How to Compare Two Pandas DataFrames and Highlight Differences Side-by-Side?

Highlighting Differences Between DataFrames

In data analysis, it's crucial to identify and compare changes between data sets effectively. This article presents an optimal method for comparing two Pandas dataframes, "StudentRoster Jan-1" and "StudentRoster Jan-2," and outputting their differences side-by-side.

To achieve this, we first utilize the boolean expression (df1 != df2). This identifies rows where values differ between the two dataframes. Next, we leverage the stack() function to create a flattened view of the boolean mask, followed by subsetting to extract only the changed entries.

For clarity, the changed dataframe shows the index and column names of the modified cells. To determine the specific changes, we utilize np.where(df1 != df2) to find the locations of these differences. Finally, we extract values from df1 and df2 at these locations and create a dataframe to display the modified values side-by-side.

By employing this approach, we can easily pinpoint and analyze the changes between dataframes, providing invaluable insights for decision-making and data exploration.

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