Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/tommyod/awesome-pandas

A collection of resources for pandas (Python) and related subjects.
https://github.com/tommyod/awesome-pandas
List: awesome-pandas
Last synced: about 1 month ago
JSON representation
A collection of resources for pandas (Python) and related subjects.
Host: GitHub
URL: https://github.com/tommyod/awesome-pandas
Owner: tommyod
Created: 2017-12-29T20:24:29.000Z (almost 7 years ago)
Default Branch: master
Last Pushed: 2022-10-12T12:31:19.000Z (about 2 years ago)
Last Synced: 2024-05-21T16:07:27.829Z (7 months ago)
Language: Jupyter Notebook
Size: 18.8 MB
Stars: 484
Watchers: 25
Forks: 102
Open Issues: 1
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

awesome-data-science-resources - Awesome Pandas
awesome-data-science-resources - Awesome Pandas
AwesomeGenomics - ...and your pandas
ultimate-awesome - awesome-pandas - A collection of resources for pandas (Python) and related subjects. (Other Lists / Monkey C Lists)
README

        ![Awesome pandas logo](/img/awesome_pandas.png)

# awesome-pandas

A collection of resources for [pandas](http://pandas.pydata.org/) 

([Python](https://www.python.org/)) 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:

1. **pandas resources** - A collection of videos, cheat-sheets, tutorials and 

   books *directly related* to pandas.

2. **Data analysis with Python resources** - Material related to *adjacent 

  Python libraries and software* such as 

  [NumPy](http://www.numpy.org/),

  [scipy](https://www.scipy.org/), 

  [matplotlib](https://matplotlib.org/), 

  [seaborn](https://seaborn.pydata.org/),

  [statsmodels](http://www.statsmodels.org/stable/) and

  [Jupyter](http://jupyter.org/).

3. **Miscellaneous related resources** - Resources related to *general* data

  analysis, Python programming, algorithms, computer science, machine learning, 

  statistics, etc.

4. **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](https://www.youtube.com/playlist?list=PL1xVF1dBM4bmy13VIiL0mD8mZQ40XePWw) | Bikram Kundu | - | > 01:20| 2K+ | 2022 | :smiley: |

| Python for Data Analysis using Pandas [part 1](https://www.youtube.com/watch?v=Bs4xyX086_I) & [part 2](https://www.youtube.com/watch?v=u83SQvWYmbI) [[repo](https://github.com/tommyod/awesome-pandas)] | tommyod | na | 2:19 | 100 | 2019 | :smiley: |

| [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: |

| [Thinking like a Panda](https://www.youtube.com/watch?v=ObUcgEO4N8w) | Hannah Stepanek | PyCon | 0:36 | 700 | 2019 | :smiley: |

| [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: |

| [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: |

| :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: |

| [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: |

| [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: |

| [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: |

| [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: |

| [A Visual Guide To Pandas](https://www.youtube.com/watch?v=9d5-Ti6onew) | Jason Wirth | Next Day Video | 0:26 | 49000 | 2015 | :smiley: |

| [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: |

| [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: |

| [Predicting sports winners with pandas](https://www.youtube.com/watch?v=k7hSD_-gWMw) | Robert Layton | PyCon Australia | 0:38 | 13000 | 2015 | :sweat_smile: |

| [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: |

| Pandas [part 1](https://www.youtube.com/watch?v=Cz_u2If7KbI) & [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: |

| [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: |

| [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: |

| [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: |

| [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: |

| [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: |

| [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: |

| [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: |

*Know of a recent, good video? Send a pull request!* :+1:

### (1.2) :exclamation: Cheat-sheets

* [Data Wrangling with pandas](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf)

* [The pandas DataFrame Object](http://www.webpages.uidaho.edu/~stevel/504/Pandas%20DataFrame%20Notes.pdf)

* [Python For Data Science - pandas Basics](https://assets.datacamp.com/blog_assets/PandasPythonForDataScience.pdf)

