This content originally appeared on DEV Community and was authored by Aditi Sharma
Getting Started with Pandas Series
Today I explored Pandas, one of the most powerful Python libraries for data analysis. I began with the Series object, which is like a 1D labeled array.
🔹 Creating a Series
import pandas as pd
data = [10, 20, 30, 40]
s = pd.Series(data)
print(s)
âś… Output:
0 10
1 20
2 30
3 40
dtype: int64
🔹 Custom Index
s = pd.Series([10, 20, 30], index=["a", "b", "c"])
print(s["b"]) # 20
🔹 From Dictionary
data = {"apples": 3, "bananas": 5, "oranges": 2}
fruits = pd.Series(data)
print(fruits)
🔹 Vectorized Operations
print(s * 2)
a 20
b 40
c 60
⚡ Interesting Facts
• A Pandas Series is built on NumPy arrays (fast + efficient).
• Labels (index) make data handling more intuitive.
• It’s the foundation for Pandas DataFrame.
✨ Reflection
Series may look simple, but they’re the first step toward mastering data manipulation in Pandas. I can already see how useful they’ll be for analytics tasks.
Next → I’ll dive into Pandas DataFrames 📊
Python #Pandas #100DaysOfCode #DataAnalytics #DevCommunity
This content originally appeared on DEV Community and was authored by Aditi Sharma

Aditi Sharma | Sciencx (2025-09-10T12:13:18+00:00) 🚀 Day 12 of My Python Learning Journey. Retrieved from https://www.scien.cx/2025/09/10/%f0%9f%9a%80-day-12-of-my-python-learning-journey/
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