├── README.md
├── ums python.txt
└── ums python2.txt


/README.md:
--------------------------------------------------------------------------------
  1 | # EV-python-project
  2 | Just completed an EDA project on electric vehicles using Python! 📊 Explored trends, cleaned real-world data, and visualized key insights.
  3 | # 1. Importing Required Libraries
  4 | import numpy as np
  5 | import pandas as pd
  6 | import matplotlib.pyplot as plt
  7 | import seaborn as sns
  8 | 
  9 | # Display settings
 10 | plt.style.use('seaborn-v0_8')  # Using the correct style name
 11 | sns.set_style("whitegrid")
 12 | plt.rcParams['figure.figsize'] = [12, 8]
 13 | plt.rcParams['font.size'] = 12
 14 | plt.rcParams['axes.labelsize'] = 14
 15 | plt.rcParams['axes.titlesize'] = 16
 16 | plt.rcParams['xtick.labelsize'] = 12
 17 | plt.rcParams['ytick.labelsize'] = 12
 18 | plt.rcParams['axes.grid'] = True
 19 | plt.rcParams['grid.alpha'] = 0.3
 20 | plt.rcParams['figure.dpi'] = 100
 21 | 
 22 | # Custom color palette
 23 | custom_palette = sns.color_palette("husl", 8)
 24 | 
 25 | # 2. Loading the Dataset
 26 | df = pd.read_csv("project.csv")
 27 | 
 28 | # 3. Data Cleaning
 29 | df.dropna(inplace=True)
 30 | df.drop(columns=["State"], inplace=True)
 31 | df.drop_duplicates(inplace=True)
 32 | 
 33 | # 4. EV Growth Over Time
 34 | ev_growth = df.groupby("Model Year").size().reset_index(name="Number of EVs")
 35 | plt.figure(figsize=(14, 7))
 36 | sns.lineplot(data=ev_growth, x="Model Year", y="Number of EVs", 
 37 |              marker="o", linewidth=2.5, color=custom_palette[0])
 38 | plt.title("Growth of Electric Vehicles Over Time", fontsize=18, pad=20)
 39 | plt.xlabel("Model Year", fontsize=14)
 40 | plt.ylabel("Number of EVs Registered", fontsize=14)
 41 | plt.xticks(rotation=45)
 42 | plt.grid(True, alpha=0.3)
 43 | plt.tight_layout()
 44 | plt.show()
 45 | 
 46 | # 5. Top 10 EV Manufacturers
 47 | top_makes = df['Make'].value_counts().head(10)
 48 | plt.figure(figsize=(12, 8))
 49 | ax = sns.barplot(x=top_makes.values, y=top_makes.index, palette=custom_palette)
 50 | plt.title("Top 10 EV Manufacturers", fontsize=18, pad=20)
 51 | plt.xlabel("Number of Vehicles", fontsize=14)
 52 | plt.ylabel("Make", fontsize=14)
 53 | for i, v in enumerate(top_makes.values):
 54 |     ax.text(v + 0.1, i, str(v), color='black', fontweight='bold')
 55 | plt.tight_layout()
 56 | plt.show()
 57 | 
 58 | # 6. Top 10 EV Models
 59 | top_models = df['Model'].value_counts().head(10)
 60 | plt.figure(figsize=(12, 8))
 61 | ax = sns.barplot(x=top_models.values, y=top_models.index, palette=custom_palette)
 62 | plt.title("Top 10 EV Models", fontsize=18, pad=20)
 63 | plt.xlabel("Number of Vehicles", fontsize=14)
 64 | plt.ylabel("Model", fontsize=14)
 65 | for i, v in enumerate(top_models.values):
 66 |     ax.text(v + 0.1, i, str(v), color='black', fontweight='bold')
 67 | plt.tight_layout()
 68 | plt.show()
 69 | 
 70 | # 7. Distribution of EV Types
 71 | type_counts = df['Electric Vehicle Type'].value_counts()
 72 | plt.figure(figsize=(10, 8))
 73 | colors = sns.color_palette("Set2", len(type_counts))
 74 | labels = [f"{label}\n({count:,} - {count/sum(type_counts)*100:.1f}%)" for label, count in type_counts.items()]
 75 | plt.pie(type_counts, labels=labels, startangle=140, colors=colors, 
 76 |         wedgeprops={'edgecolor': 'white', 'linewidth': 2}, 
 77 |         textprops={'fontsize': 12})
 78 | plt.title("Distribution of Electric Vehicle Types", fontsize=18, pad=20)
 79 | plt.axis('equal')
 80 | plt.tight_layout()
 81 | plt.show()
 82 | 
 83 | # 8. Top Cities by EV Count
 84 | top_cities = df['City'].value_counts().head(10)
 85 | plt.figure(figsize=(12, 8))
 86 | ax = sns.barplot(x=top_cities.values, y=top_cities.index, palette=custom_palette)
 87 | plt.title("Top 10 Cities by EV Count", fontsize=18, pad=20)
 88 | plt.xlabel("Number of EVs", fontsize=14)
 89 | plt.ylabel("City", fontsize=14)
 90 | for i, v in enumerate(top_cities.values):
 91 |     ax.text(v + 0.1, i, str(v), color='black', fontweight='bold')
 92 | plt.tight_layout()
 93 | plt.show()
 94 | 
 95 | # 9. Year-over-Year Growth Rate
 96 | ev_growth["Yearly Growth Rate (%)"] = ev_growth["Number of EVs"].pct_change() * 100
 97 | plt.figure(figsize=(12, 8))
 98 | ax = sns.barplot(data=ev_growth[1:], x="Model Year", y="Yearly Growth Rate (%)", 
 99 |                 palette=custom_palette)
100 | plt.title("Year-over-Year EV Growth Rate", fontsize=18, pad=20)
101 | plt.xlabel("Model Year", fontsize=14)
102 | plt.ylabel("Growth Rate (%)", fontsize=14)
103 | plt.xticks(rotation=45)
104 | for i, v in enumerate(ev_growth[1:]["Yearly Growth Rate (%)"]):
105 |     ax.text(i, v + 0.1, f"{v:.1f}%", ha='center', color='black', fontweight='bold')
106 | plt.tight_layout()
107 | plt.show()
108 | 
109 | # 10. Top EV Models by Type