* [Python For Data Science - pandas](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Pandas_Cheat_Sheet_2.pdf)

### (1.3) :mortar_board: Tutorials

* :star: [10 Minutes to pandas](http://pandas.pydata.org/pandas-docs/stable/10min.html)

* :star: [pandas_exercises](https://github.com/guipsamora/pandas_exercises)

* :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)]

* :star: [Learn-Pandas](https://github.com/tdpetrou/Learn-Pandas)

* :star: [pandas-cookbook](https://github.com/jvns/pandas-cookbook)

* :star: Modern pandas. Parts: 

  [1](http://tomaugspurger.github.io/modern-1-intro.html), 

  [2](http://tomaugspurger.github.io/method-chaining.html), 

  [3](http://tomaugspurger.github.io/modern-3-indexes.html), 

  [4](http://tomaugspurger.github.io/modern-4-performance.html), 

  [5](http://tomaugspurger.github.io/modern-5-tidy.html), 

  [6](http://tomaugspurger.github.io/modern-6-visualization.html) and 

  [7](http://tomaugspurger.github.io/modern-7-timeseries.html).

* [pandas-tutorial](https://github.com/jorisvandenbossche/pandas-tutorial) [Video: [Pandas](https://www.youtube.com/watch?v=Cz_u2If7KbI) & [Advanced Pandas](https://www.youtube.com/watch?v=gS7kVg-4ZaU)]

* [pandas_tutorial](https://github.com/jonathanrocher/pandas_tutorial) [Video: [Analyzing and Manipulating Data with Pandas](https://www.youtube.com/watch?v=6ohWS7J1hVA)]

* [scipy-2017-tutorial-pandas](https://github.com/chendaniely/scipy-2017-tutorial-pandas) [Video: [Pandas for Data Analysis](https://www.youtube.com/watch?v=oGzU688xCUs)]

* [Pandas-Tutorial](https://github.com/adeshpande3/Pandas-Tutorial)

* [sklearn_pandas_tutorial](https://github.com/GaelVaroquaux/sklearn_pandas_tutorial)

* [pandas_basics](https://github.com/vi3k6i5/pandas_basics)

* [first-python-notebook](https://github.com/california-civic-data-coalition/first-python-notebook)

* [Learn Pandas](https://bitbucket.org/hrojas/learn-pandas)

* [Pandas practice website](https://pandaspractice.com/)

### (1.4) :blue_book: Books / papers

* [[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.

* [[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.

* [[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.

* [[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.

--------------------------------------------------------------------------------

## (2) Data analysis with Python resources

### (2.1) :tv: Videos

| Title | Speaker | Uploader | Time | Views | Keyword | Year | Level |

| ----- | ------- | -------- | -------- | ----- | -------- | ---- | ----- |

| [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: |

| [Machine Learning](https://www.youtube.com/watch?v=OB1reY6IX-o) | Andreas Mueller & Sebastian Raschka | Enthought | 3:03 | 47000 | sklearn | 2016 | :sweat_smile: |

| [The Python Visualization Landscape](https://www.youtube.com/watch?v=FytuB8nFHPQ) | Jake VanderPlas | PyCon 2017 | 0:33 | 21000 | python | 2017 | :smiley: |

| [JupyterLab: Building Blocks for Interactive Computing](https://www.youtube.com/watch?v=Ejh0ftSjk6g) | Brian Granger | Enthought | 0:29 | 28000 | jupyter | 2016 | :smiley: |

| [Machine Learning with Scikit Learn](https://www.youtube.com/watch?v=80fZrVMurPM) [[repo](https://github.com/amueller/scipy_2015_sklearn_tutorial)] | Andreas Mueller & Kyle Kastner | Enthought | 3:22 | 48000 | sklearn | 2015 | :sweat_smile: |

| [Machine Learning for Time Series Data in Python](https://www.youtube.com/watch?v=ZgHGCfwExw0) | Brett Naul | Enthought | 0:24 | 24000 | cesium | 2016 | :smiley: |

