Data Inspired Insights

Month: November 2020

Pandas: Basic data interrogation

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.

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Pandas: Reading in JSON data

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?

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Pandas: Reading in tabular data

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.

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