Pandas Zero to Hero - A Beginner’s Tutorial to Using Pandas

Pandas Zero to Hero - A Beginner’s Tutorial to Using Pandas

Welcome to Pandas Zero to Hero, a weekly video series where cover simple and effective ways of using pandas. Pandas is a great data wrangling library that is built on top of the Python programming language that allows you to work with large and complex dataframes.

Welcome to Pandas Zero to Hero, a weekly video series where cover simple and effective ways of using pandas. Pandas is a great data wrangling library that is built on top of the Python programming language that allows you to work with large and complex dataframes.

GitHub: https://github.com/chongjason914/pandas-tutorial

Subscribe : https://www.youtube.com/channel/UCQXiCnjatxiAKgWjoUlM-Xg/videos

Week 1: Creating Your Own Dataframe Using Pandas

In this week's tutorial, I teach you how to read a data file using pandas as well as create your own dataframe for further data manipulation and analysis. It would really mean a lot to me if you could drop a like on the video if you enjoyed it and subscribe to my channel for future videos.

Week 2: Selecting Data Using Pandas

In last week's tutorial, we learned how to import a dataset into our Jupyter notebook also known as the IPython notebook as well as create our very own dataframe using pandas. In this week's tutorial, I go over how you can select rows and columns in a dataframe using the loc and iloc functions.

Selecting data is one of the most fundamental skills to have when working with data. Therefore, it is important that you have these basics down as we progress to more advanced topics later on in this video series.

Week 3: Applying Functions In Pandas

In this tutorial, we learn about the difference between categorical and numerical variables, the concept of encoding as well as how to create and apply different functions to our dataset.

I hope you find value in my tutorial and I would greatly appreciate if you could drop a like on the video if you did. Stay tuned for my tutorial next week where we will learn about grouping and sorting data.

Week 4: Grouping & Sorting Data Using Pandas

In this tutorial, we cover the concept of grouping and sorting. Segmentation is a powerful way technique to use when doing data analysis. It not only helps break down a large problem into smaller sub-components but more importantly, it allows us to clearly observe the patterns and characteristics within those smaller segments. There are many ways we can segment our data, for example, segmenting by customer groups, geographic region, product category and so on. I highly encourage using your creativity as well as referring to other people's projects to draw some inspirations on how you can segment your data.

Week 5: Handling Missing Data Using Pandas

In this tutorial, we cover the concept of data types as well as how to deal with missing data using pandas. Missing data is one of the most common problems that you will face in almost all data science projects. Therefore, it is important that you not only know how to detect those missing data in your dataset but also the techniques to properly handle them.

Week 6: Combining Dataframes Using Pandas

In this tutorial, we will learn how to combine data frames using two pandas functions, concat and merge.

When working on a data science or machine learning project, oftentimes, you will be working with more than one dataset. Hence, it is important that you know how to merge different data frames together to have a better view and understanding of your data. The two main functions for combining data frames in pandas are the concat and merge function.

Concat is short for concatenate which enables you to stack two separate data frames together either vertically or horizontally.

Merge, on the other hand, helps merge two data frames together based on shared columns. In the video, I demonstrate the concept of merge via two sample data frames, sales transactions and customer profile of a retail store.

These six tutorial videos are crash courses that will provide you with the foundational understanding of what you can do with the pandas library.

However, it is essential that you practise using it as much as you can in an actual data science project to fully grasp the concepts behind these videos. In other words, treat them as short introductory videos to help you get started but move on to apply them in an actual project setting to maximise your learning.

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