzl8Ziru8Y3H6++qSlevF5TXdVsYxmnn8RsWyN2DF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\nmZXGQcXMzErz/wGGjzB6wceTogAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"BO9Jl42cwUit","colab_type":"code","outputId":"fe307b4e-d769-418e-c4bc-a3c78592ade4","executionInfo":{"status":"ok","timestamp":1568616755369,"user_tz":-540,"elapsed":16178,"user":{"displayName":"Ba Thien Tran","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDZzr3dvT_qcyFXBLgb5tjA8PvIQYvRDEpL44O5=s64","userId":"05844970973101512611"}},"colab":{"base_uri":"https://localhost:8080/","height":88}},"source":["#calculating avg generated value by drivers\n","value_generated = median_ride_per_time * median_price_per_ride * lifetime_median\n","\n","value_generated\n","print(\"Median value Lyft gains per ride: \",median_ride_per_time)\n","print(\"Median value Lyft gains per ride: \",median_price_per_ride)\n","print(\"Median Lifetime value of all drivers: \",lifetime_median)\n","print(\"Average Projected Lifetime Value of a driver: \", value_generated)"],"execution_count":224,"outputs":[{"output_type":"stream","text":["Median value Lyft gains per ride:  3.5686274509803924\n","Median value Lyft gains per ride:  7.628442359027778\n","Median Lifetime value of all drivers:  57.0\n","Average Projected Lifetime Value of a driver:  1551.7149222069445\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"oie5AvlhJNBb","colab_type":"code","outputId":"33d5b349-295d-4968-99c3-8b03e68327bc","executionInfo":{"status":"ok","timestamp":1568616757562,"user_tz":-540,"elapsed":18349,"user":{"displayName":"Ba Thien Tran","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDZzr3dvT_qcyFXBLgb5tjA8PvIQYvRDEpL44O5=s64","userId":"05844970973101512611"}},"colab":{"base_uri":"https://localhost:8080/","height":88}},"source":["#Find the correlation between variables and the Lifetime Value\n","from sklearn.preprocessing import StandardScaler  # For scaling dataset\n","from sklearn.cluster import KMeans, AgglomerativeClustering, AffinityPropagation #For clustering\n","import seaborn as sns\n","\n","driver_dataframe = {\"driver_id\":[],\"lifetime\":[],\"value\":[],\"rides\":[],\"rides_per_day_first_week\":[],\"ride_frequency\":[]}\n","\n","\n","for aid, grp in df_lifetime.groupby(\"driver_id\"):\n","  grp_7 = grp.loc[(grp[\"timestamp\"] - grp[\"first_ride\"].min()) < timedelta(days = 8)]\n","  driver_dataframe[\"driver_id\"].append(aid)\n","  driver_dataframe[\"lifetime\"].append((grp[\"timestamp\"].max() - grp[\"first_ride\"].min()).days + 1)\n","  driver_dataframe[\"value\"].append(grp[\"lyft_revenue\"].sum())\n","  driver_dataframe[\"rides\"].append(len(grp[\"timestamp\"]))\n","  driver_dataframe[\"rides_per_day_first_week\"].append(len(grp_7)/7)\n","  timestamp_list = sorted(list(grp_7[\"timestamp\"]))\n","  average_time = [(x - timestamp_list[i - 1]).seconds//60 for i, x in enumerate(timestamp_list) if (x-timestamp_list[i-1]).seconds//3600 < 8]\n","  driver_dataframe[\"ride_frequency\"].append(np.mean(average_time))\n","\n","df_clustering = pd.DataFrame(driver_dataframe)"],"execution_count":225,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n","  out=out, **kwargs)\n","/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n","  ret = ret.dtype.type(ret / rcount)\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"FKP47jepNXIz","colab_type":"code","outputId":"de8bd497-07c3-4a35-ec2c-51f3a64e0db5","executionInfo":{"status":"ok","timestamp":1568616757563,"user_tz":-540,"elapsed":18329,"user":{"displayName":"Ba Thien Tran","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDZzr3dvT_qcyFXBLgb5tjA8PvIQYvRDEpL44O5=s64","userId":"05844970973101512611"}},"colab":{"base_uri":"https://localhost:8080/","height":403}},"source":["cor = df_clustering.corr()\n","sns.heatmap(abs(cor), square = True)"],"execution_count":226,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fd39de804e0>"]},"metadata":{"tags":[]},"execution_count":226},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZ4AAAFwCAYAAACIMh39AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nM4knImLIZK22iIhoVNuXzEniiYgYMlkkNCIiGpWN4CIiolHp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wA/GEoykDYEX2/6PspEN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\n/4b8p5/ofncNAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"kdds6r2csvWj","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}

Lyft Data Challenge.ipynb