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Ten Trending Data Science Tools in 2021
The fields of data science and artificial intelligence see constant growth. As more companies and industries find value in automation, analytics, and insight discovery, there comes a need for the development of new tools, frameworks, and libraries to meet increased demand. There are some tools that... Read more
How to Pivot and Plot Data With Pandas
A big challenge of working with data is manipulating its format for the analysis at hand. To make things a bit more difficult, the “proper format” can depend on what you are trying to analyze, meaning we have to know how to melt, pivot, and transpose... Read more
Saying Hello to DataFrames.jl
A majority of data scientists use Python or R to perform data preparation tasks before jumping to modeling. The Julia language is a younger player in this field that promises that you will be able to do the number-crunching-intensive parts of your pipelines fast. However, the... Read more
How to Make an Animated Gif Fit for /r/dataisbeautiful
A good visualization should capture the interest of the audience and make an impression. Few things capture interest more than bright colors and movement. In this post, I’m going to show you exactly how to make an animated gif, so that you can go farm some... Read more
Getting Started with Pandas
Pandas is a popular data analysis library built on top of the Python programming language, and getting started with Pandas is an easy task. It assists with common manipulations for data cleaning, joining, sorting, filtering, deduping, and more. First released in 2009, pandas now sits as... Read more
Getting More Value from the Pandas value_counts
Data exploration is an important aspect of the machine learning pipeline. Before we decide which model to train and how many to train, we must have an idea of what our data contains. The Pandas library is equipped with a number of useful functions for this very... Read more
Frequencies and Chaining in Python-Pandas
This article discusses chaining in Python. A few years ago, in a Q&A session following a presentation I gave on data analysis (DA) to a group of college recruits for my then consulting company, I was asked to name what I considered the most important analytic... Read more
From Pandas to Scikit-Learn — A New Exciting Workflow
Ted will present more on this topic at ODSC East 2019 this May in his presentation, “Integrating Pandas with Scikit-Learn, an Exciting New Workflow“ This article is available as a Jupyter Notebook on Google’s Colaboratory (open in playground mode to run and edit) and at the Machine Learning Github... Read more
Handling Missing Data in Python/Pandas
Key Takeaways: It’s important to describe missing data and the challenges it poses. You need to clarify a confusing terminology that further adds to the field’s complexity. You should take the time to review methods for handling missing data. You need to learn how to apply... Read more
All the Best Parts of Pandas for Data Science
Pandas has been hailed by many in the data science community as the missing link between Python and analysis, a tool that can be leveraged in order to dramatically reduce overhead in data science projects, increase understandability and speed up workflows. Pandas comes loaded with a... Read more