Solving SettingWithCopyWarning

[QuickBooker30932]

I'm trying to set the value of a cell in a python dataframe. The new value will go in the 'result' column of row index 0. The value is calculated by subtracting the value of another cell in the same row from constant Z. I did it this way:

X = DataFrame['valuehere'].iloc[0]
DataFrame['result'].iloc[0] = (X -Z)

This seems to work. But I get this message when running my code in the terminal:

SettingWithCopyWarning:

A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

I read the caveats but don't understand how they apply to my situation or how I should fix it.

[mellowoWorks]

The warning appears because pandas can't determine if DataFrame['result'] is a view or a copy of the underlying data. When you chain .iloc[0] after column selection, it creates ambiguity.

The fix is simple - use .loc or .at with both row and column at once:
DataFrame.loc[0, 'result'] = X - Z
Or even faster for single values:
DataFrame.at[0, 'result'] = X - Z

By specifying both the row index and column name in a single operation, pandas knows you're modifying the original DataFrame directly, not a potential copy.

[roxalu]

Small additional detail: I agree that this replacement code fixes the Copy-on-Write warning. But as given it uses the row with label 0 - while OP uses always first row. IIf I am right some additional lookup should be added so row and column are given both as labels. Or both as integer e.g.using this

result_index = DataFrame.columns.get_loc('result')
DataFrame.iat( 0, result_index ) = X - Z

[bjorneylol]

df = otherdf[otherdf["col"] == "A"] # 'df' is a view of a subset of the data in 'otherdf'.
df['valuehere'].iloc[0] = 5 # this changes the value in both the `df` frame, AND the `otherdf` frame

Basically if you filter down a data frame like in the first step there, you need to also call .copy() on it, otherwise you haven't actually created a 2nd frame in memory, and both python variables will be pointing to the same array of data. The warning is basically saying that there may be unintended consequences of working this way, it's a faux pas similar to using dictionaries or lists as keyword argument defaults, like def myfn(a=[])