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911 Calls Data Analysis

Data and Setup

Import numpy, pandas, other visualization libraries and set %matplotlib inline

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.rcParams['figure.figsize'] = (4.5,3.5)
sns.set_style('whitegrid')
sns.set_context(context='notebook', font_scale=0.7)
df = pd.read_csv('911.csv')
df.info()
RangeIndex: 99492 entries, 0 to 99491
Data columns (total 9 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   lat        99492 non-null  float64
 1   lng        99492 non-null  float64
 2   desc       99492 non-null  object 
 3   zip        86637 non-null  float64
 4   title      99492 non-null  object 
 5   timeStamp  99492 non-null  object 
 6   twp        99449 non-null  object 
 7   addr       98973 non-null  object 
 8   e          99492 non-null  int64  
dtypes: float64(3), int64(1), object(5)
memory usage: 6.8+ MB

df.head(5)
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 
df['e'].value_counts()
e
1    99492
Name: count, dtype: int64

Basic Questions

What are the top 5 Zipcodes for 911 calls

df['zip'].value_counts().head(5)
zip
19401.0    6979
19464.0    6643
19403.0    4854
19446.0    4748
19406.0    3174
Name: count, dtype: int64
  • These are zip codes where the maximum number of calls are coming from

What are top 5 townships(twp) for 911 calls?

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

Take a look at the title column, how many unique title codes are there?

df.nunique()
lat          14579
lng          14586
desc         99455
zip            104
title          110
timeStamp    72577
twp             68
addr         21914
e                1
dtype: int64

