Matteo  Renner

Matteo Renner

1616750700

Understanding Vectorization in NumPy and Pandas

Just days in to hands-on learning data manipulation with Pandas, my instructor paused to make a point. “Do yourself a favor,” he said to the class, with more intention than ever before, “before going too much further in learning Pandas, watch this talk on vectorization.”
The value of vectorization seemed apparent, both from our instructor’s affect when he was directing us to the clip, and from the claim that the presenter in the clip was suggesting—vectorize your code to manipulate data 1000 times faster. The video breaks down several examples of using a variety of manipulation operations—Python for-loops, NumPy array vectorization, and a variety of Pandas methods—and compares the speed that outputs are returned for such methods. The results are clear: using techniques that take advantage of vectorization in Pandas would result in, just as the video’s click-attracting headline suggests, staggeringly faster data manipulation.

#python #simd #vectorization #numpy #pandas

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Understanding Vectorization in NumPy and Pandas
Jamison  Fisher

Jamison Fisher

1620293605

Pandas Vs Numpy: Difference Between Pandas & Numpy [2021]

Python is undoubtedly one of the most popular programming languages in the software development and Data Science communities. The best part about this beginner-friendly language is that along with English-like syntax. It comes with a wide range of libraries. Pandas and NumPy are two of the most popular Python libraries.

Today’s post is all about exploring the differences between Pandas and NumPy to understand their features and aspects that make them unique.

Pandas vs. NumPy: What are they?

Pandas vs. NumPy: The core difference between Pandas and NumPy

#data science #comparison #difference between pandas and numpy #numpy #pandas #pandas vs numpy

Matteo  Renner

Matteo Renner

1616750700

Understanding Vectorization in NumPy and Pandas

Just days in to hands-on learning data manipulation with Pandas, my instructor paused to make a point. “Do yourself a favor,” he said to the class, with more intention than ever before, “before going too much further in learning Pandas, watch this talk on vectorization.”
The value of vectorization seemed apparent, both from our instructor’s affect when he was directing us to the clip, and from the claim that the presenter in the clip was suggesting—vectorize your code to manipulate data 1000 times faster. The video breaks down several examples of using a variety of manipulation operations—Python for-loops, NumPy array vectorization, and a variety of Pandas methods—and compares the speed that outputs are returned for such methods. The results are clear: using techniques that take advantage of vectorization in Pandas would result in, just as the video’s click-attracting headline suggests, staggeringly faster data manipulation.

#python #simd #vectorization #numpy #pandas

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 Applications - Uses of Numpy

In this Numpy tutorial, we will learn Numpy applications.

NumPy is a basic level external library in Python used for complex mathematical operations. NumPy overcomes slower executions with the use of multi-dimensional array objects. It has built-in functions for manipulating arrays. We can convert different algorithms to can into functions for applying on arrays.NumPy has applications that are not only limited to itself. It is a very diverse library and has a wide range of applications in other sectors. Numpy can be put to use along with Data Science, Data Analysis and Machine Learning. It is also a base for other python libraries. These libraries use the functionalities in NumPy to increase their capabilities.

numpy applications

Numpy Applications

1. An alternative for lists and arrays in Python

Arrays in Numpy are equivalent to lists in python. Like lists in python, the Numpy arrays are homogenous sets of elements. The most important feature of NumPy arrays is they are homogenous in nature. This differentiates them from python arrays. It maintains uniformity for mathematical operations that would not be possible with heterogeneous elements. Another benefit of using NumPy arrays is there are a large number of functions that are applicable to these arrays. These functions could not be performed when applied to python arrays due to their heterogeneous nature.

2. NumPy maintains minimal memory

Arrays in NumPy are objects. Python deletes and creates these objects continually, as per the requirements. Hence, the memory allocation is less as compared to Python lists. NumPy has features to avoid memory wastage in the data buffer. It consists of functions like copies, view, and indexing that helps in saving a lot of memory. Indexing helps to return the view of the original array, that implements reuse of the data. It also specifies the data type of the elements which leads to code optimization.

3. Using NumPy for multi-dimensional arrays

We can also create multi-dimensional arrays in NumPy.These arrays have multiple rows and columns. These arrays have more than one column that makes these multi-dimensional. Multi-dimensional array implements the creation of matrices. These matrices are easy to work with. With the use of matrices the code also becomes memory efficient. We have a matrix module to perform various operations on these matrices.

4. Mathematical operations with NumPy

Working with NumPy also includes easy to use functions for mathematical computations on the array data set. We have many modules for performing basic and special mathematical functions in NumPy. There are functions for Linear Algebra, bitwise operations, Fourier transform, arithmetic operations, string operations, etc.

#numpy tutorials #applications of numpy #numpy applications #uses of numpy #numpy

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