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streamlit/crypto-price-app.py

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+import streamlit as st
+from PIL import Image
+import pandas as pd
+import base64
+import matplotlib.pyplot as plt
+from bs4 import BeautifulSoup
+import requests
+import json
+import time
+#---------------------------------#
+# New feature (make sure to upgrade your streamlit library)
+# pip install --upgrade streamlit
+
+#---------------------------------#
+# Page layout
+## Page expands to full width
+st.beta_set_page_config(layout="wide")
+#---------------------------------#
+# Title
+
+image = Image.open('logo.jpg')
+
+st.image(image, width = 500)
+
+st.title('Crypto Price App')
+st.markdown("""
+This app retrieves cryptocurrency prices for the top 100 cryptocurrency from the **CoinMarketCap**!
+
+""")
+#---------------------------------#
+# About
+expander_bar = st.beta_expander("About")
+expander_bar.markdown("""
+* **Python libraries:** base64, pandas, streamlit, numpy, matplotlib, seaborn, BeautifulSoup, requests, json, time
+* **Data source:** [CoinMarketCap](http://coinmarketcap.com).
+* **Credit:** Web scraper adapted from the Medium article *[Web Scraping Crypto Prices With Python](https://towardsdatascience.com/web-scraping-crypto-prices-with-python-41072ea5b5bf)* written by [Bryan Feng](https://medium.com/@bryanf).
+""")
+
+
+#---------------------------------#
+# Page layout (continued)
+## Divide page to 3 columns (col1 = sidebar, col2 and col3 = page contents)
+col1 = st.sidebar
+col2, col3 = st.beta_columns((2,1))
+
+#---------------------------------#
+# Sidebar + Main panel
+col1.header('Input Options')
+
+## Sidebar - Currency price unit
+currency_price_unit = col1.selectbox('Select currency for price', ('USD', 'BTC', 'ETH'))
+
+# Web scraping of CoinMarketCap data
+@st.cache
+def load_data():
+    cmc = requests.get('https://coinmarketcap.com')
+    soup = BeautifulSoup(cmc.content, 'html.parser')
+
+    data = soup.find('script', id='__NEXT_DATA__', type='application/json')
+    coins = {}
+    coin_data = json.loads(data.contents[0])
+    listings = coin_data['props']['initialState']['cryptocurrency']['listingLatest']['data']
+    for i in listings:
+      coins[str(i['id'])] = i['slug']
+
+    coin_name = []
+    coin_symbol = []
+    market_cap = []
+    percent_change_1h = []
+    percent_change_24h = []
+    percent_change_7d = []
+    price = []
+    volume_24h = []
+
+    for i in listings:
+      coin_name.append(i['slug'])
+      coin_symbol.append(i['symbol'])
+      price.append(i['quote'][currency_price_unit]['price'])
+      percent_change_1h.append(i['quote'][currency_price_unit]['percent_change_1h'])
+      percent_change_24h.append(i['quote'][currency_price_unit]['percent_change_24h'])
+      percent_change_7d.append(i['quote'][currency_price_unit]['percent_change_7d'])
+      market_cap.append(i['quote'][currency_price_unit]['market_cap'])
+      volume_24h.append(i['quote'][currency_price_unit]['volume_24h'])
+
+    df = pd.DataFrame(columns=['coin_name', 'coin_symbol', 'market_cap', 'percent_change_1h', 'percent_change_24h', 'percent_change_7d', 'price', 'volume_24h'])
+    df['coin_name'] = coin_name
+    df['coin_symbol'] = coin_symbol
+    df['price'] = price
+    df['percent_change_1h'] = percent_change_1h
+    df['percent_change_24h'] = percent_change_24h
+    df['percent_change_7d'] = percent_change_7d
+    df['market_cap'] = market_cap
+    df['volume_24h'] = volume_24h
+    return df
+
+df = load_data()
+
+## Sidebar - Cryptocurrency selections
+sorted_coin = sorted( df['coin_symbol'] )
+selected_coin = col1.multiselect('Cryptocurrency', sorted_coin, sorted_coin)
+
+df_selected_coin = df[ (df['coin_symbol'].isin(selected_coin)) ] # Filtering data
+
+## Sidebar - Number of coins to display
+num_coin = col1.slider('Display Top N Coins', 1, 100, 100)
+df_coins = df_selected_coin[:num_coin]
+
+## Sidebar - Percent change timeframe
+percent_timeframe = col1.selectbox('Percent change time frame',
+                                    ['7d','24h', '1h'])
+percent_dict = {"7d":'percent_change_7d',"24h":'percent_change_24h',"1h":'percent_change_1h'}
+selected_percent_timeframe = percent_dict[percent_timeframe]
+
+## Sidebar - Sorting values
+sort_values = col1.selectbox('Sort values?', ['Yes', 'No'])
+
+col2.subheader('Price Data of Selected Cryptocurrency')
+col2.write('Data Dimension: ' + str(df_selected_coin.shape[0]) + ' rows and ' + str(df_selected_coin.shape[1]) + ' columns.')
+
+col2.dataframe(df_coins)
+
+# Download CSV 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="crypto.csv">Download CSV File</a>'
+    return href
+
+col2.markdown(filedownload(df_selected_coin), unsafe_allow_html=True)
+
+#---------------------------------#
+# Preparing data for Bar plot of % Price change
+col2.subheader('Table of % Price Change')
+df_change = pd.concat([df_coins.coin_symbol, df_coins.percent_change_1h, df_coins.percent_change_24h, df_coins.percent_change_7d], axis=1)
+df_change = df_change.set_index('coin_symbol')
+df_change['positive_percent_change_1h'] = df_change['percent_change_1h'] > 0
+df_change['positive_percent_change_24h'] = df_change['percent_change_24h'] > 0
+df_change['positive_percent_change_7d'] = df_change['percent_change_7d'] > 0
+col2.dataframe(df_change)
+
+# Conditional creation of Bar plot (time frame)
+col3.subheader('Bar plot of % Price Change')
+
+if percent_timeframe == '7d':
+    if sort_values == 'Yes':
+        df_change = df_change.sort_values(by=['percent_change_7d'])
+    col3.write('*7 days period*')
+    plt.figure(figsize=(5,25))
+    plt.subplots_adjust(top = 1, bottom = 0)
+    df_change['percent_change_7d'].plot(kind='barh', color=df_change.positive_percent_change_7d.map({True: 'g', False: 'r'}))
+    col3.pyplot(plt)
+elif percent_timeframe == '24h':
+    if sort_values == 'Yes':
+        df_change = df_change.sort_values(by=['percent_change_24h'])
+    col3.write('*24 hour period*')
+    plt.figure(figsize=(5,25))
+    plt.subplots_adjust(top = 1, bottom = 0)
+    df_change['percent_change_24h'].plot(kind='barh', color=df_change.positive_percent_change_24h.map({True: 'g', False: 'r'}))
+    col3.pyplot(plt)
+else:
+    if sort_values == 'Yes':
+        df_change = df_change.sort_values(by=['percent_change_1h'])
+    col3.write('*1 hour period*')
+    plt.figure(figsize=(5,25))
+    plt.subplots_adjust(top = 1, bottom = 0)
+    df_change['percent_change_1h'].plot(kind='barh', color=df_change.positive_percent_change_1h.map({True: 'g', False: 'r'}))
+    col3.pyplot(plt)