├── README.md
└── python code1.py


/README.md:
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1 | #PYTHON  PROJECT
2 | 
3 | 


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/python code1.py:
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  1 | import pandas as pd
  2 | import matplotlib.pyplot as plt
  3 | import seaborn as sns
  4 | 
  5 | # Load CSV
  6 | file_path = r"C:\Users\asus\OneDrive\Attachments\python project\Electric_Vehicle_Population_Data.csv"
  7 | df = pd.read_csv(file_path)
  8 | #gggggggghukgukg
  9 | #ukglhlihl
 10 | # Auto-detect columns
 11 | numerical_cols = df.select_dtypes(include=['int64', 'float64']).columns
 12 | categorical_cols = df.select_dtypes(include=['object', 'category']).columns
 13 | 
 14 | sns.set(style="whitegrid")
 15 | 
 16 | # 1️⃣ Scatter Plot
 17 | if len(numerical_cols) >= 2:
 18 |     plt.figure(figsize=(8, 5))
 19 |     sns.scatterplot(data=df, x=numerical_cols[0], y=numerical_cols[1])
 20 |     plt.title(f"Scatter Plot: {numerical_cols[0]} vs {numerical_cols[1]}")
 21 |     plt.tight_layout()
 22 |     plt.show()
 23 | 
 24 | # 2️⃣ Area Plot
 25 | if 'Model Year' in df.columns:
 26 |     year_data = df['Model Year'].value_counts().sort_index()
 27 |     plt.figure(figsize=(8, 5))
 28 |     plt.fill_between(year_data.index, year_data.values, color="skyblue", alpha=0.4)
 29 |     plt.plot(year_data.index, year_data.values, color="Slateblue", alpha=0.6)
 30 |     plt.title("EV Registration Over Years")
 31 |     plt.xlabel("Year")
 32 |     plt.ylabel("Number of EVs")
 33 |     plt.tight_layout()
 34 |     plt.show()
 35 | 
 36 | # 3️⃣ Heatmap
 37 | if len(numerical_cols) >= 2:
 38 |     plt.figure(figsize=(10, 6))
 39 |     corr = df[numerical_cols].corr()
 40 |     sns.heatmap(corr, annot=True, cmap='coolwarm', linewidths=0.5)
 41 |     plt.title("Correlation Heatmap")
 42 |     plt.tight_layout()
 43 |     plt.show()
 44 | 
 45 | # 4️⃣ Donut Chart
 46 | if 'Make' in df.columns:
 47 |     top_makes = df['Make'].value_counts().nlargest(5)
 48 |     plt.figure(figsize=(6, 6))
 49 |     wedges, texts, autotexts = plt.pie(top_makes, autopct='%1.1f%%', startangle=90, wedgeprops=dict(width=0.4))
 50 |     plt.title("Top 5 Vehicle Makes - Donut Chart")
 51 |     plt.legend(top_makes.index, title="Make", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))
 52 |     plt.tight_layout()
 53 |     plt.show()
 54 | 
 55 | # 5️⃣ Violin Plot
 56 | if len(numerical_cols) >= 1 and 'Electric Vehicle Type' in df.columns:
 57 |     plt.figure(figsize=(8, 5))
 58 |     sns.violinplot(data=df, x='Electric Vehicle Type', y=numerical_cols[0])
 59 |     plt.title(f"Violin Plot of {numerical_cols[0]} by Electric Vehicle Type")
 60 |     plt.xticks(rotation=45)
 61 |     plt.tight_layout()
 62 |     plt.show()
 63 | 
 64 | # 6️⃣ Countplot with hue
 65 | if 'Electric Vehicle Type' in df.columns and 'Make' in df.columns:
 66 |     top_5_makes = df['Make'].value_counts().nlargest(5).index
 67 |     filtered_df = df[df['Make'].isin(top_5_makes)]
 68 |     plt.figure(figsize=(10, 5))
 69 |     sns.countplot(data=filtered_df, x='Make', hue='Electric Vehicle Type')
 70 |     plt.title("Countplot with Hue (Make vs EV Type)")
 71 |     plt.tight_layout()
 72 |     plt.show()
 73 | 
 74 | # 7️⃣ Pairplot
 75 | if len(numerical_cols) >= 2:
 76 |     sns.pairplot(df[numerical_cols].dropna().sample(n=min(100, len(df))), diag_kind='kde')
 77 |     plt.suptitle("Pairplot of Numerical Features", y=1.02)
 78 |     plt.show()
 79 | 
 80 | # 8️⃣ Swarmplot
 81 | if len(numerical_cols) >= 1 and 'Electric Vehicle Type' in df.columns:
 82 |     plt.figure(figsize=(8, 5))
 83 |     sns.swarmplot(data=df, x='Electric Vehicle Type', y=numerical_cols[0])
 84 |     plt.title(f"Swarmplot of {numerical_cols[0]}")
 85 |     plt.xticks(rotation=45)
 86 |     plt.tight_layout()
 87 |     plt.show()
 88 | 
 89 | # 9️⃣ Stripplot
 90 | if len(numerical_cols) >= 1 and 'Electric Vehicle Type' in df.columns:
 91 |     plt.figure(figsize=(8, 5))
 92 |     sns.stripplot(data=df, x='Electric Vehicle Type', y=numerical_cols[0], jitter=True)
 93 |     plt.title(f"Stripplot of {numerical_cols[0]}")
 94 |     plt.xticks(rotation=45)
 95 |     plt.tight_layout()
 96 |     plt.show()
 97 | 
 98 | # 🔟 FINAL DASHBOARD (2x2 grid)
 99 | fig, axes = plt.subplots(2, 2, figsize=(14, 10))
100 | 
101 | # Histogram
102 | sns.histplot(df[numerical_cols[0]], kde=True, bins=30, ax=axes[0, 0])
103 | axes[0, 0].set_title(f"Histogram of {numerical_cols[0]}")
104 | 
105 | # Pie Chart
106 | if 'Make' in df.columns:
107 |     top_makes = df['Make'].value_counts().nlargest(5)
108 |     top_makes.plot.pie(autopct='%1.1f%%', ax=axes[0, 1], startangle=90)
109 |     axes[0, 1].set_title("Top 5 Vehicle Makes - Pie Chart")
110 |     axes[0, 1].set_ylabel("")
111 | 
112 | # Bar Chart
113 | if 'Electric Vehicle Type' in df.columns:
114 |     sns.countplot(data=df, x='Electric Vehicle Type', ax=axes[1, 0], order=df['Electric Vehicle Type'].value_counts().index)
115 |     axes[1, 0].set_title("Bar Chart of Electric Vehicle Types")
116 |     axes[1, 0].tick_params(axis='x', rotation=45)
117 | 
118 | # Boxplot
119 | sns.boxplot(y=df[numerical_cols[1]], ax=axes[1, 1])
120 | axes[1, 1].set_title(f"Boxplot of {numerical_cols[1]}")
121 | 
122 | plt.tight_layout()
123 | plt.show()
124 | 


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