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Day 3

Binary Diagnostic

view the Advent of Code 2021 Day 3 challenge

Setup

# Setup - load any packages
import pandas as pd

Load the data

# load data into Pandas DataFrame using fixed width
data_raw = pd.read_fwf('day3_data.txt', names=['a','b','c','d','e','f','g', 'h', 'i', 'j', 'k', 'l'], index_col=False, header=None, widths=[1,1,1,1,1,1,1,1,1,1,1,1])

# Check the data types of the columns
print(data_raw.dtypes)

# View the first few rows of the data
data_raw.head()

a    int64
b    int64
c    int64
d    int64
e    int64
f    int64
g    int64
h    int64
i    int64
j    int64
k    int64
l    int64
dtype: object

	a	b	c	d	e	f	g	h	i	j	k	l
0	1	1	1	0	1	0	1	1	1	0	1	1
1	0	0	1	0	1	1	1	1	0	0	1	0
2	0	0	0	1	1	0	1	1	1	1	0	0
3	1	1	0	1	0	1	1	1	0	0	0	1
4	1	0	1	1	1	0	0	1	1	1	1	1

Part 1

# copy the raw data
part1=data_raw.copy()

# declare variables as string
gamma =""
epsilon =""

# sum the columns
p1_totals = part1.sum()

#loop through each column sum
for column in p1_totals: 
    # if column sum is more than half the number of rows then there are more ones
    if column >= (part1.shape[0]/2):
        #more ones
        gamma = gamma + "1"
        epsilon = epsilon + "0"
    else:
        #more zeros
        gamma = gamma + "0"
        epsilon = epsilon + "1"

#view binary values
print(f'gamma = {gamma} ({int(gamma, 2)})')
print(f'epsilon = {epsilon} ({int(epsilon, 2)})')

# convert to integers and multiply
p1_answer = int(gamma, 2) * int(epsilon, 2)
print(f'answer = {p1_answer}')

gamma = 011101001101 (1869)
epsilon = 100010110010 (2226)
answer = 4160394

#find most common binary value in a column
def mostcommon(pd_col):
    if sum(pd_col)>=(pd_col.shape[0]/2):
        return "1"
    else:
        return "0"
    
# copy the raw data
part1=data_raw.copy()

# declare variables as string
gamma =""
epsilon =""

for i in range(part1.shape[1]): 
    # if column sum is more than half the number of rows then there are more ones
    if mostcommon(part1.iloc[:,i])=="1":
        #more ones
        gamma = gamma + "1"
        epsilon = epsilon + "0"    
    else:
        #more zeros
        gamma = gamma + "0"
        epsilon = epsilon + "1"
        
#view binary values
print(f'gamma = {gamma} ({int(gamma, 2)})')
print(f'epsilon = {epsilon} ({int(epsilon, 2)})')

# convert to integers and multiply
p1_answer = int(gamma, 2) * int(epsilon, 2)
print(f'answer = {p1_answer}')

gamma = 011101001101 (1869)
epsilon = 100010110010 (2226)
answer = 4160394

Part 2

view the Advent of Code 2021 Day 3 challenge Part 2

# copy the raw data
part2=data_raw.copy()

def findReading(sensordata, leastcommon=False):
    #copy the data as we will be filtering it
    sensordata_temp=sensordata.copy()
    
    #loop through each binary digit
    for i in range(sensordata_temp.shape[1]): 
        # get most common value in column
        most=int(mostcommon(sensordata_temp.iloc[:,i]))
        
        if leastcommon==True:
            #flip the most common value from 0->1 or 1->0
            most = 1 - most
        
        #subset the data by the most common value in the column
        sensordata_temp=sensordata_temp[sensordata_temp.iloc[:, i]==most]
        
        #if only one value then return it and exit
        if sensordata_temp.shape[0]==1:
            #Concatenate the columns together to get the binary value
            sensordata_temp['reading'] = sensordata_temp[sensordata_temp.columns[:]].apply(
            lambda x: ''.join(x.dropna().astype(str)),
            axis=1)
            return sensordata_temp.iloc[0,12]
        
oxygen = findReading(part2, False)   
CO2=findReading(part2, True)

#view binary values
print(f'oxygen = {oxygen} ({int(oxygen, 2)})')
print(f'CO2 = {CO2} ({int(CO2, 2)})')

# convert to integers and multiply
p2_answer = int(oxygen, 2) * int(CO2, 2)
print(f'answer = {p2_answer}')

oxygen = 011010110111 (1719)
CO2 = 100101100000 (2400)
answer = 4125600