| Les deux révisions précédentes Révision précédente Prochaine révision | Révision précédente |
| teaching:progappchim:pandas [2022/03/21 09:43] – villersd | teaching:progappchim:pandas [2022/11/15 10:08] (Version actuelle) – [Références] villersd |
|---|
| * [[https://towardsdatascience.com/adding-regression-lines-to-pandas-plots-with-scipy-2e23fd524adb|Adding Regression Lines to Pandas Plots with SciPy]] (yc Jupyter notebook sur GitHub) | * [[https://towardsdatascience.com/adding-regression-lines-to-pandas-plots-with-scipy-2e23fd524adb|Adding Regression Lines to Pandas Plots with SciPy]] (yc Jupyter notebook sur GitHub) |
| * cf. [[matplotlib_simple]] | * cf. [[matplotlib_simple]] |
| | * [[https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_values.html|pandas.DataFrame.sort_values]] (documentation pandas.pydata.org) |
| |
| ===== Interface utilisateur graphique ===== | ===== Interface utilisateur graphique ===== |
| * [[https://python.plainenglish.io/add-some-style-to-your-pandas-dataframe-ae3ed4ac7804|Add Some Style to your Pandas DataFrame - Putting Some Pizzaz into your Data]] Curt Beck; Medium, Oct 11, 2020 | * [[https://python.plainenglish.io/add-some-style-to-your-pandas-dataframe-ae3ed4ac7804|Add Some Style to your Pandas DataFrame - Putting Some Pizzaz into your Data]] Curt Beck; Medium, Oct 11, 2020 |
| * [[https://towardsdatascience.com/spreadsheets-to-python-its-time-to-make-the-switch-ef49cf9463d5|Spreadsheets to Python: it's time]] Clive Siviour, Towards Data Science, Medium 03/09/2021 | * [[https://towardsdatascience.com/spreadsheets-to-python-its-time-to-make-the-switch-ef49cf9463d5|Spreadsheets to Python: it's time]] Clive Siviour, Towards Data Science, Medium 03/09/2021 |
| | * [[https://towardsdatascience.com/efficiently-iterating-over-rows-in-a-pandas-dataframe-7dd5f9992c01|Efficiently iterating over rows in a Pandas DataFrame - Never use iterrows and itertuples again]] Maxime Labonne, Towards Data Science (Medium), 21/03/2022 → l'approche pythonique "list comprehension" fait gagner un facteur énorme par rapport aux codes utilisant les boucles (certaines techniques sont carrément désastreuses), alors que ce n'est pas le cas avec des langages compilés comme C ou Fortran. Pandas étant construit au dessus de NumPy, il n'est pas étonnant que ses performances peuvent être dépassés par celles de Numpy dans certains cas (x1900 plutôt que x1500 dans cet article par rapport à la plus mauvaise solution). |
| | * [[https://towardsdatascience.com/pandas-df-to-numpy-array-c1a9e7d8585f|How To Convert Pandas DataFrame Into NumPy Array - Converting a pandas DataFrame into a NumPy array]] Giorgos Myrianthous, Medium, 06/05/2022 |
| | * [[https://towardsdatascience.com/20-of-pandas-functions-that-data-scientists-use-80-of-the-time-a4ff1b694707|20% of Pandas Functions that Data Scientists Use 80% of the Time - Putting Pareto’s Principle to work on the Pandas library]] Avi Chawla, Medium, 16/05/2022 |
| | - Reading a CSV file |
| | - Saving a DataFrame to a CSV file |
| | - Creating a DataFrame from a list of lists |
| | - Creating a DataFrame from a dictionary |
| | - Merging DataFrames |
| | - Sorting a DataFrame |
| | - Concatenating DataFrames |
| | - Rename column name |
| | - Add New Column |
| | - Filter DataFrame based on condition |
| | - Drop Column(s) |
| | - GroupBy |
| | - Unique Values in a column |
| | - Fill NaN values |
| | - Apply Function on a column |
| | - Remove Duplicates |
| | - Value Counts |
| | - Size of a DataFrame |
| | * [[https://towardsdatascience.com/powerful-one-liners-in-pandas-every-data-scientist-should-know-737e721b81b6|Powerful One-liners in Pandas Every Data Scientist Should Know - Things you can do in one line using Pandas]] Avi Chawla, Medium, 12/07/2022 |
| | * [[https://medium.com/@shouke.wei/how-to-read-dataset-from-google-drive-with-python-pandas-64bc07bbe35|How to Read Dataset from Google Drive with Python Pandas - Easy and convenient Method to read a dataset from the Google Drive]] Shouke Wei, Medium, 11/11/2022 |
| | |
| |
| |