A/B test using Python

Calculating KPIs

You're now going to take what you've learned and work through calculating a KPI yourself. Specifically, you'll calculate the average amount paid per purchase within a user's first 28 days using the purchase_data DataFrame from before.

This KPI can provide a sense of the popularity of different in-app purchase price points to users within their first month.


max_purchase_date = current_date - timedelta(days=28)

# Filter to only include users who registered before our max date
purchase_data_filt = purchase_data[purchase_data.reg_date < max_purchase_date]

# Filter to contain only purchases within the first 28 days of registration
purchase_data_filt = purchase_data_filt[(purchase_data_filt.date <= 
                        purchase_data_filt.reg_date + timedelta(days=28))]

# Output the mean price paid per purchase
print(purchase_data_filt.price.mean())

Building on the previous exercise, let's look at the same KPI, average purchase price, and a similar one, median purchase price, within the first 28 days. Additionally, let's look at these metrics not limited to 28 days to compare.

We can calculate these metrics across a set of cohorts and see what differences emerge. This is a useful task as it can help us understand how behaviors vary across cohorts.

Note that in our data the price variable is given in cents.


Use np.where to create an array month1 containing:

  • the price of the purchase purchase, if

    1. the user registration .reg_date occurred at most 28 days ago (i.e. before max_reg_date), and

    2. the date of purchase .date occurred within 28 days of registration date .reg_date;

  • NaN, otherwise.

  • # Set the max registration date to be one month before today
    max_reg_date = current_date - timedelta(days=28)

    # Find the month 1 values
    month1 = np.where((purchase_data.reg_date < max_reg_date) &
                     (purchase_data.date < purchase_data.reg_date + timedelta(days=28)),
                      purchase_data.price, 
                      np.NaN)
                     
    # Update the value in the DataFrame
    purchase_data['month1'] = month1
  • purchase_data_upd = purchase_data.groupby(by=['gender', 'device'], as_index=False) 

    # Aggregate the month1 and price data 
    purchase_summary = purchase_data_upd.agg(
                            {'month1': ['mean', 'median'],
                            'price': ['mean', 'median']})

    # Examine the results 
    print(purchase_summary)

Comments

Post a Comment

Popular posts from this blog

Binomial Test in Python

  Binomial Test Let’s imagine that we are analyzing the percentage of customers who make a purchase after visiting a website. 1000 customers visited the site this month, and 58 of them made a purchase. The marketing department reports that historical data suggests about 72 of every 1000 visitors make a purchase. Thus, they estimate that the probability of any particular customer making a purchase is 7.2%. We would like to know if this month’s number, 58 purchases, is significantly different from normal or a reasonable fluctuation due to random chance. In previous exercises, we collected samples of numerical information (eg. order price) and then used the mean and standard deviation of those samples to make comparisons. In contrast, we now have a sample where each unit (a visitor) falls into one of two discrete categories: “made a purchase” “did not make a purchase” Instead of comparing sample means, we want to compare the percent in the “made a purchase” category to some expectatio...
Read more

Python Syntax and Functions Part2 (Summary Statistics)

The  .agg()  method allows you to apply your own custom functions to a DataFrame, as well as apply functions to more than one column of a DataFrame at once, making your aggregations super efficient. In the custom function for this exercise, "IQR" is short for inter-quartile range, which is the 75th percentile minus the 25th percentile. It's an alternative to standard deviation that is helpful if your data contains   import numpy as np def iqr(column):     return column.quantile(0.75) - column.quantile(0.25) # Update to print IQR and median of temperature_c, fuel_price_usd_per_l, & unemployment print(sales[["temperature_c", "fuel_price_usd_per_l", "unemployment"]].agg([iqr, np.median])) Calulating Cumulative Statistics Syntax = df.drop_duplicates sales_1_1 = sales_1_1.sort_values("date") # Get the cumulative sum of weekly_sales, add as cum_weekly_sales col sales_1_1["cum_weekly_sales"] = sales_1_1["weekly_sales...
Read more

Slicing and Indexing in Python Pandas

 # Make a list of cities to subset on cities = ["Moscow", "Saint Petersburg"] # Subset temperatures using square brackets print(temperatures[temperatures["city"].isin(cities)]) # Subset temperatures_ind using .loc[] print(temperatures_ind.loc[cities]) temperatures_ind = temperatures.set_index(['country','city']) # List of tuples: Brazil, Rio De Janeiro & Pakistan, Lahore rows_to_keep = [('Brazil','Rio De Janeiro'),('Pakistan','Lahore')] # Subset for rows to keep print(temperatures_ind.loc[rows_to_keep]) # Sort temperatures_ind by index values print(temperatures_ind.sort_index()) # Sort temperatures_ind by index values at the city level print(temperatures_ind.sort_index(level="city")) # Sort temperatures_ind by country then descending city print(temperatures_ind.sort_index(level=["country", "city"], ascending = [True, False])) Slice rows with code like  df.loc[("a", ...
Read more