dataframe to excel with styling (background and color)

I have a data frame as below and want to apply two conditions for the styling and save into excel.

I could perform either of the condition at a time but not the both simultaneously.

> input: dataframe (2 colums) and a given_list(index numbers)
>
> condition_1: [highlight ('background-color: yellow') and red color
> ('color:red') but if type(column[0])!=int then blue color
> ('color:blue')] if row.numer in the given_list.
>
> condition_2: if type(column[0])!=int then blue color ('color:blue')

data_frame={
'column0': {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10A, 10: 11B, 11: 12C, 12: 13, 13: 14, 14: 15, 15: 16, 16: 17, 17: 18, 18: 19A, 19: 20B, 20: 21, 21: 22, 22: 23, 23: 24, 24: 25, 25: 26, 26: 27},
'column1': {0: 'A', 1: 'V', 2: 'T', 3: 'L', 4: 'G', 5: 'E', 6: 'S', 7: 'G', 8: 'G', 9: 'G', 10: 'L', 11: 'Q', 12: 'T', 13: 'P', 14: 'G', 15: 'G', 16: 'G', 17: 'L', 18: 'S', 19: 'L', 20: 'V', 21: 'C', 22: 'K', 23: 'A', 24: 'S', 25: 'G', 26: 'F'}
}

given_list=[7,8,9,10,11,12,13,14,15,21,22] ### the index numbers of the dataframe

desired_output:

What I tried:

def highlight(row, row_index):
    # print(type(row[0]))
    background_color = 'background-color: yellow'
    text_color='color:red'
    text_color_1='color:blue'
    
    highlited_rows=[f'{text_color}; {background_color}' if row.name in row_index else (f'{text_color_1}' if not isinstance(row[0], int) else '')for _, cell in enumerate(row)]


highlighted_df =df.style.apply(lambda row: highlight(row, row_index), axis=1) 
aligned_df=highlighted_df.set_properties(**{'text-align': 'center'})
aligned_df.to_excel('highlighted_dataframe.xlsx', engine='openpyxl', index=False)

output=

I am not able to color the txt based on the both condition. How to apply both the conditions simultaneously so I can get the desired output?
Any help will be appreciated.

You can chain style operations (take care to sort your conditions in the right order) and use sbuset keyword to limit the styling applied to the correct cells/rows/columns:

given_list = [7, 8, 9, 10, 11, 12, 13, 14, 15, 21, 22]
given_list2 = df.columns[df.iloc[0].map(lambda x: isinstance(x, str))]


(df.style.set_properties(**{'background-color': 'yellow', 'color': 'red'}, subset=given_list)
         .set_properties(**{'color': 'blue'}, subset=pd.IndexSlice['column0', given_list2])
         .set_properties(**{'text-align': 'center'})
         .to_excel('highlighted_dataframe.xlsx', engine='openpyxl', index=False))

I have got it resolved by defining the highlighting and coloring conditions more explicitly as below:

df={ 'column0': {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10A, 10: 11B, 11: 12C, 12: 13, 13: 14, 14: 15, 15: 16, 16: 17, 17: 18, 18: 19A, 19: 20B, 20: 21, 21: 22, 22: 23, 23: 24, 24: 25, 25: 26, 26: 27}, 'column1': {0: 'A', 1: 'V', 2: 'T', 3: 'L', 4: 'G', 5: 'E', 6: 'S', 7: 'G', 8: 'G', 9: 'G', 10: 'L', 11: 'Q', 12: 'T', 13: 'P', 14: 'G', 15: 'G', 16: 'G', 17: 'L', 18: 'S', 19: 'L', 20: 'V', 21: 'C', 22: 'K', 23: 'A', 24: 'S', 25: 'G', 26: 'F'} }
first_list=[7,8,9,10,11,12,13,14,15,21,22] #row index
sec_list=df[~df.iloc[:, 0].astype(str).str.isdigit()].index.tolist() ## Makes a list of index having nonint value in column0
def highlight(row, first_list, sec_list):
background_color = ''
text_color=''
if row.name in sec_list and row.name not in first_list:
text_color = 'color: blue'
background_color = ''
elif row.name in first_list and row.name not in sec_list: 
text_color = 'color: red'
background_color = 'background-color: yellow'
elif row.name in first_list and row.name  in sec_list:
text_color = 'color: blue'
background_color = 'background-color: yellow'  
return [f'{text_color}; {background_color}' for _ in row]
highlighted_df =df.style.apply(lambda row: highlight(row, row_index,sec_list), axis=1) 
aligned_df=highlighted_df.set_properties(**{'text-align': 'center'})
aligned_df.to_excel('highlighted_dataframe.xlsx', engine='openpyxl', index=False)

import pandas as pd
from openpyxl.styles import PatternFill
from openpyxl import Workbook
# create a dataframe
df = pd.DataFrame({
'Fruit': ['Apple', 'Banana', 'Cherry', 'Dates'],
'Quantity': [10, 20, 30, 40],
'Price': [1.5, 2, 3, 4]
})
# create a workbook and worksheet
wb = Workbook()
ws = wb.active
# add data to the worksheet
for r in dataframe_to_rows(df, index=False, header=True):
ws.append(r)
# highlight cells with prices greater than 2
for row in range(2, ws.max_row + 1):
if ws.cell(row=row, column=3).value > 2:
cell = ws.cell(row=row, column=3)
cell.fill = PatternFill(start_color='FAC090', end_color='FAC090', fill_type='solid')
# save the workbook
wb.save('example.xlsx')
# This code will create an Excel file named example.xlsx with the dataframe data, and the cells with prices greater than 2 will be highlighted with an orange color using the PatternFill object.