110 | top_models_by_type = df.groupby(['Electric Vehicle Type', 'Model']).size().reset_index(name='Count')
111 | top_models_by_type = top_models_by_type.sort_values('Count', ascending=False).groupby('Electric Vehicle Type').head(5)
112 | 
113 | plt.figure(figsize=(14, 8))
114 | sns.barplot(data=top_models_by_type, y='Model', x='Count', 
115 |            hue='Electric Vehicle Type', palette=custom_palette)
116 | plt.title("Top 5 EV Models by Type (BEV vs PHEV)", fontsize=18, pad=20)
117 | plt.xlabel("Number of EVs", fontsize=14)
118 | plt.ylabel("Model", fontsize=14)
119 | plt.legend(title='Vehicle Type', title_fontsize=12, fontsize=12)
120 | plt.tight_layout()
121 | plt.show()
122 | 
123 | # 11. Correlation Heatmap
124 | numerical_cols = ['Model Year', 'Electric Range', 'Base MSRP']
125 | plt.figure(figsize=(10, 8))
126 | sns.heatmap(df[numerical_cols].corr(), annot=True, cmap='coolwarm', 
127 |            fmt=".2f", linewidths=0.5, square=True)
128 | plt.title("Correlation Heatmap", fontsize=18, pad=20)
129 | plt.tight_layout()
130 | plt.show()
131 | 
132 | # 12. Average Electric Range Over Time
133 | avg_range_by_year = df.groupby("Model Year")["Electric Range"].mean().reset_index()
134 | 
135 | plt.figure(figsize=(12, 8))
136 | sns.lineplot(data=avg_range_by_year, x="Model Year", y="Electric Range", 
137 |             marker="o", linewidth=2.5, color=custom_palette[2])
138 | plt.title("Average Electric Range Over Time", fontsize=18, pad=20)
139 | plt.xlabel("Model Year", fontsize=14)
140 | plt.ylabel("Average Electric Range (miles)", fontsize=14)
141 | plt.xticks(rotation=45)
142 | plt.grid(True, alpha=0.3)
143 | plt.tight_layout()
144 | plt.show()
145 | 
146 | # 13. Average EVs per City Over Time
147 | avg_city_count = df.groupby(['Model Year', 'City']).size().groupby('Model Year').mean().reset_index(name='Average EVs per City')
148 | 
149 | plt.figure(figsize=(12, 8))
150 | sns.lineplot(data=avg_city_count, x="Model Year", y="Average EVs per City", 
151 |             marker="o", color=custom_palette[3], linewidth=2.5)
152 | plt.title("Average EVs per City Over Time", fontsize=18, pad=20)
153 | plt.xlabel("Model Year", fontsize=14)
154 | plt.ylabel("Average Count", fontsize=14)
155 | plt.xticks(rotation=45)
156 | plt.grid(True, alpha=0.3)
157 | plt.tight_layout()
158 | plt.show()
159 | 
160 | # 14. Average MSRP Over Time
161 | avg_msrp_by_year = df.groupby("Model Year")["Base MSRP"].mean().reset_index()
162 | 
163 | plt.figure(figsize=(12, 8))
164 | sns.lineplot(data=avg_msrp_by_year, x="Model Year", y="Base MSRP", 
165 |             marker="o", color=custom_palette[4], linewidth=2.5)
166 | plt.title("Average MSRP Over Time", fontsize=18, pad=20)
167 | plt.xlabel("Model Year", fontsize=14)
168 | plt.ylabel("Average MSRP (USD)", fontsize=14)
169 | plt.xticks(rotation=45)
170 | plt.grid(True, alpha=0.3)
171 | plt.tight_layout()
172 | plt.show()
173 | 
174 | # 15. MSRP Distribution by Electric Vehicle Type
175 | msrp_trend = df.groupby("Model Year")["Base MSRP"].mean().reset_index()
176 | plt.figure(figsize=(12, 8))
177 | sns.lineplot(data=msrp_trend, x="Model Year", y="Base MSRP", 
178 |             marker="o", color=custom_palette[5], linewidth=2.5)
179 | plt.title("Average MSRP of EVs Over Time", fontsize=18, pad=20)
180 | plt.xlabel("Model Year", fontsize=14)
181 | plt.ylabel("Base MSRP (USD)", fontsize=14)
182 | plt.xticks(rotation=45)
183 | plt.grid(True, alpha=0.3)
184 | plt.tight_layout()
185 | plt.show()
186 | 
187 | 
188 | 
189 | 


--------------------------------------------------------------------------------
/ums python.txt:
--------------------------------------------------------------------------------
  1 | # 1. Importing Required Libraries
  2 | import numpy as np
  3 | import pandas as pd
  4 | import matplotlib.pyplot as plt
  5 | import seaborn as sns
  6 | 
  7 | # Display settings
  8 | plt.style.use('seaborn-v0_8')  # Using the correct style name
  9 | sns.set_style("whitegrid")
 10 | plt.rcParams['figure.figsize'] = [12, 8]
 11 | plt.rcParams['font.size'] = 12
 12 | plt.rcParams['axes.labelsize'] = 14
 13 | plt.rcParams['axes.titlesize'] = 16
 14 | plt.rcParams['xtick.labelsize'] = 12
 15 | plt.rcParams['ytick.labelsize'] = 12
 16 | plt.rcParams['axes.grid'] = True
 17 | plt.rcParams['grid.alpha'] = 0.3
 18 | plt.rcParams['figure.dpi'] = 100
 19 | 
 20 | # Custom color palette
 21 | custom_palette = sns.color_palette("husl", 8)
 22 | 
 23 | # 2. Loading the Dataset
 24 | df = pd.read_csv("project.csv")
 25 | 
 26 | # 3. Data Cleaning
 27 | df.dropna(inplace=True)
 28 | df.drop(columns=["State"], inplace=True)
 29 | df.drop_duplicates(inplace=True)
 30 | 
 31 | # 4. EV Growth Over Time
 32 | ev_growth = df.groupby("Model Year").size().reset_index(name="Number of EVs")
 33 | plt.figure(figsize=(14, 7))
