{"id":13598929,"url":"https://github.com/tommyod/awesome-pandas","last_synced_at":"2025-04-29T00:30:57.941Z","repository":{"id":30462493,"uuid":"115751788","full_name":"tommyod/awesome-pandas","owner":"tommyod","description":"A collection of resources for pandas (Python) and related subjects.","archived":false,"fork":false,"pushed_at":"2022-10-12T12:31:19.000Z","size":19665,"stargazers_count":484,"open_issues_count":1,"forks_count":102,"subscribers_count":25,"default_branch":"master","last_synced_at":"2024-05-21T16:07:27.829Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tommyod.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-12-29T20:24:29.000Z","updated_at":"2024-05-15T06:37:25.000Z","dependencies_parsed_at":"2023-01-14T17:15:22.384Z","dependency_job_id":null,"html_url":"https://github.com/tommyod/awesome-pandas","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tommyod%2Fawesome-pandas","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tommyod%2Fawesome-pandas/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tommyod%2Fawesome-pandas/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tommyod%2Fawesome-pandas/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tommyod","download_url":"https://codeload.github.com/tommyod/awesome-pandas/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224140745,"owners_count":17262733,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-01T17:00:58.173Z","updated_at":"2024-11-11T16:39:20.805Z","avatar_url":"https://github.com/tommyod.png","language":"Jupyter Notebook","funding_links":[],"categories":["Uncategorized","Jupyter Notebook","Machine Learning","Other Lists","🐍 Python","Data Engineering","Awesome awesomeness"],"sub_categories":["Uncategorized","Pandas","TeX Lists","Data Manipulation with Pandas and Numpy","Awesome python"],"readme":"![Awesome pandas logo](/img/awesome_pandas.png)\n\n# awesome-pandas\nA collection of resources for [pandas](http://pandas.pydata.org/) \n([Python](https://www.python.org/)) and related subjects. \n**Pull requests are very welcome!**\n\n**Contents:** This is an unofficial collection of resources for learning pandas,\nan open source Python library for data analysis. Here you will find videos,\ncheat-sheets, tutorials and books / papers. The curated list is divided into\nthree parts:\n\n1. **pandas resources** - A collection of videos, cheat-sheets, tutorials and \n   books *directly related* to pandas.\n2. **Data analysis with Python resources** - Material related to *adjacent \n  Python libraries and software* such as \n  [NumPy](http://www.numpy.org/),\n  [scipy](https://www.scipy.org/), \n  [matplotlib](https://matplotlib.org/), \n  [seaborn](https://seaborn.pydata.org/),\n  [statsmodels](http://www.statsmodels.org/stable/) and\n  [Jupyter](http://jupyter.org/).\n3. **Miscellaneous related resources** - Resources related to *general* data\n  analysis, Python programming, algorithms, computer science, machine learning, \n  statistics, etc.\n4. **Packages** - Python packages for helping to work with Pandas.\n\n--------------------------------------------------------------------------------\n\n## (1)  :panda_face: pandas resources\n\n### (1.1)  :tv: Videos\n\nThe videos below were collected in July of 2018.\nThey are all directly related to pandas, and the **Level** of a video is \nquantified roughly as follows:\n\n* :smiley: : **Beginner** - requires little knowledge to jump into, elementary topics.\n* :sweat_smile: : **Intermediate** - some prior knowledge needed, more technical. \n* :scream: : **Advanced** - very technical, or discusses advanced topics.\n* :star: : **Recommended video** - high quality video and audio, great presentation.\n\n| Title | Speaker | Uploader | Time | Views | Year | Level |\n| ----- | ------- | -------- | ---- | ----- | ---- | ----- |\n|[Pandas tutorial for Data Science](https://www.youtube.com/playlist?list=PL1xVF1dBM4bmy13VIiL0mD8mZQ40XePWw) | Bikram Kundu | - | \u003e 01:20| 2K+ | 2022 | :smiley: |\n| Python for Data Analysis using Pandas [part 1](https://www.youtube.com/watch?v=Bs4xyX086_I) \u0026 [part 2](https://www.youtube.com/watch?v=u83SQvWYmbI) [[repo](https://github.com/tommyod/awesome-pandas)] | tommyod | na | 2:19 | 100 | 2019 | :smiley: |\n| [Data Science Best Practices with pandas](https://www.youtube.com/watch?v=ZjrUmNq41Eo) [[repo](https://github.com/justmarkham/pycon-2019-tutorial)] | Kevin Markham | PyCon | 3:23 | 1000 | 2019 | :smiley: |\n| [Thinking like a Panda](https://www.youtube.com/watch?v=ObUcgEO4N8w) | Hannah Stepanek | PyCon | 0:36 | 700 | 2019 | :smiley: |\n| [Analyzing Census Data with