DataFrame.
dropna
Remove missing values.
Determine if rows or columns which contain missing values are removed.
0, or ‘index’ : Drop rows which contain missing values.
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.
Require that many non-NA values.
Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.
If True, do operation inplace and return None.
DataFrame with NA entries dropped from it.
See also
DataFrame.drop
Drop specified labels from columns.
DataFrame.isnull
Indicate missing values.
DataFrame.notnull
Indicate existing (non-missing) values.
Examples
>>> df = ps.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'], ... "toy": [None, 'Batmobile', 'Bullwhip'], ... "born": [None, "1940-04-25", None]}, ... columns=['name', 'toy', 'born']) >>> df name toy born 0 Alfred None None 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip None
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 None None 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip None
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 None
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