Why Does `SettingWithCopyWarning` Occur When Modifying DataFrames Derived from Subsets?

Locating the Culprit of SettingWithCopyWarning

When attempting to modify a DataFrame using .loc[row_indexer, col_indexer] = value, the "SettingWithCopyWarning" persists. This issue stems from accessing a DataFrame slice from another DataFrame without invoking the .copy() method.

Step-by-Step Error Reproduction

Consider the following code:

import pandas as pd

d = {'col1': [1, 2, 3, 4], 'col2': [3, 4, 5, 6]}
df = pd.DataFrame(data=d)

df['new_column'] = None
df.loc[0, 'new_column'] = 100

Initially, there are no warnings. However, creating a new DataFrame based on a subset of df:

new_df = df.loc[df.col1 > 2]

and then attempting to modify the new DataFrame using .loc:

new_df.loc[2, 'new_column'] = 100

triggers the warning.

Solution: Using .copy() for Data Frames Derived from a Subset

To resolve this issue, always use the .copy() method when creating a new DataFrame based on a subset of an existing DataFrame.

new_df_copy = df.loc[df.col1 > 2].copy()
new_df_copy.loc[2, 'new_column'] = 100

By invoking .copy(), you create an independent copy of the subset, avoiding the warning when modifying values.

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