How to Efficiently Set a Specific Cell Value in a Pandas DataFrame?

Set Value for a Specific Cell in a Pandas DataFrame

You've created a DataFrame and want to change the value of a particular cell. However, using df.xs('C')['x'] = 10 doesn't update the DataFrame as expected. Why?

The problem stems from how df.xs() operates. By default, it returns a new DataFrame with a copy of the data. So, df.xs('C')['x'] = 10 modifies only the new DataFrame, not the original.

Instead, you can use df['x']['C'] = 10. This method returns a view of the original DataFrame, and any modifications will be applied to df.

However, the recommended approach is using .at or .iat:

• df.at['C', 'x'] = 10: Sets the value at row 'C' and column 'x' directly.
• df.iat[2, 0] = 10: Accesses the cell using its index position (2 for row, 0 for column).

Why is df.xs('C')['x'] = 10 deprecated?

df.set_value('C', 'x', 10) is the preferred method because it's significantly faster. However, df.set_value() is slated for deprecation.

Performance Comparison

Benchmarks show that df.set_value() outperforms other options in terms of speed:

In [18]: %timeit df.set_value('C', 'x', 10)
100000 loops, best of 3: 2.9 µs per loop

In [20]: %timeit df['x']['C'] = 10
100000 loops, best of 3: 6.31 µs per loop

In [81]: %timeit df.at['C', 'x'] = 10
100000 loops, best of 3: 9.2 µs per loop

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