df = pd.DataFrame(dict(zip(features, p.random.rand(5, num_columns).transpose())))

def foo1(df):
    choose_col = 'col1'
    filter_series = df[choose_col].apply(lambda x: 0 if x < 0.5 else 1)
    filtered_sum = (df[target_col] * filter_series).sum()
    return filtered_sum

def foo2(df):
    lst  = ['col1', 'col2', 'col3']
    for cho_col in lst:
        filter_series = df[cho_col].apply(lambda x: 0 if x < 0.5 else 1)
        filtered_sum = (df[target_col] * filter_series).sum()

# foo1 without and with njit, df has 10 rows
- [4.207398879779037, 10.0]
?                  ^

+ [4.207398879779036, 10.0]
?                  ^

# foo1 without and with njit, df has 10_000_000 rows
- [2501705.7589422013, 10000000.0]
?                 ^^^

+ [2501705.7589421924, 10000000.0]
?                ++ ^

# foo2 compilation error
  File "/usr/local/anaconda3/envs/.../lib/python3.7/site-packages/numba/core/dispatcher.py", line 482, in _compile_for_args
    error_rewrite(e, 'typing')
  File "/usr/local/anaconda3/envs/.../lib/python3.7/site-packages/numba/core/dispatcher.py", line 423, in error_rewrite
    raise e.with_traceback(None)
numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Cannot request literal type.

File "test.py", line 57:
def _modified_pipeline(df):
    <source elided>
    for cho_col in lst:
        filter_series = df[cho_col].apply(lambda x: 0 if x < 0.5 else 1)
        ^

During: typing of intrinsic-call at /Users/.../sdc/tests/tests_ant/test_ant_9.py (57)

from numba import literal_unroll

@njit
def foo2_error(df):
    lst  = ('col1', 'col2', 'col3')      # a tuple of column names instead of list
    results = []
    for cho_col in literal_unroll(lst):  # literal_unroll used
        filter_series = df[cho_col].apply(lambda x: 0 if x < 0.5 else 1)
        filtered_sum = (df[target_col] * filter_series).sum()
        results.append(filtered_sum)

    return results

# on sorted data with sum via explicit loop:
arr_result:        2500270.1616518456   # numba jitted explicit loop, parallel=False
arr_result_ref:  2500270.1616518456   # python explicit loop