Interpolation for smoothing data in SciPy - Mini Project: Build & Apply

Interpolation for smoothing data

📖 Scenario: You have some noisy temperature data collected every hour. You want to smooth the data to see a clearer trend.

🎯 Goal: Use interpolation to smooth the noisy temperature data and display the smoothed values.

📋 What You'll Learn

Create a dictionary with hourly temperature data

Create a variable for new time points to interpolate

Use scipy's interp1d to create an interpolation function

Print the smoothed temperature values for the new time points

💡 Why This Matters

🌍 Real World

Smoothing noisy sensor data to better understand trends in temperature, stock prices, or other measurements.

💼 Career

Data scientists often need to clean and smooth data before analysis or modeling to improve accuracy and insights.

1

Create the noisy temperature data

Create a dictionary called temp_data with these exact entries: 0: 15.0, 1: 15.5, 2: 14.8, 3: 16.2, 4: 15.9, 5: 16.5 representing temperature readings at hours 0 to 5.

SciPy

# Create the temperature data dictionary
# Your code here

Need a hint?

Use curly braces to create a dictionary with keys 0 to 5 and the given temperature values.

2

Create new time points for interpolation

Create a list called new_hours with these exact values: 0.5, 1.5, 2.5, 3.5, 4.5 representing the half-hour points where you want to estimate temperatures.

SciPy

temp_data = {0: 15.0, 1: 15.5, 2: 14.8, 3: 16.2, 4: 15.9, 5: 16.5}
# Create the list of new hours for interpolation
# Your code here

Need a hint?

Use square brackets to create a list with the given float values.

3

Create interpolation function using scipy

Import interp1d from scipy.interpolate. Then create a variable called interp_func by calling interp1d with the keys and values of temp_data as x and y, respectively, and set kind='linear'.

SciPy

temp_data = {0: 15.0, 1: 15.5, 2: 14.8, 3: 16.2, 4: 15.9, 5: 16.5}
new_hours = [0.5, 1.5, 2.5, 3.5, 4.5]
# Import interp1d and create interp_func with linear kind
# Your code here

Need a hint?

Use list(temp_data.keys()) and list(temp_data.values()) to get x and y values for interpolation.

4

Print the smoothed temperature values

Use print() to display the result of calling interp_func with new_hours as input.

SciPy

from scipy.interpolate import interp1d

temp_data = {0: 15.0, 1: 15.5, 2: 14.8, 3: 16.2, 4: 15.9, 5: 16.5}
new_hours = [0.5, 1.5, 2.5, 3.5, 4.5]

interp_func = interp1d(list(temp_data.keys()), list(temp_data.values()), kind='linear')
# Print the interpolated temperatures for new_hours
# Your code here

Need a hint?

Call interp_func(new_hours) inside print() to see the smoothed values.