Awesome
awesome-pandas
A collection of resources for pandas (Python) and related subjects. Pull requests are very welcome!
Contents: This is an unofficial collection of resources for learning pandas, an open source Python library for data analysis. Here you will find videos, cheat-sheets, tutorials and books / papers. The curated list is divided into three parts:
- pandas resources - A collection of videos, cheat-sheets, tutorials and books directly related to pandas.
- Data analysis with Python resources - Material related to adjacent Python libraries and software such as NumPy, scipy, matplotlib, seaborn, statsmodels and Jupyter.
- Miscellaneous related resources - Resources related to general data analysis, Python programming, algorithms, computer science, machine learning, statistics, etc.
- Packages - Python packages for helping to work with Pandas.
(1) :panda_face: pandas resources
(1.1) :tv: Videos
The videos below were collected in July of 2018. They are all directly related to pandas, and the Level of a video is quantified roughly as follows:
- :smiley: : Beginner - requires little knowledge to jump into, elementary topics.
- :sweat_smile: : Intermediate - some prior knowledge needed, more technical.
- :scream: : Advanced - very technical, or discusses advanced topics.
- :star: : Recommended video - high quality video and audio, great presentation.
| Title | Speaker | Uploader | Time | Views | Year | Level |
|---|---|---|---|---|---|---|
| Pandas tutorial for Data Science | Bikram Kundu | - | > 01:20 | 2K+ | 2022 | :smiley: |
| Python for Data Analysis using Pandas part 1 & part 2 [repo] | tommyod | na | 2:19 | 100 | 2019 | :smiley: |
| Data Science Best Practices with pandas [repo] | Kevin Markham | PyCon | 3:23 | 1000 | 2019 | :smiley: |
| Thinking like a Panda | Hannah Stepanek | PyCon | 0:36 | 700 | 2019 | :smiley: |
| Analyzing Census Data with Pandas [repo] | Sergio Sánchez | PyCon | 3:15 | 600 | 2019 | :smiley: |
| Pandas is for Everyone [repo] | Daniel Chen | PyCon | 3:18 | 600 | 2019 | :smiley: |
| :star: Pandas From The Ground Up [repo] | Brandon Rhodes | PyCon 2015 | 2:24 | 91000 | 2015 | :smiley: |
| Introduction Into Pandas [repo] | Daniel Chen | Python Tutorial | 1:28 | 46000 | 2017 | :smiley: |
| Introduction To Data Analytics With Pandas [repo] | Quentin Caudron | Python Tutorial | 1:51 | 25000 | 2017 | :smiley: |
| Pandas for Data Analysis [repo] | Daniel Chen | Enthought | 3:45 | 13000 | 2017 | :sweat_smile: |
| Optimizing Pandas Code [repo] | Sofia Heisler | PyCon 2017 | 0:29 | 12000 | 2017 | :sweat_smile: |
| A Visual Guide To Pandas | Jason Wirth | Next Day Video | 0:26 | 49000 | 2015 | :smiley: |
| Analyzing and Manipulating Data with Pandas [repo] | Jonathan Rocher | Enthought | 3:33 | 22000 | 2016 | :smiley: |
| Time Series Analysis [repo] | Aileen Nielsen | PyCon 2017 | 3:11 | 9000 | 2017 | :sweat_smile: |
| Predicting sports winners with pandas | Robert Layton | PyCon Australia | 0:38 | 13000 | 2015 | :sweat_smile: |
| Pandas from the Inside [repo] [2016 talk] | Stephen Simmons | PyData | 1:17 | 3000 | 2017 | :scream: |
| Pandas part 1 & part 2 [repo] | Joris Van den Bossche | EuroSciPy | 3:03 | 1000 | 2017 | :smiley: |
| Pandas: .head() to .tail() [repo] | Tom Augspurger | PyData | 1:26 | 3000 | 2016 | :sweat_smile: |
| Performance Pandas (london) [repo] | Jeff Reback | PyData | 0:43 | 2000 | 2015 | :sweat_smile: |
| Performance Pandas (NYC) [repo] | Jeff Reback | PyData | 1:26 | 3000 | 2015 | :sweat_smile: |
| Python Data Science with pandas [repo] | Matt Harrison | JetBrainsTV | 1:09 | 2000 | 2018 | :smiley: |
| What is the Future of Pandas [slides] | Jeff Reback | PyData | 0:31 | 4000 | 2017 | :smiley: |
| Introduction to Python for Data Science [repo] | Skipper Seabold | PyData | 3:18 | 300 | 2018 | :smiley: |
| Pandas for Better (and Worse) Data Science [repo] | Kevin Markham | PyCon 2018 | 3:21 | 3000 | 2018 | :smiley: |
Know of a recent, good video? Send a pull request! :+1:
(1.2) :exclamation: Cheat-sheets
- Data Wrangling with pandas
- The pandas DataFrame Object
- Python For Data Science - pandas Basics
- Python For Data Science - pandas
(1.3) :mortar_board: Tutorials
- :star: 10 Minutes to pandas
- :star: pandas_exercises
- :star: pycon-pandas-tutorial [Video: Pandas From The Ground Up]
- :star: Learn-Pandas
- :star: pandas-cookbook
- :star: Modern pandas. Parts: 1, 2, 3, 4, 5, 6 and 7.
