Automating CoinMarketCap API Pull Using Python

from requests import Request, Session
from requests.exceptions import ConnectionError, Timeout, TooManyRedirects
import json

url = 'https://pro-api.coinmarketcap.com/v1/cryptocurrency/listings/latest'
parameters = {
  'start':'1',
  'limit':'50',
  'convert':'USD'
}
headers = {
  'Accepts': 'application/json',
  'X-CMC_PRO_API_KEY': 'c0948cf3-d185-450f-a107-75e468d020cd',
}

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)

type(data)

import pandas as pd

#This allows you to see all the columns, not just like 15

pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

#pd.set_option('display.max_rows', None)

#This normalizes the data and makes it all pretty in a dataframe

df = pd.json_normalize(data['data'])
df['timestamp']=pd.to_datetime('now')
df

def api_runner():
    global df
    url = 'https://pro-api.coinmarketcap.com/v1/cryptocurrency/listings/latest'
    parameters = {
      'start':'1',
      'limit':'15',
      'convert':'USD'
    }
    headers = {
      'Accepts': 'application/json',
      'X-CMC_PRO_API_KEY': 'c0948cf3-d185-450f-a107-75e468d020cd',
    }

    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)
        
    df2 = pd.json_normalize(data['data'])
    df2['timestamp']=pd.to_datetime('now')
    df_append = pd.DataFrame(df2)
    df = pd.concat([df, df_append])
   
    #if you want to save it in a csv format each time, can use the code below
    #df = pd.json_normalize(data['data'])
    #df['timestamp']=pd.to_datetime('now')
    #df
    
    #if not os.path.isfile(r'C:\Users\sylvi\OneDrive\Documents\Python Scripts\API.csv'):
        #df.to_csv(r'C:\Users\sylvi\OneDrive\Documents\Python Scripts\API.csv', header='column_names')
    #else: 
        #df.to_csv(r'C:\Users\sylvi\OneDrive\Documents\Python Scripts\API.csv', mode='a', header=False)

import os
from time import time
from time import sleep

for i in range(333):
    api_runner()
    print('API Runner completed Successfully')
    sleep(60) # sleep for 1 minute
exit()

df

# One thing I noticed was the scientific notation. I like it, but I want to be able to see the numbers in this case

pd.set_option('display.float_format', lambda x: '%.5f' % x)
df

# Now let's look at the coin trends over time in average

df3 = df.groupby('name', sort=False)[['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']].mean()
df3

df4= df3.stack()
df4

type(df4)

# change the df4 back to data frame
df5 = df4.to_frame(name='Value')
df5

type(df5)

df5.count()

index = pd.Index(range(300))
df6 = df5.reset_index()
df6