Compare Two DataFrames Row by Row

Pandas DataFrame.compare() function is used to compare given DataFrames row by row along with the specified align_axis. Sometimes we have two or more DataFrames having the same data with slight changes, in those situations we need to observe the difference between two DataFrames. By default, compare() function compares two DataFrames column-wise and returns the differences side by side. It can compare only DataFrames having the same shape with the same dimensions and having the same row indexes and column labels.

In this article, I will explain using compare() function, its syntax, and parameters how we can compare the two DataFrames row by row with examples.

Key Points –

• You can perform an element-wise comparison between two DataFrames to check for differences in individual values across rows.
• The compare() method allows a direct comparison between two DataFrames and highlights differences, providing a concise summary.
• The keep_equal=True parameter can be used with compare() to retain rows with no differences, including rows where values are the same across both DataFrames.
• Use the keep_shape=True parameter to preserve the shape of the DataFrames even if some rows are different, filling mismatched cells with NaN.
• To compare DataFrames row by row, set align_axis=0 to align rows while comparing their content.
• Row-by-row comparison can be computationally expensive for large DataFrames, so consider using optimized methods (e.g., vectorized operations) when performance is a concern.

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Quick Examples of Compare Two DataFrames Row by Row

If you are in a hurry, below are some quick examples of comparing two DataFrames row by row.

# Quick examples of compare two dataframes row by row

# Example 1: Compare two DataFrames row by row
diff = df.compare(df1, align_axis = 0)

# Example 2: To ignore NaN values set keep_equal=True
diff = df.compare(df1, keep_equal=True, align_axis = 0)

# Example 3: Set keep_shape = true and keep same shape 
diff = df.compare(df1, keep_shape = True, align_axis = 0)

# Example 4: Get differences of DataFrames keep equal values and shape
diff = df.compare(df1, keep_equal=True, keep_shape = True, align_axis = 0)

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Syntax of Pandas df.compare()

Following is the syntax of the Pandas compare() function.

# Following is the syntax of compare() function
DataFrame.compare(other, align_axis=1, keep_shape=False, keep_equal=False, result_names=('self', 'other'))

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Parameters

Following are the parameters of the compare() function.

• Other: It is a DataFrame Object and is used to compare with a given DataFrame.
• align_axis: It defines the axis of comparison. The default value is 1 for columns. If it is set with 0 for rows.
• keep_shape: (bool), Default value is False. If it is True, all rows and columns exist along with different values. Otherwise, only different values exist.
• keep_equal :(bool) Default value is False. If it is True, keep all equal values instead of NaN values.
• result_names : (tuple): Default (‘self’, ‘other’)

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Return Value

It returns DataFrame where the elements are not matching of given DataFrames. Resulting in DataFrame having a multi-index with ‘self’ and ‘other’ are at the innermost level of the row index.

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Usage of Compare Two DataFrames Row by Row

The DataFrame.compare() function in Pandas allows you to compare two DataFrames and clearly display the differences between them.

Now, Let’s create Pandas DataFrame using data from a Python dictionary, where the columns are Courses, Fee, Duration and Discount.

# Create DataFrame
import pandas as pd
import pandas as pd
technologies = ({
    'Courses':["Spark", "NumPY", "pandas", "Java", "PySpark"],
    'Fee' :[20000,25000,30000,22000,26000],
    'Duration':['30days','40days','35days','60days','50days'],
    'Discount':[1000,2500,1500,1200,3000]
               })
technologies1 = ({
    'Courses':["Spark", "Hadoop", "pandas", "Java", "PySpark"],
    'Fee' :[20000,24000,30000,22000,21000],
    'Duration':['30days','40days','35days','60days','50days'],
    'Discount':[1000,2500,1500,1200,3000]
               })
df = pd.DataFrame(technologies)
print("DataFrame1:\n", df)
df1 = pd.DataFrame(technologies1)
print("DataFrame2:\n", df1)

Yields below output.

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Usage of Pandas DataFrame.compare() Function

Pandas DataFrame.compare() function compares two equal sizes and dimensions of DataFrames row by row along with align_axis = 0 and returns The DataFrame with unequal values of given DataFrames. By default, it compares the DataFrames column by column. If we want to get the same sized resulting DataFrame we can use its parameter keep_shape and use keep_equal param to avoid NaN values in the resulting DataFrame.

