Python Basics for Data Analysts

Why Python for Data Analysts?

Python is the #2 most important skill after SQL. Here's why:

✅ Automate repetitive tasks (no more manual copy-paste!) ✅ Handle large datasets (millions of rows, no problem) ✅ Advanced analytics (statistics, ML, forecasting) ✅ Web scraping (collect data from websites) ✅ Free & popular (huge community, tons of libraries)

Real fact: 75% of data analyst job postings require Python. It's non-negotiable in 2026.

In this topic, you'll learn:

Variables and data types
Lists, dictionaries, and tuples
Loops and conditionals
Functions and modules
String manipulation
File operations
Real-world data analysis examples

Setting Up Python

Option 1: Anaconda (Recommended for Analysts)

Download from anaconda.com

Why Anaconda?

Includes Python + data libraries (Pandas, NumPy, Matplotlib)
Jupyter Notebook for interactive analysis
No separate installations needed

Option 2: Python.org

Download from python.org

Then install libraries:

$ terminalBash

pip install pandas numpy matplotlib jupyter

Verify Installation

code.pyPython

python --version  # Should show Python 3.10+

Variables & Data Types

Variables

Variables store data. No need to declare types — Python figures it out.

code.pyPython

# Creating variables
name = "Rahul"
age = 25
salary = 50000.50
is_employed = True

# Print variables
print(name)        # Rahul
print(f"Age: {age}")  # Age: 25

Data Types

| Type | Example | Used For | Check Type | |------|---------|----------|------------| | str | "Hello" | Text | type("Hello") | | int | 25 | Whole numbers | type(25) | | float | 3.14 | Decimals | type(3.14) | | bool | True/False | Yes/No flags | type(True) |

Type Conversion

code.pyPython

# String to integer
age_str = "25"
age_int = int(age_str)

# Integer to string
count = 100
count_str = str(count)

# String to float
price = float("99.99")

# Check type
print(type(age_int))  # <class 'int'>

String Operations

Strings are everywhere in data analysis — cleaning names, extracting info, formatting output.

Basic Operations

code.pyPython

company = "Skillsetmaster"

# Length
print(len(company))  # 14

# Uppercase / Lowercase
print(company.upper())  # SKILLSETMASTER
print(company.lower())  # skillsetmaster

# Check if contains
print("skill" in company.lower())  # True

String Methods Analysts Use Daily

code.pyPython

# Strip whitespace
name = "  Rahul  "
print(name.strip())  # "Rahul"

# Replace
email = "user@gmail.com"
cleaned = email.replace("gmail", "company")
print(cleaned)  # user@company.com

# Split
csv_row = "Rahul,Mumbai,50000"
fields = csv_row.split(",")
print(fields)  # ['Rahul', 'Mumbai', '50000']

# Join
cities = ["Mumbai", "Delhi", "Bangalore"]
result = ", ".join(cities)
print(result)  # "Mumbai, Delhi, Bangalore"

String Formatting (F-Strings)

code.pyPython

name = "Priya"
revenue = 125000

# Old way (avoid)
print("Name: " + name + ", Revenue: " + str(revenue))

# Modern way (use this!)
print(f"Name: {name}, Revenue: ₹{revenue:,}")
# Output: Name: Priya, Revenue: ₹125,000

Lists — Ordered Collections

Lists store multiple items in order.

code.pyPython

# List of sales
sales = [100, 200, 150, 300]

# Access by index (starts at 0)
print(sales[0])   # 100 (first)
print(sales[-1])  # 300 (last)

# Slicing
print(sales[1:3])  # [200, 150] (index 1 to 2)

# Length
print(len(sales))  # 4

Modifying Lists

code.pyPython

sales = [100, 200, 150]

# Add item
sales.append(250)         # [100, 200, 150, 250]

# Insert at position
sales.insert(1, 175)      # [100, 175, 200, 150, 250]

# Remove item
sales.remove(150)         # [100, 175, 200, 250]

# Remove by index
del sales[0]              # [175, 200, 250]

# Pop (remove and return last)
last = sales.pop()        # last = 250, sales = [175, 200]

List Operations Analysts Use

code.pyPython

sales = [1200, 1500, 980, 1350, 1100]

# Sum
total = sum(sales)        # 6130

# Average
average = sum(sales) / len(sales)  # 1226.0

# Min / Max
print(min(sales))  # 980
print(max(sales))  # 1500

# Sort
sales.sort()       # [980, 1100, 1200, 1350, 1500]
sales.sort(reverse=True)  # [1500, 1350, 1200, 1100, 980]

# Count occurrences
print(sales.count(1200))  # 1

List Comprehensions (Advanced)

