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Electric Vehicle Population Data EDA

This dataset shows the Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) that are currently registered through Washington State Department of Licensing (DOL).

# import libraries
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt

# load data set
electric_vehicle_df =  pd.read_csv(r"C:\Users\jki\Downloads\Electric_Vehicle_Population_Data.csv")
electric_vehicle_df.head(5)

	VIN (1-10)	County	City	State	Postal Code	Model Year	Make	Model	Electric Vehicle Type	Clean Alternative Fuel Vehicle (CAFV) Eligibility	Electric Range	Base MSRP	Legislative District	DOL Vehicle ID	Vehicle Location	Electric Utility	2020 Census Tract
0	5YJYGDEF5L	Thurston	Lacey	WA	98516.0	2020	TESLA	MODEL Y	Battery Electric Vehicle (BEV)	Clean Alternative Fuel Vehicle Eligible	291	0	22.0	124535071	POINT (-122.7474291 47.0821119)	PUGET SOUND ENERGY INC	5.306701e+10
1	1N4BZ1CP1K	King	Sammamish	WA	98074.0	2019	NISSAN	LEAF	Battery Electric Vehicle (BEV)	Clean Alternative Fuel Vehicle Eligible	150	0	45.0	102359449	POINT (-122.0313266 47.6285782)	PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)	5.303303e+10
2	5YJXCDE28G	King	Kent	WA	98031.0	2016	TESLA	MODEL X	Battery Electric Vehicle (BEV)	Clean Alternative Fuel Vehicle Eligible	200	0	33.0	228682037	POINT (-122.2012521 47.3931814)	PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)	5.303303e+10
3	JHMZC5F37M	Kitsap	Poulsbo	WA	98370.0	2021	HONDA	CLARITY	Plug-in Hybrid Electric Vehicle (PHEV)	Clean Alternative Fuel Vehicle Eligible	47	0	23.0	171566447	POINT (-122.64177 47.737525)	PUGET SOUND ENERGY INC	5.303509e+10
4	WA1F2AFY4P	Thurston	Olympia	WA	98501.0	2023	AUDI	Q5 E	Plug-in Hybrid Electric Vehicle (PHEV)	Not eligible due to low battery range	23	0	22.0	234923230	POINT (-122.89692 47.043535)	PUGET SOUND ENERGY INC	5.306701e+10

# lets confirm existance of missing values
missing_values = electric_vehicle_df.isna().sum()
print(missing_values)

VIN (1-10)                                           0
County                                               1
City                                                 1
State                                                0
Postal Code                                          1
Model Year                                           0
Make                                                 0
Model                                                0
Electric Vehicle Type                                0
Clean Alternative Fuel Vehicle (CAFV) Eligibility    0
Electric Range                                       0
Base MSRP                                            0
Legislative District                                 4
DOL Vehicle ID                                       0
Vehicle Location                                     1
Electric Utility                                     1
2020 Census Tract                                    1
dtype: int64

# lets remove missing values
electric_vehicle_df.dropna(subset=['County','City','Postal Code','Legislative District','Vehicle Location','Electric Utility','2020 Census Tract'],inplace =True)
# lets confirm existance of missing values
missing_values = electric_vehicle_df.isna().sum()
print(missing_values)

VIN (1-10)                                           0
County                                               0
City                                                 0
State                                                0
Postal Code                                          0
Model Year                                           0
Make                                                 0
Model                                                0
Electric Vehicle Type                                0
Clean Alternative Fuel Vehicle (CAFV) Eligibility    0
Electric Range                                       0
Base MSRP                                            0
Legislative District                                 0
DOL Vehicle ID                                       0
Vehicle Location                                     0
Electric Utility                                     0
2020 Census Tract                                    0
dtype: int64

# do we have unncessary negative values ?

electric_vehicle_df.describe()

	Postal Code	Model Year	Electric Range	Base MSRP	Legislative District	DOL Vehicle ID	2020 Census Tract
count	65531.000000	65531.000000	65531.000000	65531.000000	65531.000000	6.553100e+04	6.553100e+04
mean	98180.385436	2020.347881	63.562940	1184.065938	32.082022	2.158943e+08	5.303401e+10
std	214.340088	2.952367	94.513025	8566.064976	14.261470	7.913640e+07	1.216387e+07
min	98001.000000	1998.000000	0.000000	0.000000	1.000000	4.777000e+03	5.300195e+10
25%	98038.000000	2018.000000	0.000000	0.000000	21.000000	1.776101e+08	5.303301e+10
50%	98109.000000	2021.000000	10.000000	0.000000	36.000000	2.215081e+08	5.303302e+10
75%	98188.000000	2023.000000	84.000000	0.000000	45.000000	2.480819e+08	5.303303e+10
max	99362.000000	2024.000000	337.000000	184400.000000	49.000000	4.789259e+08	5.307794e+10

