How to Convert Comma-Separated Number Strings to Floats in a Pandas DataFrame?

Converting Comma-Separated Number Strings to Floats in a Pandas DataFrame

When working with Pandas DataFrames, it's common to encounter numeric columns stored as strings with thousands separators. To perform calculations or comparisons on these values, converting them to floating-point numbers is necessary. However, the process of converting these values can encounter errors.

Error #1: Using apply Directly on the DataFrame

When applying the apply function to the entire DataFrame, a TypeError is raised. This occurs because apply expects a single argument, but a DataFrame contains multiple columns.

Error #2: Using apply on a Subset of the DataFrame

Applying apply to a subset of the DataFrame, such as df[0:1], raises a ValueError. This error indicates that the first element in the subset cannot be converted to a float.

Solution

To successfully convert comma-separated number strings to floats in a Pandas DataFrame, two methods can be used:

Method 1: Using the thousands Argument While Reading

If the DataFrame is being read from a CSV file, the thousands argument in the read_csv function can be used to specify the thousands separator. This method is usually more efficient than performing the conversion as a separate step.

Method 2: Setting the Locale and Using applymap

To convert the values directly within the DataFrame, the following steps are necessary:

1. Import the locale and atof modules.
2. Set the locale to the appropriate value, such as locale.setlocale(locale.LC_NUMERIC, '').
3. Apply the applymap function to the DataFrame, using the atof function as the conversion method.

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