notebook.community

Edit and run



In [72]:

    
from __future__ import division, print_function, unicode_literals

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import seaborn as sns
from datetime import datetime
%matplotlib inline



In [8]:

    
# Get precipitation since 2000
rain_df = pd.read_csv('../n-year/notebooks/data/ohare_full_precip_hourly.csv')
rain_df['datetime'] = rain_df['datetime'].apply(lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))
rain_df = rain_df.set_index(pd.DatetimeIndex(rain_df['datetime']))
rain_df = rain_df['2000-01-01':][['HOURLYPrecip', 'datetime']].copy()
rain_df.head()









    Out[8]:






  
    
      
      HOURLYPrecip
      datetime
    
  
  
    
      2000-01-01 00:00:00
      0.0
      2000-01-01 00:00:00
    
    
      2000-01-01 01:00:00
      0.0
      2000-01-01 01:00:00
    
    
      2000-01-01 02:00:00
      0.0
      2000-01-01 02:00:00
    
    
      2000-01-01 03:00:00
      0.0
      2000-01-01 03:00:00
    
    
      2000-01-01 04:00:00
      0.0
      2000-01-01 04:00:00



In [16]:

    
daily_rain = rain_df['HOURLYPrecip'].resample('1D').sum()
daily_rain = pd.DataFrame(daily_rain)
daily_rain.head()









    Out[16]:






  
    
      
      HOURLYPrecip
    
  
  
    
      2000-01-01
      0.00
    
    
      2000-01-02
      0.01
    
    
      2000-01-03
      0.24
    
    
      2000-01-04
      0.02
    
    
      2000-01-05
      0.00



In [17]:

    
wib_df = pd.read_csv('311_data/wib_calls_311_comm.csv')
wib_df.head()









    Out[17]:






  
    
      
      Created Date
      ALBANY PARK
      ARCHER HEIGHTS
      ARMOUR SQUARE
      ASHBURN
      AUBURN GRESHAM
      AUSTIN
      AVALON PARK
      AVONDALE
      BELMONT CRAGIN
      ...
      WASHINGTON HEIGHTS
      WASHINGTON PARK
      WEST ELSDON
      WEST ENGLEWOOD
      WEST GARFIELD PARK
      WEST LAWN
      WEST PULLMAN
      WEST RIDGE
      WEST TOWN
      WOODLAWN
    
  
  
    
      0
      2000-01-01
      0
      0
      0
      0
      0
      1
      0
      0
      0
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
    
    
      1
      2000-01-02
      0
      0
      0
      0
      0
      0
      0
      0
      0
      ...
      0
      0
      0
      0
      0
      0
      0
      1
      0
      0
    
    
      2
      2000-01-03
      0
      0
      0
      4
      1
      3
      1
      1
      0
      ...
      1
      0
      0
      0
      0
      0
      2
      1
      2
      2
    
    
      3
      2000-01-04
      0
      0
      0
      0
      1
      4
      1
      0
      0
      ...
      3
      0
      0
      1
      0
      0
      0
      0
      1
      0
    
    
      4
      2000-01-05
      0
      0
      0
      1
      1
      3
      0
      0
      0
      ...
      1
      0
      0
      0
      0
      0
      1
      0
      1
      1
    
  

5 rows × 78 columns



In [32]:

    
wib_df = pd.read_csv('311_data/wib_calls_311_comm.csv')
wib_df['WIB_Calls'] = wib_df[wib_df.columns.values[1:]].sum(axis=1)
wib_sum = wib_df[['Created Date', 'WIB_Calls']].copy()
wib_sum['Created Date'] = pd.to_datetime(wib_sum['Created Date'])
wib_sum = wib_sum.set_index(wib_sum['Created Date'])
wib_sum = wib_sum[['WIB_Calls']]
wib_sum.head()









    Out[32]:






  
    
      
      WIB_Calls
    
    
      Created Date
      
    
  
  
    
      2000-01-01
      2
    
    
      2000-01-02
      3
    
    
      2000-01-03
      46
    
    
      2000-01-04
      30
    
    
      2000-01-05
      22



In [40]:

    
rain_wib = daily_rain.copy()
rain_wib['WIB_Calls'] = wib_sum['WIB_Calls']
rain_wib.head()









    Out[40]:






  
    
      
      HOURLYPrecip
      WIB_Calls
    
  
  
    
      2000-01-01
      0.00
      2.0
    
    
      2000-01-02
      0.01
      3.0
    
    
      2000-01-03
      0.24
      46.0
    
    
      2000-01-04
      0.02
      30.0
    
    
      2000-01-05
      0.00
      22.0

Rain and Flooding Notes

Doesn't seem to be a one to one relationship between the two, clear imbalances
Would be worth looking more into what is actually causing the worst ones



In [76]:

    
plt.rcParams["figure.figsize"] = [15, 5]
fix, ax = plt.subplots()
rain_plot = rain_wib['HOURLYPrecip'].plot(ax=ax, style='blue', label='Daily Precipitation')
wib_plot = rain_wib['WIB_Calls'].plot(ax=ax, style='red', secondary_y=True, label='WIB Calls')

rain_patch = mpatches.Patch(color='blue', label='Daily Precipitation')
wib_patch = mpatches.Patch(color='red', label='Basement Flooding Calls')
plt.legend(handles=[rain_patch, wib_patch])
plt.title("Precipitation and Basement Flooding Over Time")
plt.show()



In [ ]:

	HOURLYPrecip	datetime
2000-01-01 00:00:00	0.0	2000-01-01 00:00:00
2000-01-01 01:00:00	0.0	2000-01-01 01:00:00
2000-01-01 02:00:00	0.0	2000-01-01 02:00:00
2000-01-01 03:00:00	0.0	2000-01-01 03:00:00
2000-01-01 04:00:00	0.0	2000-01-01 04:00:00

	HOURLYPrecip
2000-01-01	0.00
2000-01-02	0.01
2000-01-03	0.24
2000-01-04	0.02
2000-01-05	0.00

	WIB_Calls
Created Date
2000-01-01	2
2000-01-02	3
2000-01-03	46
2000-01-04	30
2000-01-05	22