Why do Pandas DataFrame modifications sometimes affect the original DataFrame?

Understanding the Need for DataFrame Copying in Pandas

When working with Pandas dataframes, the choice of whether or not to create a copy of a dataframe can have significant implications. By default, indexing a dataframe returns a reference to the original data structure. Therefore, any modifications made to the subset will directly modify the parent frame.

To illustrate this behavior, consider the following example:

df = pd.DataFrame({'x': [1, 2]})
df_sub = df[0:1]
df_sub.x = -1
print(df)

Output:

   x
0 -1
1  2

As you can observe, modifying the subset's values directly alters the corresponding values in the original dataframe.

In situations where it is essential to safeguard the original dataframe from modifications, copying is necessary. This can be achieved using the .copy() method. Here's an example:

df_sub_copy = df[0:1].copy()
df_sub_copy.x = -1
print(df)

Output:

   x
0  1
1  2

In this case, .copy() ensures that any changes made to df_sub_copy will not affect the original df.

It is crucial to understand that this behavior applies to deep copies only, which means the entire referenced data is copied into the new object. In contrast, a shallow copy creates a new object that references the same underlying data as the original. Therefore, any changes made to a shallow copy will also affect the original dataframe.

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