Blog | Getting cryptocurrency quotes and storing them in a csv file with pandas

In the previous article the bitcoin quote was obtained from the coinmarketcap site using request or directly from a library that accesses the API.

In this article the information of the first 100 cryptocurrencies listed on coinmarketcap is obtained and handled with pandas. After sorting the information of the coins, it will be stored in a csv file.

This article is based on an article in English Importing Altcoin Data with Python and the CoinmarketCap API (enlace roto).

If you want, you can review what you can do with pandas by checking the following articles:

Here is the code in jupyter notebook format:

In [1]:

#Se importa coinmarketcap, json, pandas y time
import coinmarketcap
import json
import pandas as pd
import time

In [2]:

#SE instancia la clase Market y se consulta la cotización de ethereum.
market = coinmarketcap.Market()
coin = market.ticker("ethereum")

In [3]:

#Se muestra el tipo de la variable coin
type(coin)

Out[3]:

list

In [4]:

#Se muestra que la lista tiene 1 elemento.
print(len(coin))
1

In [5]:

#Se muestra el tipo del primer elemento de la lista
type(coin[0])

Out[5]:

dict

In [6]:

#Se muestra el primer elemento de la lista el cual es un diccionario
print (coin[0])
{'symbol': 'ETH', 'price_btc': '0.0758447', 'price_usd': '725.008', 'cached': False, 'market_cap_usd': '71145122811.0', 'available_supply': '98130121.0', '24h_volume_usd': '1559710000.0', 'percent_change_1h': '0.22', 'percent_change_24h': '6.39', 'name': 'Ethereum', 'id': 'ethereum', 'total_supply': '98130121.0', 'rank': '2', 'last_updated': '1520813352', 'max_supply': None, 'percent_change_7d': '-16.25'}

In [7]:

#Se muestra los datos de la variable coin, identado, y ordenada las claves.
print(json.dumps(coin, indent=4, sort_keys=True))
[
    {
        "24h_volume_usd": "1559710000.0",
        "available_supply": "98130121.0",
        "cached": false,
        "id": "ethereum",
        "last_updated": "1520813352",
        "market_cap_usd": "71145122811.0",
        "max_supply": null,
        "name": "Ethereum",
        "percent_change_1h": "0.22",
        "percent_change_24h": "6.39",
        "percent_change_7d": "-16.25",
        "price_btc": "0.0758447",
        "price_usd": "725.008",
        "rank": "2",
        "symbol": "ETH",
        "total_supply": "98130121.0"
    }
]

In [8]:

#Ahora se usa panda series, se toma la cotización de ethereum y se guarda en 
#la variable ether
ether = pd.Series(market.ticker("ethereum")[0])

In [9]:

#Se muestra el valor de la variable ether
print(ether)
24h_volume_usd         1559710000.0
available_supply         98130121.0
cached                         True
id                         ethereum
last_updated             1520813352
market_cap_usd        71145122811.0
max_supply                     None
name                       Ethereum
percent_change_1h              0.22
percent_change_24h             6.39
percent_change_7d            -16.25
price_btc                 0.0758447
price_usd                   725.008
rank                              2
symbol                          ETH
total_supply             98130121.0
dtype: object

In [10]:

#Se muestra la descripción de la serie
print(ether.describe)
<bound method NDFrame.describe of 24h_volume_usd         1559710000.0
available_supply         98130121.0
cached                         True
id                         ethereum
last_updated             1520813352
market_cap_usd        71145122811.0
max_supply                     None
name                       Ethereum
percent_change_1h              0.22
percent_change_24h             6.39
percent_change_7d            -16.25
price_btc                 0.0758447
price_usd                   725.008
rank                              2
symbol                          ETH
total_supply             98130121.0
dtype: object>

In [11]:

#Se guardan otras monedas alternativas en el arreglo de pandas
wowcoin = pd.Series(market.ticker("wowcoin")[0])
bitcoin = pd.Series(market.ticker("bitcoin")[0])
coinArray = pd.DataFrame([ether,bitcoin,wowcoin]).set_index("id")

In [12]:

#Se muestra los datos del arreglo panda
print(coinArray)
         24h_volume_usd available_supply  cached last_updated market_cap_usd  \
id                                                                             
ethereum   1559710000.0       98130121.0    True   1520813352  71145122811.0   
bitcoin    6292920000.0       16913662.0   False   1520813365   162866556360   
wowcoin         106.405             None   False   1520813347           None   

          max_supply      name percent_change_1h percent_change_24h  \
id                                                                    
ethereum        None  Ethereum              0.22               6.39   
bitcoin   21000000.0   Bitcoin              0.42               9.61   
wowcoin         None   Wowcoin              3.01               4.04   

         percent_change_7d    price_btc     price_usd  rank symbol  \
id                                                                   
ethereum            -16.25    0.0758447       725.008     2    ETH   
bitcoin             -15.97          1.0       9629.29     1    BTC   
wowcoin              -78.7  0.000000002  0.0000163877  1467    WOW   

         total_supply  
id                     
ethereum   98130121.0  
bitcoin    16913662.0  
wowcoin          None

In [13]:

#Ahora se extrae las cotizaciones de 100 cryptomonedas
coins = market.ticker()

In [14]:

#El tamaño de la lista es 100
len(coins)

Out[14]:

In [15]:

#Se almacena las 100 monedas en un dataframe de pandas a partir de una serie panda
coinArray2 = pd.DataFrame([pd.Series(coins[i]) for i in range(100)]).set_index('id')

In [17]:

#Obtengo los primeros 3 criptomonedas
coinArray2.head(3)

Out[17]:

| ID | 24h_volume_usd | available_supply | cached | last_updated | market_cap_usd | max_supply | name | percent_change_1h | percent_change_24h |percent_change_7d | price_btc | price_usd | rank | symbol | total_supply | | :------: | :------: | :-----: | :-----: | :------: | :-----: | :-------: | :-----: | :-----: | :-------: | :------: | :-----: | :-------: | :------: | :-----: | | bitcoin | 6292920000.0 | 16913662.0 | False | 1520813365 | 162866556360 | 21000000.0 | Bitcoin 0.42 | 9.61 | -15.97 | 1.0 | 9629.2 | 1 | BTC | 16913662.0 | |ethereum | 1559710000.0 | 98130121.0 | False | 1520813352 | 71145122811.0 | None | Ethereum 0.22 | 6.39 | -16.25 | 0.0758447 | 725.008 | 2 | ETH | 98130121.0 | |ripple | 471767000.0 | 39091956706.0 | False | 1520813341 | 32612386698.0 | 100000000000| Ripple 0.33 | 5.49 | -14.76 | 0.00008727 | 0.834248 | 3 | XRP | 99992520283.0|

In [18]:

#Se define almacenar los datos en un archivo csv que maneje el tiempo en que se creo el
#archivo como nombre del archivo
location = 'Data/'+str(time.time())+'.csv'

In [19]:

#Se almacenan los datos en formato csv
coinArray2.to_csv(location)

In [20]:

#Ahora se repiten las instrucciones anteriores con 3 ciclos de cada 2 min
coins = market.ticker()
for i in range(3):
    coinArray = pd.DataFrame([pd.Series(coins[i]) for i in range(100)]).set_index('id')
    location = 'Data/'+str(time.time())+'.csv'
    coinArray.to_csv(location)
    time.sleep(2*60)

When the program finishes running there are 4 files in csv format:

In upcoming articles the stored data will be used to do data analytics with Pandas.

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Ernesto Crespo.

Summary

Getting cryptocurrency quotes and storing them in a csv file with pandas