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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import base64
import yfinance as yf
import streamlit as st
st.set_option('deprecation.showPyplotGlobalUse', False)
st.title('Scrapping Yahoo Finance App')
st.markdown("""
This app retrieves the list of the S&P 500 (from Wikipedia) and its corresponding stock closing price (year-to-date)!
* Python libraries: base64, pandas, streamlit, numpy, matplotlib, seaborn
* Data source: [Wikipedia](https://en.wikipedia.org/wiki/List_of_S%26P_500_companies).
""")
st.sidebar.header('User Input Features')
#Scrappage des donnees sur Wikipedia
@st.cache_data
def load_data():
url = "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies"
html = pd.read_html(url,header= 0)
df = html[0]
return df
df = load_data()
sector = df.groupby('GICS Sector')
#Sidebar - Sector Selection
sorted_sector_unique = sorted(df['GICS Sector'].unique())
selected_sector = st.sidebar.multiselect('Sector',sorted_sector_unique)
#Filtering datas
df_selected_sector = df[(df['GICS Sector'].isin(selected_sector))]
st.header('Display companies in Selected sector')
st.write('Data Dimension: ' + str(df_selected_sector.shape[0]) + ' rows and ' + str(df_selected_sector.shape[1]) + ' columns.')
st.dataframe(df_selected_sector)
#Download des datas selectionnees dans le sidebar
def filedownload(df):
csv = df.to_csv(index=False)
b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions
href = f'<a href="data:file/csv;base64,{b64}" download="SP500.csv">Download CSV File</a>'
return href
st.markdown(filedownload(df_selected_sector), unsafe_allow_html=True)
#Download datas correspondantes de yahoo finance
data = yf.download(
tickers = list(df_selected_sector[:10].Symbol),
period="ytd",
interval="1d",
group_by="ticker",
auto_adjust=True,
prepost=True,
threads=True,
proxy=None
)
#Plot Closing price of Selected Symbols
def price_plot(symbol):
df = pd.DataFrame(data[symbol].Close)
df['Date'] = df.index
plt.fill_between(df.Date, df.Close, color='skyblue', alpha=0.3)
plt.plot(df.Date, df.Close, color='skyblue', alpha=0.8)
plt.xticks(rotation=90)
plt.title(symbol, fontweight='bold')
plt.xlabel('Date', fontweight='bold')
plt.ylabel('Closing Price', fontweight='bold')
return st.pyplot()
num_company = st.sidebar.slider('Number of companies',1,5)
if st.button('Show plots'):
st.header('Show Closing price')
for i in list(df_selected_sector.Symbol)[:num_company]:
price_plot(i) |