How to Compare and Display Dataframe Differences Effectively Using Python

Comparing and Displaying Dataframe Differences Effectively

Introduction

Identifying and understanding the differences between two dataframes is a common task in data analysis. Whether it's comparing historical data to current trends or tracking changes in a database, the ability to highlight these changes accurately is crucial.

Problem Statement

Suppose we have two dataframes containing student roster information from two different months: "StudentRoster Jan-1" and "StudentRoster Jan-2." Our goal is to create an HTML table that clearly displays the changes between these two dataframes, showing both new and old values for each row.

Solution

Identifying Changed Rows

The first step is to determine which rows have actually changed. We can use the any() function to check each row for any differences:

<code class="python">import pandas as pd
import numpy as np

ne = (df1 != df2).any(1)</code>

This will return a Boolean Series where True indicates a changed row.

Extracting Changed Values

Next, we need to extract the actual changed values. We use the .stack() method to transform the dataframe into a single column, then filter this column for the changed values:

<code class="python">ne_stacked = (df1 != df2).stack()
changed = ne_stacked[ne_stacked]
changed.index.names = ['id', 'col']</code>

This will give us the index and column names of the changed values.

Extracting Previous and New Values

Using the index from the changed values, we can extract the previous and new values for each changed entry:

<code class="python">difference_locations = np.where(df1 != df2)
changed_from = df1.values[difference_locations]
changed_to = df2.values[difference_locations]</code>

Creating the HTML Table

Finally, we can create the HTML table by combining the extracted values:

<code class="python">pd.DataFrame({'from': changed_from, 'to': changed_to}, index=changed.index)</code>

This dataframe contains two columns: "from" and "to," which display the original and new values for each changed entry. The index of the dataframe identifies the row and column where the change occurred.

By displaying the changed values and their previous and new values side-by-side, this HTML table provides a clear and comprehensive overview of the changes between the two dataframes.

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