- pandas-tutorial [Video: Pandas & Advanced Pandas]
- pandas_tutorial [Video: Analyzing and Manipulating Data with Pandas]
- scipy-2017-tutorial-pandas [Video: Pandas for Data Analysis]
- Pandas-Tutorial
- sklearn_pandas_tutorial
- pandas_basics
- first-python-notebook
- Learn Pandas
- Pandas practice website
(1.4) :blue_book: Books / papers
- [amazon] McKinney, Wes. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. 2 edition. O’Reilly Media, 2017.
- [amazon] VanderPlas, Jake. Python Data Science Handbook: Essential Tools for Working with Data. 1 edition. O’Reilly Media, 2016.
- [manning] Lerner, Reuven. 50 exercises that will strengthen your pandas skills to a level of automatic fluency. 1 edition. Manning Publications, 2021.
- [manning] Paskhaver, Boris. This friendly and hands-on guide shows you how to start mastering Pandas with skills you already know from spreadsheet software.. 1 edition. Manning Publications, 2021.
(2) Data analysis with Python resources
(2.1) :tv: Videos
| Title | Speaker | Uploader | Time | Views | Keyword | Year | Level |
|---|---|---|---|---|---|---|---|
| NumPy Beginner [repo] | Alexandre Chabot LeClerc | Enthought | 2:47 | 56000 | NumPy | 2016 | :sweat_smile: |
| Machine Learning | Andreas Mueller & Sebastian Raschka | Enthought | 3:03 | 47000 | sklearn | 2016 | :sweat_smile: |
| The Python Visualization Landscape | Jake VanderPlas | PyCon 2017 | 0:33 | 21000 | python | 2017 | :smiley: |
| JupyterLab: Building Blocks for Interactive Computing | Brian Granger | Enthought | 0:29 | 28000 | jupyter | 2016 | :smiley: |
| Machine Learning with Scikit Learn [repo] | Andreas Mueller & Kyle Kastner | Enthought | 3:22 | 48000 | sklearn | 2015 | :sweat_smile: |
| Machine Learning for Time Series Data in Python | Brett Naul | Enthought | 0:24 | 24000 | cesium | 2016 | :smiley: |
| Computational Statistics [repo] | Allen Downey | Enthought | 2:05 | 10000 | scipy | 2017 | :sweat_smile: |
| Time Series Analysis [repo] | Aileen Nielsen | PyCon 2017 | 3:11 | 9000 | pandas | 2017 | :sweat_smile: |
| Learning TensorFlow | Robert Layton | PyCon Australia | 0:40 | 18000 | tensorflow | 2016 | :sweat_smile: |
| JupyterHub: Deploying Jupyter Notebooks | Min Ragan Kelley & Thomas Kluyver | PyData | 1:36 | 17000 | jupyter | 2016 | :smiley: |
| Applied Time Series Econometrics | Jeffrey Yau | PyData | 1:39 | 17000 | statsmodels | 2016 | :sweat_smile: |
| Machine Learning with scikit learn [repo] | Andreas Mueller & Alexandre Gram | Enthought | 3:10 | 8000 | sklearn | 2017 | :sweat_smile: |
| Introduction to Numerical Computing with NumPy | Dillon Niederhut | Enthought | 2:27 | 8000 | NumPy | 2017 | :smiley: |
| Dask - A Pythonic Distributed Data Science Framework | Matthew Rocklin | PyCon 2017 | 0:46 | 7000 | dask | 2017 | :sweat_smile: |
| Introduction to Statistical Modeling with Python [repo] | Christopher Fonnesbeck | PyCon 2017 | 3:19 | 7000 | scipy | 2017 | :sweat_smile: |
| Fully Convolutional Networks for Image Segmentation | Daniil Pakhomov | Enthought | 0:20 | 7000 | scipy | 2017 | :smiley: |
