ソフトウェアエンジニアの技術ブログ：Software engineer tech blog

vector 123 * scalor 3 = 123123123, 369, error
these are reasonable answers

More vectorized operation
math operation
add, subtract, multiply, divide, expertise

Logical opereation
&, |, ~

Comparison operations
>, >=, <, <=, ==, !=

import numpy as np

if False:
	a = np.array([1, 2, 3, 4])
	b = np.array([1, 2, 1, 2])

	print a + b
	print a – b
	print a * b
	print a / b
	print a ** b

if False
	a = np.array([1, 2, 3, 4])
	b = 2

	print a + b
	print a – b
	print a * b
	print a / b
	print a ** b	

if False:
	a = np.array([1,2,3,4,5])
	b = np.array([5,4,3,2,1])

	print a > b
	print a >= b
	print a < b
	print a <= b
	print a == b
	print a != b

code snippet
import numpy as np
a = np.array([1,2,3,4])
b = a
a += np.array([1,1,1,1])
print b

+= operates in-place while + does not

import pandas as pandas

if False:
	s1 = pd.Series([1,2,3,4], index=['a','b','c','d'])
	s2 = pd.Series([10,20,30,40], index['a','b','c','d'])
	print s1 + s2

if False:
	s1 = pd.Series([1,2,3,4], index=['a','b','c','d'])
	s2 = pd.Series([10,20,30,40], index['a','b','c','d'])
	print s1 + s2

if False:
	s1 = pd.Series([1,2,3,4], index=['a','b','c','d'])
	s2 = pd.Series([10,20,30,40], index['a','b','c','d'])
	print s1 + s2

if False:
	s1 = pd.Series([1,2,3,4], index=['a','b','c','d'])
	s2 = pd.Series([10,20,30,40], index['a','b','c','d'])
	print s1 + s2

import numpy as np

countries = np.array([
    'Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',
    'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas',
    'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium',
    'Belize', 'Benin', 'Bhutan', 'Bolivia',
    'Bosnia and Herzegovina'
])

employment = np.array([
    55.70000076,  51.40000153,  50.5       ,  75.69999695,
    58.40000153,  40.09999847,  61.5       ,  57.09999847,
    60.90000153,  66.59999847,  60.40000153,  68.09999847,
    66.90000153,  53.40000153,  48.59999847,  56.79999924,
    71.59999847,  58.40000153,  70.40000153,  41.20000076
])

if False:
	print countries[0]
	print countries[3]

if False:
	print countries[0:3]
	print countries[:3]
	print countries[17:]
	print countries[:]

if False:
	print countries.dtype
	print employment.dtype
	print np.array([0, 1, 2, 3]).dtype
	print np.array([1.0, 1.5, 2.0, 2.5]).dtype
	print np.array([True, False, True]).dtype
	print np.array(['AL', 'AK', 'AZ', 'AR', 'CA']).dtype

if False:
	for country in countries:
		print 'Examining country {}'.format(country)

	for i in range(len(countries)):
		country = countries[i]
		country_employment = employment[i]
		print 'Country {} has employment {}'.format(country,
			country_employment)

if False:
	print employment.mean()
	print employment.std()
	print employment.max()
	print employment.sum()

def max_employment(countries, employment):
	max_country = None
	max_value = None

	return (max_country, max_value)

Gapminder Data
-employment levels
-life expectancy
-GDP
-School Completion Rates

import pandas as pd
daily_engagement = pd.read_csv('daily_engagement_full.csv')
len(daily_engagement['acct'].unique())

One-dimensional data structures
Panda, NumPy(numerical Python)
Series -> built on Array
more features, simpler

data = [1,2,1,3,3,1,4,2]

%matplotlib inline
import matplotlib.pyplot as plt
plt.hist(data)

Lots of different pieces of information to look at
These features can interact

plt.xlabel(“label for x axis”)
plt.ylabel(“label for y axis”)
plt.title(“title of plot”)

from collections import defaultdict

engagemnt_by_account = defaultdict(list)
for engagement_record in paid_engagement_in_first_week:
	account_key = engagement_record['account_key']
	engagement_by_account[account_key].append(engagement_record)

total_minutes_by_account = {}

for account_key, engegement_for_student in engagement_by_account.items():
	total_minutes = 0
	for engagement_record in engagement_for_student:
		total_minutes += engagement_record['total_minutes_visited']
	total_minutes_by_account[account_key] = total_minutes

total_minutes = total_minutes_by_account.values()

import numpy as np

num_problem_students = 0

for enrollment in enrollments:
	student = enrollment['account_key']
	if student not in unique_engagement_students
		and enrollment['join_date'] != enrollment['cancel_date']:
	num_problem_student += 1

num_problem_students

def within_one_week(join_date, engagement_date):
	time_delta = engagement_date - join_date
	return time_delta.days < 7

def remove_free_trial_cancels(data):
	new_data = []
	for data_point in data:
		if data_point['account_key'] in paid_students:
			new_data.append(data_point)
		return new_data

total_minutes = total_minutes_by_account.values()

import numpy as numpy

print 'Mean:' np.pean(total_minutes)
print 'Standard deviation:', np.std(total_minutes)
print 'Minimum:', np.min(total_minutes)
print 'Maximum:', np.max(total_minutes)

student_with_max_minutes = None
max_minutes = 0

for student, total_minutes in total_minutes_by_account.items():
	if total_minutes > max_minutes:
		max_minutes = total_minutes
		student_with_max_minutes = student

import unicodecsv
enrollments_filename=''
with open('enrollments.csv', 'rb') as f:
	reader = unicodecsv.DictReader(f)
	enrollements = list(reader)

with open('daily_engagement.csv', 'rb') as f:
	reader = unicodecsv.DictReader(f)
	daily_engagement = list(reader)

with open('project_submissions.csv', 'rb') as f:
	reader = unicodecsv.DictReader(f)
	project_submissions = list(reader)

daily_engagement = None
project_submissions = None

import unicodecsv

def read_csv(filename):
	with open(filename, 'rb') as f:
		reader = unicodecsv.DictReader(f)
		return list(reader)

enrollments = read_csv('')
daily_engagement = read_csv('')
project_submissions = read_csv('')

enrollment_num_rows = 0
enrollment_num_unique_students = 0

engagement_num_rows = 0
engagement_num_unique_students = 0

submission_num_rows = 0
submission_num_unique_students = 0