In [1]:

%matplotlib inline

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

calls = pd.read_csv("911.csv")
calls.timeStamp = pd.to_datetime(calls.timeStamp)

In [2]:

t = calls.title.str.extract("(\w*)([:\s])(\s\w*[\s-]?[/]?\w*/?\w*)",expand = False)

In [3]:

calls["call_type"] = t[0]
calls["call_detail"] = t[2]

In [4]:

calls["Month"] = calls.timeStamp.dt.month
calls["Year"] = calls.timeStamp.dt.year
calls["Hour"] = calls.timeStamp.dt.hour
calls["weekday"] = calls.timeStamp.dt.weekday_name

In [5]:

calls["hour_type"] = np.where(calls.Hour < 5,"MidNight",
                              np.where((calls.Hour >=5)& (calls.Hour <12),"Morning",
                                                                np.where((calls.Hour >=12) &(calls.Hour <17),"AfterNoon","Evening")))

In [6]:

calls.head()

Out[6]:

	lat	lng	desc	zip	title	timeStamp	twp	addr	e	call_type	call_detail	Month	Year	Hour	weekday	hour_type
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	BACK PAINS/INJURY	12	2015	17	Thursday	Evening
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	DIABETIC EMERGENCY	12	2015	17	Thursday	Evening
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	GAS-ODOR/LEAK	12	2015	14	Thursday	AfterNoon
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	CARDIAC EMERGENCY	12	2015	16	Thursday	AfterNoon
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	DIZZINESS	12	2015	16	Thursday	AfterNoon

In [58]:

calls.call_type.value_counts().plot(kind = "pie", colors =["salmon","lightsalmon","darksalmon"],
                                    autopct = "%.2f", figsize = (8,8),
                                   explode = [0.01,0.01,0.01])

Out[58]:

<matplotlib.axes._subplots.AxesSubplot at 0x19e91fd4f98>

Emergency Calls¶

In [8]:

calls[calls.call_type == "EMS"].call_detail.value_counts().plot(kind = "bar", figsize = (15,8), color = "salmon")
plt.xlabel("Cause Of Incident", size =15)
plt.ylabel("Number of Calls", size = 15)
plt.title("Emergency Calls", size = 15)

Out[8]:

<matplotlib.text.Text at 0x19e882b6ba8>

In [9]:

calls[calls.call_type == "EMS"].hour_type.value_counts().plot(figsize = (10,9),kind = "pie",autopct = "%.2f", colors = ["tan", "wheat","darksalmon","lightsalmon"],
                                                             explode = [0.2,0,0,0])
plt.ylabel("")
plt.title("Emergnecy Calls vs Time of Day", size = 15)

Out[9]:

<matplotlib.text.Text at 0x19e88595470>

In [10]:

calls[calls.call_type=="EMS"].Hour.value_counts().sort_index().plot(kind = "bar", figsize = (10,4), color = "wheat")
plt.xlabel("Hour", size = 15)
plt.ylabel("Number of Emergency Calls", size =15)
plt.title("Emergency Calls by Hour", size = 15)

Out[10]:

<matplotlib.text.Text at 0x19e8860cb70>

In [11]:

calls[calls.call_type == "EMS"].Month.value_counts().sort_index().plot(kind = "line", color = "darksalmon", figsize =(10,4))
plt.xticks(range(1,13,1));
plt.title("Emergency Calls by Month", size =15);
plt.xlabel("Month", size = 15);
plt.ylabel("Number of Calls", size = 15);

In [12]:

calls[calls.call_type == "EMS"].weekday.value_counts().plot(kind = "line", color = "maroon", figsize = (10,4));
plt.title("Emergency Calls vs Week Day", size =15);
plt.xlabel("Week Day", size =15);
plt.ylabel("Number Of Calls", size = 15);

In [13]:

calls[calls.call_type =="EMS"].groupby("twp").count().e.sort_values(ascending = False).plot(kind = "bar", figsize= (15,4), color = "lightsalmon");
plt.title("Emergency Calls vs Town", size = 15)
plt.xlabel("Town", size = 15);
plt.ylabel("Number of Calls", size = 15);

Traffic Calls¶

In [14]:

calls[calls.call_type == "Traffic"].call_detail.value_counts().plot(kind = "bar", figsize = (15,8), color = "salmon", rot = 50)
plt.xlabel("Cause Of Incident", size =15);
plt.ylabel("Number of Calls", size = 15);
plt.title("Traffic Calls", size = 15);

In [15]:

calls[calls.call_type == "Traffic"].hour_type.value_counts().plot(figsize = (10,9),kind = "pie",autopct = "%.2f", colors = ["tan", "wheat","darksalmon","lightsalmon"],
                                                             explode = [0.2,0,0,0])

plt.title("Traffic Calls vs Time of Day", size = 15)
plt.ylabel("")

Out[15]:

<matplotlib.text.Text at 0x19e8ca77a20>

In [16]:

calls[calls.call_type=="Traffic"].Hour.value_counts().sort_index().plot(kind = "bar", figsize = (10,4), color = "wheat")
plt.xlabel("Hour", size = 15);
plt.ylabel("Number of Emergency Calls", size = 15);
plt.title("Traffic Calls by Hour", size = 15);

