** NumPy split() Function:**

**Numpy split array: **The split() function of the NumPy module splits an array into numerous(multiple) sub-arrays as views into the given array.

The split() function is used to build an nd-array from nested lists of splits.

**Syntax:**

numpy.split(ary, indices_or_sections, axis=0)

**Parameters**

**ary:** This is required. It is the input array to be split into multiple sub-arrays.

**indices_or_sections:** This is required. It specifies the indices or sections as an int or a 1-D array.

- If indices_or_sections is an integer, N, the array is split into N equal arrays along the axis. An error is raised if such a split is not possible.
- If indices_or_sections is a 1-D array of sorted integers, the entries indicate where the array is split along the axis. For example, for axis=0, [2, 3] would yield: ary[:2] ary[2:3] ary[3:]

If an index exceeds the array’s dimension along the axis, an empty sub-array is returned.

**axis:** This is optional. The axis along which to split, which is set to 0 by default.

**Return Value: **

**Numpy split array: **A list of sub-arrays as views into the given ary is returned.

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## NumPy split() Function in Python

**Example1**

The split() function is used to divide(split) an array.

**Approach:**

- Import numpy module using the import keyword.
- Pass some random list(multi-dimensional) as an argument to the array() function to create an array.
- Store it in a variable.
- Print the above-given array.
- Pass the given array, indices value(here it is 3) as an argument to the split() function to split the given array.
- Store it in another variable.
- Print the above split Array.
- The Exit of the Program.

**Below is the implementation:**

# Import numpy module using the import keyword import numpy as np # Pass some random list(multi-dimensional) as an argument to the array() function to # create an array. # Store it in a variable. gvn_arry = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # Print the above given array. print("The above given array is:") print(gvn_arry) # Pass the given array, indices value(here it is 3) as an argument to the split()function to # split the given array # Store it in another variable. splt_arry = np.split(gvn_arry, 3) # Print the above split Array print("The above split Array is:") print(splt_arry)

**Output:**

The above given array is: [[1 2 3] [4 5 6] [7 8 9]] The above split Array is: [array([[1, 2, 3]]), array([[4, 5, 6]]), array([[7, 8, 9]])]

**Example2**

When indices_or_sections is given as a 1-D array of sorted integers, the entries specify where the array is split along the axis. With example, for axis=0, [2, 3] would yield: array[:2], array[2:3], array[3:].

**Below is the implementation:**

# Import numpy module using the import keyword import numpy as np # Pass some random list(multi-dimensional) as an argument to the array() function to # create an array. # Store it in a variable. gvn_arry = np.array([[11, 12, 13, 14], [15, 16, 17, 18], [19, 20, 21, 22]]) # Print the above given array. print("The above given array is:") print(gvn_arry) # Pass the given array, indices value as 1D array as an argument to the split() to # split the array # Store it in another variable. splt_arry = np.split(gvn_arry, [2,3]) # Print the above split Array print("The above split Array is:") print(splt_arry)

**Output:**

The above given array is: [[11 12 13 14] [15 16 17 18] [19 20 21 22]] The above split Array is: [array([[11, 12, 13, 14], [15, 16, 17, 18]]), array([[19, 20, 21, 22]]), array([], shape=(0, 4), dtype=int64)]