5 Best Ways to Check if Two Pandas DataFrames are Exactly the Same

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💡 Problem Formulation: When working with data analysis in Python, it’s common to have multiple Pandas DataFrames that you suspect might be identical and need to verify their equality. Ensuring two DataFrames are exactly the same, inclusive of the data types, index, and column order, is essential for many applications. For instance, you may wish to confirm that a DataFrame remains unaltered after a series of operations or that two separate data processing pipelines yield identical outputs.

Method 1: Using the equals() method

The equals() method in Pandas is designed to check if two DataFrames are of the same shape and have the same content. It returns a Boolean value, making it a straightforward and effective way to perform this check.

Here’s an example:

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df3 = pd.DataFrame({'A': [1, 2], 'B': [5, 4]})

print(df1.equals(df2))
print(df1.equals(df3))

Output:

True
False

This snippet creates two identical DataFrames, df1 and df2, and a third DataFrame, df3, that differs slightly. The equals() method confirms that df1 and df2 are the same but that df1 and df3 are not.

Method 2: Using the compare() method

In Pandas, the compare() method allows you to find differences between two DataFrames. The result details the changes per row and column, but for checking equality, you can verify if the result is empty.

Here’s an example:

comparison = df1.compare(df2)
print(comparison.empty)

Output:

True

By running df1.compare(df2), it returns an empty DataFrame if the two are equal, and by further checking if comparison.empty is True, we validate that the DataFrames are indeed the same.

Method 3: Using the assert_frame_equal() function

The assert_frame_equal() function from Pandas’ testing module can be used in test cases to assert that two DataFrames are equal. If the assertion fails, the function raises an AssertionError; if it passes, it simply continues execution.

Here’s an example:

from pandas.testing import assert_frame_equal

try:
    assert_frame_equal(df1, df2)
    print("DataFrames are equal")
except AssertionError:
    print("DataFrames are not equal")

Output:

DataFrames are equal

In the example, we use assert_frame_equal() to compare df1 and df2. Since they match, the code prints “DataFrames are equal” and does not raise an exception.

Method 4: Comparing DataFrames manually

If you want more control over the comparison process, you can manually check both the underlying data and properties such as the shape, indexes, and column names.

Here’s an example:

equal_shapes = df1.shape == df2.shape
equal_indexes = df1.index.equals(df2.index)
equal_columns = df1.columns.equals(df2.columns)
equal_values = df1.equals(df2)

dataframes_equal = all([equal_shapes, equal_indexes, equal_columns, equal_values])
print(dataframes_equal)

Output:

True

This code snippet uses four separate checks to compare structure and content, then uses the all() function to combine the results. Since all conditions are True, it prints that the DataFrames are equal.

Bonus One-Liner Method 5: Using hash()

To quickly check for equality, you can convert each DataFrame to a hashable object, such as a tuple, and compare their hashes. Keep in mind that this method requires the DataFrames to fit in memory as tuples.

Here’s an example:

print(hash(tuple(df1.values.tobytes())) == hash(tuple(df2.values.tobytes())))

Output:

True

This one-liner uses Python’s built-in hash() function to convert the DataFrames’ values to byte arrays, packages them in tuples, and compares their hashes for equality.

Summary/Discussion

  • Method 1: equals() Method. This is straightforward and directly designed for the purpose of comparing DataFrames. However, it doesn’t provide details on the differences when DataFrames aren’t equal.
  • Method 2: compare() Method. This method offers a DataFrame output that describes differences, helpful for debugging, but requires an additional check to confirm it’s empty for equality.
  • Method 3: assert_frame_equal() Function. Best suited for unit tests within a larger codebase. It provides a clear exception on failure but requires try-except handling outside of testing scenarios.
  • Method 4: Manual Comparison. It offers customizable comparison options and is transparent, but it is verbose and requires more code and computation time.
  • Method 5: Using hash(). This is a quick one-liner suitable for simple, high-level checks, but it may miss subtle differences and is not guaranteed to work on larger DataFrames.