Aggregating in pandas groupby using lambda functions

I have an aggregation statement below:

data = data.groupby(['type', 'status', 'name']).agg({
    'one' : np.mean, 
    'two' : lambda value: 100* ((value>32).sum() / reading.mean()), 
    'test2': lambda value: 100* ((value > 45).sum() / value.mean())
})

I get KeyErrors. I have been able to make it work for one lambda function but not two.

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 np
import pandas as pd

N = 100
data = 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.