styling a pandas table and merging 2 dataframes

I wanted to style a pandas table, but I'm not able to reshape it every other row, color it this way or set borders for it. I'd be thankful if anyone helps me with this. I uploaded both tables below. The right side table is my table at the moment and the left one is my target.

To color it, I tried to change tables properties, using style.properties and also used different join and merge techniques to stick two tables every other row, though none of'em worked.

You can lreshape your DataFrame, then use apply/applymap_index (which are Excel friendly) :

out = pd.lreshape(
    df, {
        "TITLE": ["Content Based MOVIES TITLE", "Colaborative Filtering MOVIES TITLE"],
         "ID": ["Content Based MOVIES ID", "Colaborative Filtering MOVIES ID"]
    }
)

import numpy as np

#rows
purple = "background-color: #ffff99; color: black"
yellow = "background-color: #b376e0; color: black"
border = ["border-right: 3px solid black", ""]

#header
bgray = [
    "border-right: 3px solid black; background-color: #e9e8fc; color: black",
    "background-color: #e9e8fc; color: black;"
]    

(
    out.style
        .apply(lambda x: np.where(x.index % 2, yellow, purple))
        .apply(lambda _: border, axis=1)
        .apply_index(lambda _: bgray, axis=1)
        # .to_excel("output.xlsx", index=False) #optional
)

Output :

Got this by referencing the docs.

https://pandas.pydata.org/docs/user_guide/style.html#Finer-Control-with-Slicing

They show a similar example using pd.IndexSlice, which I adapted a little.

Note that ::2 means every second item and is identical to 0::2.

1::2 means every second item starting from the second item.

df = pd.DataFrame({
    'columnA': [1, 3, 4, 9, 1],
    'columnB': [2, 4, 5, 1, 5],
    'columnC': [2, 4, 5, 10, 3]
})

idx = pd.IndexSlice
slice1 = idx[idx[::2, :]]
slice2 = idx[idx[1::2, :]]

df.style \
  .set_properties(**{'background-color': '#FFFF99'}, subset=slice1) \
  .set_properties(**{'background-color': '#B376E0'}, subset=slice2) 

Pandas' styling works well when it works, but for anything non-obvious it is difficult to configure it. If your project is one time thing and you don't need automation an easer solution would be to format it outside of Python environement in any document/spread-sheet software. Or if being automated is a requirement you could do formatting with HTML (extract data from the DataFrame and put it into a string that does HTML formatting); and do whatever you want with the HTML -- export it or display it inside your environement.

To put your right subtable under the left one you could use pd.concat function.