Python was used to parse the data received from the sensors.

php CO2.php

<PHP>
import serial
import time
import matplotlib.pyplot as plt

# make sure the 'COM#' is set according the Windows Device Manager
ser = serial.Serial('/dev/cu.usbmodem14101', 9600, timeout=1)
time.sleep(2)

data = []
for i in range(100):
    line = ser.readline()   # read a byte string
    if line:
        string = line.decode()  # convert the byte string to a unicode string
        num = float(string) # convert the unicode string to an int
        num2 = int(num)
        data.append(num2) # add int to data list
ser.close()

# build the plot
plt.plot(data)
plt.xlabel('Time[s]')
plt.ylabel('CO2 [ppm]')
plt.title('CO2 vs. Time')
plt.show()

</PHP>

<PHP>
# Importing library
import csv
import pandas as pd

CO2 = pd.DataFrame(data) #convert data to a pandas dataframe
  
# opening the csv file in 'w+' mode

file = open('CO2_(insert-initial.csv', 'w+', newline ='')
CO2.to_csv(file,index = False)

</PHP>

<PHP>
#combining data taken from all three locations 
import pandas as pd
L = pd.read_csv('CO2_L.csv')  #reading the csv files
K = pd.read_csv('CO2_K.csv')
B = pd.read_csv('CO2_B.csv')
df = pd.concat([L,K,B], axis = 1) #combing all the files horizontally
</PHP>

<PHP>
# calculating the mean for each location
mean = df.mean()
ppm = 400 #defining ppm of CO2 in clean air
ratio = mean/ppm #calculating the ratio of ppm of each location with the ppm of clean air

#adding the mean and ratio to the dataframe
df.loc['mean'] = mean 
df.loc['ratio'] = mean/ppm

#converting the dataframe to a csv file
file1 = open('CO2.csv', 'w+', newline ='')
df.to_csv(file1,index = False)
</PHP>