Pythonic way to find out the smallest 2D plane(box) to fit all pieces(biggest ones)-CodePudding

Im getting csv file with many pieces with length/width dimensions and need to find out the smallest box l/w. First thoughts was to transpose(sort) between pandas df columns(like this) to collect the biggest value of both in first column, so this biggest value would be one side of box and the biggest of second column would be another side of box:

length	width
200	150
100	300

transpose to this

length	width
300	100
200	150

So box would be 300x150. But Im getting error for example where I linked above. Also it possible to sort in nested list in same way. So questions:

What is the best way(pythonic) to store(manipulate) data of 2(3) dimensions l/w(thickness).
Pretty sure there will be more elegant way to solve this, so please show examples or link where I could read because dont know how to search correctly.
How sort values in pandas between same row but different columns. Thx.

CodePudding user response：

You can use numpy to sort and make a new dataframe:

import numpy as np
pd.DataFrame(np.sort(np.sort(df.values, axis=1)[:, ::-1], axis=0),
             columns=df.columns)

output:

   length  width
1     300    100
0     200    150

You can also use:

(df.apply(sorted,
          reverse=True,
          axis=1,
          result_type='expand')
   .rename(columns=dict(zip(range(len(df.columns)), df.columns)))
   .sort_values(cols, ascending=False) # optional if the goal is to get the max per column
)

output:

   length  width
1     300    100
0     200    150

CodePudding user response：

Merge your columns as a sorted tuple and sort values at the end:

cols = ['length', 'width']
f = lambda x: sorted(tuple(x), reverse=True)
df[cols] = df[cols].apply(f, axis=1, result_type='expand')
df = df.sort_values(cols)

Output:

>>> df
   length  width
0     200    150
1     300    100