Working with seaborn Python library: visualizing foot traffic based on big data of MAC addresses

  1. Split the time code to know hourly data. For example, since the first number of string ‘01:23:45’ split by colon delimiter would give ‘01’, any row of data where time field has starting with ‘01’ would be data from 1 am.
  2. Add MAC addresses to an array as the script runs through.
  3. Then, once the hour section of time changes, count number of elements in MAC addresses array, and map this number to the past hour in a dictionary.
  4. Empty the MAC addresses array.
  5. Repeat.
import numpy as np
import seaborn as sns
import pandas as pd
import netaddr
from matplotlib import pyplot as plt
import csv
import re

times = []
uniqueMACcount = []
currentcount = '0'
uniqueMAC = []

#convert the times
def split_by_hour(h):
h = re.split('[:]', h)
return h[0]

with open("seaborn.csv") as f:
reader = csv.reader(f)
next(reader) # skip header
data = [r for r in reader]

for element in data:
if currentcount == split_by_hour(element[1]):
if element[2] not in uniqueMAC:
if (netaddr.EUI(element[2]).words[0] & 0b10) == 0 and (netaddr.EUI(element[2]).words[0] & 0b01) == 0:
except (netaddr.core.AddrFormatError):

if currentcount != split_by_hour(element[1]):
currentcount = split_by_hour(element[1])
uniqueMAC = []


times = list(map(int, times))

df = pd.DataFrame(dict(hour=times, count=uniqueMACcount))
sns_plot = sns.factorplot("hour","count", data=df,kind="bar",size=6,aspect=2,legend_out=False)




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