Skip to content

Pandas Series and Dataframes (deeper)

🟦🐍 Technique Card: Understanding Series and DataFrames in Pandas (deeper)

👩‍🏫 What Are We Learning?

When we work with data using Pandas, we often hear about Series and DataFrames. But what exactly are they? This card will help you understand the difference and why they matter.

🧱 What Is a Pandas Series?

A Series is like:

  • A list of data with a label for each item.
  • A dictionary with keys (labels) and values.
  • A column in a spreadsheet.
import pandas as pd

s = pd.Series([100, 200, 300], index=["a", "b", "c"])
print(s)

a    100
b    200
c    300
dtype: int64

➡ Each item has a label (a, b, c) and a value (100, 200, 300).

📊 What Is a Pandas DataFrame?

A DataFrame is like:

  • A whole table or spreadsheet.
  • A collection of Series working together.
  • It has rows and columns.
data = {
    "Name": ["Lila", "Tom", "Ravi"],
    "Age": [10, 11, 9]
}
df = pd.DataFrame(data)
print(df)

   Name  Age
0  Lila   10
1   Tom   11
2  Ravi    9

➡ You can think of df["Name"] or df["Age"] as separate Series.

📦 Analogy: The Bookshelf

Concept Analogy
Series A single book
DataFrame A shelf of books
Index The page number
Column Name The book title

So when you grab df["Age"], you're pulling out just the Age book (a Series), and the DataFrame is the whole shelf.

🔍 How to Explore a DataFrame

df.shape      # (3, 2) → 3 rows, 2 columns
df.columns    # Index(['Name', 'Age'])
df.head()     # First 5 rows
df.info()     # Quick summary

🧠 Why This Matters

When you understand that:

  • A Series is one column.
  • A DataFrame is many columns working together.

…you’ll be able to use your code more cleverly, especially when:

  • Filtering data
  • Making graphs
  • Calculating averages
  • Loading and saving data

💬 Fun Fact

Even when you filter a DataFrame down to one column using df["column"], you're working with a Series again!

🔗 Linked Cards