felixlaumon · August 29, 2015 14:05
diff --git a/lesson02-homework.ipynb b/lesson02-homework.ipynb
diff --git a/lesson02-homework.py b/lesson02-homework.py

 # coding: utf-8

 # http://nbviewer.ipython.org/gist/felixlaumon/7e4d484a921bfccf935c
 # 
 # https://gist.github.com/felixlaumon/7e4d484a921bfccf935c

 # In[4]:

 matrix = [
  [1,3,9,2],
  [2,4,6,8]
 ]

 vector = [2,3,6,5]


 ##### 1) Complete your Vector/Matrix multiplication function

 # In[5]:

 def dot_product (a, b):
  assert len(a) is len(b)
  return sum(map(lambda x: x[0] * x[1], zip(a, b)))

 def mv_multiple (matrix, vector):
  return [dot_product(row, vector) for row in matrix]

 assert mv_multiple(matrix, vector) == [75, 92]


 # In[6]:

 from itertools import starmap, izip
 from operator import mul

 def dot_product (a, b):
  assert len(a) is len(b)
  return sum(starmap(mul, izip(a, b)))

 def mv_multiple2 (matrix, vector):
  return [dot_product(row, vector) for row in matrix]

 assert mv_multiple2(matrix, vector) == [75, 92]


 ##### 2) Add a function which does Matrix * Matrix multiplication

 # In[7]:

 def matrix_transpose (m):
  # OMFG
  # http://stackoverflow.com/questions/4937491/matrix-transpose-in-python
  return zip(*m)


 # In[8]:

 # Version that resuses matrix * vector multiplication
 def matrix_multiply (matrix1, matrix2):
  # TODO should be unnecessary to check size of matrix1 and matrix2 here?
  # Because already handled by dot_product?
  matrix2 = matrix_transpose(matrix2)
  return [mv_multiple(matrix1, m2) for m2 in matrix2]

 matrix1 = [
  [1,3,9,2],
  [2,4,6,8]
 ]

 matrix2 = [
  [2,1],
  [3,2],
  [6,0],
  [5,4]
 ]

 assert matrix_multiply(matrix, matrix2) == [[75, 92], [15, 42]]


 # In[9]:

 # Version that only relies on dot product
 def matrix_multiply2 (matrix1, matrix2):
  matrix2 = matrix_transpose(matrix2)
  return [[dot_product(v1, v2) for v1 in matrix1 ] for v2 in matrix2]

 matrix1 = [
  [1,3,9,2],
  [2,4,6,8]
 ]

 matrix2 = [
  [2,1],
  [3,2],
  [6,0],
  [5,4]
 ]

 assert matrix_multiply2(matrix, matrix2) == [[75, 92], [15, 42]]


 ##### 3) Can you find a way to generate an identity matrix for any given number with a python function?

 # In[10]:

 def i_matrix_row (n, i):
  result = [0] * n
  result[i] = 1
  return result

 def i_matrix (n):
  return [i_matrix_row(n, i) for i in range(n)]

 assert i_matrix(4) == [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]


 # In[11]:

 def i_matrix2 (n):
  return [[int(i == j) for j in range(n)] for i in range(n)]

 assert i_matrix2(4) == [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]


 ##### 4) Revise the original NYT script to  summarizes the data in such a way that you have average and max values (“avg_click”, “avg_impressions”, “max_click”, “max_impressions”) for each unique combination of “age”, “gender”, “signed_in”. so one example row would have the data for age 14 , Male, is_signed_in.

 # In[12]:

 from pandas import DataFrame, read_csv, concat
 import numpy as np
 # TODO is there a better way to lower case the header?
 df = read_csv('nytimes.csv', header=0, names=['age', 'gender', 'impressions', 'click', 'signed_in'])
 df.head()


 # In[13]:

 headers = ['age', 'gender', 'signed_in', 'avg_click', 'avg_impressions', 'max_click', 'max_impression']
 grouped = df.groupby(['age', 'gender', 'signed_in'])
 grouped_click = grouped['click']
 grouped_impressions = grouped['impressions']

