摘要： The pandas library offers core functionality when preparing your data using Python. But, many don't go beyond the basics, so learn about these lesser-known advanced methods that will make handling your data easier and cleaner.
Pandas is the gold standard library for all things data. With the functionality to load, filter, manipulate, and explore data, it’s no wonder that it’s a favorite among Data Scientists.Most of us naturally stick to the very basics of Pandas. Load up data from a CSV file, filter a few columns, and then jump right into the data visualizations. Yet Pandas actually comes with many lesser-known but useful functions that can make handling data a whole lot easier and cleaner.
(1) Configuring Options and Settings
...Pandas comes with a set of user-configurable options and settings. They’re huge productivity boosters since they let you tailor your Pandas environment exactly to your liking....Code
(2) Combining DataFrames
...Concatenating and Merging...Code
(3) Reshaping DataFrames
There are several ways to reshape and restructure Pandas DataFrames....Code
(4) Working with time data
The Datetime library is a staple in Python. Whenever you’re dealing with anything related to real-world date and time information, it’s your go-to library....Code
(5) Mapping Items into Groups
Mapping is a neat trick that helps with organizing categorical data. Imagine, for example, that we have a huge DataFrame with thousands of rows where one of the columns has items we wish to categorize. Doing so can greatly simplify both the training of Machine Learning models and visualizing the data effectively....Code
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Full Text： kdnuggets
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