Pandas Columns: Brackets `[]` or Dot `.`: Which Access Method Should You Choose?

Accessing Pandas Columns: Squared Brackets vs. Dot Notation

When working with Pandas DataFrames, there are two common ways to access a column: using squared brackets (df['col']) and using a dot (df.col). While both methods yield the same result, there are subtle differences between them.

Using Squared Brackets

The squared bracket notation, df['col'], returns a pd.Series object representing the specified column. This method is more flexible than using dot notation and can be used to access columns with spaces or integer names. It can also be used to select multiple columns at once by passing a list of column names:

df['col1']  # Returns a pd.Series
df[['col1', 'col2']]  # Returns a DataFrame with the specified columns

Using Dot Notation

The dot notation, df.col, is a convenience feature that provides attribute-like access to columns. It is equivalent to using the squared bracket notation to get a pd.Series object:

df.col1  # Same as df['col1']

However, there are a few caveats to using dot notation:

• Columns with spaces or integer names cannot be accessed using dot notation.
• Adding columns using dot notation (e.g., df.new_col = x) will silently create a new attribute instead of a column.

Conclusion

While both squared brackets and dot notation can be used to access columns in Pandas DataFrames, squared brackets are more flexible and recommended when dealing with columns with spaces or integer names or when accessing multiple columns at once.

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