pandas.DataFrame.dropna¶

DataFrame.dropna(axis=0, how=_NoDefault.no_default, thresh=_NoDefault.no_default, subset=None, inplace=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.

Changed in version 1.0.0: Pass tuple or list to drop on multiple axes. 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.

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

Keep the DataFrame with valid entries in the same variable.

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

pandas.DataFrame.droplevel

pandas.DataFrame.duplicated