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ep:labs:061 [2020/11/18 11:49] ioan_adrian.cosma [Tutorial] |
ep:labs:061 [2023/10/07 21:54] (current) emilian.radoi [[10p] Feedback] |
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| ===== Objectives ===== | ===== Objectives ===== | ||
| - | * Conditional plotting | + | * Introduction to pandas |
| - | * Time-based data when plotting in gnuplot | + | * Easy data manipulations with pandas |
| - | * Advanced plotting concepts: Histograms, animations, heatmaps, three-dimensional plots | + | * Introduction to seaborn |
| - | * Insertion of graphics in the .tex file | + | * More types of cool looking plots with seaborn |
| + | * Apply what you learned on exploring COVID data for Romania | ||
| + | ===== Resources ===== | ||
| + | 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. | ||
| + | |||
| + | 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 ===== | ||
| - | {{namespace>:ep:labs:061:contents:tasks&nofooter&noeditbutton}} | + | ==== Google Colab Notebook ==== |
| + | |||
| + | |||
| + | 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**". | ||
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| + | 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/NpSRnoEh9NLYowFr5 | feedback form]]** for this lab. | ||
