├── README.md
└── python project.py


/README.md:
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1 | # Project-Python


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/python project.py:
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 1 | import pandas as pd
 2 | import matplotlib.pyplot as plt
 3 | import seaborn as sns
 4 | 
 5 | # Set Seaborn style
 6 | sns.set(style="whitegrid")
 7 | 
 8 | # Load the dataset
 9 | file_path = r"C:\Users\tanmay\Downloads\Electric_Vehicle_Data.xlsx"
10 | df = pd.read_excel(file_path)
11 | 
12 | # Display the first few rows of the dataset
13 | print("Dataset Preview:")
14 | print(df.head())
15 | 
16 | # Objective 1: Most Popular EV Makes
17 | print("\nTop 10 EV Makes:")
18 | top_makes = df['Make'].value_counts().head(10)
19 | print(top_makes)
20 | plt.figure(figsize=(10, 6))
21 | sns.barplot(x=top_makes.values, y=top_makes.index, hue=top_makes.index, palette='viridis', legend=False)
22 | plt.title('Top 10 Most Common EV Makes')
23 | plt.xlabel('Number of Vehicles')
24 | plt.ylabel('Vehicle Make')
25 | plt.tight_layout()
26 | plt.show()
27 | 
28 | 
29 | # Objective 2: Distribution of Model Years
30 | print("\nModel Year Distribution:")
31 | plt.figure(figsize=(10, 6))
32 | sns.histplot(df['Model Year'].dropna(), bins=20, kde=True, color='skyblue')
33 | plt.title('Distribution of Electric Vehicle Model Years')
34 | plt.xlabel('Model Year')
35 | plt.ylabel('Count')
36 | plt.tight_layout()
37 | plt.show()
38 | 
39 | 
40 | # Objective 3: Top Cities for EV Adoption
41 | print("\nTop 10 Cities with Most EVs:")
42 | top_cities = df['City'].value_counts().head(10)
43 | print(top_cities)
44 | plt.figure(figsize=(10, 6))
45 | sns.barplot(x=top_cities.values, y=top_cities.index, hue=top_cities.index, palette='coolwarm', legend=False)
46 | plt.title('Top 10 Cities by EV Registrations')
47 | plt.xlabel('Number of EVs')
48 | plt.ylabel('City')
49 | plt.tight_layout()
50 | plt.show()
51 | 
52 | 
53 | # Objective 4: Electric Range Analysis
54 | print("\nElectric Range Stats:")
55 | print(df['Electric Range'].describe())
56 | plt.figure(figsize=(10, 6))
57 | sns.boxplot(x=df['Electric Range'].dropna(), color='orange')
58 | plt.title('Electric Vehicle Range Distribution')
59 | plt.xlabel('Electric Range (Miles)')
60 | plt.tight_layout()
61 | plt.show()
62 | 
63 | 
64 | # Objective 5: EV Types by Fuel Type
65 | print("\nElectric Vehicle Type Counts:")
66 | fuel_counts = df['Electric Vehicle Type'].value_counts()
67 | print(fuel_counts)
68 | plt.figure(figsize=(8, 6))
69 | sns.barplot(x=fuel_counts.index, y=fuel_counts.values, hue=fuel_counts.index, palette='pastel', legend=False)
70 | plt.title('Distribution of EV Types')
71 | plt.ylabel('Number of Vehicles')
72 | plt.xlabel('Electric Vehicle Type')
73 | plt.xticks(rotation=15)
74 | plt.tight_layout()
75 | plt.show()
76 | 


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