├── Python.py
└── README.md


/Python.py:
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  1 | import pandas as pd
  2 | import numpy as np
  3 | import matplotlib.pyplot as plt
  4 | import seaborn as sns
  5 | 
  6 | # ---------------------------------------------
  7 | # Load the dataset
  8 | # ---------------------------------------------
  9 | df = pd.read_csv("C:\\Users\\Saksham\\Downloads\\cleaned_electric_vehicle_data ###.csv")
 10 | 
 11 | # ---------------------------------------------
 12 | # Basic Data Inspection
 13 | # ---------------------------------------------
 14 | print("\nDataset Info:")
 15 | print(df.info())
 16 | 
 17 | print("\nSummary Statistics:")
 18 | print(df.describe(include='all'))  # Removed datetime_is_numeric for compatibility
 19 | 
 20 | print("\nMissing Values in Each Column:")
 21 | print(df.isnull().sum())
 22 | 
 23 | # ---------------------------------------------
 24 | # Set the visual style
 25 | # ---------------------------------------------
 26 | sns.set(style="whitegrid")
 27 | 
 28 | # ---------------------------------------------
 29 | # Objective 1: Distribution of Electric Vehicle Types
 30 | # ---------------------------------------------
 31 | plt.figure(figsize=(10, 6))
 32 | sns.countplot(
 33 |     data=df,
 34 |     y='clean_alternative_fuel_vehicle_type',
 35 |     order=df['clean_alternative_fuel_vehicle_type'].value_counts().index,
 36 |     palette='viridis'
 37 | )
 38 | plt.title('Distribution of Electric Vehicle Types')
 39 | plt.xlabel('Count')
 40 | plt.ylabel('Vehicle Type')
 41 | plt.tight_layout()
 42 | plt.show()
 43 | 
 44 | # Donut Chart
 45 | vehicle_type_counts = df['clean_alternative_fuel_vehicle_type'].value_counts()
 46 | plt.figure(figsize=(8, 8))
 47 | colors = sns.color_palette('viridis', len(vehicle_type_counts))
 48 | plt.pie(
 49 |     vehicle_type_counts,
 50 |     labels=vehicle_type_counts.index,
 51 |     colors=colors,
 52 |     wedgeprops=dict(width=0.4),
 53 |     startangle=140,
 54 |     autopct='%1.1f%%'
 55 | )
 56 | plt.title('EV Type Distribution (Donut Chart)')
 57 | plt.tight_layout()
 58 | plt.show()
 59 | 
 60 | # ---------------------------------------------
 61 | # Objective 2: Top 10 EV Makes by Count
 62 | # ---------------------------------------------
 63 | top_makes = df['make'].value_counts().nlargest(10)
 64 | 
 65 | plt.figure(figsize=(10, 6))
 66 | sns.barplot(x=top_makes.values, y=top_makes.index, palette='crest')
 67 | plt.title('Top 10 EV Makes by Count')
 68 | plt.xlabel('Number of Vehicles')
 69 | plt.ylabel('EV Make')
 70 | plt.tight_layout()
 71 | plt.show()
 72 | 
 73 | # ---------------------------------------------
 74 | # Objective 3: Electric Range Distribution
 75 | # ---------------------------------------------
 76 | plt.figure(figsize=(10, 6))
 77 | sns.histplot(df['electric_range'], bins=50, kde=True, color='skyblue')
 78 | plt.title('Electric Range Distribution')
 79 | plt.xlabel('Electric Range (miles)')
 80 | plt.ylabel('Frequency')
 81 | plt.tight_layout()
 82 | plt.show()
 83 | 
 84 | # ---------------------------------------------
 85 | # Objective 4: EV Registration Trend by Year
 86 | # ---------------------------------------------
 87 | plt.figure(figsize=(10, 6))
 88 | sns.countplot(data=df, x='model_year', palette='flare')
 89 | plt.title('EV Registration Trend by Year')
 90 | plt.xlabel('Model Year')
 91 | plt.ylabel('Number of Registrations')
 92 | plt.xticks(rotation=45)
 93 | plt.tight_layout()
 94 | plt.show()
 95 | 
 96 | # ---------------------------------------------
 97 | # Objective 5: Top Cities with the Most EV Registration
 98 | # ---------------------------------------------
 99 | top_cities = df['city'].value_counts().nlargest(10)
100 | 
101 | plt.figure(figsize=(10, 6))
102 | sns.barplot(x=top_cities.values, y=top_cities.index, palette='magma')
103 | plt.title('Top Cities with the Most EV Registration')
104 | plt.xlabel('Number of Registrations')
105 | plt.ylabel('City')
106 | plt.tight_layout()
107 | plt.show()
108 | 


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/README.md:
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1 | uighdw
2 | int main 
3 | char a
4 | int abc
5 | .
6 | int
7 | aa
8 | 


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