Handling missing values in Series in Data Analysis Python - Mini Project: Build & Apply

Handling missing values in Series

📖 Scenario: Imagine you have collected daily temperature readings for a week, but some days the sensor failed and the data is missing. You want to clean this data to analyze it properly.

🎯 Goal: You will create a pandas Series with temperature data including missing values, set a threshold for acceptable missing data, fill missing values with the average temperature, and finally display the cleaned data.

📋 What You'll Learn

Create a pandas Series named temps with the exact values: 22.5, None, 21.0, None, 23.5, 24.0, None

Create a variable named max_missing and set it to 2

Fill missing values in temps with the mean of the available temperatures and assign it to cleaned_temps

Print the cleaned_temps Series

💡 Why This Matters

🌍 Real World

Handling missing data is common in real-world datasets like weather, sales, or health records. Cleaning data helps make better decisions.

💼 Career

Data scientists and analysts often clean and prepare data before analysis. Knowing how to handle missing values is a key skill.

1

Create the temperature data Series

Create a pandas Series called temps with these exact values in order: 22.5, None, 21.0, None, 23.5, 24.0, None

Data Analysis Python

import pandas as pd
# Create the Series temps with the given values
# Your code here

Need a hint?

Use pd.Series and pass a list with the exact values including None for missing data.

2

Set the maximum allowed missing values

Create a variable called max_missing and set it to 2

Data Analysis Python

import pandas as pd

temps = pd.Series([22.5, None, 21.0, None, 23.5, 24.0, None])
# Set max_missing to 2
# Your code here

Need a hint?

Just assign the number 2 to a variable named max_missing.

3

Fill missing values with the average temperature

Create a new Series called cleaned_temps by filling the missing values in temps with the mean of the available temperatures

Data Analysis Python

import pandas as pd

temps = pd.Series([22.5, None, 21.0, None, 23.5, 24.0, None])

max_missing = 2
# Fill missing values in temps with the mean and assign to cleaned_temps
# Your code here

Need a hint?

Use temps.fillna() and pass temps.mean() as the value to fill missing data.

4

Display the cleaned temperature data

Print the cleaned_temps Series to show the temperature data with missing values filled

Data Analysis Python

import pandas as pd

temps = pd.Series([22.5, None, 21.0, None, 23.5, 24.0, None])

max_missing = 2

cleaned_temps = temps.fillna(temps.mean())
# Print cleaned_temps
# Your code here

Need a hint?

Use print(cleaned_temps) to display the Series.