Answer to bad-drivers questions

Author: Plamen Milenkov

days/37-39-csv-data-analsys/bad-drivers/README.md

@@ -1,7 +1,18 @@
 # Questions
 
 ## 1. States with highest number of died drivers.
+* North Dakota' with '23.9'
+* 'South Carolina' with '23.9'
+* 'West Virginia' with '23.8'
+* 'Arkansas' with '22.4'
+* 'Kentucky' with '21.4'
 
 ## 2. Is there a connection between incidents and speed.
+There is no intersection between top 5 states by collisions and by speeding.
+Hence, without pandas in use, I would say there is no connection.
+
+But there is a small intersection between collisions and drinking - North Dakota and South Carolina are the examples.
 
 ## 3. Are insurance companies paying more or less to racers.
+Only 3 of the top 10 states where incusrance companies are loosing money are from the top 10 states which are sorted by speeding.
+Hence -> I would say there is no connection between speeding and insurance losses. 

days/37-39-csv-data-analsys/bad-drivers/__main__.py

@@ -0,0 +1,22 @@
+from research import BadDriversResearch
+
+research = BadDriversResearch()
+
+# for state in research.sort_states_by_died_drivers()[:5]:
+#     print("'{}' and died '{}'".format(state.state, state.drivers_fatal_collisions))
+#
+# print("#" * 20)
+
+for state in research.sort_states_by_speed()[:10]:
+    print("'{}' and speeding '{}'".format(state.state, state.drivers_fatal_collisions))
+
+# print("#" * 20)
+#
+# for state in research.sort_states_by_alcohol()[:5]:
+#     print("'{}' and drinking '{}'".format(state.state, state.drivers_fatal_collisions))
+#
+print("#" * 20)
+
+for state in research.sort_states_by_insurance_company_loss()[:10]:
+    print("'{}' and insurance losses '{}'".format(state.state, state.drivers_fatal_collisions))
+

days/37-39-csv-data-analsys/bad-drivers/bad-drivers.csv

@@ -1 +1 @@
-State,Number of drivers involved in fatal collisions per billion miles,Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding,Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired,Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted,Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents,Car Insurance Premiums ($),Losses incurred by insurance companies for collisions per insured driver ($)Alabama,18.8,39,30,96,80,784.55,145.08Alaska,18.1,41,25,90,94,1053.48,133.93Arizona,18.6,35,28,84,96,899.47,110.35Arkansas,22.4,18,26,94,95,827.34,142.39California,12,35,28,91,89,878.41,165.63Colorado,13.6,37,28,79,95,835.5,139.91Connecticut,10.8,46,36,87,82,1068.73,167.02Delaware,16.2,38,30,87,99,1137.87,151.48District of Columbia,5.9,34,27,100,100,1273.89,136.05Florida,17.9,21,29,92,94,1160.13,144.18Georgia,15.6,19,25,95,93,913.15,142.8Hawaii,17.5,54,41,82,87,861.18,120.92Idaho,15.3,36,29,85,98,641.96,82.75Illinois,12.8,36,34,94,96,803.11,139.15Indiana,14.5,25,29,95,95,710.46,108.92Iowa,15.7,17,25,97,87,649.06,114.47Kansas,17.8,27,24,77,85,780.45,133.8Kentucky,21.4,19,23,78,76,872.51,137.13Louisiana,20.5,35,33,73,98,1281.55,194.78Maine,15.1,38,30,87,84,661.88,96.57Maryland,12.5,34,32,71,99,1048.78,192.7Massachusetts,8.2,23,35,87,80,1011.14,135.63Michigan,14.1,24,28,95,77,1110.61,152.26Minnesota,9.6,23,29,88,88,777.18,133.35Mississippi,17.6,15,31,10,100,896.07,155.77Missouri,16.1,43,34,92,84,790.32,144.45Montana,21.4,39,44,84,85,816.21,85.15Nebraska,14.9,13,35,93,90,732.28,114.82Nevada,14.7,37,32,95,99,1029.87,138.71New Hampshire,11.6,35,30,87,83,746.54,120.21New Jersey,11.2,16,28,86,78,1301.52,159.85New Mexico,18.4,19,27,67,98,869.85,120.75New York,12.3,32,29,88,80,1234.31,150.01North Carolina,16.8,39,31,94,81,708.24,127.82North Dakota,23.9,23,42,99,86,688.75,109.72Ohio,14.1,28,34,99,82,697.73,133.52Oklahoma,19.9,32,29,92,94,881.51,178.86Oregon,12.8,33,26,67,90,804.71,104.61Pennsylvania,18.2,50,31,96,88,905.99,153.86Rhode Island,11.1,34,38,92,79,1148.99,148.58South Carolina,23.9,38,41,96,81,858.97,116.29South Dakota,19.4,31,33,98,86,669.31,96.87Tennessee,19.5,21,29,82,81,767.91,155.57Texas,19.4,40,38,91,87,1004.75,156.83Utah,11.3,43,16,88,96,809.38,109.48Vermont,13.6,30,30,96,95,716.2,109.61Virginia,12.7,19,27,87,88,768.95,153.72Washington,10.6,42,33,82,86,890.03,111.62West Virginia,23.8,34,28,97,87,992.61,152.56Wisconsin,13.8,36,33,39,84,670.31,106.62Wyoming,17.4,42,32,81,90,791.14,122.04
+state,drivers_fatal_collisions,drivers_speeding,drivers_alcohol_impaired,drivers_not_distracted,drivers_first_collision,car_insurance,insurance_company_lossesAlabama,18.8,39,30,96,80,784.55,145.08Alaska,18.1,41,25,90,94,1053.48,133.93Arizona,18.6,35,28,84,96,899.47,110.35Arkansas,22.4,18,26,94,95,827.34,142.39California,12,35,28,91,89,878.41,165.63Colorado,13.6,37,28,79,95,835.5,139.91Connecticut,10.8,46,36,87,82,1068.73,167.02Delaware,16.2,38,30,87,99,1137.87,151.48District of Columbia,5.9,34,27,100,100,1273.89,136.05Florida,17.9,21,29,92,94,1160.13,144.18Georgia,15.6,19,25,95,93,913.15,142.8Hawaii,17.5,54,41,82,87,861.18,120.92Idaho,15.3,36,29,85,98,641.96,82.75Illinois,12.8,36,34,94,96,803.11,139.15Indiana,14.5,25,29,95,95,710.46,108.92Iowa,15.7,17,25,97,87,649.06,114.47Kansas,17.8,27,24,77,85,780.45,133.8Kentucky,21.4,19,23,78,76,872.51,137.13Louisiana,20.5,35,33,73,98,1281.55,194.78Maine,15.1,38,30,87,84,661.88,96.57Maryland,12.5,34,32,71,99,1048.78,192.7Massachusetts,8.2,23,35,87,80,1011.14,135.63Michigan,14.1,24,28,95,77,1110.61,152.26Minnesota,9.6,23,29,88,88,777.18,133.35Mississippi,17.6,15,31,10,100,896.07,155.77Missouri,16.1,43,34,92,84,790.32,144.45Montana,21.4,39,44,84,85,816.21,85.15Nebraska,14.9,13,35,93,90,732.28,114.82Nevada,14.7,37,32,95,99,1029.87,138.71New Hampshire,11.6,35,30,87,83,746.54,120.21New Jersey,11.2,16,28,86,78,1301.52,159.85New Mexico,18.4,19,27,67,98,869.85,120.75New York,12.3,32,29,88,80,1234.31,150.01North Carolina,16.8,39,31,94,81,708.24,127.82North Dakota,23.9,23,42,99,86,688.75,109.72Ohio,14.1,28,34,99,82,697.73,133.52Oklahoma,19.9,32,29,92,94,881.51,178.86Oregon,12.8,33,26,67,90,804.71,104.61Pennsylvania,18.2,50,31,96,88,905.99,153.86Rhode Island,11.1,34,38,92,79,1148.99,148.58South Carolina,23.9,38,41,96,81,858.97,116.29South Dakota,19.4,31,33,98,86,669.31,96.87Tennessee,19.5,21,29,82,81,767.91,155.57Texas,19.4,40,38,91,87,1004.75,156.83Utah,11.3,43,16,88,96,809.38,109.48Vermont,13.6,30,30,96,95,716.2,109.61Virginia,12.7,19,27,87,88,768.95,153.72Washington,10.6,42,33,82,86,890.03,111.62West Virginia,23.8,34,28,97,87,992.61,152.56Wisconsin,13.8,36,33,39,84,670.31,106.62Wyoming,17.4,42,32,81,90,791.14,122.04

