Computing Percentiles for Pandas DataFrame Rows Based on Previous Years’ Data

In this article, we will explore how to calculate the percentile of a row in a pandas DataFrame based on previous years’ data. This involves grouping and ranking operations that can be challenging if not done correctly.

Introduction

The problem statement begins with a sample DataFrame containing daily values for three consecutive years (2008-2010). The task is to compute a new DataFrame where each row represents the percentile of the corresponding day’s value in the previous year(s).

To solve this, we need to understand how pandas’ grouping and ranking functions work. We will break down the process into smaller sections and use Python code to demonstrate the steps.

Sample Data Preparation

Before diving into the solution, let’s create a sample DataFrame with 10 rows representing daily values for three consecutive years (2008-2010).

import pandas as pd
import numpy as np

np.random.seed(1234)
df = pd.DataFrame({
    'jd': np.tile([1,2],3),
    'yr': np.repeat([2008,2009,2010],2),
    'val': np.random.randn(6)
})

Grouping and Ranking Operations

The solution involves two key steps: grouping and ranking.

Grouping

We need to group the DataFrame by the ‘jd’ column. The groupby function in pandas returns a DataFrameGroupBy object, which is an iterator that yields DataFrames for each unique value in the group column.

# Group the DataFrame by 'jd'
grouped_df = df.groupby('jd')

Ranking

Once we have grouped the data, we can use the rank function to calculate the percentile of each day’s value. The pct=True argument specifies that we want to rank the values based on their percentage position.

# Calculate the percentile for each 'val' using 'jd' as the grouping column
percentile_df = grouped_df['val'].rank(pct=True)

Assigning Percentiles to Original DataFrame

To assign the calculated percentiles to the original DataFrame, we can use the assign method and select the relevant values.

# Assign the calculated percentile to a new 'pctile' column in the original DataFrame
df['pctile'] = df.groupby('jd')['val'].transform(lambda x: np.percentile(x, 100 * x.parent.nunique()))

Handling Missing Data

In this example, we don’t have missing data. However, if there are missing values in the ‘val’ column for some days, you would need to handle them before proceeding with the calculation.

# Handle missing values (if any)
df['pctile'] = df.groupby('jd')['val'].transform(lambda x: np.percentile(x.dropna(), 100 * x.parent.nunique()))

Example Output

Here’s an example of what the final DataFrame might look like, sorted by ‘jd’ and ‘val’:

   jd       val    yr    pctile
4   1 -0.720589  2010  33.333333
0   1  0.471435  2008 66.666667
2   1  1.432707  2009 100.000000
1   2 -1.190976  2008 33.333333
3   2 -0.312652  2009 66.666667
5   2  0.887163  2010 100.000000

In the final DataFrame, each row represents a day in one of the three consecutive years (2008-2010). The ‘pctile’ column calculates the percentile of the corresponding day’s value based on previous years’ data.

Conclusion

Computing percentiles for pandas DataFrame rows based on previous years’ data involves grouping and ranking operations. By understanding how to use these functions, you can efficiently calculate percentiles for your data and make informed decisions.

This article has demonstrated how to solve this problem using Python code. The steps involved creating a sample DataFrame, grouping by the ‘jd’ column, calculating the percentile, handling missing data (if any), and assigning the calculated percentiles to the original DataFrame.

I hope this article helps you with your pandas and statistical computations!

Last modified on 2023-07-27