Using Pandas DataFrame to Add Lag Feature from MultiIndex Series

Introduction

In this article, we will explore how to add a lag feature to a Pandas DataFrame using a MultiIndex Series. We will provide an example of creating a new column in the DataFrame that contains the value matching the ID_1 and ID_2 indices and the Week - 2 index from the Series.

Background

Pandas is a powerful library for data manipulation and analysis in Python. It provides several features for working with multi-indexed DataFrames, which can be particularly useful when dealing with hierarchical or categorical data.

A MultiIndex Series is a type of Series that has multiple indices. In this case, we are working with a Series that has three indices: Week, ID_1, and ID_2.

Problem Statement

We have a DataFrame df that contains all the columns used for indices in the Series above, and we want to create a new column in our DataFrame df that contains the value matching the ID_1 and ID_2 and the Week - 2 from the Series. We also want to replace NaN values with 0.

Solution

We can solve this problem by first creating a new Series that increments the Week index by 2, then merging the original DataFrame df with the new Series, and finally adding a new column to the resulting DataFrame that contains the value matching the ID_1 and ID_2 indices and the Week - 2 index from the Series.

Code

import pandas as pd
import numpy as np

# Create a sample DataFrame
np.random.seed(2016)
df = pd.DataFrame(np.random.randint(5, size=(20,5)), 
                  columns=['Week', 'ID_1', 'ID_2', 'Target', 'Foo'])

# Create the MultiIndex Series
series = df.groupby(['Week', 'ID_1', 'ID_2'])['Target'].median().reset_index()

# Increment the Week index by 2
series['Week'] += 2

# Rename the column 'Target' to 'Median'
series = series.rename(columns={'Target':'Median'})

# Merge the original DataFrame with the new Series
result = pd.merge(df, series, on=['Week', 'ID_1', 'ID_2'], how='left')

# Replace NaN values with 0
result['Median'] = result['Median'].fillna(0)

print(result)

Output

The output of this code will be a DataFrame that contains all the columns from the original DataFrame df, plus a new column named ‘Median’ that contains the value matching the ID_1 and ID_2 indices and the Week - 2 index from the Series. The NaN values in the ‘Median’ column will be replaced with 0.

    Week  ID_1  ID_2  Target  Foo  Median
0      3     2     3       4    2     0.0
1      3     3     0       3    4     0.0
2      4     3     0       1    2     0.0
3      3     4     1       1    1     0.0
4      2     4     2       0    3     2.0
5      1     0     1       4    4     0.0
6      2     3     4       0    0     0.0
7      4     0     0       2    3     0.0
8      3     4     3       2    2     0.0
9      2     2     4       0    1     0.0
10     2     0     4       4    2     0.0
11     1     1     3       0    0     0.0
12     0     1     0       2    0     0.0
13     4     0     4       0    3     4.0
14     1     2     1       3    1     0.0
15     3     0     1       3    4     2.0
16     0     4     2       2    4     0.0
17     1     1     4       4    2     0.0
18     4     1     0       3    0     0.0
19     1     0     1       0    0     0.0

Conclusion

In this article, we have shown how to add a lag feature to a Pandas DataFrame using a MultiIndex Series. We created a new column in the DataFrame that contains the value matching the ID_1 and ID_2 indices and the Week - 2 index from the Series, and replaced NaN values with 0. This technique can be useful when working with hierarchical or categorical data.

Last modified on 2025-01-13