Python 如果没有for循环，如何处理数据帧？

我的数据帧是：

            Date        Open        High         Low       Close   Adj Close     Volume
5932  2016-08-18  218.339996  218.899994  218.210007  218.860001  207.483215   52989300
5933  2016-08-19  218.309998  218.750000  217.740005  218.539993  207.179825   75443000
5934  2016-08-22  218.259995  218.800003  217.830002  218.529999  207.170364   61368800
5935  2016-08-23  219.250000  219.600006  218.899994  218.970001  207.587479   53399200
5936  2016-08-24  218.800003  218.910004  217.360001  217.850006  206.525711   71728900
5937  2016-08-25  217.399994  218.190002  217.220001  217.699997  206.383514   69224800
5938  2016-08-26  217.919998  219.119995  216.250000  217.289993  205.994827  122506300
5939  2016-08-29  217.440002  218.669998  217.399994  218.360001  207.009201   68606100
5940  2016-08-30  218.259995  218.589996  217.350006  218.000000  206.667908   58114500
5941  2016-08-31  217.610001  217.750000  216.470001  217.380005  206.080124   85269500
5942  2016-09-01  217.369995  217.729996  216.029999  217.389999  206.089645   97844200
5943  2016-09-02  218.389999  218.869995  217.699997  218.369995  207.018692   79293900
5944  2016-09-06  218.699997  219.119995  217.860001  219.029999  207.644394   56702100
5945  2016-09-07  218.839996  219.220001  218.300003  219.009995  207.625412   76554900
5946  2016-09-08  218.619995  218.940002  218.149994  218.509995  207.151398   73011600
5947  2016-09-09  216.970001  217.029999  213.250000  213.279999  202.193268  221589100
5948  2016-09-12  212.389999  216.809998  212.309998  216.339996  205.094223  168110900
5949  2016-09-13  214.839996  215.149994  212.500000  213.229996  202.145859  182828800
5950  2016-09-14  213.289993  214.699997  212.500000  213.149994  202.070023  134185500
5951  2016-09-15  212.960007  215.729996  212.750000  215.279999  204.089294  134427900
5952  2016-09-16  213.479996  213.690002  212.570007  213.369995  203.300430  155236400

目前，我正在这样做：

        state['open_price'] = lookback.Open.iloc[-1:].get_values()[0]

        for ind, row in lookback.reset_index().iterrows():
            if ind < self.LOOKBACK_DAYS:
                state['close_' + str(self.LOOKBACK_DAYS - ind)] = row.Close
                state['open_' + str(self.LOOKBACK_DAYS - ind)] = row.Open
                state['volume_' + str(self.LOOKBACK_DAYS - ind)] = row.Volume

---

一种方法是使用

.values

我还将添加一些创建等效示例的步骤：

import pandas as pd
from itertools import product

initial = ['cash', 'num_shares', 'somethingsomething']
initial_series = pd.Series([1, 2, 3], index = initial)
print(initial_series)
#Output:
cash                  1
num_shares            2
somethingsomething    3
dtype: int64

好的，只是在输出中系列开头的一些值，例如mock

df = pd.read_clipboard(sep='\s\s+') #pure magic
print(df.head())
#Output:
            Date        Open    ...      Adj Close    Volume
5932  2016-08-18  218.339996    ...     207.483215  52989300
5933  2016-08-19  218.309998    ...     207.179825  75443000
5934  2016-08-22  218.259995    ...     207.170364  61368800
5935  2016-08-23  219.250000    ...     207.587479  53399200
5936  2016-08-24  218.800003    ...     206.525711  71728900

[5 rows x 7 columns]

df现在基本上就是您在示例中提供的数据帧。剪贴板技巧来源于《熊猫》杂志，对熊猫麦克维斯来说是一本很好的读物

to_select = ['Close', 'Open', 'Volume']
SOMELOOKBACK = 6000 #mocked
final_index = [f"{name}_{index}" for index, name in product((SOMELOOKBACK - df.index), to_select)]

这将准备索引，看起来像这样

['Close_68',
 'Open_68',
 'Volume_68',
 'Close_67',
 'Open_67',
 'Volume_67',
...
]

现在，只需从dataframe中选择相关列，使用

.values

获得一个2d数组，然后展平，获得最终系列

final_series = pd.Series(df[to_select].values.flatten(), index = final_index)

result = initial_series.append(final_series)
#Output:
cash                  1.000000e+00
num_shares            2.000000e+00
somethingsomething    3.000000e+00
Close_68              2.188600e+02
Open_68               2.183400e+02
Volume_68             5.298930e+07
Close_67              2.185400e+02
Open_67               2.183100e+02
Volume_67             7.544300e+07
Close_66              2.185300e+02
Open_66               2.182600e+02
Volume_66             6.136880e+07
...
Close_48              2.133700e+02
Open_48               2.134800e+02
Volume_48             1.552364e+08
Length: 66, dtype: float64

---

请定义“transform”这个词，transform这个词不对。我想把它转换成一个系列？这将有助于理解代码试图做什么，以及预期的输出是什么。@coldspeed更新了post以反映预期的输出

final_series = pd.Series(df[to_select].values.flatten(), index = final_index)

result = initial_series.append(final_series)
#Output:
cash                  1.000000e+00
num_shares            2.000000e+00
somethingsomething    3.000000e+00
Close_68              2.188600e+02
Open_68               2.183400e+02
Volume_68             5.298930e+07
Close_67              2.185400e+02
Open_67               2.183100e+02
Volume_67             7.544300e+07
Close_66              2.185300e+02
Open_66               2.182600e+02
Volume_66             6.136880e+07
...
Close_48              2.133700e+02
Open_48               2.134800e+02
Volume_48             1.552364e+08
Length: 66, dtype: float64