我试图计算来自不同公司/股票行情公司的一些KPI。我的股票信息位于df中，具有这种结构
Ticker Open High Low Adj Close Volume
Date
2015-04-09 vws.co 315.000000 316.100000 312.500000 311.520000 1686800
2015-04-10 vws.co 317.000000 319.700000 316.400000 312.700000 1396500
2015-04-13 vws.co 317.900000 321.500000 315.200000 315.850000 1564500
2015-04-14 vws.co 320.000000 322.400000 318.700000 314.870000 1370600
2015-04-15 vws.co 320.000000 321.500000 319.200000 316.150000 945000
2015-04-16 vws.co 319.000000 320.200000 310.400000 307.870000 2236100
2015-04-17 vws.co 309.900000 310.000000 302.500000 299.100000 2711900
2015-04-20 vws.co 303.000000 312.000000 303.000000 306.490000 1629700
... ... ... ... ... ... ...
2016-03-31 mmm 166.750000 167.500000 166.500000 166.630005 1762800
2016-04-01 mmm 165.630005 167.740005 164.789993 167.529999 1993700
2016-04-04 mmm 167.110001 167.490005 165.919998 166.399994 2022800
2016-04-05 mmm 165.179993 166.550003 164.649994 165.809998 1610300
2016-04-06 mmm 165.339996 167.080002 164.839996 166.809998 2092200
2016-04-07 mmm 165.880005 167.229996 165.250000 167.160004 2721900
我需要在每个股票行情机的基础上进行计算，所以在遍历公司/股票行情机时，我不会扭曲移动平均值的结果。
有人能帮我吗？
拆分和连接dfs我如何从逻辑上将ticker分离为可能不同的dfs，并附加ticker名称，然后如何将它们再次附加到一个df？
从相同df中选择或者只选择同一df中的相关ticker行？（可能像这样将数据帧拆分为多个数据帧）
任何帮助都将不胜感激。。。
----------来自亚历山大的帖子如下。此代码
df['MA1'] = df.groupby('Ticker').df['Adj Close'].transform(lambda group: pd.rolling_mean(group, window=10))
抛出此错误：
AttributeError: 'DataFrameGroupBy' object has no attribute 'df'
或者更接近剪切粘贴
Adj_Close = df['Adj Close']
df['MA3'] = df.groupby('Ticker').Adj_Close.transform(lambda group: pd.rolling_mean(group, window=3))
不再抛出错误。这是用groupby不喜欢的空间命名的col"Adj Close"。。。这一定是一个错误
如果我尝试这种语法，它应该会起作用——它不会！
df['MA3'] = df.groupby('Ticker').df["Adj Close"].transform(lambda group: pd.rolling_mean(group, window=3))
并抛出这个错误：
AttributeError: 'DataFrameGroupBy' object has no attribute 'df'
但我可以处理以上问题。感谢Alexander
您可以在groupby对象上使用transform来维护具有相同形状的列：
例如，这里是调整收盘的3天移动平均线（Pandas<0.18.0）
df['MA3'] = df.groupby('Ticker').Adj_Close.transform(lambda group: pd.rolling_mean(group, window=3))
>>> df
Date Ticker Open High Low Adj_Close Volume MA3
0 2015-04-09 vws.co 315 316 312 312 1686800 NaN
1 2015-04-10 vws.co 317 320 316 313 1396500 NaN
2 2015-04-13 vws.co 318 322 315 316 1564500 313
3 2015-04-14 vws.co 320 322 319 315 1370600 314
4 2015-04-15 vws.co 320 322 319 316 945000 316
5 2015-04-16 vws.co 319 320 310 308 2236100 313
6 2015-04-17 vws.co 310 310 302 299 2711900 308
7 2015-04-20 vws.co 303 312 303 306 1629700 304
8 2016-03-31 mmm 167 168 166 167 1762800 NaN
9 2016-04-01 mmm 166 168 165 168 1993700 NaN
10 2016-04-04 mmm 167 167 166 166 2022800 167
11 2016-04-05 mmm 165 167 165 166 1610300 167
12 2016-04-06 mmm 165 167 165 167 2092200 166
13 2016-04-07 mmm 166 167 165 167 2721900 167
使用分组
设置
import pandas as pd
from StringIO import StringIO
text = """Date Ticker Open High Low Adj_Close Volume
2015-04-09 vws.co 315.000000 316.100000 312.500000 311.520000 1686800
2015-04-10 vws.co 317.000000 319.700000 316.400000 312.700000 1396500
2015-04-13 vws.co 317.900000 321.500000 315.200000 315.850000 1564500
2015-04-14 vws.co 320.000000 322.400000 318.700000 314.870000 1370600
2015-04-15 vws.co 320.000000 321.500000 319.200000 316.150000 945000
2015-04-16 vws.co 319.000000 320.200000 310.400000 307.870000 2236100
2015-04-17 vws.co 309.900000 310.000000 302.500000 299.100000 2711900
2015-04-20 vws.co 303.000000 312.000000 303.000000 306.490000 1629700
2016-03-31 mmm 166.750000 167.500000 166.500000 166.630005 1762800
2016-04-01 mmm 165.630005 167.740005 164.789993 167.529999 1993700
2016-04-04 mmm 167.110001 167.490005 165.919998 166.399994 2022800
2016-04-05 mmm 165.179993 166.550003 164.649994 165.809998 1610300
2016-04-06 mmm 165.339996 167.080002 164.839996 166.809998 2092200
2016-04-07 mmm 165.880005 167.229996 165.250000 167.160004 2721900"""
df = pd.read_csv(StringIO(text), delim_whitespace=1, parse_dates=[0], index_col=0)
看起来像：
print df
Ticker Open High Low Adj_Close Volume
Date
2015-04-09 vws.co 315.000000 316.100000 312.500000 311.520000 1686800
2015-04-10 vws.co 317.000000 319.700000 316.400000 312.700000 1396500
2015-04-13 vws.co 317.900000 321.500000 315.200000 315.850000 1564500
2015-04-14 vws.co 320.000000 322.400000 318.700000 314.870000 1370600
2015-04-15 vws.co 320.000000 321.500000 319.200000 316.150000 945000
2015-04-16 vws.co 319.000000 320.200000 310.400000 307.870000 2236100
2015-04-17 vws.co 309.900000 310.000000 302.500000 299.100000 2711900
2015-04-20 vws.co 303.000000 312.000000 303.000000 306.490000 1629700
2016-03-31 mmm 166.750000 167.500000 166.500000 166.630005 1762800
2016-04-01 mmm 165.630005 167.740005 164.789993 167.529999 1993700
2016-04-04 mmm 167.110001 167.490005 165.919998 166.399994 2022800
2016-04-05 mmm 165.179993 166.550003 164.649994 165.809998 1610300
2016-04-06 mmm 165.339996 167.080002 164.839996 166.809998 2092200
2016-04-07 mmm 165.880005 167.229996 165.250000 167.160004 2721900
解决方案
df.groupby('Ticker').sum()
Open High Low Adj_Close Volume
Ticker
mmm 995.89 1003.590011 991.949981 1000.339998 12203700
vws.co 2521.80 2543.400000 2497.900000 2484.550000 13541100
您可以使用groupby对象聚合和执行许多事情。