Comparing Repeated Values in a Pandas DataFrame

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In this article, we’ll explore how to compare repeated values of the same column in a pandas DataFrame. We’ll use Python and the popular pandas library to achieve this.

Introduction

When working with data, it’s not uncommon to encounter duplicate or repeated values. In this scenario, we’re interested in comparing these repeated values to determine their differences.

Let’s take a look at an example dataset that illustrates this problem. We’ll use the Kaggle housing sales prediction dataset, which contains information about individual houses and their sale prices.

Loading the Dataset

First, let’s load the dataset using pandas:

import pandas as pd

# Load the dataset
df = pd.read_csv('housing_sales.csv')

This will load a DataFrame containing information about individual houses and their sale prices.

Identifying Repeated Values

To identify repeated values in the ‘id’ column, we can use the groupby function along with the filter method:

# Group by 'id' and filter out groups with only one element
df_2 = df.groupby('id').filter(lambda x: len(x) > 1)

This will create a new DataFrame (df_2) that contains only the repeated values.

Calculating Differences

To calculate the differences between the first and last elements in each group, we can use the agg function with a lambda function:

# Calculate the difference between the first and last element for each 'id' group
diffs = df_2.groupby('id')['price'].agg(lambda x: x.iloc[-1] - x.iloc[0])

This will create a new Series (diffs) that contains the differences between the first and last elements in each ‘id’ group.

Aggregating Differences

We can aggregate these differences using any function we like. Let’s use the max function to get the maximum difference for each ‘id’ group:

# Calculate the maximum difference for each 'id' group
max_diffs = diffs.max()

This will create a new Series (max_diffs) that contains the maximum differences for each ‘id’ group.

Merging with the Original DataFrame

To merge these differences back into the original DataFrame, we can use the merge function:

# Merge the differences back into the original DataFrame
df_merged = df.iloc[:, :3].merge(max_diffs, left_on='id', right_index=True)

This will create a new DataFrame (df_merged) that contains the repeated values along with their corresponding differences.

Sorting by Difference

Finally, let’s sort the df_merged DataFrame by difference in descending order:

# Sort the DataFrame by difference in descending order
df_sorted = df_merged.sort_values(by='diff', ascending=False)

This will create a new DataFrame (df_sorted) that contains the repeated values sorted by their differences.

Conclusion

In this article, we’ve explored how to compare repeated values of the same column in a pandas DataFrame. We used Python and the popular pandas library to achieve this.

We identified repeated values using the groupby function along with the filter method. Then, we calculated the differences between the first and last elements in each group using the agg function with a lambda function.

Next, we aggregated these differences using any function we liked. Finally, we merged these differences back into the original DataFrame using the merge function.

We sorted the resulting DataFrame by difference in descending order to get our final answer.

Additional Tips and Variations

Here are some additional tips and variations you can try:

Instead of using the max function to aggregate differences, you could use other functions like min, mean, or median.
If you want to get the average difference for each ‘id’ group instead of the maximum difference, you could use the mean function: diffs.mean()
To get the standard deviation of the differences for each ‘id’ group instead of the maximum difference, you could use the std function: diffs.std()

By experimenting with different aggregation functions and variations, you can customize your analysis to suit your specific needs.

References

Last modified on 2025-04-29