How to Apply Custom Formatting to Specific Columns in Pandas DataFrames with Floating-Point Values?

Custom Formatting for Float DataFrames with Pandas

Displaying pandas DataFrames with floating-point values can often benefit from custom formatting. Consider the following DataFrame:

df = pd.DataFrame([123.4567, 234.5678, 345.6789, 456.7890],
                  index=['foo','bar','baz','quux'],
                  columns=['cost'])

print(df)

By default, pandas displays floats with precision, resulting in:

         cost
foo   123.4567
bar   234.5678
baz   345.6789
quux  456.7890

To format these values with currency, we can use the built-in display method:

import pandas as pd
pd.options.display.float_format = '${:,.2f}'.format
print(df)

This will output:

        cost
foo  3.46
bar  4.57
baz  5.68
quux 6.79

Selective Formatting

However, if only certain columns require custom formatting, we can pre-modify the DataFrame:

df = pd.DataFrame([123.4567, 234.5678, 345.6789, 456.7890],
                  index=['foo','bar','baz','quux'],
                  columns=['cost'])

df['foo'] = df['cost']
df['cost'] = df['cost'].map('${:,.2f}'.format)

This customization allows for targeted formatting within the DataFrame:

         cost       foo
foo   3.46  123.4567
bar   4.57  234.5678
baz   5.68  345.6789
quux  6.79  456.7890

The above is the detailed content of How to Apply Custom Formatting to Specific Columns in Pandas DataFrames with Floating-Point Values?. For more information, please follow other related articles on the PHP Chinese website!