Introduction to Python

Python is one of the most popular programming languages used in business analysis, artificial intelligence, automation, and data science. It is known for its simple syntax, making it easy for beginners—especially MBA students without a technical background—to learn quickly. Python allows you to automate reports, analyze datasets, work with Excel/CSV files, and perform advanced analytics with minimal code. Its readability and large community support make it ideal for business decision-making scenarios. Python programs can be written in any text editor and executed instantly, making it a flexible tool for business professionals.

Example Python Program

# Simple Python Program
print("Welcome to Python for Business Analysis!")  # prints text to the screen

Python Data Types: Numbers, Strings, Lists

1. Numbers

Numbers in Python include integers, floating-point numbers, and complex values. They are commonly used in business for calculations such as sales totals, profit margins, interest rates, and forecasting. Python allows direct mathematical operations without requiring complex formulas. You can store numbers in variables and perform addition, subtraction, multiplication, or even financial projections. Numbers can also be combined with strings using formatting, making them ideal for automated report generation.

# Numbers in Python
price = 1200         # integer
tax_rate = 0.18      # float

total = price + (price * tax_rate)   # calculating tax

print("Total Price:", total)

2. Strings

Strings are sequences of characters used to store text such as customer names, email IDs, company titles, or product descriptions. Python provides many string methods for manipulating and analyzing business-related text data. You can clean data, convert text to uppercase, extract email domains, or generate formatted business reports. Strings are flexible, making them essential for preparing automated text-based output.

# Working with Strings
company = "Global Business Solutions"
print(company.upper())             # convert to uppercase
print("Word Count:", len(company.split()))  # count words

3. Lists

Lists store multiple values in a single variable and allow easy addition, removal, or modification of items. They are widely used in business analytics for storing monthly sales numbers, customer groups, product categories, or survey responses. Lists are ordered and dynamically changeable, making them ideal for tasks where data needs to be updated frequently. Python’s list functions help analysts iterate through data and perform calculations efficiently.

# Using Lists in Python
sales = [1200, 1500, 1100, 1800]    # list of monthly sales

sales.append(2000)     # add new sale value
print("Updated Sales:", sales)

print("Highest Sale:", max(sales))

Python Data Types: Tuples and Dictionaries

1. Tuples

Tuples are similar to lists but cannot be changed after creation. This immutability makes them useful for fixed datasets such as product codes, department names, or months of the year. Tuples are faster than lists and ensure data safety by preventing accidental modifications. They are ideal for storing reference information used in business applications.

# Using Tuples
months = ("Jan", "Feb", "Mar", "Apr")

print("Total Months:", len(months))
print("First Month:", months[0])

2. Dictionaries

Dictionaries store data in key-value pairs, making them perfect for representing structured business information. They are commonly used to store customer data, employee records, product details, or business metrics. Dictionaries allow fast lookup and modification using keys, making them ideal for data analysis and report automation.

# Dictionary Example
employee = {
    "name": "John Smith",
    "department": "Finance",
    "salary": 75000
}

print("Employee Name:", employee["name"])
print("Department:", employee["department"])

Files and Exceptions

Working with Files

Python makes it easy to read and write files, which is essential for business analysts who manage reports, financial data, logs, and CSV files. Using Python file handling, analysts can automate report creation, extract information, or save processed data. The with open() structure ensures files are safely opened and closed, reducing errors.

# Writing to a File
with open("report.txt", "w") as file:
    file.write("Sales Report Generated Successfully")

print("File Created!")

Exceptions (Error Handling)

Exceptions allow your program to continue running even when an error occurs. This is important in business applications where missing files, incorrect input, or calculation issues are common. Using a try-except block ensures stability and prevents program crashes, improving overall reliability.

# Example of Exception Handling
try:
    num = int("ABC")       # this will cause an error
except ValueError:
    print("Error: Invalid number entered")

Try this example : Exceptions (Error Handling)

try:
    age = int(input("Enter your age: "))
    print("Your age:", age)
except ValueError:
    print("Please enter a valid number!")

