Python Basics

#Introduction to Python

##Python Shell Like R, Python at it's core is a fancy calculator. Typing python into the command line will give you a prompt

python

##Defining functions In Python we define functions by using the def keyword.

def addition(x, y):
	return x + y

x and y are the inputs to the function and the return keyword tells us what the function responds. The most important thing to note here is that Python respect whitespace, the second like must be indented for this to run.

##Data Structures

Python has a variety of data structures available. The most basic of them are the basic integer, float and strings.

###Lists

Lists are one of the most common data structures in Python.

l = [1,2,3,4] #Setting up a list
l.append(5) #Adding to it using the append function
l += [6] #Adding to it using the addition operation

###Dictionaries

Dictionaries in Python are equivalent to Hash Tables in any other language.

x = {} # Empty Dictionary
y = {'key' : value}
y[key] = "new-value"

##Loop Constructs

Like any other programming language, Python has loop constructs, the ability to iterate over different elements.

l = [1,2,3,4]
for i in l:
	print i

However, when possible, Python will perform better using list comprehensions.

x = [1,2,3,4]

#The following are equivalent
y = []
for i in x:
	y.append(i**2)

#OR

y = [i**2 for i in x]

##Scikits.learn

scikits.learn has become one of the most popular machine learning packages in Python. It has a most of the standard machine learning algorithms built-in and often calls faster external C-code that can make execuation fairly quick.

from sklearn.datasets import load_iris
iris = load_iris()

from sklearn import neighbors
X, y = iris.data, iris.target
knn = neighbors.KNeighborsClassifier(n_neighbors=1)
knn.fit(X, y)
# What kind of iris has 3cm x 5cm sepal and 4cm x 2cm petal?
print iris.target_names[knn.predict([[3, 5, 4, 2]])]

from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(...)
clf = ...
clf.fit(X_train, y_train)
predicted = clf.predict(X_test)

from sklearn import metrics
print metrics.classification_report(y_test, predicted)
print metrics.confusion_matrix(y_test, predicted)
print metrics.f1_score(y_test, predicted)

from sklearn.cross_validation import train_test_split
from sklearn.cross_validation import cross_val_score
X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)

scores = cross_val_score(knn, iris.data, iris.target, cv=5)

###Naive Bayes in Sklearn Download the 20 News Group Dataset

wget http://people.csail.mit.edu/jrennie/20Newsgroups/20news-bydate.tar.gz
tar -xf 20news-bydate.tar.gz

from sklearn.datasets import load_files
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

# Load the text data
categories = [
    'alt.atheism',
    'talk.religion.misc',
    'comp.graphics',
    'sci.space',
]
twenty_train_subset = load_files('20news-bydate-train/', categories=categories, charset='latin-1')
twenty_test_subset = load_files('20news-bydate-test/', categories=categories, charset='latin-1')

# Turn the text documents into vectors of word frequencies
vectorizer = CountVectorizer()
X_train = vectorizer.fit_transform(twenty_train_subset.data)
y_train = twenty_train_subset.target

# Fit a classifier on the training set
classifier = MultinomialNB().fit(X_train, y_train)
print("Training score: {0:.1f}%".format(classifier.score(X_train, y_train) * 100))

# Evaluate the classifier on the testing set
X_test = vectorizer.transform(twenty_test_subset.data)
y_test = twenty_test_subset.target
print("Testing score: {0:.1f}%".format(classifier.score(X_test, y_test) * 100))

from sklearn import metrics
metrics.confusion_matrix(y_test, classifier.predict(X_test))

##Pandas

Pandas is a Python library for data analysis that resembles the tools available in R. It is also optimized for fast I/O and data manipulation.

import pandas as pd

data = pd.read_csv('Dropbox/src/538model/data/2012_poll_data_states.csv', sep='\t')
print data['Poll']
data.describe()

data['Poll'].value_counts()

data.groupby('Poll')['Obama (D)'].mean()