How to get nth row in a Pandas DataFrame?

To get the nth row in a Pandas DataFrame, we can use the iloc[] method. For example, df.iloc[4] will return the 5th row because row numbers start from 0.

Syntax

df.iloc[n]

Where n is the index position (0-based) of the row you want to access.

Creating a Sample DataFrame

Let's create a DataFrame with student information ?

import pandas as pd

df = pd.DataFrame({
    'name': ['John', 'Jacob', 'Tom', 'Tim', 'Ally'],
    'marks': [89, 23, 100, 56, 90],
    'subjects': ["Math", "Physics", "Chemistry", "Biology", "English"]
})

print("Input DataFrame:")
print(df)

Input DataFrame:
    name  marks    subjects
0   John     89        Math
1  Jacob     23     Physics
2    Tom    100   Chemistry
3    Tim     56     Biology
4   Ally     90     English

Getting the nth Row

Use iloc[] to access the row at index 3 (4th row) ?

import pandas as pd

df = pd.DataFrame({
    'name': ['John', 'Jacob', 'Tom', 'Tim', 'Ally'],
    'marks': [89, 23, 100, 56, 90],
    'subjects': ["Math", "Physics", "Chemistry", "Biology", "English"]
})

nth_row = 3
result = df.iloc[nth_row]
print(f"Row {nth_row} of the DataFrame:")
print(result)

Row 3 of the DataFrame:
name           Tim
marks           56
subjects    Biology
Name: 3, dtype: object

Multiple Methods

Using iloc[] for Multiple Rows

Get multiple rows by passing a list of indices ?

import pandas as pd

df = pd.DataFrame({
    'name': ['John', 'Jacob', 'Tom', 'Tim', 'Ally'],
    'marks': [89, 23, 100, 56, 90],
    'subjects': ["Math", "Physics", "Chemistry", "Biology", "English"]
})

# Get rows 1, 3, and 4
selected_rows = df.iloc[[1, 3, 4]]
print("Selected rows:")
print(selected_rows)

Selected rows:
    name  marks    subjects
1  Jacob     23     Physics
3    Tim     56     Biology
4   Ally     90     English

Using loc[] with Row Labels

Use loc[] when working with labeled indices ?

import pandas as pd

df = pd.DataFrame({
    'name': ['John', 'Jacob', 'Tom', 'Tim', 'Ally'],
    'marks': [89, 23, 100, 56, 90],
    'subjects': ["Math", "Physics", "Chemistry", "Biology", "English"]
}, index=['A', 'B', 'C', 'D', 'E'])

# Get row with label 'C'
result = df.loc['C']
print("Row with label 'C':")
print(result)

Row with label 'C':
name           Tom
marks          100
subjects    Chemistry
Name: C, dtype: object

Comparison

Conclusion

Use iloc[n] to get the nth row by position, or loc[label] for labeled indices. Both methods return a Series object containing the row data.