Python Code
from requests import Request, Session
from requests.exceptions import ConnectionError, Timeout, TooManyRedirects
import json
import os
url = 'https://pro-api.coinmarketcap.com/v1/cryptocurrency/listings/latest'
parameters = {
'start':'1',
'limit':'5000',
'convert':'USD'
}
headers = {
'Accepts': 'application/json',
'X-CMC_PRO_API_KEY': 'Insert your API Key',
}
session = Session()
session.headers.update(headers)
try:
response = session.get(url, params=parameters)
data = json.loads(response.text)
print(data)
except (ConnectionError, Timeout, TooManyRedirects) as e:
print(e)
# NOTE: IOPub Data Rate Error
# Go in and put "jupyter notebook --NotebookApp.iopub_data_rate_limit=1e10"
into the Anaconda Prompt to change this to allow to pull data
pd.set_option('display.max_columns', None)
df1 = pd.json_normalize(data['data'])
df1['time_stamp'] = pd.to_datetime('now')
# Writing a Function to Run the API
def api_runner():
url = 'https://pro-api.coinmarketcap.com/v1/cryptocurrency/listings/latest'
parameters = {
'start':'1',
'limit':'5000',
'convert':'USD'
}
headers = {
'Accepts': 'application/json',
'X-CMC_PRO_API_KEY': 'Insert your API Key',
}
session = Session()
session.headers.update(headers)
try:
response = session.get(url, params=parameters)
data = json.loads(response.text)
#print(data)
except (ConnectionError, Timeout, TooManyRedirects) as e:
return
#print(e)
df1 = pd.json_normalize(data['data'])
df1['time_stamp'] = pd.to_datetime('now')
if not os.path.isfile(r"C:\Users\API.csv"):
df1.to_csv(r"C:\Users\API.csv", header = 'column_names')
else:
df1.to_csv(r"C:\Users\API.csv",mode = 'a', header = False)
# Automating the API Runner Function to Run Every 10 Minutes
import os
from time import sleep
from datetime import datetime
import pytz
def is_market_open():
est = pytz.timezone('US/Eastern')
now = datetime.now(est)
market_open = now.replace(hour=9, minute=30, second=0, microsecond=0)
market_close = now.replace(hour=16, minute=0, second=0, microsecond=0)
return market_open <= now <= market_close
for i in range(400):
if is_market_open():
api_runner()
print('API Runner Completed Successfully')
else:
print('Market Closed. Skipping API Runner.')
sleep(600) # Sleep for 10 minutes
exit()
Data Cleaning With Pandas
import pandas as pd
df1 = pd.read_csv (r"C:\Users\API.csv")
df1 = df1.drop_duplicates()
df1 = df1.drop(columns = ["slug", "num_market_pairs", "date_added", "tags","max_supply",
"Unnamed: 0"])
pd.set_option('display.float_format', lambda x: '%5f' % x)
df2 = df1[['name', 'symbol', 'quote.USD.price', 'quote.USD.volume_24h',
'quote.USD.volume_change_24h', 'quote.USD.percent_change_1h',
'quote.USD.percent_change_24h', 'quote.USD.percent_change_7d',
'quote.USD.percent_change_30d', 'quote.USD.percent_change_60d',
'quote.USD.percent_change_90d', 'quote.USD.market_cap']]
df2.columns = ['name', 'symbol', 'price', 'volume_24h', 'volume_change_24h',
'percent_change_1h', 'percent_change_24h', 'percent_change_7d',
'percent_change_30d', 'percent_change_60d', 'percent_change_90d',
'market_cap']
df3 = df2.groupby('name', sort = False)[['price', 'volume_24h',
'volume_change_24h', 'percent_change_1h',
'percent_change_24h', 'percent_change_7d',
'percent_change_30d', 'percent_change_60d',
'percent_change_90d', 'market_cap']].mean()
df3.to_csv(r'C:\Users\Panda Cleaned Crypto Data.csv', index=False)
df3_stacked = df3.stack() #Optional Step to Pivot Data
