highlight (color) a panda data frame row by index

Question

i have two data frame df1:

d1 = {\"col1\" : [\'A\', \'B\', \'C\'],
      \"Col2\": [\"home\", \"car\",\"banana\" ]}

d2 = {\"col1\" : [\'D\', \'F\',\'C\'],
              


        

Answer 1

In case you want to highlight two rows (say index 2 and 4) it is a almost a duplicate of this answer

new_df.style.apply(lambda x: ['background: lightgreen' if x.name in [2,4] 
                              else '' for i in x], 
                   axis=1)

If instead you are looking to highlight every row that contain a given name in a list (i.e. lst = ['car', 'boat']) you can use

new_df.style.apply(lambda x: ['background: lightgreen' if (set(lst).intersection(x.values)) 
                              else '' for i in x], 
                   axis=1)

Answer 2

Lots of questions link here regarding styling a DataFrame row, so I kept coming here despite the accepted answer not working for me, and no useful error message from pandas. I just came to point out that while the accepted answer may work fine for html, or Jupyter or whatever they're using. It does not work when applied to Excel. For Excel you'll need the following:

new_df.style.apply(
    lambda x: ['background-color: <color>' if x.name in [2,4] else '' for i in x],
    axis=1
)

Note the use of "backgound-color". Excel will not render "background" as you might expect

In addition, when working with Excel, you'll need to use a color name From Excel's list of recognized colors, Fiddling around with the above answer using "lightgreen" may give you black-on-black... probably not what anyone is looking for.