Loading IoT Data



In [3]:

    
import pandas as pd

df = pd.read_csv("https://data.sparkfun.com/output/n1YRX98dq9C6X0LrZdvD.csv")
df.head(10)









    Out[3]:






  
    
      
      device
      moisture
      humidity
      uv
      light
      temp
      timestamp
    
  
  
    
      0
      FZEDA446D0036D501
      0
      53.3125
      0.288086
      21
      24.06250
      2015-07-14T18:41:04.997Z
    
    
      1
      FZEDA446D0036D501
      0
      53.5625
      0.288086
      21
      24.03125
      2015-07-14T18:36:01.620Z
    
    
      2
      FZEDA446D0036D501
      0
      53.6250
      0.288086
      21
      24.03125
      2015-07-14T18:30:58.520Z
    
    
      3
      FZEDA446D0036D501
      0
      53.6250
      0.288086
      21
      23.90625
      2015-07-14T18:25:55.263Z
    
    
      4
      FZEDA446D0036D501
      0
      53.6875
      0.288086
      20
      23.87500
      2015-07-14T18:20:52.129Z
    
    
      5
      FZEDA446D0036D501
      0
      53.8750
      0.288086
      20
      23.84375
      2015-07-14T18:15:48.669Z
    
    
      6
      FZEDA446D0036D501
      0
      54.0000
      0.288086
      21
      23.87500
      2015-07-14T18:10:45.112Z
    
    
      7
      FZEDA446D0036D501
      0
      54.0000
      0.288086
      17
      23.87500
      2015-07-14T18:05:41.741Z
    
    
      8
      FZEDA446D0036D501
      0
      54.0625
      0.283203
      17
      23.87500
      2015-07-14T18:00:38.582Z
    
    
      9
      FZEDA446D0036D501
      0
      54.0625
      0.288086
      21
      23.78125
      2015-07-14T17:55:30.443Z



In [2]:

    
df.describe()



In [16]:

    
%matplotlib inline
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
import seaborn as sns



In [24]:

    
plt.figure()
df.plot('timestamp', subplots=True, figsize=(8, 6))
plt.gcf().autofmt_xdate()









    





<matplotlib.figure.Figure at 0x7f4491304b38>



In [ ]:



In [ ]:

	moisture	humidity	uv	light	temp
count	316.000000	316.000000	316.000000	316.000000	316.000000
mean	1.905063	54.337619	0.283682	20.056962	23.376483
std	30.916536	2.524159	0.001455	12.408519	0.977868
min	0.000000	50.875000	0.283203	0.000000	21.687500
25%	0.000000	52.437500	0.283203	4.000000	22.335938
50%	0.000000	52.937500	0.283203	24.000000	23.968750
75%	0.000000	56.687500	0.283203	31.000000	24.093750
max	547.000000	59.062500	0.288086	43.000000	24.656250

	device	humidity	uv	light	temp	timestamp
0	FZEDA446D0036D501	53.3125	0.288086	21	24.06250	2015-07-14T18:41:04.997Z
1	FZEDA446D0036D501	53.5625	0.288086	21	24.03125	2015-07-14T18:36:01.620Z
2	FZEDA446D0036D501	53.6250	0.288086	21	24.03125	2015-07-14T18:30:58.520Z
3	FZEDA446D0036D501	53.6250	0.288086	21	23.90625	2015-07-14T18:25:55.263Z
4	FZEDA446D0036D501	53.6875	0.288086	20	23.87500	2015-07-14T18:20:52.129Z
5	FZEDA446D0036D501	53.8750	0.288086	20	23.84375	2015-07-14T18:15:48.669Z
6	FZEDA446D0036D501	54.0000	0.288086	21	23.87500	2015-07-14T18:10:45.112Z
7	FZEDA446D0036D501	54.0000	0.288086	17	23.87500	2015-07-14T18:05:41.741Z
8	FZEDA446D0036D501	54.0625	0.283203	17	23.87500	2015-07-14T18:00:38.582Z
9	FZEDA446D0036D501	54.0625	0.288086	21	23.78125	2015-07-14T17:55:30.443Z