

In [1]:

    
from __future__ import division, print_function, unicode_literals

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline



In [2]:

    
flood_comm_df = pd.read_csv('311_data/flood_calls_311_comm.csv')
flood_comm_df.head()









    Out[2]:






  
    
      
      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
      4
      1
      1
      1
      ...
      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
      1
    
    
      4
      2000-01-05
      0
      0
      0
      1
      1
      3
      0
      0
      0
      ...
      1
      0
      1
      1
      0
      0
      1
      0
      1
      1
    
  

5 rows × 78 columns



In [3]:

    
flood_comm_df['Created Date'] = pd.to_datetime(flood_comm_df['Created Date'])
flood_comm_df = flood_comm_df.set_index(pd.DatetimeIndex(flood_comm_df['Created Date']))
flood_comm_df = flood_comm_df[flood_comm_df.columns.values[1:]]
flood_comm_df.head()









    Out[3]:






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

5 rows × 77 columns



In [4]:

    
# Get the sum for each day, plot over time
flood_comm_sum = flood_comm_df.sum(axis=1)
flood_comm_sum.head()









    Out[4]:





2000-01-01     5
2000-01-02     3
2000-01-03    56
2000-01-04    34
2000-01-05    34
dtype: int64



In [5]:

    
plt.rcParams["figure.figsize"] = [15, 5]
flood_comm_sum.plot()









    Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x9c93550>



In [6]:

    
wib_comm_df = pd.read_csv('311_data/wib_calls_311_comm.csv')
wib_comm_df['Created Date'] = pd.to_datetime(wib_comm_df['Created Date'])
wib_comm_df = wib_comm_df.set_index(pd.DatetimeIndex(wib_comm_df['Created Date']))
wib_comm_df = wib_comm_df[wib_comm_df.columns.values[1:]]
wib_comm_sum = wib_comm_df.sum(axis=1)
wib_comm_sum.head()









    Out[6]:





2000-01-01     2
2000-01-02     3
2000-01-03    46
2000-01-04    30
2000-01-05    22
dtype: int64



In [7]:

    
wos_comm_df = pd.read_csv('311_data/wos_calls_311_comm.csv')
wos_comm_df['Created Date'] = pd.to_datetime(wos_comm_df['Created Date'])
wos_comm_df = wos_comm_df.set_index(pd.DatetimeIndex(wos_comm_df['Created Date']))
wos_comm_df = wos_comm_df[wos_comm_df.columns.values[1:]]
wos_comm_sum = wos_comm_df.sum(axis=1)
wos_comm_sum.head()









    Out[7]:





2000-01-01     3
2000-01-03    10
2000-01-04     4
2000-01-05    12
2000-01-06     8
dtype: int64

Plots Over Time

From looking at the plot of calls about street vs basement flooding, it looks like people call about street flooding more consistently, but when basement flooding calls happen they're typically in much more severe spikes.



In [8]:

    
combined_calls = pd.DataFrame()
combined_calls['Water in Basement'] = wib_comm_sum
combined_calls['Water in Street'] = wos_comm_sum
combined_calls.plot()









    Out[8]:





<matplotlib.axes._subplots.AxesSubplot at 0xab88d30>



In [ ]:

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