📊 Day 39: Pareto Chart in Python

 

📊 Day 39: Pareto Chart in Python (Interactive Version)

---

🔹 What is a Pareto Chart?

A Pareto Chart combines:

• 📊 Bar Chart → shows frequency

• 📈 Line Chart → shows cumulative percentage

It follows the 80/20 Rule:

80% of problems usually come from 20% of causes.

---

🔹 When Should You Use It?

Use a Pareto chart when:

• Finding the biggest problem

• Prioritizing tasks

• Analyzing complaints

• Identifying major defect causes

---

🔹 Example Scenario

Customer complaints data:

• Late Delivery

• Damaged Product

• Wrong Item

• Poor Support

• Billing Error

We want to know which issue contributes the most.

---

🔹 Python Code (Interactive – Plotly)

import pandas as pd
import plotly.graph_objects as go 

df = pd.DataFrame({'Issue': ['Late Delivery', 'Damaged Product', 'Wrong Item', 'Poor Support', 'Billing Error'],
 'Count': [40, 25, 20, 10, 5]})

 df['cum_prct'] = df['Count'].cumsum() / df['Count'].sum() * 100 
fig = go.Figure() 

fig.add_trace(go.Bar(
    x=df['Issue'],
    y=df['Count'],
    marker_color='#A3B18A',
    name='Frequency'
 ))

fig.add_trace(go.Scatter(
    x=df['Issue'],
    y=df['cum_prct'], 
    yaxis='y2',
    line=dict(color='#BC6C25', width=3), 
    marker=dict(size=10),
    name='Cumulative %'
)) 

fig.update_layout(
   paper_bgcolor='#FAF9F6',
   plot_bgcolor='#FAF9F6',
   font_family="serif", 
   title="Customer Complaints Analysis",
   yaxis=dict(title="Frequency", showgrid=False),
   yaxis2=dict(title="Cumulative %", overlaying='y', side='right', range=[0, 110], showgrid=False),
    width=800,
    height=500,
 margin=dict(t=80, b=40, l=40, r=40)
)

fig.show()
📌 Install if needed:

pip install plotly pandas

---

🔹 Output Explanation (Beginner Friendly)

• The bars show how many complaints each issue has.

• The tallest bar (Late Delivery) is the biggest problem.

• The line shows how the percentage increases as issues are added.

👉 The first few issues make up most of the complaints.
👉 After around 80%, the remaining issues have smaller impact.

This helps you focus on solving the most important problems first.

---

🔹 Why This Version Is Better

✅ Interactive (hover to see exact values)
✅ Cleaner design
✅ No matplotlib warnings
✅ More dashboard-friendly