Practice
Resources
Contests
Online IDE
New
Free Mock
Events New Scaler
Practice
Improve your coding skills with our resources
Contests
Compete in popular contests with top coders
Events
Attend free live masterclass hosted by top tech professionals
New
Scaler
Explore Offerings by SCALER

Before you leave, take this NumPY Cheat Sheet interview guide with you.
Get a Free Personalized Career Roadmap
Answer 4 simple questions about you and get a path to a lucrative career
Expand in New Tab
/ Interview Guides / NumPY Cheat Sheet

# NumPY Cheat Sheet

Last Updated: Jan 04, 2024

Certificate included
What will you Learn?

## Introduction

Jim Hugunin originally developed Numeric, the predecessor to NumPy, with assistance from a number of other programmers. Travis Oliphant developed it in 2005 by heavily altering Numeric to incorporate features of the rival Numarray. The multidimensional array objects and the collection of operations for handling those arrays are part of an open-source library. Its sturdy n-dimensional array speeds up data processing. It offers the ability to easily interact with other Python packages and other programming languages like C, C++, etc. The foundational library for Python's scientific computing is this one. It offers a multidimensional array object with outstanding speed as well as capabilities for interacting with these arrays. The library is what enables Python's quick data manipulation.

NumPy targets the non-optimizing CPython bytecode interpreter, which is the Python reference implementation. Due to the lack of compiler optimization, mathematical algorithms created for this version of Python frequently execute considerably slower than their compiled counterparts. Multidimensional arrays, efficient array-based functions, and operators are some of the ways that NumPy addresses the slowness issue. Using them necessitates rewriting some code, primarily inner loops, in NumPy.

### What is NumPy?

The core Python library for scientific computing is called NumPy. It includes multidimensional array objects, various derived objects (masked arrays, matrices, etc.), and a variety of quick manipulations of arrays such as math, logic, shape manipulation, sorting, selection, I / O, and discrete. A Python library that provides a variety of routines. Fourier transform, basic linear algebra, basic statistical operations, random simulation, etc.

The ndarray object is the core of the NumPy package. This contains n-dimensional arrays of uniform data types, with many operations carried out in compiled code for speed. View Important Interview Questions on NumPY.

### Why Use NumPy?

Some of the key features that contribute to the popularity of NumPy are:

• It is a powerful N-dimensional array object
• It is a sophisticated broadcasting functions
• It is a tool for integrating C/C++ and Fortran code
• It is useful for linear algebra, Fourier transform, and random number capabilities

### Limitations Of NumPy

An array cannot be added to or subtracted from in the same manner as a list in Python. When extending an array, the np.pad(...) function actually creates new arrays with the required shape and padding values, copies the existing array into the new one, and then returns the new array.

The np.concatenate([a1,a2]) action in NumPy returns a new array that is filled with the sequential items from the two given arrays rather than actually linking the two arrays together. Only when the array's element count stays constant can an array's dimensionality be changed using the np.reshape(...) method. These conditions result from the need that arrays in NumPy to be viewed on contiguous memory buffers. Blaze, a substitute package, makes an effort to get around this restriction.

Numerous contemporary large-scale scientific computing applications have needs that go beyond what NumPy arrays can handle. For instance, NumPy arrays are frequently loaded into a computer's RAM, which may not have enough space to accommodate huge dataset processing. Furthermore, a single CPU is used to do NumPy computations. However, by running them on groups of CPUs or specialized hardware, such as GPUs and TPUs, which are used in many deep learning applications, many linear algebra operations may be made faster.

## Numpy Tutorial: Basic to Advanced

### 1. Importing/Exporting Commands

Note: Importing numpy as n for all codes.

Create a free personalised study plan Create a FREE custom study plan
Get into your dream companies with expert guidance
Get into your dream companies with expert..
Real-Life Problems
Prep for Target Roles
Custom Plan Duration
Flexible Plans

### 5. Adding and Removing Commands

Explore InterviewBit’s Exclusive Live Events
Explore Exclusive Events
By
No More Events to show!
No More Events to show!
No More Events to show!
No More Events to show!
Certificate included
What will you Learn?

### 7. Indexing, slicing and subsetting

Start Your Coding Journey With Tracks Start Your Coding Journey With Tracks
Master Data Structures and Algorithms with our Learning Tracks
Master Data Structures and Algorithms
Topic Buckets
Mock Assessments
Earn a Certificate

### Conclusion

This article aims to introduce you to NumPy, familiarize you with them, and provide you with a cheat sheet. It takes some effort to read through the extensive online documentation for NumPy to find what you need. We all want to use new tools as quickly and efficiently as possible. This article should have been able to help you with that.

NumPy arrays can be used for math operations, data loading and storing, and array indexing. We covered array manipulation, import/export, and statistical techniques that are crucial.

## NumPY MCQs

1.

When examining type combinations for input and output, which of the following attributes should be used?

2.

Which of the subsequent functions may combine various vectors to produce the output for each n-uplet?

3.

ndarray is also called the alias array.

4.

NumPY stands for?

5.

The buffer containing the actual array elements is named ndarray.dataitemSize.

6.

The most important object defined in NumPy is an N-dimensional array type called?

7.

What can be done with Numpy arrays?

8.

What will be output for the following code?

``````import numpy as np
a = np.array([1,2,3,4,5])
print a``````
9.

Which of the following functions creates a 2D array by stacking 1D arrays as columns?

10.

Which of the following methods generates a fresh array object that checks the same data?

11.

Which of the following Numpy operations is right?

Excel at your interview with Masterclasses Know More
Certificate included
What will you Learn?
Free Mock Assessment
Fill up the details for personalised experience.
Phone Number *
OTP will be sent to this number for verification
+91 *
+91
Change Number
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
*Enter the expected year of graduation if you're student
Current Employer
Company Name
College/University Name
Job Title
Job Title
Software Development Engineer (Backend)
Software Development Engineer (Frontend)
Software Development Engineer (Full Stack)
Data Scientist
Android Engineer
iOS Engineer
Devops Engineer
Support Engineer
Research Engineer
Engineering Intern
QA Engineer
Co-founder
SDET
Product Manager
Product Designer
Backend Architect
Program Manager
Release Engineer