return various variables in nested function based on conditional statement

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I have an original dataframe from which I am able to create a modified dataframe, however there will be cases that I am interested in selecting a subset of my data and not using the dataframe as a whole, but I want this all to be done in an entire function for which I am opting to use a subset of the data, however is it possible to return different variables based on a conditional or would this be incorrect.

The function below works fine when I run

modified_df = modify_data(protein_embeddings, protein_df, subset = False)

but when I try executing:

gal_subset_first, gal_subset_second = modify_data(protein_embeddings, protein_df, subset = True)

I get the error:

ValueError: too many values to unpack (expected 2)

The Function

def modify_data(embeddings, df, subset = False):

    """
    Modifies Original Dataframe with respective embedddings

    :return: Final Dataframe to be used in data split and modelling
    """
    #Original_DF
    OD_df = df.copy(deep = True)
    OD_df = df.reset_index()
    OD_df.loc[:,'task'] = 'stability'
    
    #Embeddings Df
    embeddings_df = pd.DataFrame(data=embeddings)
    embeddings_df = embeddings_df.reset_index()   
    
    embedded_df = pd.merge(embeddings_df, OD_df, on='index')
    embedded_df = embedded_df.drop(['index', 'sequence', 'temperature'], axis = 1)
    
    def subsetting(embedded_df, sample_no, row_no):
        "Select a Subset of rows desired from original dataframe"
        #Selecting subset
        embedded_df = embedded_df.sample(n = sample_no)
        subset_first = gal_subset[:row_no]
        subset_second = gal_subset[row_no:]
    
        return subset_first, subset_second

    if subset == True:
        gal_subset_first, gal_subset_second = subsetting(embedded_df, sample_no = 2000, row_no = 1000)
    else:
        pass  
         
    
    return embedded_df
pandas function python
1 Answers

Your function returns an iterable data frame. When you assign the result to one variable, the whole data frame will be written to the variable. However, if you assign the result multiple variables, Python will iterate over the returned value and check if the number of variables matches the data frame iterator items.

Compare the code samples:

def f():
    return (1,2,3)

a = f()  # a is a tuple (1, 2, 3)
a, b = f()  # raises the same exception ValueError: too many values to unpack (expected 2)
a, b, c = f()  # a=1 b=2 c=3 because the number of returned values matches the number of the assigned variables.
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