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GA HK Data Science Class 02 Homework
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# 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)
We can't make this file beautiful and searchable because it's too large.
"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
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68,0,7,1,1
66,0,4,
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