what is the effect of loc in a dataframe?

November 7, 2023

If I have this minimal reproducible example

import pandas as pd
 
df = pd.DataFrame({"A":[12, 4, 5, None, 1],
                "B":[7, 2, 54, 3, None],
                "C":[20, 16, 11, 3, 8],
                "D":[14, 3, None, 2, 6]})
 
index_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']
 
df.index = index_
print(df)

# Option 1
result = df[['A', 'D']]
print(result)

# Option 2
result = df.loc[:, ['A', 'D']]
print(result)

What is the effect on using loc or not. The results are quite similar.
I ask this in preparation for a more complex question in which I have been instructed to use loc.

>Solution :

The difference is that df[['A', 'D']] doesn’t necessarily generate a copy, which can trigger issues if you assign data to the slice.

result1 = df[['A', 'D']]
print(result1._is_copy)
#<weakref at 0x7f34261b69d0; to 'DataFrame' at 0x7f34260e9590>

result2 = df.loc[:, ['A', 'D']]
print(result2._is_copy)
# None

df.loc[:, ['A', 'D']] is the safe way to generate a copy if you want an independent slice.