Quite a few things being thrown out there but unfortunately it's not explicit enough as to what issue(s) you are trying to address. Also the expectation that test[['cost']] and test.cost are the same is incorrect, as the former returns a frame whereas the latter returns a series

Please refer to the contributing guide on how to open bug reports. The biggest thing here is to post a minimally reproducible example:

https://pandas.pydata.org/pandas-docs/stable/contributing.html#bug-reports-and-enhancement-requests

If you can provide the minimally reproducible example and clarify what the issue is exactly feel free to reopen

@liesb pls post a minimal example - you have lots of things going on which are unrelated
it’s actually very very hard to tell if anything is amiss

Minimal example: I'm changing the value in the cost column of df from 2 to 789. However, I don't see this when I use df, df[['cost']] or df.iloc[0]; it is visible when using df.cost.

import numpy as np
import pandas as pd

df = pd.DataFrame(index=[0])
df['x'] = 1
df['cost'] = 2
print(df)
idx = np.argmax(df.cost.values)
centroid = df.loc[[0]]
new_param = 4
df.at[idx, 'x'] = new_param
df.at[idx, 'cost'] = 789
print(df)
df.cost 
df[['cost']]
df.iloc[0]

I'm adding the pd.showversions() output again as I used a different machine:

OK thanks for the example. Just to remove unnecessary prints and make it more concise:

df = pd.DataFrame(index=[0])
df['x'] = 1
df['cost'] = 2
idx = np.argmax(df.cost.values)
centroid = df.loc[[0]]
new_param = 4
df.at[idx, 'x'] = new_param
df.at[idx, 'cost'] = 789

print(df['cost'])  # Print 789
print(df[['cost']])  # Prints 2

It is strange that the frame shows the original value before the .at assignment.

Note that almost any change to this to make it idiomatic in construction or remove elements is fine, for example even doing:

df = pd.DataFrame([[1, 2]], columns=['x', 'cost'], index=[0])
idx = np.argmax(df.cost.values)
centroid = df.loc[[0]]
new_param = 4
df.at[idx, 'x'] = new_param
df.at[idx, 'cost'] = 789

print(df['cost'])  # Print 789
print(df[['cost']])  # Prints 789

My guess is there is some wonky state management going on. Investigation and PRs are always welcome

df._item_cache needs to be cleared, not sure exactly where

Looks like the centroid = df.loc[[0]] line is triggering a "consolidate_inplace" without clearing the _item_cache.

_getitem_iterable calls self.obj._reindex_with_indexers which calls mgr.reindex_indexer which calls _consolidate_inplace