Python Pandas - Real-Time Examples

What is Pandas?

Pandas is a popular Python library used for data analysis and data manipulation. It provides powerful data structures such as Series and DataFrame.

Pandas is commonly used when working with CSV files, Excel data, databases, business reports, customer information, sales records, financial data, and other structured datasets.

Series

A one-dimensional labeled collection of data.

DataFrame

A two-dimensional table containing rows and columns.

Data Cleaning

Pandas provides tools for handling missing, duplicate, incorrect, and inconsistent data.

Data Analysis

You can filter, sort, group, summarize, and analyze large datasets efficiently.

1. Installing Pandas

Install Pandas using pip.

Terminal

pip install pandas

In Jupyter Notebook:

Jupyter Notebook

!pip install pandas

2. Importing Pandas

The standard convention is to import Pandas using the alias pd.

Python

import pandas as pd

print(pd.__version__)

Note: pd is the commonly used alias for Pandas.

3. Creating a Pandas Series

A Pandas Series is a one-dimensional labeled data structure.

Series Example

import pandas as pd

numbers = pd.Series([10, 20, 30, 40, 50])

print(numbers)

0 10 1 20 2 30 3 40 4 50 dtype: int64

Real-Time Example: Product Prices

Product Prices

import pandas as pd

prices = pd.Series(
    [499, 799, 1299, 999],
    index=["Laptop Bag", "Keyboard", "Headphones", "Mouse"]
)

print(prices)

Laptop Bag 499 Keyboard 799 Headphones 1299 Mouse 999 dtype: int64

4. Creating a DataFrame

A DataFrame is a two-dimensional table with rows and columns.

Raw Data

→

DataFrame

→

Analysis

→

Business Result

DataFrame

import pandas as pd

data = {
    "Name": ["Rahul", "Priya", "Amit"],
    "Age": [25, 28, 24],
    "City": ["Pune", "Mumbai", "Delhi"]
}

df = pd.DataFrame(data)

print(df)

Name Age City 0 Rahul 25 Pune 1 Priya 28 Mumbai 2 Amit 24 Delhi

5. Viewing Data

Pandas provides several methods for quickly inspecting a DataFrame.

head() and tail()

import pandas as pd

df = pd.DataFrame({
    "Name": ["A", "B", "C", "D", "E"],
    "Salary": [30000, 40000, 50000, 60000, 70000]
})

print(df.head(3))
print(df.tail(2))

head() displays the first rows, while tail() displays the last rows.

6. DataFrame Shape and Information

shape, columns and info

import pandas as pd


df = pd.DataFrame({
"Name": ["Rahul", "Priya", "Amit"],
"Age": [25, 28, 24],
"Salary": [45000, 55000, 40000]
})

print("Shape:", df.shape)
print("Columns:", df.columns)

df.info()

Output

Shape: (3, 3)


Columns: Index(['Name', 'Age', 'Salary'], dtype='object')


RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):

Column Non-Null Count Dtype

0 Name 3 non-null object
1 Age 3 non-null int64
2 Salary 3 non-null int64

dtypes: int64(2), object(1)
memory usage: 204.0+ bytes

shape gives the number of rows and columns. columns gives the column names. info() provides useful information about data types and non-null values.

7. Selecting a Column

You can select a single column using its column name.

Select Column

import pandas as pd

df = pd.DataFrame({
    "Name": ["Rahul", "Priya", "Amit"],
    "Salary": [45000, 55000, 40000]
})

print(df["Salary"])

0 45000 1 55000 2 40000 Name: Salary, dtype: int64

Selecting Multiple Columns

Multiple Columns

result = df[["Name", "Salary"]]

print(result)

8. Selecting Rows with iloc

iloc selects rows and columns using integer positions.

iloc

import pandas as pd


df = pd.DataFrame({
"Name": ["Rahul", "Priya", "Amit"],
"Age": [25, 28, 24],
"Salary": [45000, 55000, 40000]
})

print(df.iloc[0])
print(df.iloc[0:2])

▶ Output

Name        Rahul


Age 25
Salary 45000
Name: 0, dtype: object

Name  Age  Salary


0 Rahul 25 45000
1 Priya 28 55000

iloc is useful when you know the numerical position of the row or column.

