Basic selection from a DataFrame
When using the [] operator with a pd.DataFrame, simple selection typically involves selecting data from the column index rather than the row index. This distinction is crucial for effective data manipulation and analysis. Columns in a pd.DataFrame can be accessed by their labels, making it easy to work with named data from a pd.Series within the larger pd.DataFrame structure.
Understanding this fundamental difference in selection behavior is key to utilizing the full power of a pd.DataFrame in pandas. By leveraging the [] operator, you can efficiently access and manipulate specific columns of data, setting the stage for more advanced operations and analyses.
How to do it
Let’s start by creating a simple 3x3 pd.DataFrame. The values of the pd.DataFrame are not important, but we are intentionally going to provide our own column labels instead of having pandas create an auto-numbered column index for us:
df = pd.DataFrame(np.arange...
