Pandas Apply(), Transform() ERROR = invalid dtype determination in get_concat_dtype - pandas

Question

Flowing on from this question, which i link as background, but question is standalone.
4 questions:
I cannot understand the error I see when using apply or transform:
"invalid dtype determination in get_concat_dtype"
Why does ClipNetMean work but the other 2 methods not?
Unsure if or why i need the .copy(deep=True)
Why the slightly different syntax needed to call the InnerFoo function
The DataFrame:
cost
section item
11 1 25
2 100
3 77
4 10
12 5 50
1 39
2 7
3 32
13 4 19
1 21
2 27
The code:
import pandas as pd
import numpy as np
df = pd.DataFrame(data = {'section' : [11,11,11,11,12,12,12,12,13,13,13]
,'item' : [1,2,3,4,5,1,2,3,4,1,2]
,'cost' : [25.,100.,77.,10.,50.,39.,7.,32.,19.,21.,27.]
})
df.set_index(['section','item'],inplace=True)
upper =50
lower = 10
def ClipAndNetMean(cost,upper,lower):
avg = cost.mean()
new_cost = (cost- avg).clip(lower,upper)
return new_cost
def MiniMean(cost,upper,lower):
cost_clone = cost.copy(deep=True)
cost_clone['A'] = lower
cost_clone['B'] = upper
v = cost_clone.apply(np.mean,axis=1)
return v.to_frame()
def InnerFoo(lower,upper):
def inner(group):
group_clone = group.copy(deep=True)
group_clone['lwr'] = lower
group_clone['upr'] = upper
v = group_clone.apply(np.mean,axis=1)
return v.to_frame()
return inner
#These 2 work fine.
print df.groupby(level = 'section').apply(ClipAndNetMean,lower,upper)
print df.groupby(level = 'section').transform(ClipAndNetMean,lower,upper)
#apply works but not transform
print df.groupby(level = 'section').apply(MiniMean,lower,upper)
print df.groupby(level = 'section').transform(MiniMean,lower,upper)
#apply works but not transform
print df.groupby(level = 'section').apply(InnerFoo(lower,upper))
print df.groupby(level = 'section').transform(InnerFoo(lower,upper))
exit()
So to Chris's answer, note that if I add back the column header the methods will work in a Transform call.
see v.columns = ['cost']
def MiniMean(cost,upper,lower):
cost_clone = cost.copy(deep=True)
cost_clone['A'] = lower
cost_clone['B'] = upper
v = cost_clone.apply(np.mean,axis=1)
v = v.to_frame()
v.columns = ['cost']
return v
def InnerFoo(lower,upper):
def inner(group):
group_clone = group.copy(deep=True)
group_clone['lwr'] = lower
group_clone['upr'] = upper
v = group_clone.apply(np.mean,axis=1)
v = v.to_frame()
v.columns = ['cost']
return v
return inner

Answer 1

1 & 2) transform expects something "like-indexed", while apply is flexible. The two failing functions are adding additional columns.
3) In some cases, (e.g. if you're passing a whole DataFrame into a function) it can be necessary to copy to avoid mutating the original. It should not be necessary here.
4) The first two functions take a DataFrame with two parameters and returns data. InnerFoo actually returns another function, so it needs to be called before being passed into apply.