Swap Rows and Columns in Python Tabulate: Data Transposition

When working with tabular data in Python, you might need to swap rows and columns to better present your data.

This tutorial shows how to swap rows and columns using different methods and display the results with tabulate.

 

 

Using Python zip Function

You can use the built-in zip function to transpose your data before passing it to tabulate.

from tabulate import tabulate
data = [
    ['Amina', 28, 'Engineer'],
    ['Hassan', 35, 'Doctor'],
    ['Laila', 26, 'Teacher']
]
headers = ['Name', 'Age', 'Profession']
print("Original Table:")
print(tabulate(data, headers=headers))

# Transpose the data using zip
transposed_data = list(zip(*data))
print("\nTransposed Table:")
print(tabulate(transposed_data))

Output:

Original Table:
Name      Age  Profession
------  -----  ------------
Amina      28  Engineer
Hassan     35  Doctor
Laila      26  Teacher

Transposed Table:
--------  ------  -------
Amina     Hassan  Laila
28        35      26
Engineer  Doctor  Teacher
--------  ------  -------

In the original table, each row represents a person with their details. After transposing, each column represents a person’s details.

 

Using NumPy transpose Function

You can use NumPy transpose function to swap rows and columns:

import numpy as np
from tabulate import tabulate

# Convert data to a NumPy array
np_data = np.array(data)
print("Original Table:")
print(tabulate(np_data, headers=headers))

# Transpose the array
transposed_data = np_data.T
print("\nTransposed Table:")
print(tabulate(transposed_data))

Output:

Original Table:
Name      Age  Profession
------  -----  ------------
Amina      28  Engineer
Hassan     35  Doctor
Laila      26  Teacher

Transposed Table:
--------  ------  -------
Amina     Hassan  Laila
28        35      26
Engineer  Doctor  Teacher
--------  ------  -------

The T attribute transposes the array and swaps rows and columns.

 

Using pandas DataFrame transpose Method

The DataFrame transpose() method allows you to swap rows and columns:

import pandas as pd
from tabulate import tabulate

# Create a DataFrame from the data
df = pd.DataFrame(data, columns=headers)
print("Original Table:")
print(tabulate(df, headers='keys', tablefmt='github', showindex=False))

# Transpose the DataFrame
df_transposed = df.transpose()
print("\nTransposed Table:")
print(tabulate(df_transposed, headers='keys', tablefmt='github'))

Output:

Original Table:
| Name   |   Age | Profession   |
|--------|-------|--------------|
| Amina  |    28 | Engineer     |
| Hassan |    35 | Doctor       |
| Laila  |    26 | Teacher      |

Transposed Table:
|            |          0 |        1 |        2 |
|------------|------------|----------|----------|
| Name       | Amina      | Hassan   | Laila    |
| Age        | 28         | 35       | 26       |
| Profession | Engineer   | Doctor   | Teacher  |

 

Using List Comprehension

List comprehension allows you to transpose data without importing additional modules.

from tabulate import tabulate
transposed_data = [[row[i] for row in data] for i in range(len(data[0]))]
print("Transposed Table:")
print(tabulate(transposed_data))

Output:

Transposed Table:
--------  ------  -------
Amina     Hassan  Laila
28        35      26
Engineer  Doctor  Teacher
--------  ------  -------

By iterating over the indices of the inner lists, you extract elements at each position and group them together.

 

Using itertools.zip_longest for Uneven Lists

When dealing with lists of different lengths, itertools.zip_longest ensures all elements are included during transposition.

from tabulate import tabulate
from itertools import zip_longest
data_uneven = [
    ['Ahmed', 40, 'Architect'],
    ['Fatima', 30],
    ['Youssef', 25, 'Lawyer', 'Cairo']
]
headers = ['Name', 'Age', 'Profession', 'City']
print("Original Table:")
print(tabulate(data_uneven, headers=headers))
transposed_data = list(zip_longest(*data_uneven, fillvalue='N/A'))
print("\nTransposed Table:")
print(tabulate(transposed_data))

Output:

Original Table:
Name       Age  Profession    City
-------  -----  ------------  ------
Ahmed       40  Architect
Fatima      30
Youssef     25  Lawyer        Cairo

Transposed Table:
---------  ------  -------
Ahmed      Fatima  Youssef
40         30      25
Architect  N/A     Lawyer
N/A        N/A     Cairo
---------  ------  -------

Using zip_longest allows you to handle lists of different lengths by filling missing values with a placeholder such as 'N/A'.