How to Efficiently Compare DataFrames Side-by-Side for In-depth Change Analysis?

Compare DataFrames Side-by-Side for Comprehensive Change Analysis

To highlight discrepancies between two dataframes, an efficient method exists that eliminates the need for laborious row-by-row and column-by-column comparisons. By leveraging specific Python Pandas functions, it is possible to pinpoint changes across various data types (e.g., int, float, boolean, string) and present them in an intuitive HTML table format.

To begin, establish whether any rows have changed using the boolean function (df1 != df2).any(1). Next, to identify specific entries that have altered, employ ne_stacked = (df1 != df2).stack() and filter out non-changed values by utilizing changed = ne_stacked[ne_stacked].

To obtain the actual changed values, incorporate difference_locations = np.where(df1 != df2), which identifies the locations of changed data. Extract the values from the original dataframe (df1) at these locations using changed_from = df1.values[difference_locations]. Similarly, extract the corresponding values from the second dataframe (df2) using changed_to = df2.values[difference_locations].

To present the differences comprehensively, construct a DataFrame by combining changed_from and changed_to as columns, and setting the index to match the changed variable. This DataFrame will provide a clear side-by-side view of the changes, highlighting both the original and updated values for each data point.

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