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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 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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