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Edit and run 





In [1]:



    


from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import operator
%matplotlib inline

This notebook will investigate instances where the river is reversed, and sewage is dumped into the lake. We will take a look at rainfall before these events, to see if there is a correlation





In [2]:



    


# Get River reversals
reversals = pd.read_csv('data/lake_michigan_reversals.csv')
reversals['start_date'] = pd.to_datetime(reversals['start_date'])
reversals.head()



    


















    
Out[2]:










  
    

      
      crcw
      date_raw
      obrien
      start_date
      total
      wilmette
      year
    

  
  
    

      0
      997.5
      6/15-16/2015
      0.0
      2015-06-15
      1164.7
      167.2
      2015
    

    

      1
      362.0
      6/30-7/1/14
      0.0
      2014-06-30
      525.0
      163.0
      2014
    

    

      2
      6104.7
      4/18-19/13
      3185.6
      2013-04-18
      10719.5
      1429.2
      2013
    

    

      3
      1716.2
      7/24/2011
      0.0
      2011-07-24
      2220.5
      504.3
      2011
    

    

      4
      0.0
      5/29/2011
      0.0
      2011-05-29
      107.0
      107.0
      2011
    

  




















In [5]:



    


# Create rainfall dataframe.  Create a series that has hourly precipitation
rain_df = pd.read_csv('data/ohare_hourly_20160929.csv')
rain_df['datetime'] = pd.to_datetime(rain_df['datetime'])
rain_df = rain_df.set_index(pd.DatetimeIndex(rain_df['datetime']))
rain_df = rain_df['19700101':]
chi_rain_series = rain_df['HOURLYPrecip'].resample('1H', label='right').max()
chi_rain_series.head()



    


















    
Out[5]:








1970-01-01 04:00:00    0.0
1970-01-01 05:00:00    NaN
1970-01-01 06:00:00    NaN
1970-01-01 07:00:00    0.0
1970-01-01 08:00:00    NaN
Freq: H, Name: HOURLYPrecip, dtype: float64

















In [6]:



    


# Find the rainfall 'hours' hours before the timestamp
def cum_rain(timestamp, hours):
    end_of_day = (timestamp + timedelta(days=1)).replace(hour=0, minute=0)
    start_time = end_of_day - timedelta(hours=(hours-1))
    return chi_rain_series[start_time:end_of_day].sum()
    
t = pd.to_datetime('2015-06-15')
cum_rain(t, 240)



    


















    
Out[6]:








4.6700000000000008

















In [7]:



    


# Set the ten_day_rain field in reversals to the amount of rain that fell the previous 10 days (including the day that
# the lock was opened)
# TODO: Is there a more Pandaic way to do this?
for index, reversal in reversals.iterrows():
    reversals.loc[index,'ten_day_rain'] = cum_rain(reversal['start_date'], 240)
reversals



    


















    
Out[7]:










  
    

      
      crcw
      date_raw
      obrien
      start_date
      total
      wilmette
      year
      ten_day_rain
    

  
  
    

      0
      997.5
      6/15-16/2015
      0.0
      2015-06-15
      1164.7
      167.2
      2015
      4.67
    

    

      1
      362.0
      6/30-7/1/14
      0.0
      2014-06-30
      525.0
      163.0
      2014
      4.91
    

    

      2
      6104.7
      4/18-19/13
      3185.6
      2013-04-18
      10719.5
      1429.2
      2013
      7.40
    

    

      3
      1716.2
      7/24/2011
      0.0
      2011-07-24
      2220.5
      504.3
      2011
      8.32
    

    

      4
      0.0
      5/29/2011
      0.0
      2011-05-29
      107.0
      107.0
      2011
      5.17
    

    

      5
      5784.6
      7/24/2010
      0.0
      2010-07-24
      6534.9
      750.3
      2010
      6.63
    

    

      6
      0.0
      6/19/2009
      0.0
      2009-06-19
      191.6
      191.6
      2009
      5.68
    

    

      7
      0.0
      3/8/2009
      0.0
      2009-03-08
      143.1
      143.1
      2009
      2.82
    

    

      8
      0.0
      2/26-27/09
      0.0
      2009-02-26
      78.9
      78.9
      2009
      2.39
    

    

      9
      0.0
      12/27-28/08
      0.0
      2008-12-27
      460.8
      460.8
      2008
      3.02
    

    

