Question

我试图从binance（交换，API）中获取一些历史数据，并且我正在使用他们的模块。我可以获取数据，但无论何时使用下面的代码，它都会创建0和＃39;关闭＆＃39;和＆＃39;卷＆＃39;：

ticker = 'NANOETH'
interv = "1d"
period = "1M"
rest_client.klines(ticker, period)
klines = rest_client.klines(symbol=ticker, interval=interv)
df=[]

for kline in klines:
    df=[kline.open_time, kline.open, kline.high,kline.low, kline.close, kline.volume]
    df = pd.DataFrame([df], columns = ["Open Time","Close Time","Open","High","Low","Close", "Volume"])



    print (df)

输出结果为：

       Open Time     Close Time        Open        High         Low  \
0  1517529600000  1517615999999  0.02010000  0.02998700  0.01764600   

        Close           Volume  
0  0.01841200  448361.60000000  
       Open Time     Close Time        Open        High         Low  \
0  1517616000000  1517702399999  0.01848800  0.01890000  0.01600300   

        Close           Volume  
0  0.01700900  410642.17000000  
       Open Time     Close Time        Open        High         Low  \
0  1517529600000  1517615999999  0.02010000  0.02998700  0.01764600   

        Close           Volume  
0  0.01841200  448361.60000000

我希望它看起来像：

    Open Time     Close Time        Open        High         Low     Close           Volume   
1517529600000  1517615999999  0.02010000  0.02998700  0.01764600  0.01841200  448361.60000000
1517616000000  1517702399999  0.01848800  0.01890000  0.01600300  0.01700900  410642.17000000  
1517529600000  1517615999999  0.02010000  0.02998700  0.01764600  0.01841200  448361.60000000

并且还想索引第一个日期并将其转换为实际的日期时间。

非常感谢

Answer 1

通过列表理解创建列表列表。然后输入pd.DataFrame作为最后一步。

lst = [[k.open_time, k.open, k.high, k.low, k.close, k.volume] for kline in klines]

df = pd.DataFrame(lst, columns=['Open Time', 'Close Time', 'Open',
                                'High', 'Low', 'Close', 'Volume'])

Answer 2

我不确定type对象klines是什么，但我假设它是pd.DataFrame。你可以在将来像这样对它进行子集化：

# Get the indicies you want
idx_want = ["open_time", "open", "high", "low", "close", "volume"]
# Subset the pd.DataFrame (asseuming klines is a dataframe
df_subset = klines.loc[:,idx_want]
# If klines is a list of pd.Series then do `klines = pd.DataFrame(klines)`
# Relabel Columns
df_subset.columns = ['Open Time', 'Close Time', 'Open', 'High', 'Low', 'Close', 'Volume']

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