Python Interview Preparation Guide for Data Analysts – list, tuple, set, dictionary comparison

Python is the most widely used language for Data Analysts because of its simple syntax, powerful libraries (Pandas, NumPy, Matplotlib, Scikit-learn), and flexibility across ETL, reporting, visualization, and modeling.

This guide consolidates basic Python interview questions, explains key data structures (list, tuple, set, dictionary, array), introduces Pandas essentials (joins, groupby, pivot), and ends with FAQs and cheat sheets to clear up confusion during interviews.

1. What is Python, and what are its key features?

Python is a high-level, interpreted, object-oriented language designed for readability and productivity.

Key Features

• ✅ Simple Syntax – Easy for beginners, efficient for experts.
• ✅ Interpreted – Executes code line by line, easier debugging.
• ✅ Dynamic Typing – No need to declare variable types explicitly.
• ✅ Extensive Libraries – Pandas, NumPy, Scikit-learn, Django.
• ✅ Cross-Platform – Works on Windows, macOS, Linux.

Data Analyst Connection

• Pandas → data cleaning and wrangling.
• NumPy → numerical computations.
• Matplotlib/Seaborn → data visualization.
• SQLAlchemy → database connectivity.

2. Core Data Structures in Python

📌 Comparison Table

📌 Dimensionality

• List → 1D: [1,2,3]; 2D: [[1,2],[3,4]] (inefficient for math).
• Tuple → Similar to list, but immutable.
• Set → Always 1D (no index).
• Dictionary → 1D mapping; nested dicts simulate 2D.
• NumPy Array → True 1D, 2D, 3D, nD (efficient for matrix ops).

📌 Methods Cheat Sheet with 1D & 2D Examples

🔹 List

# 1D List
a = [3, 1, 2]
a.append(4)          # [3, 1, 2, 4]
a.sort()             # [1, 2, 3, 4]

# 2D List (List of Lists)
matrix = [[1, 2], [3, 4], [5, 6]]
matrix[1][0]         # 3
[row[1] for row in matrix]   # [2, 4, 6] → column extraction

# 1D List
a = [3, 1, 2]
a.append(4)          # [3, 1, 2, 4]
a.sort()             # [1, 2, 3, 4]

# 2D List (List of Lists)
matrix = [[1, 2], [3, 4], [5, 6]]
matrix[1][0]         # 3
[row[1] for row in matrix]   # [2, 4, 6] → column extraction

✅ Use Case: Representing tabular data before loading into Pandas.

🔹 Tuple

# 1D Tuple
t = (1, 2, 3, 2)
t.count(2)           # 2
t.index(3)           # 2

# Nested Tuple (Tuple of Tuples)
coords = ((10, 20), (30, 40))
coords[1][0]         # 30

# 1D Tuple
t = (1, 2, 3, 2)
t.count(2)           # 2
t.index(3)           # 2

# Nested Tuple (Tuple of Tuples)
coords = ((10, 20), (30, 40))
coords[1][0]         # 30

✅ Use Case: Immutable coordinates, fixed reference mappings.

🔹 Set

# 1D Set
s = {1, 2, 2, 3}
s.add(4)             # {1, 2, 3, 4}
s.intersection({2,5}) # {2}

# Set of Frozensets (to allow nested sets)
fs = {frozenset([1,2]), frozenset([3,4])}

# 1D Set
s = {1, 2, 2, 3}
s.add(4)             # {1, 2, 3, 4}
s.intersection({2,5}) # {2}

# Set of Frozensets (to allow nested sets)
fs = {frozenset([1,2]), frozenset([3,4])}

✅ Use Case: Deduplication, unique groups, fast membership tests.

🔹 Dictionary

# 1D Dictionary
d = {"Alice":100, "Bob":200}
d["Charlie"] = 300
list(d.keys())       # ['Alice','Bob','Charlie']

# Nested Dictionary
students = {
    "Alice": {"Math": 90, "Eng": 85},
    "Bob":   {"Math": 78, "Eng": 92}
}
students["Alice"]["Math"]   # 90

# 1D Dictionary
d = {"Alice":100, "Bob":200}
d["Charlie"] = 300
list(d.keys())       # ['Alice','Bob','Charlie']

# Nested Dictionary
students = {
    "Alice": {"Math": 90, "Eng": 85},
    "Bob":   {"Math": 78, "Eng": 92}
}
students["Alice"]["Math"]   # 90

✅ Use Case: Key-value storage, hierarchies like customer → orders.

