Prevent 'NA' from being interpreted as NaN in pandas

When working with pandas, it is common to encounter missing or null values represented as 'NA'. However, by default pandas interprets 'NA' as NaN (Not a Number), which can cause unexpected behavior in your data analysis.

To prevent 'NA' from being interpreted as NaN, you can use the keep_default_na parameter in the read_csv function to specify which values should be treated as null. Set keep_default_na to False to treat 'NA' as a valid value instead of NaN.

import pandas as pd
df = pd.read_csv('data.csv', keep_default_na=False)

If you already have a pandas DataFrame with 'NA' values interpreted as NaN, you can use the replace function to replace NaN values with 'NA' values:

import pandas as pd
import numpy as np
df = pd.DataFrame({'col1': [1, 2, np.nan], 'col2': ['NA', 'foo', 'bar']})
df = df.replace(np.nan, 'NA')

By setting np.nan to 'NA', all NaN values in the DataFrame will be replaced with 'NA'.

Using these techniques, you can ensure that 'NA' values are treated as expected in your pandas data analysis.

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