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ep:labs:061 [2020/11/18 12:11] ioan_adrian.cosma |
ep:labs:061 [2026/03/12 10:57] (current) radu.mantu |
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| - | ====== Lab 06 - Advanced plotting (seaborn & pandas) ====== | + | ====== Lab 06 - Network Monitoring ====== |
| ===== Objectives ===== | ===== Objectives ===== | ||
| - | * Introduction to pandas | + | * Dive into the inner workings of previously studied traffic monitoring / filtering tools |
| - | * Easy data manipulations with pandas | + | * Discuss methods of path discovery |
| - | * Introduction to seaborn | + | * Provide an introduction to protocol options |
| - | * More types of cool looking plots with seaborn | + | |
| - | * Apply what you learned on exploring COVID data for Romania | + | |
| + | ===== Contents ===== | ||
| - | ===== Resources ===== | + | {{page>:ep:labs:061:meta:nav&nofooter&noeditbutton}} |
| - | In this lab, we will study the basic API of pandas for easier data manipulations, and seaborn for some more advanced and visually appealing plots that are also easy to produce. | + | ===== Tasks ===== |
| - | For the exercises, you will explore the evolution of the COVID pandemic in Romania, using the information learned in this lab. | + | {{namespace>:ep:labs:061:contents:tasks&nofooter&noeditbutton}} |
| - | For scientific computing we need an environment that is easy to use, and provides a couple of tools like manipulating data and visualizing results. We will use Google Colab, which comes with a variety of useful tools already installed. | ||
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| - | Check out these cheetsheets for fast reference to the common libraries: | ||
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| - | **Cheat sheets:** | ||
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| - | - [[https://perso.limsi.fr/pointal/_media/python:cours:mementopython3-english.pdf)|python]] | ||
| - | - [[https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Numpy_Python_Cheat_Sheet.pdf|numpy]] | ||
| - | - [[https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Matplotlib_Cheat_Sheet.pdf|matplotlib]] | ||
| - | - [[https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf|sklearn]] | ||
| - | - [[https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf|pandas]] | ||
| - | - [[https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Seaborn_Cheat_Sheet.pdf|seaborn]] | ||
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| - | <note>This lab is organized in a Jupyer Notebook hosted on Google Colab. You will find there some intuitions and applications for pandas and seaborn. Check out the Tasks section below.</note> | ||
