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requirements.txt

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+streamlit==0.68.1
+pandas==0.25.3
+base58==2.0.0
+matplotlib==3.1.3
+numpy==1.18.1
+yfinance==0.1.54
+lxml==4.5.1

sp500-app.py

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+import streamlit as st
+import pandas as pd
+import base64
+import matplotlib.pyplot as plt
+import numpy as np
+import yfinance as yf
+
+st.title('S&P 500 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, yfinance, numpy, matplotlib
+* **Data source:** [Wikipedia](https://en.wikipedia.org/wiki/List_of_S%26P_500_companies).
+""")
+
+st.sidebar.header('User Input Features')
+
+# Web scraping of S&P 500 data
+#
+@st.cache
+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, sorted_sector_unique)
+
+# Filtering data
+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 S&P500 data
+# https://discuss.streamlit.io/t/how-to-download-file-in-streamlit/1806
+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)
+
+# https://pypi.org/project/yfinance/
+
+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 Query Symbol
+def price_plot(symbol):
+  df = pd.DataFrame(data[symbol].Close)
+  df['Date'] = df.index
+  fig = plt.figure()
+  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(fig)
+
+num_company = st.sidebar.slider('Number of Companies', 1, 5)
+
+if st.button('Show Plots'):
+    st.header('Stock Closing Price')
+    for i in list(df_selected_sector.Symbol)[:num_company]:
+        price_plot(i)