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awesome-pandas
A collection of resources for pandas (Python) and related subjects.
https://github.com/tommyod/awesome-pandas
- Pandas tutorial for Data Science - | > 01:20| 2K+ | 2022 | :smiley: |
- part 1 - pandas)] | tommyod | na | 2:19 | 100 | 2019 | :smiley: |
- Data Science Best Practices with pandas - 2019-tutorial)] | Kevin Markham | PyCon | 3:23 | 1000 | 2019 | :smiley: |
- Thinking like a Panda
- Analyzing Census Data with Pandas - census-data)] | Sergio Sánchez | PyCon | 3:15 | 600 | 2019 | :smiley: |
- Pandas is for Everyone - pandas_tutorial)] | Daniel Chen | PyCon | 3:18 | 600 | 2019 | :smiley: |
- Pandas From The Ground Up - rhodes/pycon-pandas-tutorial)] | Brandon Rhodes | PyCon 2015 | 2:24 | 91000 | 2015 | :smiley: |
- Introduction Into Pandas - pydata-carolinas-pandas)] | Daniel Chen | Python Tutorial | 1:28 | 46000 | 2017 | :smiley: |
- Introduction To Data Analytics With Pandas
- Pandas for Data Analysis - 2017-tutorial-pandas)] | Daniel Chen | Enthought | 3:45 | 13000 | 2017 | :sweat_smile: |
- Optimizing Pandas Code - optimizing-pandas)] | Sofia Heisler | PyCon 2017 | 0:29 | 12000 | 2017 | :sweat_smile: |
- A Visual Guide To Pandas
- Analyzing and Manipulating Data with Pandas
- Time Series Analysis
- Predicting sports winners with pandas
- Pandas from the Inside - berlin2017-pandas-and-dask-from-the-inside)] [[2016 talk](https://www.youtube.com/watch?v=CowlcrtSyME)] | Stephen Simmons | PyData | 1:17 | 3000 | 2017 | :scream: |
- part 1 - 4ZaU) [[repo](https://github.com/jorisvandenbossche/pandas-tutorial)] | Joris Van den Bossche | EuroSciPy | 3:03 | 1000 | 2017 | :smiley: |
- Pandas: .head() to .tail() - chi-h2t)] | Tom Augspurger | PyData | 1:26 | 3000 | 2016 | :sweat_smile: |
- Performance Pandas - london)] | Jeff Reback | PyData | 0:43 | 2000 | 2015 | :sweat_smile: |
- Performance Pandas
- Python Data Science with pandas - 2018-Webcast)] | Matt Harrison | JetBrainsTV | 1:09 | 2000 | 2018 | :smiley: |
- What is the Future of Pandas - of-pandas-82901487)] | Jeff Reback | PyData | 0:31 | 4000 | 2017 | :smiley: |
- Introduction to Python for Data Science - ds-2018)] | Skipper Seabold | PyData | 3:18 | 300 | 2018 | :smiley: |
- Pandas for Better (and Worse) Data Science - 2018-tutorial)] | Kevin Markham | PyCon 2018 | 3:21 | 3000 | 2018 | :smiley: |
- Data Wrangling with pandas
- The pandas DataFrame Object
- Python For Data Science - pandas Basics
- Python For Data Science - pandas
- 10 Minutes to pandas
- pandas_exercises
- pycon-pandas-tutorial
- Learn-Pandas
- pandas-cookbook
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- pandas-tutorial - 4ZaU)]
- pandas_tutorial
- scipy-2017-tutorial-pandas
- Pandas-Tutorial
- sklearn_pandas_tutorial
- pandas_basics
- first-python-notebook
- Learn Pandas
- Pandas practice website
- [amazon
- [amazon
- [manning
- [manning - on guide shows you how to start mastering Pandas with skills you already know from spreadsheet software.*. 1 edition. Manning Publications, 2021.
