mars.dataframe.DataFrame.dropna#
- DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)#
Remove missing values.
See the User Guide for more on which values are considered missing, and how to work with missing data.
- 参数
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.
在 1.0.0 版更改: 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.
- 返回
DataFrame with NA entries dropped from it.
- 返回类型
参见
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.
实际案例
>>> import mars.dataframe as md >>> df = md.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'], ... "toy": [np.nan, 'Batmobile', 'Bullwhip'], ... "born": [md.NaT, md.Timestamp("1940-04-25"), ... md.NaT]}) >>> df.execute() 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().execute() name toy born 1 Batman Batmobile 1940-04-25
Drop the rows where all elements are missing.
>>> df.dropna(how='all').execute() 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).execute() 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']).execute() name toy born 1 Batman Batmobile 1940-04-25
Keep the DataFrame with valid entries in the same variable.
>>> df.dropna(inplace=True) >>> df.execute() name toy born 1 Batman Batmobile 1940-04-25