When working with tables in Python, it is often necessary to add a new column to the existing table. This can be done in multiple ways, depending on the specific requirements and the libraries being used. In this article, we will explore three different approaches to solve this problem.

Option 1: Using pandas

Pandas is a powerful library for data manipulation and analysis. It provides a convenient way to add a new column to a table using the assign() method. Here is an example:

import pandas as pd

# Create a sample dataframe
data = {'Name': ['John', 'Alice', 'Bob'],
        'Age': [25, 30, 35]}
df = pd.DataFrame(data)

# Add a new column
df = df.assign(City=['New York', 'London', 'Paris'])

print(df)

This will output:

   Name  Age       City
0  John   25   New York
1 Alice   30     London
2   Bob   35      Paris

Option 2: Using SQL

If you are working with a database, you can add a new column to a table using SQL queries. Here is an example using the SQLite database:

import sqlite3

# Connect to the database
conn = sqlite3.connect('sample.db')
cursor = conn.cursor()

# Add a new column
cursor.execute("ALTER TABLE table_name ADD COLUMN City TEXT")

# Commit the changes
conn.commit()

# Close the connection
conn.close()

This will add a new column named “City” to the table.

Option 3: Using NumPy

If you are working with numerical data, you can use the NumPy library to add a new column to a table. Here is an example:

import numpy as np

# Create a sample array
data = np.array([[1, 2, 3],
                 [4, 5, 6],
                 [7, 8, 9]])

# Add a new column
new_column = np.array([10, 11, 12])
data = np.column_stack((data, new_column))

print(data)

This will output:

[[ 1  2  3 10]
 [ 4  5  6 11]
 [ 7  8  9 12]]

After exploring these three options, it is clear that the best approach depends on the specific requirements of the task at hand. If you are working with tabular data and need a comprehensive solution, using pandas is recommended. On the other hand, if you are working with a database, using SQL queries would be the most appropriate choice. Lastly, if you are dealing with numerical data, NumPy provides a convenient way to add a new column to a table. Consider the nature of your data and the functionality required to make an informed decision.