Complete Pandas for Absolute Beginners 2023 | Free Udemy Course

Welcome to Pandas For Absolute Beginners. In this lesson, we will

• introduce the pandas library

• introduce Jupyter Notebook, the environment in which we'll be writing our code

• explore sample Jupyter Notebooks to showcase some of the technology's features

The datasets for this course are available on the gitHub page here: https://github.com/Hassan-Shoayb/Complete-Pandas-Course

Download and unpack the pandas.zip file in the directory of your choice.

In this video, we're going to be learning about Pandas DataFrames and Series. We're going to be covering everything from creation to manipulation. We'll be working with some basic data sets to get a feel for what Pandas is all about. If you're a beginner looking to get started with data analysis, then this video is for you!

By the end of this video, you'll have a good understanding of how to work with Pandas DataFrames and Series, and be able to apply that knowledge to your own data sets.

In this video, we're going to learn how to set, reset, and use indexes in Pandas. Indexes are important for speeding up your data analysis, and this video will teach you everything you need to know about indexing in Pandas. If you're new to Pandas, indexes are a great way to improve your data analysis.

In this video, we'll show you how to set, reset, and use indexes in Pandas, and then explain how indexes can speed up your data analysis. So don't miss out on this important tutorial!

In this video, we'll show you how to use conditional filtering to select the data you need. We'll start by introducing the concept of boolean indexing, which allows you to filter a DataFrame based on the values in one or more columns. We'll demonstrate how to use the various comparison operators to specify the conditions that your data must meet. Next, we'll show you how to use the .loc and .iloc attributes to select rows or columns based on their position or label. We'll also demonstrate how to use the .query() method to filter your data using a simple syntax that looks like a WHERE clause in SQL. By the end of this video, you'll have a solid understanding of how to use conditional filtering in Pandas to extract the data you need from your DataFrames. Whether you're a beginner or an experienced data analyst, this tutorial has something for you.

In this video, we'll be going over the different ways you can update rows and columns in a Pandas DataFrame. We'll start with the basics of updating individual cells and then move on to more advanced techniques like updating multiple cells at once and using conditional statements.

First, we'll go over how to update a single cell in a DataFrame. To do this, we'll use the at method, which allows us to access a cell by its index and column label. We'll also show you how to update a single cell using the iat method, which allows us to access a cell by its integer index.

Next, we'll cover how to update multiple cells at once. We'll show you how to use the loc method to update rows and columns using boolean indexing and how to use the iloc method to update cells by their integer index. After that, we'll demonstrate how to update rows and columns using conditional statements. We'll use the where method to update cells that meet certain conditions and the mask method to update cells that do not meet certain conditions.

Finally, we'll show you how to update a column by reassigning the values of a DataFrame and how to rename columns by using the rename method. By the end of this video, you'll have a solid understanding of the different ways you can update rows and columns in a Pandas DataFrame and be able to modify data within DataFrames with confidence.

In this video, I'll show you how to add and remove rows and columns from a pandas dataframe. This is a useful technique if you want to manipulate the data in a dataframe using various commands.

If you're new to pandas, or need to use pandas to manipulate data in a dataframe, then this video is for you! I'll show you how to add and remove rows and columns from a pandas dataframe, and explain the syntax and usage of each command. This video is a great introduction to pandas, and will help you get started with data manipulation quickly!

By the end of this video, you'll have a solid understanding of the different ways you can update rows and columns in a Pandas DataFrame and be able to modify data within DataFrames with confidence.

In this tutorial, you will learn how to sort data in Pandas, one of the most popular data manipulation libraries in Python. This video will cover various techniques for sorting DataFrames and Series objects, including sorting by one or multiple columns, ascending or descending order, and different sorting algorithms.

Whether you are a beginner or an experienced data scientist, this tutorial will give you the knowledge and tools you need to effectively sort and organize your data.

This is a great resource for anyone looking to improve their data analysis skills and take their Pandas knowledge to the next level.

In this tutorial, we'll show you how to clean and save your dataframes to various file formats, including CSV, Excel, JSON, and SQL. We'll start by demonstrating how to remove duplicates and handle missing values in your dataframe.

Next, we'll cover different ways to save your data to CSV, Excel, JSON and SQL using popular Python libraries such as Pandas. Whether you're a beginner or an advanced user, this tutorial will provide you with the tools and techniques you need to effectively clean and save your data for further analysis.

Project Repo: https://github.com/Hassan-Shoayb/Complete-Pandas-Course