# Comparing the two DataFrames row by row
diff = df.compare(df1, align_axis = 0)
print(" After comparing two DataFrames:\n", diff)

Yields below output.

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Pass keep_equal into compare() & Compare

As we can see from the above, the resulting DataFrame has been obtained where equal values are treated as NaN values. So, overcome the NaN values by setting keep_equal as True then and pass into compare() function. It will override the NaN values with equal values of given DataFrames.

# Ignore NaN values pass keep_equal=True
diff = df.compare(df1, keep_equal=True, align_axis = 0)
print(" After comparing two DataFrames:\n", diff)

Yields below output.

# Output:
# After comparing two DataFrames:
         Courses    Fee
1 self     NumPy  25000
  other   Hadoop  24000
4 self   Pyspark  26000
  other  Pyspark  21000

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Pass keep_shape into compare() & Compare Pandas Row by Row

If we want to get the same-sized resulting DataFrame, we can set keep_shape as True and then pass it to the compare() function. It will return the same-sized DataFrame where equal values are treated as NaN values. For example,

# Set keep_shape = true and keep same shape 
diff = df.compare(df1, keep_shape = True, align_axis = 0)
print(" After comparing two DataFrames:\n", diff)

Yields below output.

# Output:
# After comparing two DataFrames:
        Courses      Fee Duration  Discount
0 self      NaN      NaN      NaN       NaN
  other     NaN      NaN      NaN       NaN
1 self    NumPy  25000.0      NaN       NaN
  other  Hadoop  24000.0      NaN       NaN
2 self      NaN      NaN      NaN       NaN
  other     NaN      NaN      NaN       NaN
3 self      NaN      NaN      NaN       NaN
  other     NaN      NaN      NaN       NaN
4 self      NaN  26000.0      NaN       NaN
  other     NaN  21000.0      NaN       NaN

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Pass keep_equal & keep_shape into compare()

Set keep_shape and keep_equal as True and pass them into the compare() function it will return the same-sized resulting DataFrame along with equal values of given DataFrames.

# Get differences of DataFrames keep equal values and shape
diff = df.compare(df1, keep_equal=True, keep_shape = True, align_axis = 0)
print(" After comparing two DataFrames:\n", diff)

Yields below output.

# Output:
# After comparing two DataFrames:
         Courses    Fee Duration  Discount
0 self     Spark  20000   30days      1000
  other    Spark  20000   30days      1000
1 self     NumPy  25000   40days      2500
  other   Hadoop  24000   40days      2500
2 self    pandas  30000   35days      1500
  other   pandas  30000   35days      1500
3 self      Java  22000   60days      1200
  other     Java  22000   60days      1200
4 self   Pyspark  26000   50days      3000
  other  Pyspark  21000   50days      3000

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FAQ on Compare Two DataFrames Row by Row

What is the purpose of the compare() method in Pandas?

The compare() method in Pandas is designed to compare two DataFrames and highlight the differences between them. It provides a convenient way to identify discrepancies in values and shapes between corresponding elements in the two DataFrames.

How does the compare() method display differences?

The compare() method displays differences in a tabular format, showing columns with hierarchical indexing. Each column has two sub-columns (‘self’ and ‘other’) to represent the values in the first and second DataFrames, respectively. Differences are highlighted by displaying the differing values, and equal values are shown as NaN.

How can I include rows with equal values in the result?

To include rows with equal values in the result when using the compare() method in Pandas, you need to set the keep_equal parameter to True. This parameter controls whether to include elements that have equal values in both DataFrames.

How can I include rows with differences in shape in the result?

To include rows with differences in shape in the result when using the compare() method in Pandas, you need to set the keep_shape parameter to True. This parameter controls whether to include elements that have different shapes in the two DataFrames.

Can I customize the behavior of the compare() method further?

You can customize the behavior by using additional parameters such as keep_equal, keep_shape, and keep_different. These parameters allow you to control which elements are included in the result based on your specific requirements.

Does the compare() method modify the original DataFrames?

The compare() method does not modify the original DataFrames. It returns a new DataFrame containing the comparison results, allowing you to analyze the differences without altering the original data.

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Conclusion

In this article, I have explained DataFrame.compare() function and using its syntax, and parameters how we can compare the two DataFrames row by row along with multiple examples

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Reference

• https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.compare.html