Documentation for applying a function with one or 2+ arguments in a Pandas Dataframe, using Pandas, Python, and Jupyter Notebook. Data from the Fantasy Premier League API — In this short tutorial, I’ll show you exactly how to apply a function that will accept both one argument and multiple arguments in a Pandas Dataframe. The approaches are somewhat different, using the .apply() method directly on a column to apply a single-parameter function, and then using a lambda function…

A comprehensive list of tutorials in Python, Pandas, and Jupyter Notebook, explaining how to use 14 of your favorite Excel-like functions — Hello Excel super-user. My name is David, and I work on a Data Science team. I’d like to introduce you to a very popular analysis stack in the great wide world of Data Science: Python, Pandas, and Jupyter notebook. This blog will guide you through setting up your computer to…

Documentation for creating a Histogram with Python, Pandas and Jupyter notebook. — A histogram is a powerful (and quick) way to visualize data in Python, especially if you’re working with Pandas dataframes. In this tutorial, we’ll create a dataframe from a public API request. Then, I’ll show you how to create a histogram, and then customize that histogram by playing with number…

Documentation for writing an IF Statement in a Jupyter notebook with Pandas and Python — Excel users know that IF statements are a powerful way to add logic to their spreadsheet. With Python, IF statements become even more flexible and powerful. In this tutorial, we’ll create a dataframe from a CSV import, then I’ll show you exactly how to write an IF statement, and how…

Documentation for finding and replacing strings and multiple strings at once with Python and Pandas in a Jupyter notebook — Excel makes finding and replacing values very straightforward and easy. Finding and replacing with Python and Pandas may not be easier than Excel, but it’s certainly more powerful and flexible. In this tutorial, I’ll show you how to search for specific strings and replace these values.

Documentation for several common Excel functions — SUM, COUNT, AVERAGE, MAX, and MIN — with Pandas, Python and Jupyter Notebook — It’s difficult to imagine any sort of data analysis without these basic, core functions of Excel: adding, counting, taking an average, finding the max and min… In this tutorial, I’ll show you exactly how to use these common Excel functions on your Pandas DataFrame. And because we’ll need a dataset…

Documentation for splitting a text column into two columns in a Pandas DataFrame, with Python and Jupyter Notebook — Spreadsheet cowboys will know that a very common data cleaning operation is to split text to columns. Data Analysts who are new to Python, Pandas, and Jupyter notebooks will appreciate the ease of executing this same task with this new Data Analysis stack. Let’s get started. Table of Contents

Documentation for importing data from a raw CSV with Pandas, Python and Jupyter notebook — The fields of Data Analysis and Data Science are all about getting data and then going something with it. In today’s tutorial, we’ll explore one of the many ways to “get data”: reading a raw CSV into our Jupyter notebook using the CSV github URL. Table of Contents Set up notebook Get data …

Documentation for concatenating two or more string columns, using Python, Pandas, and Jupyter notebook — If you’re an Excel user, you’re likely familiar with the process of joining several different string columns with either CONCATENATE or a simple series of “&” statements. We can do this exact same thing in our Jupyter notebook with Python and Pandas. Let me show you how. p.s. the header…