Why interpolation estimates between data points in SciPy - See It in Action

Why interpolation estimates between data points

📖 Scenario: Imagine you have temperature readings taken at specific hours during the day. You want to estimate the temperature at times when you did not take a reading. This is a common problem in weather forecasting and many other fields.

🎯 Goal: You will create a small dataset of temperatures at certain hours, set up an interpolation function using scipy, use it to estimate temperatures at times between your readings, and then print the estimated values.

📋 What You'll Learn

Create a dictionary called temperature_readings with exact keys 0, 3, 6, 9, 12 representing hours and values 15, 17, 20, 22, 21 representing temperatures.

Create a list called query_hours with exact values 1, 4, 7, 10 representing hours to estimate temperatures.

Use scipy.interpolate.interp1d to create an interpolation function named interp_func based on temperature_readings.

Use interp_func to estimate temperatures at query_hours and store results in estimated_temps.

Print the estimated_temps list.

💡 Why This Matters

🌍 Real World

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

💼 Career

Data scientists often use interpolation to fill missing data or create smooth curves from discrete measurements, improving analysis and predictions.

1

Create the temperature readings dictionary

Create a dictionary called temperature_readings with these exact entries: 0: 15, 3: 17, 6: 20, 9: 22, 12: 21.

SciPy

# Create the temperature_readings dictionary with given hour-temperature pairs
# Your code here

Need a hint?

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

2

Create the list of query hours

Create a list called query_hours with these exact values: 1, 4, 7, 10.

SciPy

temperature_readings = {0: 15, 3: 17, 6: 20, 9: 22, 12: 21}
# Create the query_hours list with given hours
# Your code here

Need a hint?

Use square brackets [] to create a list of hours where you want to estimate temperatures.

3

Create the interpolation function and estimate temperatures

Import interp1d from scipy.interpolate. Create an interpolation function called interp_func using interp1d with the keys and values from temperature_readings. Use interp_func to estimate temperatures at query_hours and store the results in estimated_temps.

SciPy

temperature_readings = {0: 15, 3: 17, 6: 20, 9: 22, 12: 21}
query_hours = [1, 4, 7, 10]
# Import interp1d and create interp_func
# Use interp_func to estimate temperatures at query_hours
# Your code here

Need a hint?

Convert dictionary keys and values to lists before passing to interp1d. Then call interp_func with query_hours and convert the result to a list.

4

Print the estimated temperatures

Print the estimated_temps list to display the estimated temperatures at the query hours.

SciPy

from scipy.interpolate import interp1d

temperature_readings = {0: 15, 3: 17, 6: 20, 9: 22, 12: 21}
query_hours = [1, 4, 7, 10]

interp_func = interp1d(list(temperature_readings.keys()), list(temperature_readings.values()))
estimated_temps = interp_func(query_hours).tolist()
# Print the estimated_temps list
# Your code here

Need a hint?

Use print(estimated_temps) to show the list of estimated temperatures.