df['title'].unique()
array(['EMS: BACK PAINS/INJURY', 'EMS: DIABETIC EMERGENCY',
       'Fire: GAS-ODOR/LEAK', 'EMS: CARDIAC EMERGENCY', 'EMS: DIZZINESS',
       'EMS: HEAD INJURY', 'EMS: NAUSEA/VOMITING',
       'EMS: RESPIRATORY EMERGENCY', 'EMS: SYNCOPAL EPISODE',
       'Traffic: VEHICLE ACCIDENT -', 'EMS: VEHICLE ACCIDENT',
       'Traffic: DISABLED VEHICLE -', 'Fire: APPLIANCE FIRE',
       'EMS: GENERAL WEAKNESS', 'Fire: CARBON MONOXIDE DETECTOR',
       'EMS: UNKNOWN MEDICAL EMERGENCY', 'EMS: UNRESPONSIVE SUBJECT',
       'Fire: VEHICLE ACCIDENT', 'EMS: ALTERED MENTAL STATUS',
       'Fire: FIRE ALARM', 'EMS: CVA/STROKE',
       'Traffic: ROAD OBSTRUCTION -', 'EMS: SUBJECT IN PAIN',
       'EMS: HEMORRHAGING', 'EMS: FALL VICTIM', 'EMS: ASSAULT VICTIM',
       'EMS: SEIZURES', 'EMS: MEDICAL ALERT ALARM',
       'EMS: ABDOMINAL PAINS', 'Fire: PUMP DETAIL',
       'Fire: FIRE INVESTIGATION', 'EMS: OVERDOSE', 'EMS: MATERNITY',
       'EMS: UNCONSCIOUS SUBJECT', 'EMS: CHOKING', 'EMS: LACERATIONS',
       'Fire: TRASH/DUMPSTER FIRE', 'Fire: UNKNOWN TYPE FIRE',
       'Fire: BUILDING FIRE', 'Fire: ELECTRICAL FIRE OUTSIDE',
       'Fire: DEBRIS/FLUIDS ON HIGHWAY',
       'Traffic: DEBRIS/FLUIDS ON HIGHWAY -', 'EMS: FEVER',
       'EMS: ALLERGIC REACTION', 'Traffic: VEHICLE LEAKING FUEL -',
       'EMS: FRACTURE', 'Fire: BURN VICTIM', 'EMS: BURN VICTIM',
       'Fire: RESCUE - GENERAL', 'Fire: WOODS/FIELD FIRE',
       'EMS: RESCUE - GENERAL', 'Fire: FIRE SPECIAL SERVICE',
       'Fire: VEHICLE FIRE', 'Traffic: VEHICLE FIRE -',
       'EMS: WARRANT SERVICE', 'Fire: S/B AT HELICOPTER LANDING',
       'EMS: EMS SPECIAL SERVICE', 'Traffic: HAZARDOUS ROAD CONDITIONS -',
       'Fire: RESCUE - ELEVATOR', 'EMS: FIRE SPECIAL SERVICE',
       'EMS: DEHYDRATION', 'EMS: CARBON MONOXIDE DETECTOR',
       'EMS: BUILDING FIRE', 'EMS: APPLIANCE FIRE', 'EMS: SHOOTING',
       'EMS: POISONING', 'Fire: TRANSFERRED CALL',
       'Fire: RESCUE - TECHNICAL', 'EMS: RESCUE - TECHNICAL',
       'Fire: VEHICLE LEAKING FUEL', 'EMS: EYE INJURY',
       'EMS: ELECTROCUTION', 'EMS: STABBING', 'Fire: FIRE POLICE NEEDED',
       'EMS: AMPUTATION', 'EMS: ANIMAL BITE', 'EMS: FIRE ALARM',
       'EMS: VEHICLE FIRE', 'EMS: HAZARDOUS MATERIALS INCIDENT',
       'EMS: RESCUE - ELEVATOR', 'EMS: FIRE INVESTIGATION',
       'Fire: MEDICAL ALERT ALARM', 'EMS: UNKNOWN TYPE FIRE',
       'EMS: GAS-ODOR/LEAK', 'Fire: TRAIN CRASH',
       'Fire: HAZARDOUS MATERIALS INCIDENT', 'EMS: TRANSFERRED CALL',
       'EMS: TRAIN CRASH', 'EMS: RESCUE - WATER',
       'EMS: S/B AT HELICOPTER LANDING',
       'Fire: UNKNOWN MEDICAL EMERGENCY', 'Fire: RESCUE - WATER',
       'EMS: CARDIAC ARREST', 'EMS: PLANE CRASH', 'Fire: PLANE CRASH',
       'EMS: WOODS/FIELD FIRE', 'Fire: CARDIAC ARREST',
       'Fire: EMS SPECIAL SERVICE', 'Fire: UNCONSCIOUS SUBJECT',
       'EMS: HEAT EXHAUSTION', 'EMS: DEBRIS/FLUIDS ON HIGHWAY',
       'EMS: ACTIVE SHOOTER', 'EMS: DISABLED VEHICLE',
       'Fire: POLICE INFORMATION', 'Fire: DIABETIC EMERGENCY',
       'EMS: BOMB DEVICE FOUND', 'Fire: SYNCOPAL EPISODE',
       'EMS: INDUSTRIAL ACCIDENT', 'EMS: DROWNING', 'EMS: SUSPICIOUS'],
      dtype=object)

len(df['title'].unique())
110

Creating new features

In the titles column there are 'Reasons/Departments" specified before the title code. These are EMS, Fire and Traffic. Use .apply() with a custom lambda expression to create a new column called "Reason " that contains this string values.

For Example, If the title column value is EMS: BACK PAINS/INJURY, the reason column value should be EMS.

df['Reason'] = df['title'].apply(lambda x:x.split(':')[0])
df['Reason']
0            EMS
1            EMS
2           Fire
3            EMS
4            EMS
          ...   
99487    Traffic
99488    Traffic
99489        EMS
99490        EMS
99491    Traffic
Name: Reason, Length: 99492, dtype: object