🔹 NumPy Array

import numpy as np

# 1D Array
arr = np.array([1, 2, 3, 4])
arr.mean()            # 2.5

# 2D Array (Matrix)
mat = np.array([[1, 2], [3, 4], [5, 6]])
mat.shape             # (3,2)
mat[:, 1]             # [2,4,6] → column slice

# 3D Array
cube = np.arange(8).reshape(2,2,2)

import numpy as np

# 1D Array
arr = np.array([1, 2, 3, 4])
arr.mean()            # 2.5

# 2D Array (Matrix)
mat = np.array([[1, 2], [3, 4], [5, 6]])
mat.shape             # (3,2)
mat[:, 1]             # [2,4,6] → column slice

# 3D Array
cube = np.arange(8).reshape(2,2,2)

✅ Use Case: Efficient math on 2D/3D data (images, stats, regression input).

🚀 Summary of “Why & When”

📌 Data Analyst Use Cases

3. __init__() Method in Python

The __init__() method is a constructor in OOP used to initialize objects.

class SalesRecord:
    def __init__(self, date, amount, region):
        self.date = date
        self.amount = amount
        self.region = region

record = SalesRecord("2024-01-01", 500, "North")
print(record.region)   # North

class SalesRecord:
    def __init__(self, date, amount, region):
        self.date = date
        self.amount = amount
        self.region = region

record = SalesRecord("2024-01-01", 500, "North")
print(record.region)   # North

Analyst Use Case – Create custom objects such as Customer or Transaction classes to structure datasets in ETL pipelines.

4. Mutable vs Immutable

Example

• Store transaction logs in lists/dicts (mutable).
• Store (Year, Month, ProductID) in tuples (immutable, fixed schema).

5. Comprehensions

List Comprehension

squares = [i**2 for i in range(5)]  # [0,1,4,9,16]

squares = [i**2 for i in range(5)]  # [0,1,4,9,16]

Dictionary Comprehension

sales = {region: revenue for region, revenue in [("A",1000),("B",2000)]}

sales = {region: revenue for region, revenue in [("A",1000),("B",2000)]}

Tuple Comprehension (generator)

gen = (i for i in range(5))<br>list(gen)  # [0,1,2,3,4]

gen = (i for i in range(5))<br>list(gen)  # [0,1,2,3,4]

Analyst Example – Filter out negative sales:

clean_sales = [x for x in sales if x > 0]

clean_sales = [x for x in sales if x > 0]

6. Global Interpreter Lock (GIL)

• Prevents multiple Python threads from executing simultaneously.
• ❌ Limitation → CPU-bound tasks (slow for heavy math).
• ✅ Works fine → I/O-bound tasks (API calls, file operations).

Analyst Example

• Web scraping multiple APIs → threading is effective.
• Heavy analytics models → prefer multiprocessing or NumPy vectorization.

7. Conversions Between Formats

8. Pandas Essentials

Series

import pandas as pd
s = pd.Series([10,20,30], index=["a","b","c"])
print(s["b"])   # 20

import pandas as pd
s = pd.Series([10,20,30], index=["a","b","c"])
print(s["b"])   # 20

DataFrame

df = pd.DataFrame({"Name":["Alice","Bob"], "Sales":[100,200]})

df = pd.DataFrame({"Name":["Alice","Bob"], "Sales":[100,200]})

Core Operations

Joins in Pandas

GroupBy and Pivot

# Group sales by region
df.groupby("Region")["Sales"].sum()

# Pivot table: Regions vs Products
df.pivot(index="Region", columns="Product", values="Sales")

# Group sales by region
df.groupby("Region")["Sales"].sum()

# Pivot table: Regions vs Products
df.pivot(index="Region", columns="Product", values="Sales")

9. FAQs (Common Confusions)

• Are lists 1D or 2D? → Lists are 1D; nested lists simulate 2D; use NumPy for real 2D.
• Why tuples if lists exist? → Tuples are immutable (faster, safer).
• Why sets if lists can remove duplicates? → Sets offer O(1) membership lookup.
• Are dictionaries 2D? → Single dict = 1D; nested dicts = 2D like a table.
• Which structure is closest to SQL tables? → List of dicts or Pandas DataFrame.
• When to use arrays over lists? → Use arrays for numeric-heavy computations.
• Difference between merge and concat? → Merge = SQL join logic, Concat = stack vertically/horizontally.

10. Analyst Interview Cheat Sheet

Final Takeaway

• List → staging raw data.
• Tuple → fixed, unchangeable schema.
• Set → uniqueness and fast membership checks.
• Dictionary → mapping keys to values.
• Array (NumPy) → numeric efficiency at scale.
• Pandas → indispensable for tabular wrangling, joins, and aggregations.

Mastering these fundamentals, along with their analyst-focused use cases, ensures you can confidently answer both technical and applied interview questions.