911 Calls Capstone Project¶

K.D.P Kulugammana¶

Data and Setup¶

In [3]:
import numpy as np
import pandas as pd
In [4]:
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
In [5]:
df = pd.read_csv('911.csv')
In [6]:
df.info
Out[6]:
<bound method DataFrame.info of              lat        lng  \
0      40.297876 -75.581294   
1      40.258061 -75.264680   
2      40.121182 -75.351975   
3      40.116153 -75.343513   
4      40.251492 -75.603350   
...          ...        ...   
99487  40.132869 -75.333515   
99488  40.006974 -75.289080   
99489  40.115429 -75.334679   
99490  40.186431 -75.192555   
99491  40.207055 -75.317952   

                                                    desc      zip  \
0      REINDEER CT & DEAD END;  NEW HANOVER; Station ...  19525.0   
1      BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...  19446.0   
2      HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...  19401.0   
3      AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...  19401.0   
4      CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...      NaN   
...                                                  ...      ...   
99487  MARKLEY ST & W LOGAN ST; NORRISTOWN; 2016-08-2...  19401.0   
99488  LANCASTER AVE & RITTENHOUSE PL; LOWER MERION; ...  19003.0   
99489  CHESTNUT ST & WALNUT ST;  NORRISTOWN; Station ...  19401.0   
99490  WELSH RD & WEBSTER LN;  HORSHAM; Station 352; ...  19002.0   
99491  MORRIS RD & S BROAD ST; UPPER GWYNEDD; 2016-08...  19446.0   

                             title            timeStamp                twp  \
0           EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   
1          EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   
2              Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   
3           EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   
4                   EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   
...                            ...                  ...                ...   
99487  Traffic: VEHICLE ACCIDENT -  2016-08-24 11:06:00         NORRISTOWN   
99488  Traffic: VEHICLE ACCIDENT -  2016-08-24 11:07:02       LOWER MERION   
99489             EMS: FALL VICTIM  2016-08-24 11:12:00         NORRISTOWN   
99490         EMS: NAUSEA/VOMITING  2016-08-24 11:17:01            HORSHAM   
99491  Traffic: VEHICLE ACCIDENT -  2016-08-24 11:17:02      UPPER GWYNEDD   

                                 addr  e  
0              REINDEER CT & DEAD END  1  
1          BRIAR PATH & WHITEMARSH LN  1  
2                            HAWS AVE  1  
3                  AIRY ST & SWEDE ST  1  
4            CHERRYWOOD CT & DEAD END  1  
...                               ... ..  
99487         MARKLEY ST & W LOGAN ST  1  
99488  LANCASTER AVE & RITTENHOUSE PL  1  
99489         CHESTNUT ST & WALNUT ST  1  
99490           WELSH RD & WEBSTER LN  1  
99491          MORRIS RD & S BROAD ST  1  

[99492 rows x 9 columns]>
In [7]:
df.head()
Out[7]:
lat lng desc zip title timeStamp twp addr e
0 40.297876 -75.581294 REINDEER CT & DEAD END; NEW HANOVER; Station ... 19525.0 EMS: BACK PAINS/INJURY 2015-12-10 17:40:00 NEW HANOVER REINDEER CT & DEAD END 1
1 40.258061 -75.264680 BRIAR PATH & WHITEMARSH LN; HATFIELD TOWNSHIP... 19446.0 EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00 HATFIELD TOWNSHIP BRIAR PATH & WHITEMARSH LN 1
2 40.121182 -75.351975 HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St... 19401.0 Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00 NORRISTOWN HAWS AVE 1
3 40.116153 -75.343513 AIRY ST & SWEDE ST; NORRISTOWN; Station 308A;... 19401.0 EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01 NORRISTOWN AIRY ST & SWEDE ST 1
4 40.251492 -75.603350 CHERRYWOOD CT & DEAD END; LOWER POTTSGROVE; S... NaN EMS: DIZZINESS 2015-12-10 17:40:01 LOWER POTTSGROVE CHERRYWOOD CT & DEAD END 1

Top 5 townships (twp) for 911 calls

In [9]:
df['twp'].value_counts().head(5)
Out[9]:
twp
LOWER MERION    8443
ABINGTON        5977
NORRISTOWN      5890
UPPER MERION    5227
CHELTENHAM      4575
Name: count, dtype: int64

Unique title codes

In [10]:
len(df['title'].unique())
Out[10]:
110

Creating new features¶

In [11]:
x=df['title'].iloc[0]
In [12]:
x.split(':')[0]
Out[12]:
'EMS'

The most common Reason for a 911 call based off of this new column

In [13]:
df.dtypes
Out[13]:
lat          float64
lng          float64
desc          object
zip          float64
title         object
timeStamp     object
twp           object
addr          object
e              int64
dtype: object
In [14]:
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
In [15]:
df['Reason'].value_counts()
Out[15]:
Reason
EMS        48877
Traffic    35695
Fire       14920
Name: count, dtype: int64

Countplot of 911 calls by Reason

In [16]:
sns.countplot(x = 'Reason' , data = df, palette = 'viridis')
Out[16]:
<Axes: xlabel='Reason', ylabel='count'>

