Getting the best fit match of boxes of varying dimensions in a bounding box

I have a problem wherein I have to ensure optimal packing in a 2D rectangular frame of smaller 2D rectangular/square boxes.

I read that this may be termed as bin-packing problem or may also be considered as best fit bounding-box problem, where we try to find the best solution for above problem.

The dataset is something like this:

name     |  Width  |  Height
---------+---------+--------------
Rect 1   |  146    | 276
---------+---------+-------------- 
Rect 2   |  168    | 20
---------+---------+-------------- 
Rect 3   |   53    | 297
---------+---------+-------------- 
Rect 4   |   68    | 122
---------+---------+-------------- 
Rect n   |  167    | 229
---------+---------+-------------- 

The bounding box/frame I have is 1000 units wide and 800 units high, and can have more than 1 such boxes at a time.

I have to ensure that:

1. best fit for all these boxes from Rect 1 to Rect n (where n is a whole number),
2. that whatever best fit I have as the resultant, produces the least wasted space in all the bounding boxes available.
3. Ensure least processing time and resource utilization, though I know this would not be a reality for such a problem, so this is not a priority as of now.
4. Change orientation of the boxes, if it gives a better optimization result.

On searching through google, I found out a few solution and papers (example) explaining this problem, but I am having a hard time figuring the best way to do this using a coding language.

I tried this on paper, and figured out a way.

1. Sort all the boxes, basis their surface area, from largest to smallest.
2. Split this dataset into the number of bounding boxes/frames I have to fit in. Lets call each group as a batch.
3. Start by putting the biggest boxes in each frame first, from each batch.
4. Once we've exhausted all the biggest boxes, or cannot fit in further bigger boxes in bounding box, move to the next size batch (second largest), thus reaching to the smallest size batch.

I guess, this may not be optimal, since I am taking Surface area for placement, but orientation may still be a problem that is not being looked at.

I know this a fairly complex problem, but I guess there are still a few ways to do it.

Created a Python Notebook to solve this problem, because I think Python or R is the best fit as the programming language here.

import pandas as pd
import numpy as np
#Add JSON dataset
data= 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#Convert to Pandas dataframe
df = pd.DataFrame.from_dict(data, orient='columns');

#Convert the column type from string to float 
df["width"] = pd.to_numeric(df["width"],downcast='float')
df["height"] = pd.to_numeric(df["height"],downcast='float')

#Calculate Surface area (L*B or Width * Height) for each Rectangle
df["area"] = df.width * df.height

#Sort each record on surface area (Largest to smallest)
df = df.sort_values(by=["area"], ascending= False);

#number of Rectangle frames where above Rectangles need to fit
frameCount = 6;
frameWidth = 1000; #width of rectangle frame 
frameHeight = 800; #height of rectangle frame

#Split the above dataframe into same count of frames
dfs = np.array_split(df, frameCount)

from Getting the best fit match of boxes of varying dimensions in a bounding box