应用来自 groupby 转换的函数
Apply a function from a groupby transform
我的pandas看起来像这样
Date Ticker Open High Low Adj Close Adj_Close Volume
2016-04-18 vws.co 445.0 449.2 441.7 447.3 447.3 945300
2016-04-19 vws.co 449.0 455.8 448.3 450.9 450.9 907700
2016-04-20 vws.co 451.0 452.5 435.4 436.6 436.6 1268100
2016-04-21 vws.co 440.1 442.9 428.4 435.5 435.5 1308300
2016-04-22 vws.co 435.5 435.5 435.5 435.5 435.5 0
2016-04-25 vws.co 431.0 436.7 424.4 430.0 430.0 1311700
2016-04-18 nflx 109.9 110.7 106.02 108.4 108.4 27001500
2016-04-19 nflx 99.49 101.37 94.2 94.34 94.34 55623900
2016-04-20 nflx 94.34 96.98 93.14 96.77 96.77 25633600
2016-04-21 nflx 97.31 97.38 94.78 94.98 94.98 19859400
2016-04-22 nflx 94.85 96.69 94.21 95.9 95.9 15786000
2016-04-25 nflx 95.7 95.75 92.8 93.56 93.56 14965500
我有一个程序，其中一个具有嵌入式功能的功能成功地 运行s 了一个 groupby。
这条线看起来像这样
df['MA3'] = df.groupby('Ticker').Adj_Close.transform(lambda group: pd.rolling_mean(group, window=3))
我最初的问题和数据格式在这里：
我现在明白了，与其在我有 5 个的每个嵌入式函数中执行 groupby，我宁愿在主程序中调用 top 函数的 groupby 运行，所以所有嵌入式函数可以在过滤后的 groupby pandas 数据帧上工作，只需执行一次 groupby...
如何将我的主要功能与 groupby 一起应用，以便过滤我的 pandas，以便我一次只处理一个代码（col 'Ticker' 中的值）？
'Ticker' 列包含 'aapl'、'msft'、'nflx' 公司标识符等，以及时间序列数据 -window.
非常感谢卡拉辛斯基。这接近我想要的。但是我得到一个错误。
当我运行:
def Screener(df_all, group):
# Copy df_all to df for single ticker operations
df = df_all.copy()
def diff_calc(df,ticker):
df['Difference'] = df['Adj_Close'].diff()
return df
df = diff_calc(df, ticker)
return df_all
for ticker in stocklist:
df_all[['Difference']] = df_all.groupby('Ticker').Adj_Close.apply(Screener, ticker)
我收到这个错误：
Traceback (most recent call last):
File "<ipython-input-2-d7c1835f6b2a>", line 1, in <module>
runfile('C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py', wdir='C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox')
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 682, in runfile
execfile(filename, namespace)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 85, in execfile
exec(compile(open(filename, 'rb').read(), filename, 'exec'), namespace)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 144, in <module>
df_all[['Difference']] = df_all.groupby('Ticker').Adj_Close.apply(Screener, ticker)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 663, in apply
return self._python_apply_general(f)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 667, in _python_apply_general
self.axis)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 1286, in apply
res = f(group)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 659, in f
return func(g, *args, **kwargs)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 112, in Screener
df = diff_calc(df, ticker)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 70, in diff_calc
df['Difference'] = df['Adj_Close'].diff()
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\series.py", line 514, in __getitem__
result = self.index.get_value(self, key)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\tseries\index.py", line 1221, in get_value
raise KeyError(key)
KeyError: 'Adj_Close'
当我像这样使用 functools 时
df_all = functools.partial(df_all.groupby('Ticker').Adj_Close.apply(Screener, ticker))
我得到与上面相同的错误...
