印第安纳大学布鲁明顿分校经济学研究生的Python暑期迷你课程.zip

# Intro to Python This repository consists of Python material for the 2021 summer mini course at Indiana University-Bloomington. The course expects no prior experience with python programming. *Recordings can be provided to IU econ grad students upon request.* Instructor: Kairong Chen (Ph.D. Candidate at Department of Economics, Indiana University Bloomington)<br> Email: krchen@iu.edu # The course consists of 5 classes, covering 7 chapters: - [Chapter 1: Getting Started](https://github.com/kairongchen/python_minicourse/blob/main/Chapter%201%20-%20Getting%20Started.ipynb) starts with the python basic knowledge, including ipython, terminal commands, and Jupyter Notebook basics. - [Chapter 1b: Getting Started to Research](https://github.com/kairongchen/python_minicourse/blob/main/Chapter%201b%20-%20Getting%20Started%20to%20Research.ipynb) introduces how an IU researcher can get access to the computing or university subscribed resources, like supercomputer and WRDS subscription. A simple "Hello World" example with job-sending code on Carbonate is provided. I also include a simple SQL request to pull data from the WRDS. - [Chapter 2: Data Structure and Functions](https://github.com/kairongchen/python_minicourse/blob/main/Chapter%202%20-%20Data%20Structure%20and%20Functions.ipynb) introduces you the basic python built-in data structures, functions, files and operating system. - [Chapter 3: Numpy Basics](https://github.com/kairongchen/python_minicourse/blob/main/Chapter%203%20-%20Numpy%20Basics.ipynb) deals with numpy `ndarray` (n-dim array). It contains comparisons between ndarray and built-in list, matrix operations, and linear algebra functions. - [Chapter 4: Pandas and Data Cleaning](https://github.com/kairongchen/python_minicourse/blob/main/Chapter%204%20%E2%80%93%20Pandas%20Data%20Cleaning.ipynb) introduces the pandas DataFrame and how we can use it for data cleaning work, such like dealing with missing data, duplicates, etc. - [Chapter 5: Pandas Data Wrangling](https://github.com/kairongchen/python_minicourse/blob/main/Chapter%205%20%E2%80%93%20Pandas%20Data%20Wrangling.ipynb) is the chapter that explains more on panel data analysis. More specifically, it includes combining and merging dataset, group operations, time series data, and plotting. At the end, I wrap all things up in a Compustat example. - *Note that you need WRDS subscription to get access the Compustat dataset. You can find the code of pulling wrds data in the notebook. I didn't include the corresponding sample data here.* - [Chapter 6: String Manipulation and Regular Expression](https://github.com/kairongchen/python_minicourse/blob/main/Chapter%206%20-%20String%20Manipulation%20and%20Regular%20Expression.ipynb) is a short chapter that introduces string basics, and regular expression. - [Chapter 7: Web Scraping](https://github.com/kairongchen/python_minicourse/blob/main/Chapter%207%20-%20Web%20Scraping.ipynb) shows you some web scraping basics and tools. I use FOMC statement as the example, which is publicly available on Federal Reserve website.