This short primer on Python is designed to provide a rapid "on-ramp" for computer programmers who are already familiar with basic concepts and constructs in other programming languages to learn enough about Python to effectively use open-source and proprietary Python-based machine learning and data science tools.
The primer is spread across a collection of IPython Notebooks, and the easiest way to use the primer is to install IPython Notebook on your computer. You can also install Python, and manually copy and paste the pieces of sample code into the Python interpreter, as the primer only makes use of the Python standard libraries.
There are four versions of the primer. Three versions contain the entire primer in a single notebook:
- Single IPython Notebook (cleared output cells): Python_for_Data_Science_clean.ipynb
- Single IPython Notebook (filled output cells): Python_for_Data_Science_clean.ipynb
- Single web page (HTML): Python_for_Data_Science_all.html
The other version divides the primer into 5 separate notebooks:
- Introduction
- Data Science: Basic Concepts
- Python: Basic Concepts
- Using Python to Build and Use a Simple Decision Tree Classifier
- Next Steps
There are several exercises included in the notebooks. Sample solutions to those exercises can be found in two Python source files:
simple_ml.py
: a collection of simple machine learning utility functionssimple_decision_tree.py
: a Python class to encapsulate a simplified version of a popular machine learning model
There are also 2 data files, based on the mushroom dataset in the UCI Machine Learning Repository, used for coding examples, exploratory data analysis and building and evaluating decision trees in Python:
agaricus-lepiota.data
: a machine-readable list of examples or instances of mushrooms, represented by a comma-separated list of attribute valuesagaricus-lepiota.attributes
: a machine-readable list of attribute names and possible attribute values and their abbreviations
2015-07-21
- Updated 5 subnotebooks for Python 3 compatibility
- Changed file name of
SimpleDecisionTree.py
tosimple_decision_tree.py
(class name is unchanged) - Reordered introduction of
defaultdict
andCounter
containers - Reordered the values returned by
classification_accuracy()
- Added more examples of formatted printing via
str.format()
- Various and sundry other minor changes to prepare for tutorial at PyData Seattle 2015
2015-02-23
- Added attribution for suggested changes to accommodate Python 3 to Nick Coghlan
2015-02-22
- Added
from __future__ import print_function, division
for Python 3 compatibility - Updated
simple_ml.py
andSimpleDecisionTree.py
to also use Python 3print_function
anddivision
- Replaced
xrange()
(Python 2 only) withrange()
(Python 2 or 3) - Replaced
dict.iteritems()
(Python 2 only) withdict.items()
(Python 2 or 3) - Changed "call by reference" to "call by sharing"
- Added
isinstance()
(and reference to duck typing) to section ontype()
- Added variable for
delimiter
rather than hard-coding'|'
character - Cleaned up various cells