Most data structures utilize Python's in operator to either return keys or values. Pandas Series mixes these. My question is why are they mixed? Is there a functional purpose to this?
To be clear, I'm not asking how the mechanics work; that has been asked multiple times like here and here. I am asking why it was implemented in this (arguably counterintuitive) way.
I say counterintuitive because it results in behavior like this:
import pandas as pd
import numpy as np
df = pd.DataFrame(np.ones((3,3)),columns=['a','b','c'])
df.replace(1,'abc',inplace=True)
a = df['a']
print([x in a for x in a])
# This will print
# [False, False, False]
Every other data structure I can think of (including Pandas DataFrames) will return a list of True rather than False since membership (x in a) and iteration (for x in a) operate on the same item, either keys (like dictionaries) or values (like lists).
TLDR: The Series object iterates over values and checks membership in keys. What's the reasoning behind this implementation?
P.S. For anyone landing here wondering how to iterate over the keys, simply use the `.values()' method on the Series object. It's most efficient.
