dropna(subset)_python中的head函数

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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

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原文：https://www.cnblogs.com/YingxuanZHANG/p/8807395.html