Guides

pandas DataFrame for Beginners — Series vs DataFrame, head and info

By Ravindra Bagale, Data Science & Data Analytics trainer · LinkedIn · Published: 1 October 2026 · Last updated: 3 October 2026

Read this guide in: English · मराठी · हिंदी

A Series is one labelled column. A DataFrame is a full table. In pandas you build or load a DataFrame, then use head() and info() before any fancy work. Tiny check: Mumbai 120 + Pune 70 = 190.

dict/lists → DataFrame → head → info → Series math

Language: English · मराठी · हिंदी

What do I need before this guide?

• Python + pandas in a Jupyter notebook (see the install guide).
• Comfort with a Python dict and list.
• Course: Series and DataFrame.

Before and after (look at the tables first)

Before: loose lists. After: one DataFrame whose sales sum to 190.

Why DataFrame (not only lists)?

What a DataFrame gives you

1. Column names you can trust.
2. One place for many rows.
3. Ready tools: filter, groupby, missing-value checks.

Why you care

1. Shop exports, Instagram-like engagement dumps and Infosys client sheets all arrive as tables.
2. Interviewers expect head / info muscle memory.

Build a tiny shop DataFrame

import pandas as pd

df = pd.DataFrame({
    "city": ["Mumbai", "Pune"],
    "sales": [120, 70],
})
df.head()

What you see:

1. Two rows.
2. Columns city and sales.
3. A default index 0, 1.

What it means: pandas wrapped your lists into a table.

Series vs DataFrame (plain words)

1. Series — one column with an index. Example: df["sales"].
2. DataFrame — several columns side by side. Example: df.
3. Selecting one column returns a Series; selecting two returns a DataFrame.

s = df["sales"]          # Series
both = df[["city", "sales"]]  # DataFrame
print(s.sum())           # 190

head, info, dtypes

Vertical checks every time you meet a new table:

1. df.head() — first rows (default 5).
2. df.head(2) — only two rows on tiny data.
3. df.info() — row count, nulls, dtypes.
4. df.dtypes — types only.
5. df.shape — (rows, columns).

Course: head, info, dtypes.

What you look for

1. Wrong types (sales as text).
2. Unexpected null counts.
3. Surprising column names with spaces.

Columns as Series — quick stats

df["sales"].sum()    # 190
df["sales"].mean()   # 95.0
df["sales"].max()    # 120

Course: Columns as Series — quick stats.

Instagram-like mini example

Suppose engagement:

1. DataFrame holds all three posts.
2. df["likes"].sum() → 190.
3. Later groupby will split Mumbai 120 vs Pune 70.

Common beginner mistakes