Aggregating Values with Pandas crosstab and groupby

Working with Pandas DataFrames: Aggregating Values Using Crosstab and Groupby_mean

In this article, we will explore how to use the Pandas library in Python for data analysis, specifically focusing on two popular methods for aggregating values: pd.crosstab and groupby_mean. We will delve into the details of each method, discussing their strengths, weaknesses, and when to use them.

Introduction

Pandas is a powerful library used for data manipulation and analysis in Python. It provides various functions for handling structured data, including tabular data such as spreadsheets and SQL tables. In this article, we will explore two methods for aggregating values: pd.crosstab and groupby_mean. These methods are commonly used to summarize and analyze data, but they serve different purposes and have distinct use cases.

Pandas crosstab

pd.crosstab is a function that creates a contingency table from two lists of indices. It is particularly useful for creating pivot tables with categorical values. However, when working with numerical columns, it can be challenging to get the desired output.

Example Data

Let’s consider an example dataset:

df = ({'DAY':['20210101','20210102','20210102'],'TTM':[0.1,0.1,0.5],'TTS':[0.3,0.4,0.4]})

In this example, we have a DataFrame df with three columns: DAY, TTM, and TTS. The values in the DAY column are categorical, while the values in the TTM and TTS columns are numerical.

Using pd.crosstab

When using pd.crosstab, we need to specify the index and columns as lists of indices. However, when working with multiple values for each category, it can be challenging to get the desired output.

pd.crosstab(index=df['DAY'],columns=df['DAY'],values=df[['TTM','TTS']])

In this example, we are trying to create a contingency table using pd.crosstab. However, since both columns share the same name DAY, Pandas does not know which column to use as the index and which one to use as the column. This is why we see an empty result.

Solution Using groupby_mean

One alternative method for aggregating values is by using the groupby_mean function, which was introduced in Pandas version 0.23.0.

Example Data

Let’s consider the same example dataset:

df = ({'DAY':['20210101','20210102','20210102'],'TTM':[0.1,0.1,0.5],'TTS':[0.3,0.4,0.4]})

Using groupby_mean

To use groupby_mean, we need to first create a DataFrame with the desired columns.

import pandas as pd

df = pd.DataFrame({
    'DAY': ['20210101','20210102','20210102'],
    'TTM': [0.1, 0.1, 0.5],
    'TTS': [0.3, 0.4, 0.4]
})

out = df.groupby('DAY')[['TTM','TTS']].mean().add_prefix('mean').reset_index()

In this example, we are using the groupby_mean function to calculate the mean of the TTM and TTS columns for each category in the DAY column. The resulting DataFrame contains two new columns: meanTTM and meanTTS.

Output

The output of the above code is:

        DAY  meanTTM  meanTTS
0  20210101      0.1      0.3
1  20210102      0.3      0.4

In this example, we can see that the mean value of TTM for each category in the DAY column is 0.1 and 0.3 respectively, while the mean value of TTS is 0.3 and 0.4.

Conclusion

In conclusion, both pd.crosstab and groupby_mean are powerful tools for aggregating values in Pandas DataFrames. However, they serve different purposes and have distinct use cases. While pd.crosstab is ideal for creating pivot tables with categorical values, it can be challenging to work with numerical columns.

On the other hand, groupby_mean is particularly useful when working with multiple values for each category. It provides a straightforward way to calculate the mean of one or more columns for each group in the DataFrame.

By understanding the strengths and weaknesses of both methods, we can choose the most suitable approach for our specific data analysis needs.

Last modified on 2024-08-08