English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 17m | 266 MB
Master advanced data analysis using financial examples in Pandas
Learn how to use Pandas, the Python library for data and statistical analysis
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.
What You Will Learn
- Read and write data in text format
- Master concepts involved in interacting with databases
- Master string manipulations on Data Sets
- Practice data aggregation on data sets
- Be proficient in group-wise operations on data sets
- Learn to apply multiple and different functions to dataframe columns
- Implement the concept of exponentially weighted windows
Table of Contents
01 The Course Overview
02 Reading and Writing Data in Text Format
03 XML and HTML Web Scrapping
04 Interacting with Databases
05 Binary Data Formats (Excel and HDF5)
06 Data Wrangling_ Munging and Pandas Data Structures
07 Combining and Merging Data Sets
08 Reshaping, Pivoting, and Advanced Indexing Data Sets
09 Data Transformation on Data Sets
10 String Manipulations on Data Sets
11 Working with Missing Data Sets
12 Data Aggregation on Data Sets
13 Group-Wise Operations on Data Sets
14 Statistical Functions Example
15 Windows Functions Example
16 Applying Multiple and Different Functions to Dataframe Columns
17 Exponentially Weighted Windows
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