How to Replace Blank Values with NaN in Pandas DataFrames?

Replacing Blank Values with NaN in Pandas

In Pandas dataframes, it is often necessary to identify and replace whitespace values (" ") with NaN values. This operation ensures data integrity and enables seamless analysis.

One efficient way to accomplish this is through the Pandas replace() method. Its syntax is as follows:

<code class="python">df.replace(to_replace, value, regex=True)</code>

In this context:

• to_replace is the regular expression pattern representing whitespace values (e.g., r'^s ').
• value is the desired replacement value, which can be NaN.
• regex is set to True to enable regular expression matching.

To replace blank values with NaN, you can use the following code:

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

# Create a sample dataframe
df = pd.DataFrame({
    "A": [-0.532681, 1.490752, -1.387326, 0.814772, -0.222552, -1.176781],
    "B": ['foo', 'bar', 'foo', 'baz', ' ','qux'],
    "C": [0, 1, 2, ' ', 4, ' ']
})

# Replace blank values with NaN
df = df.replace(r'^\s*$', np.nan, regex=True)

# Display the updated dataframe
print(df)</code>

Output:

          A     B     C
0 -0.532681   foo     0
1  1.490752   bar     1
2 -1.387326   foo     2
3  0.814772   baz   NaN
4 -0.222552   NaN     4
5 -1.176781   qux   NaN

This code effectively replaces all blank values in the dataframe with NaN, providing a cleaner and more accurate representation of your data.

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