前言：我是新人，但在pandas documentation搜索了几个小时并没有成功。我也读过Wes的book。
我正在为对冲基金建立股票市场数据，并且有一个简单的MultiIndexed-DataFrame，其中包含代码，日期（每日）和字段。这里的样本来自彭博社。 3个月 - 2016年12月至2017年2月，3个代码（AAPL，IBM，MSFT）。
import numpy as np
import pandas as pd
import os
# get data from Excel
curr_directory = os.getcwd()
filename = 'Sample Data File.xlsx'
filepath = os.path.join(curr_directory, filename)
df = pd.read_excel(filepath, sheetname = 'Sheet1', index_col = [0,1], parse_cols = 'A:D')
# sort
df.sort_index(inplace=True)
# sample of the data
df.head(15)
Out[4]:
PX_LAST PX_VOLUME
Security Name date
AAPL US Equity 2016-12-01 109.49 37086862
2016-12-02 109.90 26527997
2016-12-05 109.11 34324540
2016-12-06 109.95 26195462
2016-12-07 111.03 29998719
2016-12-08 112.12 27068316
2016-12-09 113.95 34402627
2016-12-12 113.30 26374377
2016-12-13 115.19 43733811
2016-12-14 115.19 34031834
2016-12-15 115.82 46524544
2016-12-16 115.97 44351134
2016-12-19 116.64 27779423
2016-12-20 116.95 21424965
2016-12-21 117.06 23783165
df.tail(15)
Out[5]:
PX_LAST PX_VOLUME
Security Name date
MSFT US Equity 2017-02-07 63.43 20277226
2017-02-08 63.34 18096358
2017-02-09 64.06 22644443
2017-02-10 64.00 18170729
2017-02-13 64.72 22920101
2017-02-14 64.57 23108426
2017-02-15 64.53 17005157
2017-02-16 64.52 20546345
2017-02-17 64.62 21248818
2017-02-21 64.49 20655869
2017-02-22 64.36 19292651
2017-02-23 64.62 20273128
2017-02-24 64.62 21796800
2017-02-27 64.23 15871507
2017-02-28 63.98 23239825
当我计算每日价格变化时，它似乎有效，只有第一天是NaN，因为它应该是：
df.head(5)
Out[7]:
PX_LAST PX_VOLUME px_change_%
Security Name date
AAPL US Equity 2016-12-01 109.49 37086862 NaN
2016-12-02 109.90 26527997 0.003745
2016-12-05 109.11 34324540 -0.007188
2016-12-06 109.95 26195462 0.007699
2016-12-07 111.03 29998719 0.009823
但是每天30天的体积并没有。它应该只是前29天的NaN，但对所有这些都是NaN：
# daily change from 30 day volume - doesn't work
df['30_day_volume'] = df.groupby(level=0,group_keys=True)['PX_VOLUME'].rolling(window=30).mean()
df['volume_change_%'] = (df['PX_VOLUME'] - df['30_day_volume']) / df['30_day_volume']
df.iloc[:,3:].tail(40)
Out[12]:
30_day_volume volume_change_%
Security Name date
MSFT US Equity 2016-12-30 NaN NaN
2017-01-03 NaN NaN
2017-01-04 NaN NaN
2017-01-05 NaN NaN
2017-01-06 NaN NaN
2017-01-09 NaN NaN
2017-01-10 NaN NaN
2017-01-11 NaN NaN
2017-01-12 NaN NaN
2017-01-13 NaN NaN
2017-01-17 NaN NaN
2017-01-18 NaN NaN
2017-01-19 NaN NaN
2017-01-20 NaN NaN
2017-01-23 NaN NaN
2017-01-24 NaN NaN
2017-01-25 NaN NaN
2017-01-26 NaN NaN
2017-01-27 NaN NaN
2017-01-30 NaN NaN
2017-01-31 NaN NaN
2017-02-01 NaN NaN
2017-02-02 NaN NaN
2017-02-03 NaN NaN
2017-02-06 NaN NaN
2017-02-07 NaN NaN
2017-02-08 NaN NaN
2017-02-09 NaN NaN
2017-02-10 NaN NaN
2017-02-13 NaN NaN
2017-02-14 NaN NaN
2017-02-15 NaN NaN
2017-02-16 NaN NaN
2017-02-17 NaN NaN
2017-02-21 NaN NaN
2017-02-22 NaN NaN
2017-02-23 NaN NaN
2017-02-24 NaN NaN
2017-02-27 NaN NaN
2017-02-28 NaN NaN
由于大熊猫似乎是专门为金融而设计的，所以我很惊讶这并不是直截了当。
编辑：我也尝试了其他一些方法。
df.index.levels[0]或...levels[1]无法正常工作。谢谢！
答案 0 :(得分：1)
您可以尝试以下方法查看它是否有效吗？
df['30_day_volume'] = df.groupby(level=0)['PX_VOLUME'].rolling(window=30).mean().values
df['volume_change_%'] = (df['PX_VOLUME'] - df['30_day_volume']) / df['30_day_volume']
答案 1 :(得分：0)
我可以在使用pandas_datareader时修复Allen的答案，修改datareader多索引的groupby操作的索引级别。
import pandas_datareader.data as web
import datetime
start = datetime.datetime(2016, 12, 1)
end = datetime.datetime(2017, 2, 28)
data = web.DataReader(['AAPL', 'IBM', 'MSFT'], 'yahoo', start, end).to_frame()
data['30_day_volume'] = data.groupby(level=1).rolling(window=30)['Volume'].mean().values
data['volume_change_%'] = (data['Volume'] - data['30_day_volume']) / data['30_day_volume']
# double-check that it computed starting at 30 trading days.
data.loc['2017-1-17':'2017-1-30']
原始海报可能会尝试编辑此行：
df['30_day_volume'] = df.groupby(level=0,group_keys=True)['PX_VOLUME'].rolling(window=30).mean()
以下，使用mean（）。values：
df['30_day_volume'] = df.groupby(level=0,group_keys=True)['PX_VOLUME'].rolling(window=30).mean().values
如果没有这个数据，数据就无法正确对齐，从而产生NaN＆＃39>。