# numpy.find() in Python

There is no `numpy.find()` function in NumPy. However, there are several functions that can be used to find elements in a NumPy array.

• `numpy.where()`: This function returns the indices of elements in an array that satisfy a given condition. For example:
``````import numpy as np

arr = np.array([1, 2, 3, 4, 5])
indices = np.where(arr > 3)
print(indices)
``````

Output:

``````(array([3, 4]),)
``````
• `numpy.argwhere()`: This function is similar to `numpy.where()`, but it returns the indices of elements that are non-zero. For example:
``````import numpy as np

arr = np.array([0, 1, 0, 2, 3])
indices = np.argwhere(arr)
print(indices)
``````

Output:

``````array([,
,
])
``````
• `numpy.nonzero()`: This function returns the indices of elements that are non-zero. It is similar to `numpy.argwhere()`, but it returns a tuple of arrays, one for each dimension of the input array. For example:
``````import numpy as np

arr = np.array([[0, 1, 0], [2, 0, 3]])
indices = np.nonzero(arr)
print(indices)
``````

Output:

``````(array([0, 1, 1]), array([1, 0, 2]))
``````

This means that the non-zero elements of `arr` are at positions `(0, 1)`, `(1, 0)`, and `(1, 2)`.

• `numpy.searchsorted()`: This function finds the indices where elements should be inserted to maintain order in a sorted array. For example:
``````import numpy as np

arr = np.array([1, 3, 5, 7])
indices = np.searchsorted(arr, [2, 4, 6])
print(indices)
``````

Output:

``````array([1, 2, 3])
``````

This means that the elements `[2, 4, 6]` should be inserted at positions `1`, `2`, and `3` in the sorted array `arr`.

Note: If you meant `numpy.ndarray.find()` instead of `numpy.find()`, then it is worth noting that there is no such method in NumPy arrays. 