modin.pandas.DataFrame.dropna¶

DataFrame.dropna(*, axis: Axis = 0, how: str | NoDefault = _NoDefault.no_default, thresh: int | NoDefault = _NoDefault.no_default, subset: IndexLabel = None, inplace: bool = False)[source]¶

Remove missing values.

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.

• thresh (int, optional) – Require that many non-NA values. Cannot be combined with how.

• subset (column 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.

• inplace (bool, default False) – Whether to modify the DataFrame rather than creating a new one.

Returns:

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

Return type:

DataFrame

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": [None, 'Batmobile', 'Bullwhip'],
...                    "born": [pd.NaT, pd.Timestamp("1940-04-25"),
...                             pd.NaT]})
>>> df
       name        toy       born
0    Alfred       None        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

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Drop the rows where at least one element is missing.

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

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Drop the rows where all elements are missing.

>>> df.dropna(how='all')
       name        toy       born
0    Alfred       None        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

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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

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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

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Keep the DataFrame with valid entries in the same variable.

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

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