In [3]:
#check for data file
import os
In [2]:
print(os.listdir())

['.ipynb_checkpoints', '911.csv', 'projectOne.ipynb']
In [4]:
# import all major libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
In [5]:
# usage of inline plotting
%matplotlib inline
In [6]:
#load data from CSV file
dataFrame = pd.read_csv('./911.csv')
In [7]:
dataFrame.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 289240 entries, 0 to 289239
Data columns (total 9 columns):
lat          289240 non-null float64
lng          289240 non-null float64
desc         289240 non-null object
zip          253942 non-null float64
title        289240 non-null object
timeStamp    289240 non-null object
twp          289137 non-null object
addr         289240 non-null object
e            289240 non-null int64
dtypes: float64(3), int64(1), object(5)
memory usage: 19.9+ MB
In [8]:
#check head for our dataframe
dataFrame.head()
Out[8]:
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 17:10:52 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 17:29:21 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 14:39:21 NORRISTOWN HAWS AVE 1
3 40.116153 -75.343513 AIRY ST & SWEDE ST; NORRISTOWN; Station 308A;... 19401.0 EMS: CARDIAC EMERGENCY 2015-12-10 16:47:36 NORRISTOWN AIRY ST & SWEDE ST 1
4 40.251492 -75.603350 CHERRYWOOD CT & DEAD END; LOWER POTTSGROVE; S... NaN EMS: DIZZINESS 2015-12-10 16:56:52 LOWER POTTSGROVE CHERRYWOOD CT & DEAD END 1
In [10]:
#top5 zipcodes
dataFrame['zip'].value_counts().head(5)
Out[10]:
19401.0    19821
19464.0    19276
19403.0    14524
19446.0    13939
19406.0     9601
Name: zip, dtype: int64
In [14]:
#top6 twp reaching to 911
dataFrame['twp'].value_counts().head(6)
Out[14]:
LOWER MERION    24826
ABINGTON        17820
NORRISTOWN      16546
UPPER MERION    15288
CHELTENHAM      13359
POTTSTOWN       12039
Name: twp, dtype: int64
In [15]:
#unique reason to call 911
dataFrame['title'].nunique()
Out[15]:
131
In [22]:
# top5 specific reasons to call 911
# assignment was there!
dataFrame['SpecificReason'] = dataFrame['title'].apply(lambda title: title.split(':')[0])
In [23]:
dataFrame.head()
Out[23]:
lat lng desc zip title timeStamp twp addr e SpecificReason
0 40.297876 -75.581294 REINDEER CT & DEAD END; NEW HANOVER; Station ... 19525.0 EMS: BACK PAINS/INJURY 2015-12-10 17:10:52 NEW HANOVER REINDEER CT & DEAD END 1 EMS
1 40.258061 -75.264680 BRIAR PATH & WHITEMARSH LN; HATFIELD TOWNSHIP... 19446.0 EMS: DIABETIC EMERGENCY 2015-12-10 17:29:21 HATFIELD TOWNSHIP BRIAR PATH & WHITEMARSH LN 1 EMS
2 40.121182 -75.351975 HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St... 19401.0 Fire: GAS-ODOR/LEAK 2015-12-10 14:39:21 NORRISTOWN HAWS AVE 1 Fire
3 40.116153 -75.343513 AIRY ST & SWEDE ST; NORRISTOWN; Station 308A;... 19401.0 EMS: CARDIAC EMERGENCY 2015-12-10 16:47:36 NORRISTOWN AIRY ST & SWEDE ST 1 EMS
4 40.251492 -75.603350 CHERRYWOOD CT & DEAD END; LOWER POTTSGROVE; S... NaN EMS: DIZZINESS 2015-12-10 16:56:52 LOWER POTTSGROVE CHERRYWOOD CT & DEAD END 1 EMS
In [25]:
dataFrame['SpecificReason'].value_counts().head()
Out[25]:
EMS        144362
Traffic    101889
Fire        42989
Name: SpecificReason, dtype: int64
In [26]:
#plot a countplot for SpecificReason
sns.countplot(x='SpecificReason', data=dataFrame)
Out[26]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a24d75390>
In [27]:
#convert timeStamp from object to actual time stamp
dataFrame['timeStamp'] = pd.to_datetime(dataFrame['timeStamp'])
In [29]:
dataFrame.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 289240 entries, 0 to 289239
Data columns (total 10 columns):
lat               289240 non-null float64
lng               289240 non-null float64
desc              289240 non-null object
zip               253942 non-null float64
title             289240 non-null object
timeStamp         289240 non-null datetime64[ns]
twp               289137 non-null object
addr              289240 non-null object
e                 289240 non-null int64
SpecificReason    289240 non-null object
dtypes: datetime64[ns](1), float64(3), int64(1), object(5)
memory usage: 22.1+ MB
In [30]:
type(dataFrame['timeStamp'].iloc[0])
Out[30]:
pandas._libs.tslib.Timestamp
In [99]:
#create 3 columns for Hour, Months and day of week
dataFrame['Hour'] = dataFrame['timeStamp'].apply(lambda time: time.hour)
dataFrame['Month'] = dataFrame['timeStamp'].apply(lambda time: time.month)
dataFrame['Day'] = dataFrame['timeStamp'].apply(lambda time: time.dayofweek)
In [102]:
dataFrame['Hour'].nunique()
Out[102]:
24
In [104]:
#plot a graph for Month
sns.countplot(x='Month', data=dataFrame, hue='SpecificReason')
Out[104]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a22fe7780>
In [108]:
#use group_by 
byMonth = dataFrame.groupby('Day').count()
In [109]:
byMonth.head()
Out[109]:
lat lng desc zip title timeStamp twp addr e SpecificReason Hour Month Day of week
Day
0 42092 42092 42092 37030 42092 42092 42081 42092 42092 42092 42092 42092 42092
1 43744 43744 43744 38385 43744 43744 43731 43744 43744 43744 43744 43744 43744
2 43442 43442 43442 38327 43442 43442 43422 43442 43442 43442 43442 43442 43442
3 42784 42784 42784 37616 42784 42784 42770 42784 42784 42784 42784 42784 42784
4 44468 44468 44468 38923 44468 44468 44450 44468 44468 44468 44468 44468 44468
In [110]:
byMonth['twp'].plot()
Out[110]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a2321e588>