Exploratory analysis of KPIs

Customer Analytics and A/B Testing in Python

Ryan Grossman

Data Scientist, EDO

KPIs

Reminder: conversion rate is just one KPI
Most companies will have many KPIs
Each serves a different purpose

Methods for calculating KPIs

Group: pandas.DataFrame.groupby()

DataFrame.groupby(by=None, axis=0, level=None, 
            as_index=True, sort=True,
            group_keys=True, squeeze=False, **kwargs)

Aggregate: pandas.DataFrame.agg()

DataFrame.agg(func, axis=0, *args, **kwargs)

Grouping Data: .groupby()

by: fields to group by
axis: axis=0 will group by columns,axis=1 will group by rows
as_index: as_index=True will use group labels as index

# sub_data_demo - combined demographics and purchase data

sub_data_grp = sub_data_demo.groupby(by=['country', 'device'],

                                     axis=0,

                                     as_index=False)

sub_data_grp

<pandas.core.groupby.DataFrameGroupBy object at 0x10ec29080>

Aggregating data - mean price paid per group

# Mean price paid for each country/device
sub_data_grp.price.mean()

    country    device    price
0    BRA        and        312.163551
1    BRA        iOS        247.884615
2    CAN        and        431.448718
3    CAN        iOS        505.659574
4    DEU        and        398.848837

Aggregate data: .agg()

Pass the name of an aggregation function to agg():

# Find the mean price paid with agg
sub_data_grp.price.agg('mean')

    country    device    price
0    BRA        and        312.163551
1    BRA        iOS        247.884615
2    CAN        and        431.448718
3    CAN        iOS        505.659574
4    DEU        and        398.848837

.agg(): multiple functions

Pass a list of names of aggregation functions:

# Mean and median price paid for each country/device 
sub_data_grp.price.agg(['mean', 'median'])

                      mean      median
country device    
BRA      and         312.163551     0
         iOS         247.884615     0
CAN      and         431.448718     699
         iOS         505.659574     699
DEU      and         398.848837     499
         iOS         313.128000     0

.agg(): multiple functions, multiple columns

Pass a dictionary of column names and aggregation functions

# Calculate multiple metrics across different groups
sub_data_grp.agg({'price': ['mean', 'min', 'max'],
                  'age': ['mean', 'min', 'max']})

      country device  price                      age
                      mean         min   max     mean          min     max
0     BRA     and     312.163551   0     999     24.303738     15      67
1     BRA     iOS     247.884615   0     999     24.024476     15      79
2     CAN     and     431.448718   0     999     23.269231     15      58
3     CAN     iOS     505.659574   0     999     22.234043     15      38
4     DEU     and     398.848837   0     999     23.848837     15      67
5     DEU     iOS     313.128000   0     999     24.208000     15      54

.agg(): custom functions

def truncated_mean(data):
    """Compute the mean excluding outliers"""
    top_val = data.quantile(.9)
    bot_val = data.quantile(.1)    
    trunc_data = data[(data <= top_val) & (data >= bot_val)]
    mean = trunc_data.mean()    
    return(mean)


# Find the truncated mean age by group
sub_data_grp.agg({'age': [truncated_mean]})

    country    device    age
                            truncated_mean
0    BRA        and                 22.636364
...

Let's practice!

Customer Analytics and A/B Testing in Python