      10
      5438.2
      9/13-16/08
      2669.2
      2008-09-13
      11049.1
      2941.7
      2008
      11.12
    

    

      11
      0.0
      8/23-24/07
      0.0
      2007-08-23
      224.0
      224.0
      2007
      5.75
    

    

      12
      1296.4
      8/22/2002
      0.0
      2002-08-22
      1751.8
      455.4
      2002
      7.26
    

    

      13
      0.0
      10/13/2001
      0.0
      2001-10-13
      90.7
      90.7
      2001
      5.69
    

    

      14
      0.0
      8/31/2001
      0.0
      2001-08-31
      75.3
      75.3
      2001
      9.26
    

    

      15
      883.1
      8/2/2001
      0.0
      2001-08-02
      1023.0
      139.9
      2001
      2.12
    

    

      16
      0.0
      6/13/1999
      0.0
      1999-06-13
      9.7
      9.7
      1999
      3.64
    

    

      17
      402.0
      8/16-8/17/97
      0.0
      1997-08-16
      559.0
      157.0
      1997
      3.46
    

    

      18
      1947.0
      2/20-2/22/97
      1458.0
      1997-02-20
      4179.0
      774.0
      1997
      0.74
    

    

      19
      519.0
      7/17-7/18/96
      1032.0
      1996-07-17
      1551.0
      0.0
      1996
      2.41
    

    

      20
      86.0
      11/27-11/28/90
      224.0
      1990-11-27
      464.0
      154.0
      1990
      3.30
    

    

      21
      0.0
      8/17-8/18/90
      0.0
      1990-08-17
      9.5
      9.5
      1990
      2.24
    

    

      22
      208.0
      5/9-5/10/90
      0.0
      1990-05-09
      497.0
      289.0
      1990
      4.83
    

    

      23
      0.0
      8/3-8/4/89
      0.0
      1989-08-03
      52.0
      52.0
      1989
      1.75
    

    

      24
      0.0
      8/25-8/26/87
      0.0
      1987-08-25
      18.0
      18.0
      1987
      4.08
    

    

      25
      986.0
      8/13-8/14/87
      0.0
      1987-08-13
      1957.0
      971.0
      1987
      3.01
    

    

      26
      0.0
      10/3/1986
      0.0
      1986-10-03
      53.0
      53.0
      1986
      5.53
    

    

      27
      0.0
      8/6/1985
      0.0
      1985-08-06
      58.0
      58.0
      1985
      2.62
    

    

      28
      0.0
      3/4/1985
      0.0
      1985-03-04
      153.3
      153.3
      1985
      4.28
    

  




















In [8]:



    


# Information about the 10 days that preceed these overflows
reversals['ten_day_rain'].describe(percentiles=[.25, .5, .75])



    


















    
Out[8]:








count    29.000000
mean      4.624138
std       2.412203
min       0.740000
25%       2.820000
50%       4.280000
75%       5.690000
max      11.120000
Name: ten_day_rain, dtype: float64

Now we will look at any n-year storms that occurred during the 10 days prior to the reversal





In [9]:



    


# N-Year Storm stuff
n_year_threshes = pd.read_csv('../../n-year/notebooks/data/n_year_definitions.csv')
n_year_threshes = n_year_threshes.set_index('Duration')
dur_str_to_hours = {
    '5-min':5/60.0,
    '10-min':10/60.0,
    '15-min':15/60.0,
    '30-min':0.5,
    '1-hr':1.0,
    '2-hr':2.0,
    '3-hr':3.0,
    '6-hr':6.0,
    '12-hr':12.0,
    '18-hr':18.0,
    '24-hr':24.0,
    '48-hr':48.0,
    '72-hr':72.0,
    '5-day':5*24.0,
    '10-day':10*24.0
}
n_s = [int(x.replace('-year','')) for x in reversed(list(n_year_threshes.columns.values))]
duration_strs = sorted(dur_str_to_hours.items(), key=operator.itemgetter(1), reverse=False)
n_year_threshes



    


















    
Out[9]:










  
    

      
      1-year
      2-year
      5-year
      10-year
      25-year
      50-year
      100-year
    

    

      Duration
      
      
      
      
      
      
      
    

  
  
    

      10-day
      4.12
      4.95
      6.04
      6.89
      8.18
      9.38
      11.14
    

    

      5-day
      3.25
      3.93
      4.91
      5.70
      6.93
      8.04
      9.96
    

