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ep:labs:061 [2019/11/18 06:22] andreea.alistar [Cheatsheet] |
ep:labs:061 [2023/10/07 21:54] (current) emilian.radoi [[10p] Feedback] |
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| - | ====== Lab 06 - Advanced plotting ====== | + | ====== Lab 06 - Advanced plotting (seaborn & pandas) ====== |
| - | + | ||
| - | <note important> | + | |
| - | Lab 06 (Advanced plotting) and Lab 07 (Test) will be switched. | + | |
| - | + | ||
| - | So this is actually Lab 07. | + | |
| - | </note> | + | |
| - | + | ||
| - | == You’ve got the basics, now let’s unleash the power! == | + | |
| ===== 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 | ||
| - | ===== Contents ===== | ||
| - | {{page>:ep:labs:061:meta:nav&nofooter&noeditbutton}} | + | ===== Resources ===== |
| - | ===== Introduction ===== | + | 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. |
| - | A quick plot is enough when you are exploring a data set or a function. But when you present your results to others you need to prepare the plots much more carefully so that they give the information to someone who does not know all the background you do. | + | For the exercises, you will explore the evolution of the COVID pandemic in Romania, using the information learned in this lab. |
| - | **Using PostScript plots with LaTeX** | + | 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. |
| - | - Make sure all the individual image files are properly trimmed EPS files. | + | Check out these cheetsheets for fast reference to the common libraries: |
| - | - Create a LaTeX document. | + | |
| - | - Process this document using LaTeX. | + | |
| - | - Use the dvips utility with the -E flag to turn the resulting DVI file into Encapsulated PostScript. | + | |
| - | ===== Cheatsheet ===== | + | **Cheat sheets:** |
| - | <code> | + | - [[https://perso.limsi.fr/pointal/_media/python:cours:mementopython3-english.pdf)|python]] |
| - | scatter plot: | + | - [[https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Numpy_Python_Cheat_Sheet.pdf|numpy]] |
| - | plot ’dataset.txt’ using 1:2 | + | - [[https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Matplotlib_Cheat_Sheet.pdf|matplotlib]] |
| - | plot ’dataset.txt’ using 1:2 with points | + | - [[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]] | ||
| - | example for the short format: | + | <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> |
| - | p ’dataset.txt’ u 1:2 w p pt 1 lt 2 lw 2 | + | |
| - | notitle | + | |
| - | line plot: | + | ===== Tasks ===== |
| - | plot ’dataset.txt’ using 1:2 with lines | + | |
| - | multiple data series: | + | ==== Google Colab Notebook ==== |
| - | use replot or separate by commas | + | |
| - | plot ’dataset.txt’ using 1:2, ’data.csv’ using 1:3 | + | |
| - | set key: | ||
| - | plot ’dataset.txt’ using 1:2 title "key" | ||
| - | </code> | ||
| - | ===== Tutorial ===== | + | 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**". |
| - | Datafile: {{:ep:labs:dataset.txt|}} | + | You can then export this python notebook as a PDF (**File -> Print**) and upload it to **Moodle**. |
| - | {{namespace>:ep:labs:061:contents:tutorial&nofooter&noeditbutton}} | + | ==== [10p] Feedback ==== |
| - | + | ||
| - | ===== Tasks ===== | + | |
| - | {{namespace>:ep:labs:061:contents:tasks&nofooter&noeditbutton}} | + | Please take a minute to fill in the **[[https://forms.gle/NpSRnoEh9NLYowFr5 | feedback form]]** for this lab. |
