pandas.DataFrame.dropna

DataFrame.dropna(self, axis=0, how='any', thresh=None, subset=None, inplace=False)

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.

Deprecated since version 0.23.0: 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.

thresh : int, optional

Require that many non-NA values.

subset : array-like, optional

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

inplace : bool, default False

If True, do operation inplace and return None.

Returns:

DataFrame

DataFrame with NA entries dropped from it.

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', 'born'])
       name        toy       born
1    Batman  Batmobile 1940-04-25

Keep the DataFrame with valid entries in the same variable.

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