尝试将每周 8 个时间点的样本减少到 2 个点,每个点代表 4 周内的平均值,我使用 resample()。我首先使用 (60*60*24*7*4) 秒定义规则,结果发现我最终出现了 3 个时间点,最新的一个是虚拟的。开始检查它,我注意到如果我将规则定义为 4W 或 28D 就可以了,但是下降到 672H 或更小的单位(分钟、秒……)就会出现额外的伪造列。本次测试代码:
import numpy as np
import pandas as pd
d = np.arange(16).reshape(2, 8)
res = []
for month in range(1,13):
start_date = str(month) + '/1/2014'
df = pd.DataFrame(data=d, index=['A', 'B'], columns=pd.date_range(start_date, periods=8, freq='7D'))
print(df, '\n')
dfw = df.resample(rule='4W', how='mean', axis=1, closed='left', label='left')
print('4 Weeks:\n', dfw, '\n')
dfd = df.resample(rule='28D', how='mean', axis=1, closed='left', label='left')
print('28 Days:\n', dfd, '\n')
dfh = df.resample(rule='672H', how='mean', axis=1, closed='left', label='left')
print('672 Hours:\n', dfh, '\n')
dfm = df.resample(rule='40320T', how='mean', axis=1, closed='left', label='left')
print('40320 Minutes:\n', dfm, '\n')
dfs = df.resample(rule='2419200S', how='mean', axis=1, closed='left', label='left')
print('2419200 Seconds:\n', dfs, '\n')
res.append(([start_date], dfh.shape[1] == dfd.shape[1]))
print('\n\n--------------------------\n\n')
[print(res[i]) for i in range(12)]
pass
打印为(我在这里仅粘贴了最后一次迭代的打印输出):
2014-11-01 2014-11-29 2014-12-27
A 1.5 5.5 NaN
B 9.5 13.5 NaN
2014-12-01 2014-12-08 2014-12-15 2014-12-22 2014-12-29 2015-01-05 \
A 0 1 2 3 4 5
B 8 9 10 11 12 13
2015-01-12 2015-01-19
A 6 7
B 14 15
4 Weeks:
2014-11-30 2014-12-28
A 1.5 5.5
B 9.5 13.5
28 Days:
2014-12-01 2014-12-29
A 1.5 5.5
B 9.5 13.5
672 Hours:
2014-12-01 2014-12-29 2015-01-26
A 1.5 5.5 NaN
B 9.5 13.5 NaN
40320 Minutes:
2014-12-01 2014-12-29 2015-01-26
A 1.5 5.5 NaN
B 9.5 13.5 NaN
2419200 Seconds:
2014-12-01 2014-12-29 2015-01-26
A 1.5 5.5 NaN
B 9.5 13.5 NaN
--------------------------
(['1/1/2014'], False)
(['2/1/2014'], True)
(['3/1/2014'], True)
(['4/1/2014'], True)
(['5/1/2014'], False)
(['6/1/2014'], False)
(['7/1/2014'], False)
(['8/1/2014'], False)
(['9/1/2014'], False)
(['10/1/2014'], False)
(['11/1/2014'], False)
(['12/1/2014'], False)
因此,从 9 月初开始的 date_range 存在错误,并且 3 个月(二月至四月)没有错误。要么我错过了什么,要么它是一个错误,是吗?
最佳答案
感谢@DSM和@Andy,确实我有pandas 0.15.1,升级到最新的0.15.2解决了它
关于python - Pandas 重采样错误?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/28332644/