 1595467140

# NumPy Array Tutorial - Python NumPy Array Operations and Methods

The most important feature of NumPy is the homogeneous high-performance n-dimensional array object. Data manipulation in Python is nearly equivalent to the manipulation of NumPy arrays. NumPy array manipulation is basically related to accessing data and sub-arrays. It also includes array splitting, reshaping, and joining of arrays. Even the other external libraries in Python relate to NumPy arrays.

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## Numpy Array Basics

Arrays in NumPy are synonymous with lists in Python with a homogenous nature. The homogeneity helps to perform smoother mathematical operations. These arrays are mutable. NumPy is useful to perform basic operations like finding the dimensions, the bite-size, and also the data types of elements of the array.

## NumPy Array Creation

### 1. Using the NumPy functions

NumPy has a variety of built-in functions to create an array.

#### a. Creating one-dimensional array in NumPy

For 1-D arrays the most common function is np.arange(…), passing any value create an array from 0 to that number.

1. import numpy as np
2. array=np.arange(20)
3. array

Output

array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19])

We can check the dimensions by using array.shape.

#numpy tutorials #array in numpy #numpy array #python numpy array

## Buddha Community  1595467140

## NumPy Array Tutorial - Python NumPy Array Operations and Methods

The most important feature of NumPy is the homogeneous high-performance n-dimensional array object. Data manipulation in Python is nearly equivalent to the manipulation of NumPy arrays. NumPy array manipulation is basically related to accessing data and sub-arrays. It also includes array splitting, reshaping, and joining of arrays. Even the other external libraries in Python relate to NumPy arrays.

_Keeping you updated with latest technology trends, _Join DataFlair on Telegram

## Numpy Array Basics

Arrays in NumPy are synonymous with lists in Python with a homogenous nature. The homogeneity helps to perform smoother mathematical operations. These arrays are mutable. NumPy is useful to perform basic operations like finding the dimensions, the bite-size, and also the data types of elements of the array.

## NumPy Array Creation

### 1. Using the NumPy functions

NumPy has a variety of built-in functions to create an array.

#### a. Creating one-dimensional array in NumPy

For 1-D arrays the most common function is np.arange(…), passing any value create an array from 0 to that number.

1. import numpy as np
2. array=np.arange(20)
3. array

Output

array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19])

We can check the dimensions by using array.shape.

#numpy tutorials #array in numpy #numpy array #python numpy array 1619518440

## top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

### 8) Check The Memory Usage Of An Object.

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## Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

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## Ternary operator in Python?

1. Ternary Operator in Python

What is a ternary operator: The ternary operator is a conditional expression that means this is a comparison operator and results come on a true or false condition and it is the shortest way to writing an if-else statement. It is a condition in a single line replacing the multiline if-else code.

syntax : condition ? value_if_true : value_if_false

condition: A boolean expression evaluates true or false

value_if_true: a value to be assigned if the expression is evaluated to true.

value_if_false: A value to be assigned if the expression is evaluated to false.

How to use ternary operator in python here are some examples of Python ternary operator if-else.

Brief description of examples we have to take two variables a and b. The value of a is 10 and b is 20. find the minimum number using a ternary operator with one line of code. ( **min = a if a < b else b ) **. if a less than b then print a otherwise print b and second examples are the same as first and the third example is check number is even or odd.

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## NumPy Array Slicing (Python Tutorial)

This article explains the NumPy array Slicing. Learn to slice an array of any dimension into another array having items of a given range with examples.

## NumPy Array Slicing

The content present in the NumPy arrays can be made accessible, and also we can make changes thorough indexing as we got to know in the previous module. Another way of data manipulation in arrays in NumPy is though slicing through the arrays. We can also try changing the position of the elements in the array with the help of their index number. Slicing is the extension of python’s basic concept of changing position in the arrays of N-d dimensions.

Slicing, in very simple words, means changing the position of elements from one given index to another given index.

We give the value of slice in this form [Start: End], and there is also another way in which we also define step attribute which could be written in this form [ Start:End:Step]

Some steps we need to keep in mind while giving these parameters:

• If we do not give any value for start, then it is considered 0 by default.
• If we do not give any value for the end, then it would consider the length of the array in the given dimension.
• Also, if we do not give value for the step, then it is considered 1 by default.

#miscellaneous #arrays #indexing #multi-dimensional arrays #numpy #python #python tutorial