days/37-39-csv-data-analsys/bad-drivers/research.py

@@ -0,0 +1,57 @@
+import os
+import csv
+from collections import namedtuple
+from typing import List
+
+Record = namedtuple(
+            'Record',
+            'state,drivers_fatal_collisions,drivers_speeding,drivers_alcohol_impaired,'
+            'drivers_not_distracted,drivers_first_collision,car_insurance,insurance_company_losses'
+        )
+
+class BadDriversResearch:
+
+    def __init__(self):
+        self.__data = []
+
+
+        base_folder = os.path.dirname(__file__)
+        data_file = os.path.join(base_folder, 'bad-drivers.csv')
+
+        with open(data_file) as f:
+            reader = csv.DictReader(f)
+
+            for row in reader:
+                record = self.__parse_row(row)
+                self.__data.append(record)
+
+    def __parse_row(self, row):
+        row['drivers_fatal_collisions'] = float(row['drivers_fatal_collisions'])
+        row['drivers_speeding'] = int(row['drivers_speeding'])
+        row['drivers_alcohol_impaired'] = float(row['drivers_alcohol_impaired'])
+        row['drivers_not_distracted'] = float(row['drivers_not_distracted'])
+        row['drivers_first_collision'] = float(row['drivers_first_collision'])
+        row['car_insurance'] = float(row['car_insurance'])
+        row['insurance_company_losses'] = float(row['insurance_company_losses'])
+
+        record = Record( **row )
+        return record
+
+    def get_states(self) -> List[str]:
+        for data in self.__data:
+            yield data.state
+
+    def sort_states_by_died_drivers(self) -> List[Record]:
+        return sorted(self.__data, key=lambda r: r.drivers_fatal_collisions, reverse=True)
+
+    def sort_states_by_speed(self) -> List[Record]:
+        return sorted(self.__data, key=lambda r: r.drivers_speeding, reverse=True)
+
+    def sort_states_by_alcohol(self) -> List[Record]:
+        return sorted(self.__data, key=lambda r: r.drivers_alcohol_impaired, reverse=True)
+
+    def sort_states_by_insurance_company_loss(self) -> List[Record]:
+        return sorted(self.__data, key=lambda r: r.insurance_company_losses, reverse=True)
+
+    def sort_states_by_alcohol(self) -> List[Record]:
+        return sorted(self.__data, key=lambda r: r.drivers_alcohol_impaired, reverse=True)