23 KiB
23 KiB
awesome-pandas
A collection of resources for pandas (Python) and related subjects.
Contents: This page is divided into three parts.
- pandas resources - A collection of videos, cheat-sheets, tutorials and books related directly to pandas.
- Data analysis with Python resources - Material related to adjacent Python libraries such as NumPy, scipy, matplotlib, seaborn statsmodels and Jupyter.
- Miscellaneous related resources - Resources related to general data analysis, algorithms, computer science, machine learning, statistics, etc.
(1) pandas resources
(1.1) Videos
The videos below were collected in December of 2017. They are all related to pandas, and the Level of a video is quantified roughly as follows:
- 😃 : Beginner - requires little knowledge to jump into, elementary topics.
- 😅 : Intermediate - some prior knowledge needed, more technical.
- 😱 : Advanced - very technical, or discusses advanced topics.
| Title | Speaker | Uploader | Time | Views | Year | Level |
|---|---|---|---|---|---|---|
| Introduction Into Pandas | Daniel Chen | Python Tutorial | 1:28 | 46000 | 2017 | 😃 |
| Introduction To Data Analytics With Pandas | Quentin Caudron | Python Tutorial | 1:51 | 25000 | 2017 | 😃 |
| Pandas From The Ground Up [repo] | Brandon Rhodes | PyCon 2015 | 2:24 | 91000 | 2015 | 😃 |
| Pandas for Data Analysis [repo] | Daniel Chen | Enthought | 3:45 | 13000 | 2017 | 😅 |
| Optimizing Pandas Code for Speed and Efficiency | Sofia Heisler | PyCon 2017 | 0:29 | 12000 | 2017 | 😅 |
| A Visual Guide To Pandas | Jason Wirth | Next Day Video | 0:26 | 49000 | 2015 | 😃 |
| Analyzing and Manipulating Data with Pandas [repo] | Jonathan Rocher | Enthought | 3:33 | 22000 | 2016 | 😃 |
| Time Series Analysis [repo] | Aileen Nielsen | PyCon 2017 | 3:11 | 9000 | 2017 | 😅 |
| Pandas from the Inside | Stephen Simmons | PyData | 1:20 | 9000 | 2016 | 😱 |
| Predicting sports winners with pandas and scikit-learn | Robert Layton | PyCon Australia | 0:38 | 13000 | 2015 | 😅 |
| Pandas from the Inside | Stephen Simmons | PyData | 1:17 | 3000 | 2017 | 😱 |
- (Brandon Rhodes - Pandas From The Ground Up - PyCon 2015) https://www.youtube.com/watch?v=5JnMutdy6Fw
- (A Visual Guide To Pandas) https://www.youtube.com/watch?v=9d5-Ti6onew
- (Stephen Simmons | Pandas from the Inside) https://www.youtube.com/watch?v=CowlcrtSyME
- (Stephen Simmons - Pandas from the Inside / “Big Pandas”) https://www.youtube.com/watch?v=YGk09nK_xnM
- (Pandas for Data Analysis | SciPy 2017 Tutorial | Daniel Chen) https://www.youtube.com/watch?v=oGzU688xCUs
- (Introduction To Data Analytics With Pandas) https://www.youtube.com/watch?v=5XGycFIe8qE
- (Introduction Into Pandas: Python Tutorial) https://www.youtube.com/watch?v=-NR-ynQg0YM
(1.2) Cheat-sheets
- https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf
- https://s3.amazonaws.com/quandl-static-content/Documents/Quandl+-+Pandas,+SciPy,+NumPy+Cheat+Sheet.pdf
- http://www.webpages.uidaho.edu/~stevel/504/Pandas%20DataFrame%20Notes.pdf