- NumPy Beginner - Tutorial-SciPyConf-2016)] | Alexandre Chabot LeClerc | Enthought | 2:47 | 56000 | NumPy | 2016 | :sweat_smile: |
- Machine Learning
- The Python Visualization Landscape
- JupyterLab: Building Blocks for Interactive Computing
- Machine Learning with Scikit Learn
- Machine Learning for Time Series Data in Python
- Computational Statistics
- Time Series Analysis
- Learning TensorFlow
- JupyterHub: Deploying Jupyter Notebooks
- Applied Time Series Econometrics
- Machine Learning with scikit learn - 2017-sklearn)] | Andreas Mueller & Alexandre Gram | Enthought | 3:10 | 8000 | sklearn | 2017 | :sweat_smile: |
- Introduction to Numerical Computing with NumPy
- Dask - A Pythonic Distributed Data Science Framework
- Introduction to Statistical Modeling with Python
- Fully Convolutional Networks for Image Segmentation
- Exploratory data analysis in python - 2017-eda-tutorial)] | Chloe Mawer & Jonathan Whitmore | PyCon 2017 | 2:54 | 7000 | scipy | 2017 | :smiley: |
- Libraries for Deep Learning with Sequences
- Numba - Tell Those C++ Bullies to Get Lost
- Deploying Interactive Jupyter Dashboards
- Data Science Using Functional Python
- Anatomy of matplotlib
- Anatomy of matplotlib
- Data Science is Software - science-is-software)] | Peter Bull & Isaac Slavitt | Enthought | 2:12 | 9000 | jupyter | 2016 | :smiley: |
- Machine Learning with Scikit Learn
- Using Jupyter notebooks - _Interactive_Data_Displays_With_Jupyter_Notebooks)] | Ioanna Ioannou | PyCon Australia | 0:28 | 8000 | jupyter | 2016 | :sweat_smile: |
- Parallel Python: Analyzing Large Datasets - tutorial)] | Matthew Rocklin | Enthought | 3:05 | 7000 | scipy | 2016 | :scream: |
- Keynote: Project Jupyter
- matplotlib beginner tutorial - tutorial)] | Nicolas Rougier | Enthought | 2:59 | 6000 | matplotlib | 2016 | :sweat_smile: |
- Awesome Big Data Algorithms
- All About Jupyter
- PyMC: Markov Chain Monte Carlo
- Jupyter Advanced Topics Tutorial - advanced-tutorial)] | Jonathan Frederic & Matthias Bussonier | Enthought | 2:48 | 4000 | jupyter | 2015 | :scream: |
- Using randomness to make code much faster
- Python Profiling & Performance
- Using List Comprehensions and Generator Expressions
- Foundations of Numerical Computing
- Numpy Cheat Sheet
- Python For Data Science - Scikit-Learn
- Scipy Lecture Notes
- [amazon - Iglesias, Juan, Stéfan van der Walt, and Harriet Dashnow. *Elegant SciPy: The Art of Scientific Python*. 1 edition. O’Reilly Media, 2017.
- From Python to Numpy
- [amazon - On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems*. 1 edition. O’Reilly Media, 2017.
- So you want to be a Python expert?
- Transforming Code into Beautiful, Idiomatic Python
- Builtin Superheroes
- How to become a Data Scientist in 6 months
- Modern Dictionaries
- Keynote on Concurrency
- The Fun of Reinvention
- Being a Core Developer in Python
- Visualizing Geographic Data
- Python's Class Development Toolkit
- The Other Async (Threads + Async = ❤️) - YouTube
- Functional Programming with Python
- Building a Recommendation Engine using Python
- Iterations of Evolution
- "Good Enough" IS Good Enough!
- Automating Code Quality
- Python 3 Cheat Sheet
- Python Cheat Sheet
- [amazon - Wesley Professional, 2015.
- [amazon
- [amazon - scm.com/book/en/v2)] Chacon, Scott, and Ben Straub. *Pro Git*. 2nd ed. edition. New York, NY: Apress, 2014.
- [amazon
- [amazon
- [amazon
- How to become a Data Scientist in 6 months
- Introduction Into Pandas
- So you want to be a Python expert?