Create lists in one line:

code.pyPython

# Traditional way
squared = []
for x in [1, 2, 3, 4, 5]:
    squared.append(x ** 2)

# List comprehension (Pythonic!)
squared = [x ** 2 for x in [1, 2, 3, 4, 5]]
print(squared)  # [1, 4, 9, 16, 25]

# With condition
even_squared = [x ** 2 for x in range(10) if x % 2 == 0]
print(even_squared)  # [0, 4, 16, 36, 64]

Dictionaries — Key-Value Pairs

Dictionaries store data as key-value pairs (like JSON).

code.pyPython

customer = {
    "name": "Priya",
    "age": 28,
    "city": "Mumbai",
    "premium": True,
    "orders": 15
}

# Access values
print(customer["name"])     # Priya
print(customer.get("age"))  # 28

# Add new key
customer["email"] = "priya@example.com"

# Update value
customer["orders"] = 16

# Check if key exists
if "premium" in customer:
    print("Premium customer!")

Dictionary Methods

code.pyPython

customer = {"name": "Rahul", "age": 25, "city": "Delhi"}

# Get all keys
print(customer.keys())    # dict_keys(['name', 'age', 'city'])

# Get all values
print(customer.values())  # dict_values(['Rahul', 25, 'Delhi'])

# Get key-value pairs
print(customer.items())
# dict_items([('name', 'Rahul'), ('age', 25), ('city', 'Delhi')])

# Loop through dictionary
for key, value in customer.items():
    print(f"{key}: {value}")

Real Example: Sales by Region

code.pyPython

sales_by_region = {
    "North": 500000,
    "South": 350000,
    "East": 280000,
    "West": 420000
}

# Total sales
total = sum(sales_by_region.values())
print(f"Total: ₹{total:,}")  # Total: ₹1,550,000

# Top region
top_region = max(sales_by_region, key=sales_by_region.get)
print(f"Top: {top_region}")  # Top: North

Tuples — Immutable Lists

Tuples are like lists but cannot be changed after creation.

code.pyPython

# Create tuple
coordinates = (19.0760, 72.8777)  # Mumbai lat, lon

# Access
print(coordinates[0])  # 19.0760

# Can't modify (this will error)
# coordinates[0] = 20  # TypeError!

# Use case: returning multiple values from function
def get_stats(numbers):
    return min(numbers), max(numbers), sum(numbers)

min_val, max_val, total = get_stats([10, 20, 30])
print(min_val, max_val, total)  # 10 30 60

Loops — Automate Repetition

For Loop

code.pyPython

# Loop through list
sales = [100, 200, 150]
for sale in sales:
    print(f"Sale amount: ₹{sale}")

# Loop with index
for i, sale in enumerate(sales):
    print(f"Sale {i+1}: ₹{sale}")
# Output:
# Sale 1: ₹100
# Sale 2: ₹200
# Sale 3: ₹150

# Loop through range
for i in range(5):
    print(i)  # 0, 1, 2, 3, 4

# Loop through dictionary
customer = {"name": "Rahul", "age": 25}
for key, value in customer.items():
    print(f"{key}: {value}")

While Loop

code.pyPython

count = 0
while count < 5:
    print(count)
    count += 1

# Real example: process until condition met
revenue = 0
day = 1
while revenue < 100000:
    daily_sales = 15000  # Simplified
    revenue += daily_sales
    print(f"Day {day}: Total ₹{revenue:,}")
    day += 1

Loop Control

code.pyPython

# break - exit loop
for i in range(10):
    if i == 5:
        break
    print(i)  # 0, 1, 2, 3, 4

# continue - skip iteration
for i in range(5):
    if i == 2:
        continue
    print(i)  # 0, 1, 3, 4

Conditional Logic

code.pyPython

revenue = 120000

if revenue > 100000:
    print("High performer")
elif revenue > 50000:
    print("Good performer")
else:
    print("Needs improvement")

Comparison Operators

code.pyPython

# == equal
# != not equal
# >  greater than
# <  less than
# >= greater or equal
# <= less or equal

age = 25
if age >= 18:
    print("Adult")

# Multiple conditions
revenue = 80000
orders = 50

if revenue > 50000 and orders > 40:
    print("Top customer")

if revenue < 10000 or orders == 0:
    print("At-risk customer")

