why should I make a copy of a data frame in pandas

This expands on Paul’s answer. In Pandas, indexing a DataFrame returns a reference to the initial DataFrame. Thus, changing the subset will change the initial DataFrame. Thus, you’d want to use the copy if you want to make sure the initial DataFrame shouldn’t change. Consider the following code:

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

You’ll get:

x
0 -1
1  2

In contrast, the following leaves df unchanged:

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