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City of New York Real-World Fuel Efficiency Exploratory Data Analysis¶

Research Question :¶

1.Display top 10 Car Makes with the highest Fuel Consumption¶

2.Display bottom 10 Car Makes with the highest Fuel Consumption¶

3.Display top 10 Car Models with the highest Fuel Consumption¶

4.Display bottom 10 Car Models with the highest Fuel Consumption¶

5.Display Hybrid Cars Vs Non Hybrid Cars Distribution¶

6.Display Car body type Fuel Consumption¶

7 Perform Case Studies on Outliers¶

8 .Provide conclusions after EDA¶

Datasource : https://catalog.data.gov/dataset/real-world-fuel-efficiency/resource/5668a995-6b32-45e9-b3a4-f45385695a06

In [2]:
# Load in some packages
import calendar
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")

fuel_df = pd.read_csv(r"C:\Users\jki\Downloads\Real-World_Fuel_Efficiency.csv")
fuel_df
Out[2]:
YEAR MAKE MODEL Hybrid/Non-Hybrid STANDARD TYPE EPA RATING (CITY) Vehicle Count TOTAL ACTUAL MILES TOTAL ACTUAL FUEL EPA EXPECTED FUEL ACTUAL FUEL ECONOMY Geotab PERCENT % DIFFERENCE ACTUAL TO EPA ESTIMATED FUEL COSTS PER GALLON FUEL COSTS PER MILE
0 2008 HONDA CIVIC GX Non-Hybrid SEDAN 24 9 4459 221.0 185.79 20.18 -0.16 $ 552.50 $ 0.12
1 2008 FORD CROWN VIC Non-Hybrid SEDAN 11 7 17162 1771.0 1560.18 9.69 -0.12 $ 4,427.50 $ 0.26
2 2008 CHEVROLET EXPRESS Non-Hybrid VAN 13 1 413 65.0 31.77 6.35 -0.51 $ 162.50 $ 0.39
3 2008 FORD FOCUS Non-Hybrid SEDAN 24 49 13074 793.0 544.75 16.49 -0.31 $ 1,982.50 $ 0.15
4 2008 FORD FUSION Non-Hybrid SEDAN 18 5 29482 1813.0 1637.89 16.26 -0.10 $ 4,532.50 $ 0.15
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
101 2019 CHEVROLET EXPRESS Non-Hybrid VAN 13 22 10316 885.0 793.54 11.66 -0.10 $ 2,212.50 $ 0.21
102 2019 FORD F150 Non-Hybrid PICKUP 16 27 56410 5419.0 3525.63 10.41 -0.35 $ 13,547.50 $ 0.24
103 2019 FORD F250 Non-Hybrid PICKUP 12 30 84521 5449.0 7043.42 15.51 0.29 $ 13,622.50 $ 0.16
104 2019 FREIGHTLINER SPRINTER 3500 Non-Hybrid VAN 15 38 3277 296.0 218.47 11.07 -0.26 $ 740.00 $ 0.23
105 2019 FORD TRANSIT VAN Non-Hybrid VAN 20 7 29068 2331.0 1453.40 12.47 -0.38 $ 5,827.50 $ 0.20

106 rows × 14 columns

In [2]:
fuel_df.describe()
Out[2]:
YEAR EPA RATING (CITY) Vehicle Count TOTAL ACTUAL MILES TOTAL ACTUAL FUEL EPA EXPECTED FUEL ACTUAL FUEL ECONOMY Geotab PERCENT % DIFFERENCE ACTUAL TO EPA
count 106.000000 106.000000 106.000000 1.060000e+02 106.000000 106.000000 106.000000 106.000000
mean 2013.698113 23.254717 38.320755 1.742588e+05 8470.372642 6903.022358 19.883491 -0.166321
std 3.165799 11.495806 51.139128 2.837817e+05 9212.284006 7641.761732 12.329095 0.226229
min 2008.000000 9.000000 1.000000 4.130000e+02 65.000000 31.770000 5.350000 -0.670000
25% 2011.000000 15.000000 10.000000 3.217475e+04 1765.750000 1412.797500 10.545000 -0.317500
50% 2014.000000 19.000000 20.500000 8.400450e+04 5365.500000 4511.125000 14.930000 -0.160000
75% 2016.000000 28.000000 45.750000 1.800435e+05 11911.500000 9723.207500 26.090000 -0.040000
max 2019.000000 55.000000 327.000000 2.144639e+06 44874.000000 44679.980000 54.580000 0.730000
In [3]:
# lets  check for missing 
missing_values =  fuel_df.isna().sum()
print(missing_values)

