911 Calls Capstone Project¶

For this capstone project we will be analyzing some 911 call data from Kaggle. The data contains the following fields:

  • lat : String variable, Latitude
  • lng: String variable, Longitude
  • desc: String variable, Description of the Emergency Call
  • zip: String variable, Zipcode
  • title: String variable, Title
  • timeStamp: String variable, YYYY-MM-DD HH:MM:SS
  • twp: String variable, Township
  • addr: String variable, Address
  • e: String variable, Dummy variable (always 1)

Just go along with this notebook and try to complete the instructions or answer the questions in bold using your Python and Data Science skills!

Data and Setup¶

Import numpy and pandas

In [1]:
import pandas as pd
import seaborn as sns
import datetime

Import visualization libraries and set %matplotlib inline.

In [34]:
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
%matplotlib inline

Read in the csv file as a dataframe called df

In [3]:
df = pd.read_csv('911.csv')

Check the info() of the df

In [4]:
df.info()

<class 'pandas.core.frame.DataFrame'>
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

Check the head of df

In [5]:
df.head()
Out[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

Basic Questions¶

What are the top 5 zipcodes for 911 calls?

In [6]:
zip_codes = df['zip']
zip_codes.head()
Out[6]:
0    19525.0
1    19446.0
2    19401.0
3    19401.0
4        NaN
Name: zip, dtype: float64

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

In [7]:
town_ships = df['twp']
town_ships.head()
Out[7]:
0          NEW HANOVER
1    HATFIELD TOWNSHIP
2           NORRISTOWN
3           NORRISTOWN
4     LOWER POTTSGROVE
Name: twp, dtype: object

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

In [8]:
df['title'].nunique()
Out[8]:
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 value.

For example, if the title column value is EMS: BACK PAINS/INJURY , the Reason column value would be EMS.

In [10]:
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])
df['Reason'].head()
Out[10]:
0     EMS
1     EMS
2    Fire
3     EMS
4     EMS
Name: Reason, dtype: object

What is the most common Reason for a 911 call based off of this new column?

In [11]:
df['Reason'].value_counts()
Out[11]:
EMS        48877
Traffic    35695
Fire       14920
Name: Reason, dtype: int64

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

In [36]:
sns.countplot(x='Reason', data=df)
Out[36]:
<AxesSubplot:xlabel='Reason', ylabel='count'>

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

In [13]:
type(df['timeStamp'].iloc[0])
Out[13]:
pandas._libs.tslibs.timestamps.Timestamp

You should have seen that these timestamps are still strings. Use pd.to_datetime to convert the column from strings to DateTime objects.

In [14]:
df['timeStamp'] = pd.to_datetime(df['timeStamp'])
In [15]:
df['Hour'] = df['timeStamp'].apply(lambda time: time.hour)
df['Month'] = df['timeStamp'].apply(lambda time: time.month)
df['Day of Week'] = df['timeStamp'].apply(lambda time: time.dayofweek)
In [16]:
df.head()
Out[16]:
lat lng desc zip title timeStamp twp addr e Hour Month Day of Week 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 17 12 3 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 17 12 3 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 17 12 3 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 17 12 3 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 17 12 3 EMS

Notice how 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 [17]:
dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}
In [18]:
df['Day of Week'] = df['Day of Week'].map(dmap)

Now use seaborn to create a countplot of the Day of Week column with the hue based off of the Reason column.

In [37]:
sns.countplot(x='Day of Week', data=df, hue='Reason')
# To relocate the legend
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
Out[37]:
<matplotlib.legend.Legend at 0x7f77ae7d56a0>

Now do the same for Month:

In [38]:
sns.countplot(x='Month', data=df, hue='Reason')
# To relocate the legend
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
Out[38]:
<matplotlib.legend.Legend at 0x7f77ae6fdcd0>

Did you notice something strange about the Plot?

You should have noticed it was missing some Months, let's see if we can maybe fill in this information by plotting the information in another way, possibly a simple line plot that fills in the missing months, in order to do this, we'll need to do some work with pandas...

Now create a gropuby 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.

In [21]:
byMonth = df.groupby('Month').count()
byMonth.head()
Out[21]:
lat lng desc zip title timeStamp twp addr e Hour Day of Week Reason
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

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

In [39]:
byMonth['twp'].plot()
Out[39]:
<AxesSubplot:xlabel='Month'>

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

In [40]:
sns.lmplot(x='Month', y='twp', data=byMonth.reset_index())
Out[40]:
<seaborn.axisgrid.FacetGrid at 0x7f77ae59adc0>

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

In [25]:
df['Date'] = df['timeStamp'].apply(lambda time: time.date())

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

In [35]:
df.groupby('Date').count()['twp'].plot()
plt.tight_layout()

Now recreate this plot but create 3 separate plots with each plot representing a Reason for the 911 call

In [41]:
df[df['Reason'] == 'Traffic'].groupby('Date').count()['twp'].plot()
plt.title('Traffic')
plt.tight_layout()
In [42]:
df[df['Reason'] == 'Fire'].groupby('Date').count()['twp'].plot()
plt.title('Fire')
plt.tight_layout()
In [43]:
df[df['Reason'] == 'EMS'].groupby('Date').count()['twp'].plot()
plt.title('EMS')
plt.tight_layout()

Now let's 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 lots of ways to do this, but I would recommend trying to combine groupby with an unstack method. Reference the solutions if you get stuck on this!

In [44]:
df.head()
Out[44]:
lat lng desc zip title timeStamp twp addr e Hour Month Day of Week Reason 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 17 12 Thu EMS 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 17 12 Thu EMS 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 17 12 Thu Fire 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 17 12 Thu EMS 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 17 12 Thu EMS 2015-12-10
In [46]:
dayHour = df.groupby(by=['Day of Week','Hour']).count()['Reason'].unstack()
dayHour.head()
Out[46]:
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

5 rows × 24 columns

Now create a HeatMap using this new DataFrame.

In [48]:
plt.figure(figsize=(12,6))
sns.heatmap(dayHour, cmap='magma')
Out[48]:
<AxesSubplot:xlabel='Hour', ylabel='Day of Week'>

Now create a clustermap using this DataFrame.

In [49]:
plt.figure(figsize=(12,6))
sns.clustermap(dayHour, cmap='magma')
Out[49]:
<seaborn.matrix.ClusterGrid at 0x7f77adb3fe20>
<Figure size 864x432 with 0 Axes>

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

In [50]:
dayMonth = df.groupby(by=['Day of Week','Month']).count()['Reason'].unstack()
dayMonth.head()
Out[50]:
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
In [51]:
plt.figure(figsize=(12,6))
sns.heatmap(dayMonth, cmap='magma')
Out[51]:
<AxesSubplot:xlabel='Month', ylabel='Day of Week'>
In [52]:
plt.figure(figsize=(12,6))
sns.clustermap(dayMonth, cmap='magma')
Out[52]:
<seaborn.matrix.ClusterGrid at 0x7f77ae8034c0>
<Figure size 864x432 with 0 Axes>