### Analysis
# • Overall adoption is clearly highest in “Urban” city types. Roughly 3x as many drivers & rides as the next highest group (“Suburban”) can be found there.
# • Average fares increase as we move away from urban areas into the rural. Not one “Urban” location appears to have an average fare over $30.
# • Whereas “Suburban” fares almost entirely average between $25-$35. “Suburban” rides, despite being a third of the size of “Urban” ridership makes up a larger share of overall fare average. If I were a driver I would consider the Suburbs. There is less overall competition and higher fares. If I were Pyber, I would also be looking to expand ridership in the Suburbs. import os
import csv
import pandas as pd
import matplotlib.pyplot as plt
import numpy as npcity_path = os.path.join('raw_data', 'city_data.csv')
ride_path = os.path.join('raw_data', 'ride_data.csv')
city_data = pd.read_csv(city_path)
ride_data = pd.read_csv(ride_path)# Your objective is to build a [Bubble Plot](https://en.wikipedia.org/wiki/Bubble_chart) that showcases the relationship between four key variables:combined_data_df = pd.merge(city_data, ride_data, on="city", how="outer")
combined_data_df.head()
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| city | driver_count | type | date | fare | ride_id | |
|---|---|---|---|---|---|---|
| 0 | Kelseyland | 63 | Urban | 2016-08-19 04:27:52 | 5.51 | 6246006544795 |
| 1 | Kelseyland | 63 | Urban | 2016-04-17 06:59:50 | 5.54 | 7466473222333 |
| 2 | Kelseyland | 63 | Urban | 2016-05-04 15:06:07 | 30.54 | 2140501382736 |
| 3 | Kelseyland | 63 | Urban | 2016-01-25 20:44:56 | 12.08 | 1896987891309 |
| 4 | Kelseyland | 63 | Urban | 2016-08-09 18:19:47 | 17.91 | 8784212854829 |
# URBAN RIDE INFO
urban_data = combined_data_df.loc[combined_data_df["type"] == "Urban"]#.count()
urban_data = pd.DataFrame(urban_data)
urban_data.head()
urban_places = urban_data.groupby("city")
urban_fares_avg = urban_places['fare'].mean()
urban_fares_avg = pd.DataFrame(urban_fares_avg).reset_index()
# * Total Number of Rides Per City
urban_rides = urban_places['ride_id'].count()
urban_rides = pd.DataFrame(urban_rides).reset_index()
# Total Number of Drivers
urban_drivers = urban_places['driver_count'].max()
urban_drivers = pd.DataFrame(urban_drivers).reset_index()
urban_drivers['driver_count'] = pd.to_numeric(urban_drivers['driver_count'])
urban_merge = pd.merge(urban_fares_avg, urban_rides, on='city', how='outer')
urban_merge = pd.merge(urban_fares_avg, urban_rides, on='city', how='outer')
urban_merge2 = pd.merge(urban_merge, urban_drivers, on='city', how='outer')# SUBURBAN RIDE INFO
suburban_data = combined_data_df.loc[combined_data_df["type"] == "Suburban"]#.count()
suburban_data = pd.DataFrame(suburban_data)
suburban_places = suburban_data.groupby("city")
suburban_fares_avg = suburban_places['fare'].mean()
suburban_fares_avg = pd.DataFrame(suburban_fares_avg).reset_index()
# * Total Number of Rides Per City
suburban_rides = suburban_places['ride_id'].count()
suburban_rides = pd.DataFrame(suburban_rides).reset_index()
# Total Number of Drivers
suburban_drivers = suburban_places['driver_count'].max()
suburban_drivers = pd.DataFrame(suburban_drivers).reset_index()
suburban_drivers['driver_count'] = pd.to_numeric(suburban_drivers['driver_count'])
suburban_merge = pd.merge(suburban_fares_avg, suburban_rides, on='city', how='outer')
suburban_merge = pd.merge(suburban_fares_avg, suburban_rides, on='city', how='outer')
