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Watch Me Code 4: More Matplotlib

  • Data Analysis of Syracuse Weather with Plotting




In [4]:



    


%matplotlib inline 
import matplotlib
matplotlib.rcParams['figure.figsize'] = (20.0, 10.0) # larger figure size
import pandas as pd
import numpy as np
from pandas import DataFrame, Series



    











In [5]:



    


weather = pd.read_csv("https://raw.githubusercontent.com/mafudge/datasets/master/weather/syracuse-ny.csv")



    











In [6]:



    


weather.head()



    


















    
Out[6]:










  
    

      
      EST
      Max TemperatureF
      Mean TemperatureF
      Min TemperatureF
      Max Dew PointF
      MeanDew PointF
      Min DewpointF
      Max Humidity
      Mean Humidity
      Min Humidity
      ...
      Max VisibilityMiles
      Mean VisibilityMiles
      Min VisibilityMiles
      Max Wind SpeedMPH
      Mean Wind SpeedMPH
      Max Gust SpeedMPH
      PrecipitationIn
      CloudCover
      Events
      WindDirDegrees
    

  
  
    

      0
      1997-1-1
      27
      12.0
      -2
      22
      4
      -8
      92
      74
      59
      ...
      10
      9
      1
      14
      5
      NaN
      0.05
      6
      Snow
      89
    

    

      1
      1997-1-2
      34
      28.0
      23
      33
      29
      21
      100
      96
      88
      ...
      9
      2
      0
      8
      4
      NaN
      0.08
      8
      Fog-Rain-Snow
      82
    

    

      2
      1997-1-3
      44
      40.0
      36
      44
      38
      34
      100
      96
      89
      ...
      10
      4
      0
      15
      6
      NaN
      0.09
      8
      Fog-Rain
      273
    

    

      3
      1997-1-4
      48
      40.0
      34
      44
      36
      33
      96
      90
      83
      ...
      10
      10
      8
      13
      4
      NaN
      0.00
      8
      Rain
      80
    

    

      4
      1997-1-5
      55
      46.0
      37
      50
      43
      29
      89
      81
      73
      ...
      10
      10
      10
      21
      11
      30.0
      0.13
      8
      Rain
      199
    

  



5 rows × 23 columns



















In [7]:



    


weather['Events'].unique()



    


















    
Out[7]:








array(['Snow', 'Fog-Rain-Snow', 'Fog-Rain', 'Rain', nan, 'Rain-Snow',
       'Fog-Snow', 'Rain-Thunderstorm', 'Fog-Rain-Thunderstorm',
       'Thunderstorm', 'Fog', 'Fog-Rain-Snow-Thunderstorm',
       'Rain-Snow-Thunderstorm', 'Fog-Snow-Thunderstorm',
       'Rain-Hail-Thunderstorm'], dtype=object)

Let's get just the thunderstorms!





In [10]:



    


thunder = weather[ weather['Events'].str.find('Thunderstorm') >=0 ]
thunder.head()



    


















    
Out[10]:










  
    

      
      EST
      Max TemperatureF
      Mean TemperatureF
      Min TemperatureF
      Max Dew PointF
      MeanDew PointF
      Min DewpointF
      Max Humidity
      Mean Humidity
      Min Humidity
      ...
      Max VisibilityMiles
      Mean VisibilityMiles
      Min VisibilityMiles
      Max Wind SpeedMPH
      Mean Wind SpeedMPH
      Max Gust SpeedMPH
      PrecipitationIn
      CloudCover
      Events
      WindDirDegrees
    

  
  
    

      128
      1997-5-9
      64
      56.0
      48
      52
      46
      38
      93
      78
      55
      ...
      10
      9
      3
      22
      9
      29.0
      0.16
      7
      Rain-Thunderstorm
      246
    

    

      138
      1997-5-19
      63
      58.0
      53
      63
      58
      42
      100
      93
      66
      ...
      10
      4
      1
      13
      6
      NaN
      0.92
      8
      Rain-Thunderstorm
      280
    

    

      163
      1997-6-13
      77
      68.0
      60
      65
      61
      57
      93
      78
      64
      ...
      10
      6
      3
      13
      6
      NaN
      0.05
      7
      Rain-Thunderstorm
      240
    

    

      170
      1997-6-20
      78
      66.0
      57
      70
      63
      56
      97
      87
      71
      ...
      10
      6
      3
      8
      5
      NaN
      0.04
      7
      Rain-Thunderstorm
      92
    

    

