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Explore Stack Internal It's good practice to always use the [] notation, one. One reason is that attribute notation (df.column_name) does not work for numbered indices:
In [1]: df = DataFrame([[1, 2, 3], [4, 5, 6]]) In [2]: df[1] Out[2]: 0 2 1 5 Name: 1 In [3]: df.1 File "<ipython-input-3-e4803c0d1066>", line 1 df.1 ^ SyntaxError: invalid syntax It's good practice to always use the [] notation, one reason is that attribute notation (df.column_name) does not work for numbered indices:
In [1]: df = DataFrame([[1, 2, 3], [4, 5, 6]]) In [2]: df[1] Out[2]: 0 2 1 5 Name: 1 In [3]: df.1 File "<ipython-input-3-e4803c0d1066>", line 1 df.1 ^ SyntaxError: invalid syntax It's good practice to always use the [] notation. One reason is that attribute notation (df.column_name) does not work for numbered indices:
In [1]: df = DataFrame([[1, 2, 3], [4, 5, 6]]) In [2]: df[1] Out[2]: 0 2 1 5 Name: 1 In [3]: df.1 File "<ipython-input-3-e4803c0d1066>", line 1 df.1 ^ SyntaxError: invalid syntax It's good practice to always use the [] notation, one reason is that attribute notation (df.column_name) does not work for numbered indices:
In [1]: df = DataFrame([[1, 2, 3], [4, 5, 6]]) In [2]: df[1] Out[2]: 0 2 1 5 Name: 1 In [3]: df.1 File "<ipython-input-3-e4803c0d1066>", line 1 df.1 ^ SyntaxError: invalid syntax