 # TODO how to rename the column here instead of setting columns below?
 avg_click = grouped_click.agg(np.mean)
 avg_impresssions = grouped_impressions.agg(np.mean)
 max_click = grouped_click.agg(np.max)
 max_impressions = grouped_impressions.agg(np.max)

 result = concat([avg_click, avg_impresssions, max_click, max_impressions], axis=1)
 result.columns = ['avg_click', 'avg_impressions', 'max_click', 'max_impressions']
 result

 # TODO how to fill the rest of the grouped column with empty data? (e.g. age = 1, gender = 1, signed_in = 0)

diff --git a/nytimes.csv b/nytimes.csv
 "Age","Gender","Impressions","Clicks","Signed_In"
 36,0,3,0,1
 73,1,3,0,1
 30,0,3,0,1
 49,1,3,0,1
 47,1,11,0,1
 47,0,11,1,1
 0,0,7,1,0
 46,0,5,0,1
 16,0,3,0,1
 52,0,4,0,1
 0,0,8,1,0
 21,0,3,0,1
 0,0,4,0,0
 57,0,6,0,1
 31,0,5,0,1
 0,0,6,0,0
 40,1,3,0,1
 31,1,5,0,1
 38,0,4,0,1
 0,0,5,0,0
 59,1,4,0,1
 61,0,6,0,1
 48,0,7,0,1
 29,1,2,0,1
 0,0,4,0,0
 19,1,4,0,1
 19,0,3,0,1
 48,1,9,0,1
 48,1,4,0,1
 21,1,5,0,1
 23,0,4,0,1
 66,1,3,0,1
 23,1,7,0,1
 44,1,4,0,1
 32,0,2,0,1
 22,1,2,0,1
 30,1,8,0,1
 74,0,2,0,1
 65,1,5,0,1
 0,0,7,2,0
 32,1,9,0,1
 0,0,4,0,0
 61,0,2,0,1
 63,1,4,0,1
 36,1,1,0,1
 35,0,7,0,1
 0,0,3,0,0
 0,0,7,0,0
 73,0,4,0,1
 0,0,5,0,0
 0,0,5,0,0
 33,0,4,0,1
 34,0,5,0,1
 48,1,1,0,1
 43,1,4,0,1
 59,1,2,0,1
 34,0,3,0,1
 22,0,5,0,1
 18,1,1,0,1
 0,0,4,0,0
 0,0,8,0,0
 17,1,6,0,1
 30,0,5,0,1
 0,0,2,1,0
 57,0,4,0,1
 62,0,5,0,1
 27,0,4,0,1
 11,0,5,0,1
 38,0,6,0,1
 21,1,4,0,1
 42,0,4,0,1
 41,0,10,0,1
 0,0,6,1,0
 42,0,6,0,1
 14,0,7,0,1
 64,1,7,1,1
 0,0,5,0,0
 24,1,5,0,1
 29,0,4,0,1
 26,1,5,0,1
 18,0,3,0,1
 22,0,5,0,1
 0,0,3,0,0
 0,0,1,0,0
 42,0,2,0,1
 0,0,7,1,0
 0,0,5,0,0
 0,0,4,0,0
 22,1,4,0,1
 39,1,4,0,1
 61,1,1,0,1
 47,0,4,0,1
 0,0,1,0,0
 0,0,5,0,0
 19,1,8,0,1
 48,1,6,0,1
 71,0,4,0,1
 31,1,3,0,1
 43,0,5,0,1
 0,0,7,0,0
 62,1,7,0,1
 0,0,6,0,0
 61,1,2,0,1
 46,0,4,0,1
 32,1,3,0,1
 47,0,4,0,1
 0,0,2,1,0
 49,0,4,0,1
 0,0,6,0,0
 31,0,5,0,1
 48,0,8,0,1
 26,1,2,0,1
 19,1,6,0,1
 45,1,9,0,1
 0,0,5,0,0
 43,1,5,0,1
 39,0,8,0,1
 0,0,8,0,0
 28,0,5,0,1
 0,0,5,0,0
 32,1,7,0,1
 38,0,2,0,1
 31,1,2,0,1
 64,1,5,0,1
 38,0,5,0,1