9. Selecting Rows with loc

loc selects rows using labels and can also be used with conditions.

loc

import pandas as pd


df = pd.DataFrame({
"Name": ["Rahul", "Priya", "Amit"],
"Age": [25, 28, 24],
"Salary": [45000, 55000, 40000]
})

print(df.loc[0])

print(df.loc[df["Salary"] > 40000])

▶ Output

Name        Rahul


Age 25
Salary 45000
Name: 0, dtype: object

Name  Age  Salary


0 Rahul 25 45000
1 Priya 28 55000

loc uses row labels, while conditions can be used to filter rows based on column values.

10. Filtering Data

Filtering is one of the most commonly used Pandas operations in real-world data analysis.

Salary Filter

import pandas as pd

employees = pd.DataFrame({
    "Name": ["Rahul", "Priya", "Amit", "Sneha"],
    "Salary": [45000, 65000, 38000, 72000]
})

high_salary = employees[
    employees["Salary"] > 50000
]

print(high_salary)

Name Salary 1 Priya 65000 3 Sneha 72000

Real-Time Example: Products Above ₹1000

Product Filtering

import pandas as pd

products = pd.DataFrame({
    "Product": ["Mouse", "Keyboard", "Monitor", "Laptop"],
    "Price": [800, 1500, 12000, 55000]
})

expensive = products[
    products["Price"] > 1000
]

print(expensive)

11. Multiple Conditions

Use & for AND conditions and | for OR conditions.

Multiple Filters

import pandas as pd

employees = pd.DataFrame({
    "Name": ["A", "B", "C", "D"],
    "Age": [24, 32, 28, 40],
    "Salary": [35000, 60000, 55000, 80000]
})

result = employees[
    (employees["Age"] > 25) &
    (employees["Salary"] > 50000)
]

print(result)

Use parentheses around individual conditions when combining multiple Pandas conditions.

12. Adding a New Column

New columns can be created using existing columns.

New Column

import pandas as pd

employees = pd.DataFrame({
    "Name": ["Rahul", "Priya", "Amit"],
    "Salary": [40000, 50000, 60000]
})

employees["Annual Salary"] = employees["Salary"] * 12

print(employees)

Name Salary Annual Salary 0 Rahul 40000 480000 1 Priya 50000 600000 2 Amit 60000 720000

13. Updating a Column

Salary Increment

import pandas as pd

employees = pd.DataFrame({
    "Name": ["Rahul", "Priya", "Amit"],
    "Salary": [40000, 50000, 60000]
})

employees["Salary"] = employees["Salary"] * 1.10

print(employees)

This applies a 10% salary increase to every employee.

14. Sorting Data

Use sort_values() to sort a DataFrame.

Sort by Salary

import pandas as pd

employees = pd.DataFrame({
    "Name": ["Rahul", "Priya", "Amit", "Sneha"],
    "Salary": [45000, 65000, 40000, 72000]
})

sorted_data = employees.sort_values(
    by="Salary",
    ascending=False
)

print(sorted_data)

Name Salary 3 Sneha 72000 1 Priya 65000 0 Rahul 45000 2 Amit 40000

15. Basic Statistical Analysis

Pandas provides convenient functions for analyzing numerical columns.

