fillna(self, value: 'object | ArrayLike | None' = None, method: 'FillnaOptions | None' = None, axis: 'Axis | None' = None, inplace: 'bool' = False, limit=None, downcast=None) -> 'DataFrame | None'
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.
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.
Axis along which to fill missing values.
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).
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.
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).
Object with missing values filled or None if inplace=True
.
Fill NA/NaN values using the specified method.
asfreq
Convert TimeSeries to specified frequency.
interpolate
Fill NaN values using interpolation.
reindex
Conform object to new index.
>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
... [3, 4, np.nan, 1],
... [np.nan, np.nan, np.nan, np.nan],
... [np.nan, 3, np.nan, 4]],
... columns=list("ABCD"))
... df A B C D 0 NaN 2.0 NaN 0.0 1 3.0 4.0 NaN 1.0 2 NaN NaN NaN NaN 3 NaN 3.0 NaN 4.0
Replace all NaN elements with 0s.
This example is valid syntax, but we were not able to check execution>>> df.fillna(0) A B C D 0 0.0 2.0 0.0 0.0 1 3.0 4.0 0.0 1.0 2 0.0 0.0 0.0 0.0 3 0.0 3.0 0.0 4.0
We can also propagate non-null values forward or backward.
This example is valid syntax, but we were not able to check execution>>> df.fillna(method="ffill") A B C D 0 NaN 2.0 NaN 0.0 1 3.0 4.0 NaN 1.0 2 3.0 4.0 NaN 1.0 3 3.0 3.0 NaN 4.0
Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1, 2, and 3 respectively.
This example is valid syntax, but we were not able to check execution>>> values = {"A": 0, "B": 1, "C": 2, "D": 3}
... df.fillna(value=values) A B C D 0 0.0 2.0 2.0 0.0 1 3.0 4.0 2.0 1.0 2 0.0 1.0 2.0 3.0 3 0.0 3.0 2.0 4.0
Only replace the first NaN element.
This example is valid syntax, but we were not able to check execution>>> df.fillna(value=values, limit=1) A B C D 0 0.0 2.0 2.0 0.0 1 3.0 4.0 NaN 1.0 2 NaN 1.0 NaN 3.0 3 NaN 3.0 NaN 4.0
When filling using a DataFrame, replacement happens along the same column names and same indices
This example is valid syntax, but we were not able to check execution>>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
... df.fillna(df2) A B C D 0 0.0 2.0 0.0 0.0 1 3.0 4.0 0.0 1.0 2 0.0 0.0 0.0 NaN 3 0.0 3.0 0.0 4.0
Note that column D is not affected since it is not present in df2.
See :The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.groupby.groupby.GroupBy.backfill
pandas.core.groupby.groupby.GroupBy.pad
pandas.core.groupby.groupby.GroupBy.ffill
pandas.core.indexes.base.Index.fillna
pandas.core.groupby.groupby.GroupBy.bfill
pandas.core.frame.DataFrame.replace
pandas.core.frame.DataFrame.dropna
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