Selecting Columns and Rows

When analyzing data, you often need to focus on specific parts — like selecting a column of interest or checking a few rows.

In Pandas, this is done using column labels, row positions, and index labels.

Selecting Columns

The most reliable way to select a column is with square brackets and the column name:

Select a column by name

df["Population"]

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

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

Selecting Rows

To select rows, use either:

• iloc[] for position-based access
• loc[] for label-based access

Select the first row by position

df.iloc[0]

Select the row with label 0

df.loc[0]

These return a row as a Series.

Selecting a Specific Value

You can combine row and column selection to get a single cell value:

Select a single value

df.loc[0, "Population"]

This gives you the value at row 0, column "Population".

Summary Table

Selector	Access Type	Example
df["col"]	Column by name	df["Age"]
df.iloc[i]	Row by position	df.iloc[3]
df.loc[i]	Row by label	df.loc[3]
df.loc[i, "col"]	Cell	df.loc[3, "Age"]

What’s Next?

Now that you know how to select data, let’s filter it using conditions!

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