Interpolation for missing numerics in Data Analysis Python - Mini Project: Build & Apply

Interpolation for Missing Numerics

📖 Scenario: You are working with a small dataset of daily temperatures. Some days have missing temperature values. You want to fill in these missing values by estimating them based on the known temperatures before and after.

🎯 Goal: Build a simple Python program that creates a temperature dataset with missing values, sets up a method to fill those missing values using interpolation, applies the interpolation, and then shows the completed dataset.

📋 What You'll Learn

Create a dictionary called temperatures with keys as days (1 to 7) and values as temperatures, including None for missing days

Create a list called days containing the days from 1 to 7

Use pandas library to convert the dictionary to a DataFrame

Use the interpolate() method to fill missing temperature values

Print the final DataFrame showing all days and temperatures with no missing values

💡 Why This Matters

🌍 Real World

In real life, weather stations or sensors often miss recording some data points. Interpolation helps estimate these missing values to keep the data useful for analysis.

💼 Career

Data scientists and analysts use interpolation to clean and prepare datasets before building models or making decisions based on incomplete data.

1

Create the temperature data with missing values

Create a dictionary called temperatures with these exact entries: 1: 22.0, 2: None, 3: 24.5, 4: None, 5: 25.0, 6: 26.5, 7: None.

Data Analysis Python

# Create the temperatures dictionary with missing values
# Your code here

Hint

Use curly braces to create a dictionary. Use None for missing values.

2

Set up the days list and import pandas

Create a list called days containing the integers from 1 to 7. Also, import the pandas library as pd.

Data Analysis Python

temperatures = {1: 22.0, 2: None, 3: 24.5, 4: None, 5: 25.0, 6: 26.5, 7: None}
# Import pandas as pd and create the days list
# Your code here

Hint

Use import pandas as pd to import pandas. Use square brackets to create the list of days.

3

Create DataFrame and interpolate missing values

Use pd.DataFrame() to create a DataFrame called df from the temperatures dictionary with days as the index. Then, use df.interpolate() to fill missing temperature values and update df with the result.

Data Analysis Python

import pandas as pd

temperatures = {1: 22.0, 2: None, 3: 24.5, 4: None, 5: 25.0, 6: 26.5, 7: None}
days = [1, 2, 3, 4, 5, 6, 7]
# Create DataFrame df and interpolate missing values
# Your code here

Hint

Use list(temperatures.items()) to convert the dictionary to a list of tuples. Use set_index('Day') to set the day as the index. Use interpolate() to fill missing values.

4

Print the DataFrame with interpolated values

Write a print() statement to display the DataFrame df showing all days and their temperatures with missing values filled.

Data Analysis Python

import pandas as pd

temperatures = {1: 22.0, 2: None, 3: 24.5, 4: None, 5: 25.0, 6: 26.5, 7: None}
days = [1, 2, 3, 4, 5, 6, 7]

df = pd.DataFrame(list(temperatures.items()), columns=['Day', 'Temperature']).set_index('Day')
df = df.interpolate()
# Print the DataFrame df
# Your code here

Hint

Use print(df) to show the DataFrame with all temperatures filled.