How can I change the text and cell colors in a Pandas DataFrame HTML table using styles and CSS?

Changing Text and Cell Colors in a Pandas DataFrame HTML Table Using Styles and CSS

Often times, when working with dataframes in Python, it is beneficial to customize the visual appearance of the data to make it more informative or visually appealing. This can be achieved by using styles and CSS to change the color of text, headers, and cells within a dataframe.

In this scenario, we are given a pandas dataframe containing values organized by both rows and columns, and the goal is to color the values in the 'MOS' rows a certain color and alter the background color of the left two index/header columns and the top header row.

To accomplish this, we can utilize the new styling functionality introduced in pandas version 0.17.1. The following Python code demonstrates how to apply styles to the dataframe:

<code class="python">import numpy as np
import pandas as pd

arrays = [['Midland', 'Midland', 'Hereford', 'Hereford', 'Hobbs','Hobbs', 'Childress',
           'Childress', 'Reese', 'Reese', 'San Angelo', 'San Angelo'],
          ['WRF','MOS','WRF','MOS','WRF','MOS','WRF','MOS','WRF','MOS','WRF','MOS']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples)
df = pd.DataFrame(np.random.randn(12, 4), index=arrays,
                  columns=['00 UTC', '06 UTC', '12 UTC', '18 UTC'])

def highlight_MOS(s):
    is_mos = s.index.get_level_values(1) == 'MOS'
    return ['color: darkorange' if v else 'color: darkblue' for v in is_mos]

s = df.style.apply(highlight_MOS)
print(s)</code>

The highlight_MOS function is defined to apply a dark orange color to the values in the 'MOS' rows and a dark blue color to all other values. The style.apply method is then used to apply this function to the dataframe, and the result is printed.

The output of the code will be a dataframe table with the 'MOS' rows colored in dark orange and the left two index/header columns and top header row colored with a different background color. This customization allows for a more visually appealing and informative representation of the data within the dataframe.

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