Numpy

NumPy Introductio

NumPy stands for?‘Numerical Python’. It is a?package?in Python to work with?arrays. It is a?basic scientific library. Its most important feature is the?n-dimensional array object. It has uses in?statistical functions,?linear algebra,?arithmetic operations,?bitwise operations, etc.

We perform all the?operations?on the?array elements. We can?initialize?these?arrays?in several ways.

Prerequisite to Learn NumPy

The?two basic prerequisites?for NumPy are?Python?and?Mathematics. We need to know the?python basics?to work with the?NumPy module.

The functions available in?NumPy?are?built?on?python language. We can hence combine the knowledge of?python arrays?and?list?for?array initialization?and?operations.

NumPy Installation

We can install Python NumPy by going to the?command prompt?and?typing?a?simple command?pip?install NumPy. Then go to the?IDE?and use the?import command import NumPy?as?np.

We can now?access?all the?functionalities?of the?NumPy module.

Uses of NumPy

NumPy is one of the most useful?external libraries?available in Python. It has a wide variety of?functions?to work with?arrays?and a?powerful multi-dimensional array object. It has?operations?that are?applicable?to a?vast range?of?platforms.

Numpy can be put to use for?storing,?manipulation, and?deletion?of?array elements. We can use it for?sorting,?indexing, and?stacking?of the?array elements. It has modules regarding various operations:

Arithmetic operations
Statistical Operations
Bitwise Operators
Linear Algebra
Copying and viewing arrays
Stacking
Searching, Sorting, and counting, etc.
Mathematical Operations
Broadcasting
Matplotlib for graphical representations
Matrix Operations, etc.
NumPy vs. Python arrays

The?NumPy library?is a great?alternative?to?python arrays. The difference is that the NumPy arrays are?homogeneous?that makes it?easier?to work with. We can?initialize?the?array elements?in many ways, one being which is through the?python lists.

The NumPy arrays are convenient as they have the following?three features–

Less Memory Requirement
Faster Processing
Convenience of use
Data types in NumPy

Numpy?supports?more data types?as compared to Python. These data types are instances of?dtype objects. Some of the?scalar data?types?are given in the table below.

Sr.No.	Data Types	Description
1.	bool_	Boolean True/False
2.	int_	Integer type
3.	intc	Same as C int
4.	intp	An integer used for indexing
5.	int8	Byte(-128 to 127)
6.	int16	Integer(-32768 to 32767)
7.	int32	Integer(-2147483648 to 2147483647)
8.	int64	Integer (-9223372036854775808 to 9223372036854775807)
9.	uint8	Unsigned integer(0 to 225)
10.	unit16	Unsigned integer(0 to 65535)
11.	unit32	Unsigned Integer(0 to 4294967295)
12.	unit64	Unsigned Integer(0 to 18446744073709551615)
13.	float_	Shorthand for float64
14.	float16	Half precision float
15.	float32	Single precision float
16.	float64	Double precision float
17.	complex_	Shorthand for comples128
18.	complex64	Two 32bit float complex number
19.	complex128	Two 64 bit float complex number
NumPy Operations

NumPy consists of a?wide range of functions?to work with?arrays.

1. Numpy ndim

It is the?function?which?determines?the?dimensions?of the?input array

import numpy as np
a = np.array([(1,1,1),(2,2,2)])
print(a.ndim)

Output

2
2. Numpy itemsize()

We use this function to determine the?size of?the?array elements.

import numpy as np
a = np.array([(1,1)])
print(a.itemsize)

Output

8
3. Numpy dtype()

We use this function to determine the?data type?of the?array elements.

import numpy as np
a = np.array([(1,1)])
print(a.dtype)

Output

int64
4. Numpy reshape()

We use this function to?reassign?the?array?a?new shape.

import numpy as np
a = np.array([(1,1,1),(2,2,2)])
print(a)
a=a.reshape(3,2)
print(a)

Output

[[1 1 1]
[2 2 2]]
[[1 1]
[1 2]
[2 2]]
5. Numpy slicing()

It is for?extracting?a?particular set?of?elements?from the?array.

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

Output

[1 2]
6. Numpy linspace()

This is for array generation of?evenly spread elements.

import numpy as np
a=np.linspace(1,5,10)
print(a)

Output

[1. 1.44444444 1.88888889 2.33333333 2.77777778 3.22222222
3.66666667 4.11111111 4.55555556 5. ]
7. Numpy min() / Numpy max()

We can find the?minimum?and?maximum?values?from the?array.

import numpy as np
 
arr= np.array([10,20,30])
print(arr.min())
print(arr.max())

Output

10
30
8. Numpy sum()

This is to?return?the?sum?of all the?array elements

import numpy as np
 
arr= np.array([10,50,100])
print(arr.sum())

Output

160
9. Numpy sqrt()/ Numpy std()

We can determine the?square root?and?standard deviation?of the?array elements.

import numpy as np
a=np.array([(1,2,3),(4,5,6)])
print(np.sqrt(a))
print(np.std(a))

Output

[[1. 1.41421356 1.73205081]
[2. 2.23606798 2.44948974]]
1.707825127659933
10. +,-,/, *

We can determine the?sum,?difference,?division, and?multiplication?of the?array elements?with the use of these?operators.

import numpy as np
x= np.array([(1,1,1),(2,2,2)])
y= np.array([(3,3,3),(4,4,4)])
print(x+y)
print(x-y)
print(x*y)
print(x/y)

Output

[[4 4 4]
[6 6 6]]
[[-2 -2 -2]
[-2 -2 -2]]
[[3 3 3]
[8 8 8]]
[[0.33333333 0.33333333 0.33333333]
[0.5 0.5 0.5 ]]
11. Numpy hstack/ Numpy vstack()

These are?stacking functions, we can perform?horizontal?and?vertical?stacking?of?arrays.

import numpy as np
x= np.array([(1,1,1),(2,2,2)])
y= np.array([(3,3,3),(4,4,4)])
print(np.vstack((x,y)))
print(np.hstack((x,y)))

Output

[[1 1 1]
[2 2 2]
[3 3 3]
[4 4 4]]
[[1 1 1 3 3 3]
[2 2 2 4 4 4]]
12. Numpy ravel()

This function concerts the?entire array?into a?single column.

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

Output

[1 1 1 2 2 2]

There are a?few special functions?available in?NumPy. We can plot the?sine,?cos,?and?tan curves?using the?matplotlib module.?It is an?alternative?to other?plotting software?like?MatLab.

It is a great alternative when working with?graphical representations.

import numpy as np
import matplotlib.pyplot as plt
arr1= np.arange(0,2*np.pi,0.5)
arr2=np.sin(arr1)
arr3=np.cos(arr1)
arr4=np.tan(arr1)
plt.plot(arr1,arr2)
plt.plot(arr1,arr3)
plt.plot(arr1,arr4)
plt.show()Nun