Statistics

import pandas as pd

sales = pd.DataFrame({
    "Sales": [12000, 15000, 18000, 22000, 25000]
})

print("Total:", sales["Sales"].sum())
print("Average:", sales["Sales"].mean())
print("Minimum:", sales["Sales"].min())
print("Maximum:", sales["Sales"].max())
print("Median:", sales["Sales"].median())

Total: 92000 Average: 18400.0 Minimum: 12000 Maximum: 25000 Median: 18000.0

16. describe() Function

The describe() method provides a statistical summary of numerical columns.

describe()

import pandas as pd


df = pd.DataFrame({
"Age": [22, 25, 28, 31, 35, 40],
"Salary": [30000, 40000, 50000, 60000, 70000, 90000]
})

print(df.describe())

▶ Output

             Age        Salary


count 6.000000 6.000000
mean 30.166667 56666.666667
std 6.645801 21602.468995
min 22.000000 30000.000000
25% 25.750000 42500.000000
50% 29.500000 55000.000000
75% 34.000000 67500.000000
max 40.000000 90000.000000

It provides values such as count, mean, standard deviation, minimum, quartiles, and maximum.

17. Handling Missing Values

Real-world datasets often contain missing values. Pandas provides isnull(), dropna(), and fillna().

Detect Missing Values

import pandas as pd

df = pd.DataFrame({
    "Name": ["Rahul", "Priya", "Amit"],
    "Age": [25, None, 30],
    "Salary": [45000, 55000, None]
})

print(df.isnull())

Fill Missing Values

fillna()

df["Age"] = df["Age"].fillna(
    df["Age"].mean()
)

df["Salary"] = df["Salary"].fillna(0)

print(df)

18. Removing Missing Rows

dropna()

import pandas as pd

df = pd.DataFrame({
    "Name": ["Rahul", "Priya", "Amit"],
    "Salary": [45000, None, 60000]
})

clean_data = df.dropna()

print(clean_data)

Important: dropna() removes rows containing missing values. In real applications, decide whether removing the row or filling the value is more appropriate for your dataset.

19. Removing Duplicate Data

Use drop_duplicates() to remove duplicate rows.

Duplicate Records

import pandas as pd

customers = pd.DataFrame({
    "Name": ["Rahul", "Priya", "Rahul", "Amit"],
    "City": ["Pune", "Mumbai", "Pune", "Delhi"]
})

clean_customers = customers.drop_duplicates()

print(clean_customers)

Name City 0 Rahul Pune 1 Priya Mumbai 3 Amit Delhi

20. Renaming Columns

rename()

import pandas as pd

df = pd.DataFrame({
    "emp_name": ["Rahul", "Priya"],
    "emp_salary": [45000, 55000]
})

df = df.rename(columns={
    "emp_name": "Name",
    "emp_salary": "Salary"
})

print(df)

21. Reading CSV Files

CSV files are commonly used to store tabular business data. Pandas can read them using read_csv().

Read CSV

import pandas as pd

df = pd.read_csv("employees.csv")

print(df.head())

Real-Time Example: Sales CSV

sales.csv

Date,Product,Sales
2026-01-01,Laptop,55000
2026-01-02,Mouse,1200
2026-01-03,Keyboard,2500
2026-01-04,Monitor,18000

Python

import pandas as pd

sales = pd.read_csv("sales.csv")

print(sales)

22. Writing Data to CSV

Use to_csv() to save a DataFrame as a CSV file.

Export CSV

import pandas as pd

df = pd.DataFrame({
    "Name": ["Rahul", "Priya", "Amit"],
    "Salary": [45000, 55000, 60000]
})

df.to_csv("employees.csv", index=False)

index=False prevents Pandas from writing the DataFrame index as an additional CSV column.

23. Reading Excel Files

Pandas can also work with Excel files.

Read Excel

import pandas as pd

df = pd.read_excel("employees.xlsx")

print(df.head())

Depending on your environment, you may need an Excel engine such as openpyxl.

24. Writing Data to Excel

Export Excel

import pandas as pd

df = pd.DataFrame({
    "Product": ["Laptop", "Mouse", "Keyboard"],
    "Sales": [55000, 1200, 2500]
})

df.to_excel(
    "sales_report.xlsx",
    index=False
)

25. GroupBy

groupby() is one of the most important Pandas features for business data analysis.

