np.where与pd.Series.where，pd.DataFrame.where的用法及区别

np.where与pd.Series.where及pd.DataFrame用法不一样，下面一一进行学习，总结：

import numpy as np
import pandas as pd

help(np.where)

Help on built-in function where in module numpy.core.multiarray:

where(...)
where(condition, [x, y])

Return elements, either from x or y, depending on condition.

If only condition is given, return condition.nonzero().

Parameters
----------
condition : array_like, bool
When True, yield x, otherwise yield y.
x, y : array_like, optional
Values from which to choose. x, y and condition need to be
broadcastable to some shape.

Returns
-------
out : ndarray or tuple of ndarrays
If both x and y are specified, the output array contains
elements of x where condition is True, and elements from
y elsewhere.

If only condition is given, return the tuple
condition.nonzero(), the indices where condition is True.

See Also
--------
nonzero, choose

Notes
-----
If x and y are given and input arrays are 1-D, where is
equivalent to::

[xv if c else yv for (c,xv,yv) in zip(condition,x,y)]

Examples
--------
>>> np.where([[True, False], [True, True]],
... [[1, 2], [3, 4]],
... [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])

>>> np.where([[0, 1], [1, 0]])
(array([0, 1]), array([1, 0]))

>>> x = np.arange(9.).reshape(3, 3)
>>> np.where( x > 5 )
(array([2, 2, 2]), array([0, 1, 2]))
>>> x[np.where( x > 3.0 )] # Note: result is 1D.
array([ 4., 5., 6., 7., 8.])
>>> np.where(x < 5, x, -1) # Note: broadcasting.
array([[ 0., 1., 2.],
[ 3., 4., -1.],
[-1., -1., -1.]])

Find the indices of elements of x that are in goodvalues.

>>> goodvalues = [3, 4, 7]
>>> ix = np.isin(x, goodvalues)
>>> ix
array([[False, False, False],
[ True, True, False],
[False, True, False]])
>>> np.where(ix)
(array([1, 1, 2]), array([0, 1, 1]))

​

• np.where用法

从上面帮助信息可以看到：np.where的参数有condition，可选参数x,y。
而有无可选参数以及可选参数x,y的维数将直接影响np.where的返回结果：如果没有可选参数x,y则相当于np.nonzero，返回condition数组的True或者非0的包含索引列表对的元组；如果有x,y则输出的数组形状首先与condition，x,y的一致（如果不一致，则广播为一致）根据condition的值来从x,y中挑选值。

（1）无可选参数,x,y

a=np.random.randint(0,high=2,size=(3,3));a

array([[0, 1, 1],
[1, 1, 0],
[1, 1, 0]])

np.where(a)

(array([0, 0, 1, 1, 2, 2], dtype=int64),
array([1, 2, 0, 1, 0, 1], dtype=int64))

(2)有x,y，输出结果的形状是condition,x,y的广播后的数组的形状，然后根据condition从x,y中挑选。

cond=np.array([True,False])

x=np.arange(6).reshape(3,2);x

array([[0, 1],
[2, 3],
[4, 5]])

y=np.array([[100,200]])

cond.shape

(2,)

x.shape

(3, 2)

y.shape

(1, 2)

所以广播后的形状应该是（3，2）

result=np.where(cond,x,y);result

array([[ 0, 200],
[ 2, 200],
[ 4, 200]])

result.shape

(3, 2)

• pandas中的where

help(pd.DataFrame.where)

Help on function where in module pandas.core.generic:

where(self, cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=False, raise_on_error=None)
Return an object of same shape as self and whose corresponding
entries are from self where cond is True and otherwise are from
other.

Parameters
----------
cond : boolean NDFrame, array-like, or callable
Where cond is True, keep the original value. Where
False, replace with corresponding value from other.
If cond is callable, it is computed on the NDFrame and
should return boolean NDFrame or array. The callable must
not change input NDFrame (though pandas doesn't check it).

.. versionadded:: 0.18.1
A callable can be used as cond.

other : scalar, NDFrame, or callable
Entries where cond is False are replaced with
corresponding value from other.
If other is callable, it is computed on the NDFrame and
should return scalar or NDFrame. The callable must not
change input NDFrame (though pandas doesn't check it).

.. versionadded:: 0.18.1
A callable can be used as other.

inplace : boolean, default False
Whether to perform the operation in place on the data
axis : alignment axis if needed, default None
level : alignment level if needed, default None
errors : str, {'raise', 'ignore'}, default 'raise'
- raise : allow exceptions to be raised
- ignore : suppress exceptions. On error return original object

Note that currently this parameter won't affect
the results and will always coerce to a suitable dtype.

try_cast : boolean, default False
try to cast the result back to the input type (if possible),
raise_on_error : boolean, default True
Whether to raise on invalid data types (e.g. trying to where on
strings)

.. deprecated:: 0.21.0

Returns
-------
wh : same type as caller

Notes
-----
The where method is an application of the if-then idiom. For each
element in the calling DataFrame, if cond is True the
element is used; otherwise the corresponding element from the DataFrame
other is used.

The signature for :func:DataFrame.where differs from
:func:numpy.where. Roughly df1.where(m, df2) is equivalent to
np.where(m, df1, df2).

For further details and examples see the where documentation in
:ref:indexing <indexing.where_mask>.

Examples
--------
>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0 NaN
1 1.0
2 2.0
3 3.0
4 4.0

>>> s.mask(s > 0)
0 0.0
1 NaN
2 NaN
3 NaN
4 NaN

>>> s.where(s > 1, 10)
0 10.0
1 10.0
2 2.0
3 3.0
4 4.0

>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> m = df % 3 == 0
>>> df.where(m, -df)
A B
0 0 -1
1 -2 3
2 -4 -5
3 6 -7
4 -8 9
>>> df.where(m, -df) == np.where(m, df, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True
>>> df.where(m, -df) == df.mask(~m, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True

See Also
--------
:func:DataFrame.mask

​

从上面帮助信息可以看到:DataFrame和Series的where函数遵循的是if-then模式,即调用者(DataFrame,或者Series)中的元素对于在condition中为True的保留,为False的,用other填充(默认为nan),inplace默认为False,即返回一个与调用者形状一样的DataFrame或者Series,如果为True,则原地修改.其与mask方法正好相反.

• np.where与DataFrame或Series的where方法的区别:

(1)numpy中是模块级别的函数,numpy模块下ndarray对象并没有where方法;而pandas没有模块级别where方法,只能通过DataFrame,Series对象来调用

(2)np.where中condition可以是数组,布尔值,而pandas的DataFrame及Series的condition不仅可以是数组,布尔值,还可以是函数句柄;
(3)前者有对于condition为True的选择集合x,而后者遵循的是if-then模式,仅对condition为False情况给出其选择集合
(4)前者返回值的形状与condition,x,y有关,是三者广播后数组的形状;而后者返回值与调用者保持一致
(5)后者有inplace参数,可以决定是返回一个新的对象还是对调用者原地修改;而前者本身就是要重组一个数组,所以没有inplace这个参数.