    

      72-hr
      2.93
      3.55
      4.44
      5.18
      6.32
      7.41
      8.78
    

    

      48-hr
      2.70
      3.30
      4.09
      4.81
      5.88
      6.84
      8.16
    

    

      24-hr
      2.51
      3.04
      3.80
      4.47
      5.51
      6.46
      7.58
    

    

      18-hr
      2.30
      2.79
      3.50
      4.11
      5.06
      5.95
      6.97
    

    

      12-hr
      2.18
      2.64
      3.31
      3.89
      4.79
      5.62
      6.59
    

    

      6-hr
      1.88
      2.28
      2.85
      3.35
      4.13
      4.85
      5.68
    

    

      3-hr
      1.60
      1.94
      2.43
      2.86
      3.53
      4.14
      4.85
    

    

      2-hr
      1.48
      1.79
      2.24
      2.64
      3.25
      3.82
      4.47
    

    

      1-hr
      1.18
      1.43
      1.79
      2.10
      2.59
      3.04
      3.56
    

    

      30-min
      0.93
      1.12
      1.41
      1.65
      2.04
      2.39
      2.80
    

    

      15-min
      0.68
      0.82
      1.03
      1.21
      1.49
      1.75
      2.05
    

    

      10-min
      0.55
      0.67
      0.84
      0.98
      1.21
      1.42
      1.67
    

    

      5-min
      0.30
      0.36
      0.46
      0.54
      0.66
      0.78
      0.91
    

  




















In [10]:



    


# This method returns the first n-year storm found in a given interval.  It starts at the 100-year storm and decriments, so
# will return the highest n-year storm found
def find_n_year_storm(start_time, end_time):
    for n in n_s:
        n_index = n_s.index(n)
        next_n = n_s[n_index-1] if n_index != 0 else None

        for duration_tuple in reversed(duration_strs):

            duration_str = duration_tuple[0]
            low_thresh = n_year_threshes.loc[duration_str, str(n) + '-year']
            high_thresh = n_year_threshes.loc[duration_str, str(next_n) + '-year'] if next_n is not None else None
        
            duration = int(dur_str_to_hours[duration_str])
            sub_series = chi_rain_series[start_time: end_time]
            rolling = sub_series.rolling(window=int(duration), min_periods=0).sum()
        
            if high_thresh is not None:
                event_endtimes = rolling[(rolling >= low_thresh) & (rolling < high_thresh)].sort_values(ascending=False)
            else:
                event_endtimes = rolling[(rolling >= low_thresh)].sort_values(ascending=False)
            if len(event_endtimes) > 0:
                return {'inches': event_endtimes[0], 'n': n, 'end_time': event_endtimes.index[0], 'hours': duration}
    return None

start_time = pd.to_datetime('2008-09-04 01:00:00')
end_time = pd.to_datetime('2008-09-14 20:00:00')
find_n_year_storm(start_time, end_time)



    


















    
Out[10]:








{'end_time': Timestamp('2008-09-14 13:00:00'),
 'hours': 240,
 'inches': 11.939999999999998,
 'n': 100}

















In [11]:



    


# Add a column to the reversals data frame to show n-year storms that occurred before the reversal
# TODO: Is there a more Pandaic way to do this?
for index, reversal in reversals.iterrows():
    end_of_day = (reversal['start_date'] + timedelta(days=1)).replace(hour=0, minute=0)
    start_time = end_of_day - timedelta(days=10)
    reversals.loc[index,'find_n_year_storm'] = str(find_n_year_storm(start_time, end_of_day))
reversals



    


















    
Out[11]:










  
    

      
      crcw
      date_raw
      obrien
      start_date
      total
      wilmette
      year
      ten_day_rain
      find_n_year_storm
    

  
  
    

      0
      997.5
      6/15-16/2015
      0.0
      2015-06-15
      1164.7
      167.2
      2015
      4.67
      {'hours': 120, 'n': 2, 'end_time': Timestamp('...
    

    

      1
      362.0
      6/30-7/1/14
      0.0
      2014-06-30
      525.0
      163.0
      2014
      4.91
      {'hours': 12, 'n': 2, 'end_time': Timestamp('2...
    

    

      2
      6104.7
      4/18-19/13
      3185.6
      2013-04-18
      10719.5
      1429.2
      2013
      7.40
      {'hours': 18, 'n': 25, 'end_time': Timestamp('...
    