YEAR                                  0
MAKE                                  0
MODEL                                 0
Hybrid/Non-Hybrid                     0
STANDARD TYPE                         0
EPA RATING (CITY)                     0
Vehicle Count                         0
TOTAL ACTUAL MILES                    0
TOTAL ACTUAL FUEL                     0
EPA EXPECTED FUEL                     0
ACTUAL FUEL ECONOMY Geotab            0
PERCENT % DIFFERENCE ACTUAL TO EPA    0
ESTIMATED FUEL COSTS PER GALLON       0
FUEL COSTS PER MILE                   0
dtype: int64
In [27]:
# lets check on data types
fuel_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 106 entries, 0 to 105
Data columns (total 14 columns):
 #   Column                              Non-Null Count  Dtype  
---  ------                              --------------  -----  
 0   YEAR                                106 non-null    int64  
 1   MAKE                                106 non-null    object 
 2   MODEL                               106 non-null    object 
 3   Hybrid/Non-Hybrid                   106 non-null    object 
 4   STANDARD TYPE                       106 non-null    object 
 5   EPA RATING (CITY)                   106 non-null    int64  
 6   Vehicle Count                       106 non-null    int64  
 7   TOTAL ACTUAL MILES                  106 non-null    int64  
 8   TOTAL ACTUAL FUEL                   106 non-null    float64
 9   EPA EXPECTED FUEL                   106 non-null    float64
 10  ACTUAL FUEL ECONOMY Geotab          106 non-null    float64
 11  PERCENT % DIFFERENCE ACTUAL TO EPA  106 non-null    float64
 12  ESTIMATED FUEL COSTS PER GALLON     106 non-null    object 
 13  FUEL COSTS PER MILE                 106 non-null    object 
dtypes: float64(4), int64(4), object(6)
memory usage: 11.7+ KB

1 Display top 10 Car Makes with the highest Fuel Consumption¶

In [14]:
# Plot the Fuel Consumtion for each Car Model

car_make =fuel_df.groupby('MAKE').sum()['TOTAL ACTUAL FUEL'] 

# Sort the values in descending order and select the top Car Makes
top_ten_car_make= car_make.sort_values(ascending=False).head(10)

# Display the result
print(top_ten_car_make)

# Plot the top ten Countries
top_ten_car_make.plot(kind='bar', color='blue',figsize=(10, 6))
plt.title('Top Ten Car Make Fuel Consumption')
plt.xlabel('Car Make')
plt.ylabel('Fuel Consumption')
plt.show()

MAKE
FORD            477336.5
TOYOTA          222598.0
CHEVROLET       183929.0
DODGE             5199.0
NISSAN            5037.0
GMC               3243.0
FREIGHTLINER       296.0
HONDA              221.0
Name: TOTAL ACTUAL FUEL, dtype: float64

Ford has the highest Fuel Consumption

2 Display bottom 10 Car Makes with the highest Fuel Consumption¶

In [15]:
# Plot the Fuel Consumtion for each Car Model

car_make =fuel_df.groupby('MAKE').sum()['TOTAL ACTUAL FUEL'] 

# Sort the values in descending order and select the top Car Makes
bottom_ten_car_make= car_make.sort_values(ascending=True).head(10)

# Display the result
print(bottom_ten_car_make)

# Plot the top ten Countries
bottom_ten_car_make.plot(kind='bar', color='blue',figsize=(10, 6))
plt.title('Top Ten Car Make Fuel Consumption')
plt.xlabel('Car Make')
plt.ylabel('Fuel Consumption')
plt.show()

MAKE
HONDA              221.0
FREIGHTLINER       296.0
GMC               3243.0
NISSAN            5037.0
DODGE             5199.0
CHEVROLET       183929.0
TOYOTA          222598.0
FORD            477336.5
Name: TOTAL ACTUAL FUEL, dtype: float64

Honda Has the lowest Fuel Consumption

3 Display top 10 Car Models with the highest Fuel Consumption¶

In [19]:
# Plot the Fuel Consumtion for each Car Model

car_model =fuel_df.groupby('MODEL').sum()['TOTAL ACTUAL FUEL']

# Sort the values in descending order and select the top Car Makes
top_ten_car_model= car_model.sort_values(ascending=False).head(10)

