Indexing in Pandas : Indexing in pandas means simply selecting particular rows and columns of data from a DataFrame. Indexing could mean selecting all the rows and some of the columns, some of the rows and all of the columns, or some of each of the rows and columns. Indexing can also be known as Subset Selection.

Let’s see some example of indexing in Pandas. In this article, we are using “nba.csv” file to download the CSV, click here.

Let’s take a DataFrame with some fake data, now we perform indexing on this DataFrame. In this, we are selecting some rows and some columns from a DataFrame. Dataframe with dataset.

Suppose we want to select columns Age, College and Salary for only rows with a labels Amir Johnson and Terry Rozier

Our final DataFrame would look like this:

Let’s say we want to select row Amir Jhonson, Terry Rozier and John Holland with all columns in a dataframe.

Our final DataFrame would look like this:

Let’s say we want to select columns Age, Height and Salary with all rows in a dataframe.

Our final DataFrame would look like this:

There are a lot of ways to pull the elements, rows, and columns from a DataFrame. There are some indexing method in Pandas which help in getting an element from a DataFrame. These indexing methods appear very similar but behave very differently. Pandas support four types of Multi-axes indexing they are:

Collectively, they are called the indexers. These are by far the most common ways to index data. These are four function which help in getting the elements, rows, and columns from a DataFrame.  

Indexing operator is used to refer to the square brackets following an object. The .loc and .iloc indexers also use the indexing operator to make selections. In this indexing operator to refer to df[].

In order to select a single column, we simply put the name of the column in-between the brackets

Output:

In order to select multiple columns, we have to pass a list of columns in an indexing operator.

Output:

This function selects data by the label of the rows and columns. The df.loc indexer selects data in a different way than just the indexing operator. It can select subsets of rows or columns. It can also simultaneously select subsets of rows and columns.

In order to select a single row using .loc[] , we put a single row label in a .loc function.

Output:

As shown in the output image, two series were returned since there was only one parameter both of the times.

In order to select multiple rows, we put all the row labels in a list and pass that to .loc function.

Output:

In order to select two rows and three columns, we select a two rows which we want to select and three columns and put it in a separate list like this:

Output:

In order to select all of the rows and some columns, we use single colon [:] to select all of rows and list of some columns which we want to select like this:

Output:

This function allows us to retrieve rows and columns by position. In order to do that, we’ll need to specify the positions of the rows that we want, and the positions of the columns that we want as well. The df.iloc indexer is very similar to df.loc but only uses integer locations to make its selections.

In order to select a single row using .iloc[], we can pass a single integer to .iloc[] function.

Output:

In order to select multiple rows, we can pass a list of integer to .iloc[] function.

Output:

In order to select two rows and two columns, we create a list of 2 integer for rows and list of 2 integer for columns then pass to a .iloc[] function.

Output:

In order to select all rows and some columns, we use single colon [:] to select all of rows and for columns we make a list of integer then pass to a .iloc[] function.

Output:

Early in the development of pandas, there existed another indexer, ix. This indexer was capable of selecting both by label and by integer location. While it was versatile, it caused lots of confusion because it’s not explicit. Sometimes integers can also be labels for rows or columns. Thus there were instances where it was ambiguous. Generally, ix is label based and acts just as the .loc indexer. However, .ix also supports integer type selections (as in .iloc) where passed an integer. This only works where the index of the DataFrame is not integer based .ix will accept any of the inputs of .loc and .iloc.

Note: The .ix indexer has been deprecated in recent versions of Pandas.

In order to select a single row, we put a single row label in a .ix function. This function act similar as .loc[] if we pass a row label as a argument of a function.

Output:

Indexing and selecting data with pandas involve specifying which data points (rows and columns) in a DataFrame or Series you want to access or modify. Pandas provides powerful tools for selecting data based on label indexing, integer indexing, or condition-based filtering.

You can select data based on the index using the loc and iloc attributes:

Pandas supports several methods of indexing:

In the broader context beyond pandas, types of indexing include:

The indexing method refers to the technique used to organize and access data within a data structure (like a DataFrame or a Series in pandas). These methods are crucial for optimizing data retrieval, updates, and management. They define how data is internally stored and how efficiently you can retrieve or manipulate it.