Wes McKinney is an open source software developer focusing on analytical computing. He created the Python pandas project and is aco-creatorof Apache Arrow, his current focus. He authored two editions of the ref
There’s a variety of ways to get started on your own project. For starters, if your daily work requires you to work with tabular data, start using pandas instead of spreadsheet software like Google Sheets and Microsoft Excel. While this could take more time initially, it will help you sha...
after which he moved to the University of Manchester, where he obtained a PhD for the project for QinetiQ related to analytical solutions for the diagnosis of helicopter gearboxes.
Presentation: Subsetting, aggregating, joining, transforming data using pandas (based on chapter 6 of Learning Data Science) Hands-on exercise: Practice with pandas on baby names dataset Q&A Break Data wrangling (80 minutes) Presentation: Quality checks; how to work with missing values...
You’ll learn the latest versions of pandas, NumPy, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python ...
The merge() function in pandas can do all types of SQL joins. We can match different columns from a different DataFrame, and we can do left join, right join, inner join, and outer join. This will be very useful when wrangling the data for your project. The groupby() function in a ...
You'll learn to create and manage virtual environments, enhancing your ability to work efficiently and maintain project dependencies. Data Manipulation and Visualization Mastery Central to effective data science is the ability to manipulate and visualize data effectively. Our course provides in-depth ...
Pandas is a great tool for working on any machine learning or data science project. It's a fundamental part of data wrangling. In this tutorial, we will
merge(project_with_outcome,essays, how='outer', on='projectid') del essays master['projectid'].nunique() print "Saving master data..." master.to_csv(clean_data_path+'master.csv') print "Saving subsample of master" firstThousandMaster = master[0:999] firstThousandMaster.to_csv(clean_...
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