Pandas iloc[]

import pandas as pd

data = {'A': [1, 2, 3], 
        'B': [4, 5, 6]}

df = pd.DataFrame(data)

# select the element in the first row and second column
element = df.iloc[0, 1]  

print(element)  

# Output: 4

iloc[] Syntax

The syntax of the iloc[] property in Pandas is:

iloc[row_selection, column_selection]

iloc[] Arguments

The iloc[] property takes following arguments:

• row_selection - specifies which rows you want to select based on integer positions
• column_selection - specifies which columns you want to select based on integer positions

iloc[] Return Value

The iloc[] property in Pandas returns a subset of a DataFrame or Series based on the integer-location-based indexing you specify.

Example 1: Select Single Element From a DataFrame

import pandas as pd

data = {'Student_ID': [101, 102, 103],
        'Name': ['Alice', 'Bob', 'Charlie'],
        'Score': [85, 92, 78]}

df = pd.DataFrame(data)

# select the score of the first student (first row, third column)
first_student_score = df.iloc[0, 2]

print("Score of the first student:", first_student_score)

Output

Score of the first student: 85

In the above example, we have the df DataFrame representing student data with columns: Student_ID, Name, and Score.

We used iloc[0,2] to select the score of the first student, which is located in the first row (index 0) and the third column (index 2).

Example 2: Select Multiple Rows and Columns Using Slices

import pandas as pd

data = {'Student_ID': [101, 102, 103, 104, 105],
        'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
        'Math_Score': [85, 92, 78, 90, 88],
        'Science_Score': [88, 94, 76, 89, 84]}

df = pd.DataFrame(data)

# select rows 1 to 3 (exclusive) and columns 1 to 3 (exclusive)
selected_data = df.iloc[1:3, 1:3]

print(selected_data)

Output

       Name  Math_Score
1      Bob          92
2      Charlie     78

Here, we used iloc[1:3, 1:3] to select a slice of rows from index 1 (inclusive) to index 3 (exclusive) and a slice of columns from index 1 (inclusive) to index 3 (exclusive).

As a result, data from rows 1 and 2 of columns 'Name' and 'Math_Score' are displayed.

Note: To learn more about how slicing works, please visit Pandas Indexing and Slicing.

Example 3: Select Specific Rows and Columns Using Lists of Integers

import pandas as pd

data = {'Student_ID': [101, 102, 103],
        'Name': ['Alice', 'Bob', 'Charlie'],
        'Math_Score': [85, 92, 78],
        'Science_Score': [90, 88, 82]}

df = pd.DataFrame(data)

# select specific rows (rows 0 and 2) and specific columns (columns 1 and 3)
selected_data = df.iloc[[0, 2], [1, 3]]

print(selected_data)

Output

      Name  Science_Score
0    Alice             90
2  Charlie             82

In the above example, we used iloc[[0, 2], [1, 3]] to select rows with indices 0 and 2 (which are the first and third rows) and columns with indices 1 and 3 (which are the second and fourth columns).

The result is a new DataFrame containing the selected rows and columns.

Example 4: Select all Rows for Specific Columns

import pandas as pd

data = {'Student_ID': [101, 102, 103],
        'Name': ['Alice', 'Bob', 'Charlie'],
        'Score': [85, 92, 78]}

df = pd.DataFrame(data)

# select all rows for the 'Student_ID' and 'Name' columns
selected_data = df.iloc[:, [0, 1]]

print(selected_data)

Output

    Student_ID     Name
0         101          Alice
1         102          Bob
2         103          Charlie

Here, the iloc[:, [0, 1]] property selects all rows and the columns at index 0, 'Student_ID', and index 1, 'Name'.

This will give us a DataFrame containing all rows for the 'Student_ID' and 'Name' columns.

Example 5: Select Specific Rows For All Columns

import pandas as pd

data = {'Student_ID': [101, 102, 103, 104, 105],
        'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
        'Score': [85, 92, 78, 88, 95]}

df = pd.DataFrame(data)

# select rows 1 and 3 for all columns
selected_rows = df.iloc[[1, 3], :]

print(selected_rows)

Output

     Student_ID    Name  Score
1         102      Bob       92
3         104      David     88

Here, iloc[[1, 3], :] allows us to select the first and third row while including all columns.