# numpy.where(): Process elements depending on conditions

Using `numpy.where()`

, elements of the NumPy array `ndarray`

that satisfy the conditions can be replaced or performed specified processing.

This post describes the following contents.

- Overview of
`np.where()`

- Multiple conditions
- Replace the elements that satisfy the condition
- Process the elements that satisfy the condition
- Get the indices of the elements that satisfy the condition

If you want to extract or delete elements, rows and columns that satisfy the conditions, see the following post.

## Overview of np.where()

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

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

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

numpy.where — NumPy v1.14 Manual

`np.where()`

is a function that returns `ndarray`

which is `x`

if `condition`

is `True`

and `y`

if `False`

. `x`

, `y`

and `condition`

need to be broadcastable to same shape.

If `x`

and`y`

are omitted, `index`

is returned. This will be described later.

```
import numpy as np
a = np.arange(9).reshape((3, 3))
print(a)
# [[0 1 2]
# [3 4 5]
# [6 7 8]]
print(np.where(a < 4, -1, 100))
# [[ -1 -1 -1]
# [ -1 100 100]
# [100 100 100]]
```

The `bool`

value `ndarray`

can be obtained by a conditional expression including `ndarray`

without using `np.where()`

.

```
print(a < 4)
# [[ True True True]
# [ True False False]
# [False False False]]
```

## Multiple conditions

If each conditional expression is enclosed in `()`

and `&`

or `|`

is used, processing is applied to multiple conditions.

```
print(np.where((a > 2) & (a < 6), -1, 100))
# [[100 100 100]
# [ -1 -1 -1]
# [100 100 100]]
print(np.where((a > 2) & (a < 6) | (a == 7), -1, 100))
# [[100 100 100]
# [ -1 -1 -1]
# [100 -1 100]]
```

Even in the case of multiple conditions, it is not necessary to use `np.where()`

to obtain `bool`

value `ndarray`

.

```
print((a > 2) & (a < 6))
# [[False False False]
# [ True True True]
# [False False False]]
print((a > 2) & (a < 6) | (a == 7))
# [[False False False]
# [ True True True]
# [False True False]]
```

## Replace the elements that satisfy the condition

It is also possible to replace elements with an arbitrary value only when the condition is satisfied or only when the condition is not satisfied.

If you pass the original `ndarray`

to `x`

and `y`

, the original value is used as it is.

```
print(np.where(a < 4, -1, a))
# [[-1 -1 -1]
# [-1 4 5]
# [ 6 7 8]]
print(np.where(a < 4, a, 100))
# [[ 0 1 2]
# [ 3 100 100]
# [100 100 100]]
```

Note that `np.where()`

returns a new `ndarray`

, and the original `ndarray`

is unchanged.

```
a_org = np.arange(9).reshape((3, 3))
print(a_org)
# [[0 1 2]
# [3 4 5]
# [6 7 8]]
a_new = np.where(a_org < 4, -1, a_org)
print(a_new)
# [[-1 -1 -1]
# [-1 4 5]
# [ 6 7 8]]
print(a_org)
# [[0 1 2]
# [3 4 5]
# [6 7 8]]
```

If you want to update the original `ndarray`

itself, you can write:

```
a_org[a_org < 4] = -1
print(a_org)
# [[-1 -1 -1]
# [-1 4 5]
# [ 6 7 8]]
```

## Process the elements that satisfy the condition

Instead of the original `ndarray`

, you can also specify the result of the operation (calculation) as `x`

, `y`

.

```
print(np.where(a < 4, a * 10, a))
# [[ 0 10 20]
# [30 4 5]
# [ 6 7 8]]
```

## Get the indices of the elements that satisfy the condition

If `x`

and `y`

are omitted, the indices of the elements satisfying the condition is returned.

A tuple of an array of indices (row number, column number) that satisfy the condition for each dimension (row, column) is returned.

```
print(np.where(a < 4))
# (array([0, 0, 0, 1]), array([0, 1, 2, 0]))
print(type(np.where(a < 4)))
# <class 'tuple'>
```

In this case, it means that the elements at `[0, 0]`

, `[0, 1]`

, `[0, 2]`

and `[1, 0]`

satisfy the condition.

It is also possible to obtain a list of each coordinate by using `list()`

, `zip()`

and `*`

as follows.

```
print(list(zip(*np.where(a < 4))))
# [(0, 0), (0, 1), (0, 2), (1, 0)]
```

The same applies to multi-dimensional arrays of three or more dimensions.

```
a_3d = np.arange(24).reshape(2, 3, 4)
print(a_3d)
# [[[ 0 1 2 3]
# [ 4 5 6 7]
# [ 8 9 10 11]]
#
# [[12 13 14 15]
# [16 17 18 19]
# [20 21 22 23]]]
print(np.where(a_3d < 5))
# (array([0, 0, 0, 0, 0]), array([0, 0, 0, 0, 1]), array([0, 1, 2, 3, 0]))
print(list(zip(*np.where(a_3d < 5))))
# [(0, 0, 0), (0, 0, 1), (0, 0, 2), (0, 0, 3), (0, 1, 0)]
```

The same applies to one-dimensional arrays. Note that using `list()`

, `zip()`

, and `*`

, each element in the resulting list is a tuple with one element.

```
a_1d = np.arange(6)
print(a_1d)
# [0 1 2 3 4 5]
print(np.where(a_1d < 3))
# (array([0, 1, 2]),)
print(list(zip(*np.where(a_1d < 3))))
# [(0,), (1,), (2,)]
```

If you know that it is one-dimensional, you can use the first element of the result of `np.where()`

as it is. In this case, it will be a `ndarray`

with an integer `int`

as an element, not a tuple with one element. If you want to convert to a list, use `tolist()`

.

```
print(np.where(a_1d < 3)[0])
# [0 1 2]
print(np.where(a_1d < 3)[0].tolist())
# [0, 1, 2]
```

The number of dimensions can be obtained with the `ndim`

attribute.