Pandas for Newbies: An Introduction Part II

Pandas for Newbies: An Introduction Part II

A Deep Dive into Data Analysis with Python.This article is a continuation of a previous article which kick-started the journey to learning Python for data analysis. You can check out the previous article here: Pandas for Newbies: An Introduction Part I. For those just starting out in data science, the Python programming language is a pre-requisite to learning data science so if you aren’t familiar with Python go make yourself familiar and then come back here to start on Pandas.

This article is a continuation of a previous article which kick-started the journey to learning Python for data analysis. You can check out the previous article here: Pandas for Newbies: An Introduction Part I.

For those just starting out in data science, the Python programming language is a pre-requisite to learning data science so if you aren’t familiar with Python go make yourself familiar and then come back here to start on Pandas.

You can start learning Python with a series of articles I just started called Minimal Python Required for Data Science.

As a reminder, what I’m doing here is a brief tour of just some of the things you can do with Pandas. It’s the deep-dive before the actual deep-dive.

Both the data and the inspiration for this series comes from Ted Petrou’s excellent courses on Dunder Data.

Prerequisites

  1. Python
  2. pandas
  3. Jupyter

You’ll be ready to begin once you have these three things in order.

Aggregation

We left off last time with the pandas query method as an alternative to regular filtering via boolean conditional logic. While it does have its limits, the query is a much more readable method.

Today we continue with aggregation which is the act of summarizing data with a single number. Examples include sum, mean, median, min and max.

Let’s try this on different dataset.

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