def get_data_bar(url, ylabel, min_index, max_index):
# import libraries
import my_function as func
# specify the url
soup = func.getData(url)
# find results within table
result_wilayah = soup.find('table', attrs={'id': 'tableLeftBottom'})
result_value = soup.find('table', attrs={'id': 'tableRightBottom'})
rows_wilayah = result_wilayah.find_all('tr')
rows_value = result_value.find_all('tr')
list_wilayah = []
value = []
# print(rows)
for id, r in enumerate(rows_wilayah[:-1]):
# find all columns per result
data_wilayah = r.find_all('td', attrs={'id': 'th4'})
data_value = rows_value[id].find_all('td', attrs={'class': 'datas'})
# check that columns have data
if len(data_wilayah) == 0:
continue
# write columns to variables
wilayah = data_wilayah[0].getText()
nilai = data_value[-1].getText()
# Remove decimal point
nilai = nilai.replace('.', '')
# Cast Data Type Integer
nilai = int(nilai)
list_wilayah.append(wilayah)
value.append(nilai)
# Create Dictionary
my_dict = {'wilayah': list_wilayah, 'value': value}
# Create Dataframe
df = pd.DataFrame(my_dict)
# Plot dataframe to histogram
plt.bar(df['wilayah'], df['value'])
plt.xlabel('Provinsi')
plt.ylabel(ylabel)
if min_index == 0 :
min_index = 0
else:
min_index = df['value'].min() - min_index
plt.gca().set_ylim([min_index, df['value'].max() + max_index])
plt.xticks(rotation='50', ha='right')
plt.show()
# import libraries
import numpy as np
import pandas as pd
import my_function as func
import matplotlib.pyplot as plt
# specify the url
url = 'https://www.bps.go.id/dynamictable/2016/06/16/1211/indeks-pembangunan-manusia-menurut-provinsi-2010-2018-metode-baru-.html'
soup = func.getData(url)
# find results within table
result_wilayah = soup.find('table', attrs={'id': 'tableLeftBottom'})
result_value = soup.find('table', attrs={'id': 'tableRightBottom'})
rows_wilayah = result_wilayah.find_all('tr')
rows_value = result_value.find_all('tr')
list_wilayah = []
value = []
# print(rows)
for id, r in enumerate(rows_wilayah[:-1]):
# find all columns per result
data_wilayah = r.find_all('td', attrs={'id': 'th4'})
data_value = rows_value[id].find_all('td', attrs={'class': 'datas'})
# check that columns have data
if len(data_wilayah) == 0:
continue
# write columns to variables
wilayah = data_wilayah[0].find('b').getText()
def get_regression_pemilu(url2, ylabel):
# import libraries
import my_function as func
# specify the url
url1 = 'https://kawalpemilu.org/#pilpres:0'
soup1 = func.getData(url1)
# find results within table
results1 = soup1.find('table', {'class': 'table'})
rows1 = results1.find_all('tr', {'class': 'row'})
# list_wilayah = []
jokowi = []
prabowo = []
# print(rows)
for r in rows1[:-1]:
# find all columns per result
data = r.find_all('td')
# check that columns have data
if len(data) == 0:
continue
# write columns to variables
# wilayah = data[1].find('a').getText()
satu = data[2].find('span', attrs={'class': 'abs'}).getText()
dua = data[3].find('span', attrs={'class': 'abs'}).getText()
# Remove decimal point
satu = satu.replace('.', '')
dua = dua.replace('.', '')
# Cast Data Type Integer
satu = int(satu)
dua = int(dua)
# list_wilayah.append(wilayah)
jokowi.append(satu)
prabowo.append(dua)
soup2 = func.getData(url2)
# find results within table
result_value = soup2.find('table', attrs={'id': 'tableRightBottom'})
rows_value = result_value.find_all('tr')
target_value = []
# print(rows)
for id, r in enumerate(rows_value[:-1]):
# find all columns per result
data_value = rows_value[id].find_all('td', attrs={'class': 'datas'})
# check that columns have data
if len(data_value) == 0:
continue
# write columns to variables
nilai = data_value[-1].getText()
# Remove decimal point
nilai = nilai.replace('.', '')
# Cast Data Type Integer
nilai = int(nilai)
target_value.append(nilai)
my_dict = {'jokowi': jokowi, 'prabowo': prabowo, 'target_value': target_value}
# Create Dataframe
df = pd.DataFrame(my_dict)
jokowi = df['jokowi'].values
prabowo = df['prabowo'].values
target_value = df['target_value'].values
jokowi = jokowi.reshape(-1, 1)
prabowo = prabowo.reshape(-1, 1)