 34 | sns.lineplot(data=ev_growth, x="Model Year", y="Number of EVs", 
 35 |              marker="o", linewidth=2.5, color=custom_palette[0])
 36 | plt.title("Growth of Electric Vehicles Over Time", fontsize=18, pad=20)
 37 | plt.xlabel("Model Year", fontsize=14)
 38 | plt.ylabel("Number of EVs Registered", fontsize=14)
 39 | plt.xticks(rotation=45)
 40 | plt.grid(True, alpha=0.3)
 41 | plt.tight_layout()
 42 | plt.show()
 43 | 
 44 | # 5. Top 10 EV Manufacturers
 45 | top_makes = df['Make'].value_counts().head(10)
 46 | plt.figure(figsize=(12, 8))
 47 | ax = sns.barplot(x=top_makes.values, y=top_makes.index, palette=custom_palette)
 48 | plt.title("Top 10 EV Manufacturers", fontsize=18, pad=20)
 49 | plt.xlabel("Number of Vehicles", fontsize=14)
 50 | plt.ylabel("Make", fontsize=14)
 51 | for i, v in enumerate(top_makes.values):
 52 |     ax.text(v + 0.1, i, str(v), color='black', fontweight='bold')
 53 | plt.tight_layout()
 54 | plt.show()
 55 | 
 56 | # 6. Top 10 EV Models
 57 | top_models = df['Model'].value_counts().head(10)
 58 | plt.figure(figsize=(12, 8))
 59 | ax = sns.barplot(x=top_models.values, y=top_models.index, palette=custom_palette)
 60 | plt.title("Top 10 EV Models", fontsize=18, pad=20)
 61 | plt.xlabel("Number of Vehicles", fontsize=14)
 62 | plt.ylabel("Model", fontsize=14)
 63 | for i, v in enumerate(top_models.values):
 64 |     ax.text(v + 0.1, i, str(v), color='black', fontweight='bold')
 65 | plt.tight_layout()
 66 | plt.show()
 67 | 
 68 | # 7. Distribution of EV Types
 69 | type_counts = df['Electric Vehicle Type'].value_counts()
 70 | plt.figure(figsize=(10, 8))
 71 | colors = sns.color_palette("Set2", len(type_counts))
 72 | labels = [f"{label}\n({count:,} - {count/sum(type_counts)*100:.1f}%)" for label, count in type_counts.items()]
 73 | plt.pie(type_counts, labels=labels, startangle=140, colors=colors, 
 74 |         wedgeprops={'edgecolor': 'white', 'linewidth': 2}, 
 75 |         textprops={'fontsize': 12})
 76 | plt.title("Distribution of Electric Vehicle Types", fontsize=18, pad=20)
 77 | plt.axis('equal')
 78 | plt.tight_layout()
 79 | plt.show()
 80 | 
 81 | # 8. Top Cities by EV Count
 82 | top_cities = df['City'].value_counts().head(10)
 83 | plt.figure(figsize=(12, 8))
 84 | ax = sns.barplot(x=top_cities.values, y=top_cities.index, palette=custom_palette)
 85 | plt.title("Top 10 Cities by EV Count", fontsize=18, pad=20)
 86 | plt.xlabel("Number of EVs", fontsize=14)
 87 | plt.ylabel("City", fontsize=14)
 88 | for i, v in enumerate(top_cities.values):
 89 |     ax.text(v + 0.1, i, str(v), color='black', fontweight='bold')
 90 | plt.tight_layout()
 91 | plt.show()
 92 | 
 93 | # 9. Year-over-Year Growth Rate
 94 | ev_growth["Yearly Growth Rate (%)"] = ev_growth["Number of EVs"].pct_change() * 100
 95 | plt.figure(figsize=(12, 8))
 96 | ax = sns.barplot(data=ev_growth[1:], x="Model Year", y="Yearly Growth Rate (%)", 
 97 |                 palette=custom_palette)
 98 | plt.title("Year-over-Year EV Growth Rate", fontsize=18, pad=20)
 99 | plt.xlabel("Model Year", fontsize=14)
100 | plt.ylabel("Growth Rate (%)", fontsize=14)
101 | plt.xticks(rotation=45)
102 | for i, v in enumerate(ev_growth[1:]["Yearly Growth Rate (%)"]):
103 |     ax.text(i, v + 0.1, f"{v:.1f}%", ha='center', color='black', fontweight='bold')
104 | plt.tight_layout()
105 | plt.show()
106 | 
107 | # 10. Top EV Models by Type
108 | top_models_by_type = df.groupby(['Electric Vehicle Type', 'Model']).size().reset_index(name='Count')
109 | top_models_by_type = top_models_by_type.sort_values('Count', ascending=False).groupby('Electric Vehicle Type').head(5)
110 | 
111 | plt.figure(figsize=(14, 8))
112 | sns.barplot(data=top_models_by_type, y='Model', x='Count', 
113 |            hue='Electric Vehicle Type', palette=custom_palette)
114 | plt.title("Top 5 EV Models by Type (BEV vs PHEV)", fontsize=18, pad=20)
115 | plt.xlabel("Number of EVs", fontsize=14)
116 | plt.ylabel("Model", fontsize=14)
117 | plt.legend(title='Vehicle Type', title_fontsize=12, fontsize=12)
118 | plt.tight_layout()
119 | plt.show()
120 | 
121 | # 11. Correlation Heatmap
122 | numerical_cols = ['Model Year', 'Electric Range', 'Base MSRP']
123 | plt.figure(figsize=(10, 8))
124 | sns.heatmap(df[numerical_cols].corr(), annot=True, cmap='coolwarm', 
125 |            fmt=".2f", linewidths=0.5, square=True)
126 | plt.title("Correlation Heatmap", fontsize=18, pad=20)
127 | plt.tight_layout()
128 | plt.show()
129 | 
130 | # 12. Average Electric Range Over Time
131 | avg_range_by_year = df.groupby("Model Year")["Electric Range"].mean().reset_index()
132 | 
133 | plt.figure(figsize=(12, 8))
134 | sns.lineplot(data=avg_range_by_year, x="Model Year", y="Electric Range", 
135 |             marker="o", linewidth=2.5, color=custom_palette[2])
136 | plt.title("Average Electric Range Over Time", fontsize=18, pad=20)