Practice Exercises

• Create a Python program that calculates profit = selling price − cost price.
• Write a program that stores 5 customer names in a list and prints the first and last name.
• Create a dictionary for a product and print each value.
• Write a script to read a text file and count the number of words.
• Use exception handling to prevent errors while dividing numbers.

Types of Operators in Python

Operators in Python are special symbols that perform operations on variables and values. They allow Python to process mathematical expressions, compare results, assign values, and perform logical checks. Operators are essential for business calculations, data filtering, analytics, and automation tasks. There are several types of operators: Arithmetic (like +, -, *), Comparison (like ==, >, <), Logical (and, or, not), Assignment (like +=, -=), and Membership (in, not in). Understanding operators allows analysts to make decisions within code, build rules, and create data-driven workflows efficiently.

# Types of Operators in Python

a = 10
b = 5

print("Arithmetic:", a + b)     # Arithmetic Operator
print("Comparison:", a > b)     # Comparison Operator
print("Logical:", a > 0 and b > 0)   # Logical Operator

x = 20
x += 5     # Assignment Operator
print("Assignment:", x)

print("Membership:", 5 in [1, 2, 3, 4, 5])  # Membership Operator

Classes and Objects

Classes and objects form the foundation of Object-Oriented Programming (OOP) in Python. A class is a blueprint or template for creating objects—similar to how a company policy defines employee roles. An object is an instance of a class, containing specific data and behaviors. This structure helps organize business logic, model real-world systems (such as customer records, invoices, or product catalogs), and create scalable applications. OOP makes programs modular, reusable, and easier to maintain. Classes contain attributes (data) and methods (functions), enabling analysts to structure complex workflows cleanly.

# Example of Classes and Objects

class Employee:
    def __init__(self, name, department):
        self.name = name
        self.department = department

    def show_details(self):
        print(f"Employee: {self.name}, Department: {self.department}")

# Creating an object
emp1 = Employee("John Smith", "Finance")
emp1.show_details()

# Example of Classes and Objects

class Employee:
    def __init__(self, name, department):
        self.name = name
        self.department = department

    def show_details(self):
        print(f"Employee: {self.name}, Department: {self.department}")

# Creating an object
emp1 = Employee("John Smith", "Finance")
emp1.show_details()

class Employee:
    ...

def __init__(self, name, department):
    self.name = name
    self.department = department

self.name = name
self.department = department

def show_details(self):
    print(f"Employee: {self.name}, Department: {self.department}")

emp1 = Employee("John Smith", "Finance")

emp1.show_details()

Employee: John Smith, Department: Finance

Reading Files Using open()

The open() function lets Python read data stored in text files. This is useful for business analysts who deal with reports, logs, survey results, or exported CRM data. Python can read files line-by-line, load complete content, or process large documents efficiently. Using modes like "r" (read), analysts can extract insights, clean raw text, or convert data into structured form. Reading files is a core part of automation workflows such as scheduled analytics reports, financial statement parsing, and batch processing.

# Reading a file using open()

with open("sample.txt", "r") as file:
    data = file.read()
    print(data)

Writing Files Using open()

Python can also write information into files using the same open() function but with the mode "w" (write) or "a" (append). This is extremely useful for automating business reports, storing processed data, creating logs, or exporting results after analysis. The write mode replaces existing content, while append mode adds new content without deleting old data. File-writing helps analysts build automated systems where Python saves updates, summaries, or financial calculations into readable documents.

