We can select both rows and columns in Pandas DataFrames using indexing operations.

For all indexing methods, we first provide the row indexer and then the column indexer with a comma separating them.

Let’s read an Excel worksheet into a Pandas Dataframe:

import pandas as pd

data = pd.read_excel("budget.xlsx")

data.columns = data.columns.str.lower().str.strip()

data

Output:

loc[ ]

We use loc[] to subset a Dataframe using label-based lookups.

Syntax

df.loc[row_indexer, column_indexer]

data.loc[0:5, ['store code', 'revenue']]

Output:

We can select all rows if we use colon (:) as the row indexer.

data.loc[:, ['store code', 'revenue']]

Output:

When using loc[], we can slice column names as well.

data.loc[:, 'store code':'revenue']

Output

iloc[ ]

We use iloc[] to subset a Dataframe using integer-based lookups.

Note that the integer slicing that iloc[] employs is exclusive of the end index.

data.iloc[:5, [0, 1, 2]]

Output:

When using iloc[], we can use the slicing syntax for the columns as well.

data.iloc[:5, 0:2]

Output:

Learn about row and column selection in Pandas using chaining in my blog post here.