suburban_merge2 = pd.merge(suburban_merge, suburban_drivers, on='city', how='outer')# RURAL RIDE INFO
rural_data = combined_data_df.loc[combined_data_df["type"] == "Rural"]#.count()
rural_data = pd.DataFrame(rural_data)
rural_places = rural_data.groupby("city")
rural_fares_avg = rural_places['fare'].mean()
rural_fares_avg = pd.DataFrame(rural_fares_avg).reset_index()
# * Total Number of Rides Per City
rural_rides = rural_places['ride_id'].count()
rural_rides = pd.DataFrame(rural_rides).reset_index()
# Total Number of Drivers
rural_drivers = rural_places['driver_count'].max()
rural_drivers = pd.DataFrame(rural_drivers).reset_index()
rural_drivers['driver_count'] = pd.to_numeric(rural_drivers['driver_count'])
rural_merge = pd.merge(rural_fares_avg, rural_rides, on='city', how='outer')
rural_merge = pd.merge(rural_fares_avg, rural_rides, on='city', how='outer')
rural_merge2 = pd.merge(rural_merge, rural_drivers, on='city', how='outer')s=(urban_merge2['driver_count'])
ax = urban_merge2.plot(kind="scatter", x="ride_id", y="fare", s=s*10, Facecolors="LightSkyBlue", Edgecolor="Black", alpha=".75", grid=True, figsize=(9,6))
s=(suburban_merge2['driver_count'])
suburban_merge2.plot(kind="scatter", x="ride_id", y="fare", s=s*10, Facecolors="Lightcoral", Edgecolor="Black", alpha=".75", ax=ax)
s=(rural_merge2['driver_count'])
rural_merge2.plot(kind="scatter", x="ride_id", y="fare", s=s*10, Facecolors="gold", Edgecolor="Black", ax=ax)
#plt.xlim = ([0,100])
#plt.ylim = ([0,100])
plt.title('Pyber Ride Sharing Data (2016)')
plt.xlabel('Number of Rides')
plt.ylabel('Average Fare ($)')
plt.grid(True)
plt.figtext(.95, .8, "NOTE: Circle size correlates with driver count")
#plt.legend()
plt.show()
#grid=True, figsize=(10, 5)
# Making the Py Charts
## % Total Fares by City Type
urban_fares_total = urban_data['fare'].sum()
suburban_fares_total = suburban_data['fare'].sum()
rural_fares_total = rural_data['fare'].sum()
city_type_fare = ["Urban", "Suburban", "Rural"]
city_rides_fares = [urban_fares_total, suburban_fares_total, rural_fares_total]
colors = ["lightskyblue", "lightcoral", "gold"]
explode = [0.05, 0, 0]
plt.pie(city_rides_fares, explode=explode, labels=city_type_fare, colors=colors, autopct='%.2f')
plt.axis("equal")
plt.title("Total % Fares by City Type")
plt.show()
## % Total Rides by City Type
urban_rides_total = urban_data['ride_id'].count()
suburban_rides_total = suburban_data['ride_id'].count()
rural_rides_total = rural_data['ride_id'].count()
city_type_rides = ["Urban", "Suburban", "Rural"]
city_rides_rides = [urban_rides_total, suburban_rides_total, rural_rides_total]
colors = ["lightskyblue", "lightcoral", "gold"]
explode = [0.05, 0, 0]
plt.pie(city_rides_rides, explode=explode, labels=city_type_fare, colors=colors, autopct='%.2f')
plt.axis("equal")
plt.title("Total % Rides by City Type")
plt.show()
## % Total Drivers by City Type
urban_drivers_total = urban_data['driver_count'].max()
suburban_drivers_total = suburban_data['driver_count'].max()
rural_drivers_total = rural_data['driver_count'].max()
city_type_drivers = ["Urban", "Suburban", "Rural"]
city_rides_drivers = [urban_drivers_total, suburban_drivers_total, rural_drivers_total]
colors = ["lightskyblue", "lightcoral", "gold"]
explode = [0.05, 0, 0]
plt.pie(city_rides_drivers, explode=explode, labels=city_type_fare, colors=colors, autopct='%.2f')
plt.axis("equal")
plt.title("Total % Drivers by City Type")
plt.show()