The data type of the objects in the timeStamp column

In [17]:
type(df['timeStamp'].iloc[0])
Out[17]:
str
In [18]:
df['timeStamp'] = pd.to_datetime(df['timeStamp'])
In [19]:
type(df['timeStamp'].iloc[0])
Out[19]:
pandas._libs.tslibs.timestamps.Timestamp
In [20]:
time = df['timeStamp'].iloc[0]
time.hour
Out[20]:
17
In [21]:
time.dayofweek
Out[21]:
3
In [22]:
df['Hour'] = df['timeStamp'].apply(lambda time : time.hour)
In [23]:
df['Hour']
Out[23]:
0        17
1        17
2        17
3        17
4        17
         ..
99487    11
99488    11
99489    11
99490    11
99491    11
Name: Hour, Length: 99492, dtype: int64
In [24]:
df['Month'] = df['timeStamp'].apply(lambda time : time.month)
In [25]:
df['Day of Week'] = df['timeStamp'].apply(lambda time : time.dayofweek)

The Day of Week is an integer 0-6. Use the .map() with this dictionary to map the actual string names to the day of the week:

dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}
In [26]:
df.head()
Out[26]:
lat lng desc zip title timeStamp twp addr e Reason Hour Month Day of Week
0 40.297876 -75.581294 REINDEER CT & DEAD END; NEW HANOVER; Station ... 19525.0 EMS: BACK PAINS/INJURY 2015-12-10 17:40:00 NEW HANOVER REINDEER CT & DEAD END 1 EMS 17 12 3
1 40.258061 -75.264680 BRIAR PATH & WHITEMARSH LN; HATFIELD TOWNSHIP... 19446.0 EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00 HATFIELD TOWNSHIP BRIAR PATH & WHITEMARSH LN 1 EMS 17 12 3
2 40.121182 -75.351975 HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St... 19401.0 Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00 NORRISTOWN HAWS AVE 1 Fire 17 12 3
3 40.116153 -75.343513 AIRY ST & SWEDE ST; NORRISTOWN; Station 308A;... 19401.0 EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01 NORRISTOWN AIRY ST & SWEDE ST 1 EMS 17 12 3
4 40.251492 -75.603350 CHERRYWOOD CT & DEAD END; LOWER POTTSGROVE; S... NaN EMS: DIZZINESS 2015-12-10 17:40:01 LOWER POTTSGROVE CHERRYWOOD CT & DEAD END 1 EMS 17 12 3
In [27]:
dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}
In [28]:
df['Day of Week'] = df['Day of Week'].map(dmap)

Countplot of the Day of Week column with the hue based off of the Reason column.

In [29]:
sns.countplot(x = 'Day of Week',data = df,hue = 'Reason',palette='viridis')

plt.legend(bbox_to_anchor = (1.05,1),loc = 2, borderaxespad = 0.)
Out[29]:
<matplotlib.legend.Legend at 0x259d9a815d0>
In [30]:
sns.countplot(x = 'Month',data = df,hue = 'Reason',palette='viridis')

plt.legend(bbox_to_anchor = (1.05,1),loc = 2, borderaxespad = 0.)
Out[30]:
<matplotlib.legend.Legend at 0x259d9a81c90>

Create a gropuby object called byMonth, where you group the DataFrame by the month column and use the count() method for aggregation.

In [31]:
byMonth = df.groupby('Month').count()

Simple plot off of the dataframe indicating the count of calls per month.

In [32]:
byMonth.head()
Out[32]:
lat lng desc zip title timeStamp twp addr e Reason Hour Day of Week
Month
1 13205 13205 13205 11527 13205 13205 13203 13096 13205 13205 13205 13205
2 11467 11467 11467 9930 11467 11467 11465 11396 11467 11467 11467 11467
3 11101 11101 11101 9755 11101 11101 11092 11059 11101 11101 11101 11101
4 11326 11326 11326 9895 11326 11326 11323 11283 11326 11326 11326 11326
5 11423 11423 11423 9946 11423 11423 11420 11378 11423 11423 11423 11423
In [33]:
sns.lmplot(x = 'Month',y = 'twp',data = byMonth.reset_index())

C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)
Out[33]:
<seaborn.axisgrid.FacetGrid at 0x259da906e10>

Create a new column called 'Date' that contains the date from the timeStamp column.

In [34]:
t = df['timeStamp'].iloc[0]
In [35]:
df['Date']=df['timeStamp'].apply(lambda t: t.date())
In [36]:
df.groupby('Date').count()['twp'].plot()
plt.tight_layout()

Groupby this Date column with the count() aggregate and create a plot of counts of 911 calls.

In [37]:
df[df['Reason']=='Traffic'].groupby('Date').count()['twp'].plot()
plt.title('Traffic')
plt.tight_layout()

Separate plots with each plot representing a Reason for the 911 call

In [38]:
df[df['Reason'] == 'Fire'].groupby('Date').count()['twp'].plot()
plt.title('Fire')
plt.tight_layout()
In [39]:
df[df['Reason'] == 'EMS'].groupby('Date').count()['twp'].plot()
plt.title('EMS')
plt.tight_layout()
In [42]:
dayHour= df.groupby(by = ['Day of Week','Hour' ]).count()['Reason'].unstack()

Heatmap & Clutermap¶

In [46]:
plt.figure(figsize = (12,6))
sns.heatmap(dayHour,cmap = 'viridis')
Out[46]:
<Axes: xlabel='Hour', ylabel='Day of Week'>
In [47]:
sns.clustermap(dayHour,cmap = 'viridis')
Out[47]:
<seaborn.matrix.ClusterGrid at 0x259e4c2ed90>
In [48]:
dayMonth= df.groupby(by = ['Day of Week','Month' ]).count()['Reason'].unstack()
In [49]:
plt.figure(figsize = (12,6))
sns.heatmap(dayMonth,cmap = 'viridis')
Out[49]:
<Axes: xlabel='Month', ylabel='Day of Week'>
In [50]:
sns.clustermap(dayMonth,cmap = 'viridis')
Out[50]:
<seaborn.matrix.ClusterGrid at 0x259e4f9d8d0>