# let have a look at the data types
electric_vehicle_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 65531 entries, 0 to 65534
Data columns (total 17 columns):
 #   Column                                             Non-Null Count  Dtype  
---  ------                                             --------------  -----  
 0   VIN (1-10)                                         65531 non-null  object 
 1   County                                             65531 non-null  object 
 2   City                                               65531 non-null  object 
 3   State                                              65531 non-null  object 
 4   Postal Code                                        65531 non-null  float64
 5   Model Year                                         65531 non-null  int64  
 6   Make                                               65531 non-null  object 
 7   Model                                              65531 non-null  object 
 8   Electric Vehicle Type                              65531 non-null  object 
 9   Clean Alternative Fuel Vehicle (CAFV) Eligibility  65531 non-null  object 
 10  Electric Range                                     65531 non-null  int64  
 11  Base MSRP                                          65531 non-null  int64  
 12  Legislative District                               65531 non-null  float64
 13  DOL Vehicle ID                                     65531 non-null  int64  
 14  Vehicle Location                                   65531 non-null  object 
 15  Electric Utility                                   65531 non-null  object 
 16  2020 Census Tract                                  65531 non-null  float64
dtypes: float64(3), int64(4), object(10)
memory usage: 9.0+ MB

# let change the date data type
electric_vehicle_df['Model Year'] = pd.to_datetime(electric_vehicle_df['Model Year'])
electric_vehicle_df['Model Year'].info()

<class 'pandas.core.series.Series'>
Int64Index: 65531 entries, 0 to 65534
Series name: Model Year
Non-Null Count  Dtype         
--------------  -----         
65531 non-null  datetime64[ns]
dtypes: datetime64[ns](1)
memory usage: 1023.9 KB

# let change the electric charge data type
electric_vehicle_df['Electric Range'] = pd.to_numeric(electric_vehicle_df['Electric Range'],errors='coerce')
electric_vehicle_df['Electric Range'].info()

<class 'pandas.core.series.Series'>
Int64Index: 65531 entries, 0 to 65534
Series name: Electric Range
Non-Null Count  Dtype
--------------  -----
65531 non-null  int64
dtypes: int64(1)
memory usage: 1023.9 KB

1.What is the average Electric range for the Vehicles

electric_vehicle_df['Electric Range'].mean()
rounded_mean = round(electric_vehicle_df['Electric Range'].mean())
print(rounded_mean)

64

2.Whats the Maximum Electric range of the Vehicles

electric_vehicle_df['Electric Range'].max()

337

3 Provide top ten counties that had the hightest number of electric vehicles used

# Load in some packages
import calendar
import warnings
import pandas as pd
import matplotlib.pyplot as plt
from itertools import combinations
from collections import Counter

# Group by 'County' and calculate the sum of 'Electric Range'
county_electric_range = electric_vehicle_df.groupby('County')['Electric Range'].sum()

# Sort the values in descending order and select the top ten
top_ten_counties = county_electric_range.sort_values(ascending=False).head(10)

# Display the result
print(top_ten_counties)

# Plot the top ten counties
top_ten_counties.plot(kind='bar', color='purple', figsize=(10, 6))
plt.title('Top Ten Counties with Most Electric Range')
plt.xlabel('County')
plt.ylabel('Total Electric Range')
plt.show()

County
King         3066482
Clark         382150
Snohomish     195511
Kitsap        193432
Thurston      156711
Jefferson      38263
Cowlitz        33711
Yakima         25979
Pierce         18402
Island         11651
Name: Electric Range, dtype: int64

4 Provide top ten Cities that had the hightest number of electric vehicles used

# Group by 'Cities' and calculate the sum of 'Electric Range'
cities_electric_range = electric_vehicle_df.groupby('City')['Electric Range'].sum()

# Sort the values in descending order and select the top ten
top_ten_cities = cities_electric_range.sort_values(ascending=False).head(10)

# Display the result
print(top_ten_cities)

# Plot the top ten Cities
top_ten_cities.plot(kind='bar', color='skyblue', figsize=(10, 6))
plt.title('Top  ten Cities that had the hightest number of electric vehicles used')
plt.xlabel('City')
plt.ylabel('Total Electric Range')
plt.show()

