The **NumPy `where()` method** tells you where, in a [NumPy array](https://www.educative.io/answers/how-to-create-an-array-in-numpy), the given condition is met. 

# Types of arguments

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## 1. Single argument

If the `where()` is called with a single argument, this argument is the *condition*. Such a function call returns an array of **indices**. 

### Examples:

1. Indices where the array elements fulfill the given condition​:

import numpy as np

arr = np.array([1,3,5,7,11]) # creating an ndarray

index_arr = np.where(arr < 6) #calling the where method

print(index_arr)

 2. An array of elements *rather* than indices:

arr = np.array([1,3,5,7,11]) # creating an ndarray

elements_arr = arr[np.where(arr < 6)]

print(elements_arr)

arr = np.array([[1,2,3],[11,22,33]])

index_multi_arr = np.where(arr < 15)

print(index_multi_arr)

## 2. Multiple arguments
The diagram below shows what multiple arguments represent in a `where()` method.

arr = np.array([1,2,3,4,5,6,7,8,9])

transformed_arr = np.where(arr<5, arr*10, 0)

print(transformed_arr)

# A common challenge
Most people who are new to NumPy find the notation, mentioned below, ​to be a bit confusing:


transformed_arr = np.where([True, False, True], [1,2,3], [10,20,30])

print(transformed_arr)

# Try to determine the output yourself before 
# executing the code or moving forward

transformed_arr = np.where([[True, False], [True, True]],
              [[1, 2], [3, 4]],
              [[10, 20], [30, 40]]) 

print(transformed_arr)

NumPy where() method

Identifies indices in NumPy arrays meeting conditions using single or multiple arguments.