137 | plt.xlabel("Model Year", fontsize=14)
138 | plt.ylabel("Average Electric Range (miles)", fontsize=14)
139 | plt.xticks(rotation=45)
140 | plt.grid(True, alpha=0.3)
141 | plt.tight_layout()
142 | plt.show()
143 | 
144 | # 13. Average EVs per City Over Time
145 | avg_city_count = df.groupby(['Model Year', 'City']).size().groupby('Model Year').mean().reset_index(name='Average EVs per City')
146 | 
147 | plt.figure(figsize=(12, 8))
148 | sns.lineplot(data=avg_city_count, x="Model Year", y="Average EVs per City", 
149 |             marker="o", color=custom_palette[3], linewidth=2.5)
150 | plt.title("Average EVs per City Over Time", fontsize=18, pad=20)
151 | plt.xlabel("Model Year", fontsize=14)
152 | plt.ylabel("Average Count", fontsize=14)
153 | plt.xticks(rotation=45)
154 | plt.grid(True, alpha=0.3)
155 | plt.tight_layout()
156 | plt.show()
157 | 
158 | # 14. Average MSRP Over Time
159 | avg_msrp_by_year = df.groupby("Model Year")["Base MSRP"].mean().reset_index()
160 | 
161 | plt.figure(figsize=(12, 8))
162 | sns.lineplot(data=avg_msrp_by_year, x="Model Year", y="Base MSRP", 
163 |             marker="o", color=custom_palette[4], linewidth=2.5)
164 | plt.title("Average MSRP Over Time", fontsize=18, pad=20)
165 | plt.xlabel("Model Year", fontsize=14)
166 | plt.ylabel("Average MSRP (USD)", fontsize=14)
167 | plt.xticks(rotation=45)
168 | plt.grid(True, alpha=0.3)
169 | plt.tight_layout()
170 | plt.show()
171 | 
172 | # 15. MSRP Distribution by Electric Vehicle Type
173 | msrp_trend = df.groupby("Model Year")["Base MSRP"].mean().reset_index()
174 | plt.figure(figsize=(12, 8))
175 | sns.lineplot(data=msrp_trend, x="Model Year", y="Base MSRP", 
176 |             marker="o", color=custom_palette[5], linewidth=2.5)
177 | plt.title("Average MSRP of EVs Over Time", fontsize=18, pad=20)
178 | plt.xlabel("Model Year", fontsize=14)
179 | plt.ylabel("Base MSRP (USD)", fontsize=14)
180 | plt.xticks(rotation=45)
181 | plt.grid(True, alpha=0.3)
182 | plt.tight_layout()
183 | plt.show()
184 | 
185 | 
186 | 
187 | 
188 | 			


--------------------------------------------------------------------------------
/ums python2.txt:
--------------------------------------------------------------------------------
  1 | 
  2 | import streamlit as st
  3 | import pandas as pd
  4 | import numpy as np
  5 | import matplotlib.pyplot as plt
  6 | import seaborn as sns
  7 | from datetime import datetime
  8 | 
  9 | # Set style and color palette
 10 | plt.style.use('seaborn-v0_8')
 11 | sns.set_style("whitegrid")
 12 | plt.rcParams['figure.figsize'] = [12, 8]
 13 | plt.rcParams['font.size'] = 12
 14 | plt.rcParams['axes.labelsize'] = 14
 15 | plt.rcParams['axes.titlesize'] = 16
 16 | plt.rcParams['xtick.labelsize'] = 12
 17 | plt.rcParams['ytick.labelsize'] = 12
 18 | plt.rcParams['axes.grid'] = True
 19 | plt.rcParams['grid.alpha'] = 0.3
 20 | plt.rcParams['figure.dpi'] = 100
 21 | 
 22 | # Custom color palette
 23 | custom_palette = sns.color_palette("husl", 8)
 24 | 
 25 | # Set page configuration
 26 | st.set_page_config(
 27 |     page_title="Electric Vehicle Analytics Dashboard",
 28 |     page_icon="🚗",
 29 |     layout="wide",
 30 |     initial_sidebar_state="expanded"
 31 | )
 32 | 
 33 | # Custom styling
 34 | st.markdown("""
 35 |     <style>
 36 |     /* Main container styling */
 37 |     .main {
 38 |         padding: 2rem;
 39 |         color: #1a202c;
 40 |     }
 41 |     
 42 |     /* Metric card styling */
 43 |     .stMetric {
 44 |         background-color: #ffffff;
 45 |         padding: 1.5rem;
 46 |         border-radius: 0.8rem;
 47 |         box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
 48 |         border: 1px solid #e2e8f0;
 49 |     }
 50 |     .stMetric:hover {
 51 |         transform: translateY(-2px);
 52 |         transition: all 0.3s ease;
 53 |     }
 54 |     .stMetric label {
 55 |         color: #4a5568 !important;
 56 |         font-size: 1rem !important;
 57 |         font-weight: 500 !important;
 58 |     }
 59 |     .stMetric .css-1wivap2 {
 60 |         color: #2d3748 !important;
 61 |         font-size: 1.8rem !important;
 62 |         font-weight: 600 !important;
 63 |     }
 64 |     
 65 |     /* Sidebar styling */
 66 |     .stSidebar {
 67 |         background-color: #ffffff;
 68 |         border-right: 1px solid #e2e8f0;
 69 |     }
 70 |     .stSidebar [data-testid="stSidebarNav"] {
 71 |         padding-top: 2rem;
 72 |     }
 73 |     
 74 |     /* Headers and text styling */
 75 |     h1 {
 76 |         color: #1a202c !important;
 77 |         font-weight: 700 !important;
 78 |     }
 79 |     h2 {
 80 |         color: #2d3748 !important;
 81 |         font-weight: 600 !important;
 82 |     }
 83 |     h3 {
 84 |         color: #4a5568 !important;
 85 |         font-weight: 600 !important;
 86 |     }