target_value = target_value.reshape(-1, 1)
# Fitting Simple Linear Regression
reg1 = LinearRegression()
reg2 = LinearRegression()
# Create the prediction space
prediction_space1 = np.linspace(min(jokowi), max(jokowi)).reshape(-1, 1)
prediction_space2 = np.linspace(min(prabowo), max(prabowo)).reshape(-1, 1)
# Fit the model to the data
reg1.fit(jokowi, target_value)
reg2.fit(prabowo, target_value)
r_sq1 = reg1.score(jokowi, target_value)
print('coefficient of determination Jokowi:', r_sq1)
print('intercept Jokowi:', reg1.intercept_)
print('slope Jokowi:', reg1.coef_)
r_sq2 = reg1.score(prabowo, target_value)
print('coefficient of determination Prabowo:', r_sq2)
print('intercept Prabowo:', reg2.intercept_)
print('slope Prabowo:', reg2.coef_)
# Compute predictions over the prediction space: y_pred
y_pred1 = reg1.predict(prediction_space1)
y_pred2 = reg2.predict(prediction_space2)
plt.scatter(jokowi, target_value, color='green', alpha=0.5, label='Jokowi')
plt.scatter(prabowo, target_value, color='red', alpha=0.5, label='Prabowo')
plt.plot(prediction_space1, y_pred1, color='green', linewidth=2, label='Jokowi Regression')
plt.plot(prediction_space2, y_pred2, color='red', linewidth=2, label='Prabowo Regression')
plt.title('Hasil Pemilu vs '+ylabel)
plt.xlabel('Hasil Pemilu')
plt.ylabel(ylabel)
plt.legend(loc='best')
plt.show()
# The path to where you have your chrome webdriver stored:
webdriver_path = 'C:/Program Files (x86)/Google/ChromeDriver/chromedriver.exe'
# Add arguments telling Selenium to not actually open a window
chrome_options = Options()
chrome_options.add_argument('--headless')
chrome_options.add_argument('--window-size=1920x1080')
# Fire up the headless browser
browser = webdriver.Chrome(executable_path=webdriver_path,
options=chrome_options)
# Load webpage
browser.get(url)
soup2 = func.getData(url2)
# It can be a good idea to wait for a few seconds before trying to parse the page
# to ensure that the page has loaded completely.
time.sleep(10)
# Parse HTML, close browser
soup = BeautifulSoup(browser.page_source, 'html.parser')
# print(soup)
pretty = soup.prettify()
browser.quit()
# find results within table
results = soup.find('table', {'class': 'table'})
rows = results.find_all('tr', {'class': 'row'})
array = []
jokowi = []
import numpy as np
import matplotlib.pyplot as plt
import my_function as func
import pandas as pd
url1 = 'https://www.bps.go.id/dynamictable/2018/04/16/1298/angka-harapan-hidup-saat-lahir-menurut-provinsi-2010-2018-metode-baru-.html'
soup1 = func.getData(url1)
# find results within table
result_wilayah = soup1.find('table', attrs={'id': 'tableLeftBottom'})
result_value = soup1.find('table', attrs={'id': 'tableRightBottom'})
rows_wilayah = result_wilayah.find_all('tr')
rows_value = result_value.find_all('tr')
data_ipm = {}
for id, r in enumerate(rows_wilayah[:-1]):
# find all columns per result
data_result = r.find_all('td', attrs={'id': 'th4'})
data_value = rows_value[id].find_all('td', attrs={'class': 'datas'})
# check that columns have data
if len(data_result) == 0:
continue
wilayah = data_result[0].find('b').getText()
wilayah = wilayah.replace('\n', '')
nilai = data_value[-1].getText()
# Remove decimal point
nilai = nilai.replace('.', '')
# Cast Data Type Integer
nilai = int(nilai)
# import libraries
import numpy as np
import my_function as func
import matplotlib.pyplot as plt
# specify the url
url1 = 'https://kawalpemilu.org/#pilpres:0'
soup1 = func.getData(url1)
results1 = soup1.find('table',{'class':'table'})
rows1 = results1.find_all('tr',{'class':'row'})
list_wilayah1 = []
jokowi1 = []
prabowo1 = []
# print(rows)
for r in rows1:
# find all columns per result
data = r.find_all('td')
# check that columns have data
if len(data) == 0:
continue
# write columns to variables
wilayah = data[1].find('a').getText()
if wilayah != 'KALIMANTAN UTARA':
satu = data[2].find('span', attrs={'class':'abs'}).getText()
dua = data[3].find('span', attrs={'class': 'abs'}).getText()
# Remove decimal point
satu = satu.replace('.','')
dua = dua.replace('.','')
# Cast Data Type Integer
satu = int(satu)