#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'

#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()

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

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

#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.