Code Sample

df = pd.DataFrame({'id': ['1001', '1001', '1002', '1002', '1003', '1004', '1005', '1006'],
                   'cat': ['A', 'B', 'C', 'C', 'C', 'B', 'B', 'D'],
                   'cat2': ['a', 'a', 'b', 'b', 'c', 'd', 'b', 'd']})
df['cat'] = df['cat'].astype('category')
df['cat2'] = df['cat2'].astype('category')

Problem description

If I subset this dataframe so groupby will return some empty Series and apply the following lambda function, it throws an index error IndexError: index 0 is out of bounds for axis 1 with size 0

df2 = df[df['cat2'].isin(['a', 'b'])]
df2.groupby('cat').apply(lambda x: x.groupby('cat2')['id'].agg('nunique'))  # throws IndexError

  • Note that df2.groupby(['cat', 'cat2'])['id'].agg('nunique') doesn't throw an error. However, I have .groupby('cat2')['id'].agg('nunique') as part of a separate function. Hence using lambda for illustrative purposes.

  • Also note that the count aggregator doesn't throw an error: df2.groupby('cat').apply(lambda x: x.groupby('cat2')['id'].agg('count'))

  • If i don't categorize the variables, it doesn't throw an error

Expected Output

cat cat2 A a 1 B a 1 B b 1 C b 1

Output of pd.show_versions()

[paste the output of ``pd.show_versions()`` here below this line] INSTALLED VERSIONS ------------------ commit: None python: 3.6.1.final.0 python-bits: 64 OS: Darwin OS-release: 17.2.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: None LOCALE: en_US.UTF-8 pandas: 0.22.0 pytest: 3.2.3 pip: 10.0.1 setuptools: 39.1.0 Cython: 0.25.2 numpy: 1.14.2 scipy: 1.0.1 pyarrow: None xarray: None IPython: 5.3.0 sphinx: 1.5.6 patsy: 0.4.1 dateutil: 2.7.2 pytz: 2018.3 blosc: None bottleneck: 1.2.1 tables: 3.3.0 numexpr: 2.6.2 feather: None matplotlib: 2.0.2 openpyxl: 2.4.7 xlrd: 1.0.0 xlwt: 1.2.0 xlsxwriter: 0.9.6 lxml: 3.7.3 bs4: 4.6.0 html5lib: 1.0b8 sqlalchemy: 1.1.9 pymysql: None psycopg2: 2.7.1 (dt dec pq3 ext lo64) jinja2: 2.9.6 s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None

Comment From: gfyoung

Admittedly, the code sample is a little unusual, but nevertheless, IMO it should work whether the variables are categorical or not.

@jreback : thoughts?

Comment From: rhshadrach

Here is a simplified example; it happens on any empty categoricals.

ser = pd.Series([1]).astype('category')[:0]
gb = ser.groupby(ser)
result = gb.nunique()
print(result)