df
lat lng desc zip title timeStamp twp addr e Reason
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
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
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
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
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
... ... ... ... ... ... ... ... ... ... ...
99487 40.132869 -75.333515 MARKLEY ST & W LOGAN ST; NORRISTOWN; 2016-08-2... 19401.0 Traffic: VEHICLE ACCIDENT - 2016-08-24 11:06:00 NORRISTOWN MARKLEY ST & W LOGAN ST 1 Traffic
99488 40.006974 -75.289080 LANCASTER AVE & RITTENHOUSE PL; LOWER MERION; ... 19003.0 Traffic: VEHICLE ACCIDENT - 2016-08-24 11:07:02 LOWER MERION LANCASTER AVE & RITTENHOUSE PL 1 Traffic
99489 40.115429 -75.334679 CHESTNUT ST & WALNUT ST; NORRISTOWN; Station ... 19401.0 EMS: FALL VICTIM 2016-08-24 11:12:00 NORRISTOWN CHESTNUT ST & WALNUT ST 1 EMS
99490 40.186431 -75.192555 WELSH RD & WEBSTER LN; HORSHAM; Station 352; ... 19002.0 EMS: NAUSEA/VOMITING 2016-08-24 11:17:01 HORSHAM WELSH RD & WEBSTER LN 1 EMS
99491 40.207055 -75.317952 MORRIS RD & S BROAD ST; UPPER GWYNEDD; 2016-08... 19446.0 Traffic: VEHICLE ACCIDENT - 2016-08-24 11:17:02 UPPER GWYNEDD MORRIS RD & S BROAD ST 1 Traffic

99492 rows × 10 columns

What is the most common Reason for a 911 call based off this new column(Reason)

values = df['timeStamp'].value_counts()
values
timeStamp
2015-12-10 17:40:01    8
2016-04-21 17:57:01    7
2015-12-23 14:12:01    7
2016-02-14 14:02:01    6
2016-03-09 16:57:01    6
                      ..
2016-03-18 11:01:01    1
2016-03-18 11:02:00    1
2016-03-18 11:02:01    1
2016-03-18 11:07:01    1
2016-08-24 11:17:02    1
Name: count, Length: 72577, dtype: int64

Now use seaborn to create a countplot of 911 calls by Reason

sns.countplot(data = df, x= 'Reason', palette='viridis');

png

  • If you look at the calls for EMS it is around 48k
  • For fire it was 13-15k
  • For Traffic it was >30k and and <36k

Now let us begin to focus on time information. What is the data type of the objects in the time Stamp column?

df['timeStamp'].dtype
print(df['timeStamp'].dtype)
object

type(df['timeStamp'][0])
str

Use pd.to_datetime to convert the column fro strings to Date Time Objects.

df['timeStamp'] = pd.to_datetime(df['timeStamp'])
df['timeStamp'].dtype
dtype('<M8[ns]')

type(df['timeStamp'][0])
pandas._libs.tslibs.timestamps.Timestamp

You can no wgrab specific attributes from a Datetime object by caling them . For example

time = df['timeStamp'].iloc[0]

time.hour

We can use jupyters tab method to explore the various attributes that we can call>Now that the timestamp column are actually DateTime Objects , use .apply to create 3 new columns called Hour, Month and Day of week. You will create this columns based off of the timeStamp column.

df['timeStamp']
0       2015-12-10 17:40:00
1       2015-12-10 17:40:00
2       2015-12-10 17:40:00
3       2015-12-10 17:40:01
4       2015-12-10 17:40:01
                ...        
99487   2016-08-24 11:06:00
99488   2016-08-24 11:07:02
99489   2016-08-24 11:12:00
99490   2016-08-24 11:17:01
99491   2016-08-24 11:17:02
Name: timeStamp, Length: 99492, dtype: datetime64[ns]

df['Hour'] = df['timeStamp'].apply(lambda x: x.hour)
df['Month'] = df['timeStamp'].apply(lambda x: x.month)
df['Day of week'] = df['timeStamp'].apply(lambda x: x.day_of_week)
df.head()
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 
df['Month'].value_counts()

Month
1     13205
7     12137
6     11786
2     11467
5     11423
4     11326
3     11101
8      9078
12     7969
Name: count, dtype: int64

df['Hour'].value_counts()
Hour
17    6517
16    6490
15    6154
12    6029
14    5997
13    5967
18    5762
11    5543
10    5413
9     5314
8     5044
19    4908
20    4377
7     3970
21    3788
22    3283
23    2559
6     2513
0     2112
1     1721
5     1629
2     1549
3     1435
4     1418
Name: count, dtype: int64

df['Day of week'].value_counts()
Day of week
1    15150
2    14879
4    14833
0    14680
3    14478
5    13336
6    12136
Name: count, dtype: int64