| [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: |

| [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: |

| [Learning TensorFlow](https://www.youtube.com/watch?v=bvHgESVuS6Q) | Robert Layton | PyCon Australia | 0:40 | 18000 | tensorflow | 2016 | :sweat_smile: |

| [JupyterHub: Deploying Jupyter Notebooks](https://www.youtube.com/watch?v=gSVvxOchT8Y) | Min Ragan Kelley & Thomas Kluyver | PyData | 1:36 | 17000 | jupyter | 2016 | :smiley: |

| [Applied Time Series Econometrics](https://www.youtube.com/watch?v=tJ-O3hk1vRw) | Jeffrey Yau | PyData | 1:39 | 17000 | statsmodels | 2016 | :sweat_smile: |

| [Machine Learning with scikit learn](https://www.youtube.com/watch?v=2kT6QOVSgSg) [[repo](https://github.com/amueller/scipy-2017-sklearn)] | Andreas Mueller & Alexandre Gram | Enthought | 3:10 | 8000 | sklearn | 2017 | :sweat_smile: |

| [Introduction to Numerical Computing with NumPy](https://www.youtube.com/watch?v=lKcwuPnSHIQ) | Dillon Niederhut | Enthought | 2:27 | 8000 | NumPy | 2017 | :smiley: |

| [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: |

| [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: |

| [Fully Convolutional Networks for Image Segmentation](https://www.youtube.com/watch?v=-lXfsWP7DJ8) | Daniil Pakhomov | Enthought | 0:20 | 7000 | scipy | 2017 | :smiley: |

| [Exploratory data analysis in python](https://www.youtube.com/watch?v=W5WE9Db2RLU) [[repo](https://github.com/cmawer/pycon-2017-eda-tutorial)] | Chloe Mawer & Jonathan Whitmore | PyCon 2017 | 2:54 | 7000 | scipy | 2017 | :smiley: |

| [Libraries for Deep Learning with Sequences](https://www.youtube.com/watch?v=E92jDCmJNek) | Alex Rubinsteyn | PyData | 0:44 | 23000 | scipy | 2015 | :sweat_smile: |

| [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 & Lorena Barba | Enthought | 2:25 | 5000 | numba | 2017 | :sweat_smile: |

| [Deploying Interactive Jupyter Dashboards](https://www.youtube.com/watch?v=8Jktm-Imt-I) | Philipp Rudiger | Enthought | 0:18 | 5000 | jupyter | 2017 | :sweat_smile: |

| [Data Science Using Functional Python](https://www.youtube.com/watch?v=ThS4juptJjQ) | Joel Grus | PyData | 0:44 | 18000 | python | 2015 | :sweat_smile: |

| [Anatomy of matplotlib](https://www.youtube.com/watch?v=MKucn8NtVeI) [[repo](https://github.com/matplotlib/AnatomyOfMatplotlib)] | Benjamin Root & Joe Kington | Enthought | 3:18 | 18000 | matplotlib | 2015 | :sweat_smile: |

| [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: |

| [Data Science is Software](https://www.youtube.com/watch?v=EKUy0TSLg04) [[repo](https://github.com/drivendata/data-science-is-software)] | Peter Bull & Isaac Slavitt | Enthought | 2:12 | 9000 | jupyter | 2016 | :smiley: |

| [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: |

| [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: |

| [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: |

| [Keynote: Project Jupyter](https://www.youtube.com/watch?v=v5mrwq7yJc4) | Brian Granger | Enthought | 0:48 | 7000 | jupyter | 2016 | :sweat_smile: |

| [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: |

| [Awesome Big Data Algorithms](https://www.youtube.com/watch?v=jKBwGlYb13w) | Titus Brown | Next Day Video | 0:39 | 41000 | python | 2013 | :scream: |

| [All About Jupyter](https://www.youtube.com/watch?v=GMKZD1Ohlzk) | Brian Granger | PyData | 0:39 | 11000 | jupyter | 2015 | :sweat_smile: |

