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#Your objective is to build a Bubble Plot that showcases the relationship between four key variables:

##Average Fare ($) Per City ##Total Number of Rides Per City ##Total Number of Drivers Per City ##City Type (Urban, Suburban, Rural)

#In addition, you will be expected to produce the following three pie charts:

##% of Total Fares by City Type ##% of Total Rides by City Type ##% of Total Drivers by City Type

#As final considerations:

##You must use the Pandas Library and the Jupyter Notebook. ##You must use the Matplotlib libraries. ##You must include a written description of three observable trends based on the data. ##You must use proper labeling of your plots, including aspects like: Plot Titles, Axes Labels, Legend ##Labels, Wedge Percentages, and Wedge Labels. ##Remember when making your plots to consider aesthetics! ###You must stick to the Pyber color scheme (Gold, Light Sky Blue, and Light Coral) in producing your ###plot and pie charts. ###When making your Bubble Plot, experiment with effects like alpha, edgecolor, and linewidths. ###When making your Pie Chart, experiment with effects like shadow, startangle, and explosion.

#You must include an exported markdown version of your Notebook called README.md in your GitHub ##repository. #See Example Solution for a reference on expected format.

#Import Dependencies
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.plotly as py
import plotly as pl
import cufflinks as cf
import plotly.tools as tls
from plotly.graph_objs import Scatter, Layout
from plotly.offline import plot
import plotly.graph_objs as go
pl.offline.init_notebook_mode(connected=True)
pl.tools.set_credentials_file(username='davidhoebbel', api_key='obcfaofD1NR2Oa9kiS3D')
<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script> 
# Read CSV
city_data = pd.read_csv("Resources/city_data.csv")
ride_data = pd.read_csv("Resources/ride_data.csv")
#Print Abridged CSV Data Tables
city_data.set_index('city')
city_data.head()
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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 
#Print abridged CSV data tables
ride_data.set_index('city')
ride_data.head()
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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 data tables on city
#pd.join(other, lsuffix='city', rsuffix='city')
#Need to get into jupyter from earlier folder
uber_merge = pd.merge(city_data, ride_data, on="city")
uber_merge.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 
#Groupby to find average per city
##Average Fare ($) Per City
city_fare = uber_merge.groupby(['city'], as_index=False)
city_fare["fare"].mean()
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city fare
0 Alvarezhaven 23.928710
1 Alyssaberg 20.609615
2 Anitamouth 37.315556
3 Antoniomouth 23.625000
4 Aprilchester 21.981579
5 Arnoldview 25.106452
6 Campbellport 33.711333
7 Carrollbury 36.606000
8 Carrollfort 25.395517
9 Clarkstad 31.051667
10 Conwaymouth 34.591818
11 Davidtown 22.978095
12 Davistown 21.497200
13 East Cherylfurt 31.416154
14 East Douglas 26.169091
15 East Erin 24.478214
16 East Jenniferchester 32.599474
17 East Leslie 33.660909
18 East Stephen 39.053000
19 East Troybury 33.244286
20 Edwardsbury 26.876667
21 Erikport 30.043750
22 Eriktown 25.478947
23 Floresberg 32.310000
24 Fosterside 23.034583
25 Hernandezshire 32.002222
26 Horneland 21.482500
27 Jacksonfort 32.006667
28 Jacobfort 24.779355
29 Jasonfort 27.831667
... ... ...
95 South Roy 26.031364
96 South Shannonborough 26.516667
97 Spencertown 23.681154
98 Stevensport 31.948000
99 Stewartview 21.614000
100 Swansonbury 27.464706
101 Thomastown 30.308333
102 Tiffanyton 28.510000
103 Torresshire 24.207308
104 Travisville 27.220870
105 Vickimouth 21.474667
106 Webstertown 29.721250
107 West Alexis 19.523000
108 West Brandy 24.157667
109 West Brittanyton 25.436250
110 West Dawnfurt 22.330345
111 West Evan 27.013333
112 West Jefferyfurt 21.072857
113 West Kevintown 21.528571
114 West Oscar 24.280000
115 West Pamelaborough 33.799286
116 West Paulport 33.278235
117 West Peter 24.875484
118 West Sydneyhaven 22.368333
119 West Tony 29.609474
120 Williamchester 34.278182
121 Williamshire 26.990323
122 Wiseborough 22.676842
123 Yolandafurt 27.205500
124 Zimmermanmouth 28.301667

125 rows × 2 columns

##Total Number of Rides Per City
ride_count = uber_merge.groupby(['city'], as_index=False)
ride_count["ride_id"].count()
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city ride_id
0 Alvarezhaven 31
1 Alyssaberg 26
2 Anitamouth 9
3 Antoniomouth 22
4 Aprilchester 19
5 Arnoldview 31
6 Campbellport 15
7 Carrollbury 10
8 Carrollfort 29
9 Clarkstad 12
10 Conwaymouth 11
11 Davidtown 21
12 Davistown 25
13 East Cherylfurt 13
14 East Douglas 22
15 East Erin 28
16 East Jenniferchester 19
17 East Leslie 11
18 East Stephen 10
19 East Troybury 7
20 Edwardsbury 27
21 Erikport 8
22 Eriktown 19
23 Floresberg 10
24 Fosterside 24
25 Hernandezshire 9
26 Horneland 4
27 Jacksonfort 6
28 Jacobfort 31
29 Jasonfort 12
... ... ...
95 South Roy 22
96 South Shannonborough 15
97 Spencertown 26
98 Stevensport 5
99 Stewartview 30
100 Swansonbury 34
101 Thomastown 24
102 Tiffanyton 13
103 Torresshire 26
104 Travisville 23
105 Vickimouth 15
106 Webstertown 16
107 West Alexis 20
108 West Brandy 30
109 West Brittanyton 24
110 West Dawnfurt 29
111 West Evan 12
112 West Jefferyfurt 21
113 West Kevintown 7
114 West Oscar 29
115 West Pamelaborough 14
116 West Paulport 17
117 West Peter 31
118 West Sydneyhaven 18
119 West Tony 19
120 Williamchester 11
121 Williamshire 31
122 Wiseborough 19
123 Yolandafurt 20
124 Zimmermanmouth 24

