How to Compare Columns Across Multiple Dataframes in Python?

To compare columns across multiple dataframes in Python, you can iterate over the columns of interest and perform the desired comparison operation. Here’s an example:

import pandas as pd

# Create sample dataframes
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [1, 2, 3], 'B': [7, 8, 9]})
df3 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Define the columns to compare
columns_to_compare = ['A', 'B']

# Iterate over the columns and compare
for column in columns_to_compare:
    if all(df1[column] == df2[column]) and all(df1[column] == df3[column]):
        print(f"Column {column} is the same across all dataframes")
    else:
        print(f"Column {column} is not the same across all dataframes")

In this example, we have three dataframes (df1, df2, and df3) with the same column names (‘A’ and ‘B’). We define a list of columns to compare (columns_to_compare) and then iterate over each column.

Within the loop, we use the all() function to check if the column values are the same across all dataframes. If the comparison returns True for all dataframes, it means the column values are identical. Otherwise, the column values differ in at least one dataframe.

You can adjust the logic inside the loop to perform different types of comparisons based on your specific requirements. For example, you can use other comparison operators (>, <, >=, <=, !=) or apply custom comparison functions to compare the columns.

By iterating over the columns of interest and performing the desired comparison, you can compare columns across multiple dataframes in Python.