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README.md
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# awesome-pandas
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A collection of resources for pandas (Python) and related subjects.
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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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**Table of contents**
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**Contents:** This page is divided into three parts.
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* pandas resources
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* Videos
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* Cheat-sheets
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* Tutorials
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* Books
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* Data analysis with Python resources
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* Videos
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* Cheat-sheets
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* Tutorials
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* Books
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* Miscellaneous related resources
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* Videos
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* Cheat-sheets
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* Tutorials
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* Books
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* (1) **pandas resources** - A collection of videos, cheat-sheets, tutorials and books related directly to pandas.
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* (2) **Data analysis with Python resources** - Material related to adjacent
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Python libraries such as
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[NumPy](http://www.numpy.org/),
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[scipy](https://www.scipy.org/),
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[matplotlib](https://matplotlib.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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[Jupyter](http://jupyter.org/).
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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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--------------------------------------------------------------------------------
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## pandas resources
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## (1) pandas resources
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### Videos
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### (1.1) Videos
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The videos below were collected in December of 2017.
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They are all related to pandas, and the **Level** of a video is quantified roughly as follows:
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* (Introduction To Data Analytics With Pandas) https://www.youtube.com/watch?v=5XGycFIe8qE
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* (Introduction Into Pandas: Python Tutorial) https://www.youtube.com/watch?v=-NR-ynQg0YM
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### Cheat-sheets
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### (1.2) Cheat-sheets
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* https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf
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* https://s3.amazonaws.com/quandl-static-content/Documents/Quandl+-+Pandas,+SciPy,+NumPy+Cheat+Sheet.pdf
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* https://assets.datacamp.com/blog_assets/PandasPythonForDataScience.pdf
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* https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Pandas_Cheat_Sheet_2.pdf
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### Tutorials
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### (1.3) Tutorials
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* https://github.com/jorisvandenbossche/pandas-tutorial
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* https://github.com/guipsamora/pandas_exercises
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* https://github.com/brandon-rhodes/pycon-pandas-tutorial
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* https://github.com/vi3k6i5/pandas_basics
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* https://github.com/california-civic-data-coalition/first-python-notebook
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### Books
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### (1.4) Books
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## Data analysis with Python resources
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--------------------------------------------------------------------------------
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### Videos
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## (2) Data analysis with Python resources
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### (2.1) Videos
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| Title | Speaker | Uploader | Time | Views | Keyword | Year | Level |
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| ----- | ------- | -------- | -------- | ----- | -------- | ---- | ----- |
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| [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: |
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| [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 |
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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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### Cheat-sheets
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### (2.2) Cheat-sheets
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* 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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### Tutorials
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### (2.3) Tutorials
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### Books
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### (2.4) Books
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## Data analysis resources
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### Books
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## (3) Miscellaneous related resources
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### Papers
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### (3.1) Books
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### (3.2) Papers
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###
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