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48 lines
1.8 KiB
48 lines
1.8 KiB
2 years ago
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# W1D02 Piscine AI - Data Science
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## Pandas
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The goal of this day is to understand practical usage of **Pandas**.
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As **Pandas** in intensively used in Data Science, other days of the piscine will be dedicated to it.
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Not only is the **Pandas** library a central component of the data science toolkit but it is used in conjunction with other libraries in that collection.
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**Pandas** is built on top of the NumPy package, meaning a lot of the structure of NumPy is used or replicated in **Pandas**. Data in **Pandas** is often used to feed statistical analysis in SciPy, plotting functions from Matplotlib, and machine learning algorithms in Scikit-learn.
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Most of the topics we will cover today are explained and describes with examples in the first resource. The number of exercises is low on purpose: Take the time to understand the chapter 5 of the resource, even if there are 40 pages.
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## Exercises of the day
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- Exercise 1 Your first DataFrame
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- Exercise 2 Electric power consumption
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- Exercise 3 E-commerce purchases
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- Exercise 4 Handling missing values
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## Virtual Environment
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- Python 3.x
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- NumPy
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- Pandas
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- Jupyter or JupyterLab
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*Version of Pandas I used to do the exercises: 1.0.1*.
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I suggest to use the most recent one.
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## Resources
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- If I had to give you one resource it would be this one:
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https://bedford-computing.co.uk/learning/wp-content/uploads/2015/10/Python-for-Data-Analysis.pdf
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It contains ALL you need to know about Pandas.
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- Pandas documentation:
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- https://pandas.pydata.org/docs/
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- https://jakevdp.github.io/PythonDataScienceHandbook/
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- https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf
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- https://www.learndatasci.com/tutorials/python-pandas-tutorial-complete-introduction-for-beginners/
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- https://jakevdp.github.io/PythonDataScienceHandbook/03.04-missing-values.html
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