Initial commit

State_wise_total(EV)/2009.csv

@@ -0,0 +1,28 @@
+State,Total Consolidated
+AP,623
+AR,1
+AS,5
+BR,17
+CG,116
+CH,1
+DD,12
+DL,724
+GA,5
+GJ,381
+HR,18
+JH,24
+JK,5
+KA,997
+KL,322
+LD,10
+MH,721
+MP,689
+OR,325
+PB,63
+PY,155
+RJ,63
+TN,1800
+UK,20
+UP,220
+WB,91
+Total,7408

State_wise_total(EV)/2010.csv

@@ -0,0 +1,32 @@
+State,Total Consolidated
+AP,254
+AR,1
+AS,6
+BR,77
+CG,172
+CH,3
+DD,3
+DL,614
+GA,24
+GJ,241
+HP,2
+HR,7
+JH,4
+JK,5
+KA,624
+KL,171
+LD,14
+MH,615
+MP,180
+MZ,2
+NL,1
+OR,73
+PB,74
+PY,79
+RJ,82
+SK,2
+TN,859
+UK,15
+UP,312
+WB,119
+Total,4635

State_wise_total(EV)/2011.csv

@@ -0,0 +1,29 @@
+State,Total Consolidated
+AP,81
+AS,2
+BR,156
+CG,270
+DD,7
+DL,741
+GA,29
+GJ,502
+HP,2
+HR,14
+JH,7
+JK,10
+KA,1028
+KL,126
+LD,16
+MH,999
+MN,5
+MP,312
+OR,102
+PB,545
+PY,40
+RJ,342
+SK,2
+TN,703
+UK,29
+UP,625
+WB,269
+Total,6964

State_wise_total(EV)/2012.csv

@@ -0,0 +1,32 @@
+State,Total Consolidated
+AP,19
+AR,1
+AS,4
+BR,20
+CG,184
+CH,2
+DD,1
+DL,369
+GA,31
+GJ,434
+HP,3
+HR,21
+JH,15
+JK,5
+KA,1104
+KL,104
+LD,9
+MH,511
+ML,1
+MP,161
+MZ,2
+OR,121
+PB,617
+PY,47
+RJ,352
+SK,3
+TN,674
+UK,48
+UP,393
+WB,243
+Total,5499

State_wise_total(EV)/2013.csv

@@ -0,0 +1,30 @@
+State,Total Consolidated
+AP,19
+AS,3
+BR,14
+CG,78
+CH,1
+DL,115
+GA,19
+GJ,108
+HP,2
+HR,6
+JH,16
+JK,4
+KA,548
+KL,63
+LD,5
+MH,571
+ML,3
+MP,51
+NL,3
+OR,102
+PB,178
+PY,25
+RJ,171
+TN,364
+TR,1
+UK,25
+UP,202
+WB,32
+Total,2729

State_wise_total(EV)/2014.csv

@@ -0,0 +1,28 @@
+State,Total Consolidated
+AP,12
+AS,4
+BR,11
+CG,123
+DL,46
+GA,9
+GJ,78
+HP,1
+HR,9
+JH,4
+JK,4
+KA,606
+KL,22
+LD,2
+MH,902
+ML,1
+MP,40
+OR,51
+PB,31
+PY,4
+RJ,89
+SK,1
+TN,177
+UK,9
+UP,143
+WB,2
+Total,2381

State_wise_total(EV)/2015.csv

@@ -0,0 +1,30 @@
+State,Total Consolidated
+AP,9
+AS,23
+BR,69
+CG,86
+CH,2
+DD,2
+DL,3564
+GA,4
+GJ,78
+HP,4
+HR,55
+JH,194
+JK,4
+KA,686
+KL,26
+LD,4
+MH,999
+MP,85
+NL,1
+OR,47
+PB,18
+PY,11
+RJ,674
+SK,1
+TN,83
+UK,15
+UP,1016
+WB,30
+Total,7790

