有没有更好的方法（最好是一个循环）

Question

我正在尝试使用按日期索引的加密货币总市值创建一个 pandas 数据框。数据来自 Coingecko API。我可以通过以下方式实现：

import requests
import json

r = requests.get('https://api.coingecko.com/api/v3/coins/bitcoin/market_chart?vs_currency=usd&days=200')
response = json.loads(r.content)

#suppresses scientific notation
pd.set_option('display.float_format','{:.2f}'.format)

marketcapdf = df(columns=['dates', 'marketcap','volume'])

dates = []
for values in response['prices']:
    dates.append(values[0])

totalmktcap = []
for values in response['market_caps']:
    totalmktcap.append(values[1])

dailymktvolume = []
for values in response['total_volumes']:
    dailymktvolume.append(values[1])

marketcapdf =  df(dates, columns=['Dates'])
marketcapdf = marketcapdf.assign(Total_Marketcap=totalmktcap)
marketcapdf = marketcapdf.assign(Daily_volume=dailymktvolume)

print (marketcapdf)

这会产生正确的数据框：

             Dates  Total_Marketcap   Daily_volume
0    1566518400000  181311699889.67 20144228964.47
1    1566604800000  187878412535.86 18313019852.75
2    1566691200000  181764456091.20 21187546608.00
3    1566777600000  180491202154.33 17899166778.01
4    1566864000000  184989701185.55 23392461462.61
..             ...              ...            ...
196  1583452800000  165070261612.79 38122635173.15
197  1583539200000  166791067805.43 41388087588.44
198  1583625600000  162543409238.00 36764763103.12
199  1583712000000  147076617271.69 39132209137.70
200  1583767255000  142178026150.74 50332115525.77

我想知道是否有更好的方法可以做到这一点，很确定可以通过列表元素访问在单个 for 循环中完成所有操作。但我需要你的帮助来解决这个问题。 如果你想查看 API 响应，就去https://api.coingecko.com/api/v3/coins/bitcoin/market_chart?vs_currency=usd&days=200

Answer 1

您可以使用list comprehensions 直接传递给DataFrame 构造函数：

import requests
import json
import pandas as pd

r = requests.get('https://api.coingecko.com/api/v3/coins/bitcoin/market_chart?vs_currency=usd&days=200')
response = json.loads(r.content)

marketcapdf = pd.DataFrame({
    'Dates': [x[0] for x in response['prices']],
    'Total_Marketcap': [x[1] for x in response['market_caps']],
    'Daily_volume': [x[1] for x in response['total_volumes']]})

---

或者，在单个理解中使用zip

marketcapdf = pd.DataFrame([(p[0], m[1], v[1])
                            for p, m, v in zip(*response.values())],
                           columns=['Dates', 'Total_Marketcap', 'Daily_volume'])
marketcapdf

[出]

             Dates  Total_Marketcap   Daily_volume
0    1566518400000  181311699889.67 20144228964.47
1    1566604800000  187878412535.86 18313019852.75
2    1566691200000  181764456091.20 21187546608.00
3    1566777600000  180491202154.33 17899166778.01
4    1566864000000  184989701185.55 23392461462.61
..             ...              ...            ...
196  1583452800000  165070261612.79 38122635173.15
197  1583539200000  166791067805.43 41388087588.44
198  1583625600000  162543409238.00 36764763103.12
199  1583712000000  147076617271.69 39132209137.70
200  1583767255000  142178026150.74 50332115525.77

[201 rows x 3 columns]