 59,0,8,0,1
 0,0,6,0,0
 83,1,6,0,1
 0,0,3,1,0
 59,0,1,0,1
 14,1,5,0,1
 22,1,3,0,1
 0,0,4,0,0
 52,0,7,0,1
 46,0,3,0,1
 0,0,6,0,0
 0,0,4,0,0
 79,0,6,0,1
 0,0,9,0,0
 48,0,6,0,1
 0,0,11,0,0
 29,0,6,0,1
 41,0,7,1,1
 39,0,4,0,1
 0,0,6,0,0
 0,0,6,1,0
 0,0,4,0,0
 22,0,7,0,1
 67,0,5,0,1
 51,1,6,0,1
 0,0,6,0,0
 47,1,3,0,1
 0,0,4,0,0
 60,1,4,1,1
 0,0,8,0,0
 29,1,7,0,1
 0,0,6,0,0
 60,0,5,0,1
 0,0,3,0,0
 38,0,3,0,1
 0,0,2,0,0
 30,1,7,0,1
 0,0,3,0,0
 58,1,3,0,1
 29,1,6,0,1
 30,0,9,0,1
 0,0,2,0,0
 18,0,8,0,1
 18,0,5,0,1
 53,0,5,0,1
 0,0,6,0,0
 0,0,8,0,0
 38,0,5,0,1
 0,0,5,0,0
 36,1,8,0,1
 25,1,7,0,1
 30,1,7,0,1
 34,1,5,0,1
 61,0,7,1,1
 0,0,5,0,0
 50,1,4,0,1
 0,0,6,0,0
 0,0,4,0,0
 44,1,4,0,1
 0,0,3,0,0
 44,0,10,0,1
 19,1,3,0,1
 0,0,5,0,0
 46,1,3,0,1
 76,0,5,0,1
 21,0,3,0,1
 52,0,7,0,1
 0,0,4,0,0
 41,0,3,0,1
 48,1,3,0,1
 34,1,8,0,1
 34,0,3,0,1
 0,0,7,2,0
 15,1,5,1,1
 41,0,6,0,1
 0,0,8,0,0
 0,0,1,0,0
 48,0,5,0,1
 78,0,5,0,1
 29,1,5,0,1
 21,0,4,0,1
 0,0,3,0,0
 32,0,6,0,1
 0,0,1,0,0
 0,0,2,0,0
 39,1,8,0,1
 38,1,6,0,1
 0,0,2,0,0
 41,1,7,0,1
 19,1,3,0,1
 0,0,3,0,0
 23,1,5,0,1
 0,0,1,0,0
 32,0,5,1,1
 0,0,8,0,0
 0,0,6,0,0
 0,0,3,0,0
 41,1,6,0,1
 19,0,2,0,1
 0,0,4,0,0
 48,0,7,0,1
 27,0,3,0,1
 0,0,11,0,0
 50,0,4,0,1
 25,1,6,0,1
 57,1,4,0,1
 0,0,2,0,0
 42,1,10,0,1
 23,1,5,0,1
 0,0,1,0,0
 67,0,2,0,1
 43,0,3,0,1
 0,0,5,0,0
 43,1,5,0,1
 55,0,9,1,1
 36,0,3,0,1
 26,0,6,0,1
 26,1,5,0,1
 23,0,5,0,1
 40,1,3,0,1
 73,0,7,0,1
 63,1,3,0,1
 47,0,6,0,1
 0,0,5,0,0
 53,1,7,0,1
 30,0,7,0,1
 0,0,4,0,0
 58,1,6,1,1
 0,0,6,0,0
 32,1,7,1,1
 50,1,5,0,1
 0,0,8,0,0
 37,0,5,0,1
 0,0,7,0,0
 29,0,4,0,1
 0,0,6,0,0
 36,1,4,0,1
 44,0,10,0,1
 18,1,3,0,1
 47,1,7,0,1
 0,0,2,0,0
 36,1,6,0,1
 47,1,3,0,1
 47,0,6,0,1
 17,1,5,1,1
 38,1,4,0,1
 18,0,6,0,1
 0,0,2,0,0
 0,0,3,0,0
 67,1,8,0,1
 56,1,3,0,1
 57,0,6,0,1
 52,1,9,1,1
 55,1,5,0,1
 39,0,3,0,1
 77,0,3,0,1
 32,1,4,0,1
 22,1,5,0,1
 46,1,3,0,1
 0,0,4,0,0
 0,0,5,0,0
 64,1,5,0,1
 0,0,3,0,0
 40,0,3,0,1
 37,0,6,0,1
 22,1,2,0,1
 85,0,5,0,1
 35,0,4,0,1
 62,1,3,0,1
 65,1,5,1,1
 39,1,4,0,1
 66,0,6,0,1
 0,0,4,1,0
 58,0,3,0,1
 46,1,5,0,1
 0,0,0,0,0
 71,0,5,0,1
 42,0,6,0,1
 46,1,4,0,1
 48,0,3,0,1
