基于多索引数据帧的价格调整最具Python式的方式是什么？

In[114]: cdm Out[114]: Last TD Date Ticker 1983-03-30 CLM83 29.40 44 CLN83 29.35 76 CLQ83 29.20 105 CLU83 28.95 139 CLV83 28.95 167 CLX83 28.90 197 CLZ83 28.75 230 1983-03-31 CLM83 29.29 43 CLN83 29.24 75 CLQ83 29.05 104 CLU83 28.85 138 CLV83 28.75 166 CLX83 28.70 196 CLZ83 28.60 229 1983-04-04 CLM83 29.44 39 CLN83 29.25 71 CLQ83 29.10 100 CLU83 29.05 134 CLV83 28.95 162 CLX83 28.95 192 CLZ83 28.85 225 1983-04-05 CLM83 29.71 38 CLN83 29.54 70 CLQ83 29.35 99 CLU83 29.20 133 CLV83 29.10 161 CLX83 29.00 191 CLZ83 29.00 224 1983-04-06 CLM83 29.90 37 CLN83 29.68 69 ... ... 2016-07-05 CLV6 47.91 72 CLX6 48.51 104 CLZ6 49.07 134 CLF7 49.54 163 CLG7 49.93 196 CLH7 50.26 226 CLJ7 50.53 254 CLK7 50.77 286 CLM7 51.00 316 CLN7 51.20 345 CLQ7 51.39 377 CLU7 51.58 408 CLV7 51.79 437 CLX7 52.03 469 2016-07-06 CLQ6 47.43 9 CLU6 48.14 42 CLV6 48.75 71 CLX6 49.34 103 CLZ6 49.89 133 CLF7 50.36 162 CLG7 50.75 195 CLH7 51.08 225 CLJ7 51.35 253 CLK7 51.60 285 CLM7 51.84 315 CLN7 52.05 344 CLQ7 52.25 376 CLU7 52.46 407 CLV7 52.69 436 CLX7 52.94 468 [289527 rows x 2 columns]

%time cdm.groupby(level='Date')['Last'].apply(rebase) Wall time: 1min 49s Out[115]: Date Ticker 1983-03-30 CLM83 100.000000 CLN83 99.829932 CLQ83 99.319728 CLU83 98.469388 CLV83 98.469388 CLX83 98.299320 CLZ83 97.789116 1983-03-31 CLM83 100.000000 CLN83 99.829293 CLQ83 99.180608 CLU83 98.497781 CLV83 98.156367 CLX83 97.985661 CLZ83 97.644247 1983-04-04 CLM83 100.000000 CLN83 99.354620 CLQ83 98.845109 CLU83 98.675272 CLV83 98.335598 CLX83 98.335598 CLZ83 97.995924 1983-04-05 CLM83 100.000000 CLN83 99.427802 CLQ83 98.788287 CLU83 98.283406 CLV83 97.946819 CLX83 97.610232 CLZ83 97.610232 1983-04-06 CLM83 100.000000 CLN83 99.264214 2016-07-05 CLV6 102.811159 CLX6 104.098712 CLZ6 105.300429 CLF7 106.309013 CLG7 107.145923 CLH7 107.854077 CLJ7 108.433476 CLK7 108.948498 CLM7 109.442060 CLN7 109.871245 CLQ7 110.278970 CLU7 110.686695 CLV7 111.137339 CLX7 111.652361 2016-07-06 CLQ6 100.000000 CLU6 101.496943 CLV6 102.783049 CLX6 104.026987 CLZ6 105.186591 CLF7 106.177525 CLG7 106.999789 CLH7 107.695551 CLJ7 108.264811 CLK7 108.791904 CLM7 109.297913 CLN7 109.740670 CLQ7 110.162345 CLU7 110.605102 CLV7 111.090027 CLX7 111.617120 Name: Last, dtype: float64

1条回答

网友

1楼 · 发布于 2024-09-30 03:24:21

设置

from StringIO import StringIO
import pandas as pd
import numpy as np

text = """Date       Ticker   Last   TD
1983-03-30 CLM83   29.40   44
1983-03-30 CLN83   29.35   76
1983-03-30 CLQ83   29.20  105
1983-03-30 CLU83   28.95  139
1983-03-30 CLV83   28.95  167
1983-03-30 CLX83   28.90  197
1983-03-30 CLZ83   28.75  230
1983-03-31 CLM83   29.29   43
1983-03-31 CLN83   29.24   75
1983-03-31 CLQ83   29.05  104
1983-03-31 CLU83   28.85  138
1983-03-31 CLV83   28.75  166
1983-03-31 CLX83   28.70  196
1983-03-31 CLZ83   28.60  229
1983-04-04 CLM83   29.44   39
1983-04-04 CLN83   29.25   71
1983-04-04 CLQ83   29.10  100
1983-04-04 CLU83   29.05  134
1983-04-04 CLV83   28.95  162
1983-04-04 CLX83   28.95  192
1983-04-04 CLZ83   28.85  225
1983-04-05 CLM83   29.71   38
1983-04-05 CLN83   29.54   70
1983-04-05 CLQ83   29.35   99
1983-04-05 CLU83   29.20  133
1983-04-05 CLV83   29.10  161
1983-04-05 CLX83   29.00  191
1983-04-05 CLZ83   29.00  224"""

cdm = pd.read_csv(StringIO(text),
                  delim_whitespace=True,
                  parse_dates=[0],
                  index_col=[0, 1])

解决方案

经常使用numpy！在

^{pr2}$

stack来取回你的系列。在

cdm_last_rebased.stack()

Date        Ticker
1983-03-30  CLM83     100.000000
            CLN83     100.000000
            CLQ83     100.000000
            CLU83     100.000000
            CLV83     100.000000
            CLX83     100.000000
            CLZ83     100.000000
1983-03-31  CLM83      99.625850
            CLN83      99.625213
            CLQ83      99.486301
            CLU83      99.654577
            CLV83      99.309154
            CLX83      99.307958
            CLZ83      99.478261
1983-04-04  CLM83     100.512120
            CLN83     100.034200
            CLQ83     100.172117
            CLU83     100.693241
            CLV83     100.695652
            CLX83     100.871080
            CLZ83     100.874126
1983-04-05  CLM83     100.917120
            CLN83     100.991453
            CLQ83     100.859107
            CLU83     100.516351
            CLV83     100.518135
            CLX83     100.172712
            CLZ83     100.519931
dtype: float64

设置

解决方案

相关问题更多 >

编程相关推荐

热门问题

热门文章