data-science-ipython-notebooks

Continually updated Data Science IPython Notebooks.

This repo is a collection of IPython Notebooks I reference while working with data. Although I developed and maintain most notebooks, some notebooks I reference were created by other authors, who are credited within their notebook(s) by providing their names and/or a link to their source.

For detailed instructions, scripts, and tools to more optimally set up your development environment for data analysis, check out the data-dev-setup repo.

Index

• spark
• mapreduce-python
• amazon web services
• kaggle-and-business-analyses
• scikit-learn
• statistical-inference-scipy
• pandas
• matplotlib
• numpy
• python-data
• command lines
• misc
• notebook-installation
• credits
• contributing
• contact-info
• license

spark

IPython Notebook(s) demonstrating spark and HDFS functionality.

Notebook	Description
spark	In-memory cluster computing framework, up to 100 times faster for certain applications and is well suited for machine learning algorithms.
hdfs	Reliably stores very large files across machines in a large cluster.

mapreduce-python

IPython Notebook(s) demonstrating Hadoop MapReduce with mrjob functionality.

Notebook	Description
mapreduce-python	Supports MapReduce jobs in Python with mrjob, running them locally or on Hadoop clusters. Demonstrates mrjob code, unit test, and config file to analyze Amazon S3 bucket logs on Elastic MapReduce. Disco is another python-based alternative.

aws

IPython Notebook(s) demonstrating Amazon Web Services (AWS) and AWS tools functionality.

Notebook	Description
boto	Official AWS SDK for Python.
s3cmd	Interacts with S3 through the command line.
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.
s3-parallel-put	Uploads multiple files to S3 in parallel.
redshift	Acts as a fast data warehouse built on top of technology from massive parallel processing (MPP).
kinesis	Streams data in real time with the ability to process thousands of data streams per second.
lambda	Runs code in response to events, automatically managing compute resources.

kaggle-and-business-analyses

IPython Notebook(s) used in kaggle competitions and business analyses.

Notebook	Description
titanic	Predicts survival on the Titanic. Demonstrates data cleaning, exploratory data analysis, and machine learning.
churn-analysis	Predicts customer churn. Exercises logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors. Discussion of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.

scikit-learn

IPython Notebook(s) demonstrating scikit-learn functionality.

Notebook	Description
intro	Intro notebook to scikit-learn. Scikit-learn adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays.
knn	K-nearest neighbors.
linear-reg	Linear regression.
svm	Support vector machine classifier, with and without kernels.
random-forest	Random forest classifier and regressor.
k-means	K-means clustering.
pca	Principal component analysis.
gmm	Gaussian mixture models.
validation	Validation and model selection.

statistical-inference-scipy

Notebook	Description
scipy	SciPy is a collection of mathematical algorithms and convenience functions built on the Numpy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data.
effect_size	Effect size.
sampling	Random sampling.
hypothesis	Hypothesis testing.

pandas

IPython Notebook(s) demonstrating pandas functionality.

Notebook	Description
pandas	Software library written for data manipulation and analysis in Python. Offers data structures and operations for manipulating numerical tables and time series.

matplotlib

IPython Notebook(s) demonstrating matplotlib functionality.

Notebook	Description
matplotlib	Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
matplotlib-applied	Matplotlib visualizations appied to Kaggle competitions for exploratory data analysis. Examples of bar plots, histograms, subplot2grid, normalized plots, scatter plots, subplots, and kernel density estimation plots.

numpy

IPython Notebook(s) demonstrating NumPy functionality.

Notebook	Description
numpy	Adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays.

python-data

IPython Notebook(s) demonstrating Python functionality geared towards data analysis.

Notebook	Description
data structures	Tuples, lists, dicts, sets.
data structure utilities	Slice, range, xrange, bisect, sort, sorted, reversed, enumerate, zip, list comprehensions.
functions	Functions as objects, lambda functions, closures, *args, **kwargs currying, generators, generator expressions, itertools.
datetime	Datetime, strftime, strptime, timedelta.
logging	Logging with RotatingFileHandler and TimedRotatingFileHandler.
pdb	Interactive source code debugger.
unit tests	Nose unit tests.

commands

IPython Notebook(s) demonstrating various command lines for Linux, Git, etc.

Notebook	Description
linux	Unix-like and mostly POSIX-compliant computer operating system. Disk usage, splitting files, grep, sed, curl, viewing running processes, terminal syntax highlighting, and Vim.
anaconda	Distribution of the Python programming language for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment.
ipython notebook	Web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document.
git	Distributed revision control system with an emphasis on speed, data integrity, and support for distributed, non-linear workflows.
ruby	Used to interact with the AWS command line and for Jekyll, a blog framework that can be hosted on GitHub Pages.
jekyll	Simple, blog-aware, static site generator for personal, project, or organization sites. Renders Markdown or Textile and Liquid templates, and produces a complete, static website ready to be served by Apache HTTP Server, Nginx or another web server. Pelican is a python-based alternative.
django	High-level Python Web framework that encourages rapid development and clean, pragmatic design. It can be useful to share reports/analyses and for blogging. Lighter-weight alternatives include Pyramid, Flask, Tornado, and Bottle.

misc

IPython Notebook(s) demonstrating miscellaneous functionality.

Notebook	Description
regex	Regular expression cheat sheet useful in data wrangling.

notebook-installation

anaconda

Anaconda is a free distribution of the Python programming language for large-scale data processing, predictive analytics, and scientific computing that aims to simplify package management and deployment.

Follow instructions to install Anaconda or the more lightweight miniconda.

pip-requirements

If you prefer to use a more lightweight installation procedure than Anaconda, first clone the repo then run the following pip command on the provided requirements.txt file:

$ pip install -r requirements.txt

running-notebooks

To view interactive content or to modify elements within the IPython notebooks, you must first clone or download the repository then run the ipython notebook. More information on IPython Notebooks can be found here.

$ git clone https://github.com/donnemartin/data-science-ipython-notebooks.git
$ cd data-science-ipython-notebooks
$ ipython notebook

Notebooks tested with Python 2.7.x.

credits

• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney
• PyCon 2015 Scikit-learn Tutorial by Jake VanderPlas
• Parallel Machine Learning with scikit-learn and IPython by Olivier Grisel
• Statistical Interference Using Computational Methods in Python by Allen Downey
• Yhat blog by Yhat
• Kaggle by Kaggle
• Spark Docs by Apache Spark
• AWS Docs by Amazon Web Services

contributing

Contributions are welcome! For bug reports or requests please submit an issue.

contact-info

Feel free to contact me to discuss any issues, questions, or comments.

• Email: donne.martin@gmail.com
• Twitter: @donne_martin
• GitHub: donnemartin
• LinkedIn: donnemartin
• Website: donnemartin.com

license

This repository contains a variety of content; some developed by Donne Martin, and some from third-parties. The third-party content is distributed under the license provided by those parties.

The content developed by Donne Martin is distributed under the following license:

Copyright 2015 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.