I think you can use Slicing in Styles for select columns B and C and then Styler.applymap for elementwise styles. import pandas as pd import numpy as np data = pd.DataFrame(np.random.randn(5, 3), columns=list(‘ABC’)) #print (data) def highlight_cols(s): color=”grey” return ‘background-color: %s’ % color data.style.applymap(highlight_cols, subset=pd.IndexSlice[:, [‘B’, ‘C’]]) If you want more colors or be more … Read more
This solution allows for you to pass a column label or a list of column labels to highlight the entire row if that value in the column(s) exceeds the threshold. import pandas as pd import numpy as np np.random.seed(24) df = pd.DataFrame({‘A’: np.linspace(1, 10, 10)}) df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list(‘BCDE’))], axis=1) df.iloc[0, 2] = … Read more
As mentioned in the comments, you can use the render property to obtain an HTML of the styled table: html = styled_table.render() You can then use a package that converts html to an image. For example, IMGKit: Python library of HTML to IMG wrapper. Bear in mind that this solution requires the installation of wkhtmltopdf, … Read more
Once you add style to your chained assignments you are operating on a Styler object. That object has a render method to get the html as a string. So in your example, you could do something like this: html = ( df.style .format(percent) .applymap(color_negative_red, subset=[‘col1’, ‘col2’]) .set_properties(**{‘font-size’: ‘9pt’, ‘font-family’: ‘Calibri’}) .bar(subset=[‘col4’, ‘col5’], color=”lightblue”) .render() ) … Read more
From the style docs: You can apply conditional formatting, the visual styling of a DataFrame depending on the data within, by using the DataFrame.style property. import pandas as pd df = pd.DataFrame([[2,3,1], [3,2,2], [2,4,4]], columns=list(“ABC”)) df.style.apply(lambda x: [“background: red” if v > x.iloc[0] else “” for v in x], axis = 1) Edit: to format … Read more