It looks like you want str.islower:

d.sort(key=lambda s: (not s.islower(), s))
print(d)
# ['abc', 'badad1', 'AAab', 'Ejc']

With your current approach, you were just lowercasing all strings, so you're just getting them sorted in alphabetical order.

Now instead you're sorting based on the result of the booleans returned by islower and the strings themselves (which seems to be what you want):

[(not s.islower(), s) for s in d]
# [(True, 'AAab'), (False, 'abc'), (True, 'Ejc'), (False, 'badad1')]

It can be done with list comprehensions

>>> [x.lower() for x in ["A", "B", "C"]]
['a', 'b', 'c']
>>> [x.upper() for x in ["a", "b", "c"]]
['A', 'B', 'C']

or with the map function

>>> list(map(lambda x: x.lower(), ["A", "B", "C"]))
['a', 'b', 'c']
>>> list(map(lambda x: x.upper(), ["a", "b", "c"]))
['A', 'B', 'C']

Changes to the data frame are not directly saved back. Replace

df['ident'].apply(capitalize)

with

df['ident'] = df['ident'].apply(capitalize)

and it will work.

So the complete code looks like:

import pandas as pd
import numpy as np
#Create a dataframe

data = {'ident': ['Jack', 'Mary', 'Teresa', 'James', 'Anna'],
        'year': [2001, 2002, 2003, 2004, 2007],
        'reports': [67, 5, 36, 9, 14],
        'scope': [17, 102, 57, 49, 77]
        }
df = pd.DataFrame(data, index = ['Bahia', 'Pico', 'Santa Fe', 'Maine','Zuma'])
#Create a capitalizer function
capitalize = lambda x: x.upper()

#Create a capitalizing function over the column 'ident'

df['ident'] = df['ident'].apply(capitalize)
print (df)

Or do it even nicer (imho) df.ident=df.ident.str.upper()