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# awesome-pandas # awesome-pandas
A collection of resources for [pandas](http://pandas.pydata.org/) A collection of resources for [pandas](http://pandas.pydata.org/)
([Python](https://www.python.org/)) and related subjects. ([Python](https://www.python.org/)) and related subjects.
**Pull requests are very welcome.**
**Contents:** This is an unofficial collection of resources for learning pandas, **Contents:** This is an unofficial collection of resources for learning pandas,
an open source Python library for data analysis. Here you will find videos, an open source Python library for data analysis. Here you will find videos,
@ -20,9 +21,8 @@ three parts:
[statsmodels](http://www.statsmodels.org/stable/) and [statsmodels](http://www.statsmodels.org/stable/) and
[Jupyter](http://jupyter.org/). [Jupyter](http://jupyter.org/).
3. **Miscellaneous related resources** - Resources related to *general* data 3. **Miscellaneous related resources** - Resources related to *general* data
analysis, algorithms, computer science, machine learning, statistics, etc. analysis, Python programming, algorithms, computer science, machine learning,
statistics, etc.
> Pull requests are very welcome.
-------------------------------------------------------------------------------- --------------------------------------------------------------------------------
@ -41,9 +41,9 @@ quantified roughly as follows:
| Title | Speaker | Uploader | Time | Views | Year | Level | | Title | Speaker | Uploader | Time | Views | Year | Level |
| ----- | ------- | -------- | ---- | ----- | ---- | ----- | | ----- | ------- | -------- | ---- | ----- | ---- | ----- |
| :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) | Daniel Chen | Python Tutorial | 1:28 | 46000 | 2017 | :smiley: | | [Introduction Into Pandas](https://www.youtube.com/watch?v=-NR-ynQg0YM) | 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: | | [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: |
| :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: |
| [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: | | [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: | | [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: | | [A Visual Guide To Pandas](https://www.youtube.com/watch?v=9d5-Ti6onew) | Jason Wirth | Next Day Video | 0:26 | 49000 | 2015 | :smiley: |
@ -51,7 +51,7 @@ quantified roughly as follows:
| [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: | | [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: | | [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 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 | 0 | 2017 | :smiley: | | 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: |
*Know of a recent, good video? Send a pull request!* :+1: *Know of a recent, good video? Send a pull request!* :+1:
@ -65,18 +65,10 @@ quantified roughly as follows:
### (1.3) :mortar_board: Tutorials ### (1.3) :mortar_board: Tutorials
* :star: [10 Minutes to pandas](http://pandas.pydata.org/pandas-docs/stable/10min.html) * :star: [10 Minutes to pandas](http://pandas.pydata.org/pandas-docs/stable/10min.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)]
* :star: [pandas_exercises](https://github.com/guipsamora/pandas_exercises) * :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: [pycon-pandas-tutorial](https://github.com/brandon-rhodes/pycon-pandas-tutorial) [Video: [Pandas From The Ground Up](https://www.youtube.com/watch?v=5JnMutdy6Fw)]
* [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)]
* :star: [Learn-Pandas](https://github.com/tdpetrou/Learn-Pandas) * :star: [Learn-Pandas](https://github.com/tdpetrou/Learn-Pandas)
* [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)
* :star: [pandas-cookbook](https://github.com/jvns/pandas-cookbook) * :star: [pandas-cookbook](https://github.com/jvns/pandas-cookbook)
* [Learn Pandas](https://bitbucket.org/hrojas/learn-pandas)
* :star: Modern pandas. Parts: * :star: Modern pandas. Parts:
[1](http://tomaugspurger.github.io/modern-1-intro.html), [1](http://tomaugspurger.github.io/modern-1-intro.html),
[2](http://tomaugspurger.github.io/method-chaining.html), [2](http://tomaugspurger.github.io/method-chaining.html),
@ -85,6 +77,14 @@ quantified roughly as follows:
[5](http://tomaugspurger.github.io/modern-5-tidy.html), [5](http://tomaugspurger.github.io/modern-5-tidy.html),
[6](http://tomaugspurger.github.io/modern-6-visualization.html) and [6](http://tomaugspurger.github.io/modern-6-visualization.html) and
[7](http://tomaugspurger.github.io/modern-7-timeseries.html). [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)
@ -156,15 +156,16 @@ quantified roughly as follows:
| Title | Speaker | Uploader | Time | Views | Keyword | Year | Level | | 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: |
| :star: [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: | | :star: [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: |
| :star: [Transforming Code into Beautiful, Idiomatic Python](https://www.youtube.com/watch?v=OSGv2VnC0go) | Raymond Hettinger | Next Day Video | 0:48 | 340000 | python | 2013 | :snake: | | :star: [Transforming Code into Beautiful, Idiomatic Python](https://www.youtube.com/watch?v=OSGv2VnC0go) | Raymond Hettinger | Next Day Video | 0:48 | 340000 | python | 2013 | :snake: |
| :star: [Builtin Superheroes](https://www.youtube.com/watch?v=j6VSAsKAj98) | David Beazley | David Beazley | 0:44 | 12000 | python | 2016 | :snake: :snake: |
| [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: |
| [Modern Dictionaries](https://www.youtube.com/watch?v=p33CVV29OG8) | Raymond Hettinger | SF Python | 1:07 | 44000 | python | 2016 | :snake: :snake: | | [Modern Dictionaries](https://www.youtube.com/watch?v=p33CVV29OG8) | Raymond Hettinger | SF Python | 1:07 | 44000 | python | 2016 | :snake: :snake: |
| [Keynote on Concurrency](https://www.youtube.com/watch?v=9zinZmE3Ogk) | Raymond Hettinger | SF Python | 1:13 | 15000 | python | 2017 | :snake::snake: | | [Keynote on Concurrency](https://www.youtube.com/watch?v=9zinZmE3Ogk) | Raymond Hettinger | SF Python | 1:13 | 15000 | python | 2017 | :snake::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: | | [The Fun of Reinvention](https://www.youtube.com/watch?v=js_0wjzuMfc) | David Beazley | David Beazley | 0:52 | 11000 | python | 2017 | :snake::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: | | [Being a Core Developer in Python](https://www.youtube.com/watch?v=voXVTjwnn-U) | Raymond Hettinger | SF Python | 1:02 | 19000 | python | 2016 | :snake: |
| [Visualizing Geographic Data](https://www.youtube.com/watch?v=ZIEyHdvF474) | Christopher Roach | PyData | 0:31 | 14000 | python | 2016 | :snake: | | [Visualizing Geographic Data](https://www.youtube.com/watch?v=ZIEyHdvF474) | Christopher Roach | PyData | 0:31 | 14000 | python | 2016 | :snake: |
| :star: [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: | | [Python's Class Development Toolkit](https://www.youtube.com/watch?v=HTLu2DFOdTg) | Raymond Hettinger | Next Day Video | 0:45 | 80000 | python | 2013 | :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:| | [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:|
| [Functional Programming with Python](https://www.youtube.com/watch?v=Ta1bAMOMFOI) | Mike Müller | Next Day Video | 0:27 | 44000 | python | 2013 | Novice | | [Functional Programming with Python](https://www.youtube.com/watch?v=Ta1bAMOMFOI) | Mike Müller | Next Day Video | 0:27 | 44000 | python | 2013 | Novice |