10_week.Rmd

# 10 Data Wrangling in Python

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This week we will turn from broader programming and software engineering concepts to practical approaches of working with data in Python. In particular, we will focus on `pandas`, a versatile library for data analysis, which often serves as the first building block in many data-science pipelines.

## Required Readings

- McKinney Chs 4: [NumPy Basics](https://wesmckinney.com/book/numpy-basics.html), [5: Getting Started with Pandas](https://wesmckinney.com/book/pandas-basics.html), [6: Data Loading, Storage and File Formats](https://wesmckinney.com/book/accessing-data.html), [7: Data Cleaning and Preparation](https://wesmckinney.com/book/plotting-and-visualization.html), [8: Data Wrangling: Join, Combine and Reshape](https://wesmckinney.com/book/data-wrangling.html);

## Additional Readings

- Guttag Ch 23: Exploring Data with Pandas;
- McKinney Ch [9: Plotting and Visualization](https://wesmckinney.com/book/plotting-and-visualization.html);

## Tutorial

- Data input and output
- Working with pandas library