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ep:labs:061 [2020/11/18 12:13] ioan_adrian.cosma [Google Colab Notebook] |
ep:labs:061 [2025/02/11 23:59] (current) cezar.craciunoiu |
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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. | ||
| - | For the exercises, you will explore the evolution of the COVID pandemic in Romania, using the information learned in this lab. | + | ===== Proof of Work ===== |
| - | 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. | + | Before you start, create a [[http://docs.google.com/|Google Doc]]. Here, you will add screenshots / code snippets / comments for each exercise. Whatever you decide to include, it must prove that you managed to solve the given task (so don't show just the output, but how you obtained it and what conclusion can be drawn from it). If you decide to complete the feedback for bonus points, include a screenshot with the form submission confirmation, but not with its contents. |
| - | Check out these cheetsheets for fast reference to the common libraries: | + | When done, export the document as a //pdf// and upload in the appropriate assignment on [[https://curs.upb.ro/2021/course/view.php?id=5665#section-5|moodle]]. Remember, the cut-off time is 15m after the lab ends. |
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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]] | + | |
| - | + | ||
| - | <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}} |
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| - | + | ||
| - | 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. | ||