Pandas](https://www.youtube.com/watch?v=sGtCgYWzOV4) [[repo](https://github.com/chekos/analyzing-census-data)] | Sergio Sánchez | PyCon | 3:15 | 600 | 2019 | :smiley: |\n| [Pandas is for Everyone](https://www.youtube.com/watch?v=3qDhDXNRgHE) [[repo](https://github.com/chendaniely/pycon_2019-pandas_tutorial)] | Daniel Chen | PyCon | 3:18 | 600 | 2019 | :smiley: |\n| :star: [Pandas From The Ground Up](https://www.youtube.com/watch?v=5JnMutdy6Fw) [[repo](https://github.com/brandon-rhodes/pycon-pandas-tutorial)] | Brandon Rhodes | PyCon 2015 | 2:24 | 91000 | 2015 | :smiley: |\n| [Introduction Into Pandas](https://www.youtube.com/watch?v=-NR-ynQg0YM) [[repo](https://github.com/chendaniely/2016-pydata-carolinas-pandas)] | Daniel Chen | Python Tutorial | 1:28 | 46000 | 2017 | :smiley: |\n| [Introduction To Data Analytics With Pandas](https://www.youtube.com/watch?v=5XGycFIe8qE) [[repo](https://github.com/QCaudron/pydata_pandas)] | Quentin Caudron | Python Tutorial | 1:51 | 25000 | 2017 | :smiley: |\n| [Pandas for Data Analysis](https://www.youtube.com/watch?v=oGzU688xCUs) [[repo](https://github.com/chendaniely/scipy-2017-tutorial-pandas)] | Daniel Chen | Enthought | 3:45 | 13000 | 2017 | :sweat_smile: |\n| [Optimizing Pandas Code](https://www.youtube.com/watch?v=HN5d490_KKk) [[repo](https://github.com/sversh/pycon2017-optimizing-pandas)] | Sofia Heisler | PyCon 2017 | 0:29 | 12000 | 2017 | :sweat_smile: |\n| [A Visual Guide To Pandas](https://www.youtube.com/watch?v=9d5-Ti6onew) | Jason Wirth | Next Day Video | 0:26 | 49000 | 2015 | :smiley: |\n| [Analyzing and Manipulating Data with Pandas](https://www.youtube.com/watch?v=6ohWS7J1hVA) [[repo](https://github.com/jonathanrocher/pandas_tutorial)] | Jonathan Rocher | Enthought | 3:33 | 22000 | 2016 | :smiley: |\n| [Time Series Analysis](https://www.youtube.com/watch?v=zmfe2RaX-14) [[repo](https://github.com/ikding/pycon_time_series)] | Aileen Nielsen | PyCon 2017 | 3:11 | 9000 | 2017 | :sweat_smile: |\n| [Predicting sports winners with pandas](https://www.youtube.com/watch?v=k7hSD_-gWMw) | Robert Layton | PyCon Australia | 0:38 | 13000 | 2015 | :sweat_smile: |\n| [Pandas from the Inside](https://www.youtube.com/watch?v=YGk09nK_xnM) [[repo](https://github.com/stevesimmons/pydata-berlin2017-pandas-and-dask-from-the-inside)] [[2016 talk](https://www.youtube.com/watch?v=CowlcrtSyME)] | Stephen Simmons | PyData | 1:17 | 3000 | 2017 | :scream: |\n| Pandas [part 1](https://www.youtube.com/watch?v=Cz_u2If7KbI) \u0026 [part 2](https://www.youtube.com/watch?v=gS7kVg-4ZaU) [[repo](https://github.com/jorisvandenbossche/pandas-tutorial)] | Joris Van den Bossche | EuroSciPy | 3:03 | 1000 | 2017 | :smiley: |\n| [Pandas: .head() to .tail()](https://www.youtube.com/watch?v=7vuO9QXDN50) [[repo](https://github.com/TomAugspurger/pydata-chi-h2t)] | Tom Augspurger | PyData | 1:26 | 3000 | 2016 | :sweat_smile: |\n| [Performance Pandas](https://www.youtube.com/watch?v=xUBoPK6FGIU) (london) [[repo](https://github.com/jreback/pydata2015-london)] | Jeff Reback | PyData | 0:43 | 2000 | 2015 | :sweat_smile: |\n| [Performance Pandas](https://www.youtube.com/watch?v=xUBoPK6FGIU) (NYC) [[repo](https://github.com/jreback/pydatanyc2015)] | Jeff Reback | PyData | 1:26 | 3000 | 2015 | :sweat_smile: |\n| [Python Data Science with pandas](https://www.youtube.com/watch?v=ikOEn8jY2Is) [[repo](https://github.com/mattharrison/PyCharm-2018-Webcast)] | Matt Harrison | JetBrainsTV | 1:09 | 2000 | 2018 | :smiley: |\n| [What is the Future of Pandas](https://www.youtube.com/watch?v=_-gJtO0XR48) [[slides](https://www.slideshare.net/JeffReback/future-of-pandas-82901487)] | Jeff Reback | PyData | 0:31 | 4000 | 2017 | :smiley: |\n| [Introduction to Python for Data Science](https://www.youtube.com/watch?v=W4WQi2OIy7o) [[repo](https://github.com/jseabold/pycon-ds-2018)] | Skipper Seabold | PyData | 3:18 | 300 | 2018 | :smiley: |\n| [Pandas for Better (and Worse) Data Science](https://www.youtube.com/watch?v=0hsKLYfyQZc) [[repo](https://github.com/justmarkham/pycon-2018-tutorial)] | Kevin Markham  | PyCon 2018 | 3:21 | 3000 | 2018 | :smiley: |\n\n*Know of a recent, good video? Send a pull request!* :+1:\n\n### (1.2) :exclamation: Cheat-sheets\n\n* [Data Wrangling with pandas](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf)\n* [The pandas DataFrame Object](http://www.webpages.uidaho.edu/~stevel/504/Pandas%20DataFrame%20Notes.pdf)\n* [Python For Data Science - pandas Basics](https://assets.datacamp.com/blog_assets/PandasPythonForDataScience.pdf)\n* [Python For Data Science - pandas](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Pandas_Cheat_Sheet_2.pdf)\n\n### (1.3) :mortar_board: Tutorials\n\n* :star: [10 Minutes to pandas](http://pandas.pydata.org/pandas-docs/stable/10min.html)\n* :star: [pandas_exercises](https://github.com/guipsamora/pandas_exercises)\n* :star: [pycon-pandas-tutorial](https://github.com/brandon-rhodes/pycon-pandas-tutorial) [Video: [Pandas From The Ground Up](https://www.youtube.com/watch?v=5JnMutdy6Fw)]\n* :star: [Learn-Pandas](https://github.com/tdpetrou/Learn-Pandas)\n* :star: [pandas-cookbook](https://github.com/jvns/pandas-cookbook)\n* :star: Modern pandas. Parts: \n  [1](http://tomaugspurger.github.io/modern-1-intro.html), \n  [2](http://tomaugspurger.github.io/method-chaining.html), \n  [3](http://tomaugspurger.github.io/modern-3-indexes.html), \n  [4](http://tomaugspurger.github.io/modern-4-performance.html), \n  [5](http://tomaugspurger.github.io/modern-5-tidy.html), \n  [6](http://tomaugspurger.github.io/modern-6-visualization.html) and \n  [7](http://tomaugspurger.github.io/modern-7-timeseries.html).\n* [pandas-tutorial](https://github.com/jorisvandenbossche/pandas-tutorial) [Video: [Pandas](https://www.youtube.com/watch?v=Cz_u2If7KbI) \u0026 [Advanced Pandas](https://www.youtube.com/watch?v=gS7kVg-4ZaU)]\n* [pandas_tutorial](https://github.com/jonathanrocher/pandas_tutorial) [Video: [Analyzing and Manipulating Data with Pandas](https://www.youtube.com/watch?v=6ohWS7J1hVA)]\n* [scipy-2017-tutorial-pandas](https://github.com/chendaniely/scipy-2017-tutorial-pandas) [Video: [Pandas for Data Analysis](https://www.youtube.com/watch?v=oGzU688xCUs)]\n* [Pandas-Tutorial](https://github.com/adeshpande3/Pandas-Tutorial)\n* [sklearn_pandas_tutorial](https://github.com/GaelVaroquaux/sklearn_pandas_tutorial)\n* [pandas_basics](https://github.com/vi3k6i5/pandas_basics)\n* [first-python-notebook](https://github.com/california-civic-data-coalition/first-python-notebook)\n* [Learn Pandas](https://bitbucket.org/hrojas/learn-pandas)\n* [Pandas practice website](https://pandaspractice.com/)\n\n\n### (1.4) :blue_book: Books / papers\n\n* [[amazon](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662/)] McKinney, Wes. *Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython*. 2 edition. O’Reilly Media, 2017.\n* [[amazon](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057/)] VanderPlas, Jake. *Python Data Science Handbook: Essential Tools for Working with Data*. 1 edition. O’Reilly Media, 2016.\n* [[manning](https://www.manning.com/books/pandas-workout)] Lerner, Reuven. *50 exercises that will strengthen your pandas skills to a level of automatic fluency*. 1 edition. Manning Publications, 2021.\n* [[manning](https://www.manning.com/books/pandas-in-action)] 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.\n\n--------------------------------------------------------------------------------\n\n## (2) Data analysis with Python resources\n\n### (2.1) :tv: Videos\n| Title | Speaker | Uploader | Time | Views | Keyword | Year | Level |\n| ----- | ------- | -------- | -------- | ----- | -------- | ---- | ----- |\n| [NumPy Beginner](https://www.youtube.com/watch?v=gtejJ3RCddE) [[repo](https://github.com/enthought/Numpy-Tutorial-SciPyConf-2016)] | Alexandre Chabot LeClerc | Enthought | 2:47 | 56000 | NumPy | 2016 | :sweat_smile: |\n| [Machine Learning](https://www.youtube.com/watch?v=OB1reY6IX-o) | Andreas Mueller \u0026 Sebastian Raschka | Enthought | 3:03 | 47000 | sklearn | 2016 | :sweat_smile: |\n| [The Python Visualization Landscape](https://www.youtube.com/watch?v=FytuB8nFHPQ) | Jake VanderPlas | PyCon 2017 | 0:33 | 21000 | python | 2017 | :smiley: |\n| [JupyterLab: Building Blocks for Interactive Computing](https://www.youtube.com/watch?v=Ejh0ftSjk6g) | Brian Granger | Enthought | 0:29 | 28000 | jupyter | 2016 | :smiley: |\n| [Machine Learning with Scikit Learn](https://www.youtube.com/watch?v=80fZrVMurPM) [[repo](https://github.com/amueller/scipy_2015_sklearn_tutorial)] | Andreas Mueller \u0026 Kyle Kastner | Enthought | 3:22 | 48000 | sklearn | 2015 | :sweat_smile: |\n| [Machine Learning for Time Series Data in Python](https://www.youtube.com/watch?v=ZgHGCfwExw0) | Brett Naul | Enthought | 0:24 | 24000 | cesium | 2016 | :smiley: |\n| [Computational Statistics](https://www.youtube.com/watch?v=He9MCbs1wgE) [[repo](https://github.com/AllenDowney/CompStats)] | Allen Downey | Enthought | 2:05 | 10000 | scipy | 2017 | :sweat_smile: |\n| [Time Series Analysis](https://www.youtube.com/watch?v=zmfe2RaX-14) [[repo](https://github.com/ikding/pycon_time_series)] | Aileen Nielsen | PyCon 2017 | 3:11 | 9000 | pandas | 2017 | :sweat_smile: |\n| [Learning TensorFlow](https://www.youtube.com/watch?v=bvHgESVuS6Q) | Robert Layton | PyCon Australia | 0:40 | 18000 | tensorflow | 2016 | :sweat_smile: |\n| [JupyterHub: Deploying Jupyter Notebooks](https://www.youtube.com/watch?v=gSVvxOchT8Y) | Min Ragan Kelley \u0026 Thomas Kluyver | PyData | 1:36 | 17000 | jupyter | 2016 | :smiley: |\n| [Applied Time Series Econometrics](https://www.youtube.com/watch?v=tJ-O3hk1vRw) | Jeffrey Yau | PyData | 1:39 | 17000 | statsmodels | 2016 | :sweat_smile: |\n| [Machine Learning with scikit learn](https://www.youtube.com/watch?v=2kT6QOVSgSg) [[repo](https://github.com/amueller/scipy-2017-sklearn)] | Andreas Mueller \u0026 Alexandre Gram | Enthought | 3:10 | 8000 | sklearn | 2017 | :sweat_smile: |\n| [Introduction to Numerical Computing with NumPy](https://www.youtube.com/watch?v=lKcwuPnSHIQ) | Dillon Niederhut | Enthought | 2:27 | 8000 | NumPy | 2017 | :smiley: |\n| [Dask - A Pythonic Distributed Data Science Framework](https://www.youtube.com/watch?v=RA_2qdipVng) | Matthew Rocklin | PyCon 2017 | 0:46 | 7000 | dask | 2017 | :sweat_smile: |\n| [Introduction to Statistical Modeling with Python](https://www.youtube.com/watch?v=TMmSESkhRtI) [[repo](https://github.com/fonnesbeck/intro_stat_modeling_2017)] | Christopher Fonnesbeck | PyCon 2017 | 3:19 | 7000 | scipy | 2017 | :sweat_smile: |\n| [Fully Convolutional Networks for Image Segmentation](https://www.youtube.com/watch?v=-lXfsWP7DJ8) | Daniil Pakhomov | Enthought | 0:20 | 7000 | scipy | 2017 | :smiley: |\n| [Exploratory data analysis in python](https://www.youtube.com/watch?v=W5WE9Db2RLU) [[repo](https://github.com/cmawer/pycon-2017-eda-tutorial)] | Chloe Mawer \u0026 Jonathan Whitmore | PyCon 2017 | 2:54 | 7000 | scipy | 2017 | :smiley: |\n| [Libraries for Deep Learning with Sequences](https://www.youtube.com/watch?v=E92jDCmJNek) | Alex Rubinsteyn | PyData | 0:44 | 23000 | scipy | 2015 | :sweat_smile: |\n| [Numba - Tell Those C++ Bullies to Get Lost](https://www.youtube.com/watch?v=1AwG0T4gaO0) [[repo](https://github.com/gforsyth/numba_tutorial_scipy2017)] | Gil Forsyth \u0026 Lorena Barba | Enthought | 2:25 | 5000 | numba | 2017 | :sweat_smile: |\n| [Deploying Interactive Jupyter Dashboards](https://www.youtube.com/watch?v=8Jktm-Imt-I) | Philipp Rudiger | Enthought | 0:18 | 5000 | jupyter | 2017 | :sweat_smile: |\n| [Data Science Using Functional Python](https://www.youtube.com/watch?v=ThS4juptJjQ) | Joel Grus | PyData | 0:44 | 18000 | python | 2015 | :sweat_smile: |\n| [Anatomy of matplotlib](https://www.youtube.com/watch?v=MKucn8NtVeI) [[repo](https://github.com/matplotlib/AnatomyOfMatplotlib)] | Benjamin Root \u0026 Joe Kington | Enthought | 3:18 | 18000 | matplotlib | 2015 | :sweat_smile: |\n| [Anatomy of matplotlib](https://www.youtube.com/watch?v=rARMKS8jE9g) [[repo](https://github.com/matplotlib/AnatomyOfMatplotlib)] | Benjamin Root | Enthought | 3:02 | 4000 | matplotlib | 2017 | :sweat_smile: |\n| [Data Science is Software](https://www.youtube.com/watch?v=EKUy0TSLg04) [[repo](https://github.com/drivendata/data-science-is-software)] | Peter Bull \u0026 Isaac Slavitt | Enthought | 2:12 | 9000 | jupyter | 2016 | :smiley: |\n| [Machine Learning with Scikit Learn](https://www.youtube.com/watch?v=HC0J_SPm9co) [[repo](https://github.com/jakevdp/sklearn_pydata2015)] | Jake VanderPlas | PyData | 1:34 | 16000 | sklearn | 2015 | :sweat_smile: |\n| [Using Jupyter notebooks](https://www.youtube.com/watch?v=aXR2d9k9-h4) [[repo](https://github.com/ch41rmn/PyConAU2016_-_Interactive_Data_Displays_With_Jupyter_Notebooks)] | Ioanna Ioannou | PyCon Australia | 0:28 | 8000 | jupyter | 2016 | :sweat_smile: |\n| [Parallel Python: Analyzing Large Datasets](https://www.youtube.com/watch?v=5Md_sSsN51k) [[repo](https://github.com/pydata/parallel-tutorial)] | Matthew Rocklin | Enthought | 3:05 | 7000 | scipy | 2016 | :scream: |\n| [Keynote: Project Jupyter](https://www.youtube.com/watch?v=v5mrwq7yJc4) | Brian Granger | Enthought | 0:48 | 7000 | jupyter | 2016 | :sweat_smile: |\n| [matplotlib beginner tutorial](https://www.youtube.com/watch?v=p7Mj-4kASmI) [[repo](https://github.com/rougier/matplotlib-tutorial)] | Nicolas Rougier | Enthought | 2:59 | 6000 | matplotlib | 2016 | :sweat_smile: |\n| [Awesome Big Data Algorithms](https://www.youtube.com/watch?v=jKBwGlYb13w) | Titus Brown | Next Day Video | 0:39 | 41000 | python | 2013 | :scream: |\n| [All About Jupyter](https://www.youtube.com/watch?v=GMKZD1Ohlzk) | Brian Granger | PyData | 0:39 | 11000 | jupyter | 2015 | :sweat_smile: |\n| [PyMC: Markov Chain Monte Carlo](https://www.youtube.com/watch?v=XbxIo7ScVzc) | Chris Fonnesbeck | Enthought | 0:20 | 9000 | pyMC | 2014 | :sweat_smile: |\n| [Jupyter Advanced Topics Tutorial](https://www.youtube.com/watch?v=38R7jiCspkw) [[repo](https://github.com/jupyter/scipy-advanced-tutorial)] | Jonathan