Latest Resources in Pandas

Pandas

Pandas Tutorial: Analyzing Video Game Data with Python and Pandas dataquest.io

Published August 30, 2019 under Data Science

Python’s pandas library is one of the things that makes Python a great programming language for data analysis. Pandas makes importing, analyzing, and visualizing data much easier. It builds on packages like NumPy and matplotlib to give you a single, convenient, place to do most of your data analysis and visualization work.

The Easiest Data Cleaning Method using Python & Pandas marsja.se

Published August 30, 2019 under Data Science

What is Pyjanitor? Before we continue learning on how to use Pandas and Pyjanitor to clean our datasets, we will learn about this package. The python package Pyjanitor extends Pandas with a verb-based API. This easy to use API is providing us with convenient data cleaning techniques. Apparently, it started out as a port of the R package janitor. Furthermore, it is inspired by the ease-of-use and expressiveness of the r-package dplyr. Note, there are some different ways how to work with the methods and this post will not cover all of them (see the documentation).

Data School’s top 25 pandas tricks jupyter.org

Published July 19, 2019 under Data Science

What it sounds like 🙂

Data science best practices with pandas dataschool.io

Published June 3, 2019 under Data Science

The pandas library is a powerful tool for multiple phases of the data science workflow, including data cleaning, visualization, and exploratory data analysis. However, the size and complexity of the pandas library makes it challenging to discover the best way to accomplish any given task.

Building a Robinhood Stock Trading Bot medium.com

Published April 26, 2019 under Trading

The bot is written in Python and relies on two core libraries for the majority of its functionality: robin-stocks and ta. robin-stocks is a library that interacts with the Robinhood API and allows one to execute buy and sell orders, get real time ticker information, and more. ta is a technical analysis library that also incorporates the Python Pandas library to generate indicators from stock data.

Pandas DataFrames for Data Analysis kite.com

Published April 19, 2019 under Data Science

In this post, we’ll learn about Pandas, a high-performance open-source package for doing data analysis in Python.

We’ll cover:

What Pandas is and why should you use it.
What a Pandas DataFrame is.
Creating and viewing a DataFrame.
Manipulating data in a DataFrame.

Vaex: A DataFrame with super strings towardsdatascience.com

Published April 19, 2019 under Data Science

String manipulations are an essential part of Data Science. The latest release of Vaex adds incredibly fast and memory efficient support for all common string manipulations. Compared to Pandas, the most popular DataFrame library in the Python ecosystem, string operations are up to ~30–100x faster on your quadcore laptop, and up to a 1000 times faster on a 32 core machine.