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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
Exercise 0 Environment and libraries
The goal of this exercise is to set up the Python work environment with the required libraries.
Note: For each quest, your first exercice will be to set up the virtual environment with the required libraries.
I recommend to use:
- the last stable versions of Python.
- the virtual environment you're the most confortable with.
virtualenvandcondaare the most used in Data Science. - one of the most recents versions of the libraries required
- Create a virtual environment named
ex00, with a version of Python >=3.8, with the following libraries:pandas,numpyandjupyter.