checking range of number and writing a value in a new column in pandas dataframe - python-3.x

Question

I need to iterate over column 'movies_rated', check the value against the conditions, and write a value in a newly create column 'expert_level'. When I test on a subset of data, it works. But when I run it against my whole dateset, it only gets filled with value 1.
for num in df_merge['movies_rated']:
if num in range(20,31):
df_merge['expert_level'] = 1
elif num in range(31,53):
df_merge['expert_level'] = 2
elif num in range(53,99):
df_merge['expert_level'] = 3
elif num in range(99,202):
df_merge['expert_level'] = 4
else:
df_merge['expert_level'] = 5
here's a sample dataframe.
movies = [88,20,35,55,1203,99,2222,847]
name = ['angie','chris','pine','benedict','alice','spock','tony','xena']
df = pd.DataFrame(movies,name,columns=['movies_rated'])
certainly there's a less verbose way of doing this?

Answer 1

You could build an IntervalIndex and then apply pd.cut. I'm sure this is a duplicate, but I can't find one right now which uses both closed='left' and .codes, though I'm sure it exists.
bins = pd.IntervalIndex.from_breaks([0, 20, 31, 53, 99, 202, np.inf], closed='left')
df["expert_level"] = pd.cut(movies, bins).codes
which gives me
In [242]: bins
Out[242]:
IntervalIndex([[0.0, 20.0), [20.0, 31.0), [31.0, 53.0), [53.0, 99.0), [99.0, 202.0), [202.0, inf)]
closed='left',
dtype='interval[float64]')
and
In [243]: df
Out[243]:
movies_rated expert_level
angie 88 3
chris 20 1
pine 35 2
benedict 55 3
alice 1203 5
spock 99 4
tony 2222 5
xena 847 5
Note that I've set this up so that scores below 20 get a 0 value, so they can be distinguished from really high rankings. If you really want everything outside the bins to get 5, it'd be straightforward to remap 0 to 5, or just pass breaks of [20, 31, 53, 99, 202] and then map anything with a code of -1 (which means 'not binned') to 5.

Answer 2

I think np.select with the pandas function between is a good choice for you:
conds = [df.movies_rated.between(20,30), df.movies_rated.between(31,52),
df.movies_rated.between(53,98), df.movies_rated.between(99,202)]
choices = [1,2,3,4]
df['expert_level'] = np.select(conds,choices, 5)
>>> df
movies_rated expert_level
angie 88 3
chris 20 1
pine 35 2
benedict 55 3
alice 1203 5
spock 99 4
tony 2222 5
xena 847 5

Answer 3

you could do it with apply and a function:
def expert_level_check(num):
if 20<= num < 31:
return 1
elif 31<= num < 53:
return 2
elif 53<= num < 99:
return 3
elif 99<= num < 202:
return 4
else:
return 5
df['expert_level'] = df['movies_rated'].apply(expert_level_check)
it is slower to manually iterate over a df, I recommend reading this