These posts are going to be a guide on how to get started playing with DataFrame. In here, I'll tutorial about: Pivoting, Stacking and Melting
This is a continuation of my last post. It’s required that you read that post first before diving into these topics. Here is the link to it.
In this part-2 of Manipulating DataFrames with Python, we’ll cover some of the following techniques:
These posts are going to be a guide on how to get started playing with DataFrame. The topics in themselves are worth writing an article for each of them. However, I’ll give a basic understanding of them and in case if you want to dig deeper I’ll attach the links to in-depth tutorials about the topics.
In this tutorial, you will know about the TED TALKS DATA ANALYSIS project from scratch.
Learn to group the data and summarize in several different ways, to use aggregate functions, data transformation, filter, map.
Exploring the leading and trailing zeros, distribution of letters and numbers, common prefixes, regular expressions, and randomization of the data set.
Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories.
We will show you the code snippet for checking the data condition. The topics will cover units of analysis, missing values, duplicated records, Is your data makes sense, and truth changing over time. The tutorial will be written in the pandas library. The most famous data manipulation library in python.