python 3.x - pandas SettingWithCopyWarning, returning view vs copy -

i quite confused when settingwithcopywarning raised. example:

import pandas pd  df0 = pd.dataframe([["fruit", "apple", 12, 0.3],                     ["fruit", "orange", 23, 0.2],                     ["dairy", "milk", 4, 1],                     ["dairy", "cheese", 1.0, 9.5],                     ["meat", "pork", 8, 11],                     ["meat", "buffalo", 2, 18],                     ["fruit", "strawberry", 45, 2.2]],                    columns=["type", "item", "quantity", "price"])  df1 = df0.loc[df0.loc[:, "price"] < 10, ["type", "item", "price"]]  # copy(?) df2 = df0.loc[df0.loc[:, "price"] < 10]  # copy, maybe not case?  df1.loc[1, "item"] = "banana"  # works fine df2.loc[1, "item"] = "banana"  # raises settingwithcopywarning 

it seems df1 copy, whereas, df2 not copy (this time df0does not change though). why happen? more interested in understanding reason avoiding warning itself. read pandas' documentation on view vs copy, not enlightening on loc returs. quoting:

dfmi.loc guaranteed dfmi modified indexing behavior

and afterwards:

of course, dfmi.loc.__ getitem__(idx) may view or copy of dfmi

i saw many interesting discussions, here, here, here (and few more), none provides reproducable example , none explains happens when .loc used. saw false positives can exist , there workarounds (turn off warning, set .is_copy = false), these fail actively tackle problem.

any insight? why ok modify df1 not df2?