How Can I Replace NaN Values in a Pandas DataFrame Column?

Replacement of NaN Values in Dataframe Column

Encountering NaN (Not-a-Number) values in a dataframe column can lead to errors when applying functions. To address this, Pandas provides a convenient solution using either DataFrame.fillna() or Series.fillna().

Example:

Consider a Pandas Dataframe with NaN values in the "Amount" column:

import pandas as pd

df = pd.DataFrame({
    "itm": [420, 421, 421, 421, 421, 485, 485, 485, 485, 489, 489],
    "Date": ['2012-09-30', '2012-09-09', '2012-09-16', '2012-09-23', '2012-09-09', '2012-09-16', '2012-09-23', '2012-09-30', '2012-09-09', '2012-09-16'],
    "Amount": [65211, 29424, 29877, 30990, 61303, 71781, np.nan, 11072, 113702, 64731, np.nan]
})

To replace the NaN values in the "Amount" column with a specific value, use fillna():

df["Amount"] = df["Amount"].fillna(0)

Alternatively, you can pass a dictionary with the desired replacement values for specific columns:

df = df.fillna({
    "Amount": 0
})

This will replace all NaN values in the "Amount" column with 0. If you want to replace NaN values with a different value or values, simply specify the desired replacement in the dictionary.

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