EV-python-project

Just completed an EDA project on electric vehicles using Python! 📊 Explored trends, cleaned real-world data, and visualized key insights.

1. Importing Required Libraries

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns

Display settings

plt.style.use('seaborn-v0_8') # Using the correct style name sns.set_style("whitegrid") plt.rcParams['figure.figsize'] = [12, 8] plt.rcParams['font.size'] = 12 plt.rcParams['axes.labelsize'] = 14 plt.rcParams['axes.titlesize'] = 16 plt.rcParams['xtick.labelsize'] = 12 plt.rcParams['ytick.labelsize'] = 12 plt.rcParams['axes.grid'] = True plt.rcParams['grid.alpha'] = 0.3 plt.rcParams['figure.dpi'] = 100

Custom color palette

custom_palette = sns.color_palette("husl", 8)

2. Loading the Dataset

df = pd.read_csv("project.csv")

3. Data Cleaning

df.dropna(inplace=True) df.drop(columns=["State"], inplace=True) df.drop_duplicates(inplace=True)

4. EV Growth Over Time

ev_growth = df.groupby("Model Year").size().reset_index(name="Number of EVs") plt.figure(figsize=(14, 7)) sns.lineplot(data=ev_growth, x="Model Year", y="Number of EVs", marker="o", linewidth=2.5, color=custom_palette[0]) plt.title("Growth of Electric Vehicles Over Time", fontsize=18, pad=20) plt.xlabel("Model Year", fontsize=14) plt.ylabel("Number of EVs Registered", fontsize=14) plt.xticks(rotation=45) plt.grid(True, alpha=0.3) plt.tight_layout() plt.show()

5. Top 10 EV Manufacturers

top_makes = df['Make'].value_counts().head(10) plt.figure(figsize=(12, 8)) ax = sns.barplot(x=top_makes.values, y=top_makes.index, palette=custom_palette) plt.title("Top 10 EV Manufacturers", fontsize=18, pad=20) plt.xlabel("Number of Vehicles", fontsize=14) plt.ylabel("Make", fontsize=14) for i, v in enumerate(top_makes.values): ax.text(v + 0.1, i, str(v), color='black', fontweight='bold') plt.tight_layout() plt.show()

6. Top 10 EV Models

top_models = df['Model'].value_counts().head(10) plt.figure(figsize=(12, 8)) ax = sns.barplot(x=top_models.values, y=top_models.index, palette=custom_palette) plt.title("Top 10 EV Models", fontsize=18, pad=20) plt.xlabel("Number of Vehicles", fontsize=14) plt.ylabel("Model", fontsize=14) for i, v in enumerate(top_models.values): ax.text(v + 0.1, i, str(v), color='black', fontweight='bold') plt.tight_layout() plt.show()

7. Distribution of EV Types

type_counts = df['Electric Vehicle Type'].value_counts() plt.figure(figsize=(10, 8)) colors = sns.color_palette("Set2", len(type_counts)) labels = [f"{label}\n({count:,} - {count/sum(type_counts)*100:.1f}%)" for label, count in type_counts.items()] plt.pie(type_counts, labels=labels, startangle=140, colors=colors, wedgeprops={'edgecolor': 'white', 'linewidth': 2}, textprops={'fontsize': 12}) plt.title("Distribution of Electric Vehicle Types", fontsize=18, pad=20) plt.axis('equal') plt.tight_layout() plt.show()

8. Top Cities by EV Count

top_cities = df['City'].value_counts().head(10) plt.figure(figsize=(12, 8)) ax = sns.barplot(x=top_cities.values, y=top_cities.index, palette=custom_palette) plt.title("Top 10 Cities by EV Count", fontsize=18, pad=20) plt.xlabel("Number of EVs", fontsize=14) plt.ylabel("City", fontsize=14) for i, v in enumerate(top_cities.values): ax.text(v + 0.1, i, str(v), color='black', fontweight='bold') plt.tight_layout() plt.show()

