bilative

import pandas as pd

SATICI = soup.find_all("a", href=True, attrs={'class':'satici-url'})
URUN = soup.find_all("a", href=True, attrs={'class':'urun-adi'})
FIYAT = soup.find_all("div", attrs={'class':'urun-fiyat'})
ESKI_FIYAT = soup.find_all("div", attrs={'class':'urun-eski-fiyat'})
INDIRIM = soup.find_all("div", attrs={'class':'urun-discount-rate'})

satici, satici_url, urun, urun_url, fiyat, eski_fiyat, indirim = [],[],[],[],[],[],[]

bilative

for k in soup.find_all("a", href=True, attrs={'class':'satici-url'}):
    print("\n",k.text)
    print(k['href'])

bilative

for k in soup.find_all("td", attrs={'class':'col9'}):
    print(k.text)

for i in soup.find_all("tr", attrs={'class':'I-harfi'}):
    print(i.text)

bilative

import requests #!pip install requests
from bs4 import BeautifulSoup #!pip install beautifulsoup4

def get_soup(TARGET_URL):
    page = requests.get(TARGET_URL)
    soup = BeautifulSoup(page.text, 'html.parser')
    return soup

URL = 'https://bilative.github.io/sisterslab/web_scraping'

bilative

for day in range(x):
    codeMore()
    andMore()

bilative

model = KMeans(n_clusters= 4, random_state=9800) # 3 grup istiyorum

cluster_df = df.iloc[:,2:8] # kumeler icin bu degiskenler secilsin olrak belirtiyorum

scaler = StandardScaler()
scaler.fit(cluster_df)
scaled_df = scaler.transform(cluster_df) # standardizasyon

scaled_df = pd.DataFrame(scaled_df, columns = cluster_df.columns)

bilative

import seaborn as sns
from sklearn.cluster import KMeans

hata = []
kume_sayisi = range(1,10)
for k in kume_sayisi:
    kmeanModel = KMeans(n_clusters = k)
    kmeanModel.fit(scaled_df)
    hata.append(kmeanModel.inertia_)

bilative

def min_max_range(a):
    return max(a) - min(a)

bmi_range = df.groupby('Age').agg({
    'bmi': min_max_range,
    'Age': 'max'
})

px.bar(bmi_range, x= 'Age', y= 'bmi', barmode='group')

bilative

fig = go.Figure()

for i in ['Weight', 'Chest', 'Abdomen', 'Hip', 'bmi']:
        fig.add_trace( go.Box(
            y = df[i],
            name = i,
            boxpoints = 'all',
            jitter = 0.4,
            whiskerwidth = 0.4,
            marker_size = 4)

bilative

df['dummy_count'] = 1

fig = px.pie(df,
             values = 'dummy_count',
             names = 'bmi_sonuc',
             title = 'BMI Gruplarina Gore Yuzdesel Dagilim')
fig.update_traces( textinfo = 'percent+label')
fig.show()