# Display the result
print(top_ten_car_model)

# Plot the top ten Countries
top_ten_car_model.plot(kind='bar', color='green',figsize=(10, 6))
plt.title('Top Ten Car Model Fuel Consumption')
plt.xlabel('Car Model')
plt.ylabel('Fuel Consumption')
plt.show()

MODEL
PRIUS              145517.0
EXPRESS            118350.0
F150                99767.5
ESCAPE              89590.0
EXPLORER            41198.0
RAV4 HYBRID         40028.0
F250                38613.0
FUSION HYBRID       37974.0
TRANSIT CONNECT     27652.0
TAURUS              26022.0
Name: TOTAL ACTUAL FUEL, dtype: float64

PRIUS has the highest Fuel Consumption

4 Display bottom 10 Car Models with the highest Fuel Consumption¶

In [21]:
# Plot the Fuel Consumtion for each Car Model

car_model =fuel_df.groupby('MODEL').sum()['TOTAL ACTUAL FUEL']

# Sort the values in descending order and select the top Car Makes
bottom_ten_car_model= car_model.sort_values(ascending=True).head(10)

# Display the result
print(bottom_ten_car_model)

# Plot the top ten Countries
bottom_ten_car_model.plot(kind='bar', color='green',figsize=(10, 6))
plt.title('Bottom Ten Car Model Fuel Consumption')
plt.xlabel('Car Model')
plt.ylabel('Fuel Consumption')
plt.show()

MODEL
CIVIC GX             221.0
SPRINTER 3500        296.0
AVALON HYBRID        569.0
FOCUS                793.0
RANGER               845.0
CHARGER             1061.0
YUKON               1064.0
CROWN VIC           2061.0
YUKON XL            2179.0
SILVERADO HYBRID    4125.0
Name: TOTAL ACTUAL FUEL, dtype: float64

CIVIC GX has the lowest fuel consumption

5 Display Hybrid Cars Vs Non Hybrid Cars Distribution¶

In [24]:
# Plot the Fuel Cocumption for hybrid cars and non hybrid cars

hybrid_category =fuel_df.groupby('Hybrid/Non-Hybrid').sum()['TOTAL ACTUAL FUEL']

# Plot the pie chart
hybrid_category.plot(kind='pie', autopct='%1.1f%%', colors=['skyblue', 'lightgreen'], figsize=(8, 8))
plt.title('Fuel Consumption for Hybrid and Non-Hybrid Cars')
plt.ylabel('')  # No need for ylabel in pie chart
plt.show()

Hybrid cars are highy fuel efficent with a significant difference from Non Hybrid Vehicles

5 Display Car body type Fuel Consumption¶

In [27]:
# Plot the Fuel Consumtion for each Car Model

car_body_type =fuel_df.groupby('STANDARD TYPE').sum()['TOTAL ACTUAL FUEL']

# Sort the values in descending order and select the top Body type
top_ten_car_body_type= car_body_type.sort_values(ascending=False).head(10)

# Display the result
print(top_ten_car_body_type)

# Plot the top ten Countries
top_ten_car_body_type.plot(kind='bar', color='brown',figsize=(10, 6))
plt.title(' Car body type  Fuel Consumption')
plt.xlabel('Car body type ')
plt.ylabel('Fuel Consumption')
plt.show()

STANDARD TYPE
SUV       274136.0
SEDAN     250728.0
VAN       207768.0
PICKUP    165227.5
Name: TOTAL ACTUAL FUEL, dtype: float64

suv body types has the highest fuel consumption

Pickup body types has the lowest fuel consumption

In [4]:
fuel_df.head(5)
Out[4]:
YEAR MAKE MODEL Hybrid/Non-Hybrid STANDARD TYPE EPA RATING (CITY) Vehicle Count TOTAL ACTUAL MILES TOTAL ACTUAL FUEL EPA EXPECTED FUEL ACTUAL FUEL ECONOMY Geotab PERCENT % DIFFERENCE ACTUAL TO EPA ESTIMATED FUEL COSTS PER GALLON FUEL COSTS PER MILE
0 2008 HONDA CIVIC GX Non-Hybrid SEDAN 24 9 4459 221.0 185.79 20.18 -0.16 $ 552.50 $ 0.12
1 2008 FORD CROWN VIC Non-Hybrid SEDAN 11 7 17162 1771.0 1560.18 9.69 -0.12 $ 4,427.50 $ 0.26
2 2008 CHEVROLET EXPRESS Non-Hybrid VAN 13 1 413 65.0 31.77 6.35 -0.51 $ 162.50 $ 0.39
3 2008 FORD FOCUS Non-Hybrid SEDAN 24 49 13074 793.0 544.75 16.49 -0.31 $ 1,982.50 $ 0.15
4 2008 FORD FUSION Non-Hybrid SEDAN 18 5 29482 1813.0 1637.89 16.26 -0.10 $ 4,532.50 $ 0.15

