911 Calls Data Capstone Project - Solutions


911 Calls Capstone Project - Solutions

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 [24]:
import numpy as np
import pandas as pd

Import visualization libraries and set %matplotlib inline.


In [25]:
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline

Read in the csv file as a dataframe called df


In [26]:
df = pd.read_csv('911.csv')

Check the info() of the df


In [27]:
df.info()


<class 'pandas.core.frame.DataFrame'>
RangeIndex: 99492 entries, 0 to 99491
Data columns (total 9 columns):
lat          99492 non-null float64
lng          99492 non-null float64
desc         99492 non-null object
zip          86637 non-null float64
title        99492 non-null object
timeStamp    99492 non-null object
twp          99449 non-null object
addr         98973 non-null object
e            99492 non-null int64
dtypes: float64(3), int64(1), object(5)
memory usage: 6.8+ MB

Check the head of df


In [28]:
df.head(3)


Out[28]:
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

Basic Questions

What are the top 5 zipcodes for 911 calls?


In [29]:
df['zip'].value_counts().head(5)


Out[29]:
19401.0    6979
19464.0    6643
19403.0    4854
19446.0    4748
19406.0    3174
Name: zip, dtype: int64

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


In [30]:
df['twp'].value_counts().head(5)


Out[30]:
LOWER MERION    8443
ABINGTON        5977
NORRISTOWN      5890
UPPER MERION    5227
CHELTENHAM      4575
Name: twp, dtype: int64

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


In [31]:
df['title'].nunique()


Out[31]:
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 [32]:
df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])

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


In [33]:
df['Reason'].value_counts()


Out[33]:
EMS        48877
Traffic    35695
Fire       14920
Name: Reason, dtype: int64

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


In [34]:
sns.countplot(x='Reason',data=df,palette='viridis')


Out[34]:
<matplotlib.axes._subplots.AxesSubplot at 0x121757b70>

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


In [35]:
type(df['timeStamp'].iloc[0])


Out[35]:
str

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


In [36]:
df['timeStamp'] = pd.to_datetime(df['timeStamp'])

You can now grab specific attributes from a Datetime object by calling them. For example:

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

You can use Jupyter's tab method to explore the various attributes you 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 these columns based off of the timeStamp column, reference the solutions if you get stuck on this step.


In [37]:
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)

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 [38]:
dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}

In [39]:
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 [40]:
sns.countplot(x='Day of Week',data=df,hue='Reason',palette='viridis')

# To relocate the legend
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)


Out[40]:
<matplotlib.legend.Legend at 0x121762710>

Now do the same for Month:


In [41]:
sns.countplot(x='Month',data=df,hue='Reason',palette='viridis')

# To relocate the legend
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)


Out[41]:
<matplotlib.legend.Legend at 0x11fa7ad68>

Did you notice something strange about the Plot?


In [42]:
# It is missing some months! 9,10, and 11 are not there.

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 [43]:
byMonth = df.groupby('Month').count()
byMonth.head()


Out[43]:
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

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


In [44]:
# Could be any column
byMonth['twp'].plot()


Out[44]:
<matplotlib.axes._subplots.AxesSubplot at 0x11fa06630>

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 [45]:
sns.lmplot(x='Month',y='twp',data=byMonth.reset_index())


Out[45]:
<seaborn.axisgrid.FacetGrid at 0x11bf002b0>

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 [46]:
df['Date']=df['timeStamp'].apply(lambda t: t.date())

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


In [47]:
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 [48]:
df[df['Reason']=='Traffic'].groupby('Date').count()['twp'].plot()
plt.title('Traffic')
plt.tight_layout()



In [49]:
df[df['Reason']=='Fire'].groupby('Date').count()['twp'].plot()
plt.title('Fire')
plt.tight_layout()



In [50]:
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 [51]:
dayHour = df.groupby(by=['Day of Week','Hour']).count()['Reason'].unstack()
dayHour.head()


Out[51]:
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 [52]:
plt.figure(figsize=(12,6))
sns.heatmap(dayHour,cmap='viridis')


Out[52]:
<matplotlib.axes._subplots.AxesSubplot at 0x12305acf8>

Now create a clustermap using this DataFrame.


In [53]:
sns.clustermap(dayHour,cmap='viridis')


Out[53]:
<seaborn.matrix.ClusterGrid at 0x103276748>

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


In [54]:
dayMonth = df.groupby(by=['Day of Week','Month']).count()['Reason'].unstack()
dayMonth.head()


Out[54]:
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 [55]:
plt.figure(figsize=(12,6))
sns.heatmap(dayMonth,cmap='viridis')


Out[55]:
<matplotlib.axes._subplots.AxesSubplot at 0x11bcabf98>

In [56]:
sns.clustermap(dayMonth,cmap='viridis')


Out[56]:
<seaborn.matrix.ClusterGrid at 0x120341e80>

Continue exploring the Data however you see fit!

Great Job!