Sales by Department

import pandas as pd

sales = pd.DataFrame({
    "Department": [
        "Electronics",
        "Clothing",
        "Electronics",
        "Clothing",
        "Electronics"
    ],
    "Sales": [
        50000,
        30000,
        45000,
        25000,
        60000
    ]
})

result = sales.groupby(
    "Department"
)["Sales"].sum()

print(result)

Department Clothing 55000 Electronics 155000 Name: Sales, dtype: int64

26. GroupBy with Multiple Aggregations

Business Summary

import pandas as pd


sales = pd.DataFrame({
"Department": [
"Electronics",
"Electronics",
"Clothing",
"Clothing"
],
"Sales": [50000, 60000, 30000, 25000]
})

summary = sales.groupby(
"Department"
)["Sales"].agg(
["sum", "mean", "max", "min"]
)

print(summary)

▶ Output

             sum     mean    max    min


Department
Clothing 55000 27500.0 30000 25000
Electronics 110000 55000.0 60000 50000

This is useful for generating department-wise business reports.

27. Counting Values

value_counts() counts how frequently each value occurs.

Customer Cities

import pandas as pd

customers = pd.DataFrame({
    "City": [
        "Pune",
        "Mumbai",
        "Pune",
        "Delhi",
        "Pune",
        "Mumbai"
    ]
})

print(customers["City"].value_counts())

City Pune 3 Mumbai 2 Delhi 1 Name: count, dtype: int64

28. Applying Functions with apply()

The apply() method can apply a function to values in a Series or DataFrame.

Salary Category

import pandas as pd

employees = pd.DataFrame({
    "Name": ["Rahul", "Priya", "Amit"],
    "Salary": [35000, 55000, 85000]
})

def category(salary):
    if salary >= 70000:
        return "High"
    elif salary >= 50000:
        return "Medium"
    else:
        return "Low"

employees["Category"] = employees["Salary"].apply(category)

print(employees)

Name Salary Category 0 Rahul 35000 Low 1 Priya 55000 Medium 2 Amit 85000 High

29. Combining DataFrames with concat()

Use pd.concat() to combine DataFrames.

Combine DataFrames

import pandas as pd


north = pd.DataFrame({
"Employee": ["Rahul", "Priya"],
"Region": ["North", "North"]
})

south = pd.DataFrame({
"Employee": ["Amit", "Sneha"],
"Region": ["South", "South"]
})

all_employees = pd.concat(
[north, south],
ignore_index=True
)

print(all_employees)

▶ Output

  Employee Region


0 Rahul North
1 Priya North
2 Amit South
3 Sneha South

30. Merging DataFrames

merge() is useful when information is stored across multiple tables.

Customer and Order Data

import pandas as pd

customers = pd.DataFrame({
    "CustomerID": [1, 2, 3],
    "Name": ["Rahul", "Priya", "Amit"]
})

orders = pd.DataFrame({
    "CustomerID": [1, 2, 3],
    "Amount": [5000, 7500, 3200]
})

result = pd.merge(
    customers,
    orders,
    on="CustomerID"
)

print(result)

CustomerID Name Amount 0 1 Rahul 5000 1 2 Priya 7500 2 3 Amit 3200

This is similar to combining related tables using a common key.

31. Working with Dates

Pandas provides powerful tools for working with dates and time-series data.

Datetime

import pandas as pd


dates = pd.to_datetime([
"2026-01-01",
"2026-02-15",
"2026-03-20"
])

print(dates)

▶ Output

DatetimeIndex(['2026-01-01', '2026-02-15', '2026-03-20'],
          dtype='datetime64[ns]', freq=None)

Real-Time Example: Order Dates

Order Analysis

import pandas as pd


orders = pd.DataFrame({
"OrderDate": [
"2026-01-10",
"2026-02-15",
"2026-03-20"
],
"Amount": [5000, 7500, 9000]
})

orders["OrderDate"] = pd.to_datetime(
orders["OrderDate"]
)

orders["Month"] = orders["OrderDate"].dt.month

print(orders)

▶ Output

   OrderDate  Amount  Month


0 2026-01-10 5000 1
1 2026-02-15 7500 2
2 2026-03-20 9000 3

32. Real-Time Example: Employee Management

Pandas can be used to analyze employee information stored in a structured table.

