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Pandas groupby on Multiple Fields with Difference Calculation
In programming, manipulating data is crucial, and Pandas is a powerful library for performing these tasks efficiently. One common question is how to group data by multiple fields and calculate differences. Let's explore how to achieve this.
Problem:
Consider a DataFrame with the following structure:
date site country score 0 2018-01-01 google us 100 1 2018-01-01 google ch 50 2 2018-01-02 google us 70 3 2018-01-03 google us 60 ...
The goal is to find the 1/3/5-day difference in scores for each 'site/country' combination.
Solution:
To solve this problem, we can utilize Pandas' groupby and diff functions:
df = df.sort_values(by=['site', 'country', 'date'])
Sorting ensures that our data is organized for proper grouping and difference calculations.
df['diff'] = df.groupby(['site', 'country'])['score'].diff().fillna(0)
This line groups the DataFrame by 'site' and 'country' columns using groupby. Then, it calculates the difference between each consecutive score within each group using diff. The result is stored in a new column called 'diff.' Any missing values are replaced with 0 using fillna(0).
Output:
The resulting DataFrame will contain the original columns along with the 'diff' column:
date site country score diff 0 2018-01-01 fb es 100 0.0 1 2018-01-02 fb gb 100 0.0 ...
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