Day 2

So here we're basically just trying to compute the maximum minus the minimum for each row of this data set. I think this looks like a good opportunity to try and use the pandas module

import pandas as pd
import numpy as np

# Ez mode:
test = pd.read_csv("H:/python_jiggering/logs/aoc_2017/d2_input.txt", header = None, sep = '\t')
test.head()

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
0	5048	177	5280	5058	4504	3805	5735	220	4362	1809	1521	230	772	1088	178	1794
1	6629	3839	258	4473	5961	6539	6870	4140	4638	387	7464	229	4173	5706	185	271
2	5149	2892	5854	2000	256	3995	5250	249	3916	184	2497	210	4601	3955	1110	5340
3	153	468	550	126	495	142	385	144	165	188	609	182	439	545	608	319
4	1123	104	567	1098	286	665	1261	107	227	942	1222	128	1001	122	69	139

We wanna figure out how to compute the difference between min and max

def check(x):
    return(np.max(x) - np.min(x))

Apply across rows:

ranges = test.apply(func = check, axis = 1)
checksum = sum(ranges)
checksum

58975

Part 2

Okay for the second part we have to find which pair of numbers even divides the other. I think my style is again to just write to do it once and see if we can get the indices. Probably two for loops will do this simply enough...

def check2(x):
    for i in range(len(x)):
        for j in range(i+1, len(x)):
            if x[i] % x[j] == 0:
                return(x[i] / x[j])
            if x[j] % x[i] == 0:
                return(x[j] / x[i])
    
quotients = test.apply(func = check2, axis = 1)
checksum2 = sum(quotients)
checksum2

308.0