Employee Analysis

import pandas as pd


employees = pd.DataFrame({
"Name": [
"Rahul",
"Priya",
"Amit",
"Sneha",
"Vikas"
],
"Department": [
"IT",
"HR",
"IT",
"Finance",
"IT"
],
"Salary": [
60000,
50000,
75000,
65000,
80000
]
})

print("Average salary:")
print(employees["Salary"].mean())

print("\nIT employees:")
print(
employees[
employees["Department"] == "IT"
]
)

print("\nDepartment salary:")
print(
employees.groupby(
"Department"
)["Salary"].mean()
)

▶ Output

Average salary:


66000.0

IT employees:
Name Department Salary
0 Rahul IT 60000
2 Amit IT 75000
4 Vikas IT 80000

Department salary:
Department
Finance 65000.0
HR 50000.0
IT 71666.666667
Name: Salary, dtype: float64

33. Real-Time Example: E-Commerce Sales Analysis

An e-commerce company can use Pandas to understand product sales and revenue.

E-Commerce Analysis

import pandas as pd


sales = pd.DataFrame({
"Product": [
"Laptop",
"Mouse",
"Keyboard",
"Laptop",
"Mouse",
"Monitor"
],
"Quantity": [
2, 10, 5, 3, 8, 4
],
"Price": [
55000,
800,
1500,
55000,
800,
12000
]
})

sales["Revenue"] = (
sales["Quantity"] *
sales["Price"]
)

print(sales)

print("\nTotal Revenue:")
print(sales["Revenue"].sum())

print("\nRevenue by Product:")
print(
sales.groupby(
"Product"
)["Revenue"].sum()
)

▶ Output

    Product  Quantity  Price  Revenue


0 Laptop 2 55000 110000
1 Mouse 10 800 8000
2 Keyboard 5 1500 7500
3 Laptop 3 55000 165000
4 Mouse 8 800 6400
5 Monitor 4 12000 48000

Total Revenue:
344000

Revenue by Product:
Product
Keyboard 7500
Laptop 275000
Monitor 48000
Mouse 14400
Name: Revenue, dtype: int64

This pattern is commonly used to transform transaction data into business reports.

34. Real-Time Example: Customer Analysis

Customer Data

import pandas as pd


customers = pd.DataFrame({
"Name": [
"Rahul",
"Priya",
"Amit",
"Sneha",
"Vikas"
],
"City": [
"Pune",
"Mumbai",
"Pune",
"Delhi",
"Mumbai"
],
"Orders": [
5, 8, 3, 10, 6
],
"TotalSpent": [
15000,
28000,
9000,
35000,
22000
]
})

print("Average spending:")
print(customers["TotalSpent"].mean())

print("\nTop customers:")
print(
customers.sort_values(
"TotalSpent",
ascending=False
).head(3)
)

print("\nCustomers by city:")
print(
customers["City"].value_counts()
)

▶ Output

Average spending:


21800.0

Top customers:
Name City Orders TotalSpent
3 Sneha Delhi 10 35000
1 Priya Mumbai 8 28000
4 Vikas Mumbai 6 22000

Customers by city:
City
Mumbai 2
Pune 2
Delhi 1
Name: count, dtype: int64

35. Real-Time Example: Inventory Management

Pandas can identify products that require restocking.

