how to compare two values in series, not the series objects? Python 3.x

Question

I am getting an error saying "ValueError: Can only compare identically-labeled Series objects". I got as far as knowing that I am comparing two series objects and not the values inside them. However, I thought series[column] gave you the value inside. Can someone elaborate more on this? I am truly stuck on where i could find this information and would gradly love to be pointed toward the right direction. all_ages and recent_grads are dataframes.

import pandas as pd

majors = recent_grads['Major'].unique()
rg_lower_count = 0

for x in majors:
    aa_major = all_ages[all_ages['Major'] == x]
    rg_major = recent_grads[recent_grads["Major"] == x]


    if rg_major["Unemployment_rate"] < aa_major["Unemployment_rate"]:
        rg_lower_count += 1

print(rg_lower_count)

Answer 1

See this old question of mine

You can't do this anymore. When you compare < , > you need to have the indices lined up . reindex one of your series like the other.

I'd edit your code like this

import pandas as pd

majors = recent_grads['Major'].unique()
rg_lower_count = 0

for x in majors:
    aa_major = all_ages[all_ages['Major'] == x]
    rg_major = recent_grads[recent_grads["Major"] == x]

rg_major_unemp = rg_major["Unemployment_rate"]
aa_major_unemp = aa_major["Unemployment_rate"].reindex_like(rg_major_unemp)

if rg_major_unemp < aa_major_unemp:
    rg_lower_count += 1

print(rg_lower_count)

---

Demonstration

Same example as in linked question

import pandas as pd
x = pd.Series([1, 1, 1, 0, 0, 0], index=['a', 'b', 'c', 'd', 'e', 'f'], name='Value')
y = pd.Series([0, 2, 0, 2, 0, 2], index=['c', 'f', 'a', 'e', 'b', 'd'], name='Value')

x > y

 ValueError: Can only compare identically-labeled Series objects 

x.reindex_like(y) > y

c     True
f    False
a     True
e    False
b     True
d    False
Name: Value, dtype: bool