pandas.DataFrame.fillna#

DataFrame.fillna(value=None,*,method=None,axis=None,inplace=False,limit=None,downcast=<no_default>)[source]#

Fill NA/NaN values using the specified method.

Parameters:

valuescalar, dict, Series, or DataFrame

Value to use to fill holes (e.g. 0), alternately adict/Series/DataFrame of values specifying which value to use foreach index (for a Series) or column (for a DataFrame). Values notin the dict/Series/DataFrame will not be filled. This value cannotbe a list.

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

Method to use for filling holes in reindexed Series:

• ffill: propagate last valid observation forward to next valid.

• backfill / bfill: use next valid observation to fill gap.

Deprecated since version 2.1.0:Use ffill or bfill instead.

axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame

Axis along which to fill missing values. ForSeriesthis parameter is unused and defaults to 0.

inplacebool, default False

If True, fill in-place. Note: this will modify anyother views on this object (e.g., a no-copy slice for a column in aDataFrame).

limitint, default None

If method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there isa gap with more than this number of consecutive NaNs, it will onlybe partially filled. If method is not specified, this is themaximum number of entries along the entire axis where NaNs will befilled. Must be greater than 0 if not None.

downcastdict, 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 appropriateequal type (e.g. float64 to int64 if possible).

Deprecated since version 2.2.0.

Returns:

Series/DataFrame or None

Object with missing values filled or None ifinplace=True.

See also

ffill

Fill values by propagating the last valid observation to next valid.

bfill

Fill values by using the next valid observation to fill the gap.

interpolate

Fill NaN values using interpolation.

reindex

Conform object to new index.

asfreq

Convert TimeSeries to specified frequency.

Examples

>>>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    D0  NaN  2.0 NaN  0.01  3.0  4.0 NaN  1.02  NaN  NaN NaN  NaN3  NaN  3.0 NaN  4.0

Replace all NaN elements with 0s.

>>>df.fillna(0)     A    B    C    D0  0.0  2.0  0.0  0.01  3.0  4.0  0.0  1.02  0.0  0.0  0.0  0.03  0.0  3.0  0.0  4.0

Replace all NaN elements in column ‘A’, ‘B’, ‘C’, and ‘D’, with 0, 1,2, and 3 respectively.

>>>values={"A":0,"B":1,"C":2,"D":3}>>>df.fillna(value=values)     A    B    C    D0  0.0  2.0  2.0  0.01  3.0  4.0  2.0  1.02  0.0  1.0  2.0  3.03  0.0  3.0  2.0  4.0

Only replace the first NaN element.

>>>df.fillna(value=values,limit=1)     A    B    C    D0  0.0  2.0  2.0  0.01  3.0  4.0  NaN  1.02  NaN  1.0  NaN  3.03  NaN  3.0  NaN  4.0

When filling using a DataFrame, replacement happens alongthe same column names and same indices

>>>df2=pd.DataFrame(np.zeros((4,4)),columns=list("ABCE"))>>>df.fillna(df2)     A    B    C    D0  0.0  2.0  0.0  0.01  3.0  4.0  0.0  1.02  0.0  0.0  0.0  NaN3  0.0  3.0  0.0  4.0

Note that column D is not affected since it is not present in df2.