Mastering Data Selection with Pandas iloc

Pandas, the Python data manipulation library, is a powerful tool for working with data. Among its many features, the iloc indexer is a valuable asset when it comes to selecting specific rows and columns from a DataFrame. In this blog post, we’ll dive into the world of Pandas iloc, understand its syntax, and explore practical examples of how to use it for data selection.

What is iloc?

iloc is a Pandas indexer used for integer-location based indexing. It allows you to select rows and columns from a DataFrame by their integer positions, similar to how you would use row and column indices in a NumPy array.

The basic syntax for using iloc is as follows:

df.iloc[row_indices, column_indices]

• row_indices can be:
• A single integer for a specific row.
• A list of integers for multiple rows.
• A slice object for a range of rows.
• A boolean array for conditional selection.
• column_indices follows the same principles as row_indices but applies to columns.

Practical Examples

Let’s explore some practical examples of how to use iloc for data selection.

Example 1: Selecting a Single Row

To select a single row by its integer position, you can use a single integer for row_indices. For example:

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)
# Select the second row
selected_row = df.iloc[1]
print(selected_row)

In this example, we select the second row of the DataFrame using iloc[1].

Example 2: Selecting Multiple Rows

To select multiple rows, you can pass a list of integers to row_indices. For instance:

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)
# Select the first and third rows
selected_rows = df.iloc[[0, 2]]
print(selected_rows)

Here, we select the first and third rows by passing a list [0, 2] to iloc.

Example 3: Selecting Specific Rows and Columns

You can also select specific rows and columns simultaneously. Use a list of integers for row_indices and a list of integers for column_indices. For example:

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)
# Select the first and third rows and the second column
selected_data = df.iloc[[0, 2], [1]]
print(selected_data)

In this case, we select the first and third rows and the second column, resulting in a DataFrame with one cell.

Example 4: Using Slices

You can use slice objects for more extensive selections. To select a range of rows and columns, you can use slices for both row_indices and column_indices. Here’s an example:

import pandas as pd
data = {'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10], 'C': [11, 12, 13, 14, 15]}
df = pd.DataFrame(data)
# Select rows 1 to 3 and columns 0 to 1
selected_data = df.iloc[1:4, 0:2]
print(selected_data)

In this example, we use slice objects to select rows 1 to 3 and columns 0 to 1.

Example 5: Conditional Selection

You can use boolean arrays to conditionally select rows and columns. For instance:

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)
# Select rows where column 'A' is greater than 1
selected_rows = df.iloc[df['A'] > 1]
print(selected_rows)

Here, we conditionally select rows where the values in column ‘A’ are greater than 1.

Conclusion

Pandas iloc is a powerful tool for selecting specific rows and columns in a DataFrame based on their integer positions. It offers various ways to tailor your data selection to your needs, whether you want to access a single row, multiple rows, specific columns, or apply conditional selection. By mastering the use of iloc, you can efficiently work with your data and perform advanced data analysis tasks with ease. Click here to learn more.