pandas.DataFrame.dropna¶
-
DataFrame.
dropna
(axis=0, how='any', thresh=None, 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.
Deprecated since version 0.23.0:: Pass tuple or list to drop on multiple
axes.
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