Handling Errors During Numeric Conversion

"Converting data to the correct numeric type, like an integer or a float, is fundamental for performing calculations and analysis. However, data is often messy. When you try to convert a column that you expect to contain numbers, you might encounter values that don't fit the expected numeric format. This can cause the conversion process to fail unexpectedly."

Imagine you have a column representing product prices, but instead of just numbers like 19.99 or 100, it contains entries like $19.99, Not Available, or even typos like 2S.00. Attempting a direct conversion of such a column to a numeric type will often result in an error, halting your data preparation workflow.

Why Numeric Conversions Fail

Conversion errors typically happen because the data contains:

• Non-Numeric Characters: Values might include currency symbols ($, £), percentage signs (%), units (kg, lbs), commas used as thousands separators (1,000), or other non-digit characters.
• Textual Placeholders: Entries meant to signify missing or unknown information, such as N/A, Unknown, missing, null, or simply empty strings ('').
• Mixed Representations: A column might inconsistently contain both numeric strings and textual descriptions, like 10 and ten in the same column.

Most data analysis tools are strict about conversions. When a function tries to convert '$19.99' or 'Unknown' into a number, it doesn't know how to interpret the non-numeric parts. By default, many tools will raise an error and stop. This is generally a safety mechanism to prevent incorrect results, but it means you need a strategy to manage these problematic values.

Strategies for Handling Conversion Errors

When faced with data that resists direct numeric conversion, you have a few approaches:

1. Clean the Data Before Conversion

This is often the most careful approach. Before attempting the conversion, you inspect the column for common non-numeric patterns and clean them up.

• Identify Problematic Values: Find the unique non-numeric entries in the column to understand the scope of the problem.
• Use String Operations: Apply string manipulation functions to remove or replace unwanted characters. For instance:
  • Remove currency symbols: '$19.99' becomes '19.99'.
  • Remove commas: '1,200' becomes '1200'.
  • Trim whitespace: ' 50 ' becomes '50'.
• Handle Text Placeholders: Decide what to do with entries like 'N/A'. You might replace them with an empty string or a specific marker that you'll handle later (perhaps converting them to a missing value indicator like NaN).

After cleaning, you can attempt the numeric conversion again on the modified data. This method gives you fine-grained control but requires identifying and addressing each type of issue present.

2. Coerce Errors During Conversion

Many data manipulation libraries offer a more direct way to handle errors during the conversion itself. A common approach is to "coerce" errors, which means forcing the conversion and automatically replacing any value that causes an error with a special marker indicating missing data.

In the popular pandas library for Python, the to_numeric() function has an errors parameter. Setting errors='coerce' instructs the function as follows:

• If a value can be converted to a number, convert it.
• If a value cannot be converted (it raises an error), replace it with NaN (Not a Number).

Example: Imagine you have this data: ['101', '102.5', '£103', 'missing', '105']

Using errors='coerce' during numeric conversion might produce: [101.0, 102.5, NaN, NaN, 105.0]

The values '£103' and 'missing' could not be directly converted to numbers, so they were replaced with NaN.

Implication: This strategy transforms conversion problems into missing data problems. You now have NaN values in your column, which you can address using the techniques discussed in Chapter 2 (like deletion or imputation). Coercion is efficient when the non-numeric values genuinely represent missing or invalid entries.

3. Ignore Errors (Generally Not Recommended for Numeric Conversion)

Some tools might offer an option to simply ignore conversion errors (like errors='ignore' in pandas to_numeric). If you use this, values that convert successfully become numeric, while values that cause errors are left unchanged in their original format (e.g., as strings).

Result: This typically leads to a column with mixed data types (containing both numbers and strings).

Caution: This approach is usually not suitable when your goal is a purely numeric column for calculations or modeling. Arithmetic operations (+, -, *, /), comparisons (>, <), and many statistical functions will fail or produce nonsensical results on columns with mixed types. While ignoring errors prevents the process from stopping, it often creates bigger problems down the line. It's better to either clean the data first or coerce errors to NaN and handle the missing values explicitly.

Choosing Your Strategy

• Clean First: Best when non-numeric characters represent formatting issues (like $, ,, %) that can be systematically removed to reveal valid numbers. It requires more upfront work but preserves valid numeric information hidden within strings.
• Coerce Errors: Ideal when non-numeric entries represent genuinely missing, invalid, or unrecoverable data (N/A, Unknown, typos). It's a quick way to get a numeric column, but you must then consciously handle the resulting NaN values.
• Ignore Errors: Rarely the right choice for numeric conversion. Avoid it unless you have a very specific reason and understand the consequences of ending up with mixed data types.

Successfully handling errors during numeric conversion is an important step in creating clean, reliable data suitable for analysis. By choosing the right strategy, you can ensure your data is in the correct format without letting unexpected values derail your work.