Python NumPy Tutorial – NumPy ndarray & NumPy Array

In our last Python Library tutorial, we studied Python SciPy. Now we are going to study Python NumPy.

In this NumPy tutorial, we are going to discuss the features, Installation and NumPy ndarray.

Moreover, we will cover the data types and array in NumPy. So, let’s begin the Python NumPy Tutorial.

Python NumPy Tutorial – NumPy ndarray & NumPy Array

What is NumPy?

A library for Python, NumPy lets you work with huge, multidimensional matrices and arrays.

Along with that, it provides a gamut of high-level functions to perform mathematical operations on these structures.

Here is a short brief about it:

• Author- Travis Oliphant
• First Release- 1995 (Released as Numeric; Changed to NumPy in 2006)
• Stable Release- June, 2018
• Written in- Python Programming, C

Python NumPy is cross-platform and BSD-licensed. We often use it with packages like Matplotlib and SciPy.

This can be seen as an alternative to MATLAB. The term ‘Numpy’ is a portmanteau of the words ‘NUMerical’ and ‘PYthon’.

Numpy Tutorial – Features of Numpy

In this Python NumPy Tutorial, we are going to study the feature of NumPy:

• NumPy stands on CPython, a non-optimizing bytecode interpreter.
• Multidimensional arrays.
• Functions and operators for these arrays.
• Python Alternative to MATLAB.
• ndarray- n-dimensional arrays.
• Fourier transforms and shapes manipulation.
• Linear algebra and random number generation.

Numpy Tutorial – How to Install NumPy?

You can use pip to install numpy-

pip install numpy

Then you can import it as-

>>> import numpy as np

Numpy Tutorial – NumPy ndarray

This is one of the most important features of numpy. ndarray is an n-dimensional array, a grid of values of the same kind.

A tuple of nonnegative integers indexes this tuple. An array’s rank is its number of dimensions.

Let’s take a few examples.

>>> a=np.array([1,2,3])
>>> type(a)

Output

>>> a.shape

Output

>>> a[0],a[2]

Output

>>> a[1]=5
>>> a

Output

As you can see, the array’s shape is (3,). What happens when we build an array of more than one dimension?

Let’s see.

>>> b=np.array([[2,7,9],[5,1,3]])
>>> b

Output

>>> b[0,1]

Output

>>> b.shape

Output

>>> b.size

Output

1. How to Create NumPy Array?

The following lines of code create a few more arrays:

>>> np.arange(7) #This is like range in Python

Output

>>> np.random.random((3,3)) #Fills in random values

Output

>>> np.ones((2,3))

Output

>>> np.zeros((1,2))

Output

>>> np.eye(3) #Identity matrix

Output

>>> np.full((3,2),7) #Matrix of constants

Output

>>> np.linspace(1,2,4) #4 values spaced evenly between, and including, 1 and 2.

Output

>>> np.empty([2,3]) #Empty array

Output

2. Some Parameters

>>> np.array([1,3,4],ndmin=3) #Minimum dimension

Output

>>> np.array([1,3,4],dtype=complex) #Data type

Output

Numpy Tutorial – Data Types

As we’ve said before, a NumPy array holds elements of the same kind.

If while creating a NumPy array, you do not specify the data type, NumPy will decide it for you.

We have the following data types-

bool_, int_, intc, intp, int8, int16, int32, int64, uint8, uint16, uint32, uint64, float_, float16, float32, float64, complex_, complex64, complex128

We can confirm:

>>> np.dtype(np.int32)

Output

>>> np.dtype('i4')

Output

>>> np.dtype('i8')

Output

Functions of NumPy Array

Let’s take a look at all that we can do to an array and what more we can find out about it.

>>> a=np.array([[1,2,3],[4,5,6]])
>>> a.reshape(3,2)

Output

>>> a.ndim #Number of array dimensions

Output

>>> np.array([[1,2,3],[4,5,6]]).itemsize #Length of each element in bytes

Output

>>> np.array([[1,2,3],[4,5,6]],dtype=np.int8).itemsize

Output

>>> a

Output

>>> a.flags

Output

Numpy Array Indexing

It is possible to slice you NumPy arrays- with multiple slices for multidimensional arrays.

>>> a=np.array([[1,2,3],[4,5,6],[7,8,9]])
>>> b=a[:2,1:3]
>>> b

Output

>>> a[1,2]

Output

>>> b[0,0]=79
>>> a

Output

As you can see, changes to a slice modify an original.

>>> a[1,:]

Output

>>> a[1:2,:]

Output

>>> a[:,1]

Output

Python NumPy Tutorial – NumPy Array Index

1. Integer Indexing

It is possible to create an array from another.

>>> a=np.array([[1,2],[3,4],[5,6]])
>>> a[[0,1,2],[0,1,0]] #Prints elements at [0,0], [1,1], and [2,0]

Output

Let’s pick elements-

>>> a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
>>> b = np.array([0, 2, 0, 1])
>>> b

Output

>>> a[np.arange(4), b]

Output

>>> a[np.arange(4), b]+=10
>>> a

Output

2. Boolean Indexing

This will let you pick elements that satisfy a condition.

>>> a=np.array([[1,2],[3,4],[5,6]])
>>> boolean=(a>3)
>>> boolean

Output

>>> a[boolean]

Output

Mathematical Functions on Arrays in NumPy

Let’s now look at some mathematical functions to call on arrays.

>>> a=np.array([[1,2,3],[4,5,6]])
>>> b=np.array([[7,8,9],[10,11,12]])
>>> np.add(a,b) #a+b does the same

Output

>>> np.subtract(a,b) #Same as a-b

Output

>>> np.multiply(a,b) #a*b works too

Output

>>> np.divide(a,b) #Same as a/b

Output

>>> np.sqrt(a) #Produces square root

Output

>>> a=np.array([[1,2],[3,4]])
>>> np.sum(a)

Output

>>> np.sum(a,axis=0) #Sum of each column

Output

>>> np.sum(a,axis=1) #Sum of each row

Output

To transpose this matrix:

>>> a.T

Output

>>> np.array([1,3,2]).T #NOP

Output

Some functions that operate on a larger context-

>>> x=np.array([[1,2],[3,4]])
>>> y=np.array([[5,6],[7,8]])
>>> v=np.array([9,10])
>>> w=np.array([11,12])
>>> v.dot(w) #Same as np.dot(v,w)

Output

>>> x.dot(v)

Output

>>> x.dot(y)

Output

So, this was all about Python NumPy Tutorial. Hope you like our explanation.

Python Interview Questions on NumPy

1. What is Python NumPy array?
2. What is the purpose of NumPy in Python?
3. What is the difference between Pandas and NumPy in Python?
4. How does NumPy work in Python?
5. What functions does NumPy provide in Python?

Conclusion

Hence, in this Python NumPy Tutorial we studied, how to install NumPy, NumPy ndarray.

In addition, we discussed NumPy Array with its Functions and data types. This sums it up for NumPy.