Frederic \u0026 Matthias Bussonier | Enthought | 2:48 | 4000 | jupyter | 2015 | :scream: |\n| [Using randomness to make code much faster](https://www.youtube.com/watch?v=7i6kBz1kZ-A) | Rachel Thomas | SF Python | 0:54 | 1000 | scipy | 2017 | :sweat_smile: |\n| [Python Profiling \u0026 Performance](https://www.youtube.com/watch?v=Dgnp28Ijm_M) | Mahmoud Hashemi | SF Python | 0:28 | 1000 | python | 2016 | :sweat_smile: |\n| [Using List Comprehensions and Generator Expressions](https://www.youtube.com/watch?v=_6U1XoxyyBY) | Trey Hunner | PyCon 2018 | 3:21 | 3000 | python | 2018 | :sweat_smile: |\n| [Foundations of Numerical Computing](https://www.youtube.com/watch?v=aGGbnMyeZs0) | Scott Sanderson | PyCon 2018 | 3:22 | 1000 | python | 2018 | :sweat_smile: |\n\n### (2.2) :exclamation: Cheat-sheets\n* [Numpy Cheat Sheet](http://datasciencefree.com/numpy.pdf)\n* [Python For Data Science - Scikit-Learn](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf)\n\n### (2.3) :mortar_board: Tutorials\n\n### (2.4) :blue_book: Books / papers\n\n* Varoquaux, Gael, Valentin Haenel, Emmanuelle Gouillart, Zbigniew Jędrzejewski-Szmek, Ralf Gommers, Fabian Pedregosa, Olav Vahtras, et al. *[Scipy Lecture Notes](http://www.scipy-lectures.org/index.html)*. Zenodo, September 28, 2015. https://doi.org/10.5281/zenodo.31521.\n* [[amazon](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877)] Nunez-Iglesias, Juan, Stéfan van der Walt, and Harriet Dashnow. *Elegant SciPy: The Art of Scientific Python*. 1 edition. O’Reilly Media, 2017.\n* Rougier, Nicolas P. *[From Python to Numpy](http://www.labri.fr/perso/nrougier/from-python-to-numpy/)* Zenodo, December 31, 2016. https://doi.org/10.5281/zenodo.225783.\n* [[amazon](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/)] 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.\n\n--------------------------------------------------------------------------------\n\n## (3) Miscellaneous related resources\n\n### (3.1)  :tv: Videos\n\n| Title | Speaker | Uploader | Time | Views | Keyword | Year | Level |\n| ----- | ------- | -------- | -------- | ----- | -------- | ---- | ----- |\n| :star: [So you want to be a Python expert?](https://www.youtube.com/watch?v=cKPlPJyQrt4) | James Powell | PyData | 1:54 | 28000 | python | 2017 | :scream: |\n| :star: [Transforming Code into Beautiful, Idiomatic Python](https://www.youtube.com/watch?v=OSGv2VnC0go) |  Raymond Hettinger | Next Day Video | 0:48 | 340000 | python | 2013 | :smiley: |\n| :star: [Builtin Superheroes](https://www.youtube.com/watch?v=j6VSAsKAj98) | David Beazley | David Beazley | 0:44 | 12000 | python | 2016 | :sweat_smile: |\n| [How to become a Data Scientist in 6 months](https://www.youtube.com/watch?v=rIofV14c0tc) | Tetiana Ivanova | PyData | 0:56 | 148000 | misc | 2016 | :smiley: |\n| [Modern Dictionaries](https://www.youtube.com/watch?v=p33CVV29OG8) | Raymond Hettinger | SF Python | 1:07 | 44000 | python | 2016 | :sweat_smile: |\n| [Keynote on Concurrency](https://www.youtube.com/watch?v=9zinZmE3Ogk) | Raymond Hettinger | SF Python | 1:13 | 15000 | python | 2017 | :sweat_smile: |\n| [The Fun of Reinvention](https://www.youtube.com/watch?v=js_0wjzuMfc) | David Beazley | David Beazley | 0:52 | 11000 | python | 2017 | :scream: |\n| [Being a Core Developer in Python](https://www.youtube.com/watch?v=voXVTjwnn-U) | Raymond Hettinger | SF Python | 1:02 | 19000 | python | 2016 | :smiley: |\n| [Visualizing Geographic Data](https://www.youtube.com/watch?v=ZIEyHdvF474) | Christopher Roach | PyData | 0:31 | 14000 | python | 2016 | :smiley: |\n| [Python's Class Development Toolkit](https://www.youtube.com/watch?v=HTLu2DFOdTg) | Raymond Hettinger | Next Day Video | 0:45 | 80000 | python | 2013 | :sweat_smile: |\n| [The Other Async (Threads + Async = ❤️) - YouTube](https://www.youtube.com/watch?v=x1ndXuw7S0s) | David Beazley | David Beazley | 0:47 | 5000 | python | 2017 | :scream:|\n| [Functional Programming with Python](https://www.youtube.com/watch?v=Ta1bAMOMFOI) | Mike Müller | Next Day Video | 0:27 | 44000 | python | 2013 | Novice |\n| [Building a Recommendation Engine using Python](https://www.youtube.com/watch?v=E9XTOnEgqRY) | Anusua Trivedi | PyData | 0:37 | 11000 | python | 2015 | Novice |\n| [Iterations of Evolution](https://www.youtube.com/watch?v=2AXuhgid7E4) | David Beazley | David Beazley | 0:34 | 2000 | python | 2017 | Novice |\n| [\"Good Enough\" IS Good Enough!](https://www.youtube.com/watch?v=_Ek3A2b-nHU) | Alex Martelli | SF Python | 0:53 | 4000 | python | 2016 | Novice |\n| [Automating Code Quality](https://www.youtube.com/watch?v=G1lDk_WKXvY) | Kyle Knapp | PyCon 2018 | 0:30 | 3000 | python | 2018 | :sweat_smile: |\n\n\n\n### (3.2) :exclamation: Cheat-sheets\n\n* [Python 3 Cheat Sheet](https://perso.limsi.fr/pointal/_media/python:cours:mementopython3-english.pdf)\n* [Python Cheat Sheet](http://datasciencefree.com/python.pdf)\n\n### (3.3) :mortar_board: Tutorials\n\n### (3.4) :blue_book: Books / papers\n\n* [[amazon](https://www.amazon.com/Effective-Python-Specific-Software-Development/dp/0134034287/)] Slatkin, Brett. *Effective Python: 59 Specific Ways to Write Better Python*. 