      176
      1997-6-26
      79
      74.0
      64
      72
      66
      53
      100
      79
      45
      ...
      10
      7
      2
      16
      8
      NaN
      0.15
      6
      Rain-Thunderstorm
      275
    

  



5 rows × 23 columns

The percentage of days it thunders in Syracuse, historically





In [9]:



    


thunder.EST.count() / weather.EST.count()



    


















    
Out[9]:








0.067640613586559539

















In [8]:



    


weather.columns



    


















    
Out[8]:








Index(['EST', 'Max TemperatureF', 'Mean TemperatureF', 'Min TemperatureF',
       'Max Dew PointF', 'MeanDew PointF', 'Min DewpointF', 'Max Humidity',
       'Mean Humidity', 'Min Humidity', 'Max Sea Level PressureIn',
       'Mean Sea Level PressureIn', 'Min Sea Level PressureIn',
       'Max VisibilityMiles', 'Mean VisibilityMiles', 'Min VisibilityMiles',
       'Max Wind SpeedMPH', 'Mean Wind SpeedMPH', 'Max Gust SpeedMPH',
       'PrecipitationIn', 'CloudCover', 'Events', 'WindDirDegrees'],
      dtype='object')

What is the relationship between Temperature and Dewpoint?





In [11]:



    


weather.plot.scatter( x = 'Mean TemperatureF', y = 'MeanDew PointF')



    


















    
Out[11]:








<matplotlib.axes._subplots.AxesSubplot at 0x1e624c0dc18>

INSIGHT: Positive correlation between tem and dewpoint. Every meteroloogist knows this. ;-)

What is the relationship between cloud cover and visibility?





In [12]:



    


weather.plot.scatter( x = 'CloudCover', y = 'Mean VisibilityMiles')



    


















    
Out[12]:








<matplotlib.axes._subplots.AxesSubplot at 0x1e6240d0be0>

As one would expect the less cloud cover the greater visibility.

How about temperature and wind speed?





In [13]:



    


weather.plot.scatter( x = 'Mean TemperatureF', y = 'Mean Wind SpeedMPH')



    


















    
Out[13]:








<matplotlib.axes._subplots.AxesSubplot at 0x1e6242a54e0>

not much of an insight there, but...

when you look at the relationship on days where it thunders:





In [12]:



    


thunder.plot.scatter( x = 'Mean TemperatureF', y = 'Mean Wind SpeedMPH')



    


















    
Out[12]:








<matplotlib.axes._subplots.AxesSubplot at 0x2718d89c198>

We see that it doesn't really thunder when its cold out!

This plot it interesting. It shows when the temperature is cold, the wind isn't coming out of the south. Make sense for Syracuse.





In [14]:



    


weather.plot.scatter( x = 'Mean TemperatureF', y = 'WindDirDegrees')



    


















    
Out[14]:








<matplotlib.axes._subplots.AxesSubplot at 0x1e624a4f4e0>










    
























In [15]:



    


weather['Events'].unique()



    


















    
Out[15]:








array(['Snow', 'Fog-Rain-Snow', 'Fog-Rain', 'Rain', nan, 'Rain-Snow',
       'Fog-Snow', 'Rain-Thunderstorm', 'Fog-Rain-Thunderstorm',
       'Thunderstorm', 'Fog', 'Fog-Rain-Snow-Thunderstorm',
       'Rain-Snow-Thunderstorm', 'Fog-Snow-Thunderstorm',
       'Rain-Hail-Thunderstorm'], dtype=object)

















In [19]:



    


weather['Events'] = weather.Events.fillna('None')



    











In [20]:



    


weather['Diff TemperatureF'] = weather['Max TemperatureF'] - weather['Min TemperatureF']



    











In [21]:



    


import matplotlib 
matplotlib.rcParams['figure.figsize'] = (20.0, 10.0) # larger figure size



    











In [22]:



    


weather['date'] = pd.to_datetime(weather.EST) # make timeseries data



    











In [23]:



    


# let's plot the temperature swings For may 2015
weather[weather['EST'].str.find("2015-5") >=0 ].plot.line( x = 'date', y = 'Diff TemperatureF')



    


















    
Out[23]:








<matplotlib.axes._subplots.AxesSubplot at 0x1e626390710>










    
























In [24]:



    


w2015 = weather[ weather.date > '2015-01-01']
w2015.plot.line(x = 'date', y =['Max TemperatureF', 'Min TemperatureF'] )



    


















    
Out[24]:








<matplotlib.axes._subplots.AxesSubplot at 0x1e624aae470>










    
























In [ ]:

Content source: IST256/learn-python

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