- https://assets.datacamp.com/blog_assets/PandasPythonForDataScience.pdf
- https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Pandas_Cheat_Sheet_2.pdf
(1.3) Tutorials
- https://github.com/jorisvandenbossche/pandas-tutorial
- https://github.com/guipsamora/pandas_exercises
- https://github.com/brandon-rhodes/pycon-pandas-tutorial
- https://github.com/jadianes/winerama-recommender-tutorial
- https://github.com/jonathanrocher/pandas_tutorial
- https://github.com/chendaniely/scipy-2017-tutorial-pandas
- https://github.com/tdpetrou/Learn-Pandas
- https://github.com/adeshpande3/Pandas-Tutorial
- https://github.com/GaelVaroquaux/sklearn_pandas_tutorial
- https://github.com/vi3k6i5/pandas_basics
- https://github.com/california-civic-data-coalition/first-python-notebook
(1.4) Books
(2) Data analysis with Python resources
(2.1) Videos
| Title | Speaker | Uploader | Time | Views | Keyword | Year | Level |
|---|---|---|---|---|---|---|---|
| NumPy Beginner [repo] | Alexandre Chabot LeClerc | Enthought | 2:47 | 56000 | NumPy | 2016 | 🐍 🐍 |
| Machine Learning | Andreas Mueller & Sebastian Raschka | Enthought | 3:03 | 47000 | sklearn | 2016 | 🐍 🐍 |
| The Python Visualization Landscape | Jake VanderPlas | PyCon 2017 | 0:33 | 21000 | python | 2017 | 🐍 |
| JupyterLab: Building Blocks for Interactive Computing | Brian Granger | Enthought | 0:29 | 28000 | jupyter | 2016 | 🐍 |
| Machine Learning with Scikit Learn [repo] | Andreas Mueller & Kyle Kastner | Enthought | 3:22 | 48000 | sklearn | 2015 | 🐍 🐍 |
| Machine Learning for Time Series Data in Python | Brett Naul | Enthought | 0:24 | 24000 | cesium | 2016 | 🐍 |
| Computational Statistics [repo] | Allen Downey | Enthought | 2:05 | 10000 | scipy | 2017 | 🐍 🐍 |
| Time Series Analysis [repo] | Aileen Nielsen | PyCon 2017 | 3:11 | 9000 | pandas | 2017 | 🐍 🐍 |
| Learning TensorFlow | Robert Layton | PyCon Australia | 0:40 | 18000 | tensorflow | 2016 | 🐍 🐍 |
| JupyterHub: Deploying Jupyter Notebooks | Min Ragan Kelley & Thomas Kluyver | PyData | 1:36 | 17000 | jupyter | 2016 | 🐍 |
| Applied Time Series Econometrics | Jeffrey Yau | PyData | 1:39 | 17000 | statsmodels | 2016 | 🐍 🐍 |
| Machine Learning with scikit learn [repo] | Andreas Mueller & Alexandre Gram | Enthought | 3:10 | 8000 | sklearn | 2017 | 🐍 🐍 |
| Introduction to Numerical Computing with NumPy | Dillon Niederhut | Enthought | 2:27 | 8000 | NumPy | 2017 | 🐍 |
| Dask - A Pythonic Distributed Data Science Framework | Matthew Rocklin | PyCon 2017 | 0:46 | 7000 | dask | 2017 | 🐍 🐍 |
| Introduction to Statistical Modeling with Python [repo] | Christopher Fonnesbeck | PyCon 2017 | 3:19 | 7000 | scipy | 2017 | 🐍 🐍 |
| Fully Convolutional Networks for Image Segmentation | Daniil Pakhomov | Enthought | 0:20 | 7000 | scipy | 2017 | 🐍 |
| Exploratory data analysis in python [repo] | Chloe Mawer & Jonathan Whitmore | PyCon 2017 | 2:54 | 7000 | scipy | 2017 | 🐍 |
