Accelerating Pandas Concatenation

Accelerating Pandas Concatenation

How to quickly concatenate MultiIndexed Series with pandas only

I was recently faced with the problem of concatenating a fair amount of MultiIndexed Pandas Series (stacked DataFrames) into one single DataFrame. This can take a fair amount of time if you have many and/or large Series, and because of the MultiIndex, Dask cannot be used. I first present a sample of code using Dask’s logic to concatenate the Series pairwise in parallel jobs. I then show the acceleration performance for different parallel processing methods, for several number of CPUs ranging from 8 to 80 and several number of Series ranging from 10 to 90.

An optimal computation time of 37 % compared to the benchmark is found with only 8 CPUs. A higher number of CPUs does not reduce the relative computation time significantly.

pandas multiprocessing speed pandas-dataframe data

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Fluent Pandas: Pandas Data Structures

Let’s uncover practical details of Pandas’s Series, DataFrame, and Panel. Pandas is a column-oriented data analysis API. It’s a great tool for handling and analyzing input data.

Basic Dataframe Manipulation using Pandas

Basic Dataframe Manipulation using Pandas. Pandas DataFrames make manipulating your data easy, from selecting or replacing columns and indices to reshaping your data.

Python Pandas Objects - Pandas Series and Pandas Dataframe

In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:- ### Pandas Series Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float...

Applications Of Data Science On 3D Imagery Data

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.

Data Processing with Python Pandas — Part 2 Data Formatting

This tutorial explains how to preprocess data using the Pandas library. Preprocessing is the process of doing a pre-analysis of data, in…