Pandas DataFrame iloc Property – Explained with Examples

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Pandas is an essential library in Python for data manipulation and analysis. One of the fundamental aspects of Pandas is the DataFrame, which is a two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns). To effectively work with data in DataFrames, it’s crucial to understand how to access and manipulate data. One powerful way to do this is by using the iloc property.

What is iloc?

The iloc property in Pandas stands for “integer location” and is used for integer-based indexing. It allows you to select data by specifying row and column positions (using integers) rather than labels. This makes it particularly useful when the DataFrame’s index and column labels are not numeric.

Basic Usage

The basic syntax for using iloc is:

Python
df.iloc[row_indexer, column_indexer]

Here:

  • row_indexer refers to the rows you want to select.
  • column_indexer refers to the columns you want to select.
Selecting Rows and Columns

Let’s dive into some practical examples to illustrate the versatility of the iloc property.

Example DataFrame

First, let’s create a sample DataFrame to work with:

Python
import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'Age': [24, 27, 22, 32, 29],
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix']
}

df = pd.DataFrame(data)
print(df)

This will output:

Markdown
      Name  Age         City
0    Alice   24     New York
1      Bob   27  Los Angeles
2  Charlie   22      Chicago
3    David   32      Houston
4      Eve   29      Phoenix

Selecting a Single Row

To select the first row:

Python
print(df.iloc[0])

Output:

Markdown
Name       Alice
Age           24
City    New York
Name: 0, dtype: object

Selecting Multiple Rows

To select the first three rows:

Python
print(df.iloc[0:3])

Output:

Markdown
      Name  Age         City
0    Alice   24     New York
1      Bob   27  Los Angeles
2  Charlie   22      Chicago

Selecting a Single Column

To select the first column:

Python
print(df.iloc[:, 0])

Output:

Markdown
0      Alice
1        Bob
2    Charlie
3      David
4        Eve
Name: Name, dtype: object

Selecting Multiple Columns

To select the first two columns:

Python
print(df.iloc[:, 0:2])

Output:

Markdown
      Name  Age
0    Alice   24
1      Bob   27
2  Charlie   22
3    David   32
4      Eve   29

Selecting Specific Rows and Columns

To select specific rows and columns (e.g., rows 0, 1 and columns 0, 2):

Python
print(df.iloc[0:2, [0, 2]])

Output:

Markdown
     Name         City
0   Alice     New York
1     Bob  Los Angeles

Using Negative Indexing

Negative indexing can be used to select rows or columns from the end. For example, to select the last row:

Python
print(df.iloc[-1])

Output:

Markdown
Name         Eve
Age           29
City     Phoenix
Name: 4, dtype: object

Conditional Selection with iloc

While iloc primarily uses integer-based indexing, you can combine it with boolean conditions to filter data. For example, to select rows where the age is greater than 25:

Python
print(df.iloc[(df['Age'] > 25).values])

Output:

Markdown
   Name  Age         City
1   Bob   27  Los Angeles
3 David   32      Houston
4   Eve   29      Phoenix

Conclusion

The iloc property is a powerful tool in Pandas that provides flexible and efficient ways to access and manipulate data within DataFrames using integer-based indexing. Whether you need to select specific rows and columns, perform conditional filtering, or navigate your data using negative indexing, iloc has you covered.

By mastering iloc, you can enhance your data manipulation capabilities and handle a wide range of data analysis tasks more effectively. Remember, practice is key, so try experimenting with different datasets and scenarios to become proficient in using iloc.

Happy data wrangling!

Explore Also : Python Pandas DataFrame .loc[] Method

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