Pyber Analysis:

• The number of drivers increase with the city levels, rural has the least amount of drivers while urban has the most.
• According to the bubble chart, the average fares seems to decrease with the city levels, the average fares is rural is generally higher than Urban areas. Perhaps further study of average distances with city levels will help for exploring this trend.
• Rural area seems to have less drivers than other city levels, it only has around 1% of the total drivers, but it has around 5.2% of total riders.

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from scipy import stats

city = pd.read_csv("raw_data/city_data.csv")
print(city.head())
ride = pd.read_csv("raw_data/ride_data.csv")
ride.head()

             city  driver_count   type
0      Kelseyland            63  Urban
1      Nguyenbury             8  Urban
2    East Douglas            12  Urban
3   West Dawnfurt            34  Urban
4  Rodriguezburgh            52  Urban

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	city	date	fare	ride_id
0	Sarabury	2016-01-16 13:49:27	38.35	5403689035038
1	South Roy	2016-01-02 18:42:34	17.49	4036272335942
2	Wiseborough	2016-01-21 17:35:29	44.18	3645042422587
3	Spencertown	2016-07-31 14:53:22	6.87	2242596575892
4	Nguyenbury	2016-07-09 04:42:44	6.28	1543057793673

merge = pd.merge(city, ride, on = "city",how = "left")
#Average Fare ($) Per City
city_grouped = merge.groupby("city")
fare_per_city = city_grouped["fare"].mean()
#Total Number of Rides Per City
total_riders = city_grouped["ride_id"].count()
#Total Number of Drivers Per City
total_drivers = city_grouped["driver_count"].mean()
#City Type (Urban, Suburban, Rural)
city_type = city_grouped['type'].max()
city_data = pd.DataFrame({
    "Average Fare ($) Per City": fare_per_city,
    "Total Riders": total_riders ,
    "Total Drivers": total_drivers,
    "City Types": city_type
})
city_data.head()

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	Average Fare ($) Per City	City Types	Total Drivers	Total Riders
city
Alvarezhaven	23.928710	Urban	21	31
Alyssaberg	20.609615	Urban	67	26
Anitamouth	37.315556	Suburban	16	9
Antoniomouth	23.625000	Urban	21	22
Aprilchester	21.981579	Urban	49	19

rural = city_data[city_data['City Types'] == 'Rural']
urban = city_data[city_data['City Types'] == 'Urban']
suburban = city_data[city_data['City Types'] == 'Suburban']
plt.scatter(rural["Total Riders"],rural["Average Fare ($) Per City"],marker = "o", facecolors = "gold",edgecolors = "black", s = rural["Total Drivers"]*10,alpha = 0.75, label = "Rural")
plt.scatter(urban["Total Riders"],urban["Average Fare ($) Per City"],marker = "o", facecolors = "lightcoral",edgecolors = "black", s = urban["Total Drivers"]*10,alpha = 0.75, label = "Urban")
plt.scatter(suburban["Total Riders"],suburban["Average Fare ($) Per City"],marker = "o", facecolors = "skyblue",edgecolors = "black", s = suburban["Total Drivers"]*10,alpha = 0.75, label = "Suburban")

legend = plt.legend(loc = "best", numpoints = 1, fontsize = 10)
legend.legendHandles[0]._sizes = [30]
legend.legendHandles[1]._sizes = [30]
legend.legendHandles[2]._sizes = [30]
plt.xlabel("Total Number of Riders (Per City)")
plt.ylabel("Average Fares($)")
plt.suptitle("Pyber Ride Sharing Data",fontsize = 14,fontweight = "bold")
plt.grid(True,color = 'white')
text = "Note: circle sizes correlates with driver count per day"
plt.text(45,30,text,fontsize = 10)
plt.subplots_adjust()
plt.xlim(0,40)
plt.ylim(15,45)
plt.rcParams['axes.facecolor'] = 'lightgrey'
plt.show()

#% of Total Fares by City Type
city_group = merge.groupby(["type"])
fares = city_group["fare"].sum()
labels = fares.index
colors = ["gold","skyblue","lightcoral"]
explode = [0,0,0.1]
plt.pie(fares,explode = explode,colors = colors, labels = labels, startangle = 90,autopct = "%1.1f%%",shadow = True)
plt.axis("equal")
plt.title("% of Total Fares by City Type")
plt.show()

#% of Total Rides by City Type
rides = city_group["ride_id"].count()
colors = ["gold","skyblue","lightcoral"]
explode = [0,0,0.1]
plt.pie(rides,explode = explode,colors = colors, labels = labels, startangle = 90,autopct = "%1.1f%%",shadow = True)
plt.axis("equal")
plt.title("% of Total Rides by City Type")
plt.show()

#% of Total Drivers by City Type
drivers = city_group["driver_count"].sum()
colors = ["gold","skyblue","lightcoral"]
explode = [0,0,0.1]
plt.pie(drivers,explode = explode,colors = colors, labels = labels, startangle = 90,autopct = "%1.1f%%",shadow = True)
plt.axis("equal")
plt.title("% of Total Drivers by City Type")
plt.show()