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

dmap = {'0' :'Mon', 1:'Tue', 2:'Wed', 3:'Thu', 4:'Fri',5:'Sat', 6:'Sun'}

dmap = {0 :'Mon', 1:'Tue', 2:'Wed', 3:'Thu', 4:'Fri',5:'Sat', 6:'Sun'}
df['Day of week'] = df['Day of week'].map(dmap)
df.head()
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 Thu
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 Thu
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 Thu
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 Thu
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 Thu 
df['Day of week'].value_counts()
Day of week
Tue    15150
Wed    14879
Fri    14833
Mon    14680
Thu    14478
Sat    13336
Sun    12136
Name: count, dtype: int64

Now use Seaborn to create a countplot of the Day of the week column with the hue based off of the reason column.

sns.countplot(data = df , x = 'Day of week', hue ='Reason', palette = 'viridis')
plt.legend(bbox_to_anchor=(1,1))

png

sns.countplot(data = df , x = 'Month', hue ='Reason', palette = 'viridis')
plt.legend(bbox_to_anchor=(1.25,1))

png

Did you notice anything strange about the plot?

  • You should have notice that some months(sep,oct, and Nov) are missing lets see if we can fill the information by plotting the information in another way, possiby a simple line plot that fills in the missing months, in order to do this , we need to work with pandas again.

Now create a groupby object called byMonth, where you group the dataframe by the month column and use the count method for aggregation. Use the head() method on this returned DataFrame.

byMonth = df.groupby('Month').count()
byMonth
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
6 11786 11786 11786 10212 11786 11786 11777 11732 11786 11786 11786 11786
7 12137 12137 12137 10633 12137 12137 12133 12088 12137 12137 12137 12137
8 9078 9078 9078 7832 9078 9078 9073 9025 9078 9078 9078 9078
12 7969 7969 7969 6907 7969 7969 7963 7916 7969 7969 7969 7969

Now create a simple plot off the dataframe indicating the count of calls per month?

byMonth.plot(kind='line', y='e', legend = False, xlim=(0,12))

png

  • What we see here is number of calls comes down from january and goes up again during the summer months and and falls of drastically off October and december months.
  • This also produces an estimated value for the number of calls, for the missing months.

Now see if you can use seaborn's lmplot to create a linear fit on the number per month.Keep in mind you may need to reset the index to a column.

byMonth.reset_index(inplace=True)
byMonth
Month lat lng desc zip title timeStamp twp addr e Reason Hour Day of week
0 1 13205 13205 13205 11527 13205 13205 13203 13096 13205 13205 13205 13205
1 2 11467 11467 11467 9930 11467 11467 11465 11396 11467 11467 11467 11467
2 3 11101 11101 11101 9755 11101 11101 11092 11059 11101 11101 11101 11101
3 4 11326 11326 11326 9895 11326 11326 11323 11283 11326 11326 11326 11326
4 5 11423 11423 11423 9946 11423 11423 11420 11378 11423 11423 11423 11423
5 6 11786 11786 11786 10212 11786 11786 11777 11732 11786 11786 11786 11786
6 7 12137 12137 12137 10633 12137 12137 12133 12088 12137 12137 12137 12137
7 8 9078 9078 9078 7832 9078 9078 9073 9025 9078 9078 9078 9078
8 12 7969 7969 7969 6907 7969 7969 7963 7916 7969 7969 7969 7969 
sns.lmplot(data = byMonth, x = 'Month' ,y= 'e', height=4, scatter_kws= {'s':10}, line_kws={'lw':2}) #scatter_kws-> s represents the size

png

  • The shadow represents the error
  • What we see is as we go from january to decemeber, The regression analysis is showing that number of calls per month goes lower and lower .
  • However if you see the the scattered point it goes down and then it goes up again before it crashes in august and december
  • -Thats why its showing a much larger margin of error there over the shadow portion.
  • So the actual data points could lie anywhere between 8500 to 12000 for the month October.so it desnt give a good fit here because of this high error.

