Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
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README.md Combined pandas notebooks until pandas I/O and pandas cleaning are further developed. 2015-06-12 20:51:00 -04:00

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.

Index


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.

aws

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

Notebook Description
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

IPython Notebook(s) used in kaggle competitions.

Notebook Description
titanic Predicts survival on the Titanic. Demonstrates data cleaning, exploratory data analysis, and machine learning.

scikit-learn

IPython Notebook(s) demonstrating scikit-learn functionality.

Credits: Forked from PyCon 2015 Scikit-learn Tutorial by Jake VanderPlas

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.

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.

Credits: Some content forked from Parallel Machine Learning with scikit-learn and IPython by Olivier Grisel

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.

Credits: Forked from Parallel Machine Learning with scikit-learn and IPython by Olivier Grisel

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.

scipy

[Coming Soon] IPython Notebook(s) demonstrating SciPy functionality.


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, and Bottle.

notebook-installation

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.

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 [downloaded repo directory name]
$ ipython notebook

credits

contact-info

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

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.