 36,1,8,0,1
 0,0,4,0,0
 0,0,6,0,0
 19,0,3,0,1
 60,0,1,0,1
 17,1,0,0,1
 55,1,4,0,1
 23,1,8,0,1
 82,1,4,1,1
 35,0,2,0,1
 28,0,4,0,1
 57,1,4,0,1
 0,0,1,0,0
 0,0,5,0,0
 23,0,6,0,1
 45,1,3,0,1
 0,0,6,0,0
 16,1,10,0,1
 0,0,4,0,0
 0,0,2,0,0
 17,1,9,0,1
 0,0,9,0,0
 0,0,4,0,0
 0,0,6,0,0
 40,1,7,0,1
 59,1,10,0,1
 50,1,3,0,1
 0,0,6,0,0
 0,0,2,0,0
 0,0,6,0,0
 60,0,5,0,1
 16,1,3,0,1
 31,0,4,1,1
 31,0,2,0,1
 0,0,8,0,0
 21,0,1,0,1
 0,0,4,0,0
 18,1,6,0,1
 13,1,3,0,1
 0,0,3,0,0
 32,0,4,0,1
 36,0,1,0,1
 45,0,3,0,1
 43,0,6,0,1
 0,0,3,0,0
 0,0,8,1,0
 0,0,4,0,0
 25,0,5,0,1
 0,0,7,0,0
 70,1,3,0,1
 33,0,6,0,1
 21,1,4,0,1
 26,1,7,0,1
 0,0,6,0,0
 70,0,6,0,1
 33,0,5,0,1
 43,1,9,0,1
 18,0,2,0,1
 63,1,8,0,1
 45,0,7,0,1
 0,0,7,0,0
 0,0,3,0,0
 37,1,7,0,1
 57,1,6,1,1
 31,0,7,0,1
 42,0,9,0,1
 22,0,4,0,1
 23,1,4,0,1
 0,0,3,0,0
 0,0,2,0,0
 43,0,7,0,1
 85,1,6,1,1
 0,0,2,0,0
 0,0,5,1,0
 0,0,8,0,0
 0,0,2,0,0
 46,1,3,0,1
 38,0,6,0,1
 40,1,9,0,1
 50,0,6,0,1
 67,1,7,0,1
 40,1,6,0,1
 0,0,3,0,0
 31,1,4,0,1
 21,0,4,0,1
 0,0,6,0,0
 40,1,9,0,1
 66,0,6,0,1
 27,0,8,0,1
 36,0,9,0,1
 0,0,8,0,0
 0,0,3,0,0
 43,1,7,0,1
 0,0,5,0,0
 0,0,4,0,0
 27,1,8,0,1
 48,1,8,0,1
 39,1,3,0,1
 60,1,2,0,1
 47,1,7,0,1
 0,0,4,0,0
 0,0,5,0,0
 61,0,2,0,1
 51,1,2,0,1
 42,0,5,0,1
 17,1,1,0,1
 0,0,6,0,0
 59,1,5,0,1
 41,1,2,0,1
 0,0,6,0,0
 84,1,2,0,1
 0,0,5,0,0
 49,1,5,0,1
 0,0,7,0,0
 36,0,5,0,1
 39,0,10,2,1
 40,0,4,0,1
 0,0,2,0,0
 0,0,3,0,0
 0,0,2,0,0
 16,1,2,0,1
 50,0,6,0,1
 0,0,5,0,0
 22,1,14,0,1
 0,0,3,0,0
 52,0,5,0,1
 62,1,3,0,1
 46,1,3,0,1
 0,0,3,0,0
 27,0,10,0,1
 62,1,4,0,1
 0,0,9,1,0
 53,0,4,0,1
 53,0,4,0,1
 29,0,8,0,1
 0,0,8,0,0
 19,0,5,0,1
 31,0,5,1,1
 44,0,3,0,1
 63,0,6,0,1
 0,0,3,0,0
 70,0,7,0,1
 45,0,4,0,1
 54,0,5,0,1
 44,1,9,0,1
 0,0,4,0,0
 59,0,4,0,1
 30,0,6,0,1
 46,0,8,0,1
 38,1,3,0,1
 23,1,7,0,1
 33,1,6,0,1
 19,1,4,0,1
 0,0,6,0,0
 0,0,4,0,0
 45,0,5,0,1
 39,0,6,0,1
 0,0,4,1,0
 0,0,6,0,0
 27,1,4,1,1
 0,0,6,0,0
 17,1,9,0,1
 0,0,6,0,0
 36,0,1,0,1
 0,0,5,0,0
 0,0,5,1,0
 51,0,2,0,1
 0,0,11,0,0
 43,0,9,0,1
 53,0,8,0,1
 22,1,8,0,1
 71,0,4,0,1
 18,1,10,0,1
 40,0,5,0,1
 36,1,10,0,1
 41,0,7,0,1
 24,1,5,0,1
 0,0,4,0,0
 0,0,3,0,0
 66,0,5,0,1