Traceback (most recent call last):
File "<ipython-input-5-d7c1835f6b2a>", line 1, in <module>
runfile('C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py', wdir='C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox')
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 682, in runfile
execfile(filename, namespace)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 85, in execfile
exec(compile(open(filename, 'rb').read(), filename, 'exec'), namespace)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 148, in <module>
df_all = functools.partial(df_all.groupby('Ticker').Adj_Close.apply(Screener, [ticker]))
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 663, in apply
return self._python_apply_general(f)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 667, in _python_apply_general
self.axis)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 1286, in apply
res = f(group)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 659, in f
return func(g, *args, **kwargs)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 114, in Screener
df = diff_calc(df, ticker)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 72, in diff_calc
df['Difference'] = df['Adj_Close'].diff()
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\series.py", line 514, in __getitem__
result = self.index.get_value(self, key)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-
3.3.5.amd64\lib\site-packages\pandas\tseries\index.py", line 1221, in get_value
raise KeyError(key)
KeyError: 'Adj_Close'
从 Karasinski 从 31/5 开始编辑。
当我 运行 Karasinski 的最后一个建议时，我得到了这个错误。
mmm
mmm
nflx
vws.co
Traceback (most recent call last):
File "<ipython-input-4-d7c1835f6b2a>", line 1, in <module>
runfile('C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py', wdir='C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox')
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 682, in runfile
execfile(filename, namespace)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 85, in execfile
exec(compile(open(filename, 'rb').read(), filename, 'exec'), namespace)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 173, in <module>
df_all[['mean', 'max', 'median', 'min']] = df_all.groupby('Ticker').apply(group_func)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 663, in apply
return self._python_apply_general(f)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 670, in _python_apply_general
not_indexed_same=mutated)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 2785, in _wrap_applied_output
not_indexed_same=not_indexed_same)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 1142, in _concat_objects
result = result.reindex_axis(ax, axis=self.axis)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\frame.py", line 2508, in reindex_axis
fill_value=fill_value)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\generic.py", line 1841, in reindex_axis
{axis: [new_index, indexer]}, fill_value=fill_value, copy=copy)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\generic.py", line 1865, in _reindex_with_indexers
copy=copy)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\internals.py", line 3144, in reindex_indexer
raise ValueError("cannot reindex from a duplicate axis")
ValueError: cannot reindex from a duplicate axis
根据您上一个问题的答案，我们可以设置
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)
然后您可以创建一个函数来计算您想要的任何统计数据，例如：
def various_indicators(group):
mean = pd.rolling_mean(group, window=3)
max = pd.rolling_max(group, window=3)
median = pd.rolling_median(group, window=3)
min = pd.rolling_min(group, window=3)
return pd.DataFrame({'mean': mean,
'max': max,
'median': median,
'min': min})
要将这些新列分配给您的数据框，您可以执行 groupby 然后 apply 函数
df[['mean', 'max', 'median', 'min']] = df.groupby('Ticker').Adj_Close.apply(various_indicators)
编辑
关于您在答案评论中提出的进一步问题：要从数据框中提取更多信息，您应该传递整个组，而不仅仅是单个列。
def group_func(group):
ticker = group.Ticker.unique()[0]
adj_close = group.Adj_Close
return Screener(ticker, adj_close)
def Screener(ticker, adj_close):
print(ticker)
mean = pd.rolling_mean(adj_close, window=3)
max = pd.rolling_max(adj_close, window=3)
median = pd.rolling_median(adj_close, window=3)
min = pd.rolling_min(adj_close, window=3)
return pd.DataFrame({'mean': mean,
'max': max,
'median': median,
'min': min})
然后您可以按照与上述类似的方式分配这些列
df[['mean', 'max', 'median', 'min']] = df.groupby('Ticker').apply(group_func)
我的pandas看起来像这样
Date Ticker Open High Low Adj Close Adj_Close Volume
2016-04-18 vws.co 445.0 449.2 441.7 447.3 447.3 945300
2016-04-19 vws.co 449.0 455.8 448.3 450.9 450.9 907700
2016-04-20 vws.co 451.0 452.5 435.4 436.6 436.6 1268100
2016-04-21 vws.co 440.1 442.9 428.4 435.5 435.5 1308300
2016-04-22 vws.co 435.5 435.5 435.5 435.5 435.5 0
2016-04-25 vws.co 431.0 436.7 424.4 430.0 430.0 1311700
2016-04-18 nflx 109.9 110.7 106.02 108.4 108.4 27001500
2016-04-19 nflx 99.49 101.37 94.2 94.34 94.34 55623900
2016-04-20 nflx 94.34 96.98 93.14 96.77 96.77 25633600
2016-04-21 nflx 97.31 97.38 94.78 94.98 94.98 19859400
2016-04-22 nflx 94.85 96.69 94.21 95.9 95.9 15786000
2016-04-25 nflx 95.7 95.75 92.8 93.56 93.56 14965500
我有一个程序，其中一个具有嵌入式功能的功能成功地 运行s 了一个 groupby。
这条线看起来像这样
df['MA3'] = df.groupby('Ticker').Adj_Close.transform(lambda group: pd.rolling_mean(group, window=3))
我最初的问题和数据格式在这里：
我现在明白了，与其在我有 5 个的每个嵌入式函数中执行 groupby，我宁愿在主程序中调用 top 函数的 groupby 运行，所以所有嵌入式函数可以在过滤后的 groupby pandas 数据帧上工作，只需执行一次 groupby...
如何将我的主要功能与 groupby 一起应用，以便过滤我的 pandas，以便我一次只处理一个代码（col 'Ticker' 中的值）？
'Ticker' 列包含 'aapl'、'msft'、'nflx' 公司标识符等，以及时间序列数据 -window.
