Aggregating lambda functions in pandas and numpy

You need to specify the column in data whose values are to be aggregated. For example,

data = data.groupby(['type', 'status', 'name'])['value'].agg(...)

instead of

data = data.groupby(['type', 'status', 'name']).agg(...)

If you don't mention the column (e.g. 'value'), then the keys in dict passed to agg are taken to be the column names. The KeyErrors are Pandas' way of telling you that it can't find columns named one, two or test2 in the DataFrame data.

Note: Passing a dict to groupby/agg has been deprecated. Instead, going forward you should pass a list-of-tuples instead. Each tuple is expected to be of the form ('new_column_name', callable).

Here is runnable example:

import numpy as npimport pandas as pdN = 100data = pd.DataFrame({    'type': np.random.randint(10, size=N),    'status': np.random.randint(10, size=N),    'name': np.random.randint(10, size=N),    'value': np.random.randint(10, size=N),})reading = np.random.random(10,)data = data.groupby(['type', 'status', 'name'])['value'].agg(    [('one',  np.mean),     ('two', lambda value: 100* ((value>32).sum() / reading.mean())),     ('test2', lambda value: 100* ((value > 45).sum() / value.mean()))])print(data)#                   one  two  test2# type status name                 # 0    1      3     3.0    0    0.0#             7     4.0    0    0.0#             9     8.0    0    0.0#      3      1     5.0    0    0.0#             6     3.0    0    0.0# ...

If this does not match your situation, then please provide runnable code that does.