Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas dataframe.bfill() is used to backward fill the missing values in the dataset. It will backward fill the NaN values that are present in the pandas dataframe.
Syntax: DataFrame.bfill(axis=None, inplace=False, limit=None, downcast=None)
Parameters:
axis : ‘rows’ or ‘columns’
inplace : boolean, default False
limit : integer value, No. of consecutivenacells to be populated.
Example #1: Use bfill() function to populate missing values na values in the dataframe across rows.
# importing pandas as pdimport pandas as pd # Creating a dataframe with "na" values. df = pd.DataFrame({"A":[None, 1, 2, 3, None, None], "B":[11, 5, None, None, None, 8], "C":[None, 5, 10, 11, None, 8]}) # Printing the dataframedf |
When axis='rows', then value in current na cells are filled from the corresponding value in the next row. If the next row is also na value then it won’t be populated.
# Fill across the rowdf.bfill(axis ='rows') |
Output :
Example #2: Use bfill() function to populate missing values na values in the dataframe across columns.
when axis='columns', then the current na cells will be filled from the value present in the next column in the same row. If the next column is also na cell then it won’t be filled.
# importing pandas as pdimport pandas as pd # Creating a dataframe with "na" values. df = pd.DataFrame({"A":[None, 1, 2, 3, None, None], "B":[11, 5, None, None, None, 8], "C":[None, 5, 10, 11, None, 8]}) # bfill values using values from next columndf.bfill(axis ='columns') |
Output :
Notice the 4th row. All values are na because the rightmost cell was originally na and there is no cell to its right from which it can populate itself. So, it could not populate the previous na cells as well.