Inventory

import pandas as pd


inventory = pd.DataFrame({
"Product": [
"Laptop",
"Mouse",
"Keyboard",
"Monitor",
"Printer"
],
"Stock": [
25,
5,
8,
3,
20
],
"ReorderLevel": [
10,
10,
10,
5,
10
]
})

inventory["Needs Restock"] = (
inventory["Stock"] <
inventory["ReorderLevel"]
)

print(inventory)

print("\nProducts requiring restock:")
print(
inventory[
inventory["Needs Restock"]
]
)

▶ Output

    Product  Stock  ReorderLevel  Needs Restock


0 Laptop 25 10 False
1 Mouse 5 10 True
2 Keyboard 8 10 True
3 Monitor 3 5 True
4 Printer 20 10 False

Products requiring restock:
Product Stock ReorderLevel Needs Restock
1 Mouse 5 10 True
2 Keyboard 8 10 True
3 Monitor 3 5 True

36. Real-Time Example: Student Result Analysis

Student Results

import pandas as pd


students = pd.DataFrame({
"Name": [
"Rahul",
"Priya",
"Amit",
"Sneha"
],
"Math": [85, 92, 68, 78],
"Science": [80, 95, 70, 82],
"English": [88, 90, 65, 85]
})

students["Average"] = students[
["Math", "Science", "English"]
].mean(axis=1)

students["Result"] = students["Average"].apply(
lambda x: "Pass" if x >= 40 else "Fail"
)

print(students)

print("\nTop students:")
print(
students.sort_values(
"Average",
ascending=False
)
)

▶ Output

    Name  Math  Science  English    Average Result


0 Rahul 85 80 88 84.333333 Pass
1 Priya 92 95 90 92.333333 Pass
2 Amit 68 70 65 67.666667 Pass
3 Sneha 78 82 85 81.666667 Pass

Top students:
Name Math Science English Average Result
1 Priya 92 95 90 92.333333 Pass
0 Rahul 85 80 88 84.333333 Pass
3 Sneha 78 82 85 81.666667 Pass
2 Amit 68 70 65 67.666667 Pass

37. Real-Time Example: Monthly Sales Report

This example demonstrates a common reporting workflow: load data, calculate revenue, group data, and generate a summary.

Monthly Sales Report

import pandas as pd


sales = pd.DataFrame({
"Month": [
"January",
"January",
"February",
"February",
"March",
"March"
],
"Product": [
"Laptop",
"Mouse",
"Laptop",
"Keyboard",
"Laptop",
"Monitor"
],
"Quantity": [
2, 10, 3, 8, 4, 5
],
"Price": [
55000, 800,
55000, 1500,
55000, 12000
]
})

sales["Revenue"] = (
sales["Quantity"] *
sales["Price"]
)

monthly_report = sales.groupby(
"Month"
)["Revenue"].sum()

print("Monthly Revenue:")
print(monthly_report)

print("\nTotal Revenue:")
print(sales["Revenue"].sum())

▶ Output

Monthly Revenue:


Month
February 177000
January 118000
March 280000
Name: Revenue, dtype: int64

Total Revenue:
575000

38. Useful Pandas Functions

Function / Method	Purpose
`pd.Series()`	Create a Series
`pd.DataFrame()`	Create a DataFrame
`pd.read_csv()`	Read CSV data
`pd.read_excel()`	Read Excel data
`df.head()`	View first rows
`df.tail()`	View last rows
`df.info()`	View DataFrame information
`df.describe()`	Generate statistical summary
`df.sort_values()`	Sort data
`df.groupby()`	Group data
`df.dropna()`	Remove missing rows
`df.fillna()`	Fill missing values
`df.drop_duplicates()`	Remove duplicate records
`df.merge()`	Combine related DataFrames
`pd.concat()`	Concatenate DataFrames
`df.to_csv()`	Export to CSV
`df.to_excel()`	Export to Excel

39. Pandas DataFrame vs Series

Feature	Series	DataFrame
Dimensions	1D	2D
Looks like	Single column	Table
Example	`pd.Series()`	`pd.DataFrame()`
Common use	Single column of data	Complete dataset

40. Complete Real-Time Example: Business Sales Analysis

This example combines several important Pandas concepts into one realistic business workflow.

