Day 47 of My Data Analytics Journey !

#datascience #beginners #devjournal #python

Today, I explored one of the most important Python libraries used in Data Analytics — Pandas 🐼

📘 What I Learned:

Pandas Library – A powerful open-source Python library used for data manipulation and analysis.
I practiced creating a dataset with columns like Name, Age, and Location, and also learned how to add an Index for better data organization.

import pandas as pd

# Creating a simple DataFrame
data = {
    'Name': ['Ramya', 'Visky', 'Maha'],
    'Age': [22, 21, 23],
    'Location': ['Chennai', 'Salem', 'Madurai']
}

df = pd.DataFrame(data, index=['S1', 'S2', 'S3'])
print(df)

Output:

    Name   Age  Location
S1  Ramya   22   Chennai
S2  Visky   21     Salem
S3   Maha   23   Madurai

💡 Additional Concepts I Learned Today:

🔓 Open Source:

Software whose source code is freely available for anyone to use, modify, and distribute.
Example: Python, Pandas, Linux

🔒 Non Open Source:

Software whose source code is private and cannot be modified or shared without permission.
Example: Windows OS, MS Office

🧩 Third-Party Library:

A library developed by someone other than the official vendor (Python in this case).
It adds extra functionality to make coding faster and easier.
Example: Pandas, NumPy, Matplotlib

🚀 Summary:

Today’s focus on Pandas helped me understand how data is structured and manipulated in Python — a key skill for any Data Analyst!

🏷️ Hashtags:

Day47 #RamyaAnalyticsJourney #DataAnalytics #Python #Pandas #OpenSource #LearningJourney #DevCommunity #DataScience

DEV Community