 87 |     p {
 88 |         color: #4a5568 !important;
 89 |     }
 90 |     
 91 |     /* Expander styling */
 92 |     div[data-testid="stExpander"] {
 93 |         border: 1px solid #e2e8f0;
 94 |         border-radius: 0.5rem;
 95 |         margin-bottom: 1rem;
 96 |         background-color: #ffffff;
 97 |     }
 98 |     
 99 |     /* Filter section styling */
100 |     .stSlider {
101 |         padding: 1rem 0;
102 |     }
103 |     .stSlider label {
104 |         color: #4a5568 !important;
105 |     }
106 |     
107 |     /* Section dividers */
108 |     .section-divider {
109 |         margin: 2rem 0;
110 |         border-top: 1px solid #e2e8f0;
111 |     }
112 |     
113 |     /* Chart containers */
114 |     div[data-testid="stBlock"] {
115 |         background-color: #ffffff;
116 |         padding: 1rem;
117 |         border-radius: 0.5rem;
118 |         border: 1px solid #e2e8f0;
119 |         margin-bottom: 1rem;
120 |     }
121 |     
122 |     /* Button styling */
123 |     .stButton button {
124 |         background-color: #3182ce;
125 |         color: #ffffff;
126 |         border-radius: 0.5rem;
127 |     }
128 |     .stButton button:hover {
129 |         background-color: #2c5282;
130 |     }
131 |     
132 |     /* Multiselect styling */
133 |     .stMultiSelect {
134 |         color: #4a5568;
135 |     }
136 |     
137 |     /* Caption text */
138 |     .caption-text {
139 |         color: #718096 !important;
140 |         font-size: 0.875rem !important;
141 |     }
142 |     
143 |     /* Alert/Info boxes */
144 |     .stAlert {
145 |         background-color: #ebf8ff;
146 |         border: 1px solid #bee3f8;
147 |         color: #2c5282;
148 |         border-radius: 0.5rem;
149 |         padding: 1rem;
150 |     }
151 |     </style>
152 |     """, unsafe_allow_html=True)
153 | 
154 | # Define helper functions
155 | def filter_summary(years, types, price_range, range_values):
156 |     st.markdown(f"""
157 |     <div style='background-color: #f7fafc; padding: 1.2rem; border-radius: 0.8rem; border: 1px solid #e2e8f0;'>
158 |         <h4 style='margin: 0 0 0.8rem 0; color: #2d3748; font-weight: 600;'>Active Filters</h4>
159 |         <ul style='margin: 0; padding-left: 1.2rem; color: #4a5568; font-size: 0.95rem;'>
160 |             <li style='margin-bottom: 0.4rem;'>Years: {years[0]}-{years[1]}</li>
161 |             <li style='margin-bottom: 0.4rem;'>Types: {len(types)} selected</li>
162 |             <li style='margin-bottom: 0.4rem;'>Price: ${price_range[0]:,.0f}-${price_range[1]:,.0f}</li>
163 |             <li>Range: {range_values[0]}-{range_values[1]} miles</li>
164 |         </ul>
165 |     </div>
166 |     """, unsafe_allow_html=True)
167 | 
168 | def section_header(title, description):
169 |     st.markdown(f"""
170 |     <div style='margin-bottom: 1.5rem; background-color: #ffffff; padding: 1.2rem; border-radius: 0.8rem; border: 1px solid #e2e8f0;'>
171 |         <h3 style='color: #2d3748; margin-bottom: 0.5rem; font-weight: 600;'>{title}</h3>
172 |         <p style='color: #4a5568; font-size: 0.95rem; margin: 0; line-height: 1.5;'>{description}</p>
173 |     </div>
174 |     """, unsafe_allow_html=True)
175 | 
176 | def footer():
177 |     st.markdown(f"""
178 |     <div style='text-align: center; padding: 2rem; background-color: #f7fafc; border-radius: 0.8rem; border: 1px solid #e2e8f0; margin-top: 3rem;'>
179 |         <h4 style='color: #2d3748; margin-bottom: 0.8rem; font-weight: 600;'>Electric Vehicle Analytics Dashboard</h4>
180 |         <p style='color: #4a5568; font-size: 0.95rem; margin-bottom: 0.4rem;'>Last Updated: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}</p>
181 |         <p style='color: #718096; font-size: 0.875rem;'>Data refreshed automatically on load</p>
182 |     </div>
183 |     """, unsafe_allow_html=True)
184 | 
185 | # Load data
186 | @st.cache_data
187 | def load_data():
188 |     df = pd.read_csv("project.csv")
189 |     df.dropna(inplace=True)
190 |     df.drop(columns=["State"], inplace=True)
191 |     df.drop_duplicates(inplace=True)
192 |     return df
193 | 
194 | # Load the data
195 | df = load_data()
196 | 
197 | # Sidebar with enhanced styling
198 | with st.sidebar:
199 |     st.title("📊 Dashboard Controls")
200 |     st.markdown("---")
201 | 
202 |     # Update the dashboard description box styling
203 |     st.markdown("""
204 |     <div style='background-color: #ebf8ff; padding: 1.5rem; border-radius: 0.8rem; margin-bottom: 1.5rem; border: 1px solid #bee3f8;'>
205 |         <h4 style='margin: 0; color: #2c5282; font-weight: 600;'>About this Dashboard</h4>
206 |         <p style='margin-top: 0.8rem; color: #4a5568; font-size: 0.95rem; line-height: 1.5;'>
207 |             Explore electric vehicle trends, distributions, and patterns using interactive filters below.