| [PyMC: Markov Chain Monte Carlo](https://www.youtube.com/watch?v=XbxIo7ScVzc) | Chris Fonnesbeck | Enthought | 0:20 | 9000 | pyMC | 2014 | :sweat_smile: |

| [Jupyter Advanced Topics Tutorial](https://www.youtube.com/watch?v=38R7jiCspkw) [[repo](https://github.com/jupyter/scipy-advanced-tutorial)] | Jonathan Frederic & Matthias Bussonier | Enthought | 2:48 | 4000 | jupyter | 2015 | :scream: |

| [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: |

| [Python Profiling & Performance](https://www.youtube.com/watch?v=Dgnp28Ijm_M) | Mahmoud Hashemi | SF Python | 0:28 | 1000 | python | 2016 | :sweat_smile: |

| [Using List Comprehensions and Generator Expressions](https://www.youtube.com/watch?v=_6U1XoxyyBY) | Trey Hunner | PyCon 2018 | 3:21 | 3000 | python | 2018 | :sweat_smile: |

| [Foundations of Numerical Computing](https://www.youtube.com/watch?v=aGGbnMyeZs0) | Scott Sanderson | PyCon 2018 | 3:22 | 1000 | python | 2018 | :sweat_smile: |

### (2.2) :exclamation: Cheat-sheets

* [Numpy Cheat Sheet](http://datasciencefree.com/numpy.pdf)

* [Python For Data Science - Scikit-Learn](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf)

### (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](http://www.scipy-lectures.org/index.html)*. Zenodo, September 28, 2015. https://doi.org/10.5281/zenodo.31521.

* [[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.

* 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.

* [[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.

--------------------------------------------------------------------------------

## (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?](https://www.youtube.com/watch?v=cKPlPJyQrt4) | James Powell | PyData | 1:54 | 28000 | python | 2017 | :scream: |

| :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: |

| :star: [Builtin Superheroes](https://www.youtube.com/watch?v=j6VSAsKAj98) | David Beazley | David Beazley | 0:44 | 12000 | python | 2016 | :sweat_smile: |

| [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: |

| [Modern Dictionaries](https://www.youtube.com/watch?v=p33CVV29OG8) | Raymond Hettinger | SF Python | 1:07 | 44000 | python | 2016 | :sweat_smile: |

| [Keynote on Concurrency](https://www.youtube.com/watch?v=9zinZmE3Ogk) | Raymond Hettinger | SF Python | 1:13 | 15000 | python | 2017 | :sweat_smile: |

| [The Fun of Reinvention](https://www.youtube.com/watch?v=js_0wjzuMfc) | David Beazley | David Beazley | 0:52 | 11000 | python | 2017 | :scream: |

| [Being a Core Developer in Python](https://www.youtube.com/watch?v=voXVTjwnn-U) | Raymond Hettinger | SF Python | 1:02 | 19000 | python | 2016 | :smiley: |

| [Visualizing Geographic Data](https://www.youtube.com/watch?v=ZIEyHdvF474) | Christopher Roach | PyData | 0:31 | 14000 | python | 2016 | :smiley: |

| [Python's Class Development Toolkit](https://www.youtube.com/watch?v=HTLu2DFOdTg) | Raymond Hettinger | Next Day Video | 0:45 | 80000 | python | 2013 | :sweat_smile: |

| [The Other Async (Threads + Async = ❤️) - YouTube](https://www.youtube.com/watch?v=x1ndXuw7S0s) | David Beazley | David Beazley | 0:47 | 5000 | python | 2017 | :scream:|

| [Functional Programming with Python](https://www.youtube.com/watch?v=Ta1bAMOMFOI) | Mike Müller | Next Day Video | 0:27 | 44000 | python | 2013 | Novice |

| [Building a Recommendation Engine using Python](https://www.youtube.com/watch?v=E9XTOnEgqRY) | Anusua Trivedi | PyData | 0:37 | 11000 | python | 2015 | Novice |

| [Iterations of Evolution](https://www.youtube.com/watch?v=2AXuhgid7E4) | David Beazley | David Beazley | 0:34 | 2000 | python | 2017 | Novice |

| ["Good Enough" IS Good Enough!](https://www.youtube.com/watch?v=_Ek3A2b-nHU) | Alex Martelli | SF Python | 0:53 | 4000 | python | 2016 | Novice |

| [Automating Code Quality](https://www.youtube.com/watch?v=G1lDk_WKXvY) | Kyle Knapp | PyCon 2018 | 0:30 | 3000 | python | 2018 | :sweat_smile: |

