pandas.DataFrame.dropna

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

Return object with labels on given axis omitted where alternately any or all of the data are missing

Parameters:

axis : {0 or ‘index’, 1 or ‘columns’}, or tuple/list thereof

Pass tuple or list to drop on multiple axes

how : {‘any’, ‘all’}

• any : if any NA values are present, drop that label
• all : if all values are NA, drop that label

thresh : int, default None

int value : require that many non-NA values

subset : array-like

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

inplace : boolean, default False

If True, do operation inplace and return None.

Returns:

dropped : DataFrame

Examples

>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1],
...                    [np.nan, np.nan, np.nan, 5]],
...                   columns=list('ABCD'))
>>> df
     A    B   C  D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1
2  NaN  NaN NaN  5

Drop the columns where all elements are nan:

>>> df.dropna(axis=1, how='all')
     A    B  D
0  NaN  2.0  0
1  3.0  4.0  1
2  NaN  NaN  5

Drop the columns where any of the elements is nan

>>> df.dropna(axis=1, how='any')
   D
0  0
1  1
2  5

Drop the rows where all of the elements are nan (there is no row to drop, so df stays the same):

>>> df.dropna(axis=0, how='all')
     A    B   C  D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1
2  NaN  NaN NaN  5

Keep only the rows with at least 2 non-na values:

>>> df.dropna(thresh=2)
     A    B   C  D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1