| Libraries for Deep Learning with Sequences | Alex Rubinsteyn | PyData | 0:44 | 23000 | scipy | 2015 | 🐍 🐍 |
| Numba - Tell Those C++ Bullies to Get Lost [repo] | Gil Forsyth & Lorena Barba | Enthought | 2:25 | 5000 | numba | 2017 | 🐍 🐍 |
| Deploying Interactive Jupyter Dashboards | Philipp Rudiger | Enthought | 0:18 | 5000 | jupyter | 2017 | 🐍 🐍 |
| Data Science Using Functional Python | Joel Grus | PyData | 0:44 | 18000 | python | 2015 | 🐍 🐍 |
| Anatomy of matplotlib [repo] | Benjamin Root & Joe Kington | Enthought | 3:18 | 18000 | matplotlib | 2015 | 🐍 🐍 |
| Anatomy of matplotlib [repo] | Benjamin Root | Enthought | 3:02 | 4000 | matplotlib | 2017 | 🐍 🐍 |
| Data Science is Software [repo] | Peter Bull & Isaac Slavitt | Enthought | 2:12 | 9000 | jupyter | 2016 | 🐍 |
| 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 |
| 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 |
| All About Jupyter | Brian Granger | PyData | 0:39 | 11000 | jupyter | 2015 | 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 |
(2.2) Cheat-sheets
- http://datasciencefree.com/numpy.pdf
- https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf
(2.3) Tutorials
(2.4) Books
(3) Miscellaneous related resources
(3.1) Books
(3.2) Papers
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 | 🐍 |
| Introduction Into Pandas | Daniel Chen | Python Tutorial | 1:28 | 46000 | pandas | 2017 | 🐍 |
| So you want to be a Python expert? | James Powell | PyData | 1:54 | 28000 | python | 2017 | 🐍🐍🐍 |
| NumPy Beginner [repo] | Alexandre Chabot LeClerc | Enthought | 2:47 | 56000 | NumPy | 2016 | 🐍 🐍 |
| Introduction To Data Analytics With Pandas | Quentin Caudron | Python Tutorial | 1:51 | 25000 | pandas | 2017 | 🐍 |
| Transforming Code into Beautiful, Idiomatic Python | Raymond Hettinger | Next Day Video | 0:48 | 340000 | python | 2013 | 🐍 |
| Machine Learning | Andreas Mueller & Sebastian Raschka | Enthought | 3:03 | 47000 | sklearn | 2016 | 🐍 🐍 |
| Pandas From The Ground Up [repo] | Brandon Rhodes | PyCon 2015 | 2:24 | 91000 | pandas | 2015 | 🐍 🐍 |
| Modern Dictionaries | Raymond Hettinger | SF Python | 1:07 | 44000 | python | 2016 | 🐍 🐍 |
| The Python Visualization Landscape | Jake VanderPlas | PyCon 2017 | 0:33 | 21000 | python | 2017 | 🐍 |
| Keynote on Concurrency | Raymond Hettinger | SF Python | 1:13 | 15000 | python | 2017 | 🐍🐍 |
| Pandas for Data Analysis [repo] | Daniel Chen | Enthought | 3:45 | 13000 | pandas | 2017 | 🐍🐍 |
| JupyterLab: Building Blocks for Interactive Computing | Brian Granger | Enthought | 0:29 | 28000 | jupyter | 2016 | 🐍 |
| Optimizing Pandas Code for Speed and Efficiency | Sofia Heisler | PyCon 2017 | 0:29 | 12000 | pandas | 2017 | 🐍 🐍 |
| A Visual Guide To Pandas | Jason Wirth | Next Day Video | 0:26 | 49000 | pandas | 2015 | 🐍 |
| Machine Learning with Scikit Learn [repo] | Andreas Mueller & Kyle Kastner | Enthought | 3:22 | 48000 | sklearn | 2015 | 🐍 🐍 |
| Machine Learning for Time Series Data in Python | Brett Naul | Enthought | 0:24 | 24000 | cesium | 2016 | 🐍 |
