custom pretty print descriptor for a pandas dataframe

pretty_print_df.py

@pd.api.extensions.register_dataframe_accessor("pt")

# Performs formatting on a dataframe
# Usage: df.pt.pretty()
class Pretty:
    def __init__(self, pandas_obj):
        self._validate(pandas_obj)
        self._obj = pandas_obj

    @staticmethod
    def _validate(obj):
        # verify this is a DataFrame
        if not isinstance(obj, pd.DataFrame):
            raise AttributeError("Must be a pandas DataFrame")
    
    # Code from https://github.com/azaitsev/millify
    def _pretty_col(self, col, precision, percent):
        millnames = ['', 'k', 'M', 'B', 'T', 'P', 'E', 'Z', 'Y']
        max_val = col.astype('float').max(axis=0)
        magnitude = int(math.floor(0 if max_val==0 else math.log10(abs(max_val)) / 3))
        millindex = max(0, min(len(millnames) - 1, magnitude))
        values = col.div(10**(3 * millindex))
        format_letter = millnames[millindex]
        precision = f'.{precision}'
        format_string = '{:' + precision +'f}'+f'{format_letter}'
        if percent and max_val < 1:
            format_string = "{:.2%}"
            values = col
        return values, format_string
    
    def pretty(self, precision=2, percent=True):
        numeric_cols = self._obj.select_dtypes(include='number').columns
        other_cols = self._obj.select_dtypes(exclude='number')
        format_dict = {}
        results = []
        for col in numeric_cols:
            new_col, format_data = self._pretty_col(self._obj[col], precision, percent)
            format_dict[col] = format_data
            results.append(pd.Series(new_col, name=col))
        formatted_df = pd.concat(results, axis=1)
        full_df = pd.concat([other_cols, formatted_df], axis=1)
        # Max sure the column order is preserved
        return full_df[self._obj.columns].style.format(format_dict)