208 |         </p>
209 |     </div>
210 |     """, unsafe_allow_html=True)
211 | 
212 |     # Filters in expandable sections
213 |     with st.expander("🔍 Time Period Filter", expanded=True):
214 |         # Year range selector with min/max display
215 |         min_year = int(df['Model Year'].min())
216 |         max_year = int(df['Model Year'].max())
217 |         selected_years = st.slider(
218 |             "Select Year Range",
219 |             min_value=min_year,
220 |             max_value=max_year,
221 |             value=(min_year, max_year)
222 |         )
223 |         st.caption(f"Selected period: {selected_years[0]} - {selected_years[1]}")
224 | 
225 |     with st.expander("🚗 Vehicle Type Filter", expanded=True):
226 |         # Vehicle type filter with select all/none options
227 |         all_types = df['Electric Vehicle Type'].unique()
228 |         select_all = st.checkbox("Select All Types", value=True)
229 |         
230 |         if select_all:
231 |             vehicle_types = st.multiselect(
232 |                 "Choose Vehicle Types",
233 |                 options=all_types,
234 |                 default=all_types
235 |             )
236 |         else:
237 |             vehicle_types = st.multiselect(
238 |                 "Choose Vehicle Types",
239 |                 options=all_types
240 |             )
241 | 
242 |     with st.expander("📈 Additional Filters", expanded=False):
243 |         # Price range filter
244 |         price_range = st.slider(
245 |             "Price Range (MSRP)",
246 |             min_value=float(df['Base MSRP'].min()),
247 |             max_value=float(df['Base MSRP'].max()),
248 |             value=(float(df['Base MSRP'].min()), float(df['Base MSRP'].max())),
249 |             format="$%f"
250 |         )
251 |         
252 |         # Range filter
253 |         range_values = st.slider(
254 |             "Electric Range (miles)",
255 |             min_value=int(df['Electric Range'].min()),
256 |             max_value=int(df['Electric Range'].max()),
257 |             value=(int(df['Electric Range'].min()), int(df['Electric Range'].max()))
258 |         )
259 | 
260 |     # Update the filter summary call in the sidebar
261 |     filter_summary(selected_years, vehicle_types, price_range, range_values)
262 |     
263 |     # Add separator
264 |     st.markdown("---")
265 |     
266 |     # About Me Section
267 |     st.markdown("""
268 |     <div style='background-color: #f8fafc; padding: 1.5rem; border-radius: 0.8rem; margin-top: 2rem; border: 1px solid #e2e8f0;'>
269 |         <h4 style='margin: 0; color: #2d3748; font-weight: 600; margin-bottom: 1rem;'>👨‍🎓 About Me</h4>
270 |         <div style='color: #4a5568; font-size: 0.95rem; line-height: 1.5;'>
271 |             <p style='margin: 0.5rem 0;'><strong>Name:</strong>AMAN KUMAR</p>
272 |             <p style='margin: 0.5rem 0;'><strong>Reg. Number:</strong>12318896</p>
273 |             <p style='margin: 0.5rem 0;'><strong>Section:</strong>K23GP</p>
274 |             <p style='margin: 0.5rem 0;'><strong>Course:</strong>BTECH-CSE</p>
275 |             <div style='margin-top: 1rem; padding-top: 1rem; border-top: 1px solid #e2e8f0;'>
276 |                 <p style='margin: 0; font-size: 0.875rem; color: #718096;'>
277 |                     Academic Project - Electric Vehicle Analytics Dashboard
278 |                 </p>
279 |             </div>
280 |         </div>
281 |     </div>
282 |     """, unsafe_allow_html=True)
283 | 
284 | # Apply filters to the dataframe
285 | filtered_df = df[
286 |     (df['Model Year'] >= selected_years[0]) & 
287 |     (df['Model Year'] <= selected_years[1]) &
288 |     (df['Electric Vehicle Type'].isin(vehicle_types)) &
289 |     (df['Base MSRP'] >= price_range[0]) &
290 |     (df['Base MSRP'] <= price_range[1]) &
291 |     (df['Electric Range'] >= range_values[0]) &
292 |     (df['Electric Range'] <= range_values[1])
293 | ]
294 | 
295 | # Main content
296 | st.title("🚗 Electric Vehicle Analytics Dashboard")
297 | st.markdown("""
298 | <div style='background-color: #f8fafc; padding: 1rem; border-radius: 0.5rem; margin-bottom: 2rem;'>
299 |     <p style='margin: 0; color: #4a5568; font-size: 1.1rem;'>
300 |         Comprehensive analysis of electric vehicle trends and patterns across different dimensions.
