Python Pandas is a popular python library that allows you to easily work with data and files. Sometimes, while using pandas, you may get SettingWithCopyWarning message. In this article, we will learn how to fix this problem.
What is SettingWithCopyWarning?
When you filter a dataframe, you can slice it to get a view or copy, depending on its layout and other factors. A view is a view into original data, so if you modify the view it may modify the original object. On the other hand, a copy as the name suggests is a copy, and modifying it does not affect the original data. The SettingWithCopyWarning error appears in case of chained assignments because the interpreter is unable to predict if the filtered dataframe will return a copy or view, and so it does not know if it will have to assign values back to original data or not.
How to Fix SettingWithCopyWarning in Pandas
This warning was created to flag confusing chained assignments such as the one shown below, which does not always work as expected. For example, if the first selection returns a copy, then you will get SettingWithCopyWarning.
df[df['A'] > 2]['B'] = new_val # new_val not set in df
In fact, it will also offer a suggestion to rewrite as follows.
df.loc[df['A'] > 2, 'B'] = new_val
You can either fix your existing command as per the suggestion offered by your python interpreter, or completely disable it using the following command.
import pandas as pd pd.options.mode.chained_assignment = None # default='warn'
In this article, we have learnt how to fix SettingWithCopyWarning messages in Python pandas.
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