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Data Science in Python: Pandas Cheat Sheet

#artificialintelligence

This cheat sheet, along with explanations, was first published on DataCamp. Click on the picture to zoom in. To view other cheat sheets (Python, R, Machine Learning, Probability, Visualizations, Deel Learning, Data Science, and so on) click here. To view a better version of the cheat sheet and read the explanations, click here.


Pandas Cheat Sheet for Data Science in Python

#artificialintelligence

The Pandas library is one of the most preferred tools for data scientists to do data manipulation and analysis, next to matplotlib for data visualization and NumPy, the fundamental library for scientific computing in Python on which Pandas was built. The fast, flexible, and expressive Pandas data structures are designed to make real-world data analysis significantly easier, but this might not be immediately the case for those who are just getting started with it. Exactly because there is so much functionality built into this package that the options are overwhelming. That's where this Pandas cheat sheet might come in handy. It's a quick guide through the basics of Pandas that you will need to get started on wrangling your data with Python.


Pandas Cheat Sheet for Data Science in Python

#artificialintelligence

The Pandas library is one of the most preferred tools for data scientists to do data manipulation and analysis, next to matplotlib for data visualization and NumPy, the fundamental library for scientific computing in Python on which Pandas was built. The fast, flexible, and expressive Pandas data structures are designed to make real-world data analysis significantly easier, but this might not be immediately the case for those who are just getting started with it. Exactly because there is so much functionality built into this package that the options are overwhelming. That's where this cheat sheet might come in handy. It's a quick guide through the basics of Pandas that you will need to get started on wrangling your data with Python.


Importing Data in Python Cheat Sheet

#artificialintelligence

Before doing any data cleaning, wrangling, visualizing, ... You'll need to know how to get data into Python. As you know, there are many ways to import data into Python, depending also on which files you're dealing with.


Pandas Cheat Sheet: Data Science and Data Wrangling in Python

@machinelearnbot

A very important component in the data science workflow is data wrangling. And just like matplotlib is one of the preferred tools for data visualization in data science, the Pandas library is the one to use if you want to do data manipulation and analysis in Python. This library was originally built on NumPy, the fundamental library for scientific computing in Python. The data structures that the Pandas library offers are fast, flexible and expressive and are specifically designed to make real-world data analysis significantly easier. However, this flexibility might come at a cost for beginners; When you're first starting out, the Pandas library can seem very elaborate and it might be hard to find a single point of entry to the material: with other learning materials focusing on different aspects of this library, you can definitely use a reference sheet to help you get the hang of it.