2023年6月13日 08:26:09go评论65阅读模式

英文:

DataFrame object is not callable when sharing a variable between functions

问题

以下是您提供的代码的翻译部分：

这一行导致错误：`SKU_Metrics = SKU_Details(SAS_full_dataset)`。
我得到的错误是`Dataframe对象不可调用`。基本上，我想要做的是在函数内调用`SAS_full_dataset()`函数，将其存储为变量，然后在这些函数中对这些变量进行转换。请注意，我将函数作为要调用该函数的函数的参数。我认为这是一个合理的方法。在这里也让我感到困惑的是，代码在某些地方没有出错，比如`SAS_full_dataset = SAS_full_dataset()`。

我无法弄清楚为什么会出现这个错误。在下面的代码中，出于长度考虑，一些文件导入赋值被省略了。

这是一个函数，用于合并基础和当前期发票数据集，因为我们只想对过去一年购买的SKU进行推荐。

这是一个函数，用于合并基础和当前期发票数据集，因为我们只想对过去一年购买的SKU进行推荐。

这是一个函数，用于合并基础和当前期发票数据集，因为我们只想对过去一年购买的SKU进行推荐。

这是一个函数，用于合并基础和当前期发票数据集，因为我们只想对过去一年购买的SKU进行推荐。

以下是追溯信息：

以下是追溯信息：

以下是追溯信息：

以下是追溯信息：

希望这有助于您理解代码和错误信息。如果您需要进一步的帮助，请随时提出。

英文:

This line is causing an error: SKU_Metrics = SKU_Details(SAS_full_dataset).
The error I get is Dataframe object is not callable. Basically, I want to do this pattern where I call the SAS_full_dataset() function within functions, store that as a variable and do transformations on those variables in those functions. Notice I put the function as parameters in the functions I want to call that function in. I think this is a sensible approach. What puzzles me here too is that the code doesn't error out in some places, like SAS_full_dataset = SAS_full_dataset()

I can't figure out why I am getting this error. In the code below, some file import assignments were left out of this code to for length sake.

def data_cleaning_and_filtering(SAS_current_period,SAS_prior_period,primary_vendor):
    ## filtering out OT,LT Verticals
    SAS_current_period = SAS_current_period.loc[(SAS_current_period[&#39;Vertical Code&#39;] != &#39;LT&#39;) &amp; (SAS_current_period[&#39;Vertical Code&#39;] != &#39;OT&#39; )]
    SAS_prior_period = SAS_prior_period.loc[(SAS_prior_period[&#39;Vertical Code&#39;] != &#39;LT&#39;) &amp; (SAS_prior_period[&#39;Vertical Code&#39;] != &#39;OT&#39; )]

    ## filtering out CustTypes UPS, S&amp;S Latam, S&amp;S Canada
    SAS_current_period = SAS_current_period.loc[(SAS_current_period[&#39;Customer Type Code&#39;] != &#39;NT&#39;) &amp; (SAS_current_period[&#39;Customer Type Code&#39;] != &#39;GH&#39; ) &amp; (SAS_current_period[&#39;Customer Type Code&#39;] != &#39;NJ&#39; )]
    SAS_prior_period = SAS_prior_period.loc[(SAS_prior_period[&#39;Customer Type Code&#39;] != &#39;NT&#39;) &amp; (SAS_prior_period[&#39;Customer Type Code&#39;] != &#39;GH&#39; ) &amp; (SAS_prior_period[&#39;Customer Type Code&#39;] != &#39;NJ&#39; )]

    ## filtering out negative quantities and Revenue
    SAS_current_period = SAS_current_period.loc[(SAS_current_period[&#39;Qty Shipped&#39;] &gt; 0) &amp; (SAS_current_period[&#39;Ext. Sell Price&#39;] &gt; 0)]
    SAS_prior_period = SAS_prior_period.loc[(SAS_prior_period[&#39;Qty Shipped&#39;] &gt; 0) &amp; (SAS_prior_period[&#39;Ext. Sell Price&#39;] &gt; 0)]

    ## filtering to shipBR = 01
    SAS_current = SAS_current_period.loc[(SAS_current_period[&#39;Ship Br.&#39;] == 1)]
    SAS_prior = SAS_prior_period.loc[(SAS_prior_period[&#39;Ship Br.&#39;] == 1)]

    ## dropping/Excluding SKU&#39;s where primary vendor = 1708 (Midwest)
    primary_vendor[&#39;SKU&#39;] = primary_vendor[&#39;PRDLIN&#39;] + &#39;-&#39; + primary_vendor[&#39;PRODNO&#39;]
    exclude_list = primary_vendor.loc[(primary_vendor[&#39;VENDNO&#39;] == 1708),[&#39;SKU&#39;]]
    #SAS_current = SAS_current_period.drop(SAS_current_period[SAS_current_period.SKU.isin(exclude_list[:])].index)
    #SAS_prior = SAS_prior_period.drop(SAS_prior_period[SAS_prior_period.SKU.isin(exclude_list[:])].index)

    return SAS_current,SAS_prior

The overall goal through the next handful of functions is to build sections of the overall sp1 file in blocks, then join them in a final function at the end to return the full sp1 adjustment file.

