python file uploaded for sharing

x2 Demo with sample data (random 'NaN' in every runtime):
- Linear interpolation
- Polynomial interpolation

Author: ieee-sun

interpolation.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Module Name: Linear Interpolation
+Description: A brief demo of inspecting, handling, imputating Missing Data, i.e. 'NaN', with Linear Interpolation.
+
+Credit / Prepared by: 
+Sun CHUNG, SMIEEE 
+M.Sc., HKU
+License: MIT License
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Sample time series data with missing values
+dates = pd.date_range(start='2020-01-01', end='2020-01-10')
+sales = [200, np.nan, 210, np.nan, np.nan, 250, 260, np.nan, 280, 290]
+data = pd.DataFrame({'date': dates, 'sales': sales})
+
+print("Original Data:")
+data
+
+### By Observing this example, we know it is 'sales' data with the most missing data.
+
+# Check for missing values
+missing_data = data['sales'].isnull().sum()
+total_data = len(data)
+missing_percentage = (missing_data / total_data) * 100
+
+print("\nMissing Values in Each Column:")
+print(data.isnull().sum())
+print(f"Percentage of Missing Data: {missing_percentage:.2f}%")
+
+# Plot the original data to observe its shape
+plt.figure(figsize=(10, 5))
+plt.plot(data['date'], data['sales'], label='Original Sales', marker='o')
+plt.title('Original Sales Data with Missing Values')
+plt.xlabel('Date')
+plt.ylabel('Sales')
+plt.legend()
+plt.grid(True)
+plt.xticks(rotation=45)
+plt.show()
+
+# Apply linear interpolation
+data['sales_interpolated'] = data['sales'].interpolate(method='linear')
+
+print("\nData After Linear Interpolation:")
+print(data)
+
+# Plot the original and interpolated data
+plt.figure(figsize=(10, 5))
+plt.plot(data['date'], data['sales'], label='Original Sales', marker='o')
+plt.plot(data['date'], data['sales_interpolated'], label='Interpolated Sales', marker='x')
+plt.title('Sales Data with Linear Interpolation')
+plt.xlabel('Date')
+plt.ylabel('Sales')
+plt.legend()
+plt.grid(True)
+plt.xticks(rotation=45)
+plt.show()

polynomial.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Module Name: Polynomial Interpolation
+Description: A brief demo of inspecting, handling, imputating Curved Missing Data, i.e. 'NaN', with Polynomial Interpolation.
+
+Credit / Prepared by: 
+Sun CHUNG, SMIEEE 
+M.Sc., HKU
+License: MIT License
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Sample time series data with missing values
+dates = pd.date_range(start='2020-01-01', end='2020-01-20')
+sales = [200, np.nan, 210, np.nan, np.nan, 250, 260, np.nan, 280, 290,
+         285, np.nan, 280, 270, np.nan, np.nan, 320, 325, np.nan, 330]
+data = pd.DataFrame({'date': dates, 'sales': sales})
+
+print("Original Data:")
+print(data)
+
+# Check for missing values
+missing_data = data['sales'].isnull().sum()
+total_data = len(data)
+missing_percentage = (missing_data / total_data) * 100
+
+print("\nMissing Values in Each Column:")
+print(data.isnull().sum())
+print(f"Percentage of Missing Data: {missing_percentage:.2f}%")
+
+data['date_formatted'] = data['date'].dt.strftime('%d %b') 
+
+# Plot the original data to observe its shape
+plt.figure(figsize=(10, 5))
+plt.plot(data['date_formatted'], data['sales'], label='Original Sales', marker='o')
+plt.title('Original Sales Data with Missing Values')
+plt.xlabel('Date')
+plt.ylabel('Sales')
+plt.legend()
+plt.grid(True)
+plt.xticks(rotation=45)
+plt.show()
+
+# Polynomial Interpolation (Order 2)
+data['sales_poly'] = data['sales'].interpolate(method='polynomial', order=2)
+
+print("\nData After Polynomial Interpolation (Order 2):")
+print(data[['date', 'sales', 'sales_poly']])
+
+# Plot the original and interpolated data
+plt.figure(figsize=(10, 5))
+plt.plot(data['date'], data['sales'], label='Original Sales', marker='o')
+plt.plot(data['date'], data['sales_poly'], label='Polynomial Interpolation', marker='x')
+plt.title('Sales Data with Polynomial Interpolation')
+plt.xlabel('Date')
+plt.ylabel('Sales')
+plt.legend()
+plt.grid(True)
+plt.xticks(rotation=45)
+plt.show()
+
+# Spline Interpolation (Order 3)
+data['sales_spline'] = data['sales'].interpolate(method='spline', order=3)
+
+print("\nData After Spline Interpolation (Order 3):")
+print(data[['date', 'sales', 'sales_spline']])
+
+# Plot the original and interpolated data
+plt.figure(figsize=(10, 5))
+plt.plot(data['date'], data['sales'], label='Original Sales', marker='o')
+plt.plot(data['date'], data['sales_spline'], label='Spline Interpolation', marker='x')
+plt.title('Sales Data with Spline Interpolation')
+plt.xlabel('Date')
+plt.ylabel('Sales')
+plt.legend()
+plt.grid(True)
+plt.xticks(rotation=45)
+plt.show()