### (3.2) :exclamation: Cheat-sheets

* [Python 3 Cheat Sheet](https://perso.limsi.fr/pointal/_media/python:cours:mementopython3-english.pdf)

* [Python Cheat Sheet](http://datasciencefree.com/python.pdf)

### (3.3) :mortar_board: Tutorials

### (3.4) :blue_book: Books / papers

* [[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.

* [[amazon](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1491946008/)] Ramalho, Luciano. *Fluent Python*. 1st edition. O’Reilly, 2015.

* [[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.

* [[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.

* [[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.

The books below are perhaps of an even more general nature.

* [[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.

* [[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.

* [[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.

--------------------------------------------------------------------------------

Every video is below.

| Title | Speaker | Uploader | Time | Views | Keyword | Year | Level |

| ----- | ------- | -------- | -------- | ----- | -------- | ---- | ----- |

| [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: |

| [Introduction Into Pandas](https://www.youtube.com/watch?v=-NR-ynQg0YM) | Daniel Chen | Python Tutorial | 1:28 | 46000 | pandas | 2017 | :snake: |

| [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: |

| [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: |

| [Introduction To Data Analytics With Pandas](https://www.youtube.com/watch?v=5XGycFIe8qE) | Quentin Caudron | Python Tutorial | 1:51 | 25000 | pandas | 2017 | :snake: |

| [Transforming Code into Beautiful, Idiomatic Python](https://www.youtube.com/watch?v=OSGv2VnC0go) |  Raymond Hettinger | Next Day Video | 0:48 | 340000 | python | 2013 | :snake: |

| [Machine Learning](https://www.youtube.com/watch?v=OB1reY6IX-o) | Andreas Mueller & Sebastian Raschka | Enthought | 3:03 | 47000 | sklearn | 2016 | :snake: :snake: |

| [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: |

| [Modern Dictionaries](https://www.youtube.com/watch?v=p33CVV29OG8) | Raymond Hettinger | SF Python | 1:07 | 44000 | python | 2016 | :snake: :snake: |

| [The Python Visualization Landscape](https://www.youtube.com/watch?v=FytuB8nFHPQ) | Jake VanderPlas | PyCon 2017 | 0:33 | 21000 | python | 2017 | :snake: |

| [Keynote on Concurrency](https://www.youtube.com/watch?v=9zinZmE3Ogk) | Raymond Hettinger | SF Python | 1:13 | 15000 | python | 2017 | :snake::snake: |

| [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: |

| [JupyterLab: Building Blocks for Interactive Computing](https://www.youtube.com/watch?v=Ejh0ftSjk6g) | Brian Granger | Enthought | 0:29 | 28000 | jupyter | 2016 | :snake: |

| [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: |

| [A Visual Guide To Pandas](https://www.youtube.com/watch?v=9d5-Ti6onew) | Jason Wirth | Next Day Video | 0:26 | 49000 | pandas | 2015 | :snake: |

| [Machine Learning with Scikit Learn](https://www.youtube.com/watch?v=80fZrVMurPM) [[repo](https://github.com/amueller/scipy_2015_sklearn_tutorial)] | Andreas Mueller & Kyle Kastner | Enthought | 3:22 | 48000 | sklearn | 2015 | :snake: :snake: |

| [Machine Learning for Time Series Data in Python](https://www.youtube.com/watch?v=ZgHGCfwExw0) | Brett Naul | Enthought | 0:24 | 24000 | cesium | 2016 | :snake: |