Ternary Operator (One-Liner)

code.pyPython

# Traditional
if revenue > 100000:
    tier = "Premium"
else:
    tier = "Standard"

# Ternary (concise)
tier = "Premium" if revenue > 100000 else "Standard"

Functions — Reusable Code

Functions prevent repeating code.

code.pyPython

def calculate_gst(amount):
    return amount * 1.18

total = calculate_gst(1000)
print(total)  # 1180.0

Functions with Multiple Parameters

code.pyPython

def calculate_discount(price, discount_pct):
    discount_amount = price * (discount_pct / 100)
    final_price = price - discount_amount
    return final_price

final = calculate_discount(1000, 20)
print(final)  # 800.0

Default Parameters

code.pyPython

def greet(name, greeting="Hello"):
    return f"{greeting}, {name}!"

print(greet("Rahul"))              # Hello, Rahul!
print(greet("Priya", "Namaste"))   # Namaste, Priya!

Real Example: Sales Analysis Function

code.pyPython

def analyze_sales(sales_list):
    total = sum(sales_list)
    average = total / len(sales_list)
    highest = max(sales_list)
    lowest = min(sales_list)

    return {
        "total": total,
        "average": average,
        "highest": highest,
        "lowest": lowest
    }

sales = [1200, 1500, 980, 1350, 1100]
stats = analyze_sales(sales)

print(f"Total: ₹{stats['total']:,}")
print(f"Average: ₹{stats['average']:.2f}")
print(f"Highest: ₹{stats['highest']}")
print(f"Lowest: ₹{stats['lowest']}")

Working with Files

Reading Files

code.pyPython

# Read entire file
with open('data.txt', 'r') as file:
    content = file.read()
    print(content)

# Read line by line
with open('data.txt', 'r') as file:
    for line in file:
        print(line.strip())

Writing Files

code.pyPython

# Write (overwrites file)
with open('output.txt', 'w') as file:
    file.write("Total revenue: ₹500,000\n")
    file.write("Orders: 250\n")

# Append (adds to end)
with open('output.txt', 'a') as file:
    file.write("New data\n")

Real Example: Process CSV

code.pyPython

with open('sales.csv', 'r') as file:
    header = file.readline().strip()  # Skip header

    total_sales = 0
    for line in file:
        fields = line.strip().split(',')
        amount = float(fields[2])  # Assuming amount is 3rd column
        total_sales += amount

    print(f"Total sales: ₹{total_sales:,}")

Importing Modules

Python's power comes from libraries.

code.pyPython

# Import entire module
import math
print(math.sqrt(16))  # 4.0

# Import specific function
from math import sqrt, ceil
print(sqrt(16))  # 4.0
print(ceil(3.2))  # 4

# Import with alias
import pandas as pd
import numpy as np

# Common modules for analysts
import datetime
import random
import csv

Error Handling

Handle errors gracefully instead of crashing.

code.pyPython

# Without error handling (will crash if error)
# age = int(input("Enter age: "))

# With error handling
try:
    age = int(input("Enter age: "))
    print(f"Age: {age}")
except ValueError:
    print("Please enter a valid number!")

# Real example: Reading file
try:
    with open('data.txt', 'r') as file:
        content = file.read()
except FileNotFoundError:
    print("File not found!")
except Exception as e:
    print(f"Error: {e}")

Real-World Example: Sales Report Generator

code.pyPython

def generate_sales_report(sales_data):
    """
    Generate sales report from daily sales data

    Args:
        sales_data: list of dictionaries with 'date' and 'amount'

    Returns:
        Dictionary with summary statistics
    """
    if not sales_data:
        return {"error": "No data provided"}

    # Calculate metrics
    amounts = [sale['amount'] for sale in sales_data]
    total = sum(amounts)
    average = total / len(amounts)
    highest_day = max(sales_data, key=lambda x: x['amount'])
    lowest_day = min(sales_data, key=lambda x: x['amount'])

    # Count days above average
    above_avg = sum(1 for amt in amounts if amt > average)

    report = {
        "total_revenue": total,
        "average_daily": average,
        "highest_day": highest_day,
        "lowest_day": lowest_day,
        "days_above_average": above_avg,
        "total_days": len(sales_data)
    }

    return report

# Sample data
sales_data = [
    {"date": "2026-03-01", "amount": 12000},
    {"date": "2026-03-02", "amount": 15000},
    {"date": "2026-03-03", "amount": 9000},
    {"date": "2026-03-04", "amount": 18000},
    {"date": "2026-03-05", "amount": 11000}
]