Case Study 1: Ford¶

In [4]:
# Selecting data for Honda
Ford_data = fuel_df[fuel_df['MAKE'] == "FORD"]

# Extracting specific columns
Fordf1 = Ford_data ['Vehicle Count']
Fordf2 = Ford_data ['TOTAL ACTUAL FUEL']

# Checking if both conditions hold true
Ford_filtered_data = Ford_data[(Fordf1.notnull()) & (Fordf2.notnull())]

Ford_filtered_data.head(5)
Out[4]:
YEAR MAKE MODEL Hybrid/Non-Hybrid STANDARD TYPE EPA RATING (CITY) Vehicle Count TOTAL ACTUAL MILES TOTAL ACTUAL FUEL EPA EXPECTED FUEL ACTUAL FUEL ECONOMY Geotab PERCENT % DIFFERENCE ACTUAL TO EPA ESTIMATED FUEL COSTS PER GALLON FUEL COSTS PER MILE
1 2008 FORD CROWN VIC Non-Hybrid SEDAN 11 7 17162 1771.0 1560.18 9.69 -0.12 $ 4,427.50 $ 0.26
3 2008 FORD FOCUS Non-Hybrid SEDAN 24 49 13074 793.0 544.75 16.49 -0.31 $ 1,982.50 $ 0.15
4 2008 FORD FUSION Non-Hybrid SEDAN 18 5 29482 1813.0 1637.89 16.26 -0.10 $ 4,532.50 $ 0.15
7 2008 FORD RANGER Non-Hybrid PICKUP 15 14 8820 845.0 588.00 10.44 -0.30 $ 2,112.50 $ 0.24
9 2009 FORD CROWN VIC Non-Hybrid SEDAN 16 39 3263 290.0 203.94 11.25 -0.30 $ 725.00 $ 0.22
In [5]:
Ford_filtered_data.describe()
Out[5]:
YEAR EPA RATING (CITY) Vehicle Count TOTAL ACTUAL MILES TOTAL ACTUAL FUEL EPA EXPECTED FUEL ACTUAL FUEL ECONOMY Geotab PERCENT % DIFFERENCE ACTUAL TO EPA
count 55.00000 55.000000 55.000000 55.000000 55.000000 55.000000 55.000000 55.000000
mean 2013.60000 19.981818 29.163636 124784.927273 8678.845455 6577.763636 15.788364 -0.209818
std 3.18329 8.154733 36.182031 140896.517633 9032.667358 6769.011186 8.346563 0.221138
min 2008.00000 10.000000 5.000000 3263.000000 289.000000 203.940000 6.620000 -0.560000
25% 2011.00000 15.000000 9.000000 32502.000000 1897.000000 1449.540000 10.425000 -0.380000
50% 2014.00000 18.000000 16.000000 83488.000000 5681.000000 4538.200000 12.470000 -0.210000
75% 2016.00000 21.500000 30.500000 161921.500000 10707.750000 8714.580000 16.695000 -0.090000
max 2019.00000 43.000000 189.000000 689589.000000 40809.000000 32837.570000 41.460000 0.520000

Ford vehicle were mostly used between 2012 to 2014

In [6]:
import plotly.express as px

# Plot relationship between life expectancy and years
fig = px.bar(Ford_filtered_data, x='YEAR', y='TOTAL ACTUAL FUEL', height=320, labels={'TOTAL ACTUAL FUEL':'Fuel Consumption'}, title='Relationship between Fuel Consumption and Years - Ford')
fig.update_layout(xaxis_tickangle=-45)  # Rotate x-axis labels for better readability

# Show the plot
fig.show()

Between 2016 and 2018 Ford vehicle had the highest Fuel Consumption

Case Study 2: PRIUS¶

In [9]:
# Selecting data for Honda
PRIUS_data = fuel_df[fuel_df['MODEL'] == "PRIUS"]