| [The Fun of Reinvention](https://www.youtube.com/watch?v=js_0wjzuMfc) | David Beazley | David Beazley | 0:52 | 11000 | python | 2017 | :snake::snake::snake: |

| [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: |

| [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: |

| [Being a Core Developer in Python](https://www.youtube.com/watch?v=voXVTjwnn-U) | Raymond Hettinger | SF Python | 1:02 | 19000 | python | 2016 | :snake: |

| [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: |

| [Learning TensorFlow](https://www.youtube.com/watch?v=bvHgESVuS6Q) | Robert Layton | PyCon Australia | 0:40 | 18000 | tensorflow | 2016 | :snake: :snake: |

| [JupyterHub: Deploying Jupyter Notebooks](https://www.youtube.com/watch?v=gSVvxOchT8Y) | Min Ragan Kelley & Thomas Kluyver | PyData | 1:36 | 17000 | jupyter | 2016 | :snake: |

| [Applied Time Series Econometrics](https://www.youtube.com/watch?v=tJ-O3hk1vRw) | Jeffrey Yau | PyData | 1:39 | 17000 | statsmodels | 2016 | :snake: :snake: |

| [Machine Learning with scikit learn](https://www.youtube.com/watch?v=2kT6QOVSgSg) [[repo](https://github.com/amueller/scipy-2017-sklearn)] | Andreas Mueller & Alexandre Gram | Enthought | 3:10 | 8000 | sklearn | 2017 | :snake: :snake: |

| [Introduction to Numerical Computing with NumPy](https://www.youtube.com/watch?v=lKcwuPnSHIQ) | Dillon Niederhut | Enthought | 2:27 | 8000 | NumPy | 2017 | :snake: |

| [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: |

| [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: |

| [Fully Convolutional Networks for Image Segmentation](https://www.youtube.com/watch?v=-lXfsWP7DJ8) | Daniil Pakhomov | Enthought | 0:20 | 7000 | scipy | 2017 | :snake: |

| [Exploratory data analysis in python](https://www.youtube.com/watch?v=W5WE9Db2RLU) [[repo](https://github.com/cmawer/pycon-2017-eda-tutorial)] | Chloe Mawer & Jonathan Whitmore | PyCon 2017 | 2:54 | 7000 | scipy | 2017 | :snake: |

| [Visualizing Geographic Data](https://www.youtube.com/watch?v=ZIEyHdvF474) | Christopher Roach | PyData | 0:31 | 14000 | python | 2016 | :snake: |

| [Builtin Superheroes](https://www.youtube.com/watch?v=j6VSAsKAj98) | David Beazley | David Beazley | 0:44 | 12000 | python | 2016 | :snake: :snake: |

| [Python's Class Development Toolkit](https://www.youtube.com/watch?v=HTLu2DFOdTg) | Raymond Hettinger | Next Day Video | 0:45 | 80000 | python | 2013 | :snake: :snake: |

| [Libraries for Deep Learning with Sequences](https://www.youtube.com/watch?v=E92jDCmJNek) | Alex Rubinsteyn | PyData | 0:44 | 23000 | scipy | 2015 | :snake: :snake: |

| [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:|

| [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 & Lorena Barba | Enthought | 2:25 | 5000 | numba | 2017 | :snake: :snake: |

| [Deploying Interactive Jupyter Dashboards](https://www.youtube.com/watch?v=8Jktm-Imt-I) | Philipp Rudiger | Enthought | 0:18 | 5000 | jupyter | 2017 | :snake: :snake: |

| [Eyal Trabelsi - Practical Optimisations for Pandas](https://www.youtube.com/watch?v=RXMiMpMfzXw) | Eyal Trabelsi | Europython | 0:45 | 5000 | jupyter | 2020 | :snake: :snake: |

| [Data Science Using Functional Python](https://www.youtube.com/watch?v=ThS4juptJjQ) | Joel Grus | PyData | 0:44 | 18000 | python | 2015 | :snake: :snake: |

