Michael  Hamill

Michael Hamill

1616074200

The Basics of Numpy Arrays

Data manipulation in Python is nearly synonymous with Numpy array manipulation, even newer tools like Pandas are built around the Numpy array. This section will present several examples using Numpy and manipulation to access data and subarrays, and to split, reshape and join arrays.
Let’s start by defining three random arrays: a one-dimensional, two-dimensional, and three dimensional array. We’ll use Numpy’s random number generator, which we will seed with a set value in order to ensure that the same random arrays are generated each time this code is run.

#data-science #numpy #numpy-tutorial #numpy-array #learning

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The Basics of Numpy Arrays
Luna  Hermann

Luna Hermann

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

Array Creation in NumPy

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

Bailee  Streich

Bailee Streich

1623895117

Data Science Basics: Numpy

Getting started with NumPy

Numpy

Numpy is a python library popular in the data science community. It can be used for multidimensional large array manipulation. It is also used for mathematical operations on arrays.

Numpy is useful as it is easy to learn, efficient, and has high computing capabilities.

To use NumPy, we first have to import it. We use a shorter alias np to use numpy.

import numpy as np

#numpy #numpy-tutorial #numpy-array #python3 #data science basics: numpy #data science basics

Michael  Hamill

Michael Hamill

1616074200

The Basics of Numpy Arrays

Data manipulation in Python is nearly synonymous with Numpy array manipulation, even newer tools like Pandas are built around the Numpy array. This section will present several examples using Numpy and manipulation to access data and subarrays, and to split, reshape and join arrays.
Let’s start by defining three random arrays: a one-dimensional, two-dimensional, and three dimensional array. We’ll use Numpy’s random number generator, which we will seed with a set value in order to ensure that the same random arrays are generated each time this code is run.

#data-science #numpy #numpy-tutorial #numpy-array #learning

Bailee  Streich

Bailee Streich

1619660285

Linear Algebra for Data Scientists with NumPy - Analytics India Magazine

A geek in Machine Learning with a Master’s degree in…

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Delhivery Promises To Fly Charters With Oxygen Concentrators In India

NumPy is an essential Python library to perform mathematical and scientific computations. NumPy offers Python’s array-like data structures with exclusive operations and methods. Many data science libraries and frameworks, including PandasScikit-Learn, Statsmodels, Matplotlib and SciPy, are built on top of NumPy with Numpy arrays in their building blocks. Some frameworks, including TensorFlow and PyTorch, introduce NumPy arrays or NumPy-alike arrays as their fundamental data structure in the name of tensors.

NumPy in data scienceHow NumPy becomes the base of Data Science computing system (source)

Data Science relies heavily on Linear Algebra. NumPy is famous for its Linear Algebra operations. This article discusses methods available in the NumPy library to perform various Linear Algebra operations with examples. These examples assume that the readers have a basic understanding of NumPy arrays. Check out the following articles to have a better understanding of NumPy fundamentals:

  1. Fundamental Concepts of NumPy
  2. Basic Programming with NumPy
  3. Top Resources to Learn NumPy

#developers corner #linear algebra #matrices #numpy #numpy array #numpy dot product #numpy matrix multiplication #numpy tutorial #svd #vectors

NumPy Features - Why we should use Numpy?

Welcome to DataFlair!!! In this tutorial, we will learn Numpy Features and its importance.

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays

NumPy (Numerical Python) is an open-source core Python library for scientific computations. It is a general-purpose array and matrices processing package. Python is slower as compared to Fortran and other languages to perform looping. To overcome this we use NumPy that converts monotonous code into the compiled form.

numpy features

NumPy Features

These are the important features of NumPy:

1. High-performance N-dimensional array object

This is the most important feature of the NumPy library. It is the homogeneous array object. We perform all the operations on the array elements. The arrays in NumPy can be one dimensional or multidimensional.

a. One dimensional array

The one-dimensional array is an array consisting of a single row or column. The elements of the array are of homogeneous nature.

b. Multidimensional array

In this case, we have various rows and columns. We consider each column as a dimension. The structure is similar to an excel sheet. The elements are homogenous.

2. It contains tools for integrating code from C/C++ and Fortran

We can use the functions in NumPy to work with code written in other languages. We can hence integrate the functionalities available in various programming languages. This helps implement inter-platform functions.

#numpy tutorials #features of numpy #numpy features #why use numpy #numpy