data-science-ipython-notebooks/README.md

![alt text](http://i2.wp.com/donnemartin.com/wp-content/uploads/2015/02/ipython_notebook_cover2-e1425213196820.png)

# ipython-data-notebooks
Continually updated IPython Data Science Notebooks geared towards processing big data (AWS, Spark, Hadoop, Linux command line, Python, NumPy, pandas, matplotlib, SciPy, scikit-learn, Kaggle).

## kaggle

IPython Notebooks used in [kaggle](https://www.kaggle.com/) competitions.

* [titanic](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/kaggle/titanic.ipynb): Predicts survival on the Titanic.  Demonstrates data cleaning, exploratory data analysis, and machine learning.

## aws

IPython Notebooks demonstrating Amazon Web Services functionality.

* [aws commands index](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb)

* [s3cmd](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#s3cmd): Interacts with S3 through the command line.

* [s3-parallel-put](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#s3-parallel-put): Uploads multiple files to S3 in parallel.

* [s3distcp](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#s3distcp): Combines smaller files and aggregates them together by taking in a pattern and target file.  S3DistCp can also be used to transfer large volumes of data from S3 to your Hadoop cluster.

* [mrjob](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#mrjob): Supports MapReduce jobs in Python 2.5+ and runs them locally or on Hadoop clusters.

* [redshift](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#redshift): Acts as a fast data warehouse built on top of technology from massive parallel processing (MPP).

* [kinesis](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#kinesis): Streams data in real time with the ability to process thousands of data streams per second.

* [lambda](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/aws/aws.ipynb#lambda): Runs code in response to events, automatically managing compute resources.

## spark

IPython Notebooks demonstrating spark and HDFS functionality.

* [spark](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/spark/spark.ipynb): Open-source in-memory cluster computing framework, up to 100 times faster for certain applications and is well suited for machine learning algorithms.

* [hdfs](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/spark/hdfs.ipynb): Reliably stores very large files across machines in a large cluster.

## python-core

IPython Notebooks demonstrating core Python functionality geared towards data analysis.

* [data structures](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/python-core/structs.ipynb)
* [data structure utilities](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/python-core/structs_utils.ipynb)
* [functions](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/python-core/functions.ipynb)
* [datetime](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/python-core/datetime.ipynb)
* [unit tests](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/python-core/unit_tests.ipynb)

## pandas

IPython Notebooks demonstrating pandas functionality.

* [pandas](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/pandas/pandas.ipynb)
* [pandas io](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/pandas/pandas_io.ipynb)
* [pandas cleaning](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/pandas/pandas_clean.ipynb)

## commands

IPython Notebooks demonstrating various command lines for Linux, Git, etc.

* [linux](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/commands/linux.ipynb)
* [anaconda](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/commands/misc.ipynb#anaconda)
* [ipython notebook](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/commands/misc.ipynb#ipython-notebook)
* [git](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/commands/misc.ipynb#git)
* [ruby](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/commands/misc.ipynb#ruby)
* [jekyll](http://nbviewer.ipython.org/github/donnemartin/ipython-data-notebooks/blob/master/commands/misc.ipynb#jekyll)

## matplotlib

[Coming Soon] IPython Notebooks demonstrating matplotlib functionality.

## scikit-learn

[Coming Soon] IPython Notebooks demonstrating scikit-learn functionality.

## scipy

[Coming Soon] IPython Notebooks demonstrating SciPy functionality.

## numpy

[Coming Soon] IPython Notebooks demonstrating NumPy functionality.

## References

* [Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython](http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793)
* [Building Machine Learning Systems with Python](http://www.amazon.com/Building-Machine-Learning-Systems-Python/dp/1782161406)
* [Think Bayes](http://www.amazon.com/Think-Bayes-Allen-B-Downey/dp/1449370780)
* [Think Stats](http://www.amazon.com/Think-Stats-Allen-B-Downey/dp/1449307116)

## License

    Copyright 2014 Donne Martin

    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing, software
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.