NumPy is stand for **“Numerical Python”**. it is a Python library. NumPy is use for works with arrays. It is provides support in mathematical, scientific, engineering, and data science programming. NumPy** **is an open-source library in** **Python. Numpy was create by** **Travis Oliphan in 2005.

I am going to explain in a very basic approach, and you will thank me later after reading this small capsule sharing with you here. It will really help you to understand better.

#### Creating a Numpy Array

Numpy can be creates by multiple ways, with various number of Ranks, defining the size of the Array. Arrays can also be creates with the use of various data types is knows Creating a Numpy Array.

**Create a NumPy ndarray Object**

import numpy as np

what = np.array((0,1,2,3,4,5))

print(what)

**Output**: 0,1,2,3,4,5.

**Dimensions of Arrays**

**four types of Dimension of Arrays**

- 0-D Arrays
- 1-D Arrays
- 2-D Arrays
- 3-D Arrays

#### 0-D Arrays

0-D arrays are the elements in array. Each value in array is 0-D array is knows as 0-D Array.

**Example of 0-D**

import numpy as np

what = np.array(5)

print(what)

**Output** : 5

#### 1-D Arrays

Array that has 0-D arrays as its elements is knows as 1-D array.

**Example of 1-D Array**

import numpy as np

what = np.array((0,1,2,3,4,5))

print(what)

**Output** : [0 1 2 3 4 5]

#### 2-D Array

Array that has 1-D arrays as its elements is knows as 2-D array.

**Example of 2-D Array**

import numpy as np

what = np.array([[0, 1, 3],[9, 10, 11]])

print(what)

**Output**: [[ 0 1 3] [ 9 10 11]]

#### 3-D Array

Array that has 2-D arrays as its elements is knows as 3-D array.

**Example of 3-D Array**

import numpy as np

what = np.array([[[0, 1, 2], [3, 4, 5]], [[2, 4, 6], [8, 10, 12]]])

print(what)

**Output: **[[[ 0 1 2 ][3 4 5 ]][[2 4 6 ][8 10 12 ]]]

#### Indexing Array

Indexing can be done in numpy by using an array as an index. Numpy arrays can be index with other arrays or any other sequence with the exception of tuples is knows as Indexing Array.

**Example of Indexing Array**

import numpy as np

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

print(what[3])

**Output: 4**

**Other** **Example of Indexing Array**

import numpy as np

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

print(what[3] + what[5])

**Output: 10**

#### Slicing Array

Slicing means taking elements from one given index to another given index is knows as Slicing Array.

**Example of Slicing Array**

import numpy as np

what = np.array([11, 12,13,14,15,16,17])

print(what[2:5])

**Output: [13 14 15 ]**

**Other Example of Slicing Array**

import numpy as np

what = np.array([11, 12,13,14,15,16,17])

print(what[2:])

**Output: [13 14 15 ]**

**Other Example of Slicing Array**

import numpy as np

what = np.array([11, 12,13,14,15,16,17])

print(what[:2])

**Output: [11 12 13 ]**

#### Datatypes

Every ndarray has an associates data type object. Some Datatypes

- i – integer
- b – boolean
- f – float
- s – string
- o – object
- u – unsigned integer
- c – complex float
- m – timedelta
- M – datetime
- U – unicode string

**Check the Data Type Array**

**Example of Data Type Array**

import numpy as np

what = np.array([0,1,2,3])

print(what.dtype)

Output: int64

**Create an array with data type**

Example

import numpy as np

arr = np.array([11, 12, 13, 14], dtype=’i’)

print(arr)

print(arr.dtype)

Output : [11 12 13] int32

#### numpy.reshape()

The numpy.reshape() function is available in NumPy package. The shape of an array is the number of elements in each dimension.

**Example of numpy.reshape()**

import numpy as np

what = np.array([[11, 12, 13, 14], [15, 16, 17, 18]])

print(what.shape)

**Output: (2,4)**

#### NumPy Array Reshaping

The numpy.reshaping() function is available in NumPy package. reshape means is a **‘changes in shape’** is knows as reshaping

Example of NumPy Array Reshaping

Reshape From 1-D to 2-D

import numpy as np

what = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16])

newwhat = what.reshape(4, 4)

print(newwhat)

**Output:**

[[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12] [13 14 15 16]]

Example of NumPy Array Reshaping

**Reshape From 1-D to 3-D**

import numpy as np

what = np.array([11,12,13,14,15,16,17,18,19,20,21,22])

newwhat = what.reshape(2, 3, 2)

print(newwhat)

**Output**

[[[11 12] [13 14] [15 16]] [[17 18] [19 20] [21 22]]]

#### Iterating Array

Iterating Array is a means going through elements one by one. It is a multi-dimensional arrays in numpy.

Example of Iterating Array

import numpy as np

what = np.array([4,5,6])

for y in what:

print(y)

**Output: **

4 5 6

#### Join NumPy Arrays

Exmaple

import numpy as np

what = np.array([4, 5, 6])

why = np.array([8,7,9])

where = np.concatenate((what, why))

print(where)

**Output**

[4 5 6 8 7 9]

#### Split NumPy Arrays

Example of Splitting NumPy Arrays

import numpy as np

what = np.array([4, 5, 6, 7, 8, 9])

newwhat = np.array_split(what, 2)

print(newwhat)

**Output:**

[array([4, 5, 6]), array([7, 8, 9])]

If you have any queries regarding this article or if I have missed something on this topic, please feel free to add in the comment down below for the audience. See you guys in another article.

To know more about pandas please Wikipedia click here

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