# Writing data to a file

with open("report.txt", "w") as file:
    file.write("Business Report Generated Successfully")

print("File Saved")

Loading Data with Pandas

import os
import pandas as pd

# 1) Show current working directory (where the CSV will be saved)
print("Current working directory:", os.getcwd())

# 2) Create a small DataFrame
data = {
    "student_id": [101, 102, 103],
    "name": ["Asha", "Ravi", "Maya"],
    "grade": ["5th", "6th", "5th"],
    "score": [88, 92, 79]
}
df = pd.DataFrame(data)
print("\nDataFrame created:")
print(df)

# 3) Save DataFrame to CSV in the current working directory
csv_filename = "students.csv"
df.to_csv(csv_filename, index=False)   # index=False avoids writing the row numbers to file
print(f"\nSaved DataFrame to CSV: {csv_filename}")

# 4) Confirm file exists where expected
full_path = os.path.join(os.getcwd(), csv_filename)
print("Full CSV path:", full_path)
print("File exists?:", os.path.exists(full_path))

# 5) Read the CSV back into a new DataFrame
df_loaded = pd.read_csv(csv_filename)
print("\nCSV loaded back into DataFrame:")
print(df_loaded)

import os
import pandas as pd

print("Current working directory:", os.getcwd())

data = {
    "student_id": [101, 102, 103],
    "name": ["Asha", "Ravi", "Maya"],
    "grade": ["5th", "6th", "5th"],
    "score": [88, 92, 79]
}
df = pd.DataFrame(data)

csv_filename = "students.csv"
df.to_csv(csv_filename, index=False)

full_path = os.path.join(os.getcwd(), csv_filename)
print("File exists?:", os.path.exists(full_path))

df_loaded = pd.read_csv(csv_filename)
print(df_loaded)

Pandas is a powerful data analysis library used heavily in business analytics. It provides a DataFrame—a table-like structure ideal for working with financial reports, sales datasets, customer information, and large CSV/Excel files. With Pandas, loading data is straightforward using functions like read_csv() or read_excel(). Pandas automatically structures raw data, making it easy to clean, filter, summarize, visualize, or export. This allows analysts to convert raw business data into insights with just a few lines of code.

import pandas as pd

# Loading CSV data
df = pd.read_csv("sales.csv")

print(df.head())     # view first 5 rows

Working With and Saving Data Using Pandas

After loading data, Pandas enables powerful operations such as filtering rows, adding new columns, calculating summaries, grouping data, and merging datasets. This makes it ideal for business intelligence tasks such as revenue analysis, customer segmentation, and financial modeling. Once analysis is complete, Pandas allows saving results back into CSV or Excel formats using to_csv() or to_excel(). This helps automate data pipelines and create shareable business reports.

import pandas as pd

# Working with data
df = pd.DataFrame({
    "Product": ["A", "B", "C"],
    "Sales": [1200, 1500, 1000]
})

df["Tax"] = df["Sales"] * 0.18    # adding new column

# Saving the updated data
df.to_csv("updated_sales.csv", index=False)

print(df)

Array-Oriented Programming with NumPy

NumPy (Numerical Python) is the foundation of numerical and scientific computing in Python. It introduces the powerful ndarray object, which is significantly faster and more memory-efficient than Python lists. NumPy allows analysts to perform complex mathematical operations—such as matrix multiplication, statistical analysis, or forecasting—using just a few lines of code. Its vectorized operations eliminate slow Python loops, enabling large-scale business analytics and financial modeling. NumPy arrays support element-wise calculations, broadcasting, reshaping, filtering, and aggregation—making them essential for data preprocessing, machine learning, simulation models, demand forecasting, and quantitative analysis.

import numpy as np

# Creating a NumPy array
sales = np.array([1200, 1500, 1700, 1600])

# Vectorized operations (faster than Python lists)
tax = sales * 0.18       # calculate 18% tax for all values
total_sales = sales + tax

print("Sales:", sales)
print("Tax:", tax)
print("Total Sales:", total_sales)

More NumPy Features (Reshaping, Aggregation)

import numpy as np

data = np.array([[10, 20, 30],
                 [40, 50, 60]])

print("Sum of all values:", data.sum())
print("Row-wise sum:", data.sum(axis=1))
print("Column-wise max:", data.max(axis=0))

