Access Index of Last Element in pandas DataFrame in Python

Let's first create a DataFrame to work with throughout our examples ?

import pandas as pd

# Create sample DataFrame
df = pd.DataFrame({
    'Subject': ['Maths', 'Science', 'Computer', 'English'],
    'Marks': [70, 85, 90, 45],
    'Grade': ['B', 'A', 'A', 'C']
})

print("Original DataFrame:")
print(df)

Original DataFrame:
    Subject  Marks Grade
0     Maths     70     B
1   Science     85     A
2  Computer     90     A
3   English     45     C

Using Index Attribute

The index attribute provides access to row indices. To get the last row index, use negative indexing with -1 ?

Syntax

df.index[-1]

Example

import pandas as pd

df = pd.DataFrame({
    'Subject': ['Maths', 'Science', 'Computer', 'English'],
    'Marks': [70, 85, 90, 45],
    'Grade': ['B', 'A', 'A', 'C']
})

# Get the last row index
last_index = df.index[-1]
print("Last row index:", last_index)

# Alternative: Get all indices and select the last one
all_indices = df.index.tolist()
print("All indices:", all_indices)
print("Last index using max():", max(df.index))

Last row index: 3
All indices: [0, 1, 2, 3]
Last index using max(): 3

Using tail() Method

The tail(n) method returns the last n rows. Combined with the index attribute, it provides another way to access the last row index ?

Syntax

df.tail(1).index[0]

Example

import pandas as pd

df = pd.DataFrame({
    'Subject': ['Maths', 'Science', 'Computer', 'English'],
    'Marks': [70, 85, 90, 45],
    'Grade': ['B', 'A', 'A', 'C']
})

# Get last row using tail()
last_row = df.tail(1)
print("Last row:")
print(last_row)

# Get the index of the last row
last_index = df.tail(1).index[0]
print("\nLast row index using tail():", last_index)

Last row:
    Subject  Marks Grade
3   English     45     C

Last row index using tail(): 3

Working with Custom Indices

DataFrames can have custom indices. Here's how to handle the last index in such cases ?

import pandas as pd

# Create DataFrame with custom index
df_custom = pd.DataFrame({
    'Subject': ['Maths', 'Science', 'Computer', 'English'],
    'Marks': [70, 85, 90, 45],
    'Grade': ['B', 'A', 'A', 'C']
}, index=['A', 'B', 'C', 'D'])

print("DataFrame with custom index:")
print(df_custom)

# Get last index with custom indices
print("\nLast index:", df_custom.index[-1])
print("Last index using tail():", df_custom.tail(1).index[0])

DataFrame with custom index:
    Subject  Marks Grade
A     Maths     70     B
B   Science     85     A
C  Computer     90     A
D   English     45     C

Last index: D
Last index using tail(): D

Comparison of Methods

Conclusion

Use df.index[-1] for the fastest way to get the last row index. The tail() method is useful when you need both the last row data and its index. Both methods work with custom indices as well.