Why Should You Always Copy Pandas DataFrames When Selecting Subsets?

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.

The above is the detailed content of Why Should You Always Copy Pandas DataFrames When Selecting Subsets?. For more information, please follow other related articles on the PHP Chinese website!