REST API example - Coingecko API

👋 저번 시간에 <tools for DS> section에서 REST API에 대해 배운 적이 있다..!

 

Tools for Data Science (from Coursera)

 

 

💪 요약하자면, API중 HTTP 방식으로 client와 web service 간 메세지를 주고 받는 방식을 REST API라고 함.

 

'* Rest API’s function by sending a request, the request is communicated via HTTP message. The HTTP message usually contains a JSON file. This contains instructions for what operation we would like the service or resource to perform. In a similar manner, API returns a response, via an HTTP message, this response is usually contained within a JSON. In cryptocurrency a popular method to display the movements of the price of a currency.'

 

'* we will be using the CoinGecko API to create one of these candlestick graphs for Bitcoin. We will use the API to get the price data for 30 days with 24 observation per day, 1 per hour. We will find the max, min, open, and close price per day meaning we will have 30 candlesticks and use that to generate the candlestick graph. Although we are using the CoinGecko API we will use a Python client/wrapper for the API called PyCoinGecko. PyCoinGecko will make performing the requests easy and it will deal with the endpoint targeting.

 

💪 RESTAPI의 예시 중 하나인 CoinGeckoAPI를 사용해 간단한 시각화를 해보려 한다. 💪

 

1> 준비

 

!pip install pycoingecko
!pip install plotly
!pip install mplfinance

import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.offline import plot
import matplotlib.pyplot as plt
import datetime
from pycoingecko import CoinGeckoAPI #REST API example!
from mplfinance.original_flavor import candlestick2_ohlc

 

2> get_coin_market_chart_by_id(id, vs_currency, days) 사용

(* id is the name of the coin you want & vs_currency is the currency you want the price in & days is how many days back from today you want)

 

cg = CoinGeckoAPI()

bitcoin_data = cg.get_coin_market_chart_by_id(id='bitcoin', vs_currency='usd', days=30)

type(bitcoin_data ) #prints 'dict'

 

> return 결과 → * in the form of a JSON which includes the price, market caps, and total volumes along with timestamps for each observation. We are focused on the prices so we will select that data. (가격에 초점)

 

bitcoin_price_data = bitcoin_data['prices']

bitcoin_price_data[0:5]

"""

[[1614305260769, 46542.2532873948],
 [1614308746201, 47671.0461272975],
 [1614312224381, 47629.322477694484],
 [1614315749395, 47074.58598303568],
 [1614319450318, 45823.394963459454]]
 
 """

 

3> pandas df로 바꾸고 datetime형태로 한 column data type 변경 - date별 group by 이후 min, max, open, close 준비

 

data = pd.DataFrame(bitcoin_price_data, columns=['TimeStamp', 'Price'])
data['Date'] = pd.to_datetime(data['TimeStamp'], unit='ms')

candlestick_data = data.groupby(data.Date.dt.date, as_index=False).agg({"Price": ['min', 'max', 'first', 'last']})

 

4> 시각화

 

fig = go.Figure(data=[go.Candlestick(x=data['Date'],
                open=candlestick_data['Price']['first'], 
                high=candlestick_data['Price']['max'],
                low=candlestick_data['Price']['min'], 
                close=candlestick_data['Price']['last'])
                ])

fig.update_layout(xaxis_rangeslider_visible=True)

fig.show()

 

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* 코드 & 내용 출처) IBM Data Science course (from Coursera)

* 썸네일 출처) https://www.coingecko.com/