 38,0,4,0,1
 28,0,2,0,1
 19,0,5,0,1
 13,0,3,0,1
 0,0,4,0,0
 61,0,12,1,1
 60,0,5,0,1
 19,0,6,0,1
 23,1,2,0,1
 29,0,4,0,1
 0,0,5,0,0
 14,1,4,0,1
 0,0,2,0,0
 52,0,8,0,1
 52,1,5,0,1
 0,0,14,0,0
 51,0,4,0,1
 0,0,3,0,0
 46,0,4,0,1
 20,0,5,0,1
 32,0,1,0,1
 64,1,5,0,1
 0,0,3,0,0
 32,1,8,1,1
 0,0,6,0,0
 0,0,4,0,0
 0,0,3,0,0
 27,0,5,0,1
 0,0,6,1,0
 21,1,6,0,1
 51,1,5,0,1
 0,0,7,0,0
 21,0,2,0,1
 0,0,6,0,0
 21,0,10,1,1
 24,1,9,0,1
 42,1,8,0,1
 33,1,6,0,1
 32,0,1,0,1
 70,0,4,0,1
 20,0,2,0,1
 21,1,3,0,1
 42,1,7,0,1
 39,0,5,0,1
 17,1,4,0,1
 57,0,5,0,1
 0,0,5,0,0
 50,1,5,0,1
 0,0,4,0,0
 59,1,7,0,1
 43,1,2,0,1
 16,1,5,0,1
 0,0,3,0,0
 0,0,9,0,0
 0,0,4,0,0
 0,0,8,0,0
 50,1,6,0,1
 29,0,5,0,1
 15,0,7,0,1
 21,1,2,0,1
 40,0,6,0,1
 19,0,3,0,1
 63,0,2,0,1
 22,0,7,0,1
 59,1,3,0,1
 40,0,3,0,1
 10,0,4,0,1
 0,0,3,0,0
 0,0,4,0,0
 65,1,0,0,1
 26,1,3,0,1
 40,1,8,0,1
 0,0,4,1,0
 0,0,3,0,0
 32,0,2,0,1
 39,0,7,0,1
 63,1,7,0,1
 38,1,4,0,1
 42,1,2,0,1
 43,0,7,0,1
 25,0,4,0,1
 0,0,2,0,0
 0,0,7,0,0
 32,0,4,0,1
 0,0,6,0,0
 0,0,3,0,0
 0,0,9,0,0
 51,1,10,1,1
 37,1,2,0,1
 59,1,7,0,1
 44,1,4,0,1
 18,0,3,0,1
 33,1,5,0,1
 49,1,6,0,1
 36,1,4,0,1
 0,0,4,0,0
 32,1,4,0,1
 53,0,6,0,1
 0,0,7,0,0
 33,0,2,0,1
 35,1,3,0,1
 43,0,6,0,1
 29,1,5,0,1
 19,0,5,0,1
 0,0,5,0,0
 15,1,4,0,1
 0,0,5,0,0
 27,1,4,0,1
 46,0,7,0,1
 0,0,4,0,0
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 0,0,7,1,0
 26,0,6,0,1
 0,0,7,1,0
 55,0,2,0,1
 25,1,6,0,1
 57,1,4,0,1
 49,1,7,0,1
 57,0,3,0,1
 30,1,2,0,1
 22,1,3,0,1
 62,0,7,1,1
 18,1,5,0,1
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 0,0,4,0,0
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 45,0,3,0,1
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 57,1,4,0,1
 0,0,4,1,0
 53,1,7,0,1
 37,1,2,0,1
 30,1,5,0,1
 62,1,3,1,1
 61,1,6,2,1
 32,0,2,0,1
 64,1,8,0,1
 50,1,4,0,1
 52,0,7,0,1
 46,1,10,0,1
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 57,1,12,0,1
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 52,0,7,0,1
 38,0,2,0,1
 49,1,7,0,1
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 50,0,4,0,1
 35,0,8,0,1
 63,1,4,0,1
 0,0,3,0,0
 0,0,3,1,0
 43,1,5,0,1
 61,0,7,1,1
