assignment 2 numpy array operations

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Numpy Array Operations [closed]

What does the NumPy array[1,2] operation do? This is an image of me printing the output of this operations This is an image of the output

I try looking at the documentation, but I haven't found anything yet. I also tried finding something on You tube about, but i still haven't found anything yet. I hope that you will be able to help. Thank you in advance.

Please provide enough code so others can better understand or reproduce the problem. – Community Bot Commented Jul 2 at 13:44
2 Welcome to SO; please do not post screenshots, see how to create a minimal reproducible example . – desertnaut Commented Jul 2 at 13:46
3 numpy.org/doc/stable/user/basics.indexing.html – BigBen Commented Jul 2 at 13:46
what are x and y ? If they are already numoy arrays, you don't need the np.array wrappers. But if they are something like tensorflow objects, then the conversion may be useful. Context matters when trying to understand code snipets. – hpaulj Commented Jul 2 at 15:26

Browse other questions tagged python numpy or ask your own question .

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Assignment 2 - Numpy Array Operations This assignment is part of the course "Data Analysis with Python: Zero to Pandas" . The objective of this assignment is to develop a solid understanding of Numpy array operations. In this assignment you will: Pick 5 interesting Numpy array functions by going through the documentation: https://numpy.org/doc/stable/reference/routines.html Run and modify this Jupyter notebook to illustrate their usage (some explanation and 3 examples for each function). Use your imagination to come up with interesting and unique examples. Upload this notebook to your Jovian profile using jovian.commit and make a submission here: https://jovian.ml/learn/data-analysis-with-python-zero-to-pandas/assignment/assignment-2-numpy-array-operations (Optional) Share your notebook online (on Twitter, LinkedIn, Facebook) and on the community forum thread: https://jovian.ml/forum/t/assignment-2-numpy-array-operations-share-your-work/10575 . (Optional) Check out the notebooks shared by other participants and give feedback & appreciation. The recommended way to run this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. Try to give your notebook a catchy title & subtitle e.g. "All about Numpy array operations", "5 Numpy functions you didn't know you needed", "A beginner's guide to broadcasting in Numpy", "Interesting ways to create Numpy arrays", "Trigonometic functions in Numpy", "How to use Python for Linear Algebra" etc. NOTE : Remove this block of explanation text before submitting or sharing your notebook online - to make it more presentable.

5 Numpy Functions you should know

The most important numpy functions you should know when learning python.

Numpy is a python package for scientific computing that provides high-performance multidimensional arrays objects. This library is widely used for numerical analysis, matrix computations, and mathematical operations. In this article, we present 10 useful numpy functions along with data science and artificial intelligence applications. Let’s get started!

function 1 - numpy.linspace
function 2 - numpy.digitize
function 3 - numpy.repeat
function 4 - numpy.random.randint
function 5 - numpy.reshape

Let's begin by importing Numpy and listing out the functions covered in this notebook.

NumPy reference #

June 16, 2024

This reference manual details functions, modules, and objects included in NumPy, describing what they are and what they do. For learning how to use NumPy, see the complete documentation .

Python API #

NumPy’s module structure
The N-dimensional array ( ndarray )
Data type objects ( dtype )
Iterating over arrays
Standard array subclasses
Masked arrays
The array interface protocol
Datetimes and timedeltas
Universal functions ( ufunc )
Array creation routines
Array manipulation routines
Bit-wise operations
String functionality
Datetime support functions
Data type routines
Mathematical functions with automatic domain
Floating point error handling
Exceptions and Warnings ( numpy.exceptions )
Discrete Fourier Transform ( numpy.fft )
Functional programming
Input and output
Indexing routines
Linear algebra ( numpy.linalg )
Logic functions
Masked array operations
Mathematical functions
Miscellaneous routines
Polynomials
Random sampling ( numpy.random )
Set routines
Sorting, searching, and counting
Test support ( numpy.testing )
Window functions
Typing ( numpy.typing )
Packaging ( numpy.distutils )
Python types and C-structures
System configuration
Data type API
Array iterator API
Generalized universal function API
NpyString API
NumPy core math library
Datetime API
C API deprecations
Memory management in NumPy

Acknowledgements #

Large parts of this manual originate from Travis E. Oliphant’s book Guide to NumPy (which generously entered Public Domain in August 2008). The reference documentation for many of the functions are written by numerous contributors and developers of NumPy.

Assignment 2 - Numpy Array Operations This assignment is part of the course "Data Analysis with Python: Zero to Pandas" . The objective of this assignment is to develop a solid understanding of Numpy array operations. In this assignment you will: Pick 5 interesting Numpy array functions by going through the documentation: https://numpy.org/doc/stable/reference/routines.html Run and modify this Jupyter notebook to illustrate their usage (some explanation and 3 examples for each function). Use your imagination to come up with interesting and unique examples. Upload this notebook to your Jovian profile using jovian.commit and make a submission here: https://jovian.ml/learn/data-analysis-with-python-zero-to-pandas/assignment/assignment-2-numpy-array-operations (Optional) Share your notebook online (on Twitter, LinkedIn, Facebook) and on the community forum thread: https://jovian.ml/forum/t/assignment-2-numpy-array-operations-share-your-work/10575 . (Optional) Check out the notebooks shared by other participants and give feedback & appreciation. The recommended way to run this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. Try to give your notebook a catchy title & subtitle e.g. "All about Numpy array operations", "5 Numpy functions you didn't know you needed", "A beginner's guide to broadcasting in Numpy", "Interesting ways to create Numpy arrays", "Trigonometic functions in Numpy", "How to use Python for Linear Algebra" etc. NOTE : Remove this block of explanation text before submitting or sharing your notebook online - to make it more presentable.

5 Numpy function in celebration of Liverpool Premier League Champions

The recommended way to run this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks.

Numpy is a special python libary which stands for Numerical Python. It was developed in 2005 by Travis Oliphant. It is an open source, free project with many conributors. It comprises of several multidimensional array objects and functions/methods to work with the arrays. It is widely used for performing logical as well as mathematical operations such as Linear Algebra, Matrices, Statistics, Fourier Transform, Trignometry, Financial maths, etc.

Lets look at some of the numpy methods/functions :-

transpose()