我在Pandas中使用OHLC重新采样1分钟时间序列数据，15分钟将完美地工作，例如在以下数据帧上：
ohlc_dict = {'Open':'first', 'High':'max', 'Low':'min', 'Close': 'last'}
df.resample('15Min').apply(ohlc_dict).dropna(how='any').loc['2011-02-01']
Date Time Open High Low Close
------------------------------------------------------------------
2011-02-01 09:30:00 3081.940 3086.860 3077.832 3081.214
2011-02-01 09:45:00 3082.422 3083.730 3071.922 3073.801
2011-02-01 10:00:00 3073.303 3078.345 3069.130 3078.345
2011-02-01 10:15:00 3078.563 3078.563 3071.522 3072.279
2011-02-01 10:30:00 3071.873 3071.873 3063.497 3067.364
2011-02-01 10:45:00 3066.735 3070.523 3063.402 3069.974
2011-02-01 11:00:00 3069.561 3069.981 3066.286 3069.981
2011-02-01 11:15:00 3070.602 3074.088 3070.373 3073.919
2011-02-01 13:00:00 3074.778 3074.823 3069.925 3069.925
2011-02-01 13:15:00 3070.096 3070.903 3063.457 3063.457
2011-02-01 13:30:00 3063.929 3067.358 3063.929 3067.358
2011-02-01 13:45:00 3067.570 3072.455 3067.570 3072.247
2011-02-01 14:00:00 3072.927 3081.357 3072.767 3080.175
2011-02-01 14:15:00 3078.843 3079.435 3076.733 3076.782
2011-02-01 14:30:00 3076.721 3081.980 3076.721 3081.912
2011-02-01 14:45:00 3082.822 3083.381 3076.722 3077.283
然而，当我重新采样1分钟到1H时，问题出现了。我使用默认设置，并从上午9点开始查找时间，但市场营业时间为上午9:30。
df.resample('1H').apply(ohlc_dict).dropna(how='any').loc['2011-02-01']
然后我尝试更改base设置，但在下午会话中失败。市场应该在下午13点开放，并在下午15点结束，所以应该是晚上13点，下午14点，下午15点，总共3个吧。
df.resample('60MIN',base=30).apply(ohlc_dict).dropna(how='any').loc['2011-02-01']
总之，问题是我希望它适合市场并有6个(9:30,10:30,11:30,1:00,2:00,3:00)条，但resample中的pandas只给我5个条(9:30,10:30,11:30,1:30,2:30)
我在网上搜索了很长时间。但没用。请帮助或尝试提供一些如何实现这一点的想法。 感谢。
答案 0 :(得分：0)
以下是数据框中仅Close的答案的一部分。
耶利说，resample中的pandas可能无法满足我的初衷。
因此，我尝试通过iterrows提取所需的项目。
from datetime import datetime
from datetime import timedelta
def extract(df):
data = pd.DataFrame()
for index, row in df.iterrows():
if index.to_pydatetime().minute == 30 and index.to_pydatetime().hour < 12 :
data = data.append(row)
elif index.to_pydatetime().minute == 0 and index.to_pydatetime().hour > 12 :
data = data.append(row)
elif index.to_pydatetime().minute == 29 and index.to_pydatetime().hour == 11 :
row = row = row.rename(index.to_pydatetime() + timedelta(minutes = 1))
data = data.append(row)
elif index.to_pydatetime().minute == 59 and index.to_pydatetime().hour == 14 :
row = row = row.rename(index.to_pydatetime() + timedelta(minutes = 1))
data = data.append(row)
return data
data = extract(df.loc['2011-02-01'])
data
但是，除close外，其他项目不正确。
结果如下所示：
Close High Low Open Volume turnover
2011-02-01 09:30:00 3081.940 3081.940 3081.940 3081.940 74767100.0 996328900.0
2011-02-01 10:30:00 3071.873 3071.873 3071.873 3071.873 18754100.0 250694100.0
2011-02-01 11:30:00 3073.919 3073.919 3073.919 3073.919 13762700.0 179169200.0
2011-02-01 13:00:00 3074.778 3074.778 3074.778 3074.778 25992700.0 321678500.0
2011-02-01 14:00:00 3072.927 3072.927 3072.927 3072.927 11682300.0 161534600.0
2011-02-01 15:00:00 3077.283 3077.283 3077.283 3077.283 68184500.0 930561900.0
答案 1 :(得分：0)
我遇到了同样的问题，无法在线找到帮助。所以我写了这个脚本，将1分钟的OHLC数据转换为1小时。
这假设市场时间为上午9:15至下午3:30。如果市场时机不同，只需编辑start_time和end_time以适合您的需求。
我没有放任何其他支票，以防在市场交易时间内交易被暂停。
希望代码对某人有帮助。 ：）
csv格式示例
Date,O,H,L,C,V
2020-03-12 09:15:00,3860,3867.8,3763.35,3830,58630
2020-03-12 09:16:00,3840.05,3859.4,3809.65,3834.6,67155
2020-03-12 09:17:00,3832.55,3855.4,3823.75,3852,51891
2020-03-12 09:18:00,3851.65,3860.95,3846.35,3859,42205
2020-03-12 09:19:00,3859.45,3860,3848.1,3851.55,33194
代码
from pandas import read_csv, to_datetime, DataFrame
from datetime import time
file_path = 'BAJFINANCE-EQ.csv'
def add(data, b):
# utility function
# appends the value in dictionary 'b'
# to corresponding key in dictionary 'data'
for (key, value) in b.items():
data[key].append(value)
df = read_csv(file_path,
parse_dates=True,
infer_datetime_format=True,
na_filter=False)
df['Date'] = to_datetime(df['Date'], format='%Y-%m-%d %H:%M:%S')
# stores hourly data to convert to dataframe
data = {
'Date': [],
'O': [],
'H': [],
'L': [],
'C': [],
'V': []
}
start_time = [time(9, 15), time(10, 15), time(11, 15), time(
12, 15), time(13, 15), time(14, 15), time(15, 15)]
end_time = [time(10, 14), time(11, 14), time(12, 14), time(
13, 14), time(14, 14), time(15, 14), time(15, 29)]
# Market timings 9:15am to 3:30pm (6 hours 15 mins)
# We create 6 hourly bars and one 15 min bar
# as usually depicted in candlestick charts
i = 0
no_bars = df.shape[0]
while i < no_bars:
if df.loc[i]['Date'].time() in end_time:
end_idx = i + 1
hour_df = df[start_idx:end_idx]
add(data, {
'Date': df.loc[start_idx]['Date'],
'O': hour_df['O'].iloc[0],
'H': hour_df['H'].max(),
'L': hour_df['L'].min(),
'C': hour_df['C'].iloc[-1],
'V': hour_df['V'].sum()
})
if df.loc[i]['Date'].time() in start_time:
start_idx = i
# optional optimisation for large datasets
# skip ahead to loop faster
i += 55
i += 1
df = DataFrame(data=data).set_index(keys=['Date'])
# df.to_csv('out.csv')
print(df)