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ep:labs:061 [2020/11/18 12:13] ioan_adrian.cosma [Google Colab Notebook] |
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. | + | |
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| - | For the exercises, you will explore the evolution of the COVID pandemic in Romania, using the information learned in this lab. | + | |
| - | + | ||
| - | 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. | + | |
| - | + | ||
| - | Check out these cheetsheets for fast reference to the common libraries: | + | |
| - | + | ||
| - | **Cheat sheets:** | + | |
| - | + | ||
| - | - [[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]] | + | |
| - | + | ||
| - | <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> | + | |
| ===== Tasks ===== | ===== Tasks ===== | ||
| - | ==== Google Colab Notebook ==== | + | {{namespace>:ep:labs:061:contents:tasks&nofooter&noeditbutton}} |
| - | + | ||
| - | + | ||
| - | For this lab, we will use Google Colab for exploring pandas and seaborn. Please solve your tasks [[https://github.com/cosmaadrian/ml-environment/blob/master/EP_Plotting_II.ipynb|here]] by clicking "**Open in Colaboratory**". | + | |
| - | + | ||
| - | You can then export this python notebook as a PDF (**File -> Print**) and upload it to **Moodle**. | + | |
| - | + | ||
| - | ==== [10p] Feedback ==== | + | |
| - | Please take a minute to fill in the **[[https://forms.gle/k7FqUM16AcrkgKTJA | feedback form]]** for this lab. | ||
