# W1D04  Piscine AI - Data Science

## Data wrangling with Pandas

Data wrangling is one of the crucial tasks in data science and analysis which includes operations like:

- Data Sorting: To rearrange values in ascending or descending order.
- Data Filtration: To create a subset of available data.
- Data Reduction: To eliminate or replace unwanted values.
- Data Access: To read or write data files.
- Data Processing: To perform aggregation, statistical, and similar operations on specific values.
Ax explained before, Pandas is an open source library, specifically developed for data science and analysis. It is built upon the Numpy (to handle numeric data in tabular form) package and has inbuilt data structures to ease-up the process of data manipulation, aka data munging/wrangling.

## Exercises of the day

- Exercise 1 Concatenate
- Exercise 2 Merge
- Exercise 3 Merge MultiIndex
- Exercise 4 Groupby Apply
- Exercise 5 Groupby Agg
- Exercise 6 Unstack

## Virtual Environment 
- Python 3.x
- NumPy
- Pandas
- Jupyter or JupyterLab

*Version of Pandas I used to do the exercises: 1.0.1*. 
I suggest to use the most recent one.

## Resources

- https://jakevdp.github.io/PythonDataScienceHandbook/

- https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf

- https://www.learndatasci.com/tutorials/python-pandas-tutorial-complete-introduction-for-beginners/

- https://towardsdatascience.com/different-ways-to-iterate-over-rows-in-a-pandas-dataframe-performance-comparison-dc0d5dcef8fe