    

      3
      1716.2
      7/24/2011
      0.0
      2011-07-24
      2220.5
      504.3
      2011
      8.32
      {'hours': 24, 'n': 100, 'end_time': Timestamp(...
    

    

      4
      0.0
      5/29/2011
      0.0
      2011-05-29
      107.0
      107.0
      2011
      5.17
      {'hours': 240, 'n': 2, 'end_time': Timestamp('...
    

    

      5
      5784.6
      7/24/2010
      0.0
      2010-07-24
      6534.9
      750.3
      2010
      6.63
      {'hours': 24, 'n': 50, 'end_time': Timestamp('...
    

    

      6
      0.0
      6/19/2009
      0.0
      2009-06-19
      191.6
      191.6
      2009
      5.68
      {'hours': 120, 'n': 5, 'end_time': Timestamp('...
    

    

      7
      0.0
      3/8/2009
      0.0
      2009-03-08
      143.1
      143.1
      2009
      2.82
      {'hours': 48, 'n': 1, 'end_time': Timestamp('2...
    

    

      8
      0.0
      2/26-27/09
      0.0
      2009-02-26
      78.9
      78.9
      2009
      2.39
      None
    

    

      9
      0.0
      12/27-28/08
      0.0
      2008-12-27
      460.8
      460.8
      2008
      3.02
      None
    

    

      10
      5438.2
      9/13-16/08
      2669.2
      2008-09-13
      11049.1
      2941.7
      2008
      11.12
      {'hours': 240, 'n': 50, 'end_time': Timestamp(...
    

    

      11
      0.0
      8/23-24/07
      0.0
      2007-08-23
      224.0
      224.0
      2007
      5.75
      {'hours': 120, 'n': 5, 'end_time': Timestamp('...
    

    

      12
      1296.4
      8/22/2002
      0.0
      2002-08-22
      1751.8
      455.4
      2002
      7.26
      {'hours': 240, 'n': 10, 'end_time': Timestamp(...
    

    

      13
      0.0
      10/13/2001
      0.0
      2001-10-13
      90.7
      90.7
      2001
      5.69
      {'hours': 48, 'n': 5, 'end_time': Timestamp('2...
    

    

      14
      0.0
      8/31/2001
      0.0
      2001-08-31
      75.3
      75.3
      2001
      9.26
      {'hours': 3, 'n': 50, 'end_time': Timestamp('2...
    

    

      15
      883.1
      8/2/2001
      0.0
      2001-08-02
      1023.0
      139.9
      2001
      2.12
      None
    

    

      16
      0.0
      6/13/1999
      0.0
      1999-06-13
      9.7
      9.7
      1999
      3.64
      {'hours': 72, 'n': 2, 'end_time': Timestamp('1...
    

    

      17
      402.0
      8/16-8/17/97
      0.0
      1997-08-16
      559.0
      157.0
      1997
      3.46
      None
    

    

      18
      1947.0
      2/20-2/22/97
      1458.0
      1997-02-20
      4179.0
      774.0
      1997
      0.74
      None
    

    

      19
      519.0
      7/17-7/18/96
      1032.0
      1996-07-17
      1551.0
      0.0
      1996
      2.41
      None
    

    

      20
      86.0
      11/27-11/28/90
      224.0
      1990-11-27
      464.0
      154.0
      1990
      3.30
      {'hours': 72, 'n': 1, 'end_time': Timestamp('1...
    

    

      21
      0.0
      8/17-8/18/90
      0.0
      1990-08-17
      9.5
      9.5
      1990
      2.24
      None
    

    

      22
      208.0
      5/9-5/10/90
      0.0
      1990-05-09
      497.0
      289.0
      1990
      4.83
      {'hours': 24, 'n': 2, 'end_time': Timestamp('1...
    

    

      23
      0.0
      8/3-8/4/89
      0.0
      1989-08-03
      52.0
      52.0
      1989
      1.75
      None
    

    

      24
      0.0
      8/25-8/26/87
      0.0
      1987-08-25
      18.0
      18.0
      1987
      4.08
      {'hours': 2, 'n': 10, 'end_time': Timestamp('1...
    

    

      25
      986.0
      8/13-8/14/87
      0.0
      1987-08-13
      1957.0
      971.0
      1987
      3.01
      {'hours': 3, 'n': 5, 'end_time': Timestamp('19...
    

    

      26
      0.0
      10/3/1986
      0.0
      1986-10-03
      53.0
      53.0
      1986
      5.53
      {'hours': 240, 'n': 2, 'end_time': Timestamp('...
    

    

      27
      0.0
      8/6/1985
      0.0
      1985-08-06
      58.0
      58.0
      1985
      2.62
      None
    

    

      28
      0.0
      3/4/1985
      0.0
      1985-03-04
      153.3
      153.3
      1985
      4.28
      {'hours': 240, 'n': 1, 'end_time': Timestamp('...
    

  




















In [12]:



    


no_n_year = reversals.loc[reversals['find_n_year_storm'] == 'None']
print("There are %s reversals without an n-year event" % len(no_n_year))
no_n_year



    


















    






There are 9 reversals without an n-year event










    
Out[12]:










  
    

      
      crcw
      date_raw
      obrien
      start_date
      total
      wilmette
      year
      ten_day_rain
      find_n_year_storm
    

  
  
    

      8
      0.0
      2/26-27/09
      0.0
      2009-02-26
      78.9
      78.9
      2009
      2.39
      None
    

    

      9
      0.0
      12/27-28/08
      0.0
      2008-12-27
      460.8
      460.8
      2008
      3.02
      None
    

    

      15
      883.1
      8/2/2001
      0.0
      2001-08-02
      1023.0
      139.9
      2001
      2.12
      None
    

    

      17
      402.0
      8/16-8/17/97
      0.0
      1997-08-16
      559.0
      157.0
      1997
      3.46
      None
    

    

      18
      1947.0
      2/20-2/22/97
      1458.0
      1997-02-20
      4179.0
      774.0
      1997
      0.74
      None
    

    

      19
      519.0
      7/17-7/18/96
      1032.0
      1996-07-17
      1551.0
      0.0
      1996
      2.41
      None
    

    

      21
      0.0
      8/17-8/18/90
      0.0
      1990-08-17
      9.5
      9.5
      1990
      2.24
      None
    

    

      23
      0.0
      8/3-8/4/89
      0.0
      1989-08-03
      52.0
      52.0
      1989
      1.75
      None
    

    

      27
      0.0
      8/6/1985
      0.0
      1985-08-06
      58.0
      58.0
      1985
      2.62
      None
    

  




















In [13]:



    


reversals.loc[reversals['year'] == 1997]



    


















    
Out[13]:










  
    

      
      crcw
      date_raw
      obrien
      start_date
      total
      wilmette
      year
      ten_day_rain
      find_n_year_storm
    

  
  
    

      17
      402.0
      8/16-8/17/97
      0.0
      1997-08-16
      559.0
      157.0
      1997
      3.46
      None
    

    

      18
      1947.0
      2/20-2/22/97
      1458.0
      1997-02-20
      4179.0
      774.0
      1997
      0.74
      None
    

  




















In [15]:



    


reversals.sort_values('crcw', ascending=False)



    


















    
Out[15]:










  
    

      
      crcw
      date_raw
      obrien
      start_date
      total
      wilmette
      year
      ten_day_rain
      find_n_year_storm
    

  
  
    

      2
      6104.7
      4/18-19/13
      3185.6
      2013-04-18
      10719.5
      1429.2
      2013
      7.40
      {'hours': 18, 'n': 25, 'end_time': Timestamp('...
    

    

      5
      5784.6
      7/24/2010
      0.0
      2010-07-24
      6534.9
      750.3
      2010
      6.63
      {'hours': 24, 'n': 50, 'end_time': Timestamp('...
    

    

      10
      5438.2
      9/13-16/08
      2669.2
      2008-09-13
      11049.1
      2941.7
      2008
      11.12
      {'hours': 240, 'n': 50, 'end_time': Timestamp(...
    

    

      18
      1947.0
      2/20-2/22/97
      1458.0
      1997-02-20
      4179.0
      774.0
      1997
      0.74
      None
    

    

      3
      1716.2
      7/24/2011
      0.0
      2011-07-24
      2220.5
      504.3
      2011
      8.32
      {'hours': 24, 'n': 100, 'end_time': Timestamp(...
    

    

      12
      1296.4
      8/22/2002
      0.0
      2002-08-22
      1751.8
      455.4
      2002
      7.26
      {'hours': 240, 'n': 10, 'end_time': Timestamp(...
    

    

      0
      997.5
      6/15-16/2015
      0.0
      2015-06-15
      1164.7
      167.2
      2015
      4.67
      {'hours': 120, 'n': 2, 'end_time': Timestamp('...
    

    

      25
      986.0
      8/13-8/14/87
      0.0
      1987-08-13
      1957.0
      971.0
      1987
      3.01
      {'hours': 3, 'n': 5, 'end_time': Timestamp('19...
    

    

      15
      883.1
      8/2/2001
      0.0
      2001-08-02
      1023.0
      139.9
      2001
      2.12
      None
    

    

      19
      519.0
      7/17-7/18/96
      1032.0
      1996-07-17
      1551.0
      0.0
      1996
      2.41
      None
    

    

      17
      402.0
      8/16-8/17/97
      0.0
      1997-08-16
      559.0
      157.0
      1997
      3.46
      None
    

    

      1
      362.0
      6/30-7/1/14
      0.0
      2014-06-30
      525.0
      163.0
      2014
      4.91
      {'hours': 12, 'n': 2, 'end_time': Timestamp('2...
    

    

      22
      208.0
      5/9-5/10/90
      0.0
      1990-05-09
      497.0
      289.0
      1990
      4.83
      {'hours': 24, 'n': 2, 'end_time': Timestamp('1...
    

    

      20
      86.0
      11/27-11/28/90
      224.0
      1990-11-27
      464.0
      154.0
      1990
      3.30
      {'hours': 72, 'n': 1, 'end_time': Timestamp('1...
    

    

      24
      0.0
      8/25-8/26/87
      0.0
      1987-08-25
      18.0
      18.0
      1987
      4.08
      {'hours': 2, 'n': 10, 'end_time': Timestamp('1...
    

    

      26
      0.0
      10/3/1986
      0.0
      1986-10-03
      53.0
      53.0
      1986
      5.53
      {'hours': 240, 'n': 2, 'end_time': Timestamp('...
    

    

      27
      0.0
      8/6/1985
      0.0
      1985-08-06
      58.0
      58.0
      1985
      2.62
      None
    

    

      21
      0.0
      8/17-8/18/90
      0.0
      1990-08-17
      9.5
      9.5
      1990
      2.24
      None
    

    

      23
      0.0
      8/3-8/4/89
      0.0
      1989-08-03
      52.0
      52.0
      1989
      1.75
      None
    

    

      14
      0.0
      8/31/2001
      0.0
      2001-08-31
      75.3
      75.3
      2001
      9.26
      {'hours': 3, 'n': 50, 'end_time': Timestamp('2...
    

    

      16
      0.0
      6/13/1999
      0.0
      1999-06-13
      9.7
      9.7
      1999
      3.64
      {'hours': 72, 'n': 2, 'end_time': Timestamp('1...
    

    

      13
      0.0
      10/13/2001
      0.0
      2001-10-13
      90.7
      90.7
      2001
      5.69
      {'hours': 48, 'n': 5, 'end_time': Timestamp('2...
    

    

      11
      0.0
      8/23-24/07
      0.0
      2007-08-23
      224.0
      224.0
      2007
      5.75
      {'hours': 120, 'n': 5, 'end_time': Timestamp('...
    

    

      9
      0.0
      12/27-28/08
      0.0
      2008-12-27
      460.8
      460.8
      2008
      3.02
      None
    

    

      8
      0.0
      2/26-27/09
      0.0
      2009-02-26
      78.9
      78.9
      2009
      2.39
      None
    

    

      7
      0.0
      3/8/2009
      0.0
      2009-03-08
      143.1
      143.1
      2009
      2.82
      {'hours': 48, 'n': 1, 'end_time': Timestamp('2...
    

    

      6
      0.0
      6/19/2009
      0.0
      2009-06-19
      191.6
      191.6
      2009
      5.68
      {'hours': 120, 'n': 5, 'end_time': Timestamp('...
    

    

      4
      0.0
      5/29/2011
      0.0
      2011-05-29
      107.0
      107.0
      2011
      5.17
      {'hours': 240, 'n': 2, 'end_time': Timestamp('...
    

    

      28
      0.0
      3/4/1985
      0.0
      1985-03-04
      153.3
      153.3
      1985
      4.28
      {'hours': 240, 'n': 1, 'end_time': Timestamp('...
    

  




















In [ ]:

Content source: NORCatUofC/rain

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