In [17]:

calls[calls.call_type == "Traffic"].Month.value_counts().sort_index().plot(kind = "line", color = "darksalmon", figsize =(10,4))
plt.xticks(range(1,13,1));
plt.title("Traffic Calls by Month", size = 15);
plt.xlabel("Month", size = 15);
plt.ylabel("Number of Calls", size = 15);

In [18]:

calls[calls.call_type == "Traffic"].weekday.value_counts().plot(kind = "line", color = "maroon", figsize = (10,4));
plt.title("Traffic Calls vs Week Day", size = 15);
plt.xlabel("Week Day", size = 15);
plt.ylabel("Number Of Calls", size = 15);

In [19]:

calls[calls.call_type =="Traffic"].groupby("twp").count().e.sort_values(ascending= False).plot(kind = "bar", figsize= (15,4), color = "tan");
plt.title("Traffic Calls vs Town", size = 15);
plt.xlabel("Town", size = 15);
plt.ylabel("Number of Calls", size = 15);

Fire Calls¶

In [20]:

calls[calls.call_type == "Fire"].call_detail.value_counts().plot(kind = "bar", figsize = (15,8), color = "salmon")
plt.xlabel("Cause Of Incident", size =15);
plt.ylabel("Number of Calls", size = 15);
plt.title("Fire Calls", size = 15);

In [21]:

calls[calls.call_type == "Fire"].hour_type.value_counts().plot(figsize = (10,9),kind = "pie",autopct = "%.2f", colors = ["tan", "wheat","darksalmon","lightsalmon"],
                                                             explode = [0.2,0,0,0])

plt.title("Fire Calls vs Time of Day", size = 15)
plt.ylabel("")

Out[21]:

<matplotlib.text.Text at 0x19e8d452b00>

In [22]:

calls[calls.call_type=="Fire"].Hour.value_counts().sort_index().plot(kind = "bar", figsize = (10,4), color = "wheat")
plt.xlabel("Hour", size = 15);
plt.ylabel("Number of Emergency Calls", size = 15);
plt.title("Fire Calls by Hour", size = 15);

In [23]:

calls[calls.call_type == "Fire"].Month.value_counts().sort_index().plot(kind = "line", color = "darksalmon", figsize =(10,4))
plt.xticks(range(1,13,1));
plt.title("Fire Calls by Month", size = 15);
plt.xlabel("Month", size = 15);
plt.ylabel("Number of Calls", size = 15);

In [24]:

calls[calls.call_type == "Fire"].weekday.value_counts().plot(kind = "line", color = "maroon", figsize = (10,4));
plt.title("Fire Calls vs Week Day", size = 15);
plt.xlabel("Week Day", size = 15);
plt.ylabel("Number Of Calls", size = 15);

In [25]:

calls[calls.call_type =="Fire"].groupby("twp").count().e.sort_values(ascending= False).plot(kind = "bar", figsize= (15,4), color = "tan");
plt.title("Fire Calls vs Town", size = 15);
plt.xlabel("Town", size = 15);
plt.ylabel("Number of Calls", size = 15);

Overall Analysis¶

In [26]:

calls.groupby("Hour").call_type.value_counts().unstack().plot(kind = "line",figsize = (16,6))
plt.title("Calls by Hour",size = 15)
plt.ylabel("Number of incidents",size = 15)
plt.xlabel("Hours", size = 15)
plt.xticks(range(0,24,1));

In [27]:

calls.groupby("Month").call_type.value_counts().unstack().plot(kind = "line",figsize = (16,6))
plt.title("Calls by Month",size = 15)
plt.ylabel("Number of incidents",size = 15)
plt.xlabel("Month", size = 15)
plt.xticks(range(0,13,1));

In [28]:

calls.groupby("weekday").call_type.value_counts().unstack().plot(kind = "line",figsize = (16,6))
plt.title("Calls by Weekday",size = 15)
plt.ylabel("Number of incidents",size = 15)
plt.xlabel("Weekday", size = 15)
# plt.xticks(range(0,24,1));

Out[28]:

<matplotlib.text.Text at 0x19e8c9a1828>

In [29]:

calls.groupby("twp").call_type.value_counts().unstack().sum(1).sort_values(ascending = False).plot(kind = "bar",figsize = (18,8))
# pct_town_details = town_details.div(town_details.sum(0),1)
# pct_town_details.sort_values("EMS",ascending = False).plot(kind = "bar",rot = 90,figsize = (18,8),color = ["red","orange","green"])
plt.xlabel("Town", size = 15);
plt.title("Total Number of Calls by Town", size = 15)
plt.ylabel("Number of Calls", size = 15)

Out[29]:

<matplotlib.text.Text at 0x19e8de8d978>

In [47]:

town_details = calls.groupby("twp").call_type.value_counts().unstack()
pct_town_details = town_details.div(town_details.sum(1),0)
pct_town_details.plot(kind = "kde",rot = 90,figsize = (18,8),color = ["red","orange","green"])
# plt.xlabel("Town", size = 15);
# plt.title("Proportion of Call type by Town", size = 15)
# plt.ylabel("Number of Calls", size = 15)

Out[47]:

<matplotlib.axes._subplots.AxesSubplot at 0x19e91322748>

911 Calls Analysis

Emergency Calls¶

Traffic Calls¶

Fire Calls¶

Overall Analysis¶