| Les deux révisions précédentes Révision précédente Prochaine révision | Révision précédente Prochaine révisionLes deux révisions suivantes |
| teaching:progappchim:pandas [2019/03/25 12:17] – [Références] villersd | teaching:progappchim:pandas [2020/03/02 10:23] – [Références] villersd |
|---|
| * autres représentations | * autres représentations |
| * différentiation suivant le genre, l'âge | * différentiation suivant le genre, l'âge |
| | * utiliser d'autres fonctions, comme nsmallest() et nlargest(), value_counts() (se baser sur la documentation officielle) |
| * ... | * ... |
| |
| df.Weight.plot.hist() | df.Weight.plot.hist() |
| </code> | </code> |
| | |
| | ===== Interface utilisateur graphique ===== |
| | * [[https://github.com/adamerose/pandasgui|PandasGUI]] |
| | * [[https://github.com/tkrabel/bamboolib|bamboolib]] (closed source - non libre) |
| |
| ===== Références ===== | ===== Références ===== |
| * [[https://www.edureka.co/blog/python-pandas-tutorial/|Python Pandas Tutorial : Learn Pandas for Data Analysis]] | * [[https://www.edureka.co/blog/python-pandas-tutorial/|Python Pandas Tutorial : Learn Pandas for Data Analysis]] |
| * [[https://medium.com/dunder-data/minimally-sufficient-pandas-a8e67f2a2428|Minimally Sufficient Pandas]] | * [[https://medium.com/dunder-data/minimally-sufficient-pandas-a8e67f2a2428|Minimally Sufficient Pandas]] |
| | * //cf.// aussi [[https://github.com/tdpetrou/Master-Data-Analysis-with-Python|Master Data Analysis with Python]] et les données disponibles comme exemples |
| * [[https://towardsdatascience.com/python-for-data-science-8-concepts-you-may-have-forgotten-i-did-825966908393|Python for Data Science: 8 Concepts You May Have Forgotten]] | * [[https://towardsdatascience.com/python-for-data-science-8-concepts-you-may-have-forgotten-i-did-825966908393|Python for Data Science: 8 Concepts You May Have Forgotten]] |
| * [[https://towardsdatascience.com/23-great-pandas-codes-for-data-scientists-cca5ed9d8a38|23 great Pandas codes for Data Scientists]] | * [[https://towardsdatascience.com/23-great-pandas-codes-for-data-scientists-cca5ed9d8a38|23 great Pandas codes for Data Scientists]] |
| * [[https://github.com/fonnesbeck/statistical-analysis-python-tutorial|Statistical Data Analysis in Python]] | * [[https://github.com/fonnesbeck/statistical-analysis-python-tutorial|Statistical Data Analysis in Python]] |
| * [[http://cmdlinetips.com/2018/03/how-to-make-boxplots-in-python-with-pandas-and-seaborn/|How to Make Boxplots in Python with Pandas and Seaborn?]] (and Gapminder dataset) | * [[http://cmdlinetips.com/2018/03/how-to-make-boxplots-in-python-with-pandas-and-seaborn/|How to Make Boxplots in Python with Pandas and Seaborn?]] (and Gapminder dataset) |
| * [[https://www.geeksforgeeks.org/box-plot-visualization-with-pandas-and-seaborn/|Box plot visualization with Pandas and Seaborn]] | * [[https://www.geeksforgeeks.org/box-plot-visualization-with-pandas-and-seaborn/|Box plot visualization with Pandas and Seaborn] |
| | * [[https://towardsdatascience.com/complete-guide-to-data-visualization-with-python-2dd74df12b5e|Complete Guide to Data Visualization with Python]] (avec différentes librairies : matplotlib, Seaborn, bokeh, Altair, Folium avec des cartes,...) |
| |
| | ==== Datasets ==== |
| | * [[https://archive.ics.uci.edu/ml/datasets/Adult|Adult Data Set]] Predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset |
| | * [[https://archive.ics.uci.edu/ml/datasets.php]] |
| |
| ==== Exemples divers ==== | ==== Exemples divers ==== |