Understanding the Importance of Data Frame Copying in Pandas

In Pandas, when selecting a portion of a data frame, it's common practice to use the '.copy()' method to create a copy of the original data frame. This approach ensures that any changes made to the subset will not affect the parent data frame.

Why Make a Copy?

By default, indexing a data frame returns a view of the original data frame, rather than a copy. This means that any modifications made to the subset will directly impact the parent data frame. To maintain the integrity of the parent data frame, it's essential to create a copy using the '.copy()' method.

Consequences of Not Copying

Consider the following code snippet:

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

In this example, df_sub is a view of df. As a result, setting df_sub.x to -1 also modifies df.x:

print(df)
   x
0 -1
1  2

Benefits of Copying

Copying data frames ensures that the parent data frame remains untouched. This is particularly important when multiple operations are performed on a data frame and it is crucial to preserve the original data for later analysis or comparison.

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

print(df)
   x
0  1
1  2

In this modified code snippet, df_sub_copy is a copy of df. As a result, changing df_sub_copy.x has no impact on df.

Note: It's important to note that the behavior of data frame indexing has changed in newer versions of Pandas. In Pandas 1.0 and earlier, indexing a data frame returns a copy by default. However, in Pandas 1.1 and later, indexing returns a view. To ensure consistent behavior across versions, it's recommended to always use the '.copy()' method when creating subsets of data frames.