Fastest way to calculate in Pandas?

Question

Given these two dataframes:

df1 =
     Name  Start  End
  0  A     10     20
  1  B     20     30
  2  C     30     40

df2 =
     0   1
  0  5   10
  1  15  20
         


        

Answer 1

This is one way to go about it:

 #create numpy arrays of df1 and 2

df1_start = df1.loc[:,'Start'].to_numpy()
df1_end = df1.loc[:,'End'].to_numpy()

df2_start = df2[0].to_numpy()
df2_end = df2[1].to_numpy()

#use np tile to create shapes
#that allow element wise subtraction
tiled_start = np.tile(df1_start,(len(df2),1)).T
tiled_end = np.tile(df1_end,(len(df2),1)).T

#subtract df2 from df1
start = np.subtract(tiled_start,df2_start)
end = np.subtract(tiled_end, df2_end)

#create columns for start and end
start_columns = [f'Start_Diff_{num}' for num in range(len(df2))]
end_columns = [f'End_Diff_{num}' for num in range(len(df2))]

#create dataframes of start and end
start_df = pd.DataFrame(start,columns=start_columns)
end_df = pd.DataFrame(end, columns = end_columns)

#lump start and end into one dataframe
lump = pd.concat([start_df,end_df],axis=1)

#sort the columns by the digits at the end
filtered = final.columns[final.columns.str.contains('\d')]

cols = sorted(filtered, key = lambda x: x[-1])

lump = lump.reindex(cols,axis='columns')

#hook lump back to df1
final = pd.concat([df1,lump],axis=1)