301 |     </p>
302 | </div>
303 | """, unsafe_allow_html=True)
304 | 
305 | # Key Metrics with enhanced styling
306 | st.markdown("""
307 | <div style='margin-bottom: 2rem;'>
308 |     <h3 style='color: #2d3748; margin-bottom: 1rem;'>Key Metrics Overview</h3>
309 | </div>
310 | """, unsafe_allow_html=True)
311 | 
312 | col1, col2, col3, col4 = st.columns(4)
313 | with col1:
314 |     st.metric(
315 |         "Total Vehicles",
316 |         f"{len(filtered_df):,}"
317 |     )
318 | with col2:
319 |     st.metric(
320 |         "Average Range",
321 |         f"{filtered_df['Electric Range'].mean():.0f} miles"
322 |     )
323 | with col3:
324 |     st.metric(
325 |         "Average MSRP",
326 |         f"${filtered_df['Base MSRP'].mean():,.0f}"
327 |     )
328 | with col4:
329 |     st.metric(
330 |         "Unique Models",
331 |         f"{filtered_df['Model'].nunique():,}"
332 |     )
333 | 
334 | # Continue with the existing visualization code, but add section headers with enhanced styling
335 | st.markdown("---")
336 | 
337 | # Add section headers with enhanced styling before each row
338 | def section_header(title, description):
339 |     st.markdown(f"""
340 |     <div style='margin-bottom: 1.5rem; background-color: #ffffff; padding: 1.2rem; border-radius: 0.8rem; border: 1px solid #e2e8f0;'>
341 |         <h3 style='color: #2d3748; margin-bottom: 0.5rem; font-weight: 600;'>{title}</h3>
342 |         <p style='color: #4a5568; font-size: 0.95rem; margin: 0; line-height: 1.5;'>{description}</p>
343 |     </div>
344 |     """, unsafe_allow_html=True)
345 | 
346 | # First Row
347 | section_header(
348 |     "Growth Trends & Distribution",
349 |     "Analysis of EV adoption over time and distribution of vehicle types"
350 | )
351 | row1_col1, row1_col2 = st.columns(2)
352 | 
353 | # Left Column - Growth and Trends
354 | with row1_col1:
355 |     st.subheader("Growth and Trends")
356 |     
357 |     # EV Growth Over Time
358 |     ev_growth = filtered_df.groupby("Model Year").size().reset_index(name="Number of EVs")
359 |     fig, ax = plt.subplots(figsize=(10, 6))
360 |     sns.lineplot(data=ev_growth, x="Model Year", y="Number of EVs", 
361 |                  marker="o", linewidth=2.5, color='#1f77b4')
362 |     plt.title("Growth of Electric Vehicles Over Time")
363 |     plt.grid(True, alpha=0.3)
364 |     plt.xticks(rotation=45)
365 |     plt.tight_layout()
366 |     st.pyplot(fig)
367 | 
368 | # Right Column - Distribution
369 | with row1_col2:
370 |     st.subheader("Distribution and Composition")
371 |     
372 |     # Vehicle Type Distribution
373 |     type_counts = filtered_df['Electric Vehicle Type'].value_counts()
374 |     fig, ax = plt.subplots(figsize=(10, 6))
375 |     colors = sns.color_palette("Set2", len(type_counts))
376 |     plt.pie(type_counts, labels=type_counts.index, autopct='%1.1f%%',
377 |             colors=colors, wedgeprops={'edgecolor': 'white', 'linewidth': 2})
378 |     plt.title("Distribution of Electric Vehicle Types")
379 |     plt.axis('equal')
380 |     plt.tight_layout()
381 |     st.pyplot(fig)
382 | 
383 | st.markdown("---")
384 | 
385 | # Second Row - Models and Range
386 | row2_col1, row2_col2 = st.columns(2)
387 | 
388 | with row2_col1:
389 |     st.subheader("Top EV Models")
390 |     # Top Models
391 |     top_models = filtered_df['Model'].value_counts().head(10)
392 |     fig, ax = plt.subplots(figsize=(10, 6))
393 |     sns.barplot(x=top_models.values, y=top_models.index, palette="magma")
394 |     plt.title("Top 10 EV Models")
395 |     for i, v in enumerate(top_models.values):
396 |         ax.text(v + 0.1, i, str(v), color='black', fontweight='bold')
397 |     plt.tight_layout()
398 |     st.pyplot(fig)
399 | 
400 | with row2_col2:
401 |     st.subheader("Range Analysis")
402 |     # Average Range Over Time
403 |     avg_range = filtered_df.groupby("Model Year")["Electric Range"].mean().reset_index()
404 |     fig, ax = plt.subplots(figsize=(10, 6))
405 |     sns.lineplot(data=avg_range, x="Model Year", y="Electric Range", 
406 |                  marker="o", linewidth=2.5, color='#2ca02c')
407 |     plt.title("Average Electric Range Over Time")
408 |     plt.grid(True, alpha=0.3)
409 |     plt.xticks(rotation=45)
410 |     plt.tight_layout()
411 |     st.pyplot(fig)
412 | 
413 | st.markdown("---")
414 | 
415 | # Third Row - Price Analysis and Cities
416 | row3_col1, row3_col2 = st.columns(2)
417 | 
418 | with row3_col1:
419 |     st.subheader("Price Analysis")
420 |     # Average MSRP Over Time
421 |     avg_msrp = filtered_df.groupby("Model Year")["Base MSRP"].mean().reset_index()
422 |     fig, ax = plt.subplots(figsize=(10, 6))
423 |     sns.lineplot(data=avg_msrp, x="Model Year", y="Base MSRP", 
424 |                 marker="o", linewidth=2.5, color='#ff7f0e')
