How do you replace NaN values in a pandas DataFrame with column averages?

Replacing NaN Values in pandas DataFrame with Column Averages

Filling NaN values in a pandas DataFrame with the average of corresponding columns is a common task in data analysis. While numpy offers a straightforward approach for arrays, pandas DataFrames require a tailored solution.

Approach:

To replace NaN values in a DataFrame with column averages, we can use the DataFrame.fillna method:

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

Example:

Consider a DataFrame with NaN values:

<code class="python">import pandas as pd

df = pd.DataFrame({
    'A': [-0.166919, -0.297953, -0.120211, np.nan, np.nan, -0.788073, -0.916080, -0.887858, 1.948430, 0.019698],
    'B': [0.979728, -0.912674, -0.540679, -2.027325, np.nan, np.nan, -0.612343, 1.033826, 1.025011, -0.795876],
    'C': [-0.632955, -1.365463, -0.680481, 1.533582, 0.461821, np.nan, np.nan, np.nan, -2.982224, -0.046431]
})</code>

Calculating the mean of each column:

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

And finally, replacing the NaN values:

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

Result:

<code class="python">print(df_filled)

          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</code>

As seen in the output, the NaN values are successfully replaced with the average of their respective columns.

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