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Temperature and Severe Weather Occurences in Southern Maine

a search for correlation

May 9, 2016





In [101]:



    


import openpyxl
import sys                             # system module 
import pandas as pd                    # data package
import matplotlib.pyplot as plt        # graphics module  
import datetime as dt                  # date and time module
import numpy as np                     # foundation for Pandas 
from pandas_datareader import data, wb 
%matplotlib inline 
import seaborn as sns

# check versions (overkill, but why not?)
print('Python version: ', sys.version)
print('Pandas version: ', pd.__version__)
print('Today: ', dt.date.today())



    


















    






Python version:  3.5.1 |Anaconda 4.0.0 (x86_64)| (default, Dec  7 2015, 11:24:55) 
[GCC 4.2.1 (Apple Inc. build 5577)]
Pandas version:  0.18.0
Today:  2016-05-07

















In [300]:



    


##Recordings of severe weather in York, Maine, from 1996 to 2015:

storms = pd.read_excel('/Users/jamesalbright/Desktop/Bootcamp Final Master Data.xlsx')
print('Dimensions:', storms.shape)
print('Variable dtypes:\n', storms.dtypes, sep='')
storms.head



    


















    






Dimensions: (864, 10)
Variable dtypes:
CZ_NAME_STR                    object
BEGIN_DATE             datetime64[ns]
EVENT_TYPE                     object
MAGNITUDE                      object
DAMAGE_PROPERTY_NUM             int64
EPISODE_ID                      int64
CZ_FIPS                         int64
Unnamed: 7                     object
Unnamed: 8                     object
Unnamed: 9                    float64
dtype: object










    
Out[300]:








<bound method NDFrame.head of               CZ_NAME_STR BEGIN_DATE         EVENT_TYPE MAGNITUDE  \
0     COASTAL YORK (ZONE) 1996-01-03         Heavy Snow             
1    INTERIOR YORK (ZONE) 1996-01-03         Heavy Snow             
2    INTERIOR YORK (ZONE) 1996-01-08         Heavy Snow             
3     COASTAL YORK (ZONE) 1996-01-08         Heavy Snow             
4     COASTAL YORK (ZONE) 1996-01-12         Heavy Snow             
5    INTERIOR YORK (ZONE) 1996-01-12         Heavy Snow             
6    INTERIOR YORK (ZONE) 1996-01-19          High Wind             
7     COASTAL YORK (ZONE) 1996-01-19          High Wind             
8    INTERIOR YORK (ZONE) 1996-01-27     Winter Weather             
9     COASTAL YORK (ZONE) 1996-01-27          High Wind             
10   INTERIOR YORK (ZONE) 1996-02-25          High Wind             
11    COASTAL YORK (ZONE) 1996-03-05         Heavy Snow             
12   INTERIOR YORK (ZONE) 1996-03-05         Heavy Snow             
13    COASTAL YORK (ZONE) 1996-03-07         Heavy Snow             
14   INTERIOR YORK (ZONE) 1996-03-07         Heavy Snow             
15   INTERIOR YORK (ZONE) 1996-04-09         Heavy Snow             
16    COASTAL YORK (ZONE) 1996-04-09         Heavy Snow             
17   INTERIOR YORK (ZONE) 1996-04-16         Heavy Rain             
18    COASTAL YORK (ZONE) 1996-04-16         Heavy Rain             
19   INTERIOR YORK (ZONE) 1996-04-16              Flood             
20    COASTAL YORK (ZONE) 1996-04-16              Flood             
21    COASTAL YORK (ZONE) 1996-04-16      Coastal Flood             
22               YORK CO. 1996-05-12        Flash Flood             
23               YORK CO. 1996-07-08               Hail      1.75   
24               YORK CO. 1996-07-08               Hail      1.75   
25               YORK CO. 1996-07-08               Hail      0.75   
26               YORK CO. 1996-07-08               Hail       1.5   
27               YORK CO. 1996-07-16  Thunderstorm Wind             
28               YORK CO. 1996-07-16  Thunderstorm Wind             
29               YORK CO. 1996-07-16  Thunderstorm Wind             
..                    ...        ...                ...       ...   
834   COASTAL YORK (ZONE) 2015-02-02         Heavy Snow             
835   COASTAL YORK (ZONE) 2015-02-07         Heavy Snow             
836  INTERIOR YORK (ZONE) 2015-02-14         Heavy Snow             
837   COASTAL YORK (ZONE) 2015-02-14         Heavy Snow             
838   COASTAL YORK (ZONE) 2015-02-15      Coastal Flood             
839  INTERIOR YORK (ZONE) 2015-02-18         Heavy Snow             
840   COASTAL YORK (ZONE) 2015-04-20      Coastal Flood             
841              YORK CO. 2015-05-28  Thunderstorm Wind        50   
842              YORK CO. 2015-05-28  Thunderstorm Wind        50   
843              YORK CO. 2015-05-28  Thunderstorm Wind        50   
844              YORK CO. 2015-06-23  Thunderstorm Wind        50   
845              YORK CO. 2015-07-19               Hail      2.75   
846              YORK CO. 2015-07-19               Hail         1   
847              YORK CO. 2015-07-19  Thunderstorm Wind        50   
848              YORK CO. 2015-07-19  Thunderstorm Wind        50   
849              YORK CO. 2015-07-19  Thunderstorm Wind        50   
850              YORK CO. 2015-08-04  Thunderstorm Wind        50   
851              YORK CO. 2015-08-04               Hail      0.75   
852              YORK CO. 2015-08-04               Hail         2   
853              YORK CO. 2015-08-04               Hail       1.5   
854              YORK CO. 2015-08-04               Hail         1   
855              YORK CO. 2015-08-04               Hail      1.75   
856              YORK CO. 2015-08-04               Hail         1   
857              YORK CO. 2015-08-04               Hail      0.75   
858              YORK CO. 2015-08-04               Hail       2.5   
859              YORK CO. 2015-08-04               Hail         1   
860   COASTAL YORK (ZONE) 2015-09-30      Coastal Flood             
861   COASTAL YORK (ZONE) 2015-10-29      Coastal Flood             
862   COASTAL YORK (ZONE) 2015-12-29         Heavy Snow             
863  INTERIOR YORK (ZONE) 2015-12-29         Heavy Snow             

     DAMAGE_PROPERTY_NUM  EPISODE_ID  CZ_FIPS Unnamed: 7 Unnamed: 8  \
0                      0     2041201       23        NaN        NaN   
1                      0     2041201       18        NaN        NaN   
2                      0     2041202       18        NaN        NaN   
3                      0     2041202       23        NaN        NaN   
4                      0     2041204       23        NaN        NaN   
5                      0     2041204       18        NaN        NaN   
6                      0     2041206       18        NaN        NaN   
7                      0     2041206       23        NaN        NaN   
8                      0     2041208       18        NaN        NaN   
9                      0     2041209       23        NaN        NaN   
10                     0     2041224       18        NaN        NaN   
11                     0     2055154       23        NaN        NaN   
12                     0     2055154       18        NaN        NaN   
13                     0     2055155       23        NaN        NaN   
14                     0     2055155       18        NaN        NaN   
15                     0     2054860       18        NaN        NaN   
16                     0     2054860       23        NaN        NaN   
17                     0     2054862       18        NaN        NaN   
18                     0     2054862       23        NaN        NaN   
19                     0     2054864       18        NaN        NaN   
20                820000     2054864       23        NaN        NaN   
21                     0     2054863       23        NaN        NaN   
22                     0     2055784       31        NaN        NaN   
23                     0     2055796       31        NaN        NaN   
24                     0     2055797       31        NaN        NaN   
25                     0     2055798       31        NaN        NaN   
26                     0     2055799       31        NaN        NaN   
27                     0     2055810       31        NaN        NaN   
28                     0     2055811       31        NaN        NaN   
29                     0     2055812       31        NaN        NaN   
..                   ...         ...      ...        ...        ...   
834                    0       93914       23        NaN        NaN   
835                    0       93904       23        NaN        NaN   
836                    0       93897       18        NaN        NaN   
837                    0       93897       23        NaN        NaN   
838                 5000       93118       23        NaN        NaN   
839                    0       93885       18        NaN        NaN   
840               125000       95443       23        NaN        NaN   
841                    0       96583       31        NaN        NaN   
842                    0       96583       31        NaN        NaN   
843                    0       96583       31        NaN        NaN   
844                    0       98095       31        NaN        NaN   
845                    0       98969       31        NaN        NaN   
846                    0       98969       31        NaN        NaN   
847                    0       98969       31        NaN        NaN   
848                    0       98969       31        NaN        NaN   
849                    0       98969       31        NaN        NaN   
850                    0      100161       31        NaN        NaN   
851                    0      100161       31        NaN        NaN   
852                    0      100161       31        NaN        NaN   
853                    0      100161       31        NaN        NaN   
854                    0      100161       31        NaN        NaN   
855                    0      100161       31        NaN        NaN   
856                    0      100161       31        NaN        NaN   
857                    0      100161       31        NaN        NaN   
858                    0      100161       31        NaN        NaN   
859                    0      100161       31        NaN        NaN   
860                75000      100586       23        NaN        NaN   
861                 2000      101799       23        NaN        NaN   
862                    0      102612       23        NaN        NaN   
863                    0      102612       18        NaN        NaN   

     Unnamed: 9  
0           NaN  
1           NaN  
2           NaN  
3           NaN  
4           NaN  
5           NaN  
6           NaN  
7           NaN  
8           NaN  
9           NaN  
10          NaN  
11          NaN  
12          NaN  
13          NaN  
14          NaN  
15          NaN  
16          NaN  
17          NaN  
18          NaN  
19          NaN  
20          NaN  
21          NaN  
22          NaN  
23          NaN  
24          NaN  
25          NaN  
26          NaN  
27          NaN  
28          NaN  
29          NaN  
..          ...  
834         NaN  
835         NaN  
836         NaN  
837         NaN  
838         NaN  
839         NaN  
840         NaN  
841         NaN  
842         NaN  
843         NaN  
844         NaN  
845         NaN  
846         NaN  
847         NaN  
848         NaN  
849         NaN  
850         NaN  
851         NaN  
852         NaN  
853         NaN  
854         NaN  
855         NaN  
856         NaN  
857         NaN  
858         NaN  
859         NaN  
860         NaN  
861         NaN  
862         NaN  
863         NaN  

[864 rows x 10 columns]>

















In [3]:



    


del storms['Unnamed: 8']



    











In [4]:



    


del storms['Unnamed: 9']



    











In [5]:



    


del storms['Unnamed: 7']



    











In [6]:



    


storms.head()



    


















    
Out[6]:










  
    

      
      CZ_NAME_STR
      BEGIN_DATE
      EVENT_TYPE
      MAGNITUDE
      DAMAGE_PROPERTY_NUM
      EPISODE_ID
      CZ_FIPS
    

  
  
    

      0
      COASTAL YORK (ZONE)
      1996-01-03
      Heavy Snow
      
      0
      2041201
      23
    

    

      1
      INTERIOR YORK (ZONE)
      1996-01-03
      Heavy Snow
      
      0
      2041201
      18
    

    

      2
      INTERIOR YORK (ZONE)
      1996-01-08
      Heavy Snow
      
      0
      2041202
      18
    

    

      3
      COASTAL YORK (ZONE)
      1996-01-08
      Heavy Snow
      
      0
      2041202
      23
    

    

      4
      COASTAL YORK (ZONE)
      1996-01-12
      Heavy Snow
      
      0
      2041204
      23
    

  




















In [7]:



    


storms = storms.set_index('BEGIN_DATE')



    











In [8]:



    


storms



    


















    
Out[8]:










  
    

      
      CZ_NAME_STR
      EVENT_TYPE
      MAGNITUDE
      DAMAGE_PROPERTY_NUM
      EPISODE_ID
      CZ_FIPS
    

    

      BEGIN_DATE
      
      
      
      
      
      
    

  
  
    

      1996-01-03
      COASTAL YORK (ZONE)
      Heavy Snow
      
      0
      2041201
      23
    

    

      1996-01-03
      INTERIOR YORK (ZONE)
      Heavy Snow
      
      0
      2041201
      18
    

    

      1996-01-08
      INTERIOR YORK (ZONE)
      Heavy Snow
      
      0
      2041202
      18
    

    

      1996-01-08
      COASTAL YORK (ZONE)
      Heavy Snow
      
      0
      2041202
      23
    

    

      1996-01-12
      COASTAL YORK (ZONE)
      Heavy Snow
      
      0
      2041204
      23
    

    

      1996-01-12
      INTERIOR YORK (ZONE)
      Heavy Snow
      
      0
      2041204
      18
    

    

      1996-01-19
      INTERIOR YORK (ZONE)
      High Wind
      
      0
      2041206
      18
    

    

      1996-01-19
      COASTAL YORK (ZONE)
      High Wind
      
      0
      2041206
      23
    

    

      1996-01-27
      INTERIOR YORK (ZONE)
      Winter Weather
      
      0
      2041208
      18
    

    

      1996-01-27
      COASTAL YORK (ZONE)
      High Wind
      
      0
      2041209
      23
    

    

      1996-02-25
      INTERIOR YORK (ZONE)
      High Wind
      
      0
      2041224
      18
    

    

      1996-03-05
      COASTAL YORK (ZONE)
      Heavy Snow
      
      0
      2055154
      23
    

    

      1996-03-05
      INTERIOR YORK (ZONE)
      Heavy Snow
      
      0
      2055154
      18
    

    

      1996-03-07
      COASTAL YORK (ZONE)
      Heavy Snow
      
      0
      2055155
      23
    

    

      1996-03-07
      INTERIOR YORK (ZONE)
      Heavy Snow
      
      0
      2055155
      18
    

    

      1996-04-09
      INTERIOR YORK (ZONE)
      Heavy Snow
      
      0
      2054860
      18
    

    

      1996-04-09
      COASTAL YORK (ZONE)
      Heavy Snow
      
      0
      2054860
      23
    

    

      1996-04-16
      INTERIOR YORK (ZONE)
      Heavy Rain
      
      0
      2054862
      18
    

    

      1996-04-16
      COASTAL YORK (ZONE)
      Heavy Rain
      
      0
      2054862
      23
    

    

      1996-04-16
      INTERIOR YORK (ZONE)
      Flood
      
      0
      2054864
      18
    

    

      1996-04-16
      COASTAL YORK (ZONE)
      Flood
      
      820000
      2054864
      23
    

    

      1996-04-16
      COASTAL YORK (ZONE)
      Coastal Flood
      
      0
      2054863
      23
    

    

      1996-05-12
      YORK CO.
      Flash Flood
      
      0
      2055784
      31
    

    

      1996-07-08
      YORK CO.
      Hail
      1.75
      0
      2055796
      31
    

    

      1996-07-08
      YORK CO.
      Hail
      1.75
      0
      2055797
      31
    

    

      1996-07-08
      YORK CO.
      Hail
      0.75
      0
      2055798
      31
    

    

      1996-07-08
      YORK CO.
      Hail
      1.5
      0
      2055799
      31
    

    

      1996-07-16
      YORK CO.
      Thunderstorm Wind
      
      0
      2055810
      31
    

    

      1996-07-16
      YORK CO.
      Thunderstorm Wind
      
      0
      2055811
      31
    

    

      1996-07-16
      YORK CO.
      Thunderstorm Wind
      
      0
      2055812
      31
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      2015-02-02
      COASTAL YORK (ZONE)
      Heavy Snow
      
      0
      93914
      23
    

    

      2015-02-07
      COASTAL YORK (ZONE)
      Heavy Snow
      
      0
      93904
      23
    

    

      2015-02-14
      INTERIOR YORK (ZONE)
      Heavy Snow
      
      0
      93897
      18
    

    

      2015-02-14
      COASTAL YORK (ZONE)
      Heavy Snow
      
      0
      93897
      23
    

    

      2015-02-15
      COASTAL YORK (ZONE)
      Coastal Flood
      
      5000
      93118
      23
    

    

      2015-02-18
      INTERIOR YORK (ZONE)
      Heavy Snow
      
      0
      93885
      18
    

    

      2015-04-20
      COASTAL YORK (ZONE)
      Coastal Flood
      
      125000
      95443
      23
    

    

      2015-05-28
      YORK CO.
      Thunderstorm Wind
      50
      0
      96583
      31
    

    

      2015-05-28
      YORK CO.
      Thunderstorm Wind
      50
      0
      96583
      31
    

    

      2015-05-28
      YORK CO.
      Thunderstorm Wind
      50
      0
      96583
      31
    

    

      2015-06-23
      YORK CO.
      Thunderstorm Wind
      50
      0
      98095
      31
    

    

      2015-07-19
      YORK CO.
      Hail
      2.75
      0
      98969
      31
    

    

      2015-07-19
      YORK CO.
      Hail
      1
      0
      98969
      31
    

    

      2015-07-19
      YORK CO.
      Thunderstorm Wind
      50
      0
      98969
      31
    

    

      2015-07-19
      YORK CO.
      Thunderstorm Wind
      50
      0
      98969
      31
    

    

      2015-07-19
      YORK CO.
      Thunderstorm Wind
      50
      0
      98969
      31
    

    

      2015-08-04
      YORK CO.
      Thunderstorm Wind
      50
      0
      100161
      31
    

    

      2015-08-04
      YORK CO.
      Hail
      0.75
      0
      100161
      31
    

    

      2015-08-04
      YORK CO.
      Hail
      2
      0
      100161
      31
    

    

      2015-08-04
      YORK CO.
      Hail
      1.5
      0
      100161
      31
    

    

      2015-08-04
      YORK CO.
      Hail
      1
      0
      100161
      31
    

    

      2015-08-04
      YORK CO.
      Hail
      1.75
      0
      100161
      31
    

    

      2015-08-04
      YORK CO.
      Hail
      1
      0
      100161
      31
    

    

      2015-08-04
      YORK CO.
      Hail
      0.75
      0
      100161
      31
    

    

      2015-08-04
      YORK CO.
      Hail
      2.5
      0
      100161
      31
    

    

      2015-08-04
      YORK CO.
      Hail
      1
      0
      100161
      31
    

    

      2015-09-30
      COASTAL YORK (ZONE)
      Coastal Flood
      
      75000
      100586
      23
    

    

      2015-10-29
      COASTAL YORK (ZONE)
      Coastal Flood
      
      2000
      101799
      23
    

    

      2015-12-29
      COASTAL YORK (ZONE)
      Heavy Snow
      
      0
      102612
      23
    

    

      2015-12-29
      INTERIOR YORK (ZONE)
      Heavy Snow
      
      0
      102612
      18
    

  



864 rows × 6 columns



















In [305]:



    


##This error only started appearing on May 9, up until this point there were zero issues in this notebook... at a loss for words to explain

stormtotals = storms['EVENT_TYPE'].resample('M', how='count')
stormtotals.head()



    


















    






---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-305-6e6f586c2697> in <module>()
      1 ##This error only started appearing on May 9, up until this point there were zero issues in this notebook... at a loss for words to explain
      2 
----> 3 stormtotals = storms['EVENT_TYPE'].resample('M', how='count')
      4 stormtotals.head()

//anaconda/lib/python3.5/site-packages/pandas/core/generic.py in resample(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base)
   3944                      axis=axis, kind=kind, loffset=loffset,
   3945                      convention=convention,
-> 3946                      base=base)
   3947         return _maybe_process_deprecations(r,
   3948                                            how=how,

//anaconda/lib/python3.5/site-packages/pandas/tseries/resample.py in resample(obj, kind, **kwds)
    758     """ create a TimeGrouper and return our resampler """
    759     tg = TimeGrouper(**kwds)
--> 760     return tg._get_resampler(obj, kind=kind)
    761 resample.__doc__ = Resampler.__doc__
    762 

//anaconda/lib/python3.5/site-packages/pandas/tseries/resample.py in _get_resampler(self, obj, kind)
    859         raise TypeError("Only valid with DatetimeIndex, "
    860                         "TimedeltaIndex or PeriodIndex, "
--> 861                         "but got an instance of %r" % type(ax).__name__)
    862 
    863     def _get_grouper(self, obj):

TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'RangeIndex'
















In [10]:



    


stormtotals



    


















    
Out[10]:








BEGIN_DATE
1996-01-31    10
1996-02-29     1
1996-03-31     4
1996-04-30     7
1996-05-31     1
1996-06-30     0
1996-07-31     7
1996-08-31     0
1996-09-30     3
1996-10-31     6
1996-11-30     1
1996-12-31     1
1997-01-31     2
1997-02-28     0
1997-03-31     4
1997-04-30     2
1997-05-31     0
1997-06-30     0
1997-07-31     0
1997-08-31     3
1997-09-30     1
1997-10-31     0
1997-11-30     5
1997-12-31     3
1998-01-31    16
1998-02-28     1
1998-03-31     5
1998-04-30     0
1998-05-31     3
1998-06-30     9
              ..
2013-07-31     0
2013-08-31     2
2013-09-30     4
2013-10-31     5
2013-11-30     4
2013-12-31     4
2014-01-31     4
2014-02-28     6
2014-03-31     0
2014-04-30     0
2014-05-31     0
2014-06-30     0
2014-07-31     7
2014-08-31     4
2014-09-30     0
2014-10-31     0
2014-11-30     2
2014-12-31     0
2015-01-31     5
2015-02-28     7
2015-03-31     0
2015-04-30     1
2015-05-31     3
2015-06-30     1
2015-07-31     5
2015-08-31    10
2015-09-30     1
2015-10-31     1
2015-11-30     0
2015-12-31     2
Freq: M, dtype: int64

slicing data to create datasets of month-over-month comparisons:





In [30]:



    


jandata = ['1996-01-31','1997-01-31','1998-01-31','1999-01-31','2000-01-31','2001-01-31','2002-01-31','2003-01-31',
           '2004-01-31','2005-01-31','2006-01-31','2007-01-31','2008-01-31','2009-01-31','2010-01-31','2011-01-31',
          '2012-01-31','2013-01-31','2014-01-31','2015-01-31']
january=stormtotals[stormtotals.index.isin(jandata)]



    











In [32]:



    


febdata = ['1996-02-29','1997-02-28','1998-02-28','1999-02-28','2000-02-29','2001-02-28','2002-02-28','2003-02-28',
           '2004-02-29','2005-02-28','2006-02-28','2007-02-28','2008-02-29','2009-02-28','2010-02-28','2011-02-28',
          '2012-02-29','2013-02-28','2014-02-28','2015-02-28']
february=stormtotals[stormtotals.index.isin(febdata)]



    











In [33]:



    


mardata = ['1996-03-31','1997-03-31','1998-03-31','1999-03-31','2000-03-31','2001-03-31','2002-03-31','2003-03-31',
           '2004-03-31','2005-03-31','2006-03-31','2007-03-31','2008-03-31','2009-03-31','2010-03-31','2011-03-31',
          '2012-03-31','2013-03-31','2014-03-31','2015-03-31']
march=stormtotals[stormtotals.index.isin(mardata)]



    











In [34]:



    


aprdata = ['1996-04-30','1997-04-30','1998-04-30','1999-04-30','2000-04-30','2001-04-30','2002-04-30','2003-04-30',
           '2004-04-30','2005-04-30','2006-04-30','2007-04-30','2008-04-30','2009-04-30','2010-04-30','2011-04-30',
          '2012-04-30','2013-04-30','2014-04-30','2015-04-30']
april=stormtotals[stormtotals.index.isin(aprdata)]



    











In [35]:



    


maydata = ['1996-05-31','1997-05-31','1998-05-31','1999-05-31','2000-05-31','2001-05-31','2002-05-31','2003-05-31',
           '2004-05-31','2005-05-31','2006-05-31','2007-05-31','2008-05-31','2009-05-31','2010-05-31','2011-05-31',
          '2012-05-31','2013-05-31','2014-05-31','2015-05-31']
may=stormtotals[stormtotals.index.isin(maydata)]



    











In [36]:



    


jundata = ['1996-06-30','1997-06-30','1998-06-30','1999-06-30','2000-06-30','2001-06-30','2002-06-30','2003-06-30',
           '2004-06-30','2005-06-30','2006-06-30','2007-06-30','2008-06-30','2009-06-30','2010-06-30','2011-06-30',
          '2012-06-30','2013-06-30','2014-06-30','2015-06-30']
june=stormtotals[stormtotals.index.isin(jundata)]



    











In [37]:



    


juldata = ['1996-07-31','1997-07-31','1998-07-31','1999-07-31','2000-07-31','2001-07-31','2002-07-31','2003-07-31',
           '2004-07-31','2005-07-31','2006-07-31','2007-07-31','2008-07-31','2009-07-31','2010-07-31','2011-07-31',
          '2012-07-31','2013-07-31','2014-07-31','2015-07-31']
july=stormtotals[stormtotals.index.isin(juldata)]



    











In [51]:



    


augdata = ['1996-08-31','1997-08-31','1998-08-31','1999-08-31','2000-08-31','2001-08-31','2002-08-31','2003-08-31',
           '2004-08-31','2005-08-31','2006-08-31','2007-08-31','2008-08-31','2009-08-31','2010-08-31','2011-08-31',
          '2012-08-31','2013-08-31','2014-08-31','2015-08-31']
august=stormtotals[stormtotals.index.isin(augdata)]



    











In [39]:



    


sepdata = ['1996-09-30','1997-09-30','1998-09-30','1999-09-30','2000-09-30','2001-09-30','2002-09-30','2003-09-30',
           '2004-09-30','2005-09-30','2006-09-30','2007-09-30','2008-09-30','2009-09-30','2010-09-30','2011-09-30',
          '2012-09-30','2013-09-30','2014-09-30','2015-09-30']
september=stormtotals[stormtotals.index.isin(sepdata)]



    











In [40]:



    


octdata = ['1996-10-31','1997-10-31','1998-10-31','1999-10-31','2000-10-31','2001-10-31','2002-10-31','2003-10-31',
           '2004-10-31','2005-10-31','2006-10-31','2007-10-31','2008-10-31','2009-10-31','2010-10-31','2011-10-31',
          '2012-10-31','2013-10-31','2014-10-31','2015-10-31']
october=stormtotals[stormtotals.index.isin(octdata)]



    











In [41]:



    


novdata = ['1996-11-30','1997-11-30','1998-11-30','1999-11-30','2000-11-30','2001-11-30','2002-11-30','2003-11-30',
           '2004-11-30','2005-11-30','2006-11-30','2007-11-30','2008-11-30','2009-11-30','2010-11-30','2011-11-30',
          '2012-11-30','2013-11-30','2014-11-30','2015-11-30']
november=stormtotals[stormtotals.index.isin(novdata)]



    











In [254]:



    


decdata = ['1996-12-31','1997-12-31','1998-12-31','1999-12-31','2000-12-31','2001-12-31','2002-12-31','2003-12-31',
           '2004-12-31','2005-12-31','2006-12-31','2007-12-31','2008-12-31','2009-12-31','2010-12-31','2011-12-31',
          '2012-12-31','2013-12-31','2014-12-31','2015-12-31']
december=stormtotals[stormtotals.index.isin(decdata)]



    











In [55]:



    


november



    


















    
Out[55]:








BEGIN_DATE
1996-11-30    1
1997-11-30    5
1998-11-30    1
1999-11-30    2
2000-11-30    2
2001-11-30    4
2002-11-30    4
2003-11-30    2
2004-11-30    6
2005-11-30    0
2006-11-30    0
2007-11-30    0
2008-11-30    1
2009-11-30    1
2010-11-30    2
2011-11-30    1
2012-11-30    0
2013-11-30    4
2014-11-30    2
2015-11-30    0
dtype: int64

















In [16]:



    


stormtotals_y = stormtotals.resample('A', how='sum')
stormtotals_y



    


















    






//anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:4: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).sum()










    
Out[16]:








BEGIN_DATE
1996-12-31    41
1997-12-31    20
1998-12-31    57
1999-12-31    63
2000-12-31    62
2001-12-31    75
2002-12-31    42
2003-12-31    28
2004-12-31    22
2005-12-31    43
2006-12-31    32
2007-12-31    78
2008-12-31    80
2009-12-31    30
2010-12-31    48
2011-12-31    32
2012-12-31    23
2013-12-31    29
2014-12-31    23
2015-12-31    36
Freq: A-DEC, dtype: int64

















In [70]:



    


##Daily temperature readings:

temps = pd.read_excel('/Users/jamesalbright/Desktop/Daily Temps.xlsx')
temps.head()



    


















    
Out[70]:










  
    

      
      Month
      Day
      Year
      Temp
    

  
  
    

      0
      1
      1
      1995
      32.2
    

    

      1
      1
      2
      1995
      31.5
    

    

      2
      1
      3
      1995
      23.9
    

    

      3
      1
      4
      1995
      23.8
    

    

      4
      1
      5
      1995
      12.9
    

  




















In [71]:



    


print('Variable dtypes:\n', temps.dtypes, sep='')



    


















    






Variable dtypes:
Month      int64
Day        int64
Year       int64
Temp     float64
dtype: object

















In [72]:



    


temps['Date'] = pd.to_datetime(temps['Day'] + 100*temps['Month'] + 10000*temps['Year'], format="%Y%m%d")
temps.head()



    


















    
Out[72]:










  
    

      
      Month
      Day
      Year
      Temp
      Date
    

  
  
    

      0
      1
      1
      1995
      32.2
      1995-01-01
    

    

      1
      1
      2
      1995
      31.5
      1995-01-02
    

    

      2
      1
      3
      1995
      23.9
      1995-01-03
    

    

      3
      1
      4
      1995
      23.8
      1995-01-04
    

    

      4
      1
      5
      1995
      12.9
      1995-01-05
    

  




















In [73]:



    


del temps['Month']



    











In [74]:



    


del temps['Day']



    











In [75]:



    


temps = temps.set_index('Date')



    











In [76]:



    


temps



    


















    
Out[76]:










  
    

      
      Year
      Temp
    

    

      Date
      
      
    

  
  
    

      1995-01-01
      1995
      32.2
    

    

      1995-01-02
      1995
      31.5
    

    

      1995-01-03
      1995
      23.9
    

    

      1995-01-04
      1995
      23.8
    

    

      1995-01-05
      1995
      12.9
    

    

      1995-01-06
      1995
      20.1
    

    

      1995-01-07
      1995
      33.1
    

    

      1995-01-08
      1995
      25.0
    

    

      1995-01-09
      1995
      22.4
    

    

      1995-01-10
      1995
      14.6
    

    

      1995-01-11
      1995
      2.9
    

    

      1995-01-12
      1995
      9.4
    

    

      1995-01-13
      1995
      30.8
    

    

      1995-01-14
      1995
      35.5
    

    

      1995-01-15
      1995
      43.5
    

    

      1995-01-16
      1995
      49.8
    

    

      1995-01-17
      1995
      47.3
    

    

      1995-01-18
      1995
      34.6
    

    

      1995-01-19
      1995
      32.4
    

    

      1995-01-20
      1995
      34.2
    

    

      1995-01-21
      1995
      39.6
    

    

      1995-01-22
      1995
      35.5
    

    

      1995-01-23
      1995
      32.0
    

    

      1995-01-24
      1995
      28.1
    

    

      1995-01-25
      1995
      26.8
    

    

      1995-01-26
      1995
      24.4
    

    

      1995-01-27
      1995
      17.9
    

    

      1995-01-28
      1995
      14.7
    

    

      1995-01-29
      1995
      20.9
    

    

      1995-01-30
      1995
      27.2
    

    

      ...
      ...
      ...
    

    

      2016-03-07
      2016
      32.4
    

    

      2016-03-08
      2016
      35.8
    

    

      2016-03-09
      2016
      47.7
    

    

      2016-03-10
      2016
      56.7
    

    

      2016-03-11
      2016
      44.2
    

    

      2016-03-12
      2016
      39.0
    

    

      2016-03-13
      2016
      50.0
    

    

      2016-03-14
      2016
      37.8
    

    

      2016-03-15
      2016
      39.3
    

    

      2016-03-16
      2016
      44.3
    

    

      2016-03-17
      2016
      46.8
    

    

      2016-03-18
      2016
      43.9
    

    

      2016-03-19
      2016
      32.3
    

    

      2016-03-20
      2016
      28.1
    

    

      2016-03-21
      2016
      30.8
    

    

      2016-03-22
      2016
      35.2
    

    

      2016-03-23
      2016
      43.8
    

    

      2016-03-24
      2016
      33.5
    

    

      2016-03-25
      2016
      29.9
    

    

      2016-03-26
      2016
      36.0
    

    

      2016-03-27
      2016
      36.3
    

    

      2016-03-28
      2016
      37.6
    

    

      2016-03-29
      2016
      42.0
    

    

      2016-03-30
      2016
      38.5
    

    

      2016-03-31
      2016
      52.2
    

    

      2016-04-01
      2016
      55.7
    

    

      2016-04-02
      2016
      49.7
    

    

      2016-04-03
      2016
      35.0
    

    

      2016-04-04
      2016
      22.1
    

    

      2016-04-05
      2016
      23.5
    

  



7766 rows × 2 columns



















In [79]:



    


del temps['Year']



    











In [80]:



    


temps.resample('M').mean()



    


















    
Out[80]:










  
    

      
      Temp
    

    

      Date
      
    

  
  
    

      1995-01-31
      27.593548
    

    

      1995-02-28
      22.364286
    

    

      1995-03-31
      34.896774
    

    

      1995-04-30
      41.140000
    

    

      1995-05-31
      51.819355
    

    

      1995-06-30
      64.856667
    

    

      1995-07-31
      69.829032
    

    

      1995-08-31
      67.164516
    

    

      1995-09-30
      57.256667
    

    

      1995-10-31
      52.851613
    

    

      1995-11-30
      36.753333
    

    

      1995-12-31
      24.622581
    

    

      1996-01-31
      22.045161
    

    

      1996-02-29
      24.424138
    

    

      1996-03-31
      30.019355
    

    

      1996-04-30
      42.236667
    

    

      1996-05-31
      47.925806
    

    

      1996-06-30
      62.466667
    

    

      1996-07-31
      66.206452
    

    

      1996-08-31
      66.935484
    

    

      1996-09-30
      59.913333
    

    

      1996-10-31
      47.593548
    

    

      1996-11-30
      35.390000
    

    

      1996-12-31
      34.745161
    

    

      1997-01-31
      23.480645
    

    

      1997-02-28
      29.032143
    

    

      1997-03-31
      31.106452
    

    

      1997-04-30
      41.920000
    

    

      1997-05-31
      51.232258
    

    

      1997-06-30
      63.803333
    

    

      ...
      ...
    

    

      2013-11-30
      37.860000
    

    

      2013-12-31
      25.319355
    

    

      2014-01-31
      21.309677
    

    

      2014-02-28
      24.007143
    

    

      2014-03-31
      27.629032
    

    

      2014-04-30
      43.210000
    

    

      2014-05-31
      53.935484
    

    

      2014-06-30
      64.260000
    

    

      2014-07-31
      69.754839
    

    

      2014-08-31
      66.529032
    

    

      2014-09-30
      60.363333
    

    

      2014-10-31
      54.658065
    

    

      2014-11-30
      39.476667
    

    

      2014-12-31
      34.025806
    

    

      2015-01-31
      22.016129
    

    

      2015-02-28
      15.078571
    

    

      2015-03-31
      30.819355
    

    

      2015-04-30
      45.280000
    

    

      2015-05-31
      59.532258
    

    

      2015-06-30
      61.256667
    

    

      2015-07-31
      68.380645
    

    

      2015-08-31
      70.048387
    

    

      2015-09-30
      65.056667
    

    

      2015-10-31
      49.319355
    

    

      2015-11-30
      43.040000
    

    

      2015-12-31
      37.654839
    

    

      2016-01-31
      27.670968
    

    

      2016-02-29
      29.689655
    

    

      2016-03-31
      37.509677
    

    

      2016-04-30
      37.200000
    

  



256 rows × 1 columns



















In [113]:



    


annual_temps=temps.resample('A').mean()



    











In [125]:



    


annual_temps



    


















    
Out[125]:










  
    

      
      Temp
    

    

      Date
      
    

  
  
    

      1995-12-31
      46.078082
    

    

      1996-12-31
      45.049454
    

    

      1997-12-31
      45.904658
    

    

      1998-12-31
      47.442740
    

    

      1999-12-31
      48.066575
    

    

      2000-12-31
      46.556011
    

    

      2001-12-31
      47.652329
    

    

      2002-12-31
      45.391781
    

    

      2003-12-31
      44.947123
    

    

      2004-12-31
      45.840984
    

    

      2005-12-31
      46.648219
    

    

      2006-12-31
      48.890685
    

    

      2007-12-31
      46.091507
    

    

      2008-12-31
      45.993716
    

    

      2009-12-31
      45.975342
    

    

      2010-12-31
      49.246849
    

    

      2011-12-31
      48.093973
    

    

      2012-12-31
      49.377869
    

    

      2013-12-31
      47.215616
    

    

      2014-12-31
      46.724932
    

    

      2015-12-31
      47.485205
    

    

      2016-12-31
      31.954167

slicing data to create datasets of month-over-month comparisons:





In [84]:



    


jantdata = ['1996-01-31','1997-01-31','1998-01-31','1999-01-31','2000-01-31','2001-01-31','2002-01-31','2003-01-31',
           '2004-01-31','2005-01-31','2006-01-31','2007-01-31','2008-01-31','2009-01-31','2010-01-31','2011-01-31',
          '2012-01-31','2013-01-31','2014-01-31','2015-01-31']
jantemp=temps[temps.index.isin(jantdata)]



    











In [86]:



    


febtdata = ['1996-02-29','1997-02-28','1998-02-28','1999-02-28','2000-02-29','2001-02-28','2002-02-28','2003-02-28',
           '2004-02-29','2005-02-28','2006-02-28','2007-02-28','2008-02-29','2009-02-28','2010-02-28','2011-02-28',
          '2012-02-29','2013-02-28','2014-02-28','2015-02-28']
febtemp=temps[temps.index.isin(febtdata)]



    











In [87]:



    


martdata = ['1996-03-31','1997-03-31','1998-03-31','1999-03-31','2000-03-31','2001-03-31','2002-03-31','2003-03-31',
           '2004-03-31','2005-03-31','2006-03-31','2007-03-31','2008-03-31','2009-03-31','2010-03-31','2011-03-31',
          '2012-03-31','2013-03-31','2014-03-31','2015-03-31']
marchtemp=temps[temps.index.isin(martdata)]



    











In [270]:



    


aprtdata = ['1996-04-30','1997-04-30','1998-04-30','1999-04-30','2000-04-30','2001-04-30','2002-04-30','2003-04-30',
           '2004-04-30','2005-04-30','2006-04-30','2007-04-30','2008-04-30','2009-04-30','2010-04-30','2011-04-30',
          '2012-04-30','2013-04-30','2014-04-30','2015-04-30']
apriltemp=temps[temps.index.isin(aprtdata)]



    











In [89]:



    


maytdata = ['1996-05-31','1997-05-31','1998-05-31','1999-05-31','2000-05-31','2001-05-31','2002-05-31','2003-05-31',
           '2004-05-31','2005-05-31','2006-05-31','2007-05-31','2008-05-31','2009-05-31','2010-05-31','2011-05-31',
          '2012-05-31','2013-05-31','2014-05-31','2015-05-31']
maytemp=temps[temps.index.isin(maytdata)]



    











In [90]:



    


juntdata = ['1996-06-30','1997-06-30','1998-06-30','1999-06-30','2000-06-30','2001-06-30','2002-06-30','2003-06-30',
           '2004-06-30','2005-06-30','2006-06-30','2007-06-30','2008-06-30','2009-06-30','2010-06-30','2011-06-30',
          '2012-06-30','2013-06-30','2014-06-30','2015-06-30']
junetemp=temps[temps.index.isin(juntdata)]



    











In [91]:



    


jultdata = ['1996-07-31','1997-07-31','1998-07-31','1999-07-31','2000-07-31','2001-07-31','2002-07-31','2003-07-31',
           '2004-07-31','2005-07-31','2006-07-31','2007-07-31','2008-07-31','2009-07-31','2010-07-31','2011-07-31',
          '2012-07-31','2013-07-31','2014-07-31','2015-07-31']
julytemp=temps[temps.index.isin(jultdata)]



    











In [92]:



    


augtdata = ['1996-08-31','1997-08-31','1998-08-31','1999-08-31','2000-08-31','2001-08-31','2002-08-31','2003-08-31',
           '2004-08-31','2005-08-31','2006-08-31','2007-08-31','2008-08-31','2009-08-31','2010-08-31','2011-08-31',
          '2012-08-31','2013-08-31','2014-08-31','2015-08-31']
augusttemp=temps[temps.index.isin(augtdata)]



    











In [93]:



    


septdata = ['1996-09-30','1997-09-30','1998-09-30','1999-09-30','2000-09-30','2001-09-30','2002-09-30','2003-09-30',
           '2004-09-30','2005-09-30','2006-09-30','2007-09-30','2008-09-30','2009-09-30','2010-09-30','2011-09-30',
          '2012-09-30','2013-09-30','2014-09-30','2015-09-30']
septembertemp=temps[temps.index.isin(septdata)]



    











In [94]:



    


octtdata = ['1996-10-31','1997-10-31','1998-10-31','1999-10-31','2000-10-31','2001-10-31','2002-10-31','2003-10-31',
           '2004-10-31','2005-10-31','2006-10-31','2007-10-31','2008-10-31','2009-10-31','2010-10-31','2011-10-31',
          '2012-10-31','2013-10-31','2014-10-31','2015-10-31']
octobertemp=temps[temps.index.isin(octtdata)]



    











In [95]:



    


novtdata = ['1996-11-30','1997-11-30','1998-11-30','1999-11-30','2000-11-30','2001-11-30','2002-11-30','2003-11-30',
           '2004-11-30','2005-11-30','2006-11-30','2007-11-30','2008-11-30','2009-11-30','2010-11-30','2011-11-30',
          '2012-11-30','2013-11-30','2014-11-30','2015-11-30']
novembertemp=temps[temps.index.isin(novdata)]



    











In [284]:



    


dectdata = ['1996-12-31','1997-12-31','1998-12-31','1999-12-31','2000-12-31','2001-12-31','2002-12-31','2003-12-31',
           '2004-12-31','2005-12-31','2006-12-31','2007-12-31','2008-12-31','2009-12-31','2010-12-31','2011-12-31',
          '2012-12-31','2013-12-31','2014-12-31','2015-12-31']
decembertemp=temps[temps.index.isin(dectdata)]

charts comparing monthly average temperatures to the number of severe weather occurences. Do you notice any patterns or correlation?





