This article is part of a series of practical guides for using the Python data processing library pandas. To see view all the available parts, click here.

In other sections in this series, we’ve looked at how we can use booleans (a value that is either True or False) in pandas. Specifically, we’ve looked at how a list or array of booleans can be used to filter a DataFrame. In those examples we generated lists of booleans using simple comparisons like “are the values in the fixed acidity column > 12?” However, simple comparisons like this are only one of many ways we can create booleans. In this guide we are going to look at a range of methods that allow us to do more complex comparisons, while also making our code more concise and easier to understand.

This article is part of a series of practical guides for using the Python data processing library pandas. To see view all the available parts, click here.

One of the most common ways you will interact with a pandas DataFrame is by selecting different combinations of columns and rows. This can be done using the numerical positions of columns and rows in the DataFrame, column names and row indices, or by filtering the rows by applying some criteria to the data in the DataFrame. All of these options (and combinations of them) are available, so let’s dig in!

This article is part of a series of practical guides for using the Python data processing library pandas. To see view all the available parts, click here.

Once we have our data in a pandas DataFrame, the basic table structure in pandas, the next step is how do we assess what we have? If you are coming from Excel or R Studio, you are probably used to being able to look at the data any time you want. In python/pandas, we don’t have a spreadsheet to work with, and we don’t even have an equivalent of R Studio (although Jupyter notebooks are a similar concept), but we do have several tools available that can help you get a handle on what your data looks like.

This article is part of a series of practical guides for using the Python data processing library pandas. To see view all the available parts, click here.

When we are working with data in software development or when the data comes from APIs, it is often not provided in a tabular form. Instead it is provided in some combination of key-value stores and arrays broadly denoted as JavaScript Object Notation (JSON). So how do we read this type of non-tabular data into a tabular format like a pandas DataFrame?

This article is part of a series of practical guides for using the Python data processing library pandas. To see view all the available parts, click here.

To get started with pandas, the first thing you are going to need to understand is how to get data into pandas. For this guide we are going to focus on reading in tabular data (i.e. data stored in a table with rows and columns). If you don’t have some data available but want to try some things out, a great place to get some data to play with is the UCI Machine Learning Repository.