| Exploratory data analysis in python [repo] | Chloe Mawer & Jonathan Whitmore | PyCon 2017 | 2:54 | 7000 | scipy | 2017 | :smiley: |
| Libraries for Deep Learning with Sequences | Alex Rubinsteyn | PyData | 0:44 | 23000 | scipy | 2015 | :sweat_smile: |
| Numba - Tell Those C++ Bullies to Get Lost [repo] | Gil Forsyth & Lorena Barba | Enthought | 2:25 | 5000 | numba | 2017 | :sweat_smile: |
| Deploying Interactive Jupyter Dashboards | Philipp Rudiger | Enthought | 0:18 | 5000 | jupyter | 2017 | :sweat_smile: |
| Data Science Using Functional Python | Joel Grus | PyData | 0:44 | 18000 | python | 2015 | :sweat_smile: |
| Anatomy of matplotlib [repo] | Benjamin Root & Joe Kington | Enthought | 3:18 | 18000 | matplotlib | 2015 | :sweat_smile: |
| Anatomy of matplotlib [repo] | Benjamin Root | Enthought | 3:02 | 4000 | matplotlib | 2017 | :sweat_smile: |
| Data Science is Software [repo] | Peter Bull & Isaac Slavitt | Enthought | 2:12 | 9000 | jupyter | 2016 | :smiley: |
| Machine Learning with Scikit Learn [repo] | Jake VanderPlas | PyData | 1:34 | 16000 | sklearn | 2015 | :sweat_smile: |
| Using Jupyter notebooks [repo] | Ioanna Ioannou | PyCon Australia | 0:28 | 8000 | jupyter | 2016 | :sweat_smile: |
| Parallel Python: Analyzing Large Datasets [repo] | Matthew Rocklin | Enthought | 3:05 | 7000 | scipy | 2016 | :scream: |
| Keynote: Project Jupyter | Brian Granger | Enthought | 0:48 | 7000 | jupyter | 2016 | :sweat_smile: |
| matplotlib beginner tutorial [repo] | Nicolas Rougier | Enthought | 2:59 | 6000 | matplotlib | 2016 | :sweat_smile: |
| Awesome Big Data Algorithms | Titus Brown | Next Day Video | 0:39 | 41000 | python | 2013 | :scream: |
| All About Jupyter | Brian Granger | PyData | 0:39 | 11000 | jupyter | 2015 | :sweat_smile: |
| PyMC: Markov Chain Monte Carlo | Chris Fonnesbeck | Enthought | 0:20 | 9000 | pyMC | 2014 | :sweat_smile: |
| Jupyter Advanced Topics Tutorial [repo] | Jonathan Frederic & Matthias Bussonier | Enthought | 2:48 | 4000 | jupyter | 2015 | :scream: |
| Using randomness to make code much faster | Rachel Thomas | SF Python | 0:54 | 1000 | scipy | 2017 | :sweat_smile: |
| Python Profiling & Performance | Mahmoud Hashemi | SF Python | 0:28 | 1000 | python | 2016 | :sweat_smile: |
| Using List Comprehensions and Generator Expressions | Trey Hunner | PyCon 2018 | 3:21 | 3000 | python | 2018 | :sweat_smile: |
| Foundations of Numerical Computing | Scott Sanderson | PyCon 2018 | 3:22 | 1000 | python | 2018 | :sweat_smile: |
(2.2) :exclamation: Cheat-sheets
(2.3) :mortar_board: Tutorials
(2.4) :blue_book: Books / papers
- Varoquaux, Gael, Valentin Haenel, Emmanuelle Gouillart, Zbigniew Jędrzejewski-Szmek, Ralf Gommers, Fabian Pedregosa, Olav Vahtras, et al. Scipy Lecture Notes. Zenodo, September 28, 2015. https://doi.org/10.5281/zenodo.31521.
- [amazon] Nunez-Iglesias, Juan, Stéfan van der Walt, and Harriet Dashnow. Elegant SciPy: The Art of Scientific Python. 1 edition. O’Reilly Media, 2017.
- Rougier, Nicolas P. From Python to Numpy Zenodo, December 31, 2016. https://doi.org/10.5281/zenodo.225783.
- [amazon] Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. 1 edition. O’Reilly Media, 2017.