Create a new column called 'Date' that contains the date from the rimestamp column. You will need to use apply along with the .date() method.

df['Date']= df['timeStamp'].apply(lambda x:x.date())
df['Date']
0        2015-12-10
1        2015-12-10
2        2015-12-10
3        2015-12-10
4        2015-12-10
            ...    
99487    2016-08-24
99488    2016-08-24
99489    2016-08-24
99490    2016-08-24
99491    2016-08-24
Name: Date, Length: 99492, dtype: object

df.head()
lat lng desc zip title timeStamp twp addr e Reason Hour Month Day of week Date
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 Thu 2015-12-10
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 Thu 2015-12-10
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 Thu 2015-12-10
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 Thu 2015-12-10
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 Thu 2015-12-10 
type(df['Date'][0])
 datetime.date

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

byDate = df.groupby('Date').count()
byDate
lat lng desc zip title timeStamp twp addr e Reason Hour Month Day of week
Date
2015-12-10 115 115 115 100 115 115 115 113 115 115 115 115 115
2015-12-11 396 396 396 333 396 396 395 391 396 396 396 396 396
2015-12-12 403 403 403 333 403 403 403 401 403 403 403 403 403
2015-12-13 319 319 319 280 319 319 319 317 319 319 319 319 319
2015-12-14 447 447 447 387 447 447 446 445 447 447 447 447 447
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2016-08-20 328 328 328 279 328 328 328 327 328 328 328 328 328
2016-08-21 357 357 357 299 357 357 357 352 357 357 357 357 357
2016-08-22 389 389 389 336 389 389 388 384 389 389 389 389 389
2016-08-23 439 439 439 390 439 439 439 437 439 439 439 439 439
2016-08-24 132 132 132 106 132 132 132 132 132 132 132 132 132

259 rows × 13 columns

byDate.head()
lat lng desc zip title timeStamp twp addr e Reason Hour Month Day of week
Date
2015-12-10 115 115 115 100 115 115 115 113 115 115 115 115 115
2015-12-11 396 396 396 333 396 396 395 391 396 396 396 396 396
2015-12-12 403 403 403 333 403 403 403 401 403 403 403 403 403
2015-12-13 319 319 319 280 319 319 319 317 319 319 319 319 319
2015-12-14 447 447 447 387 447 447 446 445 447 447 447 447 447 
byDate.tail()
lat lng desc zip title timeStamp twp addr e Reason Hour Month Day of week
Date
2016-08-20 328 328 328 279 328 328 328 327 328 328 328 328 328
2016-08-21 357 357 357 299 357 357 357 352 357 357 357 357 357
2016-08-22 389 389 389 336 389 389 388 384 389 389 389 389 389
2016-08-23 439 439 439 390 439 439 439 437 439 439 439 439 439
2016-08-24 132 132 132 106 132 132 132 132 132 132 132 132 132 


byDate.plot(kind='line', y ='e', figsize=(8,4), legend = False)


png


    

  • The data actually starts from 2015 , then it goes till the 8th month i.e, is August of 2016.
    • 

  • In the begining i have predicted that we might have a missing data in the year 2016 rather this graph explains us that the data starts somewhere in the month of december  in 2015 and continues only till 2016.
    • 

  • The data starts from 10th of Dec 2015 till 24th of August 2016.
    • 

  • The number of calls is less in August and Decmeber because we do not have the data of the complete months.
    • 

  • If you analyse this data this is the total calls byDate,  On any individual day it looks like the total number of calls varies between 300 and 450 calls with spikes on certain days.
    • 



Now recreate the plot but create 3 separate plots with each plot representing a Reason for the call


byDateTraffic = df[df['Reason'] == 'Traffic'].groupby('Date').count()
byDateTraffic.head()



  
    

      
      lat
      lng
      desc
      zip
      title
      timeStamp
      twp
      addr
      e
      Reason
      Hour
      Month
      Day of week
    

    

      Date
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      2015-12-10
      43
      43
      43
      35
      43
      43
      43
      41
      43
      43
      43
      43
      43
    

    

      2015-12-11
      141
      141
      141
      108
      141
      141
      141
      137
      141
      141
      141
      141
      141
    

    

      2015-12-12
      146
      146
      146
      109
      146
      146
      146
      144
      146
      146
      146
      146
      146
    

    

      2015-12-13
      78
      78
      78
      54
      78
      78
      78
      76
      78
      78
      78
      78
      78
    

    

      2015-12-14
      186
      186
      186
      150
      186
      186
      186
      184
      186
      186
      186
      186
      186
    

