Styling negative numbers in pandas

I have a dataframe that I am exporting to Excel. I would also like to style it before the export.

I have this code which changes the background color and text color and works fine, but I would like to add to it:

df.style.set_properties(**{'background-color': 'black',
                           'color': 'lawngreen',
                           'border-color': 'white'}).to_excel(writer, sheet_name='Sheet1', startrow=rowPos, float_format = "%0.5f")

I need columns with strings and dates to have a white text color, and then positive numbers to be green and negative numbers to be red. I pulled these styles directly from pandas documentation on styling since I have never used it before, and am unsure how to achieve these results.

Lets say my dataframe looks like this:

StartDate   ExpiryDate  Commodity   Quantity  Price   Total
---------   ----------  ----------  -------   -----   -----
02/28/2024  12/28/2024  HO          10000     -3.89   -38900
02/28/2024  12/28/2024  WPI         10000      4.20    42000

how could I achieve what I am looking for?

I'd break it down into three steps (see the comments #) :

st = (
    df.style
    # 1-applying the default styles
    .set_properties(**default_css)
    # 2-formatting the numeric columns
    .apply(
        lambda df_: df_.select_dtypes("number")
        .lt(0).replace({True: tc(neg), False: tc(pos)}),
        axis=None,
    )
    .format(precision=2) # this one is optional
    # 3-formatting the string-like dates and strings
    .map(lambda v: tc(obj) if isinstance(v, str) else "")
)

# st.to_excel("output.xlsx", index=False) # uncomment to make an Excel

Output :

Used CSS :

default_css = {
    "background-color": "black",
    "border": "1px solid white",
}

tc = "color: {}".format # css text color
obj, pos, neg = "white", "lawngreen", "red"