Dropping missing values (dropna) in Data Analysis Python - Mini Project: Build & Apply

Dropping Missing Values with dropna

📖 Scenario: You work in a small shop that keeps track of daily sales data. Sometimes, some sales records are missing because the cashier forgot to enter them. You want to clean the data by removing these missing entries before analyzing the sales.

🎯 Goal: You will create a small sales data table with some missing values, then use a configuration variable to decide how to drop these missing values using dropna. Finally, you will display the cleaned data.

📋 What You'll Learn

Create a pandas DataFrame called sales_data with specific sales records including missing values

Create a variable called drop_axis to decide whether to drop rows or columns with missing values

Use dropna on sales_data with the axis=drop_axis parameter to remove missing values

Print the cleaned DataFrame

💡 Why This Matters

🌍 Real World

Cleaning data by removing missing values is a common first step before analyzing or visualizing data in many fields like sales, healthcare, and finance.

💼 Career

Data analysts and data scientists often need to handle missing data to ensure their models and reports are accurate and reliable.

1

Create the sales data with missing values

Create a pandas DataFrame called sales_data with these exact entries: columns 'Day', 'Sales', and 'Customers'. The data should be:
Day: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']
Sales: [200, 220, null, 250, 270]
Customers: [20, null, 25, 30, null]
Use None for missing values.

Data Analysis Python

# Create the sales_data DataFrame with missing values
# Your code here

Hint

Use pd.DataFrame with a dictionary. Use None for missing values.

2

Set the axis to drop missing values

Create a variable called drop_axis and set it to 0 to drop rows with missing values.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, 220, None, 250, 270],
    'Customers': [20, None, 25, 30, None]
})

# Set drop_axis to 0 to drop rows with missing values
# Your code here

Hint

Set drop_axis to 0 to drop rows.

3

Drop missing values using dropna

Create a new DataFrame called cleaned_data by dropping missing values from sales_data using dropna with the parameter axis=drop_axis.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, 220, None, 250, 270],
    'Customers': [20, None, 25, 30, None]
})

drop_axis = 0

# Use dropna on sales_data with axis=drop_axis and save to cleaned_data
# Your code here

Hint

Use dropna(axis=drop_axis) on sales_data.

4

Print the cleaned data

Print the cleaned_data DataFrame to see the result after dropping missing values.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, 220, None, 250, 270],
    'Customers': [20, None, 25, 30, None]
})

drop_axis = 0

cleaned_data = sales_data.dropna(axis=drop_axis)

# Print cleaned_data
# Your code here

Hint

Use print(cleaned_data) to show the cleaned DataFrame.