How to Replace Missing Values in Pandas DataFrames with Column Averages?

Replacing NaN Values with Column Averages in Pandas DataFrames

When working with pandas DataFrames, encountering NaN (missing) values is common. To effectively handle these values, it is crucial to replace them with appropriate values. One efficient way is to replace NaN values with the average of their respective columns.

Solution Using DataFrame.fillna

Unlike the approach mentioned in the referenced question, pandas DataFrames can be handled differently. The DataFrame.fillna method provides a straightforward solution for filling NaN values:

<code class="python">df.fillna(df.mean())</code>

Detailed Explanation:

• The df.mean() function calculates the average of each column in the DataFrame.
• The fillna method takes the calculated averages and fills the NaN values in each column with the corresponding average.

Example:

Let's consider the following DataFrame:

          A         B         C
0 -0.166919  0.979728 -0.632955
1 -0.297953 -0.912674 -1.365463
2 -0.120211 -0.540679 -0.680481
3       NaN -2.027325  1.533582
4       NaN       NaN  0.461821
5 -0.788073       NaN       NaN
6 -0.916080 -0.612343       NaN
7 -0.887858  1.033826       NaN
8  1.948430  1.025011 -2.982224
9  0.019698 -0.795876 -0.046431

After applying the fillna method with averages:

          A         B         C
0 -0.166919  0.979728 -0.632955
1 -0.297953 -0.912674 -1.365463
2 -0.120211 -0.540679 -0.680481
3 -0.151121 -2.027325  1.533582
4 -0.151121 -0.231291  0.461821
5 -0.788073 -0.231291 -0.530307
6 -0.916080 -0.612343 -0.530307
7 -0.887858  1.033826 -0.530307
8  1.948430  1.025011 -2.982224
9  0.019698 -0.795876 -0.046431

As demonstrated, the NaN values have been replaced with the corresponding column averages.

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