# Generate report
report = generate_sales_report(sales_data)

# Print formatted report
print("=" * 40)
print("SALES REPORT")
print("=" * 40)
print(f"Total Revenue: ₹{report['total_revenue']:,}")
print(f"Average Daily: ₹{report['average_daily']:,.2f}")
print(f"Highest Day: {report['highest_day']['date']} (₹{report['highest_day']['amount']:,})")
print(f"Lowest Day: {report['lowest_day']['date']} (₹{report['lowest_day']['amount']:,})")
print(f"Days Above Average: {report['days_above_average']}/{report['total_days']}")
print("=" * 40)

Common Mistakes & Fixes

❌ Mistake 1: Forgetting Indentation

Python uses indentation for code blocks.

code.pyPython

# Wrong
if revenue > 100000:
print("High")  # IndentationError!

# Correct
if revenue > 100000:
    print("High")  # 4 spaces or 1 tab

❌ Mistake 2: Using = Instead of ==

code.pyPython

# Wrong (assignment, not comparison)
if revenue = 100000:  # SyntaxError!

# Correct
if revenue == 100000:
    print("Exactly 100k")

❌ Mistake 3: Modifying List While Looping

code.pyPython

# Wrong (can skip items)
sales = [100, 200, 150, 300]
for sale in sales:
    if sale < 200:
        sales.remove(sale)  # Don't do this!

# Correct
sales = [s for s in sales if s >= 200]

❌ Mistake 4: Not Closing Files

code.pyPython

# Wrong (file stays open)
file = open('data.txt', 'r')
content = file.read()
# ... forgot file.close()

# Correct (auto-closes)
with open('data.txt', 'r') as file:
    content = file.read()
# File automatically closed here

Best Practices for Analysts

Use meaningful variable names

code.pyPython

# Bad
x = 50000
y = 12

# Good
monthly_revenue = 50000
num_orders = 12

Comment your code

code.pyPython

# Calculate 20% discount for premium customers
discount = price * 0.20

Use f-strings for formatting

code.pyPython

# Bad
print("Revenue: " + str(revenue))

# Good
print(f"Revenue: ₹{revenue:,}")

Keep functions small and focused
- One function = one task
- If > 20 lines, consider splitting
Handle errors gracefully
- Use try/except for file operations
- Validate user input

Summary

✅ Variables & types - int, float, str, bool ✅ Strings - strip(), split(), replace(), f-strings ✅ Lists - append(), sort(), sum(), list comprehensions ✅ Dictionaries - key-value storage for structured data ✅ Loops - for, while, enumerate(), break, continue ✅ Conditionals - if/elif/else, comparison operators ✅ Functions - reusable code, parameters, return values ✅ Files - read/write with with open() ✅ Modules - import libraries for advanced features ✅ Error handling - try/except for robust code

Next Topic: Pandas for data manipulation! 🐼

You now have Python fundamentals. In the next topic, you'll use Pandas to analyze real datasets with millions of rows!

Python Basics for Analysts

Why Python for Data Analysts?

Setting Up Python

Option 1: Anaconda (Recommended for Analysts)

Option 2: Python.org

Verify Installation

Variables & Data Types

Variables

Data Types

Type Conversion

String Operations

Basic Operations

String Methods Analysts Use Daily

String Formatting (F-Strings)

Lists — Ordered Collections

Modifying Lists

List Operations Analysts Use

List Comprehensions (Advanced)

Dictionaries — Key-Value Pairs

Dictionary Methods

Real Example: Sales by Region

Tuples — Immutable Lists

Loops — Automate Repetition

For Loop

While Loop

Loop Control

Conditional Logic

Comparison Operators

Ternary Operator (One-Liner)

Functions — Reusable Code

Functions with Multiple Parameters

Default Parameters

Real Example: Sales Analysis Function

Working with Files

Reading Files

Writing Files

Real Example: Process CSV

Importing Modules

Error Handling

Real-World Example: Sales Report Generator

Common Mistakes & Fixes

❌ Mistake 1: Forgetting Indentation

❌ Mistake 2: Using = Instead of ==

❌ Mistake 3: Modifying List While Looping

❌ Mistake 4: Not Closing Files

Best Practices for Analysts

Summary