Question

我试图遍历多个JSON数据，然后将列表中的每个值添加到DataFrame中。对于每个JSON数据，我创建一个列标题。我似乎总是只获取最后一列的数据，所以我附加我相信的数据的方式显然存在问题。

from pycoingecko import CoinGeckoAPI

cg = CoinGeckoAPI()
df = pd.DataFrame()


timePeriod = 120


for x in range(10):
    try:
        data = cg.get_coin_market_chart_by_id(id=geckoList[x], 
                                 vs_currency ='btc', days = 'timePeriod')

        for y in range(timePeriod):
            df = df.append({geckoList[x]: data['prices'][y][1]}, 
                                          ignore_index= True)
        print(geckoList[x])

    except:
        pass

Geckolist示例：

['bitcoin',
 'ethereum',
 'xrp',
 'bitcoin-cash',
 'litecoin',
 'binance-coin']

其中一个硬币的JSON示例：

'prices': [[1565176840078, 0.029035263522626625],
  [1565177102060, 0.029079747150763842],
  [1565177434439, 0.029128983083947863],
  [1565177700686, 0.029136960678700433],
  [1565178005716, 0.0290826667213779],
  [1565178303855, 0.029173025688296675],
  [1565178602640, 0.029204331218623796],
  [1565178911561, 0.029211943928343167],

预期结果将是一个具有每个加密硬币的数据列和数据行的DataFrame。现在只有最后一列显示数据

当前，它看起来像这样：

    bitcoin ethereum bitcoin-cash
0   NaN    NaN    0.33  
1   NaN    NaN    0.32  
2   NaN    NaN    0.21  
3   NaN    NaN    0.22  
4   NaN    NaN    0.25  
5   NaN    NaN    0.26  
6   NaN    NaN    0.22  
7   NaN    NaN    0.22

Answer 1

好的，我想我找到了问题。

问题是您将仅包含一列的数据结构逐行追加到框架，因此所有其他列都用NaN填充。我想您想要的是按其时间戳加入各列。这就是我在下面的示例中所做的。让我知道这是否是您需要的：

from pycoingecko import CoinGeckoAPI
import pandas as pd

cg = CoinGeckoAPI()

timePeriod = 120

gecko_list = ['bitcoin',
              'ethereum',
              'xrp',
              'bitcoin-cash',
              'litecoin',
              'binance-coin']

data = {}
for coin in gecko_list:
    try:
        nested_lists = cg.get_coin_market_chart_by_id(
            id=coin, vs_currency='btc', days='timePeriod')['prices']
        data[coin] = {}
        data[coin]['timestamps'], data[coin]['values'] = zip(*nested_lists)

    except Exception as e:
        print(e)
        print('coin: ' + coin)

frame_list = [pd.DataFrame(
              data[coin]['values'],
              index=data[coin]['timestamps'],
              columns=[coin])
              for coin in gecko_list
              if coin in data]

df = pd.concat(frame_list, axis=1).sort_index()
df.index = pd.to_datetime(df.index, unit='ms')

print(df)

这使我得到输出

                         bitcoin  ethereum  bitcoin-cash  litecoin
2019-08-07 12:20:14.490      NaN       NaN      0.029068       NaN
2019-08-07 12:20:17.420      NaN       NaN           NaN  0.007890
2019-08-07 12:20:21.532      1.0       NaN           NaN       NaN
2019-08-07 12:20:27.730      NaN  0.019424           NaN       NaN
2019-08-07 12:24:45.309      NaN       NaN      0.029021       NaN
...                          ...       ...           ...       ...
2019-08-08 12:15:47.548      NaN       NaN           NaN  0.007578
2019-08-08 12:18:41.000      NaN  0.018965           NaN       NaN
2019-08-08 12:18:44.000      1.0       NaN           NaN       NaN
2019-08-08 12:18:54.000      NaN       NaN           NaN  0.007577
2019-08-08 12:18:59.000      NaN       NaN      0.028144       NaN

[1153 rows x 4 columns]

这是我将days切换为180时获得的数据。

要获取每日数据，请使用groupby function：

df = df.groupby(pd.Grouper(freq='D')).mean()

在5天的数据帧中，这给了我

            bitcoin  ethereum  bitcoin-cash  litecoin
2019-08-03      1.0  0.020525      0.031274  0.008765
2019-08-04      1.0  0.020395      0.031029  0.008583
2019-08-05      1.0  0.019792      0.029805  0.008360
2019-08-06      1.0  0.019511      0.029196  0.008082
2019-08-07      1.0  0.019319      0.028837  0.007854
2019-08-08      1.0  0.018949      0.028227  0.007593

我的数据框除最后一列外均具有NaN

1 个答案: