Linear vs cubic interpolation in SciPy - Hands-On Comparison

Linear vs Cubic Interpolation with SciPy

📖 Scenario: Imagine you have some temperature readings taken at certain hours of the day. You want to estimate the temperature at times between these readings. This is called interpolation.There are different ways to do interpolation. Two common methods are linear interpolation and cubic interpolation.

🎯 Goal: You will create a small dataset of temperature readings, set up interpolation functions using SciPy, apply both linear and cubic interpolation to estimate temperatures at new times, and finally print the interpolated results to compare.

📋 What You'll Learn

Create a dictionary with exact time points and temperature values

Create a list of new time points where interpolation will be done

Use SciPy's interp1d function to create linear and cubic interpolation functions

Calculate interpolated temperatures at new time points using both methods

Print the interpolated temperature lists exactly as specified

💡 Why This Matters

🌍 Real World

Interpolation helps estimate unknown values between measured data points, useful in weather forecasting, engineering, and finance.

💼 Career

Data scientists often use interpolation to fill missing data or smooth data trends for better analysis and predictions.

1

Create the temperature data dictionary

Create a dictionary called temperature_readings with these exact entries: 0: 15.0, 3: 18.5, 6: 21.0, 9: 19.0, 12: 16.5 representing hours and temperature in Celsius.

SciPy

# Create the temperature_readings dictionary with exact values
# Your code here

Need a hint?

Use curly braces to create a dictionary with keys as hours and values as temperatures.

2

Create new time points for interpolation

Create a list called new_times with these exact values: 1.5, 4.5, 7.5, 10.5 representing hours where you want to estimate temperatures.

SciPy

temperature_readings = {0: 15.0, 3: 18.5, 6: 21.0, 9: 19.0, 12: 16.5}
# Create the new_times list with exact values
# Your code here

Need a hint?

Create a list with the exact floating point numbers inside square brackets.

3

Create linear and cubic interpolation functions

Import interp1d from scipy.interpolate. Then create two interpolation functions: linear_interp and cubic_interp. Use interp1d with kind='linear' for linear_interp and kind='cubic' for cubic_interp. Use the keys and values from temperature_readings as x and y values respectively.

SciPy

temperature_readings = {0: 15.0, 3: 18.5, 6: 21.0, 9: 19.0, 12: 16.5}
new_times = [1.5, 4.5, 7.5, 10.5]
# Import interp1d and create linear_interp and cubic_interp functions
# Your code here

Need a hint?

Use list(temperature_readings.keys()) and list(temperature_readings.values()) to get x and y values.

4

Calculate and print interpolated temperatures

Calculate interpolated temperatures at new_times using linear_interp and cubic_interp. Store results in linear_temps and cubic_temps respectively. Then print linear_temps and cubic_temps exactly as lists.

SciPy

from scipy.interpolate import interp1d

temperature_readings = {0: 15.0, 3: 18.5, 6: 21.0, 9: 19.0, 12: 16.5}
new_times = [1.5, 4.5, 7.5, 10.5]

linear_interp = interp1d(list(temperature_readings.keys()), list(temperature_readings.values()), kind='linear')
cubic_interp = interp1d(list(temperature_readings.keys()), list(temperature_readings.values()), kind='cubic')
# Calculate linear_temps and cubic_temps using interpolation functions
# Print linear_temps and cubic_temps
# Your code here

Need a hint?

Convert the interpolation results to lists before printing to match the expected output format.