 23,0,5,0,1
 0,0,7,0,0
 21,1,1,0,1
 50,1,2,0,1
 29,1,4,0,1
 45,1,7,1,1
 48,1,2,0,1
 51,1,3,0,1
 37,1,7,0,1
 37,0,3,0,1
 60,1,11,0,1
 0,0,7,0,0
 68,0,6,0,1
 40,1,4,0,1
 31,0,6,0,1
 42,1,7,0,1
 27,1,5,0,1
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 41,1,3,0,1
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 49,1,3,0,1
 46,0,5,0,1
 43,1,5,0,1
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 41,1,3,0,1
 19,1,4,0,1
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 50,1,5,0,1
 39,1,8,0,1
 46,1,5,0,1
 0,0,6,0,0
 32,0,5,0,1
 34,0,3,0,1
 36,1,3,0,1
 0,0,5,0,0
 34,1,9,0,1
 43,1,7,0,1
 54,0,5,0,1
 28,1,8,1,1
 20,0,2,0,1
 62,1,7,0,1
 0,0,4,0,0
 0,0,6,1,0
 0,0,6,0,0
 25,0,3,0,1
 35,1,2,0,1
 43,0,5,0,1
 42,1,4,0,1
 70,0,4,0,1
 32,1,8,0,1
 65,0,5,0,1
 61,1,2,0,1
 17,1,3,0,1
 0,0,4,0,0
 60,0,5,0,1
 0,0,6,0,0
 75,0,6,0,1
 21,0,6,0,1
 38,1,5,0,1
 0,0,3,0,0
 58,1,9,0,1
 0,0,4,0,0
 62,0,6,1,1
 42,0,6,0,1
 25,1,7,0,1
 59,0,11,1,1
 52,0,6,0,1
 0,0,3,0,0
 17,1,5,0,1
 19,0,9,0,1
 55,0,5,0,1
 38,0,4,0,1
 47,1,6,0,1
 54,1,5,0,1
 30,1,3,0,1
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 28,1,3,0,1
 12,0,6,1,1
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 37,0,7,0,1
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 69,0,7,0,1
 54,0,5,0,1
 34,1,5,0,1
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 62,1,4,0,1
 58,0,9,0,1
 49,1,5,1,1
 41,1,5,0,1
 31,1,8,0,1
 33,0,5,0,1
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 47,0,2,0,1
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 19,0,3,0,1
 19,1,7,0,1
 63,0,7,0,1
 0,0,14,2,0
 19,0,3,0,1
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 0,0,3,0,0
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 32,0,3,0,1
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 57,1,4,0,1
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 28,1,4,0,1
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 61,0,5,0,1
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 78,0,6,0,1
 36,0,10,0,1
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 31,1,1,0,1
 0,0,1,0,0
 61,1,7,0,1
 0,0,3,0,0
 68,0,7,1,1
 66,0,4,