1 edition. Addison-Wesley Professional, 2015.\n* [[amazon](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1491946008/)] Ramalho, Luciano. *Fluent Python*. 1st edition. O’Reilly, 2015.\n* [[pdf](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4161295/pdf/pcbi.1003833.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.\n* [[pdf](http://vita.had.co.nz/papers/tidy-data.pdf)] *Tidy Data* | Wickham | Journal of Statistical Software. Accessed December 31, 2017. https://doi.org/10.18637/jss.v059.i10.\n* [[amazon](https://www.amazon.com/Pro-Git-Scott-Chacon/dp/1484200772/)] [[online](https://git-scm.com/book/en/v2)] Chacon, Scott, and Ben Straub. *Pro Git*. 2nd ed. edition. New York, NY: Apress, 2014.\n\nThe books below are perhaps of an even more general nature.\n\n* [[amazon](https://www.amazon.com/Algorithms-Sanjoy-Dasgupta/dp/0073523402/)] Dasgupta, Sanjoy, Christos H. . Papadimitriou, and Umesh Virkumar. Vazirani. *Algorithms*. Boston, Mass: McGraw Hill, 2008.\n* [[amazon](https://www.amazon.com/Numerical-Linear-Algebra-Lloyd-Trefethen/dp/0898713617/)] Lloyd N. Trefethen. *Numerical Linear Algebra*. Society for Industrial and Applied Mathematics, 1997.\n* [[amazon](https://www.amazon.com/Computations-Hopkins-Studies-Mathematical-Sciences/dp/1421407949/)] Gene H. Golub. *Matrix Computations*. 4th ed. Johns Hopkins Studies in the Mathematical Sciences. Baltimore: Johns Hopkins University Press, 2013.\n--------------------------------------------------------------------------------\n\nEvery video is below.\n\n| Title | Speaker | Uploader | Time | Views | Keyword | Year | Level |\n| ----- | ------- | -------- | -------- | ----- | -------- | ---- | ----- |\n| [How to become a Data Scientist in 6 months](https://www.youtube.com/watch?v=rIofV14c0tc) | Tetiana Ivanova | PyData | 0:56 | 148000 | misc | 2016 | :snake: |\n| [Introduction Into Pandas](https://www.youtube.com/watch?v=-NR-ynQg0YM) | Daniel Chen | Python Tutorial | 1:28 | 46000 | pandas | 2017 | :snake: |\n| [So you want to be a Python expert?](https://www.youtube.com/watch?v=cKPlPJyQrt4) | James Powell | PyData | 1:54 | 28000 | python | 2017 | :snake::snake::snake: |\n| [NumPy Beginner](https://www.youtube.com/watch?v=gtejJ3RCddE) [[repo](https://github.com/enthought/Numpy-Tutorial-SciPyConf-2016)] | Alexandre Chabot LeClerc | Enthought | 2:47 | 56000 | NumPy | 2016 | :snake: :snake: |\n| [Introduction To Data Analytics With Pandas](https://www.youtube.com/watch?v=5XGycFIe8qE) | Quentin Caudron | Python Tutorial | 1:51 | 25000 | pandas | 2017 | :snake: |\n| [Transforming Code into Beautiful, Idiomatic Python](https://www.youtube.com/watch?v=OSGv2VnC0go) |  Raymond Hettinger | Next Day Video | 0:48 | 340000 | python | 2013 | :snake: |\n| [Machine Learning](https://www.youtube.com/watch?v=OB1reY6IX-o) | Andreas Mueller \u0026 Sebastian Raschka | Enthought | 3:03 | 47000 | sklearn | 2016 | :snake: :snake: |\n| [Pandas From The Ground Up](https://www.youtube.com/watch?v=5JnMutdy6Fw) [[repo](https://github.com/brandon-rhodes/pycon-pandas-tutorial)] | Brandon Rhodes | PyCon 2015 | 2:24 | 91000 | pandas | 2015 | :snake: :snake: |\n| [Modern Dictionaries](https://www.youtube.com/watch?v=p33CVV29OG8) | Raymond Hettinger | SF Python | 1:07 | 44000 | python | 2016 | :snake: :snake: |\n| [The Python Visualization Landscape](https://www.youtube.com/watch?v=FytuB8nFHPQ) | Jake VanderPlas | PyCon 2017 | 0:33 | 21000 | python | 2017 | :snake: |\n| [Keynote on Concurrency](https://www.youtube.com/watch?v=9zinZmE3Ogk) | Raymond Hettinger | SF Python | 1:13 | 15000 | python | 2017 | :snake::snake: |\n| [Pandas for Data Analysis](https://www.youtube.com/watch?v=oGzU688xCUs) [[repo](https://github.com/chendaniely/scipy-2017-tutorial-pandas)] | Daniel Chen | Enthought | 3:45 | 13000 | pandas | 2017 | :snake::snake: |\n| [JupyterLab: Building Blocks for Interactive Computing](https://www.youtube.com/watch?v=Ejh0ftSjk6g) | Brian Granger | Enthought | 0:29 | 28000 | jupyter | 2016 | :snake: |\n| [Optimizing Pandas Code for Speed and Efficiency](https://www.youtube.com/watch?v=HN5d490_KKk) | Sofia