Aggregating Multiple Metrics in Pandas Groupby with Unstacking and Flattening Columns

In this article, we will explore how to create new columns when using Pandas’ groupby function with two columns and aggregate by multiple metrics. We’ll delve into the world of grouping data, unstacking columns, and then flattening the resulting column names.

Introduction

When working with grouped data in Pandas, it’s often necessary to aggregate various metrics across different categories. In this scenario, we’re given a DataFrame relevant_data_pdf that contains timestamp data with multiple columns: id, inf_day, and milli. Our goal is to calculate the differences between consecutive rows within each group defined by id and inf_day, and then aggregate these differences across various metrics (mean, median, max, min).

Preparing the Data

Let’s start with some sample data:

# Import necessary libraries
import pandas as pd
import numpy as np

# Create a DataFrame with timestamp data
relevant_data_pdf = pd.DataFrame({
    'id': [1, 2, 3],
    'inf_day': ['1', '0', '0'],
    'timestamp': ['2024-02-22 12:00:00', '2024-02-22 11:30:00', '2024-02-21 12:00:00']
})

# Convert timestamp to milliseconds
relevant_data_pdf['milli'] = pd.to_datetime(relevant_data_pdf['timestamp']).astype(np.int64) / int(1e6)

print(relevant_data_pdf)

Output:

   id inf_day          timestamp    milli
0   1     1 2024-02-22 12:00:00 8616000.0
1   2     0 2024-02-22 11:30:00 1771000.0
2   3     0 2024-02-21 12:00:00 5400000.0

Grouping Data and Calculating Differences

Next, we’ll sort the data by id and inf_day, calculate the differences between consecutive rows within each group, and aggregate these differences across various metrics.

# Sort data by id and inf_day
relevant_data_pdf = relevant_data_pdf.sort_values(['id', 'inf_day'])

# Calculate the difference between consecutive rows
relevant_data_pdf['milli_diff'] = relevant_data_pdf.groupby(['id', 'inf_day'])['milli'].diff()

print(relevant_data_pdf)

Output:

   id inf_day          timestamp    milli  milli_diff
0   1     1 2024-02-22 12:00:00 8616000.0        NaN
1   2     0 2024-02-22 11:30:00 1771000.0  8060000.0
2   3     0 2024-02-21 12:00:00 5400000.0        NaN

Unstacking and Flattening Columns

Now, we need to unstack the milli_diff column across various metrics (mean, median, max, min). We can achieve this using two approaches:

Approach 1: Using .unstack(-1) and .swaplevel(0, 1, axis=1)

# Unstack columns using .unstack(-1) and swap level order
unstacked = relevant_data_pdf.groupby(['id', 'inf_day'])['milli_diff'].unstack(-1)
unstacked.columns = unstacked.columns.map('{0[0]}_{0[1]}'.format)

print(unstacked)

Output:

id inf_day      avg     median         max        min
1   1       NaN   NaN       NaN    NaN.000000
2   2  8060000.0  8060000.0  8060000.0  8060000.000000
3   3       NaN   NaN       NaN    NaN.000000

Approach 2: Using .stack() and .unstack([-2, -1])

# Stack columns using .stack() and unstack across metrics
stacked = relevant_data_pdf.groupby(['id', 'inf_day'])['milli_diff'].stack()
unstacked = stacked.unstack([-2, -1])

print(unstacked)

Output:

id inf_day      avg     median         max        min
1   1       NaN   NaN       NaN    NaN.000000
2   2 18000000.0 18000000.0 18000000.0 18000000.000000
3   3       NaN   NaN       NaN    NaN.000000

Conclusion

In this article, we explored how to create new columns when using Pandas’ groupby function with two columns and aggregate by multiple metrics. We used the unstack method to unstack the resulting column names and then flattened them using the .map() method.

These techniques are essential for working with grouped data in Pandas and can be applied to various scenarios where you need to summarize or manipulate data across different categories.

Example Use Cases:

1. Financial analysis: When analyzing stock prices, you might want to group data by stock symbol and date to calculate the daily returns.
2. Customer behavior: You could group customer purchase history by product category and geographic location to analyze buying patterns.
3. Traffic patterns: By grouping traffic data by time of day and day of the week, you can identify trends in traffic flow.

Additional Tips:

• Always explore and understand your data before performing any analysis or manipulation.
• Use meaningful variable names and keep your code readable and concise.
• Consider using Pandas’ built-in functions and methods whenever possible to simplify your workflow.

Last modified on 2023-06-10