Create function to return multiple dataframes

Question

I am looking to create a simple function to return two different dataframes depending on the values in a certain column.

Here's my data:

np.random.seed(1111)
df = pd.DataFrame({
'Category':np.random.choice( ['Group A','Group B','Group C','Group D'], 10000),
'Sub-Category':np.random.choice( ['X','Y','Z'], 10000),
'Sub-Category-2':np.random.choice( ['G','F','I'], 10000),
'Product':np.random.choice( ['Product 1','Product 2','Product 3'], 10000),
'Units_Sold':np.random.randint(1,100, size=(10000)),
'Dollars_Sold':np.random.randint(100,1000, size=10000),
'Customer':np.random.choice(pd.util.testing.rands_array(10,25,dtype='str'),10000),
'Date':np.random.choice( pd.date_range('1/1/2016','12/31/2018',  
                      freq='M'), 10000)})

Here's a simplified version of what I'm trying to do:

def summary(df_use):
    if df_use['Sub-Category'] == 'X':
        df1 = df_use.groupby(['Category','Sub-Category','Date']).agg({'Units_Sold':'sum'})\
            .unstack()
    elif df_use['Sub-Category'] == 'Y':
        df2 = df_use.groupby(['Category','Sub-Category','Date']).agg({'Dolars_Sold':'sum'})\
            .unstack()
    return df1, df2

I'm getting the following error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Any help would be greatly appreciated! I am looking to use an if statement if at all possible.

You are not looking to use an if statement.

Mad Physicist
– Mad Physicist

2019-11-08 00:23:22 +00:00
Commented Nov 8, 2019 at 0:23 — Mad Physicist
– Mad Physicist, Commented Nov 8, 2019 at 0:23

It_is_Chris · Accepted Answer · 2019-11-08 00:16:44Z

1

If i understand what you are trying to do (you did not post an expected output) just remove the if statement and use boolean indexing:

def summary(df_use):
    df1 = df_use[df_use['Sub-Category'] == 'X'].groupby(['Category','Sub-Category','Date']).agg({'Units_Sold':'sum'})\
            .unstack()
    df2 = df_use[df_use['Sub-Category'] == 'Y'].groupby(['Category','Sub-Category','Date']).agg({'Dollars_Sold':'sum'})\
            .unstack()
    return df1, df2

answered Nov 8, 2019 at 0:16

It_is_Chris

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1 Comment

keg5038 Over a year ago

Wow I made that way more complicated than it needed to be. Thanks for the quick response, much appreciated!

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