9. Year-over-Year Growth Rate

ev_growth["Yearly Growth Rate (%)"] = ev_growth["Number of EVs"].pct_change() * 100 plt.figure(figsize=(12, 8)) ax = sns.barplot(data=ev_growth[1:], x="Model Year", y="Yearly Growth Rate (%)", palette=custom_palette) plt.title("Year-over-Year EV Growth Rate", fontsize=18, pad=20) plt.xlabel("Model Year", fontsize=14) plt.ylabel("Growth Rate (%)", fontsize=14) plt.xticks(rotation=45) for i, v in enumerate(ev_growth[1:]["Yearly Growth Rate (%)"]): ax.text(i, v + 0.1, f"{v:.1f}%", ha='center', color='black', fontweight='bold') plt.tight_layout() plt.show()

10. Top EV Models by Type

top_models_by_type = df.groupby(['Electric Vehicle Type', 'Model']).size().reset_index(name='Count') top_models_by_type = top_models_by_type.sort_values('Count', ascending=False).groupby('Electric Vehicle Type').head(5)

plt.figure(figsize=(14, 8)) sns.barplot(data=top_models_by_type, y='Model', x='Count', hue='Electric Vehicle Type', palette=custom_palette) plt.title("Top 5 EV Models by Type (BEV vs PHEV)", fontsize=18, pad=20) plt.xlabel("Number of EVs", fontsize=14) plt.ylabel("Model", fontsize=14) plt.legend(title='Vehicle Type', title_fontsize=12, fontsize=12) plt.tight_layout() plt.show()

11. Correlation Heatmap

numerical_cols = ['Model Year', 'Electric Range', 'Base MSRP'] plt.figure(figsize=(10, 8)) sns.heatmap(df[numerical_cols].corr(), annot=True, cmap='coolwarm', fmt=".2f", linewidths=0.5, square=True) plt.title("Correlation Heatmap", fontsize=18, pad=20) plt.tight_layout() plt.show()

12. Average Electric Range Over Time

avg_range_by_year = df.groupby("Model Year")["Electric Range"].mean().reset_index()

plt.figure(figsize=(12, 8)) sns.lineplot(data=avg_range_by_year, x="Model Year", y="Electric Range", marker="o", linewidth=2.5, color=custom_palette[2]) plt.title("Average Electric Range Over Time", fontsize=18, pad=20) plt.xlabel("Model Year", fontsize=14) plt.ylabel("Average Electric Range (miles)", fontsize=14) plt.xticks(rotation=45) plt.grid(True, alpha=0.3) plt.tight_layout() plt.show()

13. Average EVs per City Over Time

avg_city_count = df.groupby(['Model Year', 'City']).size().groupby('Model Year').mean().reset_index(name='Average EVs per City')

plt.figure(figsize=(12, 8)) sns.lineplot(data=avg_city_count, x="Model Year", y="Average EVs per City", marker="o", color=custom_palette[3], linewidth=2.5) plt.title("Average EVs per City Over Time", fontsize=18, pad=20) plt.xlabel("Model Year", fontsize=14) plt.ylabel("Average Count", fontsize=14) plt.xticks(rotation=45) plt.grid(True, alpha=0.3) plt.tight_layout() plt.show()

14. Average MSRP Over Time

avg_msrp_by_year = df.groupby("Model Year")["Base MSRP"].mean().reset_index()

plt.figure(figsize=(12, 8)) sns.lineplot(data=avg_msrp_by_year, x="Model Year", y="Base MSRP", marker="o", color=custom_palette[4], linewidth=2.5) plt.title("Average MSRP Over Time", fontsize=18, pad=20) plt.xlabel("Model Year", fontsize=14) plt.ylabel("Average MSRP (USD)", fontsize=14) plt.xticks(rotation=45) plt.grid(True, alpha=0.3) plt.tight_layout() plt.show()

15. MSRP Distribution by Electric Vehicle Type

msrp_trend = df.groupby("Model Year")["Base MSRP"].mean().reset_index() plt.figure(figsize=(12, 8)) sns.lineplot(data=msrp_trend, x="Model Year", y="Base MSRP", marker="o", color=custom_palette[5], linewidth=2.5) plt.title("Average MSRP of EVs Over Time", fontsize=18, pad=20) plt.xlabel("Model Year", fontsize=14) plt.ylabel("Base MSRP (USD)", fontsize=14) plt.xticks(rotation=45) plt.grid(True, alpha=0.3) plt.tight_layout() plt.show()