Heisler | PyCon 2017 | 0:29 | 12000 | pandas | 2017 | :snake: :snake: |\n| [A Visual Guide To Pandas](https://www.youtube.com/watch?v=9d5-Ti6onew) | Jason Wirth | Next Day Video | 0:26 | 49000 | pandas | 2015 | :snake: |\n| [Machine Learning with Scikit Learn](https://www.youtube.com/watch?v=80fZrVMurPM) [[repo](https://github.com/amueller/scipy_2015_sklearn_tutorial)] | Andreas Mueller \u0026 Kyle Kastner | Enthought | 3:22 | 48000 | sklearn | 2015 | :snake: :snake: |\n| [Machine Learning for Time Series Data in Python](https://www.youtube.com/watch?v=ZgHGCfwExw0) | Brett Naul | Enthought | 0:24 | 24000 | cesium | 2016 | :snake: |\n| [The Fun of Reinvention](https://www.youtube.com/watch?v=js_0wjzuMfc) | David Beazley | David Beazley | 0:52 | 11000 | python | 2017 | :snake::snake::snake: |\n| [Analyzing and Manipulating Data with Pandas](https://www.youtube.com/watch?v=6ohWS7J1hVA) [[repo](https://github.com/jonathanrocher/pandas_tutorial)] | Jonathan Rocher | Enthought | 3:33 | 22000 | pandas | 2016 | :snake: |\n| [Computational Statistics](https://www.youtube.com/watch?v=He9MCbs1wgE) [[repo](https://github.com/AllenDowney/CompStats)] | Allen Downey | Enthought | 2:05 | 10000 | scipy | 2017 | :snake: :snake: |\n| [Being a Core Developer in Python](https://www.youtube.com/watch?v=voXVTjwnn-U) | Raymond Hettinger | SF Python | 1:02 | 19000 | python | 2016 | :snake: |\n| [Time Series Analysis](https://www.youtube.com/watch?v=zmfe2RaX-14) [[repo](https://github.com/ikding/pycon_time_series)] | Aileen Nielsen | PyCon 2017 | 3:11 | 9000 | pandas | 2017 | :snake: :snake: |\n| [Learning TensorFlow](https://www.youtube.com/watch?v=bvHgESVuS6Q) | Robert Layton | PyCon Australia | 0:40 | 18000 | tensorflow | 2016 | :snake: :snake: |\n| [JupyterHub: Deploying Jupyter Notebooks](https://www.youtube.com/watch?v=gSVvxOchT8Y) | Min Ragan Kelley \u0026 Thomas Kluyver | PyData | 1:36 | 17000 | jupyter | 2016 | :snake: |\n| [Applied Time Series Econometrics](https://www.youtube.com/watch?v=tJ-O3hk1vRw) | Jeffrey Yau | PyData | 1:39 | 17000 | statsmodels | 2016 | :snake: :snake: |\n| [Machine Learning with scikit learn](https://www.youtube.com/watch?v=2kT6QOVSgSg) [[repo](https://github.com/amueller/scipy-2017-sklearn)] | Andreas Mueller \u0026 Alexandre Gram | Enthought | 3:10 | 8000 | sklearn | 2017 | :snake: :snake: |\n| [Introduction to Numerical Computing with NumPy](https://www.youtube.com/watch?v=lKcwuPnSHIQ) | Dillon Niederhut | Enthought | 2:27 | 8000 | NumPy | 2017 | :snake: |\n| [Dask - A Pythonic Distributed Data Science Framework](https://www.youtube.com/watch?v=RA_2qdipVng) | Matthew Rocklin | PyCon 2017 | 0:46 | 7000 | dask | 2017 | :snake: :snake: |\n| [Introduction to Statistical Modeling with Python](https://www.youtube.com/watch?v=TMmSESkhRtI) [[repo](https://github.com/fonnesbeck/intro_stat_modeling_2017)] | Christopher Fonnesbeck | PyCon 2017 | 3:19 | 7000 | scipy | 2017 | :snake: :snake: |\n| [Fully Convolutional Networks for Image Segmentation](https://www.youtube.com/watch?v=-lXfsWP7DJ8) | Daniil Pakhomov | Enthought | 0:20 | 7000 | scipy | 2017 | :snake: |\n| [Exploratory data analysis in python](https://www.youtube.com/watch?v=W5WE9Db2RLU) [[repo](https://github.com/cmawer/pycon-2017-eda-tutorial)] | Chloe Mawer \u0026 Jonathan Whitmore | PyCon 2017 | 2:54 | 7000 | scipy | 2017 | :snake: |\n| [Visualizing Geographic Data](https://www.youtube.com/watch?v=ZIEyHdvF474) | Christopher Roach | PyData | 0:31 | 14000 | python | 2016 | :snake: |\n| [Builtin Superheroes](https://www.youtube.com/watch?v=j6VSAsKAj98) | David Beazley | David Beazley | 0:44 | 12000 | python | 2016 | :snake: :snake: |\n| [Python's Class Development Toolkit](https://www.youtube.com/watch?v=HTLu2DFOdTg) | Raymond Hettinger | Next Day Video | 0:45 | 80000 | python | 2013 | :snake: :snake: |\n| [Libraries for Deep Learning with Sequences](https://www.youtube.com/watch?v=E92jDCmJNek) | Alex Rubinsteyn | PyData | 0:44 | 23000 | scipy | 2015 | :snake: :snake: |\n| [The Other Async (Threads + Async = ❤️) - YouTube](https://www.youtube.com/watch?v=x1ndXuw7S0s) | David Beazley | David Beazley | 0:47 | 5000 | python | 2017 | :snake: :snake: :snake:|\n| [Numba - Tell Those C++ Bullies to Get Lost](https://www.youtube.com/watch?v=1AwG0T4gaO0) [[repo](https://github.com/gforsyth/numba_tutorial_scipy2017)] | Gil Forsyth \u0026 Lorena Barba | Enthought | 2:25 | 5000 | numba | 2017 | :snake: :snake: |\n| [Deploying Interactive Jupyter Dashboards](https://www.youtube.com/watch?v=8Jktm-Imt-I) | Philipp Rudiger | Enthought | 0:18 | 5000 | jupyter | 2017 | :snake: :snake: |\n| [Eyal Trabelsi - Practical Optimisations for