When only a single argument is supplied to numpy's `where`

function it returns the indices of the input array (the `condition`

) that evaluate as true (same behaviour as `numpy.nonzero`

). This can be used to extract the indices of an array that satisfy a given condition.

```
import numpy as np
a = np.arange(20).reshape(2,10)
# a = array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
# [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]])
# Generate boolean array indicating which values in a are both greater than 7 and less than 13
condition = np.bitwise_and(a>7, a<13)
# condition = array([[False, False, False, False, False, False, False, False, True, True],
# [True, True, True, False, False, False, False, False, False, False]], dtype=bool)
# Get the indices of a where the condition is True
ind = np.where(condition)
# ind = (array([0, 0, 1, 1, 1]), array([8, 9, 0, 1, 2]))
keep = a[ind]
# keep = [ 8 9 10 11 12]
```

If you do not need the indices, this can be achieved in one step using `extract`

, where you agian specify the `condition`

as the first argument, but give the `array`

to return the values from where the condition is true as the second argument.

```
# np.extract(condition, array)
keep = np.extract(condition, a)
# keep = [ 8 9 10 11 12]
```

Two further arguments `x`

and `y`

can be supplied to `where`

, in which case the output will contain the values of `x`

where the condition is `True`

and the values of `y`

where the condition is `False`

.

```
# Set elements of a which are NOT greater than 7 and less than 13 to zero, np.where(condition, x, y)
a = np.where(condition, a, a*0)
print(a)
# Out: array([[ 0, 0, 0, 0, 0, 0, 0, 0, 8, 9],
# [10, 11, 12, 0, 0, 0, 0, 0, 0, 0]])
```

For simple cases, you can filter data directly.

```
a = np.random.normal(size=10)
print(a)
#[-1.19423121 1.10481873 0.26332982 -0.53300387 -0.04809928 1.77107775
# 1.16741359 0.17699948 -0.06342169 -1.74213078]
b = a[a>0]
print(b)
#[ 1.10481873 0.26332982 1.77107775 1.16741359 0.17699948]
```

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