pandas.DataFrame.dropna — pandas 0.24.2 documentation (original) (raw)

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. 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.

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.

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