%matplotlib inline

import requests

print requests.get("http://example.com").text

response = requests.get("https://www.googleapis.com/books/v1/volumes", params={"q":"machine learning"})
raw_data = response.json()
titles = [item[‘volumeInfo‘][‘title‘] for item in raw_data[‘items‘]]
titles

import lxml.html

page = lxml.html.parse("http://www.blocket.se/stockholm?q=apple")
# ^ This is probably illegal. Blocket, please don‘t sue me!
items_data = []
for el in page.getroot().find_class("item_row"):
    links = el.find_class("item_link")
    images = el.find_class("item_image")
    prices = el.find_class("list_price")
    if links and images and prices and prices[0].text:
        items_data.append({"name": links[0].text,
                           "image": images[0].attrib[‘src‘],
                           "price": int(prices[0].text.split(":")[0].replace(" ", ""))})
items_data

import pandas

df = pandas.read_csv(‘sample.csv‘)

# Display the DataFrame
df

# DataFrame‘s columns
df.columns

# Values of a given column
df.Model

# Any missing values?
df[‘Price‘]

df[‘Description‘]

# Fill missing prices by a linear interpolation
df[‘Description‘] = df[‘Description‘].fillna("No description is available.")
df[‘Price‘] = df[‘Price‘].interpolate()

df

import matplotlib.pyplot as plt

df = pandas.read_csv(‘sample2.csv‘)

df

# This table has 3 columns: Office, Year, Sales
print df.columns

# It‘s really easy to query data with Pandas:
print df[(df[‘Office‘] == ‘Stockholm‘) & (df[‘Sales‘] > 260)]

# It‘s also easy to do aggregations...
aggregated_sales = df.groupby(‘Year‘).sum()
print aggregated_sales

# ... and generate plots
%matplotlib inline
aggregated_sales.plot(kind=‘bar‘)

from sklearn import feature_extraction

corpus = [‘All the cats really are great.‘,
          ‘I like the cats but I still prefer the dogs.‘,
          ‘Dogs are the best.‘,
          ‘I like all the trains‘,
          ]

tfidf = feature_extraction.text.TfidfVectorizer()

print tfidf.fit_transform(corpus).toarray()
print tfidf.get_feature_names()

import json


data = [json.loads("""{"weight": 194.0, "sex": "female", "student": true}"""),
        {"weight": 60., "sex": ‘female‘, "student": True},
        {"weight": 80.1, "sex": ‘male‘, "student": False},
        {"weight": 65.3, "sex": ‘male‘, "student": True},
        {"weight": 58.5, "sex": ‘female‘, "student": False}]

vectorizer = feature_extraction.DictVectorizer(sparse=False)

vectors = vectorizer.fit_transform(data)
print vectors
print vectorizer.get_feature_names()

class A:
    def __init__(self, x):
        self.x = x
        self.blabla = ‘test‘
        
a = A(20)
a.__dict__

from sklearn import preprocessing

data = [[10., 2345., 0., 2.],
        [3., -3490., 0.1, 1.99],
        [13., 3903., -0.2, 2.11]]

print preprocessing.normalize(data)

from sklearn import decomposition

data = [[0.3, 0.2, 0.4,  0.32],
        [0.3, 0.5, 1.0, 0.19],
        [0.3, -0.4, -0.8, 0.22]]

pca = decomposition.PCA()
print pca.fit_transform(data)
print pca.explained_variance_ratio_

from sklearn import datasets
from sklearn import svm

iris = datasets.load_iris()

X = iris.data[:, :2]
y = iris.target

# Training the model
clf = svm.SVC(kernel=‘rbf‘)
clf.fit(X, y)

# Doing predictions
new_data = [[4.85, 3.1], [5.61, 3.02]]
print clf.predict(new_data)

import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt

def f(x):
    return x + np.random.random() * 3.

X = np.arange(0, 5, 0.5)
X = X.reshape((len(X), 1))
y = map(f, X)

clf = linear_model.LinearRegression()
clf.fit(X, y)

new_X = np.arange(0.2, 5.2, 0.3)
new_X = new_X.reshape((len(new_X), 1))
new_y = clf.predict(new_X)

plt.scatter(X, y, color=‘g‘, label=‘Training data‘)

plt.plot(new_X, new_y, ‘.-‘, label=‘Predicted‘)
plt.legend()

from sklearn.cluster import DBSCAN
from sklearn.datasets.samples_generator import make_blobs
from sklearn.preprocessing import StandardScaler

# Generate sample data
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=200, centers=centers, cluster_std=0.4,
                            random_state=0)
X = StandardScaler().fit_transform(X)

# Compute DBSCAN
db = DBSCAN(eps=0.3, min_samples=10).fit(X)
db.labels_

import matplotlib.pyplot as plt
plt.scatter(X[:, 0], X[:, 1], c=db.labels_)

from sklearn import svm, cross_validation, datasets

iris = datasets.load_iris()
X, y = iris.data, iris.target

model = svm.SVC()
print cross_validation.cross_val_score(model, X, y, scoring=‘precision‘)
print cross_validation.cross_val_score(model, X, y, scoring=‘mean_squared_error‘)