Complete Business Example

import pandas as pd


sales = pd.DataFrame({
"Date": [
"2026-01-05",
"2026-01-06",
"2026-01-07",
"2026-01-08",
"2026-01-09",
"2026-01-10"
],
"Product": [
"Laptop",
"Mouse",
"Laptop",
"Keyboard",
"Monitor",
"Mouse"
],
"Category": [
"Electronics",
"Accessories",
"Electronics",
"Accessories",
"Electronics",
"Accessories"
],
"Quantity": [
2, 10, 3, 8, 4, 12
],
"Price": [
55000,
800,
55000,
1500,
12000,
800
]
})

Convert date column

sales["Date"] = pd.to_datetime(
sales["Date"]
)

Calculate revenue

sales["Revenue"] = (
sales["Quantity"] *
sales["Price"]
)

print("Complete Sales Data:")
print(sales)

Total revenue

total_revenue = sales["Revenue"].sum()

print("\nTotal Revenue:")
print(total_revenue)

Product-wise revenue

product_sales = sales.groupby(
"Product"
)["Revenue"].sum()

print("\nProduct-wise Revenue:")
print(product_sales)

Category-wise revenue

category_sales = sales.groupby(
"Category"
)["Revenue"].sum()

print("\nCategory-wise Revenue:")
print(category_sales)

Best-selling transactions

top_sales = sales.sort_values(
"Revenue",
ascending=False
).head(3)

print("\nTop 3 Transactions:")
print(top_sales)

▶ Output

Complete Sales Data:
    Date   Product     Category  Quantity  Price  Revenue


0 2026-01-05 Laptop Electronics 2 55000 110000
1 2026-01-06 Mouse Accessories 10 800 8000
2 2026-01-07 Laptop Electronics 3 55000 165000
3 2026-01-08 Keyboard Accessories 8 1500 12000
4 2026-01-09 Monitor Electronics 4 12000 48000
5 2026-01-10 Mouse Accessories 12 800 9600

Total Revenue:
352600

Product-wise Revenue:
Product
Keyboard 12000
Laptop 275000
Monitor 48000
Mouse 17600
Name: Revenue, dtype: int64

Category-wise Revenue:
Category
Accessories 29600
Electronics 323000
Name: Revenue, dtype: int64

Top 3 Transactions:
Date Product Category Quantity Price Revenue
2 2026-01-07 Laptop Electronics 3 55000 165000
0 2026-01-05 Laptop Electronics 2 55000 110000
4 2026-01-09 Monitor Electronics 4 12000 48000

This demonstrates a typical Pandas workflow: load data → clean data → transform data → calculate values → group data → sort data → generate business insights.

41. Pandas Best Practices

Use import pandas as pd as the standard import.
Inspect data using head(), tail(), and info().
Use describe() to understand numerical data.
Check missing values before performing analysis.
Use meaningful column names.
Use vectorized Pandas operations when possible.
Use groupby() for grouped business analysis.
Use sort_values() to identify top or bottom records.
Use merge() when combining related datasets.
Use concat() when stacking compatible DataFrames.
Convert date columns using pd.to_datetime().
Be careful when using dropna() because it removes data.
Save processed datasets using to_csv() or to_excel().

42. Important Pandas Rules

Pandas is primarily used for data manipulation and analysis.
Series represents one-dimensional labeled data.
DataFrame represents two-dimensional tabular data.
Use loc for label-based selection.
Use iloc for position-based selection.
Use boolean conditions to filter rows.
Use groupby() to summarize data by categories.
Use fillna() or dropna() to handle missing values.
Use drop_duplicates() to remove duplicate records.
Use merge() to combine related datasets.
Use pd.concat() to combine compatible DataFrames.
Use read_csv() and read_excel() to load external data.
Use to_csv() and to_excel() to export processed data.
Pandas works especially well with NumPy for numerical data processing.