  




byDateTraffic.plot(kind='line', y='e' , figsize=(8,4), legend=False, title = 'Traffic')


png


    

  • If you see the graph for the traffic reason , the number of calls are varying between 80 and 200 with spikes on certain days.
    • 



byDateFire = df[df['Reason'] == 'Fire'].groupby('Date').count()
byDateFire.head()



  
    

      
      lat
      lng
      desc
      zip
      title
      timeStamp
      twp
      addr
      e
      Reason
      Hour
      Month
      Day of week
    

    

      Date
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      2015-12-10
      15
      15
      15
      13
      15
      15
      15
      15
      15
      15
      15
      15
      15
    

    

      2015-12-11
      69
      69
      69
      59
      69
      69
      68
      68
      69
      69
      69
      69
      69
    

    

      2015-12-12
      68
      68
      68
      53
      68
      68
      68
      68
      68
      68
      68
      68
      68
    

    

      2015-12-13
      51
      51
      51
      48
      51
      51
      51
      51
      51
      51
      51
      51
      51
    

    

      2015-12-14
      39
      39
      39
      36
      39
      39
      38
      39
      39
      39
      39
      39
      39
    

  




byDateFire.plot(kind='line', y='e' , figsize=(8,4), legend=False, title = 'Fire')


png


    

  • If you see the graph for the Fire reason the number of calls on each day varies in between 30 to 80 calls on a normal days with a spike on certain days.
    • 



byDateEMS = df[df['Reason'] == 'EMS'].groupby('Date').count()
byDateEMS.head()



  
    

      
      lat
      lng
      desc
      zip
      title
      timeStamp
      twp
      addr
      e
      Reason
      Hour
      Month
      Day of week
    

    

      Date
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      2015-12-10
      57
      57
      57
      52
      57
      57
      57
      57
      57
      57
      57
      57
      57
    

    

      2015-12-11
      186
      186
      186
      166
      186
      186
      186
      186
      186
      186
      186
      186
      186
    

    

      2015-12-12
      189
      189
      189
      171
      189
      189
      189
      189
      189
      189
      189
      189
      189
    

    

      2015-12-13
      190
      190
      190
      178
      190
      190
      190
      190
      190
      190
      190
      190
      190
    

    

      2015-12-14
      222
      222
      222
      201
      222
      222
      222
      222
      222
      222
      222
      222
      222
    

  




byDateEMS.plot(kind = 'line', y = 'e',  figsize=(8,4), legend=False, title = 'EMS');


png


    

  • Here it looks like the calls vary between 150 and 225 with spikes on downward direction at the end of april or first of may.
    • 



Now lets move on to creating heatmaps with seaborn and our data. We'll first need to restructure the dataframe so that the columns become the Hours and the Index becomes the day of the week. There are a lots of ways to do this , but I would recommend trying to combine group by with an unstack method.


df.head()



  
    

      
      lat
      lng
      desc
      zip
      title
      timeStamp
      twp
      addr
      e
      Reason
      Hour
      Month
      Day of week
      Date
    

  
  
    

      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
      Thu
      2015-12-10
    

    

      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
      Thu
      2015-12-10
    

    

      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
      Thu
      2015-12-10
    

    

      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
      Thu
      2015-12-10
    

    

      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
      Thu
      2015-12-10
    

  




    

  • The easiest way to do this is using pivot table. If you check the dataframe we have added the new columns Reason, Hour Month and Day of the week and Date. What I want here is, 'Day of week' to be the index and the hour column actually be the columns and the hour varies between 0 to 23.
    • 

  • So I want 24 columns for each hour of day and seven rows for each day of the week.
    • 

  • Easiest way to to it is using the pivot table.
    • 



df.pivot_table(index = 'Day of week', columns = 'Hour', values = 'e', aggfunc = 'sum')



  
    

      Hour
      0
      1
      2
      3
      4
      5
      6
      7
      8
      9
      ...
      14
      15
      16
      17
      18
      19
      20
      21
      22
      23
    

    

      Day of week
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      Fri
      275
      235
      191
      175
      201
      194
      372
      598
      742
      752
      ...
      932
      980
      1039
      980
      820
      696
      667
      559
      514
      474
    

