divide columns vertically by max value in a dataframe-CodePudding

I have a following table:

id	summary	summary_len	apple	book	computer
1	....	210	2	1	0
2	...	120	3	0	1
3	...	50	2	2	1

summary is basically some sort of description, summary_len <- the length of those descriptions and the rest - apple/book/computer and the keywords and the values presented in the table - those are the occurrences in each description.

I need to normalize this table, in a way to find max value - PER COLUMN (vertically) and then divide by this value, so the output will be as below (I put it in a format 2/3 - just to emphasis max value per column):

id	summary	summary_len	apple	book	computer
1	....	210	2/3	1/2	0/1
2	...	120	3/3	0/2	1/1
3	...	50	2/3	2/2	1/1

My problem here is that I don't have to find max in each columns - only for those keywords, which I am checking the occurrences for. I stored them in a list and got max value per column:

max_per_col = df_freq[keywords].max()
max_per_col

this is how it looks (with the original data):

Could you help me apply it "back" to the former dataframe and divide vertically each column by the max value?

CodePudding user response：

You can divide only filtered columns by maximal values:

keywords = ['apple','book','computer']

df_freq[keywords] /= df_freq[keywords].max()

#working like
#df_freq[keywords] = df_freq[keywords] / df_freq[keywords].max()
print (df_freq)
   id  summary_len     apple  book  computer
0   1          210  0.666667   0.5       0.0
1   2          120  1.000000   0.0       1.0
2   3           50  0.666667   1.0       1.0