Why Should I Use .copy() When Selecting Subsets of Pandas DataFrames?

The Importance of Data Frame Copying in Pandas

Scenario:

While selecting a subset of a DataFrame, it's common to encounter code that explicitly makes a copy of the parent DataFrame using the .copy() method. The question arises: why is this necessary?

Reasoning:

Pandas dataframes behave differently from traditional programming language arrays. When indexing a pandas DataFrame (e.g., my_dataframe[features_list]), the returned value does not create a new copy but rather returns a view or reference to the original DataFrame. Any modifications made to this view will directly affect the original DataFrame.

Example:

Consider the following code:

df = pd.DataFrame({'x': [1, 2]})
df_view = df[0:1]  # Returns a view of the first row
df_view['x'] = -1

# Check the original DataFrame
print(df)

Output:

   x
0 -1
1  2

As you can see, modifying df_view also modified the original df DataFrame.

Solution:

To prevent such unintended consequences, it's recommended to make a copy of the DataFrame using the .copy() method before modifying it. This ensures that any changes made to the copy will not affect the original DataFrame.

Revised Code:

df = pd.DataFrame({'x': [1, 2]})
df_copy = df[0:1].copy()  # Makes a copy of the first row
df_copy['x'] = -1

# Check the original DataFrame
print(df)

Output:

   x
0  1
1  2

In this case, df remains unchanged.

Benefits of Data Frame Copying:

• Protection of original data: Prevents accidental modifications to the parent DataFrame.
• Data isolation: Allows for independent operations on different subsets of a DataFrame.
• Enhanced performance: Copying allows for optimizations by isolating data that is not required for current operations.

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