UnivariateSpline in SciPy - Mini Project: Build & Apply

Smooth Data with UnivariateSpline

📖 Scenario: You have collected daily temperature data for a week. The data is a bit noisy, and you want to create a smooth curve to better understand the trend.

🎯 Goal: Use UnivariateSpline from scipy.interpolate to create a smooth curve from the temperature data and visualize the result.

📋 What You'll Learn

Create a dictionary called temperature_data with days as keys and temperatures as values

Create a variable called smoothing_factor to control the smoothness

Use UnivariateSpline with the data and smoothing factor to create a spline object

Generate smooth temperature values for the days using the spline

Print the list of smoothed temperature values

💡 Why This Matters

🌍 Real World

Smoothing noisy data is common in weather forecasting, stock price analysis, and sensor data cleaning.

💼 Career

Data scientists often use smoothing techniques like splines to prepare data for modeling and to visualize trends clearly.

1

Create the temperature data dictionary

Create a dictionary called temperature_data with these exact entries: 1: 22.1, 2: 21.8, 3: 23.0, 4: 22.5, 5: 24.0, 6: 23.5, 7: 24.2.

SciPy

# Create the temperature_data dictionary with days and temperatures
# Your code here

Need a hint?

Use curly braces {} to create a dictionary with keys as days (1 to 7) and values as temperatures.

2

Set the smoothing factor

Create a variable called smoothing_factor and set it to 1 to control the smoothness of the spline.

SciPy

temperature_data = {1: 22.1, 2: 21.8, 3: 23.0, 4: 22.5, 5: 24.0, 6: 23.5, 7: 24.2}
# Create smoothing_factor and set it to 1
# Your code here

Need a hint?

Just create a variable named smoothing_factor and assign the number 1 to it.

3

Create the UnivariateSpline and generate smooth values

Import UnivariateSpline from scipy.interpolate. Then create a spline object called spline using the days and temperatures from temperature_data and the smoothing_factor. Finally, create a list called smoothed_temps by applying spline to each day in temperature_data.

SciPy

temperature_data = {1: 22.1, 2: 21.8, 3: 23.0, 4: 22.5, 5: 24.0, 6: 23.5, 7: 24.2}
smoothing_factor = 1
# Import UnivariateSpline
# Create spline with days, temperatures, and smoothing_factor
# Create smoothed_temps list by applying spline to each day
# Your code here

Need a hint?

Use list(temperature_data.keys()) and list(temperature_data.values()) to get days and temperatures. Then create the spline with s=smoothing_factor. Use a list comprehension to get smoothed values.

4

Print the smoothed temperature values

Write a print statement to display the smoothed_temps list.

SciPy

from scipy.interpolate import UnivariateSpline

days = list(temperature_data.keys())
temps = list(temperature_data.values())
spline = UnivariateSpline(days, temps, s=smoothing_factor)
smoothed_temps = [spline(day) for day in days]
# Print the smoothed_temps list
# Your code here

Need a hint?

Use print(smoothed_temps) to show the smooth temperature values.