    

      Mon
      282
      221
      201
      194
      204
      267
      397
      653
      819
      786
      ...
      869
      913
      989
      997
      885
      746
      613
      497
      472
      325
    

    

      Sat
      375
      301
      263
      260
      224
      231
      257
      391
      459
      640
      ...
      789
      796
      848
      757
      778
      696
      628
      572
      506
      467
    

    

      Sun
      383
      306
      286
      268
      242
      240
      300
      402
      483
      620
      ...
      684
      691
      663
      714
      670
      655
      537
      461
      415
      330
    

    

      Thu
      278
      202
      233
      159
      182
      203
      362
      570
      777
      828
      ...
      876
      969
      935
      1013
      810
      698
      617
      553
      424
      354
    

    

      Tue
      269
      240
      186
      170
      209
      239
      415
      655
      889
      880
      ...
      943
      938
      1026
      1019
      905
      731
      647
      571
      462
      274
    

    

      Wed
      250
      216
      189
      209
      156
      255
      410
      701
      875
      808
      ...
      904
      867
      990
      1037
      894
      686
      668
      575
      490
      335
    

  



7 rows × 24 columns



## Getting the same results using unstack method
## Here we use the groupby method and rather than grouping by single column, we will pass in a list of two columns and that
## will produce a multi index dataframe

df.groupby(['Day of week', 'Hour']).count()



  
    

      
      
      lat
      lng
      desc
      zip
      title
      timeStamp
      twp
      addr
      e
      Reason
      Month
      Date
    

    

      Day of week
      Hour
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      Fri
      0
      275
      275
      275
      248
      275
      275
      275
      275
      275
      275
      275
      275
    

    

      1
      235
      235
      235
      200
      235
      235
      235
      232
      235
      235
      235
      235
    

    

      2
      191
      191
      191
      165
      191
      191
      191
      191
      191
      191
      191
      191
    

    

      3
      175
      175
      175
      164
      175
      175
      175
      175
      175
      175
      175
      175
    

    

      4
      201
      201
      201
      184
      201
      201
      201
      201
      201
      201
      201
      201
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      Wed
      19
      686
      686
      686
      590
      686
      686
      686
      682
      686
      686
      686
      686
    

    

      20
      668
      668
      668
      597
      668
      668
      668
      662
      668
      668
      668
      668
    

    

      21
      575
      575
      575
      508
      575
      575
      574
      572
      575
      575
      575
      575
    

    

      22
      490
      490
      490
      432
      490
      490
      490
      485
      490
      490
      490
      490
    

    

      23
      335
      335
      335
      294
      335
      335
      334
      335
      335
      335
      335
      335
    

  



168 rows × 12 columns



    

  • If  we observe the dataframe we can see two indexes here one is 'Day of Week' and 'Hour' for each day of the week
    • 



## Lets use unstack on it 

byDayHour = df.groupby(['Day of week', 'Hour']).count()['e'].unstack()
byDayHour



  
    

      Hour
      0
      1
      2
      3
      4
      5
      6
      7
      8
      9
      ...
      14
      15
      16
      17
      18
      19
      20
      21
      22
      23
    

    

      Day of week
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      Fri
      275
      235
      191
      175
      201
      194
      372
      598
      742
      752
      ...
      932
      980
      1039
      980
      820
      696
      667
      559
      514
      474
    

    

      Mon
      282
      221
      201
      194
      204
      267
      397
      653
      819
      786
      ...
      869
      913
      989
      997
      885
      746
      613
      497
      472
      325
    

    

      Sat
      375
      301
      263
      260
      224
      231
      257
      391
      459
      640
      ...
      789
      796
      848
      757
      778
      696
      628
      572
      506
      467
    

    

      Sun
      383
      306
      286
      268
      242
      240
      300
      402
      483
      620
      ...
      684
      691
      663
      714
      670
      655
      537
      461
      415
      330
    

    

      Thu
      278
      202
      233
      159
      182
      203
      362
      570
      777
      828
      ...
      876
      969
      935
      1013
      810
      698
      617
      553
      424
      354
    

    

