pandas.DataFrame.dropna#

DataFrame.dropna(*, axis=0, how=_NoDefault.no_default, thresh=_NoDefault.no_default, subset=None, inplace=False, ignore_index=False)[source]#

Remove missing values.

See the User Guide for more on which values are considered missing, and how to work with missing data.

Parameters:
axis{0 or ‘index’, 1 or ‘columns’}, default 0

Determine if rows or columns which contain missing values are removed.

  • 0, or ‘index’ : Drop rows which contain missing values.

  • 1, or ‘columns’ : Drop columns which contain missing value.

Only a single axis is allowed.

how{‘any’, ‘all’}, default ‘any’

Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.

  • ‘any’ : If any NA values are present, drop that row or column.

  • ‘all’ : If all values are NA, drop that row or column.

threshint, optional

Require that many non-NA values. Cannot be combined with how.

subsetcolumn label or sequence of labels, optional

Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.

inplacebool, default False

Whether to modify the DataFrame rather than creating a new one.

ignore_indexbool, default False

If True, the resulting axis will be labeled 0, 1, …, n - 1.

New in version 2.0.0.

Returns:
DataFrame or None

DataFrame with NA entries dropped from it or None if inplace=True.

See also

DataFrame.isna

Indicate missing values.

DataFrame.notna

Indicate existing (non-missing) values.

DataFrame.fillna

Replace missing values.

Series.dropna

Drop missing values.

Index.dropna

Drop missing indices.

Examples

>>> df = pd.DataFrame(
...     {
...         "name": ["Alfred", "Batman", "Catwoman"],
...         "toy": [np.nan, "Batmobile", "Bullwhip"],
...         "born": [pd.NaT, pd.Timestamp("1940-04-25"), pd.NaT],
...     }
... )
>>> df
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Drop the rows where at least one element is missing.

>>> df.dropna()
     name        toy       born
1  Batman  Batmobile 1940-04-25

Drop the columns where at least one element is missing.

>>> df.dropna(axis="columns")
       name
0    Alfred
1    Batman
2  Catwoman

Drop the rows where all elements are missing.

>>> df.dropna(how="all")
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep only the rows with at least 2 non-NA values.

>>> df.dropna(thresh=2)
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Define in which columns to look for missing values.

>>> df.dropna(subset=["name", "toy"])
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT