Pandas
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chevron_leftHandling Missing Values
Adding missing dates in Datetime IndexChecking if a certain value in a DataFrame is NaNChecking if a DataFrame contains any missing valuesConverting a column with missing values to integer typeCounting non-missing valuesCounting number of rows with missing valuesCounting the number of NaN in each row of a DataFrameCounting number of NaN values in each column of a DataFrameCounting the total number of NaN values of a DataFrameFilling missing values using another columnFilling missing values with the mean of the columnFinding columns with missing valuesGetting integer indexes of rows with NaNGetting rows with missing valuesGetting rows with missing values in certain columnsGetting index of rows with missing values (NaNs)Getting index of rows without missing valuesMapping NaN values to 0 and non-NaN values to 1Mapping NaN values to False and non-NaN values to TrueRemoving columns where some rows contain missing valuesRemoving rows from a DataFrame with missing valuesReplacing all NaN values of a DataFrameReplacing all NaN values with zeros in a DataFrameReplacing missing valuesReplacing missing values with constantsReplacing NaN with blank stringReplacing NaNs for certain columnsReplacing NaNs with preceding valuesReplacing values with NaNsUsing interpolation to fill missing values
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Replacing missing values (NaNs) with preceding values in Pandas DataFrame
schedule Aug 12, 2023
Last updated local_offer
Tags Python●Pandas
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To replace NaN with preceding values, use the DataFrame's fillna(method="ffill") method.
Examples
Consider the following DataFrame:
df = pd.DataFrame({"A":[None,5,6],"B":[7,None,8],"C":[9,None,None]})df
A B C0 NaN 7.0 9.01 5.0 NaN NaN2 6.0 8.0 NaN
Replace NaN with preceding values in column
To fill NaN with the previous non-NaN value in the same column:
df.fillna(method="ffill") # or method="pad"
A B C0 NaN 7.0 9.01 5.0 7.0 9.02 6.0 8.0 9.0
Notice how we still have a NaN on the top-left. This is because there is no preceding value for that entry in that column.
Replace NaN with preceding values in row
To fill NaN with the previous non-NaN value in the same row, set axis=1 like so:
df.fillna(method="ffill", axis=1)
A B C0 NaN 7.0 9.01 5.0 5.0 5.02 6.0 8.0 8.0
Again, we end up with NaN on the top-left since it does not have any preceding value in that row.
Published by Isshin Inada
Edited by 0 others
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