Generate Realistic Fake Data in Python: Pandas, Numpy & Faker Tutorial!

Generate Realistic Fake Data in Python: Pandas, Numpy & Faker Tutorial!
Welcome back to my channel! 🎥 In this video, we're diving into the fascinating world of data generation using Python. Whether you're a data scientist, a developer, or just someone curious about data manipulation, this tutorial will show you how to create realistic fake data for testing and prototyping your projects.
What You'll Learn:
How to use the Pandas library to create and manipulate DataFrames.
How to utilize Numpy for generating random numbers.
How to harness the power of the Faker library to produce realistic names, addresses, emails, and phone numbers.
Why Generate Fake Data?
Generating fake data is incredibly useful for:
Testing algorithms and models without the need for real data.
Prototyping new features or applications.
Learning and teaching data analysis techniques.
Code Snippet:
import pandas as pd
import numpy as np
from faker import Faker
fake = Faker()
num_samples = 100
data = {
'Name': [fake.name() for _ in range(num_samples)],
'Age': np.random.randint(18, 80, size=num_samples),
'Address': [fake.address() for _ in range(num_samples)],
'Email': [fake.email() for _ in range(num_samples)],
'Phone Number': [fake.phone_number() for _ in range(num_samples)]
}
df = pd.DataFrame(data)
print(df.head())
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Thank you for watching, and happy coding! 🚀

[@zaidshan1830]

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