funzione macro trend scraping commodities/energy/metals

#funzione macro trend scraping commodities/energy/metals
import pandas as pd
import requests
from bs4 import BeautifulSoup

#legenda assets
#oil: s_number = 1369 / assets = crude-oil-price-history-chart
#nat_gas: s_number = 2478 / assets = natural-gas-prices-historical-chart
#copper: s_number = 1476 / assets = copper-prices-historical-chart-data
#gold: s_number = 1333 / assets = historical-gold-prices-100-year-chart
#silver: s_number = 1470 / assets = historical-silver-prices-100-year-chart
#platinum: s_number = 2540 / assets = platinum-prices-historical-chart-data

def no_dollar (x):
    x = x.replace('$','')
    x = x.replace('%','')
    return x

def assets_needed(s_number,assets):
    url = "https://www.macrotrends.net/"+s_number+"/"+assets+""
    #url = "https://www.macrotrends.net/1369/crude-oil-price-history-chart"
    response = requests.get(url)
    print(response)
    if response.status_code != 200:
        print('ERRORE DOWNLOAD PAGINA')

    soup = BeautifulSoup(response.content,'html.parser')
    tables = soup.find_all('table',{'class':'table'})

    # liste dei parametri
    Years_th = []
    Average_Closing_Price_th = []
    Year_Open_th = []
    Year_High_th = []
    Year_Low_th= []
    Year_Close_th = []
    Annual_Percentual_Change_th = []


    for table in tables:
        header = table.th.text
        if header.find('Price') == -1:
            continue
        #print(table)


        rows = table.find_all('tr')
        for row in rows[0:]:
            performances = row.find_all('td')
            for performance in performances:
                year_td = performances[0].text
                acp_td = performances[1].text
                yo_td = performances[2].text
                yh_td = performances[3].text
                yl_td = performances[4].text
                yc_td = performances[5].text
                apc_td = performances[6].text
                Years_th.append(year_td)
                Average_Closing_Price_th.append(acp_td)
                Year_Open_th.append(yo_td)
                Year_High_th.append(yh_td)
                Year_Low_th.append(yl_td)
                Year_Close_th.append(yc_td)
                Annual_Percentual_Change_th.append(apc_td)

    performance = pd.DataFrame({'Years':Years_th,'ACP':Average_Closing_Price_th,
    'YO':Year_Open_th,'YH':Year_High_th,'YL':Year_Low_th,'YC':Year_Close_th,
    'APC':Annual_Percentual_Change_th})

    performance['APC'] = performance['APC'].apply(no_dollar)
    performance['ACP'] = performance['ACP'].apply(no_dollar)
    performance['YO'] = performance['YO'].apply(no_dollar)
    performance['YH'] = performance['YH'].apply(no_dollar)
    performance['YL'] = performance['YL'].apply(no_dollar)
    performance['YC'] = performance['YC'].apply(no_dollar)
    performance['APC'] = pd.to_numeric(performance['APC'])
    performance['ACP'] = pd.to_numeric(performance['ACP'])
    performance['YO'] = pd.to_numeric(performance['YO'])
    performance['YH'] = pd.to_numeric(performance['YH'])
    performance['YL'] = pd.to_numeric(performance['YL'])
    performance['YC'] = pd.to_numeric(performance['YC'])
    performance['Years']= pd.to_datetime(performance['Years'])
    performance.set_index('Years',inplace=True)
    return performance


#assets_needed("2478","natural-gas-prices-historical-chart")
#assets_needed("1369","crude-oil-price-history-chart")
assets_needed("1476","copper-prices-historical-chart-data").loc['1999':'2020'].plot(y = 'YC')
assets_needed("1476","copper-prices-historical-chart-data").loc['1999':'2020'].min()