      Tue
      269
      240
      186
      170
      209
      239
      415
      655
      889
      880
      ...
      943
      938
      1026
      1019
      905
      731
      647
      571
      462
      274
    

    

      Wed
      250
      216
      189
      209
      156
      255
      410
      701
      875
      808
      ...
      904
      867
      990
      1037
      894
      686
      668
      575
      490
      335
    

  



7 rows × 24 columns



    

  • This is a 7 x 24 df . We have seven days of the week and 24 hours of the day ranging from 0 to 23 and we have the sum that is the count for each row which has 'e' value as 1
    • 



Now create a HeatMap using this DataFrame.


plt.figure(figsize=(9,5))
sns.heatmap(data = byDayHour, cmap = 'viridis_r');


png


    

  • If you see the color bar the darker color has the higher value and the lighter color has the lower value.
    • 

  • We have the days of the week on the y axis and hours of the day on the x axis.
    • 

  • At midnight i.e, between 01 A.M to 5 A.M the values are lower and if you go through the day from the morning to the afternoon and evening the the colors become darker and darker thrpught the day and starts getting lighter again as you reach 9 P.m, 10 P.m and 11 P.m.
    • 

  • If you look at the Y axis the days of the week sat and sun has lighter colors compared to the other days of the week
    • 

  • The number of calls increased as you go through the day.
    • 

  • Most of the calls have been around three to seven in the evening and similiarly most of the calls happen on the weekdays than on the weekends.
    • 



Create a clustermap using the dataframe


sns.clustermap(data =byDayHour, cmap= 'viridis_r',figsize=(6, 6));


png


Lets repeat these same plots and operations, for a DataFrame that shows the Month as the column.


byDayMonth = df.groupby(['Day of week', 'Month']).count()['e'].unstack()
byDayMonth



  
    

      Month
      1
      2
      3
      4
      5
      6
      7
      8
      12
    

    

      Day of week
      
      
      
      
      
      
      
      
      
    

  
  
    

      Fri
      1970
      1581
      1525
      1958
      1730
      1649
      2045
      1310
      1065
    

    

      Mon
      1727
      1964
      1535
      1598
      1779
      1617
      1692
      1511
      1257
    

    

      Sat
      2291
      1441
      1266
      1734
      1444
      1388
      1695
      1099
      978
    

    

      Sun
      1960
      1229
      1102
      1488
      1424
      1333
      1672
      1021
      907
    

    

      Thu
      1584
      1596
      1900
      1601
      1590
      2065
      1646
      1230
      1266
    

    

      Tue
      1973
      1753
      1884
      1430
      1918
      1676
      1670
      1612
      1234
    

    

      Wed
      1700
      1903
      1889
      1517
      1538
      2058
      1717
      1295
      1262
    

  




    

  • We have our days on the row index and months on the column  and we have total number of calls and the data is given as a form of matrix where the actual data relates to the column and row index.
    • 



plt.figure(figsize=(8,5))
sns.heatmap(data = byDayMonth, cmap = 'viridis_r');


png


    

  • So here we have the day data on thr row index and the month data on the column index.
    • 

  • Analysing earlier i have realised that the data actually starts from december 10th of 2015 and went till 24 th of august 2016.
    • 

  • The data was incompte for the months december and august so obiviously number of calls were lesser compared to other months.
    • 



sns.clustermap(data = byDayMonth, cmap = 'viridis_r', figsize=(6,6));


png


Observations


    

  • 

    In this Project Its seen that more calls are made on weekdays than on weekends.
    

    • 

  • 

    And also more calls were made on late afternoon and evening hours than compared to late nighs and morning hours
    

    • 

  • 

    That kind of make sense as people are more active during these times so there would be more traffic calls or maybe EMS calls.
    

    • 

  • 

    However there was no pattern observed for months data partly due to the missing daa for the fall and the winter months.
    

    • 

  • 

    If the missing data is found then it could be analysed for the different reasons for summer and winter months as there could be more fire incidents in the summers and more traffic related calls in the winter months.
    

    Author - Thanveer Ahmed Shaik
    

    • 



This project is part of my portfolio, showcasing the Python skills essential for data analyst roles. If you have any questions, feedback, or would like to collaborate, feel free to get in touch!