(3) Miscellaneous related resources
(3.1) :tv: Videos
| Title | Speaker | Uploader | Time | Views | Keyword | Year | Level |
|---|---|---|---|---|---|---|---|
| :star: So you want to be a Python expert? | James Powell | PyData | 1:54 | 28000 | python | 2017 | :scream: |
| :star: Transforming Code into Beautiful, Idiomatic Python | Raymond Hettinger | Next Day Video | 0:48 | 340000 | python | 2013 | :smiley: |
| :star: Builtin Superheroes | David Beazley | David Beazley | 0:44 | 12000 | python | 2016 | :sweat_smile: |
| How to become a Data Scientist in 6 months | Tetiana Ivanova | PyData | 0:56 | 148000 | misc | 2016 | :smiley: |
| Modern Dictionaries | Raymond Hettinger | SF Python | 1:07 | 44000 | python | 2016 | :sweat_smile: |
| Keynote on Concurrency | Raymond Hettinger | SF Python | 1:13 | 15000 | python | 2017 | :sweat_smile: |
| The Fun of Reinvention | David Beazley | David Beazley | 0:52 | 11000 | python | 2017 | :scream: |
| Being a Core Developer in Python | Raymond Hettinger | SF Python | 1:02 | 19000 | python | 2016 | :smiley: |
| Visualizing Geographic Data | Christopher Roach | PyData | 0:31 | 14000 | python | 2016 | :smiley: |
| Python's Class Development Toolkit | Raymond Hettinger | Next Day Video | 0:45 | 80000 | python | 2013 | :sweat_smile: |
| The Other Async (Threads + Async = ❤️) - YouTube | David Beazley | David Beazley | 0:47 | 5000 | python | 2017 | :scream: |
| Functional Programming with Python | Mike Müller | Next Day Video | 0:27 | 44000 | python | 2013 | Novice |
| Building a Recommendation Engine using Python | Anusua Trivedi | PyData | 0:37 | 11000 | python | 2015 | Novice |
| Iterations of Evolution | David Beazley | David Beazley | 0:34 | 2000 | python | 2017 | Novice |
| "Good Enough" IS Good Enough! | Alex Martelli | SF Python | 0:53 | 4000 | python | 2016 | Novice |
| Automating Code Quality | Kyle Knapp | PyCon 2018 | 0:30 | 3000 | python | 2018 | :sweat_smile: |
(3.2) :exclamation: Cheat-sheets
(3.3) :mortar_board: Tutorials
(3.4) :blue_book: Books / papers
- [amazon] Slatkin, Brett. Effective Python: 59 Specific Ways to Write Better Python. 1 edition. Addison-Wesley Professional, 2015.
- [amazon] Ramalho, Luciano. Fluent Python. 1st edition. O’Reilly, 2015.
- [pdf] P Rougier, Nicolas, Michael Droettboom, and Philip Bourne. "Ten Simple Rules for Better Figures." PLoS Computational Biology 10 (September 1, 2014): e1003833. https://doi.org/10.1371/journal.pcbi.1003833.
- [pdf] Tidy Data | Wickham | Journal of Statistical Software. Accessed December 31, 2017. https://doi.org/10.18637/jss.v059.i10.
- [amazon] [online] Chacon, Scott, and Ben Straub. Pro Git. 2nd ed. edition. New York, NY: Apress, 2014.
The books below are perhaps of an even more general nature.
- [amazon] Dasgupta, Sanjoy, Christos H. . Papadimitriou, and Umesh Virkumar. Vazirani. Algorithms. Boston, Mass: McGraw Hill, 2008.
- [amazon] Lloyd N. Trefethen. Numerical Linear Algebra. Society for Industrial and Applied Mathematics, 1997.
- [amazon] Gene H. Golub. Matrix Computations. 4th ed. Johns Hopkins Studies in the Mathematical Sciences. Baltimore: Johns Hopkins University Press, 2013.
