Updated with more cheat-sheets and intro
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README.md
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README.md
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![Awesome pandas logo](/img/awesome_pandas.png)
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![Awesome pandas logo](/img/awesome_pandas.png)
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# awesome-pandas
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# awesome-pandas
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A collection of resources for [pandas](http://pandas.pydata.org/)
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A collection of resources for [pandas](http://pandas.pydata.org/)
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([Python](https://www.python.org/)) and related subjects.
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([Python](https://www.python.org/)) and related subjects.
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**Contents:** This page is divided into three parts.
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**Contents:** This is an unofficial collection of resources for learning pandas,
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an open source Python library for data analysis. Here you will find videos,
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cheat-sheets, tutorials and books / papers. The curated list is divided into
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three parts:
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1. **pandas resources** - A collection of videos, cheat-sheets, tutorials and books related directly to pandas.
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1. **pandas resources** - A collection of videos, cheat-sheets, tutorials and
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2. **Data analysis with Python resources** - Material related to adjacent
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books *directly related* to pandas.
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Python libraries and software such as
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2. **Data analysis with Python resources** - Material related to *adjacent
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Python libraries and software* such as
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[NumPy](http://www.numpy.org/),
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[NumPy](http://www.numpy.org/),
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[scipy](https://www.scipy.org/),
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[scipy](https://www.scipy.org/),
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[matplotlib](https://matplotlib.org/),
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[matplotlib](https://matplotlib.org/),
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[seaborn](https://seaborn.pydata.org/),
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[seaborn](https://seaborn.pydata.org/),
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[statsmodels](http://www.statsmodels.org/stable/) and
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[statsmodels](http://www.statsmodels.org/stable/) and
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[Jupyter](http://jupyter.org/).
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[Jupyter](http://jupyter.org/).
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3. **Miscellaneous related resources** - Resources related to general data
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3. **Miscellaneous related resources** - Resources related to *general* data
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analysis, algorithms, computer science, machine learning, statistics, etc.
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analysis, algorithms, computer science, machine learning, statistics, etc.
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> Pull requests are very welcome.
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## (1) :panda_face: pandas resources
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## (1) :panda_face: pandas resources
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| [Predicting sports winners with pandas](https://www.youtube.com/watch?v=k7hSD_-gWMw) | Robert Layton | PyCon Australia | 0:38 | 13000 | 2015 | :sweat_smile: |
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| [Predicting sports winners with pandas](https://www.youtube.com/watch?v=k7hSD_-gWMw) | Robert Layton | PyCon Australia | 0:38 | 13000 | 2015 | :sweat_smile: |
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| [Pandas from the Inside](https://www.youtube.com/watch?v=YGk09nK_xnM) | Stephen Simmons | PyData | 1:17 | 3000 | 2017 | :scream: |
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| [Pandas from the Inside](https://www.youtube.com/watch?v=YGk09nK_xnM) | Stephen Simmons | PyData | 1:17 | 3000 | 2017 | :scream: |
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*Know of a recent, good video? Send a pull request!* +1
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*Know of a recent, good video? Send a pull request!* :+1:
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### (1.2) :exclamation: Cheat-sheets
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### (1.2) :exclamation: Cheat-sheets
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| [Python Profiling & Performance](https://www.youtube.com/watch?v=Dgnp28Ijm_M) | Mahmoud Hashemi | SF Python | 0:28 | 1000 | python | 2016 | Novice |
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| [Python Profiling & Performance](https://www.youtube.com/watch?v=Dgnp28Ijm_M) | Mahmoud Hashemi | SF Python | 0:28 | 1000 | python | 2016 | Novice |
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### (2.2) :exclamation: Cheat-sheets
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### (2.2) :exclamation: Cheat-sheets
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* http://datasciencefree.com/numpy.pdf
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* [Numpy Cheat Sheet](http://datasciencefree.com/numpy.pdf)
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* https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf
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* [Python For Data Science - Scikit-Learn](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf)
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### (2.3) :mortar_board: Tutorials
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### (2.3) :mortar_board: Tutorials
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