Creating time series graphs with Matplotlib

Customer Analytics and A/B Testing in Python

Ryan Grossman

Data Scientist, EDO

Conversion rate over time

Useful Ways to Explore Metrics

By user type
Over time

Monitoring the impact of changes

Week one conversion rate by day

import pandas as pd
from datetime import timedelta

# The maximum date in our dataset 
current_date = pd.to_datetime('2018-03-17')


# Limit to users who have had a week to subscribe
max_lapse_date = current_date - timedelta(days=7)
conv_sub_data = sub_data_demo[
    sub_data_demo.lapse_date < max_lapse_date]


# Calculate how many days it took the user to subscribe
conv_sub_data['sub_time'] = (conv_sub_data.subscription_date 
    - conv_sub_data.lapse_date.dt.days)

Conversion Rate by Day

First day to register, reg_date, equals the free trial lapse_date
These terms are used interchangeably throughout

# Find the convsersion rate for each daily cohort
conversion_data = conv_sub_data.groupby(
    by=['lapse_date'],as_index=False

).agg({'sub_time': [gc7]})


# Clean up the dataframe columns 
conversion_data.head()

    lapse_date    sub_time
0    2017-09-01    0.224775
1    2017-09-02    0.223749
...

Plotting Daily Conversion Rate

Use the.plot() method to generate graphs of DataFrames

# Convert the lapse_date value from a string to a  datetime value 
conversion_data["lapse_date"] = pd.to_datetime(
    conversion_data.lapse_date
)


# Plot a line graph of the average conversion rate for each user registration cohort
conversion_data.plot(x='lapse_date', y='sub_time')

Plotting Daily Conversion Rate

# Print the generated graph to the screen 
plt.show()

alt

Trends in different cohorts

See how changes interact with different groups
Compare users of different genders
Evaluate the impact of a change across regions
See the impact for different devices

Trends across time and user groups

Is the holiday dip consistent across different countries?

conversion_data.head()

Conversion rate by day, broken out by our top selling countries

    lapse_date    country    sub_time
0    2017-09-01    BRA        0.184000
1    2017-09-01    CAN        0.285714
2    2017-09-01    DEU        0.276119
3    2017-09-01    FRA        0.240506
4    2017-09-01    TUR        0.161905

Conversion rate by country

# Break out our conversion rate by country 
reformatted_cntry_data = pd.pivot_table(
    conversion_data, # DataFrame to reshape

    values=['sub_time'], # Our primary value

    columns=['country'], # what to break out by

    index=['lapse_date'], # "earliest sub date for cohort"
    fill_value=0 
)

lapse_date        BRA         CAN        DEU    
2017-09-01        0.184000    0.285714    0.276119    ...
2017-09-02        0.171296    0.244444    0.276190    ...    
2017-09-03        0.177305    0.295082    0.266055    ...

Plotting trends in different cohorts

# Plot each countries conversion rate
reformatted_cntry_data.plot(

    x='lapse_date',

    y=['BRA','FRA','DEU','TUR','USA','CAN']    
)

plt.show()

alt

Let's practice!

Customer Analytics and A/B Testing in Python