CAPSTONE PROJECT. OPEN-ONES PROJECT MANAGEMENT SYSTEM. CONTENTS Introduction Plan Requirement Design Implementation Summary Demo and QA.CAPSTONE PROJECT. OPEN-ONES PROJECT MANAGEMENT SYSTEM. CONTENTS Introduction Plan Requirement Design Implementation Summary Demo and QA.CAPSTONE PROJECT. OPEN-ONES PROJECT MANAGEMENT SYSTEM. CONTENTS Introduction Plan Requirement Design Implementation Summary Demo and QA.CAPSTONE PROJECT. OPEN-ONES PROJECT MANAGEMENT SYSTEM. CONTENTS Introduction Plan Requirement Design Implementation Summary Demo and QA.

1. PRESENTATION
TITLE
1
SALES DATA ANALYSIS WITH
SALES REPORTS -PDF
DONE BY
LENIN KUMAR (CH.EN.U4CSE22127)
SRI HARI K (CH.EN.U4CSE22153)
PRITHIVI RS (CH.EN.U4CSE22179)

2. CONTENTS
• INTRODUCTION
• ABSTRACT
• OVERVIEW
• PYTHON USE IN DATA VISUALATION
• LIBRARIES
• REPORT GENERATION
• FUTURE ENHANCEMENT
• CONCLUSION

3. INTRODUCTION
• Data visualization and analysis are powerful tools that empower us to unlock insights,
detect patterns, and uncover hidden trends within data.
• By transforming data into visual representations and applying analytical techniques, we
can make informed decisions, solve problems, and drive innovation.
• Data analysis involves the examination of data to draw conclusions, identify trends, and
make predictions.
• Statistical methods, machine learning algorithms, and exploratory data analysis
techniques play pivotal roles in uncovering insights. Through rigorous analysis,
businesses can optimize processes, enhance decision-making, and gain a competitive
edge.

4. ABSTRACT
• Data visualization is a vital component of modern data analysis and reporting. It involves
representing complex data sets through graphical elements such as charts, graphs, and maps,
making it easier for data analysts, decision-makers, and stakeholders to gain insights and draw
conclusions.
• Data visualization plays a pivotal role in understanding patterns, trends, and anomalies within
data, allowing for data driven decision-making.The Python programming language offers a rich
ecosystem of libraries, such as Matplotlib,Seaborn, pandas , fpdf and Plotly for creating static and
interactive visualizations.
• Python's Pandas library assists in data preparation and manipulation, making it a valuable
companion in the data visualization process

5. OVERVIEW
• Creating a sales analysis and generating reports using Python and a Database Management System (DBMS)
involves several key steps. Initially, a well-designed database schema should be established to store relevant
sales data, encompassing tables for customers, products, orders, and sales transactions.
• SQL queries are then crafted to retrieve pertinent sales data, which is subsequently processed using Python,
often with the aid of libraries like Pandas for data manipulation and analysis.
• Visualization plays a crucial role in sales analysis, and libraries such as Matplotlib or Seaborn can be utilized
to create informative graphs, such as line charts for sales trends or bar charts for product comparisons.
• To formalize insights, reports can be generated using reporting libraries like ReportLab or FPDF, incorporating
visualizations and additional information. For automation, tools like cron jobs or Task Scheduler can be
employed to generate reports periodically.

6. USE OF PYTHON IN DATA VISUALISATION
• Initially, Python interfaces with the Database Management System (DBMS) through libraries like SQLlite3,
establishing a connection that allows the seamless retrieval of sales-related data.
• The subsequent deployment of SQL queries for data retrieval is executed with Python, and the acquired
dataset undergoes intricate processing using Pandas, enabling nuanced analyses.
• Python's prowess extends further into the realm of visualization, where Matplotlib or Seaborn aids in crafting
insightful graphs such as line charts or bar graphs, providing a visual narrative of sales trends.
• Python's scripting capabilities enable the automation of report generation, ensuring the periodic delivery of
up-to-date analyses. Altogether, Python's role in this project is pivotal, seamlessly harmonizing database
interaction, data manipulation, visualization, and report generation to create an efficient and comprehensive
sales analysis system.