City
Seattle      1061779
Bellevue      375993
Vancouver     230644
Kirkland      221131
Sammamish     216944
Redmond       166695
Bothell       120145
Renton        114256
Olympia       107140
Shoreline      95226
Name: Electric Range, dtype: int64

5 Provide top ten Make that had the hightest Electric range

# Group by 'Make' and calculate the sum of 'Electric Range'
make_electric_range = electric_vehicle_df.groupby('Make')['Electric Range'].sum()

# Sort the values in descending order and select the top ten
top_ten_make = make_electric_range.sort_values(ascending=False).head(10)

# Display the result
print(top_ten_make)

# Plot the top ten Car Makes
top_ten_make.plot(kind='bar', color='skyblue', figsize=(10, 6))
plt.title('Top Ten Car Makes with Most Electric Range')
plt.xlabel('Make')
plt.ylabel('Total Electric Range')
plt.show()

Make
TESLA         2535240
NISSAN         458797
CHEVROLET      433072
KIA            135821
BMW            109367
AUDI            77393
TOYOTA          58439
VOLKSWAGEN      49897
CHRYSLER        44622
JEEP            39125
Name: Electric Range, dtype: int64

6 Provide top ten Model that had the hightest Electric range

# Group by 'Make' and calculate the sum of 'Electric Range'
model_electric_range = electric_vehicle_df.groupby('Model')['Electric Range'].sum()

# Sort the values in descending order and select the top ten
top_ten_model = model_electric_range.sort_values(ascending=False).head(10)

# Display the result
print(top_ten_model)

# Plot the top ten Car Models
top_ten_model.plot(kind='bar', color='green', figsize=(10, 6))
plt.title('Top Ten Car Models with Most Electric Range')
plt.xlabel('Model')
plt.ylabel('Total Electric Range')
plt.show()

Model
MODEL 3    1368811
MODEL S     558802
LEAF        458797
BOLT EV     353010
MODEL X     335789
MODEL Y     267138
NIRO         99290
I3           73525
VOLT         72846
E-TRON       60438
Name: Electric Range, dtype: int64

7 Provide top ten Electric Vehicle Type that had the hightest Electric range

# Group by 'Vehicle Type' and calculate the sum of 'Electric Range'
vehicle_type_electric_range = electric_vehicle_df.groupby('Electric Vehicle Type')['Electric Range'].sum()

# Sort the values in descending order and select the top ten
top_ten_vehicle_type = vehicle_type_electric_range.sort_values(ascending=False).head(10)

# Display the result
print(top_ten_vehicle_type)

# Plot the top ten counties
top_ten_vehicle_type.plot(kind='bar', color='skyblue', figsize=(10, 6))
plt.title('Top Ten Electic Type with Most Electric Range')
plt.xlabel('Electric Vehicle Type')
plt.ylabel('Total Electric Range')
plt.show()

Electric Vehicle Type
Battery Electric Vehicle (BEV)            3735456
Plug-in Hybrid Electric Vehicle (PHEV)     429887
Name: Electric Range, dtype: int64

7 Provide top ten Electric Utility that had the hightest Electric range

# Group by 'Electric Utility' and calculate the sum of 'Electric Range'
Electric_Utility_electric_range = electric_vehicle_df.groupby('Electric Utility')['Electric Range'].sum()

# Sort the values in descending order and select the top ten
top_ten_Electric_Utility= Electric_Utility_electric_range.sort_values(ascending=False).head(10)

# Display the result
print(top_ten_Electric_Utility)

# Plot the top ten Electric Utility
top_ten_Electric_Utility.plot(kind='bar', color='skyblue', figsize=(10, 6))
plt.title('Top Ten Electric Utility with Most Electric Range')
plt.xlabel('Electric Vehicle Type')
plt.ylabel('Total Electric Range')
plt.show()

Electric Utility
PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)                                            1889741
CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)                                             1179951
PUGET SOUND ENERGY INC                                                                    548023
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA)                          372264
BONNEVILLE POWER ADMINISTRATION||PUGET SOUND ENERGY INC||PUD NO 1 OF JEFFERSON COUNTY      37016
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF COWLITZ COUNTY                                33711
PACIFICORP                                                                                 23543
BONNEVILLE POWER ADMINISTRATION||PUD 1 OF SNOHOMISH COUNTY                                 11004
BONNEVILLE POWER ADMINISTRATION||PACIFICORP||PUD NO 1 OF CLARK COUNTY - (WA)                9886
BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY             9655
Name: Electric Range, dtype: int64