pandas.DataFrame.dropna — pandas 2.2.3 documentation (original) (raw)

DataFrame.dropna(*, axis=0, how=<no_default>, thresh=<no_default>, subset=None, inplace=False, ignore_index=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.

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.

threshint, optional

Require that many non-NA values. Cannot be combined with how.

subsetcolumn 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.

inplacebool, default False

Whether to modify the DataFrame rather than creating a new one.

ignore_indexbool, default False

If True, the resulting axis will be labeled 0, 1, …, n - 1.

Added in version 2.0.0.

Returns:

DataFrame or None

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

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', 'toy']) name toy born 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT