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ep:labs:02 [2022/10/16 18:23] radu.mantu |
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| - | ====== Lab 02 - Memory Monitoring (Linux) ====== | + | ====== Lab 02 - Advanced Plotting ====== |
| ===== Objectives ===== | ===== Objectives ===== | ||
| - | * Offer an introduction to Virtual Memory. | + | * Introduction to pandas |
| - | * Get you acquainted with relevant commands and their outputs for monitoring memory related aspects. | + | * Easy data manipulations with pandas |
| - | * Introduce the concept of page de-duplication. | + | * Introduction to seaborn |
| - | * Present a step-by-step guide to Intel PIN for dynamic instrumentation. | + | * More types of cool looking plots with seaborn |
| - | ===== Contents ===== | + | * Apply what you learned on exploring COVID data for Romania |
| - | {{page>:ep:labs:02:meta:nav&nofooter&noeditbutton}} | ||
| - | ===== Proof of Work ===== | + | ===== Resources ===== |
| - | 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. | + | 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. |
| - | 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-3|moodle]]. The deadline is 23:55 on Friday. | + | 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. | ||
| - | ===== Introduction ===== | + | 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 ===== | ||
