You will analyzing some 911 call data from

You will analyzing some 911 call data from kaggle
(kaggle.com/mchirico/montcoalert).

-Import the necessary libraries.

-Read csv file into a dataframe

– Check the whole dataframe

-Find what are the top 5 zipcodes for 911 calls?

– What are the top 5 towships for 911 calls?

-Find unique title codes?

-In the titles column, there are “Reasons/Departments” specified
before the title code. These are EMS, Fire and Traffic. Use
.apply() with a costum 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.

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

-Use any plotting method to create a countplot 911 calls by
Reason.

-What is the data type of the objects in the timeStamp column?
If needed, use pd.to_datetime to convert to DateTime objects. Then
grab specific attributes from a Datetime object by calling
them.

-Use .apply() to create 3 new columns called Hour, Month and Day
of Week. Use any plot to countplot of the Day of Week column with
the hue based off of the Reason column.

-Repeat previous step for Month. Count calls per Month. Create a
linear fit on the number of calls per month,

-Use apply along woth the .date() to create a new column called
Date. Now groupby this Date column with the count() and create a
plot of counts for 911 calls.

-Reconstruct the dataframe so that the columns become the Hours
and the Index becomes the Day of the Week. Now create the HeatMap
using this new dataframe. Repeat this by creating clustermap.

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