125 rows × 2 columns

##Total Number of Drivers Per City
city_drivers = uber_merge.groupby(['city'], as_index=False)
city_drivers["driver_count"].mean()
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city driver_count
0 Alvarezhaven 21
1 Alyssaberg 67
2 Anitamouth 16
3 Antoniomouth 21
4 Aprilchester 49
5 Arnoldview 41
6 Campbellport 26
7 Carrollbury 4
8 Carrollfort 55
9 Clarkstad 21
10 Conwaymouth 18
11 Davidtown 73
12 Davistown 25
13 East Cherylfurt 9
14 East Douglas 12
15 East Erin 43
16 East Jenniferchester 22
17 East Leslie 9
18 East Stephen 6
19 East Troybury 3
20 Edwardsbury 11
21 Erikport 3
22 Eriktown 15
23 Floresberg 7
24 Fosterside 69
25 Hernandezshire 10
26 Horneland 8
27 Jacksonfort 6
28 Jacobfort 52
29 Jasonfort 25
... ... ...
95 South Roy 35
96 South Shannonborough 9
97 Spencertown 68
98 Stevensport 6
99 Stewartview 49
100 Swansonbury 64
101 Thomastown 1
102 Tiffanyton 21
103 Torresshire 70
104 Travisville 37
105 Vickimouth 13
106 Webstertown 26
107 West Alexis 47
108 West Brandy 12
109 West Brittanyton 9
110 West Dawnfurt 34
111 West Evan 4
112 West Jefferyfurt 65
113 West Kevintown 5
114 West Oscar 11
115 West Pamelaborough 27
116 West Paulport 5
117 West Peter 61
118 West Sydneyhaven 70
119 West Tony 17
120 Williamchester 26
121 Williamshire 70
122 Wiseborough 55
123 Yolandafurt 7
124 Zimmermanmouth 45

125 rows × 2 columns

#uber_merge
#city_fare
#ride_count
#city_drivers
#merge into single table
#pd.merge(uber_merge, city_fare, ride_count, city_drivers, on=["Index"], how='inner',suffixes=('_fare','_count','_drivers'))
#Consolidate groupby values into single table for charting purposes

f = {'fare':['mean'], 'ride_id':['count'], 'driver_count':['mean']}

merged_df = uber_merge.groupby('city').agg(f)
merged_df.head()
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fare ride_id driver_count
mean count mean
city
Alvarezhaven 23.928710 31 21
Alyssaberg 20.609615 26 67
Anitamouth 37.315556 9 16
Antoniomouth 23.625000 22 21
Aprilchester 21.981579 19 49 
# Use DataFrame.plot() in order to create a bar chart of the data
merged_df.iplot(kind='bubble', x='fare', y='ride_id', size='driver_count', xTitle='Avg Fare', yTitle='Rider Count',
             filename='bubblechart')

# Set a title for the chart
plt.title("Avg Price vs. Ride Count vs. Driver Count")

axes = plt.gca()
axes.set_xlim([0,70])
axes.set_ylim([0,70])

plt.show()
High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~davidhoebbel/0 or inside your plot.ly account where it is named 'bubblechart'

png

#Consolidate groupby values into single table for charting purposes

m = {'fare':['sum'], 'ride_id':['count'], 'driver_count':['mean']}

merged_df2 = uber_merge.groupby(['type']).agg(m)
merged_df2.head()
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fare ride_id driver_count
sum count mean
type
Rural 4255.09 125 5.816000
Suburban 20335.69 657 14.809741
Urban 40078.34 1625 39.692923 
##City Type (Urban, Suburban, Rural)
#In addition, you will be expected to produce the following three pie charts:
##% of Total Fares by City Type
##% of Total Rides by City Type
##% of Total Drivers by City Type
merged_df2['fare %'] = (merged_df2['fare'])/(merged_df2['fare'].sum())
merged_df2['ride %'] = (merged_df2['ride_id'])/(merged_df2['ride_id'].sum())
merged_df2['driver %'] = (merged_df2['driver_count'])/(merged_df2['driver_count'].sum())
merged_df2
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fare ride_id driver_count fare % ride % driver %
sum count mean
type
Rural 4255.09 125 5.816000 0.065798 0.051932 0.096421
Suburban 20335.69 657 14.809741 0.314458 0.272954 0.245525
Urban 40078.34 1625 39.692923 0.619745 0.675114 0.658054 
# Fare % by city type
plt.pie(merged_df2['fare %'])
plt.show()

png

# ride % by city type
plt.pie(merged_df2['ride %'])
plt.show()

png

# driver % by city type
plt.pie(merged_df2['driver %'])
plt.show()

png

#Observable Trends
#1. Higher average fares associated with cities that have smaller amounts of Uber drivers
#2. Rider count and driver count are positively correlated
#3. Urban cities have more riders, drivers and a higher share of the overall fares. This is followed by Suburban then Rural cities.