Every video is below.
| Title | Speaker | Uploader | Time | Views | Keyword | Year | Level |
|---|---|---|---|---|---|---|---|
| How to become a Data Scientist in 6 months | Tetiana Ivanova | PyData | 0:56 | 148000 | misc | 2016 | :snake: |
| Introduction Into Pandas | Daniel Chen | Python Tutorial | 1:28 | 46000 | pandas | 2017 | :snake: |
| So you want to be a Python expert? | James Powell | PyData | 1:54 | 28000 | python | 2017 | :snake::snake::snake: |
| NumPy Beginner [repo] | Alexandre Chabot LeClerc | Enthought | 2:47 | 56000 | NumPy | 2016 | :snake: :snake: |
| Introduction To Data Analytics With Pandas | Quentin Caudron | Python Tutorial | 1:51 | 25000 | pandas | 2017 | :snake: |
| Transforming Code into Beautiful, Idiomatic Python | Raymond Hettinger | Next Day Video | 0:48 | 340000 | python | 2013 | :snake: |
| Machine Learning | Andreas Mueller & Sebastian Raschka | Enthought | 3:03 | 47000 | sklearn | 2016 | :snake: :snake: |
| Pandas From The Ground Up [repo] | Brandon Rhodes | PyCon 2015 | 2:24 | 91000 | pandas | 2015 | :snake: :snake: |
| Modern Dictionaries | Raymond Hettinger | SF Python | 1:07 | 44000 | python | 2016 | :snake: :snake: |
| The Python Visualization Landscape | Jake VanderPlas | PyCon 2017 | 0:33 | 21000 | python | 2017 | :snake: |
| Keynote on Concurrency | Raymond Hettinger | SF Python | 1:13 | 15000 | python | 2017 | :snake::snake: |
| Pandas for Data Analysis [repo] | Daniel Chen | Enthought | 3:45 | 13000 | pandas | 2017 | :snake::snake: |
| JupyterLab: Building Blocks for Interactive Computing | Brian Granger | Enthought | 0:29 | 28000 | jupyter | 2016 | :snake: |
| Optimizing Pandas Code for Speed and Efficiency | Sofia Heisler | PyCon 2017 | 0:29 | 12000 | pandas | 2017 | :snake: :snake: |
| A Visual Guide To Pandas | Jason Wirth | Next Day Video | 0:26 | 49000 | pandas | 2015 | :snake: |
| Machine Learning with Scikit Learn [repo] | Andreas Mueller & Kyle Kastner | Enthought | 3:22 | 48000 | sklearn | 2015 | :snake: :snake: |
| Machine Learning for Time Series Data in Python | Brett Naul | Enthought | 0:24 | 24000 | cesium | 2016 | :snake: |
| The Fun of Reinvention | David Beazley | David Beazley | 0:52 | 11000 | python | 2017 | :snake::snake::snake: |
| Analyzing and Manipulating Data with Pandas [repo] | Jonathan Rocher | Enthought | 3:33 | 22000 | pandas | 2016 | :snake: |
| Computational Statistics [repo] | Allen Downey | Enthought | 2:05 | 10000 | scipy | 2017 | :snake: :snake: |
| Being a Core Developer in Python | Raymond Hettinger | SF Python | 1:02 | 19000 | python | 2016 | :snake: |
| Time Series Analysis [repo] | Aileen Nielsen | PyCon 2017 | 3:11 | 9000 | pandas | 2017 | :snake: :snake: |
| Learning TensorFlow | Robert Layton | PyCon Australia | 0:40 | 18000 | tensorflow | 2016 | :snake: :snake: |
| JupyterHub: Deploying Jupyter Notebooks | Min Ragan Kelley & Thomas Kluyver | PyData | 1:36 | 17000 | jupyter | 2016 | :snake: |
| Applied Time Series Econometrics | Jeffrey Yau | PyData | 1:39 | 17000 | statsmodels | 2016 | :snake: :snake: |
| Machine Learning with scikit learn [repo] | Andreas Mueller & Alexandre Gram | Enthought | 3:10 | 8000 | sklearn | 2017 | :snake: :snake: |
| Introduction to Numerical Computing with NumPy | Dillon Niederhut | Enthought | 2:27 | 8000 | NumPy | 2017 | :snake: |
| Dask - A Pythonic Distributed Data Science Framework | Matthew Rocklin | PyCon 2017 | 0:46 | 7000 | dask | 2017 | :snake: :snake: |
| Introduction to Statistical Modeling with Python [repo] | Christopher Fonnesbeck | PyCon 2017 | 3:19 | 7000 | scipy | 2017 | :snake: :snake: |
| Fully Convolutional Networks for Image Segmentation | Daniil Pakhomov | Enthought | 0:20 | 7000 | scipy | 2017 | :snake: |
