Fill NA/NaN values using the specified method.

param value
scalar, dict, Series, or DataFrame

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.

param method
{‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}, default None

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.

param axis
{0 or ‘index’}

Axis along which to fill missing values.

param inplace
bool, default False

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).

param limit
int, default None

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.

param downcast
dict, default is 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).


Series Object with missing values filled.


  • Parameters method, limit and downcast are currently unsupported by Intel Scalable Dataframe Compiler.

  • Parameter inplace is supported as literal value only.


Fill NA/NaN values using the specified method.
import numpy as np
import pandas as pd
from numba import njit

def series_fillna():
    s = pd.Series([4, np.nan, 2, 1])

    return s.fillna(0)

$ python ./series/
0    4.0
1    0.0
2    2.0
3    1.0
dtype: float64

See also


Fill NaN values using interpolation.


Conform object to new index.


Convert TimeSeries to specified frequency.