	# coding: utf-8

	# http://nbviewer.ipython.org/gist/felixlaumon/7e4d484a921bfccf935c
	#
	# https://gist.github.com/felixlaumon/7e4d484a921bfccf935c

	# In[4]:

	matrix = [
	[1,3,9,2],
	[2,4,6,8]
	]

	vector = [2,3,6,5]


	##### 1) Complete your Vector/Matrix multiplication function

	# In[5]:

	def dot_product (a, b):
	assert len(a) is len(b)
	return sum(map(lambda x: x[0] * x[1], zip(a, b)))

	def mv_multiple (matrix, vector):
	return [dot_product(row, vector) for row in matrix]

	assert mv_multiple(matrix, vector) == [75, 92]


	# In[6]:

	from itertools import starmap, izip
	from operator import mul

	def dot_product (a, b):
	assert len(a) is len(b)
	return sum(starmap(mul, izip(a, b)))

	def mv_multiple2 (matrix, vector):
	return [dot_product(row, vector) for row in matrix]

	assert mv_multiple2(matrix, vector) == [75, 92]


	##### 2) Add a function which does Matrix * Matrix multiplication

	# In[7]:

	def matrix_transpose (m):
	# OMFG
	# http://stackoverflow.com/questions/4937491/matrix-transpose-in-python
	return zip(*m)


	# In[8]:

	# Version that resuses matrix * vector multiplication
	def matrix_multiply (matrix1, matrix2):
	# TODO should be unnecessary to check size of matrix1 and matrix2 here?
	# Because already handled by dot_product?
	matrix2 = matrix_transpose(matrix2)
	return [mv_multiple(matrix1, m2) for m2 in matrix2]

	matrix1 = [
	[1,3,9,2],
	[2,4,6,8]
	]

	matrix2 = [
	[2,1],
	[3,2],
	[6,0],
	[5,4]
	]

	assert matrix_multiply(matrix, matrix2) == [[75, 92], [15, 42]]


	# In[9]:

	# Version that only relies on dot product
	def matrix_multiply2 (matrix1, matrix2):
	matrix2 = matrix_transpose(matrix2)
	return [[dot_product(v1, v2) for v1 in matrix1 ] for v2 in matrix2]

	matrix1 = [
	[1,3,9,2],
	[2,4,6,8]
	]

	matrix2 = [
	[2,1],
	[3,2],
	[6,0],
	[5,4]
	]

	assert matrix_multiply2(matrix, matrix2) == [[75, 92], [15, 42]]


	##### 3) Can you find a way to generate an identity matrix for any given number with a python function?

	# In[10]:

	def i_matrix_row (n, i):
	result = [0] * n
	result[i] = 1
	return result

	def i_matrix (n):
	return [i_matrix_row(n, i) for i in range(n)]

	assert i_matrix(4) == [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]


	# In[11]:

	def i_matrix2 (n):
	return [[int(i == j) for j in range(n)] for i in range(n)]

	assert i_matrix2(4) == [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]


	##### 4) Revise the original NYT script to summarizes the data in such a way that you have average and max values (“avg_click”, “avg_impressions”, “max_click”, “max_impressions”) for each unique combination of “age”, “gender”, “signed_in”. so one example row would have the data for age 14 , Male, is_signed_in.

	# In[12]:

	from pandas import DataFrame, read_csv, concat
	import numpy as np
	# TODO is there a better way to lower case the header?
	df = read_csv('nytimes.csv', header=0, names=['age', 'gender', 'impressions', 'click', 'signed_in'])
	df.head()


	# In[13]:

	headers = ['age', 'gender', 'signed_in', 'avg_click', 'avg_impressions', 'max_click', 'max_impression']
	grouped = df.groupby(['age', 'gender', 'signed_in'])
	grouped_click = grouped['click']
	grouped_impressions = grouped['impressions']

	# TODO how to rename the column here instead of setting columns below?
	avg_click = grouped_click.agg(np.mean)
	avg_impresssions = grouped_impressions.agg(np.mean)
	max_click = grouped_click.agg(np.max)
	max_impressions = grouped_impressions.agg(np.max)

	result = concat([avg_click, avg_impresssions, max_click, max_impressions], axis=1)
	result.columns = ['avg_click', 'avg_impressions', 'max_click', 'max_impressions']
	result

	# TODO how to fill the rest of the grouped column with empty data? (e.g. age = 1, gender = 1, signed_in = 0)
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