<\/span><\/h3>\n<\/p>\n
The fillna()<\/b> function in pandas allows us to replace NaN values with a specified value or with the result of a transformation. This function is quite versatile and provides several options for handling missing data.<\/p>\n
Let’s assume we have a pandas DataFrame named ‘df’ that contains NaN values. To replace these NaN values with a specific value, such as 0, we can use the following code:<\/p>\n
“`python
\ndf.fillna(0, inplace=True)
\n“`<\/p>\n
This code will replace all the NaN values in the DataFrame ‘df’ with the value 0. The ‘inplace=True’ parameter ensures that the changes are made directly to the original DataFrame.<\/p>\n
Note that using ‘inplace=True’ is optional. If we omit this parameter, a new DataFrame with the replaced NaN values will be returned, and the original DataFrame ‘df’ will remain unchanged.<\/p>\n
We can also replace NaN values with the mean, median, or mode of the respective column. To replace NaN values with the mean of each column, we can use the following code:<\/p>\n
“`python
\ndf.fillna(df.mean(), inplace=True)
\n“`<\/p>\n
This code replaces all the NaN values in the DataFrame ‘df’ with the mean value of each column.<\/p>\n
Similarly, we can fill NaN values with the median or mode by using the median()<\/b> or mode()<\/b> functions instead of mean()<\/b>.<\/p>\n