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我的熊猫看起来像这样

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

我有一个程序可以在其中一个具有嵌入式功能的功能上成功运行 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))

在此处查看我的初始问题和数据格式：

仅在同一 df 中的 df col 行中选择一个值来计算来自不同 val 的结果，并且一次仅在一个代码上计算 df

现在我意识到，与其在我有 5 个的每个嵌入式函数中执行 groupby，我宁愿让 groupby 在调用顶部函数的主程序中运行，这样所有嵌入式函数都可以在过滤后的 groupby pandas 上运行数据框只做一次 groupby ......

如何使用 groupby 应用我的主要功能，以过滤我的熊猫，所以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，以及时间窗口的时间序列数据。

非常感谢卡拉辛斯基。这接近我想要的。但我得到一个错误。

当我运行时：

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)

我收到此错误：

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

df_all = functools.partial(df_all.groupby('Ticker').Adj_Close.apply(Screener, ticker))

我得到与上述相同的错误...

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'

编辑自卡拉辛斯基 31/5 的编辑。

当我运行 Karasinski 的最后一个建议时，我收到了这个错误。

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
4

1 回答 1

1

从您上一个问题的答案中，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信息，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，您将执行 a groupby，然后apply执行函数

df[['mean', 'max', 'median', 'min']] = df.groupby('Ticker').Adj_Close.apply(various_indicators)

编辑

关于答案评论中的进一步问题：要从数据框中提取其他信息，您应该传递整个组，而不仅仅是传递单个列。

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，您可以以与上述类似的方式分配这些列

df[['mean', 'max', 'median', 'min']] = df.groupby('Ticker').apply(group_func)
于 2016-05-22T07:04:56.760 回答