Session 18 · Phase 3: Python

Pandas — Series, DataFrame & Reading Data

Pandas is the heart of Python data analysis — the DataFrame and Series let you load, inspect, and slice tabular data like a spreadsheet, in code.

⏱ ~2 hrs 📚 Core content 🎯 Highest priority

Learning Objectives

Explain what Pandas is and why it's the core data-analysis library.
Distinguish a Series (1-D) from a DataFrame (2-D).
Read data with pd.read_csv() and pd.read_excel().
Inspect data with head, info, describe, shape, columns.
Select data with loc (labels) vs iloc (positions).

1. What Pandas Is & Why It Matters

Pandas is the Python library for data manipulation and analysis. It provides two core data structures — the DataFrame (a table) and the Series (a single column) — and is the tool you'll use for almost every real data task.

import pandas as pd

2. Series — the 1-D Structure

A Series is a single labeled column of data — like one column in a spreadsheet.

import pandas as pd

prices = pd.Series([25, 120, 10], index=["Pen", "Notebook", "Eraser"])
print(prices)
# Pen        25
# Notebook   120
# Eraser     10
# dtype: int64

The index is the set of labels; the values are the data.

3. DataFrame — the 2-D Structure

A DataFrame is a table of rows and columns — essentially a collection of Series sharing an index.

df = pd.DataFrame({
    "product": ["Pen", "Notebook", "Eraser"],
    "price":   [25, 120, 10],
    "units":   [10, 5, 20]
})
print(df)
#      product  price  units
# 0        Pen     25     10
# 1   Notebook    120      5
# 2     Eraser     10     20

	Series	DataFrame
Dimensions	1-D (one column)	2-D (rows × columns)
Analogy	a single column	a whole table
Data types	one type	different types per column

4. Reading Data

# Read a CSV file
df = pd.read_csv("sales.csv")

# Read an Excel file
df = pd.read_excel("sales.xlsx")

# Read a CSV with a custom delimiter / no header
df = pd.read_csv("sales.txt", sep="\t", header=None)

📝

Note: read_csv is the single most-used Pandas function — you'll type it in almost every analysis you ever do.

5. Inspecting Data

df.head()       # first 5 rows
df.tail()       # last 5 rows
df.info()       # columns, dtypes, non-null counts
df.describe()   # summary statistics for numeric columns
df.shape        # (rows, columns)  — an ATTRIBUTE, no parentheses
df.columns      # list of column names
df.dtypes       # data type of each column

💡

Why does df.shape have no parentheses? Because shape is an attribute (a stored value), while head() and describe() are methods (functions you call). This is a surprisingly common interview question.

6. Selecting Data — loc vs iloc

This is one of the most-asked Pandas questions. Both select data, but by different things:

	`loc`	`iloc`
Selection by	labels (names)	positions (integers)
Example row	`df.loc["Pen"]`	`df.iloc[0]`
Example col	`df.loc[:, "price"]`	`df.iloc[:, 1]`

df = pd.DataFrame(
    {"price": [25, 120, 10]},
    index=["Pen", "Notebook", "Eraser"]
)

df.loc["Pen"]        # label-based → 25
df.iloc[0]           # position-based → 25

df.loc[:, "price"]   # all rows, "price" column
df.iloc[:, 0]        # all rows, first column

⚠️

Memory hook: loc = label, iloc = integer position. When in doubt about which to use, ask: "am I selecting by name or by position?"

📋 Stable content — Reviewed: August 2026

7. Interview Questions (with Model Answers)

The Pandas fundamentals questions interviewers ask. Self-test before revealing.

IQ1. What is Pandas, and why is it used in data analysis?

Model answer: "Pandas is the core Python library for data manipulation and analysis. It provides DataFrames and Series for working with tabular data — cleaning, transforming, filtering, and summarizing."

IQ2. What's the difference between a Series and a DataFrame?

Model answer: "A Series is 1-dimensional — a single labeled column. A DataFrame is 2-dimensional — a table of rows and columns, essentially a collection of Series sharing an index."

IQ3. How do you read a CSV file into a DataFrame?

Model answer: "With pd.read_csv('file.csv'). For Excel I use pd.read_excel(). I can also pass arguments like sep for the delimiter."

IQ4. What's the difference between loc and iloc?

Model answer: "loc selects by label (row/column names); iloc selects by integer position. So df.loc["Pen"] uses the name, while df.iloc[0] uses the position."

IQ5. What does df.describe() do?

Model answer: "It returns summary statistics — count, mean, std, min, quartiles, and max — for the numeric columns. It's my first step for a quick feel of the data."

IQ6. What's the difference between df.info() and df.describe()?

Model answer: "info() tells me about structure — column names, data types, and non-null counts (so I can spot missing data). describe() gives statistical summaries of the numeric columns."

IQ7. Why does df.shape have no parentheses?

Model answer: "Because shape is an attribute — a stored property of the DataFrame — not a method. Methods like head() or describe() are functions you call, so they need parentheses."

IQ8. How do you see the first or last few rows of a DataFrame?

Model answer: "With df.head() for the first five rows and df.tail() for the last five. I can pass a number — like df.head(10) — to see more."

IQ9. How do you check column names and data types?

Model answer: "df.columns gives the column names, and df.dtypes gives the data type of each column. df.info() shows both together with non-null counts."

Hands-On Project: Load and Inspect a Dataset

Write Pandas code for each task. Run it and check the output.

Steps

Create a DataFrame with columns product, price, and units.
Print its shape and column names.
Show the first 5 rows (and the summary statistics).
Select the price column using both loc and iloc.
Select the first row using both loc and iloc.
Read a CSV (you can create a tiny one first) using pd.read_csv.

View Solution / Walkthrough

import pandas as pd

# 1. Create DataFrame
df = pd.DataFrame({
    "product": ["Pen", "Notebook", "Eraser", "Marker"],
    "price":   [25, 120, 10, 45],
    "units":   [10, 5, 20, 8]
})

# 2. Shape and columns
print(df.shape)      # (4, 3)
print(df.columns)    # Index(['product', 'price', 'units'])

# 3. Head and describe
print(df.head())
print(df.describe())

# 4. Select the price column
print(df.loc[:, "price"])    # label-based
print(df.iloc[:, 1])         # position-based

# 5. Select the first row
print(df.loc[0])             # label 0 (default index)
print(df.iloc[0])            # position 0

# 6. Read a CSV
df = pd.read_csv("sales.csv")

Key Takeaways

Series = 1-D column; DataFrame = 2-D table.

pd.read_csv() is the single most-used Pandas function.

head/info/describe/shape are your first-look toolkit.

loc = labels; iloc = positions.

shape is an attribute (no parens); head() is a method (parens).

Learning Objectives

1. What Pandas Is & Why It Matters

2. Series — the 1-D Structure

3. DataFrame — the 2-D Structure

4. Reading Data

5. Inspecting Data

6. Selecting Data — loc vs iloc

7. Interview Questions (with Model Answers)

Hands-On Project: Load and Inspect a Dataset

Steps

Key Takeaways

Objective Questions — Test Your Understanding