Interpolation for missing values in Pandas - Mini Project: Build & Apply

Interpolation for missing values

📖 Scenario: You have collected daily temperature data for a week, but some days are missing values. You want to fill in these missing temperatures by estimating them based on the available data.

🎯 Goal: Build a small program that creates a pandas DataFrame with temperature data including missing values, sets up a method to fill those missing values using interpolation, applies the interpolation, and then shows the completed data.

📋 What You'll Learn

Create a pandas DataFrame named temps with a Day column and a Temperature column containing some missing values (NaN).

Create a variable called method and set it to the string 'linear' to specify the interpolation method.

Use the interpolate() method on the Temperature column of temps with the method variable to fill missing values.

Print the resulting DataFrame after interpolation.

💡 Why This Matters

🌍 Real World

Data collected from sensors or surveys often have missing values. Interpolation helps estimate these missing points to keep data analysis accurate.

💼 Career

Data scientists and analysts use interpolation to clean and prepare data before building models or reports.

1

Create the temperature data with missing values

Create a pandas DataFrame called temps with two columns: Day containing the days from 'Mon' to 'Sun', and Temperature containing these values exactly: 22.0, NaN, 24.5, NaN, 26.0, 27.5, NaN. Use import pandas as pd at the start.

Pandas

import pandas as pd
# Create the DataFrame temps with Day and Temperature columns
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary for columns. Use float('nan') for missing values.

2

Set the interpolation method

Create a variable called method and set it to the string 'linear' to specify the interpolation method.

Pandas

import pandas as pd

temps = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'],
    'Temperature': [22.0, float('nan'), 24.5, float('nan'), 26.0, 27.5, float('nan')]
})

# Set the interpolation method to 'linear'
# Your code here

Need a hint?

Just assign the string 'linear' to the variable method.

3

Apply interpolation to fill missing values

Use the interpolate() method on the Temperature column of temps with the method variable to fill missing values. Assign the result back to the Temperature column of temps.

Pandas

import pandas as pd

temps = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'],
    'Temperature': [22.0, float('nan'), 24.5, float('nan'), 26.0, 27.5, float('nan')]
})

method = 'linear'

# Fill missing Temperature values using interpolate with method
# Your code here

Need a hint?

Use temps['Temperature'].interpolate(method=method) and assign it back to temps['Temperature'].

4

Print the DataFrame after interpolation

Write a print statement to display the temps DataFrame after interpolation.

Pandas

import pandas as pd

temps = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'],
    'Temperature': [22.0, float('nan'), 24.5, float('nan'), 26.0, 27.5, float('nan')]
})

method = 'linear'

temps['Temperature'] = temps['Temperature'].interpolate(method=method)

# Print the temps DataFrame
# Your code here

Need a hint?

Use print(temps) to show the DataFrame.