Python Pandas – How to use Pandas DataFrame tail( ) function

The Pandas DataFrame tail() function returns the last n rows of a DataFrame. This is particularly useful when combined with filtering operations to examine the bottom portion of your filtered data.

Syntax

DataFrame.tail(n=5)

Parameters:

• n (int, optional): Number of rows to select. Default is 5.

Creating Sample Data

Let's create a sample dataset to demonstrate the tail() function ?

import pandas as pd

# Create sample products data
data = {
    'id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'product': ['Laptop', 'Phone', 'Tablet', 'Watch', 'Camera', 'Speaker', 'Headset', 'Monitor', 'Keyboard', 'Mouse'],
    'price': [45000, 25000, 35000, 15000, 55000, 8000, 12000, 42000, 3000, 2500],
    'category': ['Electronics', 'Electronics', 'Electronics', 'Accessories', 'Electronics', 'Audio', 'Audio', 'Electronics', 'Accessories', 'Accessories']
}

df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

Original DataFrame:
   id   product  price    category
0   1    Laptop  45000  Electronics
1   2     Phone  25000  Electronics
2   3    Tablet  35000  Electronics
3   4     Watch  15000  Accessories
4   5    Camera  55000  Electronics
5   6   Speaker   8000       Audio
6   7   Headset  12000       Audio
7   8   Monitor  42000  Electronics
8   9  Keyboard   3000  Accessories
9  10     Mouse   2500  Accessories

Using tail() with Filtering

Now let's filter products with price between 30000 to 70000 and get the last 3 rows ?

import pandas as pd

# Create sample data
data = {
    'id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'product': ['Laptop', 'Phone', 'Tablet', 'Watch', 'Camera', 'Speaker', 'Headset', 'Monitor', 'Keyboard', 'Mouse'],
    'price': [45000, 25000, 35000, 15000, 55000, 8000, 12000, 42000, 3000, 2500]
}

df = pd.DataFrame(data)

# Filter products with price between 30000 to 70000
filtered_df = df[(df['price'] >= 30000) & (df['price'] <= 70000)]
print("Filtered products (price 30000-70000):")
print(filtered_df)

# Get last 3 rows with specific columns
result = filtered_df[['id', 'product']].tail(3)
print("\nLast 3 rows (id and product columns):")
print(result)

Filtered products (price 30000-70000):
   id  product  price
0   1   Laptop  45000
2   3   Tablet  35000
4   5   Camera  55000
7   8  Monitor  42000

Last 3 rows (id and product columns):
   id  product
2   3   Tablet
4   5   Camera
7   8  Monitor

Method 1: Using between() Function

The between() method provides a clean way to filter numeric ranges ?

import pandas as pd

# Create sample data
data = {
    'id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'product': ['Laptop', 'Phone', 'Tablet', 'Watch', 'Camera', 'Speaker', 'Headset', 'Monitor', 'Keyboard', 'Mouse'],
    'price': [45000, 25000, 35000, 15000, 55000, 8000, 12000, 42000, 3000, 2500]
}

df = pd.DataFrame(data)

# Using between() for price filtering
filtered_df = df[df['price'].between(30000, 70000)]
result = filtered_df[['id', 'product']].tail(3)
print(result)

   id  product
2   3   Tablet
4   5   Camera
7   8  Monitor

Method 2: Using Conditional Operators

Alternative approach using comparison operators for more explicit filtering ?

import pandas as pd

# Create sample data
data = {
    'id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'product': ['Laptop', 'Phone', 'Tablet', 'Watch', 'Camera', 'Speaker', 'Headset', 'Monitor', 'Keyboard', 'Mouse'],
    'price': [45000, 25000, 35000, 15000, 55000, 8000, 12000, 42000, 3000, 2500]
}

df = pd.DataFrame(data)

# Using conditional operators
filtered_df = df[(df['price'] >= 30000) & (df['price'] <= 70000)]
result = filtered_df[['id', 'product']].tail(3)
print(result)

   id  product
2   3   Tablet
4   5   Camera
7   8  Monitor

Common Use Cases

The tail() function is commonly used for ?

• Examining the last few records after sorting
• Quality checking filtered datasets
• Getting recent entries in time-series data
• Sampling bottom rows for analysis

Comparison

Conclusion

The tail() function is essential for examining the bottom rows of filtered DataFrames. Use between() for clean range filtering, or conditional operators for more complex conditions. Both methods work effectively with tail() to get the last n rows of your filtered data.