已经有一个PR Request，将来会支持这个，根据这条评论，很可能会与pandas 1.3.0一起发布。同时，我在另一个issue中发现了这个解决方案，它只强调每列的最大值：

from functools import partial

import pandas as pd
import numpy as np

def bold_formatter(x, value, num_decimals=2):
    """Format a number in bold when (almost) identical to a given value.
    
    Args:
        x: Input number.
        
        value: Value to compare x with.
        
        num_decimals: Number of decimals to use for output format.

    Returns:
        String converted output.

    """
    # Consider values equal, when rounded results are equal
    # otherwise, it may look surprising in the table where they seem identical
    if round(x, num_decimals) == round(value, num_decimals):
        return f"{{\\bfseries\\num{{{x:.{num_decimals}f}}}}}"
    else:
        return f"\\num{{{x:.{num_decimals}f}}}"

df = pd.DataFrame(np.array([[1.123456, 2.123456, 3.123456, 4.123456],
                            [11.123456, 22.123456, 33.123456, 44.123456],
                            [111.123456, 222.123456, 333.123456, 444.123456],]),
                   columns=['a', 'b', 'c', 'd'])

col_names = ['a in \\si{\\meter}',
             'b in \\si{\\volt}',
             'c in \\si{\\seconds}',
             'd']

# Colums to format with maximum condition and 2 floating decimals
max_columns_2f = ['a']

# Colums to format with minimum condition and 2 floating decimals
min_columns_2f = ['b', 'c']

# Colums to format with minimum condition and 4 floating decimals
min_columns_4f= ['d']

fmts_max_2f = {column: partial(bold_formatter, value=df[column].max(), num_decimals=2) for column in max_columns_2f}
fmts_min_2f = {column: partial(bold_formatter, value=df[column].min(), num_decimals=2) for column in min_columns_2f}
fmts_min_4f = {column: partial(bold_formatter, value=df[column].min(), num_decimals=4) for column in min_columns_4f}

fmts = dict(**fmts_max_2f, **fmts_min_2f, **fmts_min_4f)

with open("test_table.tex", "w") as fh:
    df.to_latex(buf=fh,
                index=False,
                header=col_names,
                formatters=fmts,
                escape=False)

循环遍历行以查找最大值的位置，并在这些特定位置应用格式设置。

import numpy as np
import pandas as pd

# generate a dataframe with 10 rows and 4 columns filled with random numbers
df = pd.DataFrame(data=np.random.rand(10, 4), index= [f"row_{i}" for i in range(10)], columns=[f"col_{i}" for i in range(4)])

# apply some formatting for all numbers (optional)
df_s = df.style.format("{:.2f}")

# loop through rows and find which column for each row has the highest value
for row in df.index:
    col = df.loc[row].idxmax()
    # redo formatting for a specific cell
    df_s = df_s.format(lambda x: "\\textbf{" + f'{x:.2f}' + "}", subset=(row, col))

print(df_s.to_latex())

结果：

\begin{tabular}{lrrrr}
 & col_0 & col_1 & col_2 & col_3 \\
row_0 & 0.56 & \textbf{0.74} & 0.48 & 0.70 \\
row_1 & 0.22 & 0.02 & 0.08 & \textbf{0.97} \\
row_2 & \textbf{0.80} & 0.26 & 0.39 & 0.30 \\
row_3 & \textbf{0.93} & 0.26 & 0.28 & 0.75 \\
row_4 & 0.39 & \textbf{0.45} & 0.10 & 0.30 \\
row_5 & 0.31 & \textbf{0.73} & 0.19 & 0.45 \\
row_6 & 0.23 & \textbf{0.61} & 0.31 & 0.21 \\
row_7 & 0.27 & 0.38 & 0.64 & \textbf{0.93} \\
row_8 & 0.15 & 0.09 & \textbf{0.48} & 0.44 \\
row_9 & \textbf{0.84} & 0.59 & 0.57 & 0.44 \\
\end{tabular}