catch exception and return empty dataframe

Question

I query a database and save result as a dataframe which I then transform by factorize with pivot_table. This works fine when database query returns data but it throws an error when no data is returned(this is to be expected). How to catch this exception and return empty dataframe?

#When dataframe is non-empty, transformation works fine:
print df

   sale name  year
0   41  Jason  2012
1   24  Molly  2012
2   31  Jason  2013
3   32  Jason  2014
4   31  Molly  2014


df['groups'] = (pd.factorize(df.year)[0] + 1).astype(str)

df1 = (df.pivot_table(index='name', columns='groups', values=['sale', 'year']))
df1.columns = [''.join(col) for col in df1.columns]
print (df1)

       sale1  sale2  sale3   year1   year2   year3
name                                              
Jason   41.0   31.0   32.0  2012.0  2013.0  2014.0
Molly   24.0    NaN   31.0  2012.0     NaN  2014.0

#But when dataframe is empty, factorize by pivot_table throws error

df  = pd.DataFrame(columns=['sales','name','year'])
df1 = (df.pivot_table(index='name', columns='groups', values=['sale', 'year']))
df1.columns = [''.join(col) for col in df1.columns]
print (df1)

DataError: No numeric types to aggregate

Community · Accepted Answer · 2017-05-23 11:44:15Z

12

try:
    df1 = df.pivot_table(index='name', columns='name', values=['sale', 'year'])
except pd.core.groupby.DataError:
    df1 = pd.DataFrame()
except:
    raise

Credits to brenbarn who found the error name for dataerror at How can I catch a pandas DataError?

edited May 23, 2017 at 11:44

CommunityBot

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answered Jun 30, 2016 at 12:14

Yarnspinner

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Comments

Régis Makhmara · Accepted Answer · 2016-06-30 12:14:31Z

2

You should probably check whether the dataframe is empty or not

if df.empty:
    return df

before calling pivot_table

answered Jun 30, 2016 at 12:14

Régis Makhmara

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Comments

Mukesh Kumar · Accepted Answer · 2021-04-09 21:24:40Z

1

Avoid using df.empty

Checking whether a Dask DataFrame has any rows may be expensive. However, checking the number of columns is fast. Depending on which of these results you need, use either len(df.index) == 0 or len(df.columns) == 0

answered Apr 9, 2021 at 21:24

Mukesh Kumar

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