When working with data frames in Python, it is often useful to add additional columns to store relevant information. One common requirement is to add a column that contains the current date. In this article, we will explore three different ways to achieve this using Python.

Option 1: Using the datetime module

The datetime module in Python provides various classes and functions to work with dates and times. We can utilize the datetime class to get the current date and then add it as a new column to the data frame.

import pandas as pd
from datetime import datetime

# Create a sample data frame
data = {'Name': ['John', 'Emma', 'Michael'],
        'Age': [25, 28, 32]}
df = pd.DataFrame(data)

# Get the current date
current_date = datetime.now().date()

# Add the current date as a new column
df['Current Date'] = current_date

print(df)

This code snippet imports the necessary libraries, creates a sample data frame, and then uses the datetime.now().date() function to get the current date. Finally, it adds the current date as a new column named ‘Current Date’ to the data frame.

Option 2: Using the pandas to_datetime() function

The pandas library provides a to_datetime() function that can convert a string, series, or list-like object to a datetime object. We can leverage this function to add the current date as a new column to the data frame.

import pandas as pd

# Create a sample data frame
data = {'Name': ['John', 'Emma', 'Michael'],
        'Age': [25, 28, 32]}
df = pd.DataFrame(data)

# Add the current date as a new column
df['Current Date'] = pd.to_datetime('today').date()

print(df)

In this code snippet, we create a sample data frame and then use the pd.to_datetime(‘today’).date() function to get the current date. We directly assign this value to a new column named ‘Current Date’ in the data frame.

Option 3: Using the apply() function

The apply() function in pandas allows us to apply a function along an axis of the data frame. We can define a custom function that returns the current date and then use the apply() function to add it as a new column.

import pandas as pd
from datetime import datetime

# Create a sample data frame
data = {'Name': ['John', 'Emma', 'Michael'],
        'Age': [25, 28, 32]}
df = pd.DataFrame(data)

# Define a function to get the current date
def get_current_date():
    return datetime.now().date()

# Add the current date as a new column using apply()
df['Current Date'] = df.apply(lambda x: get_current_date(), axis=1)

print(df)

In this code snippet, we define a custom function get_current_date() that returns the current date. We then use the apply() function along the axis=1 (row-wise) to apply this function to each row of the data frame and add the current date as a new column.

After exploring these three options, it is evident that Option 2 using the pandas to_datetime() function is the most concise and efficient way to add the current date as a new column to a data frame. It eliminates the need for importing the datetime module separately and provides a direct method to obtain the current date.