Python 函数 returns 只有第一个值而不是数据帧

Question

我构建了一个函数，我将 5 个投资组合的 return 附加到一个数据框，我想将其 return 附加到一个变量。当我运行函数中的命令逐行（一种调试）时，我最终得到变量 'folioReturn'（这是我希望我的脚本 return 的变量）具有正确的数量值（e.x 5）。但是如果我调用函数，只有数据帧的第一个值是 returned。有谁知道我怎样才能得到整个数据框？


def portfolioReturns (securities, quintilesNo, perReturns):
    '''
    this function receives 
    1)securities: array with the security names and values ** for the purpose of our work the names
    should already be sorted
    2)quintilesNo: the number of portfolios we want to create 
    3)perReturns: an array with the returns that will be used for performance measuremnt

    It returns an array with the returns for each portfolio

    '''

    # we calculate the number of securities per portfolio 
    stdFolioSize = np.divmod(securities.size, quintilesNo)[0] # we take the floor division
    folioReturn = [] # pd.DataFrame()
    # we create portfolios with equal number of securities except of the last one where we use all the remaining securities
    for k in range(0, quintilesNo, 1): # in folio list we store the name of the securities we must include in each portfolio
        if k < (quintilesNo - 1):           
            folioList = securities.index.get_level_values(1)[k * stdFolioSize : (k + 1) * stdFolioSize]

        else: # the last portfolio will also include the remainder securities 
            folioList = securities.index.get_level_values(1)[k * stdFolioSize : securities.size]

        # now that we have the list of the securities to be included in the folio, we use the table
        # with the periodical returns to check the performance. The portfolio we construct is equally weighted

        # first we drop one index(the first index of the country) and then we store all the periodical returns in one-array 
        perRetFinalTable = pd.DataFrame(perReturns.reset_index(level = 0, drop = True)).T  

        # using the list of the bonds we want to include in our portfolio we pick the bond returns and
        # we store them in one array. Then we calculate the folio return
        folio = perRetFinalTable[folioList]
        folioReturn = np.append(folioReturn, folio.sum(axis = 1) * (1 / folio.size))
        folioReturn = pd.DataFrame(folioReturn).T
        # folioReturn = pd.Series(folioReturn).T

        return (folioReturn)

Answer 1

return 语句必须在 for 循环之后，如果您希望在第一个循环期间在您的案例中使用整个列表，仅值是 returned。只需从 for 循环中删除 return 它就可以正常工作。

def portfolioReturns (securities, quintilesNo, perReturns):
    '''
    this function receives 
    1)securities: array with the security names and values ** for the purpose of our work the names
    should already be sorted
    2)quintilesNo: the number of portfolios we want to create 
    3)perReturns: an array with the returns that will be used for performance measuremnt

    It returns an array with the returns for each portfolio

    '''

    # we calculate the number of securities per portfolio 
    stdFolioSize = np.divmod(securities.size, quintilesNo)[0] # we take the floor division
    folioReturn = [] # pd.DataFrame()
    # we create portfolios with equal number of securities except of the last one where we use all the remaining securities
    for k in range(0, quintilesNo, 1): # in folio list we store the name of the securities we must include in each portfolio
        if k < (quintilesNo - 1):           
            folioList = securities.index.get_level_values(1)[k * stdFolioSize : (k + 1) * stdFolioSize]

        else: # the last portfolio will also include the remainder securities 
            folioList = securities.index.get_level_values(1)[k * stdFolioSize : securities.size]

        # now that we have the list of the securities to be included in the folio, we use the table
        # with the periodical returns to check the performance. The portfolio we construct is equally weighted

        # first we drop one index(the first index of the country) and then we store all the periodical returns in one-array 
        perRetFinalTable = pd.DataFrame(perReturns.reset_index(level = 0, drop = True)).T  

        # using the list of the bonds we want to include in our portfolio we pick the bond returns and
        # we store them in one array. Then we calculate the folio return
        folio = perRetFinalTable[folioList]
        folioReturn = np.append(folioReturn, folio.sum(axis = 1) * (1 / folio.size))
        folioReturn = pd.DataFrame(folioReturn).T
        # folioReturn = pd.Series(folioReturn).T

    return (folioReturn)

Answer 2

你基本上太早退出了这个功能。

这里有两个例子，可以更容易理解：

def test():
    lst = []
    for num in range(10):
        lst = num
        return lst

def test2():
    lst = []
    for num in range(10):
        lst.append(num)
    return lst

print(test()) # Output: 0
print(test2()) # Output: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

在test中，我们创建一个名为lst的列表变量，然后在for循环中，我们用当前变量num覆盖lst，然后立即发出一个 return 语句，它只是 returns 0 因为它是 for 循环中的第一个条目。

在test2中，我们在循环开始时做同样的事情，但是，我们没有覆盖lst，而是使用方法list.append()，它添加了[=的值14=] 到已经创建的列表。

完成整个 for 循环后，我们使用 return 语句返回变量 lst，它现在包含 0 中的所有值至 9.

编辑：看来我没能解决您代码中的实际问题。主要有两个错误：

您正在使用 return 语句退出 for 循环，因为它在 for 循环内部。修复缩进，应该可以解决
在您的 for 循环内部，您正在覆盖 folioReturn 而不是将在 for 循环内部计算的值附加到它。替换 for 循环内的变量 folioReturn，然后在创建 DataFrame 后，将该值附加到 folioReturn.

Python 函数 returns 只有第一个值而不是数据帧

Python function returns only the first value instead of a dataframe

python

portfolio

return

function

dataframe