In today’s world, data analysis has become increasingly important, and one of the most popular tools used by data analysts and data scientists is Python with the pandas library. Pandas is a powerful, open-source data analysis and manipulation tool that allows for easy manipulation of data structures and series. One common problem that users encounter is converting birth dates to ages for more accurate and practical analysis. In this article, we will delve into how to tackle this issue with clear examples and explanations of the code implementation.
Pandas is a versatile tool that often involves working with DateTime objects – this is the case when dealing with birth dates. The first step to convert birth dates into age requires simple arithmetic with the DateTime library. This will enable us to find the age of individuals by calculating the difference between their birth date and the current date.
Let’s start by importing the necessary libraries:
import pandas as pd from datetime import datetime
Next, consider a simple dataset containing the following data about individuals:
data = {'Name': ['John', 'Paul', 'George', 'Ringo'],
'Birth_Date': ['1940-10-09', '1942-06-18', '1943-02-25', '1940-07-07']
}
df = pd.DataFrame(data)
df['Birth_Date'] = pd.to_datetime(df['Birth_Date'])
In the above code, we’re converting the ‘Birth_Date’ column to DateTime objects.
Calculating Age
Now, we are ready to calculate the ages of these individuals by finding the difference between their birth date and the current date. To do this, follow these steps:
1. Create a function called ‘calculate_age’ that takes a birthdate as input and returns the person’s age.
2. Apply this function to the ‘Birth_Date’ column in the DataFrame.
Here’s the code to implement the above logic:
def calculate_age(birth_date):
today = datetime.now()
age = today.year - birth_date.year - ((today.month, today.day) <
(birth_date.month, birth_date.day))
return age
df['Age'] = df['Birth_Date'].apply(calculate_age)
In this code snippet, we created a function called ‘calculate_age’ that receives a birth_date as input, calculates the current date using datetime.now(), and calculates the person’s age by subtracting their birth year from the current year. If their birthdate has not occurred this year, we subtract an additional year.
Finally, we apply this function on the ‘Birth_Date’ column using apply() method, and the calculated ages are stored in a new ‘Age’ column in the DataFrame.
Using Numpy and Pandas for Age Calculation
Alternatively, we can make use of the powerful numpy library in combination with pandas for this task. To convert the birth dates to ages using numpy, follow these steps:
1. Import the numpy library.
2. Use the numpy ‘floor’ function to calculate the age.
Here’s an example of how to do this:
import numpy as np df['Age'] = np.floor((datetime.now() - df['Birth_Date']).dt.days / 365.25)
This code uses numpy’s ‘floor’ function to round down the floating-point division result of the number of days since the birth date by 365.25 (taking into account leap years).
In summary, by leveraging libraries like pandas and datetime or pandas and numpy, it becomes seamless to convert birth date columns to age within a dataset. Following the explained steps and understanding the code provided in this article will arm you with the knowledge to manipulate such data and carry out a more efficient and accurate analysis.