In [263]:



    


plt.style.use('fivethirtyeight')

plt.figure(figsize=(20,10))
january.plot()
plt.xlim('1996', '2015') 
plt.ylim(0, 30) 
plt.title('January Severe Weather Occurences: 1996-2015', fontsize=18, loc='left')

jantemp.plot(figsize=(20,10),color='red')
plt.xlim('1996', '2015') 
plt.ylim(0, 60)
plt.title('January Average Temperatures: 1996-2015', fontsize=18, loc='left')



    


















    
Out[263]:








<matplotlib.text.Text at 0x13809c898>










    

















    
























In [265]:



    


plt.figure(figsize=(20,10))
february.plot()
plt.xlim('1996', '2015') 
plt.ylim(0, 30) 
plt.title('February Severe Weather Occurences: 1996-2015', fontsize=18, loc='left')

febtemp.plot(figsize=(20,10),color='red')
plt.xlim('1996', '2015') 
plt.ylim(0, 60)
plt.title('February Average Temperatures: 1996-2015', fontsize=18, loc='left')



    


















    
Out[265]:








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In [268]:



    


plt.figure(figsize=(20,10))
march.plot()
plt.xlim('1996', '2015') 
plt.ylim(0, 30) 
plt.title('March Severe Weather Occurences: 1996-2015', fontsize=18, loc='left')

marchtemp.plot(figsize=(20,10),color='red')
plt.xlim('1996', '2015') 
plt.ylim(20, 80)
plt.title('March Average Temperatures: 1996-2015', fontsize=18, loc='left')



    


















    
Out[268]:








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In [286]:



    


plt.figure(figsize=(20,10))
april.plot()
plt.xlim('1996', '2015') 
plt.ylim(0, 30) 
plt.title('April Severe Weather Occurences: 1996-2015', fontsize=18, loc='left')

apriltemp.plot(figsize=(20,10),color='red')
plt.xlim('1996', '2015') 
plt.ylim(20, 80)
plt.title('April Average Temperatures: 1996-2015', fontsize=18, loc='left')



    


















    
Out[286]:








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In [272]:



    


plt.figure(figsize=(20,10))
may.plot()
plt.xlim('1996', '2015') 
plt.ylim(0, 30) 
plt.title('May Severe Weather Occurences: 1996-2015', fontsize=18, loc='left')

maytemp.plot(figsize=(20,10),color='red')
plt.xlim('1996', '2015') 
plt.ylim(20, 80)
plt.title('May Average Temperatures: 1996-2015', fontsize=18, loc='left')



    


















    
Out[272]:








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In [273]:



    


plt.figure(figsize=(20,10))
june.plot()
plt.xlim('1996', '2015') 
plt.ylim(0, 30) 
plt.title('June Severe Weather Occurences: 1996-2015', fontsize=18, loc='left')

junetemp.plot(figsize=(20,10),color='red')
plt.xlim('1996', '2015') 
plt.ylim(20, 80)
plt.title('June Average Temperatures: 1996-2015', fontsize=18, loc='left')



    


















    
Out[273]:








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In [274]:



    


plt.figure(figsize=(20,10))
july.plot()
plt.xlim('1996', '2015') 
plt.ylim(0, 30) 
plt.title('July Severe Weather Occurences: 1996-2015', fontsize=18, loc='left')

julytemp.plot(figsize=(20,10),color='red')
plt.xlim('1996', '2015') 
plt.ylim(20, 80)
plt.title('July Average Temperatures: 1996-2015', fontsize=18, loc='left')



    


















    
Out[274]:








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In [275]:



    


plt.figure(figsize=(20,10))
august.plot()
plt.xlim('1996', '2015') 
plt.ylim(0, 30) 
plt.title('August Severe Weather Occurences: 1996-2015', fontsize=18, loc='left')

augusttemp.plot(figsize=(20,10),color='red')
plt.xlim('1996', '2015') 
plt.ylim(20, 80)
plt.title('August Average Temperatures: 1996-2015', fontsize=18, loc='left')



    


















    
Out[275]:








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In [276]:



    


plt.figure(figsize=(20,10))
september.plot()
plt.xlim('1996', '2015') 
plt.ylim(0, 30) 
plt.title('September Severe Weather Occurences: 1996-2015', fontsize=18, loc='left')

septembertemp.plot(figsize=(20,10),color='red')
plt.xlim('1996', '2015') 
plt.ylim(20, 80)
plt.title('September Average Temperatures: 1996-2015', fontsize=18, loc='left')



    


















    
Out[276]:








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In [277]:



    


plt.figure(figsize=(20,10))
october.plot()
plt.xlim('1996', '2015') 
plt.ylim(0, 30) 
plt.title('October Severe Weather Occurences: 1996-2015', fontsize=18, loc='left')

octobertemp.plot(figsize=(20,10),color='red')
plt.xlim('1996', '2015') 
plt.ylim(20, 80)
plt.title('October Average Temperatures: 1996-2015', fontsize=18, loc='left')



    


















    
Out[277]:








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In [278]:



    


plt.figure(figsize=(20,10))
november.plot()
plt.xlim('1996', '2015') 
plt.ylim(0, 30) 
plt.title('November Severe Weather Occurences: 1996-2015', fontsize=18, loc='left')

novembertemp.plot(figsize=(20,10),color='red')
plt.xlim('1996', '2015') 
plt.ylim(20, 80)
plt.title('November Average Temperatures: 1996-2015', fontsize=18, loc='left')



    


















    
Out[278]:








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In [281]:



    


plt.figure(figsize=(20,10))
december.plot()
plt.xlim('1996', '2015') 
plt.ylim(0, 30) 
plt.title('December Severe Weather Occurences: 1996-2015', fontsize=18, loc='left')

decembertemp.plot(figsize=(20,10),color='red')
plt.xlim('1996', '2015') 
plt.ylim(0, 60)
plt.title('December Average Temperatures: 1996-2015', fontsize=18, loc='left')



    


















    
Out[281]:








<matplotlib.text.Text at 0x1380772e8>

With data only available back to 1996, and without being able to confirm the veracity of severe weather recordings, unfortunately it is difficult to pull conclusive correlations between tempuerature fluctations and frequency of severe weather occurances in Souther Maine. Ideally, we would have liked to have used data going back a century or so. What is interesting, is that one can see some mirroring overall with respect to higher observed monthly average tempuratures being accompanied by a higher number of severe weather occurances.





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