非常感谢卡拉辛斯基。这接近我想要的。但是我得到一个错误。
当我运行:
def Screener(df_all, group):
# Copy df_all to df for single ticker operations
df = df_all.copy()
def diff_calc(df,ticker):
df['Difference'] = df['Adj_Close'].diff()
return df
df = diff_calc(df, ticker)
return df_all
for ticker in stocklist:
df_all[['Difference']] = df_all.groupby('Ticker').Adj_Close.apply(Screener, ticker)
我收到这个错误：
Traceback (most recent call last):
File "<ipython-input-2-d7c1835f6b2a>", line 1, in <module>
runfile('C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py', wdir='C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox')
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 682, in runfile
execfile(filename, namespace)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 85, in execfile
exec(compile(open(filename, 'rb').read(), filename, 'exec'), namespace)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 144, in <module>
df_all[['Difference']] = df_all.groupby('Ticker').Adj_Close.apply(Screener, ticker)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 663, in apply
return self._python_apply_general(f)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 667, in _python_apply_general
self.axis)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 1286, in apply
res = f(group)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 659, in f
return func(g, *args, **kwargs)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 112, in Screener
df = diff_calc(df, ticker)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 70, in diff_calc
df['Difference'] = df['Adj_Close'].diff()
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\series.py", line 514, in __getitem__
result = self.index.get_value(self, key)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\tseries\index.py", line 1221, in get_value
raise KeyError(key)
KeyError: 'Adj_Close'
当我像这样使用 functools 时
df_all = functools.partial(df_all.groupby('Ticker').Adj_Close.apply(Screener, ticker))
我得到与上面相同的错误...
Traceback (most recent call last):
File "<ipython-input-5-d7c1835f6b2a>", line 1, in <module>
runfile('C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py', wdir='C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox')
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 682, in runfile
execfile(filename, namespace)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 85, in execfile
exec(compile(open(filename, 'rb').read(), filename, 'exec'), namespace)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 148, in <module>
df_all = functools.partial(df_all.groupby('Ticker').Adj_Close.apply(Screener, [ticker]))
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 663, in apply
return self._python_apply_general(f)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 667, in _python_apply_general
self.axis)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 1286, in apply
res = f(group)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 659, in f
return func(g, *args, **kwargs)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 114, in Screener
df = diff_calc(df, ticker)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 72, in diff_calc
df['Difference'] = df['Adj_Close'].diff()
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\series.py", line 514, in __getitem__
result = self.index.get_value(self, key)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-
3.3.5.amd64\lib\site-packages\pandas\tseries\index.py", line 1221, in get_value
raise KeyError(key)
KeyError: 'Adj_Close'
从 Karasinski 从 31/5 开始编辑。
当我 运行 Karasinski 的最后一个建议时，我得到了这个错误。
mmm
mmm
nflx
vws.co
Traceback (most recent call last):
File "<ipython-input-4-d7c1835f6b2a>", line 1, in <module>
runfile('C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py', wdir='C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox')
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 682, in runfile
execfile(filename, namespace)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 85, in execfile
exec(compile(open(filename, 'rb').read(), filename, 'exec'), namespace)
File "C:/Users/Morten/Documents/Design/Python/CrystalBall - Local - Git/Git - CrystalBall/sandbox/screener_test simple for StockOverflowNestedFct_Getstock.py", line 173, in <module>
df_all[['mean', 'max', 'median', 'min']] = df_all.groupby('Ticker').apply(group_func)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 663, in apply
return self._python_apply_general(f)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 670, in _python_apply_general
not_indexed_same=mutated)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 2785, in _wrap_applied_output
not_indexed_same=not_indexed_same)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\groupby.py", line 1142, in _concat_objects
result = result.reindex_axis(ax, axis=self.axis)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\frame.py", line 2508, in reindex_axis
fill_value=fill_value)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\generic.py", line 1841, in reindex_axis
{axis: [new_index, indexer]}, fill_value=fill_value, copy=copy)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\generic.py", line 1865, in _reindex_with_indexers
copy=copy)
File "C:\Program Files\WinPython-64bit-3.3.5.7\python-3.3.5.amd64\lib\site-packages\pandas\core\internals.py", line 3144, in reindex_indexer
raise ValueError("cannot reindex from a duplicate axis")
ValueError: cannot reindex from a duplicate axis
根据您上一个问题的答案，我们可以设置
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)
然后您可以创建一个函数来计算您想要的任何统计数据，例如：
def various_indicators(group):
mean = pd.rolling_mean(group, window=3)
max = pd.rolling_max(group, window=3)
median = pd.rolling_median(group, window=3)
min = pd.rolling_min(group, window=3)
return pd.DataFrame({'mean': mean,
'max': max,
'median': median,
'min': min})
要将这些新列分配给您的数据框，您可以执行 groupby 然后 apply 函数
df[['mean', 'max', 'median', 'min']] = df.groupby('Ticker').Adj_Close.apply(various_indicators)
编辑
关于您在答案评论中提出的进一步问题：要从数据框中提取更多信息，您应该传递整个组，而不仅仅是单个列。
def group_func(group):
ticker = group.Ticker.unique()[0]
adj_close = group.Adj_Close
return Screener(ticker, adj_close)
def Screener(ticker, adj_close):
print(ticker)
mean = pd.rolling_mean(adj_close, window=3)
max = pd.rolling_max(adj_close, window=3)
median = pd.rolling_median(adj_close, window=3)
min = pd.rolling_min(adj_close, window=3)
return pd.DataFrame({'mean': mean,
'max': max,
'median': median,
'min': min})
然后您可以按照与上述类似的方式分配这些列
df[['mean', 'max', 'median', 'min']] = df.groupby('Ticker').apply(group_func)