Video Description
Learn how to use Pandas, the Python library for data and statistical analysis
About This Video
- Know what is needed for Mastering Python Data Analysis with Pandas
- Understand why is Mastering python data analysis with Pandas really useful
- Learn how can we Master Python Data Analysis with Pandas
In Detail
This course is your guide to implementing the more advanced offerings of the popular Pandas library and explains how it can solve real-world problems. After a brief overview of the basics—such as data structures and various data manipulation tasks such as grouping, merging, and reshaping data—this video also teaches you how to manipulate, analyze, and visualize your time-series financial data.
You will learn how to apply Pandas to important but simple financial tasks such as modeling portfolios, calculating optimal portfolios based upon risk, and more. This video not only teaches you why Pandas is a great tool for solving real-world problems in quantitative finance, it also takes you meticulously through every step of the way, with practical, real-world examples, especially from the financial domain where Pandas is a popular choice.
By the end of this video, you will be an expert in using the Pandas library for any data analysis problem, especially related to finance.
Table of Contents
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Chapter 1 : Data Ingestion
- The Course Overview 00:03:51
- Reading and Writing Data in Text Format 00:04:36
- XML and HTML Web Scrapping 00:04:20
- Interacting with Databases 00:03:43
- Binary Data Formats (Excel and HDF5) 00:06:42
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Chapter 2 : Data Wrangling and Munging in Pandas
- Data Wrangling/ Munging and Pandas Data Structures 00:08:00
- Combining and Merging Data Sets 00:04:14
- Reshaping, Pivoting, and Advanced Indexing Data Sets 00:03:37
- Data Transformation on Data Sets 00:03:31
- String Manipulations on Data Sets 00:04:10
- Working with Missing Data Sets 00:03:29
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Chapter 3 : Data Analysis in Pandas
- Data Aggregation on Data Sets 00:04:04
- Group-Wise Operations on Data Sets 00:05:11
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Chapter 4 : Computational Tools in Pandas
- Statistical Functions Example 00:04:49
- Windows Functions Example 00:05:46
- Applying Multiple and Different Functions to Dataframe Columns 00:03:27
- Exponentially Weighted Windows 00:03:36