- NumPy Beginner - Tutorial-SciPyConf-2016)] | Alexandre Chabot LeClerc | Enthought | 2:47 | 56000 | NumPy | 2016 | :snake: :snake: |
- Introduction To Data Analytics With Pandas
- Transforming Code into Beautiful, Idiomatic Python
- Machine Learning
- Pandas From The Ground Up - rhodes/pycon-pandas-tutorial)] | Brandon Rhodes | PyCon 2015 | 2:24 | 91000 | pandas | 2015 | :snake: :snake: |
- Modern Dictionaries
- The Python Visualization Landscape
- Keynote on Concurrency
- Pandas for Data Analysis - 2017-tutorial-pandas)] | Daniel Chen | Enthought | 3:45 | 13000 | pandas | 2017 | :snake::snake: |
- JupyterLab: Building Blocks for Interactive Computing
- Optimizing Pandas Code for Speed and Efficiency
- A Visual Guide To Pandas
- Machine Learning with Scikit Learn
- Machine Learning for Time Series Data in Python
- The Fun of Reinvention
- Analyzing and Manipulating Data with Pandas
- Computational Statistics
- Being a Core Developer in Python
- Time Series Analysis
- Learning TensorFlow
- JupyterHub: Deploying Jupyter Notebooks
- Applied Time Series Econometrics
- Machine Learning with scikit learn - 2017-sklearn)] | Andreas Mueller & Alexandre Gram | Enthought | 3:10 | 8000 | sklearn | 2017 | :snake: :snake: |
- Introduction to Numerical Computing with NumPy
- Dask - A Pythonic Distributed Data Science Framework
- Introduction to Statistical Modeling with Python
- Fully Convolutional Networks for Image Segmentation
- Exploratory data analysis in python - 2017-eda-tutorial)] | Chloe Mawer & Jonathan Whitmore | PyCon 2017 | 2:54 | 7000 | scipy | 2017 | :snake: |
- Visualizing Geographic Data
- Builtin Superheroes
- Python's Class Development Toolkit
- Libraries for Deep Learning with Sequences
- The Other Async (Threads + Async = ❤️) - YouTube
- Numba - Tell Those C++ Bullies to Get Lost
- Deploying Interactive Jupyter Dashboards
- Eyal Trabelsi - Practical Optimisations for Pandas
- Data Science Using Functional Python
- Pandas from the Inside
- Anatomy of matplotlib
- Anatomy of matplotlib
- Data Science is Software - science-is-software)] | Peter Bull & Isaac Slavitt | Enthought | 2:12 | 9000 | jupyter | 2016 | :snake: |
- Machine Learning with Scikit Learn
- Using Jupyter notebooks
- Parallel Python: Analyzing Large Datasets - tutorial)] | Matthew Rocklin | Enthought | 3:05 | 7000 | scipy | 2016 | Novice |
- Functional Programming with Python
- Predicting sports winners with pandas and scikit-learn
- Keynote: Project Jupyter
- matplotlib beginner tutorial - tutorial)] | Nicolas Rougier | Enthought | 2:59 | 6000 | matplotlib | 2016 | Novice |
- Awesome Big Data Algorithms
- Pandas from the Inside
- All About Jupyter
- Building a Recommendation Engine using Python
- Iterations of Evolution
- "Good Enough" IS Good Enough!
- PyMC: Markov Chain Monte Carlo
- Jupyter Advanced Topics Tutorial - advanced-tutorial)] | Jonathan Frederic & Matthias Bussonier | Enthought | 2:48 | 4000 | jupyter | 2015 | Novice |
- Using randomness to make code much faster
- Python Profiling & Performance
- datatest - Tools for test driven data-wrangling and data validation (DataFrame, Series, Index, MultiIndex).
- pandera - A light-weight, flexible, and expressive data validation library for dataframes.
- pandas-vet - A plugin for Flake8 that checks pandas code.
Programming Languages
Keywords
pandas
5
python
4
data-analysis
2
tutorial
2
exercise
1
practice
1
training
1
ipython-notebook
1
tutorial-code
1
altair
1
data-journalism
1
education
1
journalism
1
jupyter
1
jupyter-notebook
1
jupyterlab
1
news
1
sphinx
1
data-wrangling
1
pytest-plugin
1
quality-assurance
1
testing
1
unittest
1
flake8
1
flake8-plugin
1
linter
1