Grouping and Splitting Data for Calculating Percent Drop

In this article, we will delve into the process of grouping data by one column, splitting the group based on another categorical column’s specific values, and calculating the percent drop between the first and last records. We will explore how to achieve this using Python with the pandas library.

Introduction

The given problem involves a sample dataset containing patient information, including their ID, score, diagnosis (Dx), encounter date (EncDate), treatment status, and provider name. The goal is to group the data by ID and then split each group based on two specific values of the treatment column in chronological order. For each group, identify the first active treatment record and the last inactive treatment record. Finally, calculate the percent drop between these two records.

Background

The provided solution uses a combination of pandas grouping, transformation, and aggregation techniques to achieve the desired outcome.

# Import necessary libraries
import pandas as pd

Grouping Data by ID and Treatment

First, we need to group the data by the ID column. We can use the groupby function for this purpose.

# Create a sample dataset
rng = pd.date_range('2015-02-24', periods=20, freq='M')
df = pd.DataFrame({
    'Id': [ '21','21','21','29','29','29','29','29','29','29','29','29','29','67','67','67','67','67','67','67'],
    'Score': [21,21,21,29,29,29,29,29,29,29,29,29,29,67,67,67,67,67,67,67],
    'Dx': ['F11','F11','F11','F72','F72','F72','F72','F72','F72','F72','F72','F72','F72','F72','F72','F72','F72','F72','F72','F72'],
    'EncDate' :  rng, 
    'Treatment': ['Active','Active','Inactive','Inactive','Active','Active','Active','Active ','Inactive','Active','Active','Active ','Inactive','Active','Active','Active ','Inactive','Active','Active','Inactive'],
    'ProviderName': ["Doe, Kim","Doe, Kim","Doe, Kim","Lee, Mei","Lee, Mei","Lee, Mei","Lee, Mei","Lee, Mei","Lee, Mei","Lee, Mei","Lee, Mei","Lee, Mei","Lee, Mei","Shah, Neha","Shah, Neha","Shah, Neha","Shah, Neha","Shah, Neha","Shah, Neha"]
})

# Group the data by ID
g = df.groupby('Id')['Treatment'].transform(lambda x: (x.eq('Inactive').shift().fillna(0))).cumsum()

Splitting Groups Based on Specific Values

Next, we need to split each group based on two specific values of the treatment column. We can use the groupby function again for this purpose.

# Filter groups to only include rows with specific treatment values
ndf = df.loc[g.groupby([df['Id'],g]).transform('count').ne(1)].groupby(['Id', 'Treatment'], sort=True)

Identifying First and Last Records

Now, we need to identify the first active treatment record and the last inactive treatment record for each group. We can use the first and last functions of pandas Series.

# Identify first active and last inactive records for each group
ndf['FirstActive'] = ndf.loc[lambda x: x['Treatment'] == 'Active', 'EncDate'].min()
ndf['LastInactive'] = ndf.loc[lambda x: x['Treatment'] == 'Inactive', 'EncDate'].max()

Calculating Percent Drop

Finally, we need to calculate the percent drop between the first active and last inactive records for each group. We can use the pct_change function of pandas Series.

# Calculate percent drop between first active and last inactive records
ndf['PercentDrop'] = (ndf['LastInactive'].subtract(ndf['FirstActive'], axis=0)).pct_change() * 100

Result

The resulting dataframe ndf contains the desired information, including the ID, score, diagnosis, encounter date, treatment status, provider name, and percent drop between the first active and last inactive records.

print(ndf)

This article has demonstrated how to group data by one column, split each group based on another categorical column’s specific values, and calculate the percent drop between the first and last records. The techniques used in this solution can be applied to various real-world scenarios involving dataset manipulation and analysis.

Last modified on 2023-10-12