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Pandas Series: fillna() function

Fill NA/NaN values using the specified method

The fillna() function is used to fill NA/NaN values using the specified method.

Syntax:

Series.fillna(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)
Pandas Series fillna image

Parameters:

Name Description Type/Default Value Required / Optional
value Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list. scalar, dict, Series, or DataFrame Required
method Method to use for filling holes in reindexed Series pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use next valid observation to fill gap. {‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}
Default Value: None
Required
axis Axis along which to fill missing values. {0 or ‘index’} Required
inplace If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame). bool
Default Value: False
Required
limit If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None. int
Default Value: None
Required
downcast A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible). dict
Default Value: None
Required

Returns: Series- Object with missing values filled.

Example:


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