Data Cleaning and Preparation

Data cleaning and preparation are critical steps in business analytics because raw data often contains missing values, duplicates, inconsistent formatting, or incorrect types. Before analysis or modeling, data must be standardized, cleaned, and transformed into a usable format. Pandas provides powerful tools for handling missing data, removing duplicates, converting data types, renaming columns, filtering records, and creating new calculated fields. Clean data leads to accurate insights and ensures decisions are based on reliable information. This step is considered the backbone of data science, representing nearly 60–70% of the analytics workflow in most business environments.

import pandas as pd

# Sample raw data
data = {
    "Product": ["A", "B", "C", "C"],
    "Sales": [1200, None, 1500, 1500],
    "City": ["Delhi", "Mumbai", "Delhi", "Delhi"]
}

df = pd.DataFrame(data)

# Cleaning operations
df["Sales"] = df["Sales"].fillna(df["Sales"].mean())   # fill missing values
df = df.drop_duplicates()                              # remove duplicate rows
df["City"] = df["City"].str.upper()                    # standardize text

print(df)

Additional Cleaning Tasks

# More cleaning operations

df["Sales_Tax"] = df["Sales"] * 0.18   # new calculated column

filtered = df[df["Sales"] > 1300]      # filter by condition

print(filtered)

Plotting and Visualization

Plotting and visualization play a crucial role in business analytics, helping convert raw data into meaningful visual patterns. Python’s Matplotlib library is widely used to create charts such as line graphs, bar charts, histograms, pie charts, and scatter plots. These visuals help students and managers quickly observe trends, compare categories, and detect business patterns like seasonality, fluctuations, or outliers. Visualization simplifies communication and supports decision-making by presenting complex information in an easy-to-understand manner. Matplotlib is flexible, customizable, and works well with Pandas, making it an essential tool for reporting, dashboards, and presentations.

Example: Monthly Sales Line Graph

import matplotlib.pyplot as plt

# Sample sales data
months = ["Jan", "Feb", "Mar", "Apr", "May"]
sales = [1200, 1500, 1700, 1600, 2000]

plt.plot(months, sales, marker='o')
plt.title("Monthly Sales Trend")
plt.xlabel("Months")
plt.ylabel("Sales in USD")
plt.grid(True)
plt.show()

Example: Bar Chart of City-wise Revenue

cities = ["Delhi", "Mumbai", "Chennai", "Kolkata"]
revenue = [50000, 60000, 45000, 52000]

plt.bar(cities, revenue)
plt.title("Revenue by City")
plt.xlabel("City")
plt.ylabel("Revenue")
plt.show()

Data Aggregation and Group Operations

Data aggregation is the process of summarizing information to identify meaningful patterns. In business analytics, this includes performing operations such as sum, mean, count, max, or min across different groups. Pandas provides the groupby() function, which allows analysts to group data based on categories like region, product type, or month, and perform descriptive statistics. Group operations are essential for tasks such as analyzing total sales by region, average revenue per product, customer segmentation, or comparing performance across time periods. Aggregation transforms raw transactional data into insights suitable for dashboards and strategic decisions.

Example: Aggregating Sales by City

import pandas as pd

# Sample business data
data = {
    "City": ["Delhi", "Mumbai", "Delhi", "Chennai", "Mumbai"],
    "Sales": [1200, 1500, 1800, 1300, 1700]
}

df = pd.DataFrame(data)

# Group by City and calculate total sales
city_sales = df.groupby("City")["Sales"].sum()

print(city_sales)

Example: Multiple Aggregations (Sum, Mean)

product_data = {
    "Product": ["A", "A", "B", "B", "C"],
    "Revenue": [2000, 2500, 1800, 2200, 3000]
}

df2 = pd.DataFrame(product_data)

# Apply multiple aggregation functions
summary = df2.groupby("Product")["Revenue"].agg(["sum", "mean", "max"])

print(summary)