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.
Selecting Columns
The most common and reliable way to select a column is by using square brackets with its column name:
Select a column by name
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.
Selecting Rows
To access rows, use one of two methods:
iloc[]— selects rows by positionloc[]— selects rows by label
Select the first row by position
df.iloc[0]
Select the row with label 0
df.loc[0]
Both return a row as a Series.
Selecting a Specific Value
Combine row and column selection to get a single cell value:
Select a single value
df.loc[0, "Population"]
This retrieves the value at row 0 in the "Population" column.
Summary
| 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"] |
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