| The Fun of Reinvention | David Beazley | David Beazley | 0:52 | 11000 | python | 2017 | 🐍🐍🐍 |
| Analyzing and Manipulating Data with Pandas [repo] | Jonathan Rocher | Enthought | 3:33 | 22000 | pandas | 2016 | 🐍 |
| Computational Statistics [repo] | Allen Downey | Enthought | 2:05 | 10000 | scipy | 2017 | 🐍 🐍 |
| Being a Core Developer in Python | Raymond Hettinger | SF Python | 1:02 | 19000 | python | 2016 | 🐍 |
| Time Series Analysis [repo] | Aileen Nielsen | PyCon 2017 | 3:11 | 9000 | pandas | 2017 | 🐍 🐍 |
| Learning TensorFlow | Robert Layton | PyCon Australia | 0:40 | 18000 | tensorflow | 2016 | 🐍 🐍 |
| JupyterHub: Deploying Jupyter Notebooks | Min Ragan Kelley & Thomas Kluyver | PyData | 1:36 | 17000 | jupyter | 2016 | 🐍 |
| Applied Time Series Econometrics | Jeffrey Yau | PyData | 1:39 | 17000 | statsmodels | 2016 | 🐍 🐍 |
| Machine Learning with scikit learn [repo] | Andreas Mueller & Alexandre Gram | Enthought | 3:10 | 8000 | sklearn | 2017 | 🐍 🐍 |
| Introduction to Numerical Computing with NumPy | Dillon Niederhut | Enthought | 2:27 | 8000 | NumPy | 2017 | 🐍 |
| Dask - A Pythonic Distributed Data Science Framework | Matthew Rocklin | PyCon 2017 | 0:46 | 7000 | dask | 2017 | 🐍 🐍 |
| Introduction to Statistical Modeling with Python [repo] | Christopher Fonnesbeck | PyCon 2017 | 3:19 | 7000 | scipy | 2017 | 🐍 🐍 |
| Fully Convolutional Networks for Image Segmentation | Daniil Pakhomov | Enthought | 0:20 | 7000 | scipy | 2017 | 🐍 |
| Exploratory data analysis in python [repo] | Chloe Mawer & Jonathan Whitmore | PyCon 2017 | 2:54 | 7000 | scipy | 2017 | 🐍 |
| Visualizing Geographic Data | Christopher Roach | PyData | 0:31 | 14000 | python | 2016 | 🐍 |
| Builtin Superheroes | David Beazley | David Beazley | 0:44 | 12000 | python | 2016 | 🐍 🐍 |
| Python’s Class Development Toolkit | Raymond Hettinger | Next Day Video | 0:45 | 80000 | python | 2013 | 🐍 🐍 |
| Libraries for Deep Learning with Sequences | Alex Rubinsteyn | PyData | 0:44 | 23000 | scipy | 2015 | 🐍 🐍 |
| The Other Async (Threads + Async = ❤️) - YouTube | David Beazley | David Beazley | 0:47 | 5000 | python | 2017 | 🐍 🐍 🐍 |
| Numba - Tell Those C++ Bullies to Get Lost [repo] | Gil Forsyth & Lorena Barba | Enthought | 2:25 | 5000 | numba | 2017 | 🐍 🐍 |
| Deploying Interactive Jupyter Dashboards | Philipp Rudiger | Enthought | 0:18 | 5000 | jupyter | 2017 | 🐍 🐍 |
| Data Science Using Functional Python | Joel Grus | PyData | 0:44 | 18000 | python | 2015 | 🐍 🐍 |
| Pandas from the Inside | Stephen Simmons | PyData | 1:20 | 9000 | pandas | 2016 | 🐍 🐍 🐍 |
| Anatomy of matplotlib [repo] | Benjamin Root & Joe Kington | Enthought | 3:18 | 18000 | matplotlib | 2015 | 🐍 🐍 |
| Anatomy of matplotlib [repo] | Benjamin Root | Enthought | 3:02 | 4000 | matplotlib | 2017 | 🐍 🐍 |
| Data Science is Software [repo] | Peter Bull & Isaac Slavitt | Enthought | 2:12 | 9000 | jupyter | 2016 | 🐍 |
| 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 |