425 |     plt.title("Average MSRP Over Time")
426 |     plt.grid(True, alpha=0.3)
427 |     plt.xticks(rotation=45)
428 |     plt.tight_layout()
429 |     st.pyplot(fig)
430 | 
431 | with row3_col2:
432 |     st.subheader("Geographic Distribution")
433 |     # Top Cities
434 |     top_cities = filtered_df['City'].value_counts().head(10)
435 |     fig, ax = plt.subplots(figsize=(10, 6))
436 |     sns.barplot(x=top_cities.values, y=top_cities.index, palette="coolwarm")
437 |     plt.title("Top 10 Cities by EV Count")
438 |     for i, v in enumerate(top_cities.values):
439 |         ax.text(v + 0.1, i, str(v), color='black', fontweight='bold')
440 |     plt.tight_layout()
441 |     st.pyplot(fig)
442 | 
443 | # After the third row (Price Analysis and Cities), add new rows:
444 | 
445 | st.markdown("---")
446 | 
447 | # Fourth Row - Growth Rate and Manufacturers
448 | row4_col1, row4_col2 = st.columns(2)
449 | 
450 | with row4_col1:
451 |     st.subheader("Year-over-Year Growth Rate")
452 |     # Growth Rate Analysis
453 |     ev_growth["Yearly Growth Rate (%)"] = ev_growth["Number of EVs"].pct_change() * 100
454 |     fig, ax = plt.subplots(figsize=(10, 6))
455 |     sns.barplot(data=ev_growth[1:], x="Model Year", y="Yearly Growth Rate (%)", 
456 |                 palette=custom_palette)
457 |     plt.title("Year-over-Year EV Growth Rate")
458 |     plt.xticks(rotation=45)
459 |     for i, v in enumerate(ev_growth[1:]["Yearly Growth Rate (%)"]):
460 |         ax.text(i, v + 0.1, f"{v:.1f}%", ha='center', color='black', fontweight='bold')
461 |     plt.tight_layout()
462 |     st.pyplot(fig)
463 | 
464 | with row4_col2:
465 |     st.subheader("Top Manufacturers")
466 |     # Top Manufacturers
467 |     top_makes = filtered_df['Make'].value_counts().head(10)
468 |     fig, ax = plt.subplots(figsize=(10, 6))
469 |     sns.barplot(x=top_makes.values, y=top_makes.index, palette=custom_palette)
470 |     plt.title("Top 10 EV Manufacturers")
471 |     for i, v in enumerate(top_makes.values):
472 |         ax.text(v + 0.1, i, str(v), color='black', fontweight='bold')
473 |     plt.tight_layout()
474 |     st.pyplot(fig)
475 | 
476 | st.markdown("---")
477 | 
478 | # Fifth Row - Models by Type and Correlation
479 | row5_col1, row5_col2 = st.columns(2)
480 | 
481 | with row5_col1:
482 |     st.subheader("Top Models by Vehicle Type")
483 |     # Models by Type
484 |     top_models_by_type = filtered_df.groupby(['Electric Vehicle Type', 'Model']).size().reset_index(name='Count')
485 |     top_models_by_type = top_models_by_type.sort_values('Count', ascending=False).groupby('Electric Vehicle Type').head(5)
486 |     fig, ax = plt.subplots(figsize=(10, 6))
487 |     sns.barplot(data=top_models_by_type, y='Model', x='Count', 
488 |                hue='Electric Vehicle Type', palette=custom_palette)
489 |     plt.title("Top 5 EV Models by Type")
490 |     plt.legend(title='Vehicle Type', title_fontsize=12, fontsize=10)
491 |     plt.tight_layout()
492 |     st.pyplot(fig)
493 | 
494 | with row5_col2:
495 |     st.subheader("Correlation Analysis")
496 |     # Correlation Heatmap
497 |     numerical_cols = ['Model Year', 'Electric Range', 'Base MSRP']
498 |     fig, ax = plt.subplots(figsize=(10, 6))
499 |     sns.heatmap(filtered_df[numerical_cols].corr(), annot=True, 
500 |                cmap='coolwarm', fmt=".2f", linewidths=0.5, square=True)
501 |     plt.title("Correlation Heatmap")
502 |     plt.tight_layout()
503 |     st.pyplot(fig)
504 | 
505 | st.markdown("---")
506 | 
507 | # Sixth Row - Average EVs per City and MSRP Distribution
508 | row6_col1, row6_col2 = st.columns(2)
509 | 
510 | with row6_col1:
511 |     st.subheader("City-wise EV Distribution")
512 |     # Average EVs per City Over Time
513 |     avg_city_count = filtered_df.groupby(['Model Year', 'City']).size().groupby('Model Year').mean().reset_index(name='Average EVs per City')
514 |     fig, ax = plt.subplots(figsize=(10, 6))
515 |     sns.lineplot(data=avg_city_count, x="Model Year", y="Average EVs per City", 
516 |                 marker="o", color=custom_palette[3], linewidth=2.5)
517 |     plt.title("Average EVs per City Over Time")
518 |     plt.grid(True, alpha=0.3)
519 |     plt.xticks(rotation=45)
520 |     plt.tight_layout()
521 |     st.pyplot(fig)
522 | 
523 | with row6_col2:
524 |     st.subheader("MSRP Distribution")
525 |     # MSRP Distribution by Vehicle Type
526 |     fig, ax = plt.subplots(figsize=(10, 6))
527 |     sns.boxplot(data=filtered_df, x='Electric Vehicle Type', y='Base MSRP', 
528 |                 palette=custom_palette)
529 |     plt.title("MSRP Distribution by Vehicle Type")
530 |     plt.xticks(rotation=45)
531 |     plt.tight_layout()
532 |     st.pyplot(fig)
533 | 


--------------------------------------------------------------------------------