🎯 Project using Excel

 

🎯 Project: Sales Analysis from Excel Using pandas & matplotlib

📌 Objective

• Read sales data from Excel

• Clean the data

• Analyze monthly performance

• Create graphs using matplotlib

• Save processed data back to Excel

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📂 Sample Excel File (sales.xlsx)

Your Excel file may look like this:

Month	Sales
Jan	12000
Feb	15000
Mar	18000
Apr	16000
May	20000
Jun	22000
Jul	21000
Aug	25000
Sep	23000
Oct	24000
Nov	26000
Dec	30000

Save as sales.xlsx.

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✅ Step 1: Read Excel File

import pandas as pd

df = pd.read_excel("sales.xlsx")
print(df)

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✅ Step 2: Check for missing values

print(df.isnull().sum())

Fill missing values with 0 (if any):

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

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✅ Step 3: Basic Analysis

✔ Total yearly sales

total_sales = df["Sales"].sum()
print("Total Sales:", total_sales)

✔ Highest & lowest month

max_month = df.loc[df["Sales"].idxmax()]
min_month = df.loc[df["Sales"].idxmin()]

print("Best Month:", max_month["Month"], max_month["Sales"])
print("Worst Month:", min_month["Month"], min_month["Sales"])

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📊 Step 4: Create Charts using matplotlib

Import library

import matplotlib.pyplot as plt

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📈 Line Chart – Monthly Sales Trend

plt.figure(figsize=(10,5))
plt.plot(df["Month"], df["Sales"], marker='o')
plt.xlabel("Month")
plt.ylabel("Sales")
plt.title("Monthly Sales Trend")
plt.grid(True)
plt.show()

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📊 Bar Chart

plt.figure(figsize=(10,5))
plt.bar(df["Month"], df["Sales"])
plt.xlabel("Month")
plt.ylabel("Sales")
plt.title("Sales by Month")
plt.show()

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🥧 Pie Chart

plt.figure(figsize=(8,8))
plt.pie(df["Sales"], labels=df["Month"], autopct="%1.1f%%")
plt.title("Sales Contribution by Month")
plt.show()

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📝 Step 5: Save Results Back to Excel

with pd.ExcelWriter("sales_analysis.xlsx") as writer:
    df.to_excel(writer, sheet_name="Cleaned_Data", index=False)

print("New Excel file saved!")