In my previous post, I explored how to use Pandas to work with data frames and similar structures. In this post, I want to go to the next level and discuss the magical operations available with the NumPy (Numerical Python) library, including fast array manipulation.

### Numerical Python = NumPy

**Why should go with NumPy**

- Provides Data Structure, Algorithm for the Scientific application which requires numerical data.
- Which supports multi-dimensional array manipulation. NumPy’s array object is called ndarray.
- Easy to reshape, slice, and dice the array. And fast array process capability.
- Makes complex mathematical implementations very simple.
- To perform different numerical and trigonometry functions (i.e., sin, cos, tan, mean, median, etc.)
- Excellent support for Linear Algebra, Fourier Transformer, etc.,
- NumPy arrays are very efficient than list arrays, The way it processes manipulate id fast.
- It is often used along with other packages in Python environments like
**SciPy**and**Matplotlib.**

**What library supports and how should we import NumPy?**

import numpy as np

**What NumPy Cando? **

The below picture represented the capabilities of NumPy. Let’s discuss it one by one.

**I. Exploring the dimensions in the array**

**a. ONE Dimensions and Multi-Dimensions**

**(a.1) 1-D**

import numpy as np

a = np.array([100,200,300,400,500])

print (a)

OUTPUT

[100 200 300 400 500]

**(a.2) n-D**

a = np.array([[100, 200], [300, 400]])

print (a)

**OUTPUT**

[[100 200]

[300 400]]

**b. Number of Dimensions**

x = np.array(1)

y = np.array([1, 2, 3, 4, 5])

z = np.array([[1, 2, 3], [4, 5, 6]])

print(x.ndim)

print(y.ndim)

print(z.ndim)

**OUTPUT**

0

1

2

**c. Finding Type of the array**

arr = np.array([1, 2, 3, 4, 5])

print(arr)

print(type(arr))

**OUTPUT**

[1 2 3 4 5]

<class ‘numpy.ndarray’>

**d. Accessing array elements**

arr = np.array([1, 2, 3, 4])

print(arr[0])

**OUTPUT**

1

**e.Slicing array element**

import numpy as np

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

print(arr[1:5])

**OUTPUT**

[2 3 4 5]

**II. Data Type Objects Specfic to NumPy**

NumPy has additional data types, Let’s see those and simple code to find the type of the variable.

**III. Finding shape and re-shape of the ndarray.**

## a. Shape of the array

(a.1) The Array has the attribute that returns array dimensions.

import numpy as np

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

print (“Shape of the array:”,a.shape)

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

print (“Shape of the array:”,a.shape)

**OUTPUT**

Shape of the array: (3, 3)

Shape of the array: (2, 4)

## b. Re-shape of the array

(b.1) Certainly, you can resize the array, with…

Continue reading: https://www.datasciencecentral.com/xn/detail/6448529%3ABlogPost%3A1058699

Source: www.datasciencecentral.com

## Comments by halbot