# Extracting specific columns
PRIUSf1 = PRIUS_data['Vehicle Count']
PRIUSf2 = PRIUS_data['TOTAL ACTUAL FUEL']

# Checking if both conditions hold true
PRIUS_filtered_data = PRIUS_data[(PRIUSf1.notnull()) & (PRIUSf2.notnull())]

PRIUS_filtered_data.head(5)
Out[9]:
YEAR MAKE MODEL Hybrid/Non-Hybrid STANDARD TYPE EPA RATING (CITY) Vehicle Count TOTAL ACTUAL MILES TOTAL ACTUAL FUEL EPA EXPECTED FUEL ACTUAL FUEL ECONOMY Geotab PERCENT % DIFFERENCE ACTUAL TO EPA ESTIMATED FUEL COSTS PER GALLON FUEL COSTS PER MILE
6 2008 TOYOTA PRIUS Hybrid SEDAN 48 189 1034445 25453.0 21550.94 40.64 -0.15 $ 63,632.50 $ 0.06
13 2009 TOYOTA PRIUS Hybrid SEDAN 48 86 341538 8249.0 7115.38 41.40 -0.14 $ 20,622.50 $ 0.06
18 2010 TOYOTA PRIUS Hybrid SEDAN 51 84 266257 5760.0 5220.73 46.23 -0.09 $ 14,400.00 $ 0.05
26 2011 TOYOTA PRIUS Hybrid SEDAN 49 86 374960 7968.0 7652.24 47.06 -0.04 $ 19,920.00 $ 0.05
38 2012 TOYOTA PRIUS Hybrid SEDAN 49 15 55592 1184.0 1134.53 46.95 -0.04 $ 2,960.00 $ 0.05
In [10]:
import plotly.express as px

# Plot relationship between life expectancy and years
fig = px.bar(PRIUS_filtered_data, x='YEAR', y='TOTAL ACTUAL FUEL', height=320, labels={'TOTAL ACTUAL FUEL':'Fuel Consumption'}, title='Relationship between Fuel Consumption and Years - PRIUS')
fig.update_layout(xaxis_tickangle=-45)  # Rotate x-axis labels for better readability

# Show the plot
fig.show()

Prius models had the highest fuel consumption between 2015 and 2016

Case Study 3: SUV Body type¶

In [11]:
# Selecting data for Honda
SUV_data = fuel_df[fuel_df['STANDARD TYPE'] == "SUV"]

# Extracting specific columns
SUVf1 = SUV_data['Vehicle Count']
SUVf2 = SUV_data['TOTAL ACTUAL FUEL']

# Checking if both conditions hold true
SUV_filtered_data = SUV_data[(SUVf1.notnull()) & (SUVf2.notnull())]

SUV_filtered_data.head(3)
Out[11]:
YEAR MAKE MODEL Hybrid/Non-Hybrid STANDARD TYPE EPA RATING (CITY) Vehicle Count TOTAL ACTUAL MILES TOTAL ACTUAL FUEL EPA EXPECTED FUEL ACTUAL FUEL ECONOMY Geotab PERCENT % DIFFERENCE ACTUAL TO EPA ESTIMATED FUEL COSTS PER GALLON FUEL COSTS PER MILE
5 2008 TOYOTA HIGHLANDER HYBRID Hybrid SUV 27 15 36095 1667.0 1336.85 21.65 -0.20 $ 4,167.50 $ 0.12
10 2009 FORD ESCAPE Non-Hybrid SUV 19 14 3903 430.0 205.42 9.08 -0.52 $ 1,075.00 $ 0.28
11 2009 FORD ESCAPE HYBRID Hybrid SUV 34 32 180992 8729.0 5323.29 20.73 -0.39 $ 21,822.50 $ 0.12
In [12]:
# Plot relationship between life expectancy and years
fig = px.bar(SUV_filtered_data, x='YEAR', y='TOTAL ACTUAL FUEL', height=320, labels={'TOTAL ACTUAL FUEL':'Fuel Consumption'}, title='SUV Fuel Consuption Histogram - SUVS')
fig.update_layout(xaxis_tickangle=-45)  # Rotate x-axis labels for better readability

# Show the plot
fig.show()

SUVs had the highest fuel consumption between 2014 to 2016

Conclusion¶

1.Ford has the highest Fuel Consumption

2.HPRIUS has the highest Fuel Consumption

3.Honda Has the lowest Fuel Consumption

4.CIVIC GX has the lowest fuel consumption

5.Hybrid cars are highy fuel efficent with a significant difference from Non Hybrid Vehicles

6.suv body types has the highest fuel consumption

7.Pickup body types has the lowest fuel consumption

8.Between 2016 and 2018 Ford vehicle had the highest Fuel Consumption

9.Prius models had the highest fuel consumption between 2015 and 2016

10.SUVs had the highest fuel consumption between 2014 to 2016

In [ ]: