In Python lists serve the purpose of arrays, but they are slow to process.
It provides an array object that is faster than Python lists  which is called ndarray.
NumPy arrays are stored at one continuous place in memory so processes can access and manipulate them very efficiently.

Once NumPy is installed, import it by adding the import keyword:
Syntax: import numpy

We can create a NumPy ndarray object by using the array()function.
Converting a list to array using array() function.

Example:

import numpy as n
ar = n.array([1, 2, 3, 4, 5])
print(ar)

Converting tuple to array using array() function.
Example:
import numpy as n
ar = n.array((1, 2, 3, 4, 5))
print(ar)

0-D arrays are the elements in an array. Each value in an array is a 0-D array.

Example 0-D array:

import numpy as n
ar = n.array(42)
print(ar)

A One-Dimensional Array is the simplest form of an Array in which the elements are stored linearly. These are the most common and basic arrays.

Example of 1-D array:

import numpy as n
ar = n.array([1, 2, 3, 4, 5])
print(ar)

2D array can be defined as an array of arrays. The 2D array is organized as matrices which can be represented as the collection of rows and columns. These are often used to represent matrix. NumPy has a whole sub module dedicated towards matrix operations called numpy.mat

Example of a 2-D array:

import numpy as n
ar = n.array([[1, 2, 3], [4, 5, 6]])
print(ar)

A 3D array is a multi-dimensional array(array of arrays). A 3D array is a collection of 2D arrays.
Example of 3-D array:
import numpy as n
ar = n.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
print(ar)

To check the number of dimensions of an array, we can use ndim attribute.
ndim attribute returns an integer that tells us how many dimensions the array have.

Example of ndim attribute:

import numpy as np
a = np.array(42)
b = np.array([1, 2, 3, 4, 5])
print(a.ndim)
print(b.ndim)

You can access an array element by referring to its index number.

Example of accessing the elements in an array:

import numpy as n
ar = n.array([1, 2, 3, 4])
print(ar[0])

To access the elements from a 2-D array we can use comma separated integers representing the dimension and the index of the element.

Example to access elements of 2-D array:

import numpy as n
ar = n.array([[1,2,3,4,5], [6,7,8,9,10]])
print('2nd element on 1st row: ', ar[0, 1])

To access the elements from a 3-D array we can use comma separated integers representing the dimensions and the index of the element.

Example to access the elements from 3-D array:

import numpy as n
ar = n.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
print(ar[0, 1, 2])

NumPy has some extra data types, and refer to data types with one character.
Below is a list of all data types in NumPy and the characters used to represent them.

1. i - integer
2. b - boolean
3. u - unsigned integer
4. f - float
5. c - complex float
6. m - timedelta
7. M - datetime
8. O - object
9. S - string
10. U - unicode string
11. V - fixed chunk of memory for other type ( void )

We can check the data type of an array using dtype.
dtype returns the data type of the array:

Example to check the data type of an array:

import numpy as n
ar = n.array([4, 8, 2, 7])
print(ar.dtype)

We can also create a new array with pre defined data type using the dtype as an argument.
array() function can take an optional argument: dtype that allows us to define the expected data type of the array elements:

Example:

import numpy as n
ar = n.array([1, 2, 3, 4], dtype='S')
print(ar)
print(ar.dtype)

The best way to change the data type of an existing array, is to make a copy of the array with the astype() method.
The data type can be specified using a string.

Example:

We can change the data type from float to integer by using 'i' as parameter value:
import numpy as n
ar= n.array([1.1, 2.1, 3.1])
newar = ar.astype('i')
print(newar)
print(newar.dtype)

Example:
We can convert data type from int to bool.
import numpy as np
arr = np.array([1, 0, 3])
newarr = arr.astype(bool)
print(newarr)
print(newarr.dtype)

Example:

Make a copy of the original array, change value from original array and display both arrays:
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
x = arr.copy()
arr[0] = 42
print(arr)
print(x)

Example

Make a view of the original array, change the value from original array and display both arrays:
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
x = arr.view()
arr[0] = 42
print(arr)
print(x)

You can search an array for a certain value. To search an array, use the where() method.

Example to search the array for a certain value:

import numpy as p
ar = p.array([1, 2, 3, 4, 5, 4, 4])
x = p.where(ar == 4)
print(x)

To perform a binary search in array, we can use the searchsorted() method.
Example:
import numpy as np
arr = np.array([6, 7, 8, 9])
x = np.searchsorted(arr, 7)
print(x)

By default the left most index is returned, but we can give side='right' to return the right most index instead.

Example:

import numpy as np
arr = np.array([6, 7, 8, 9])
x = np.searchsorted(arr, 7, side='right')
print(x)

To search for more than one value, use an array with the specified values.

Example

import numpy as np
arr = np.array([1, 3, 5, 7])
x = np.searchsorted(arr, [2, 4, 6])
print(x)

Sorting means putting elements in an ordered sequence.
We can sort an array in numpy using the sort() function.

Example:

import numpy as np
arr = np.array([3, 2, 0, 1])
print(np.sort(arr))

When you use the sort() method on a 2-D array, both arrays will be sorted:

Example:

import numpy as np
arr = np.array([[3, 2, 4], [5, 0, 1]])
print(np.sort(arr))

Getting some filtered elements out of an existing array.
In NumPy, you can filter an array using a boolean index list.

Example:

import numpy as np
arr = np.array([41, 42, 43, 44])
x = [True, False, True, False]
newarr = arr[x]
print(newarr)

We can also create a new filtered array using conditions.

Example

import numpy as np
arr = np.array([41, 42, 43, 44])
filter_arr = []
for element in arr:
  if element > 42:
    filter_arr.append(True)
  else:
    filter_arr.append(False)
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)

We can substitute the array in our condition.

Example

import numpy as np
arr = np.array([41, 42, 43, 44])
filter_arr = arr > 42
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)