This is a function to join the base and current period invoice datasets, because we only want to make recommendations on SKU's that have been bought in the last year.

def SAS_full_dataset():
    SAS_current,SAS_prior = data_cleaning_and_filtering(SAS_current_period,SAS_prior_period,primary_vendor)
    SAS_full_dataset = pd.concat([SAS_current, SAS_prior])
    SAS_full_dataset[&#39;Invoiced Date&#39;] = pd.to_datetime(SAS_full_dataset[&#39;Invoiced Date&#39;]).dt.strftime(&#39;%Y-%m-%d&#39;)
    SAS_full_dataset = SAS_full_dataset.sort_values(by=&#39;Invoiced Date&#39;,ascending=False)
    return SAS_full_dataset

here we are putting together the SKU details tab of the adjustment table

def SKU_Details(SAS_full_dataset):
    SAS_full_dataset = SAS_full_dataset()
    SAS_full_dataset = pd.merge(SAS_full_dataset,AVG_cost[[&#39;SKU&#39;,&#39;inventory code&#39;]],how=&#39;left&#39;,on=&#39;SKU&#39;)
    SKU_Details=SAS_full_dataset
    SKU_Details = pd.merge(SKU_Details,AVG_cost[[&#39;SKU&#39;,&#39;inventory code&#39;]],how=&#39;left&#39;,on=&#39;SKU&#39;)
    SKU_Details = SAS_full_dataset[[&#39;Tracking Ln&#39;,&#39;PCAT&#39;,&#39;SKU&#39;,&#39;inventory code&#39;]]
    SKU_Details[&#39;Tracking Ln|PCAT&#39;] = SKU_Details[&#39;Tracking Ln&#39;]+&#39;|&#39;+SKU_Details[&#39;PCAT&#39;]
    SKU_Details = SKU_Details.drop_duplicates(subset=&#39;SKU&#39;)
    return SKU_Details

# this function returns the the dataframe associated with the &#39;sku metrics&#39; tab of
# the sp1 adjustment file 
def SKU_Metrics(SAS_full_dataset):
    # doing aggregate calculation on base dataset. keep in mind this dataset has been filtered
    SAS_full_dataset = SAS_full_dataset()
    SAS_full_dataset[&#39;Ext. Sell Price&#39;].astype(float).inplace=True
    SAS_full_dataset[&#39;TTM&#39;]=SAS_full_dataset.groupby([&#39;SKU&#39;])[&#39;Ext. Sell Price&#39;].transform(&#39;sum&#39;)
    
    # creating SKU metrics tabs
    SKU_Metrics = SKU_Details(SAS_full_dataset)
    SKU_Metrics = pd.merge(SKU_Metrics,SAS_full_dataset[[&#39;SKU&#39;,&#39;TTM Revenue&#39;]],how=&#39;left&#39;,on=&#39;SKU&#39;)
    
    Historical_SP1[&#39;Effective Date&#39;] = pd.to_datetime(Historical_SP1[&#39;Effective Date&#39;]).dt.strftime(&#39;%Y-%m-%d&#39;)
    Historical_SP1.sort_values([&#39;Effective Date&#39;],ascending=False).groupby(&#39;SKU&#39;).inplace=True
    current_period_sp1 = Historical_SP1.drop_duplicates(subset=&#39;SKU&#39;,keep=&#39;first&#39;)
    base_period_sp1 = np.where(Historical_SP1[&#39;Effective Date&#39;].isin(base_period),base_period_sp1)
    return SKU_Metrics
    
SKU_Metrics = SKU_Metrics(SAS_full_dataset)

The traceback is the following:

TypeError                                 Traceback (most recent call last)
c:\Users\mstevens\python_test _Copy_for_pricing\python_sns\pricing_test_docs\Sp1 Adjustment June23.py in line 141
    145     base_period_sp1 = np.where(Historical_SP1[&#39;Effective Date&#39;].isin(base_period),base_period_sp1)
    146     return SKU_Metrics
--&gt; 148 SKU_Metrics = SKU_Metrics(SAS_full_dataset)

c:\Users\mstevens\python_test _Copy_for_pricing\python_sns\pricing_test_docs\Sp1 Adjustment June23.py in line 133, in SKU_Metrics(SAS_full_dataset)
    138 SAS_full_dataset[&#39;TTM&#39;]=SAS_full_dataset.groupby([&#39;SKU&#39;])[&#39;Ext. Sell Price&#39;].transform(&#39;sum&#39;)
    139 # creating SKU metrics tabs
--&gt; 140 SKU_Metrics = SKU_Details(SAS_full_dataset)
    141 SKU_Metrics = pd.merge(SKU_Metrics,SAS_full_dataset[[&#39;SKU&#39;,&#39;TTM Revenue&#39;]],how=&#39;left&#39;,on=&#39;SKU&#39;)
    142 Historical_SP1[&#39;Effective Date&#39;] = pd.to_datetime(Historical_SP1[&#39;Effective Date&#39;]).dt.strftime(&#39;%Y-%m-%d&#39;)

c:\Users\mstevens\python_test _Copy_for_pricing\python_sns\pricing_test_docs\Sp1 Adjustment June23.py in line 115, in SKU_Details(SAS_full_dataset)
    121 def SKU_Details(SAS_full_dataset):
--&gt; 122     SAS_full_dataset = SAS_full_dataset()
    123     SAS_full_dataset = pd.merge(SAS_full_dataset,AVG_cost[[&#39;SKU&#39;,&#39;inventory code&#39;]],how=&#39;left&#39;,on=&#39;SKU&#39;)
    124     SKU_Details=SAS_full_dataset

TypeError: &#39;DataFrame&#39; object is not callable

答案1

得分: 1

SKU_Details 在执行这行代码后不再是一个函数。

你已经重新赋值了名称 SKU_Details，使其成为从函数调用中返回的值，这种情况下是一个数据框对象。

英文:

SKU_Details=SKU_Details(SAS_full_dataset)

After executing this line, SKU_Details isn't a function anymore.

You've reassigned the name SKU_Details to be the value that was returned from the function call, in this case a Dataframe object.

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DataFrame对象在函数之间共享变量时不可调用。

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