| [Pandas from the Inside](https://www.youtube.com/watch?v=CowlcrtSyME) | Stephen Simmons | PyData | 1:20 | 9000 | pandas | 2016 | :snake: :snake: :snake: |

| [Anatomy of matplotlib](https://www.youtube.com/watch?v=MKucn8NtVeI) [[repo](https://github.com/matplotlib/AnatomyOfMatplotlib)] | Benjamin Root & Joe Kington | Enthought | 3:18 | 18000 | matplotlib | 2015 | :snake: :snake: |

| [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: |

| [Data Science is Software](https://www.youtube.com/watch?v=EKUy0TSLg04) [[repo](https://github.com/drivendata/data-science-is-software)] | Peter Bull & Isaac Slavitt | Enthought | 2:12 | 9000 | jupyter | 2016 | :snake: |

| [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 |

| [Using Jupyter notebooks](https://www.youtube.com/watch?v=aXR2d9k9-h4) | Ioanna Ioannou | PyCon Australia | 0:28 | 8000 | jupyter | 2016 | Novice |

| [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 |

| [Functional Programming with Python](https://www.youtube.com/watch?v=Ta1bAMOMFOI) | Mike Müller | Next Day Video | 0:27 | 44000 | python | 2013 | Novice |

| [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 |

| [Keynote: Project Jupyter](https://www.youtube.com/watch?v=v5mrwq7yJc4) | Brian Granger | Enthought | 0:48 | 7000 | jupyter | 2016 | Novice |

| [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 |

| [Awesome Big Data Algorithms](https://www.youtube.com/watch?v=jKBwGlYb13w) | Titus Brown | Next Day Video | 0:39 | 41000 | python | 2013 | Novice |

| [Pandas from the Inside](https://www.youtube.com/watch?v=YGk09nK_xnM) | Stephen Simmons | PyData | 1:17 | 3000 | pandas | 2017 | Novice |

| [All About Jupyter](https://www.youtube.com/watch?v=GMKZD1Ohlzk) | Brian Granger | PyData | 0:39 | 11000 | jupyter | 2015 | Novice |

| [Building a Recommendation Engine using Python](https://www.youtube.com/watch?v=E9XTOnEgqRY) | Anusua Trivedi | PyData | 0:37 | 11000 | python | 2015 | Novice |

| [Iterations of Evolution](https://www.youtube.com/watch?v=2AXuhgid7E4) | David Beazley | David Beazley | 0:34 | 2000 | python | 2017 | Novice |

| ["Good Enough" IS Good Enough!](https://www.youtube.com/watch?v=_Ek3A2b-nHU) | Alex Martelli | SF Python | 0:53 | 4000 | python | 2016 | Novice |

| [PyMC: Markov Chain Monte Carlo](https://www.youtube.com/watch?v=XbxIo7ScVzc) | Chris Fonnesbeck | Enthought | 0:20 | 9000 | pyMC | 2014 | Novice |

| [Jupyter Advanced Topics Tutorial](https://www.youtube.com/watch?v=38R7jiCspkw) [[repo](https://github.com/jupyter/scipy-advanced-tutorial)] | Jonathan Frederic & Matthias Bussonier | Enthought | 2:48 | 4000 | jupyter | 2015 | Novice |

| [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 |

| [Python Profiling & Performance](https://www.youtube.com/watch?v=Dgnp28Ijm_M) | Mahmoud Hashemi | SF Python | 0:28 | 1000 | python | 2016 | Novice |

## (4) Packages

- [datatest](https://github.com/shawnbrown/datatest) - Tools for test driven data-wrangling and data validation (DataFrame, Series, Index, MultiIndex).

- [pandera](https://github.com/pandera-dev/pandera) - A light-weight, flexible, and expressive data validation library for dataframes.

- [pandas-vet](https://github.com/deppen8/pandas-vet) - A plugin for Flake8 that checks pandas code.