to_numeric() for safe conversion in Pandas - Mini Project: Build & Apply

Safe Numeric Conversion with pandas to_numeric()

📖 Scenario: You work in a sales team and receive monthly sales data as text. Some values are numbers, but others have errors like 'N/A' or 'unknown'. You want to convert these sales figures into numbers safely to analyze them.

🎯 Goal: Learn how to use pandas to_numeric() to convert text data into numbers safely, handling errors without crashing your program.

📋 What You'll Learn

Create a pandas DataFrame with sales data as strings

Create a configuration variable to handle errors during conversion

Use pandas.to_numeric() with the error handling configuration

Print the converted numeric sales data

💡 Why This Matters

🌍 Real World

Sales and finance data often come as text with errors. Safe conversion helps clean data for analysis.

💼 Career

Data analysts and scientists frequently convert messy text data to numbers for calculations and reporting.

1

Create sales data as a pandas DataFrame

Create a pandas DataFrame called sales_data with one column named 'sales' containing these exact string values: '100', '200', 'N/A', '300', 'unknown'.

Pandas

import pandas as pd
# Create the sales_data DataFrame with the specified sales strings
# Your code here

Need a hint?

Use pd.DataFrame() with a dictionary where the key is 'sales' and the value is the list of strings.

2

Set error handling configuration for conversion

Create a variable called error_handling and set it to the string 'coerce' to tell pandas to convert invalid values to NaN.

Pandas

import pandas as pd
sales_data = pd.DataFrame({'sales': ['100', '200', 'N/A', '300', 'unknown']})
# Create error_handling variable with value 'coerce'
# Your code here

Need a hint?

Set error_handling to the string 'coerce' to convert invalid entries to NaN.

3

Convert sales strings to numeric values safely

Use pandas.to_numeric() on the sales_data['sales'] column with the errors=error_handling argument. Save the result in a new column called sales_data['sales_numeric'].

Pandas

import pandas as pd
sales_data = pd.DataFrame({'sales': ['100', '200', 'N/A', '300', 'unknown']})
error_handling = 'coerce'
# Convert sales column to numeric safely and save as sales_numeric
# Your code here

Need a hint?

Use pd.to_numeric() with the errors parameter set to error_handling.

4

Print the converted numeric sales data

Print the sales_data['sales_numeric'] column to see the numeric conversion results.

Pandas

import pandas as pd
sales_data = pd.DataFrame({'sales': ['100', '200', 'N/A', '300', 'unknown']})
error_handling = 'coerce'
sales_data['sales_numeric'] = pd.to_numeric(sales_data['sales'], errors=error_handling)
# Print the sales_numeric column
# Your code here

Need a hint?

Use print() to display the sales_numeric column.