Why Does the SettingWithCopyWarning Persist with .loc Indexing?

SettingWithCopyWarning Persists with .loc Indexing

When using .loc[row_indexer,col_indexer] = value for data manipulation, you may still encounter the SettingWithCopyWarning. This is typically caused by copying the underlying dataframe before applying changes.

Step-by-Step Error Reproduction

Consider the following dataframe df:

<code class="python">import pandas as pd

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

Initially, modifying a column using .loc does not trigger any warnings:

<code class="python">df['new_column'] = None
df.loc[0, 'new_column'] = 100</code>

However, if df is filtered to create a new dataframe new_df, subsequent modifications using .loc may result in the warning:

<code class="python">new_df = df.loc[df.col1 > 2]
new_df.loc[2, 'new_column'] = 100</code>

This happens because new_df is a view or slice of df, and changes applied to new_df are not propagated back to df.

Solution

To eliminate the warning, it is necessary to make a copy of df before applying the filter:

<code class="python">new_df_copy = df.loc[df.col1 > 2].copy()
new_df_copy.loc[2, 'new_column'] = 100</code>

By using .copy(), a new dataframe is created that is independent of df, allowing changes to new_df_copy without modifying df.

Avoidance for convert_objects

The SettingWithCopyWarning can also be triggered by the convert_objects function. To avoid this, use .astype(str) before calling convert_objects:

<code class="python">value1['Total Population'] = value1['Total Population'].astype(str).convert_objects(convert_numeric=True)</code>

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