Pandas](https://www.youtube.com/watch?v=RXMiMpMfzXw) | Eyal Trabelsi | Europython | 0:45 | 5000 | jupyter | 2020 | :snake: :snake: |\n| [Data Science Using Functional Python](https://www.youtube.com/watch?v=ThS4juptJjQ) | Joel Grus | PyData | 0:44 | 18000 | python | 2015 | :snake: :snake: |\n| [Pandas from the Inside](https://www.youtube.com/watch?v=CowlcrtSyME) | Stephen Simmons | PyData | 1:20 | 9000 | pandas | 2016 | :snake: :snake: :snake: |\n| [Anatomy of matplotlib](https://www.youtube.com/watch?v=MKucn8NtVeI) [[repo](https://github.com/matplotlib/AnatomyOfMatplotlib)] | Benjamin Root \u0026 Joe Kington | Enthought | 3:18 | 18000 | matplotlib | 2015 | :snake: :snake: |\n| [Anatomy of matplotlib](https://www.youtube.com/watch?v=rARMKS8jE9g) [[repo](https://github.com/matplotlib/AnatomyOfMatplotlib)] | Benjamin Root | Enthought | 3:02 | 4000 | matplotlib | 2017 | :snake: :snake: |\n| [Data Science is Software](https://www.youtube.com/watch?v=EKUy0TSLg04) [[repo](https://github.com/drivendata/data-science-is-software)] | Peter Bull \u0026 Isaac Slavitt | Enthought | 2:12 | 9000 | jupyter | 2016 | :snake: |\n| [Machine Learning with Scikit Learn](https://www.youtube.com/watch?v=HC0J_SPm9co) [[repo](https://github.com/jakevdp/sklearn_pydata2015)] | Jake VanderPlas | PyData | 1:34 | 16000 | sklearn | 2015 | Novice |\n| [Using Jupyter notebooks](https://www.youtube.com/watch?v=aXR2d9k9-h4) | Ioanna Ioannou | PyCon Australia | 0:28 | 8000 | jupyter | 2016 | Novice |\n| [Parallel Python: Analyzing Large Datasets](https://www.youtube.com/watch?v=5Md_sSsN51k) [[repo](https://github.com/pydata/parallel-tutorial)] | Matthew Rocklin | Enthought | 3:05 | 7000 | scipy | 2016 | Novice |\n| [Functional Programming with Python](https://www.youtube.com/watch?v=Ta1bAMOMFOI) | Mike Müller | Next Day Video | 0:27 | 44000 | python | 2013 | Novice |\n| [Predicting sports winners with pandas and scikit-learn](https://www.youtube.com/watch?v=k7hSD_-gWMw) | Robert Layton | PyCon Australia | 0:38 | 13000 | pandas | 2015 | Novice |\n| [Keynote: Project Jupyter](https://www.youtube.com/watch?v=v5mrwq7yJc4) | Brian Granger | Enthought | 0:48 | 7000 | jupyter | 2016 | Novice |\n| [matplotlib beginner tutorial](https://www.youtube.com/watch?v=p7Mj-4kASmI) [[repo](https://github.com/rougier/matplotlib-tutorial)] | Nicolas Rougier | Enthought | 2:59 | 6000 | matplotlib | 2016 | Novice |\n| [Awesome Big Data Algorithms](https://www.youtube.com/watch?v=jKBwGlYb13w) | Titus Brown | Next Day Video | 0:39 | 41000 | python | 2013 | Novice |\n| [Pandas from the Inside](https://www.youtube.com/watch?v=YGk09nK_xnM) | Stephen Simmons | PyData | 1:17 | 3000 | pandas | 2017 | Novice |\n| [All About Jupyter](https://www.youtube.com/watch?v=GMKZD1Ohlzk) | Brian Granger | PyData | 0:39 | 11000 | jupyter | 2015 | Novice |\n| [Building a Recommendation Engine using Python](https://www.youtube.com/watch?v=E9XTOnEgqRY) | Anusua Trivedi | PyData | 0:37 | 11000 | python | 2015 | Novice |\n| [Iterations of Evolution](https://www.youtube.com/watch?v=2AXuhgid7E4) | David Beazley | David Beazley | 0:34 | 2000 | python | 2017 | Novice |\n| [\"Good Enough\" IS Good Enough!](https://www.youtube.com/watch?v=_Ek3A2b-nHU) | Alex Martelli | SF Python | 0:53 | 4000 | python | 2016 | Novice |\n| [PyMC: Markov Chain Monte Carlo](https://www.youtube.com/watch?v=XbxIo7ScVzc) | Chris Fonnesbeck | Enthought | 0:20 | 9000 | pyMC | 2014 | Novice |\n| [Jupyter Advanced Topics Tutorial](https://www.youtube.com/watch?v=38R7jiCspkw) [[repo](https://github.com/jupyter/scipy-advanced-tutorial)] | Jonathan Frederic \u0026 Matthias Bussonier | Enthought | 2:48 | 4000 | jupyter | 2015 | Novice |\n| [Using randomness to make code much faster](https://www.youtube.com/watch?v=7i6kBz1kZ-A) | Rachel Thomas | SF Python | 0:54 | 1000 | scipy | 2017 | Novice |\n| [Python Profiling \u0026 Performance](https://www.youtube.com/watch?v=Dgnp28Ijm_M) | Mahmoud Hashemi | SF Python | 0:28 | 1000 | python | 2016 | Novice |\n\n## (4) Packages\n\n- [datatest](https://github.com/shawnbrown/datatest) - Tools for test driven data-wrangling and data validation (DataFrame, Series, Index, MultiIndex).\n- [pandera](https://github.com/pandera-dev/pandera) - A light-weight, flexible, and expressive data validation library for dataframes.\n- [pandas-vet](https://github.com/deppen8/pandas-vet) - A plugin for Flake8 that checks pandas code.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftommyod%2Fawesome-pandas","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftommyod%2Fawesome-pandas","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftommyod%2Fawesome-pandas/lists"}