✅ 📘 Advanced (Hard) Level Projects of DataFrame

 

Beginner Level Projects of DataFrame

 Intermediate Level Projects

7️⃣ Project: Stock Market Trend Analysis

Objective: Compare Open, Close prices + Moving Average.

Concepts Used:

• Time-series indexing

• Rolling window

• Multiple line plots

Example Code:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("stocks.csv")  # date, open, close
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)

df['MA20'] = df['close'].rolling(20).mean()

plt.plot(df.index, df['close'], label='Close Price')
plt.plot(df.index, df['MA20'], label='20-Day MA')
plt.legend()
plt.title("Stock Trend Analysis")
plt.show()

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8️⃣ Project: Weather Data – Heatmap

Objective: Show temperature of 12 months & 30 days in a heatmap.

Concepts Used: Pivot table, .imshow(), color mapping.

Example Code:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("weather.csv")  # columns: month, day, temp

pivot = df.pivot('day', 'month', 'temp')

plt.imshow(pivot, aspect='auto')
plt.colorbar(label='Temperature')
plt.title("Monthly Temperature Heatmap")
plt.show()

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9️⃣ Project: Advanced Sales Dashboard (Bar + Line + Pie)

Objective: Build a mini dashboard using matplotlib subplots.

Concepts Used:

• Subplots

• Aggregation

• Advanced plotting

Example Code:

import pandas as pd
import matplotlib.pyplot as plt

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

plt.figure(figsize=(12,8))

# 1. Bar – category sales
plt.subplot(2,2,1)
cat = df.groupby('category')['sales'].sum()
plt.bar(cat.index, cat.values)
plt.title("Category Sales")

# 2. Line – monthly trend
plt.subplot(2,2,2)
monthly = df.groupby('month')['sales'].sum()
plt.plot(monthly.index, monthly.values, marker='o')
plt.title("Monthly Sales Trend")

# 3. Pie – region split
plt.subplot(2,2,3)
region = df.groupby('region')['sales'].sum()
plt.pie(region.values, labels=region.index, autopct="%1.1f%%")
plt.title("Region Sales Share")

plt.tight_layout()
plt.show()