| Exploratory data analysis in python [repo] | Chloe Mawer & Jonathan Whitmore | PyCon 2017 | 2:54 | 7000 | scipy | 2017 | :snake: |
| Visualizing Geographic Data | Christopher Roach | PyData | 0:31 | 14000 | python | 2016 | :snake: |
| Builtin Superheroes | David Beazley | David Beazley | 0:44 | 12000 | python | 2016 | :snake: :snake: |
| Python's Class Development Toolkit | Raymond Hettinger | Next Day Video | 0:45 | 80000 | python | 2013 | :snake: :snake: |
| Libraries for Deep Learning with Sequences | Alex Rubinsteyn | PyData | 0:44 | 23000 | scipy | 2015 | :snake: :snake: |
| The Other Async (Threads + Async = ❤️) - YouTube | David Beazley | David Beazley | 0:47 | 5000 | python | 2017 | :snake: :snake: :snake: |
| Numba - Tell Those C++ Bullies to Get Lost [repo] | Gil Forsyth & Lorena Barba | Enthought | 2:25 | 5000 | numba | 2017 | :snake: :snake: |
| Deploying Interactive Jupyter Dashboards | Philipp Rudiger | Enthought | 0:18 | 5000 | jupyter | 2017 | :snake: :snake: |
| Eyal Trabelsi - Practical Optimisations for Pandas | Eyal Trabelsi | Europython | 0:45 | 5000 | jupyter | 2020 | :snake: :snake: |
| Data Science Using Functional Python | Joel Grus | PyData | 0:44 | 18000 | python | 2015 | :snake: :snake: |
| Pandas from the Inside | Stephen Simmons | PyData | 1:20 | 9000 | pandas | 2016 | :snake: :snake: :snake: |
| Anatomy of matplotlib [repo] | Benjamin Root & Joe Kington | Enthought | 3:18 | 18000 | matplotlib | 2015 | :snake: :snake: |
| Anatomy of matplotlib [repo] | Benjamin Root | Enthought | 3:02 | 4000 | matplotlib | 2017 | :snake: :snake: |
| Data Science is Software [repo] | Peter Bull & Isaac Slavitt | Enthought | 2:12 | 9000 | jupyter | 2016 | :snake: |
| Machine Learning with Scikit Learn [repo] | Jake VanderPlas | PyData | 1:34 | 16000 | sklearn | 2015 | Novice |
| Using Jupyter notebooks | Ioanna Ioannou | PyCon Australia | 0:28 | 8000 | jupyter | 2016 | Novice |
| Parallel Python: Analyzing Large Datasets [repo] | Matthew Rocklin | Enthought | 3:05 | 7000 | scipy | 2016 | Novice |
| Functional Programming with Python | Mike Müller | Next Day Video | 0:27 | 44000 | python | 2013 | Novice |
| Predicting sports winners with pandas and scikit-learn | Robert Layton | PyCon Australia | 0:38 | 13000 | pandas | 2015 | Novice |
| Keynote: Project Jupyter | Brian Granger | Enthought | 0:48 | 7000 | jupyter | 2016 | Novice |
| matplotlib beginner tutorial [repo] | Nicolas Rougier | Enthought | 2:59 | 6000 | matplotlib | 2016 | Novice |
| Awesome Big Data Algorithms | Titus Brown | Next Day Video | 0:39 | 41000 | python | 2013 | Novice |
| Pandas from the Inside | Stephen Simmons | PyData | 1:17 | 3000 | pandas | 2017 | Novice |
| All About Jupyter | Brian Granger | PyData | 0:39 | 11000 | jupyter | 2015 | Novice |
| Building a Recommendation Engine using Python | Anusua Trivedi | PyData | 0:37 | 11000 | python | 2015 | Novice |
| Iterations of Evolution | David Beazley | David Beazley | 0:34 | 2000 | python | 2017 | Novice |
| "Good Enough" IS Good Enough! | Alex Martelli | SF Python | 0:53 | 4000 | python | 2016 | Novice |
| PyMC: Markov Chain Monte Carlo | Chris Fonnesbeck | Enthought | 0:20 | 9000 | pyMC | 2014 | Novice |
| Jupyter Advanced Topics Tutorial [repo] | Jonathan Frederic & Matthias Bussonier | Enthought | 2:48 | 4000 | jupyter | 2015 | Novice |
| Using randomness to make code much faster | Rachel Thomas | SF Python | 0:54 | 1000 | scipy | 2017 | Novice |
| Python Profiling & Performance | Mahmoud Hashemi | SF Python | 0:28 | 1000 | python | 2016 | Novice |
(4) Packages
- 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.