Selecting Columns and Rows

When analyzing data, you often need to focus on specific subsets — like selecting one column of interest or reviewing certain rows.

In Pandas, you can do this easily using column labels, row positions, or index labels.

The most common and reliable way to select a column is by using square brackets with its column name:

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df["Population"]

This returns a Series. You can assign it to a variable, combine it with conditions, or perform calculations.

Avoid using df.ColumnName (dot notation) because it only works when column names are valid Python identifiers.

To access rows, use one of two methods:

• iloc[] — selects rows by position
• loc[] — selects rows by label

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df.iloc[0]

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df.loc[0]

Both return a row as a Series.

Combine row and column selection to get a single cell value:

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df.loc[0, "Population"]

This retrieves the value at row 0 in the "Population" column.