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In [1]:

    
%%capture

import numpy as np
import pandas as pd
import os

# Suppress unwatned warnings
import warnings
warnings.filterwarnings('ignore')
import logging
logging.getLogger("requests").setLevel(logging.WARNING)

# Load our favorite visualization library

import plotly
import plotly.plotly as py
import plotly.figure_factory as ff
import plotly.graph_objs as go
import cufflinks as cf
plotly.offline.init_notebook_mode(connected=True)

# Sign into Plotly with masked, encrypted API key

myPlotlyKey = os.environ['SECRET_ENV_BRETTS_PLOTLY_KEY']
py.sign_in(username='bretto777',api_key=myPlotlyKey)

# Set some defaults for how pandas displays
pd.set_option('display.height', 1000)
pd.set_option('display.max_rows', 200)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)



In [27]:

    
fig = {
  "data": [
    {
      "values": churnDF['Churn'].value_counts(),
      "labels": churnDF['Churn'].unique(),
      "domain": {"x": [0, .48]},
      "name": "Predicted to Churn",
      "hoverinfo":"label+percent+name",
      "hole": .4,
      "type": "pie"
    },
    {
      "values": churnDF['CustServ Calls'].value_counts(),
      "textposition":"inside",
      "domain": {"x": [.52, 1]},
      "name": "Customer Service Calls",
      "hoverinfo":"label+value+name",
      "hole": .4,
      "type": "pie"
    }],
  "layout": {
        "title":"Customer Churn Predictions",
        "annotations": [
            {
                "font": {
                    "size": 20
                },
                "showarrow": False,
                "text": "Churn",
                "x": 0.20,
                "y": 0.5
            },
            {
                "font": {
                    "size": 20
                },
                "showarrow": False,
                "text": "Calls",
                "x": 0.8,
                "y": 0.5
            }
        ]
    }
}
py.iplot(fig, filename='donut')









    Out[27]:



In [2]:

    
s3file = 'https://dsclouddata.s3.amazonaws.com/churn.csv'
churnDF = pd.read_csv(s3file, delimiter=',')
churnDF.head(5)









    Out[2]:







  
    
      
      State
      Account Length
      Area Code
      Phone
      Int'l Plan
      VMail Plan
      VMail Message
      Day Mins
      Day Calls
      Day Charge
      Eve Mins
      Eve Calls
      Eve Charge
      Night Mins
      Night Calls
      Night Charge
      Intl Mins
      Intl Calls
      Intl Charge
      CustServ Calls
      Churn
    
  
  
    
      0
      KS
      128
      415
      382-4657
      no
      yes
      25
      265.1
      110
      45.07
      197.4
      99
      16.78
      244.7
      91
      11.01
      10.0
      3
      2.70
      1
      False
    
    
      1
      OH
      107
      415
      371-7191
      no
      yes
      26
      161.6
      123
      27.47
      195.5
      103
      16.62
      254.4
      103
      11.45
      13.7
      3
      3.70
      1
      False
    
    
      2
      NJ
      137
      415
      358-1921
      no
      no
      0
      243.4
      114
      41.38
      121.2
      110
      10.30
      162.6
      104
      7.32
      12.2
      5
      3.29
      0
      False
    
    
      3
      OH
      84
      408
      375-9999
      yes
      no
      0
      299.4
      71
      50.90
      61.9
      88
      5.26
      196.9
      89
      8.86
      6.6
      7
      1.78
      2
      False
    
    
      4
      OK
      75
      415
      330-6626
      yes
      no
      0
      166.7
      113
      28.34
      148.3
      122
      12.61
      186.9
      121
      8.41
      10.1
      3
      2.73
      3
      False

	State	Account Length	Area Code	Phone	Int'l Plan	VMail Plan	VMail Message	Day Mins	Day Calls	Day Charge	Eve Mins	Eve Calls	Eve Charge	Night Mins	Night Calls	Night Charge	Intl Mins	Intl Calls	Intl Charge	CustServ Calls	Churn
0	KS	128	415	382-4657	no	yes	25	265.1	110	45.07	197.4	99	16.78	244.7	91	11.01	10.0	3	2.70	1	False
1	OH	107	415	371-7191	no	yes	26	161.6	123	27.47	195.5	103	16.62	254.4	103	11.45	13.7	3	3.70	1	False
2	NJ	137	415	358-1921	no	no	0	243.4	114	41.38	121.2	110	10.30	162.6	104	7.32	12.2	5	3.29	0	False
3	OH	84	408	375-9999	yes	no	0	299.4	71	50.90	61.9	88	5.26	196.9	89	8.86	6.6	7	1.78	2	False
4	OK	75	415	330-6626	yes	no	0	166.7	113	28.34	148.3	122	12.61	186.9	121	8.41	10.1	3	2.73	3	False