Pandas Column Access: Dot Notation vs. Square Brackets – When to Use Which?

Attribute Access: Dot vs. Square Brackets in Pandas

In Pandas, accessing a column is possible both through attribute access (dot notation) and square brackets. While both approaches yield the same result, there are subtle differences to consider.

Consider the following example:

import pandas

d = {'col1': 2, 'col2': 2.5}
df = pandas.DataFrame(data=d, index=[0])

print(df['col2'])
print(df.col2)

Both methods return the same result:

2.5

Dot Notation: Attribute Access

df.col2 uses the attribute access feature. It directly exposes the attribute corresponding to the column name. This is a convenient shorthand that is functionally equivalent to:

df.__getitem__('col2')

Square Brackets: Column Indexing

df['col2'] uses square bracket indexing. This approach is more flexible and allows for various manipulations beyond attribute access. For instance, you can index multiple columns:

df[['col1', 'col2']]

Caveats of Attribute Access

While attribute access is convenient, it has some limitations:

• No Column Addition: Attribute access does not permit adding new columns to a DataFrame (e.g., df.new_col = x will not work).
• Namespaced Column Names: Attribute access fails for columns with spaces or integer names (e.g., df.'col 1' or df.2).

In such scenarios, it is recommended to use square bracket indexing to ensure proper functionality.

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