What is the difference between using squared brackets or dot to access a column?

Question

In both the bellow cases:

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

Both methods can be used to index on a column and yield the same result, so is there any difference between them?

Answer 1

The "dot notation", i.e. df.col2 is the attribute access that's exposed as a convenience.

You may access an index on a Series, column on a DataFrame, and an item on a Panel directly as an attribute:

df['col2'] does the same: it returns a pd.Series of the column.

A few caveats about attribute access:

• you cannot add a column (df.new_col = x won't work, worse: it will silently actually create a new attribute rather than a column - think monkey-patching here)
• it won't work if you have spaces in the column name or if the column name is an integer.