What is Indexing in Pandas?

• Indexing in Pandas means selecting particular rows and columns from a DataFrame.
• Indexing in Pandas is the same as we did for a Python List and a NumPy array.
• There are two different methods of indexing in Pandas:
  • loc - label based selection
  • iloc - index-based selection

Index-Based Selection

• Index-based selection is to select data based on its numerical position in DataFrame.
• iloc is used for selecting data based on numerical position.
• The syntax for using the iloc operator is
  1 df.iloc[ ]
  , where df is a DataFrame name. You can pass the numerical positions of rows and columns to select in the square bracket.
• Do you remember the indexing in a NumPy array? If not, don’t worry; you will soon see its implementation on a dataset.
• Import Pandas Library and load the ‘exam_scores.csv’ file

Selecting data using iloc:

• You can also pass list of indexes

• You can also pass negative indexes

Label-Based Selection

• Label-based selection selects data based on the column or row names/index. This becomes important while selecting data from a DataFrame.
• Label-based selection is made with loc.
• Do you remember the python default indexing of the DataFrame and the indexes/names that you changed in the previous topics?
• loc and iloc are conceptually similar. The difference is that iloc considers the default indexing while loc ignores the default indexing.
• loc is used for selecting data based on the data index value/name, not the numerical positions.
• The syntax for loc is similar to iloc:
  1 df.loc[ ]
  , where df is the DataFrame name.
• We will learn about its implementation on a dataset soon.

Selecting data using loc: