How to Convert Number Strings with Commas in Pandas DataFrame to Float?

Convert Number Strings with Commas in Pandas DataFrame to Float

When working with pandas DataFrames, it is common to encounter number strings formatted with commas. To facilitate numerical operations and calculations, it is often necessary to convert these strings to floats.

One approach to achieving this conversion is to use Python's locale.atof function. However, when applying this function to a DataFrame, it is important to set the appropriate locale. Setting the locale ensures that the function can interpret the comma-separated values correctly as numbers.

</p>
<p>import locale<br>from locale import atof<br>locale.setlocale(locale.LC_NUMERIC, '')</p>
<p>

Once the locale is set, atof can be applied to the DataFrame using the applymap method, which applies a function element-wise to a DataFrame.

</p>
<p>df.applymap(atof)</p>
<p>

This approach effectively converts the number strings with commas to floats, enabling seamless numerical operations and calculations within the DataFrame.

Alternatively, if the DataFrame is being read from a CSV file, the thousands argument when reading the file can be set to ',' to automatically convert the comma-separated values to floats. This method is more efficient than performing the conversion as a separate step.

</p>
<p>df.read_csv('foo.tsv', sep='t', thousands=',')</p>
<p>

The above is the detailed content of How to Convert Number Strings with Commas in Pandas DataFrame to Float?. For more information, please follow other related articles on the PHP Chinese website!