7. LIBRARIES USED IN THE CODE
Importing the necessary libraries for the generation:
• from datetime import date
• from pathlib import Path
• from create_database import
• import pandas as pd
• import plotly.express as px
• from fpdf import FPDF

8. 8
PANDA LIBRARY USES
The Pandas library is a popular and powerful data manipulation and analysis
tool in Python.It provides data structures and functions that allow you to work
with structured data efficiently.
• Data Loading: Pandas can load data from various file formats, such as CSV,
Excel, SQL databases, and more. You can use functions like read_csv,
read_excel, and read_sql to import data into Pandas DataFrames.
• Data Exploration: You can explore your data using functions like head, tail, info,
and describe to get a quick overview of your dataset, including summary
statistics.
• Data Analysis and Statistics: You can perform advanced data analysis and
statistics using Pandas.

9. 9
PLOTLY LIBRARY USES
• Plotly is a versatile Python library for creating interactive
visualizations.
• Using the plotly.express module, you can easily generate
interactive charts. For instance, a simple line chart depicting
sales trends over time can be created with just a few lines of
code.
• The library supports various chart types, and its interactive
nature allows users to zoom, pan, and hover over data points
for detailed insights. To utilize Plotly, install it with
pip install plotly

10. 1 0
DATETIME LIBRARY USES
• The datetime module in Python is a versatile tool for
handling dates and times.
• It includes the datetime class, allowing the representation
of specific points in time, and provides methods like
strftime and strptime for formatting and parsing date
strings.
• It is a fundamental module for managing temporal data
in Python, crucial in various applications requiring precise
handling of dates and times.

11. 1 1
FPDF LIBRARY USES
• The FPDF library in Python provides a simple and effective
way to generate PDF documents.
• With basic functionality for adding pages, setting fonts, and
inserting text, it offers a quick solution for creating PDFs from
scratch.
• After installation, users can instantiate an FPDF object, add
pages, set fonts, and include text with ease.
• The library also supports more advanced features such as
images and tables, allowing for the creation of complex and
customized PDF documents.

12. REPORT GENERATING
1 2
In generating a report for the sales analysis project using Python's
FPDF library, the process involves combining analytical insights,
visualizations, and relevant information into a cohesive
document.. Visual representations, such as sales trend charts
created with libraries like Matplotlib or Plotly, can be integrated
into the report for a clear depiction of patterns and trends. Utilizing
the FPDF library, these charts, saved as images, can be
seamlessly embedded into the document. The final PDF report is
then generated, encapsulating the outcomes of the analysis in a
structured and visually appealing document. The flexibility of the
FPDF library allows for customization of the report's structure and
content, making it a versatile tool for communicating insights from
the sales analysis project.

13. BAR GRAPH USES IN DATA ANALYSIS
Bar graphs are commonly used in data analysis for several purposes.
They are a type of data visualization that represents data using
rectangular bars of varying lengths. Here are some of the key uses of
bar graphs in data analysis
# Create the Plotly figure
fig = px.bar(df,
x='customer_name',
y='total_sales',
template=plotly_template,
text='total_sales')
# Set the layout
fig.update_layout(
title='Top Customers by Sales',
xaxis_title='Customer',
yaxis_title='Total Sales ($)',
yaxis_tickprefix='$',

14. TOP COSTUMER SALES : MONTHLY SALES :
PRODUCT SALE:

15. GENERATED PDF REPORT:

16. APPLICATIONS OF DATA VISUALIZATION AND ANALYSIS
Business Analytics and Intelligence:
Visualize sales data, market trends, and key performance indicators (KPIs) to make informed business
decisions
.
Finance:
Create financial reports with visualizations of stock prices, portfolio performance, and economic
indicators.
Healthcare:
Visualize patient data, medical records, and epidemiological trends to improve patient care and public
health.
Marketing:
Analyze customer behavior, campaign performance, and website traffic to optimize marketing strategies.

17. CONCLUSION
1 7
In conclusion, the sales analysis project, rooted in Python and a Database
Management System (DBMS), stands as a robust framework for deriving
meaningful insights from saledata.The integration of SQL facilitates
seamless data retrieval and manipulation, while Python, coupled with the
Pandas library, empowers in-depth analysis and visualization. The
incorporation of the FPDF library enables efficient report generation,
offering a concise and visually appealing representation of sales trends
and key metrics.