通过数据框传递数据时,删除最后一个小数
b= self.client.Trade.Trade_getBucketed(
binSize=self.timeframe,
partial=True,
symbol='EOSZ19',
count=1,
reverse=True
).result()[0]
print (b)
cd = parse_dataframe(b)
print (cd)
print (b) 返回我:
[{'timestamp': datetime.datetime(2019, 10, 4, 0, 40, tzinfo=tzutc()),
'symbol': 'EOSZ19', 'open': 0.0003728, 'high': 0.0003728, 'low':
0.0003728, 'close': 0.0003728, 'trades': 0, 'volume': 0, 'vwap': None,
'lastSize': 0, 'turnover': 0, 'homeNotional': 0.0, 'foreignNotional':
0.0}]
但 print (cd) 返回我:
date open high low close volume
0 2019-10-04 00:40:00+00:00 0.000373 0.000373 0.000373 0.000373 0
删除最后一个小数,我需要:
date open high low close volume
0 2019-10-04 00:40:00+00:00 0.0003728 0.0003728 0.0003728 0.0003728 0
不要去掉最后一个小数
data_frame 函数来自 util:
from pandas import DatetimeIndex, merge, DataFrame, to_datetime
from configuration import TICKER_INTERVAL_MINUTES
def parse_dataframe1(ticker: list) -> DataFrame:
"""
builds dataframe based on the given trades
:param ticker: see /trade/bucketed API
:return: DataFrame
"""
cols = ['timestamp', 'symbol', 'open', 'high', 'low', 'close', 'trades',
'volume', 'vwap', 'lastSize', 'turnover', 'homeNotional',
'foreignNotional']
frame = DataFrame(ticker, columns=cols)
# drop unnecessary columns
frame.drop(['symbol', 'trades', 'vwap', 'lastSize', 'turnover',
'homeNotional', 'foreignNotional'], axis=1)
# rename timestamp column Y
frame = frame.rename(columns={'timestamp': 'date'})
# reformat date column
frame['date'] = to_datetime(frame['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
这里的 data_frame 函数不四舍五入到小数点后 7 位
holdtom
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