chained-assignment

Answers from HYRY and Marius in comments! One can check either by: testing equivalence of the values.base attribute rather than the values attribute, as in: df.values.base is df2.values.base instead of df.values is df2.values. or using the (admittedly internal) _is_view attribute (df2._is_view is True). Thanks everyone!

This is a very common warning from pandas. It means you are writing in a copy slice, not the original data so it might not apply to the original columns due to confusing chained assignment. Please read this post. It has detailed discussion on this SettingWithCopyWarning. In your case I think you can try data.loc[data[‘name’] … Read more

try using apply function. df[‘quantity’] = df[‘quantity’].apply(lambda x: x*-1)

There is a way of doing this and it actually looks similar to R new = old[[‘A’, ‘C’, ‘D’]].copy() Here you are just selecting the columns you want from the original data frame and creating a variable for those. If you want to modify the new dataframe at all you’ll probably want to use .copy() … Read more

This expands on Paul’s answer. In Pandas, indexing a DataFrame returns a reference to the initial DataFrame. Thus, changing the subset will change the initial DataFrame. Thus, you’d want to use the copy if you want to make sure the initial DataFrame shouldn’t change. Consider the following code: df = DataFrame({‘x’: [1,2]}) df_sub = df[0:1] … Read more