How to Replace NaN Values in a Pandas DataFrame Column?

Replacing NaN Values in a DataFrame Column

When working with pandas DataFrames, it is common to encounter missing values represented as NaN (Not a Number). Handling these values is crucial to ensure accurate data analysis and prevent errors. This article provides a comprehensive guide on how to replace NaN values in a DataFrame column.

Background

The following DataFrame contains a column named "Amount" with some NaN values:

       Date                  Amount
67    2012-09-30 00:00:00   65211
68    2012-09-09 00:00:00   29424
69    2012-09-16 00:00:00   29877
70    2012-09-23 00:00:00   30990
71    2012-09-30 00:00:00   61303
72    2012-09-09 00:00:00   71781
73    2012-09-16 00:00:00     NaN
74    2012-09-23 00:00:00   11072
75    2012-09-30 00:00:00  113702
76    2012-09-09 00:00:00   64731
77    2012-09-16 00:00:00     NaN

Using DataFrame.fillna() or Series.fillna()

The most straightforward method to replace NaN values is using the fillna() method. It allows you to specify a value or a function to fill the missing data:

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

This will replace all NaN values in the "Amount" column with 0.

Filling NaN Values with Specific Values

To fill NaN values with specific values, use:

df['Amount'].fillna({NaN: 100})

This will replace NaN values with 100.

Filling NaN Values Based on Other Columns

You can also fill NaN values based on values in other columns:

df['Amount'].fillna(df['Amount'].mean())

This will fill NaN values with the mean value of the "Amount" column.

The above is the detailed content of How to Replace NaN Values in a Pandas DataFrame Column?. For more information, please follow other related articles on the PHP Chinese website!