State_wise_total(EV)/2016.csv

@@ -0,0 +1,32 @@
+State,Total Consolidated
+AN,1
+AP,20
+AR,1
+AS,19
+BR,1100
+CG,192
+CH,1
+DD,2
+DL,21680
+GA,33
+GJ,141
+HP,4
+HR,1375
+JH,705
+KA,587
+KL,20
+LD,5
+MH,928
+ML,1
+MN,2
+MP,847
+MZ,2
+OR,25
+PB,37
+PY,7
+RJ,3999
+TN,82
+UK,1158
+UP,15315
+WB,1530
+Total,49819

State_wise_total(EV)/2017.csv

@@ -0,0 +1,28 @@
+State,Total Consolidated
+AS,3831
+BR,3594
+CG,945
+CH,23
+DD,1
+DL,17850
+GA,38
+GJ,173
+HP,15
+HR,2487
+JH,1352
+JK,1
+KA,708
+KL,79
+MH,943
+MN,5
+MP,1409
+OR,212
+PB,137
+PY,9
+RJ,3945
+TN,109
+TR,2
+UK,4223
+UP,40648
+WB,4452
+Total,87191

State_wise_total(EV)/2018.csv

@@ -0,0 +1,32 @@
+State,Total Consolidated
+AN,20
+AP,1180
+AR,1
+AS,7398
+BR,8465
+CG,1903
+CH,213
+DD,19
+DL,20902
+GA,25
+GJ,500
+HP,62
+HR,4626
+JH,1957
+JK,90
+KA,2259
+KL,288
+MH,4632
+ML,2
+MN,39
+MP,2743
+OR,1358
+PB,364
+PY,87
+RJ,4676
+TN,1338
+TR,32
+UK,5067
+UP,53199
+WB,6728
+Total,130173

State_wise_total(EV)/2019.csv

@@ -0,0 +1,36 @@
+State,Total Consolidated
+AN,2
+AP,2157
+AR,1
+AS,10859
+BR,12380
+CG,2744
+CH,507
+DD,19
+DL,23221
+GA,42
+GJ,947
+HP,54
+HR,5107
+JH,1932
+JK,75
+KA,6138
+KL,503
+LA,1
+LD,1
+MH,7316
+ML,13
+MN,251
+MP,3686
+MZ,1
+NL,29
+OR,1160
+PB,961
+PY,81
+RJ,6636
+TN,3450
+TR,1459
+UK,5817
+UP,55785
+WB,13533
+Total,166868

State_wise_total(EV)/2020.csv

@@ -0,0 +1,35 @@
+State,Total Consolidated
+AN,36
+AP,1643
+AR,4
+AS,8357
+BR,12446
+CG,1490
+CH,369
+DD,24
+DL,12377
+GA,82
+GJ,1123
+HP,181
+HR,2980
+JH,1516
+JK,74
+KA,9688
+KL,1370
+LD,7
+MH,7130
+ML,3
+MN,102
+MP,3361
+MZ,1
+NL,11
+OR,903
+PB,833
+PY,89
+RJ,5605
+TN,5697
+TR,3419
+UK,2392
+UP,31256
+WB,10079
+Total,124648

State_wise_total(EV)/2021.csv

@@ -0,0 +1,36 @@
+State,Total Consolidated
+AN,92
+AP,9678
+AR,2
+AS,15632
+BR,23084
+CG,4216
+CH,731
+DD,29
+DL,25806
+GA,1095
+GJ,9762
+HP,327
+HR,8658
+JH,3740
+JK,1147
+KA,33298
+KL,8732
+LA,6
+LD,4
+MH,29916
+ML,5
+MN,114
+MP,10442
+MZ,1
+NL,2
+OR,5626
+PB,4648
+PY,405
+RJ,23460
+TN,30033
+TR,2432
+UK,5322
+UP,66684
+WB,6408
+Total,331537

State_wise_total(EV)/2022.csv

@@ -0,0 +1,36 @@
+State,Total Consolidated
+AN,23
+AP,29358
+AR,2
+AS,40699
+BR,55751
+CG,22364
+CH,2719
+DD,142
+DL,62249
+GA,5682
+GJ,68993
+HP,1011
+HR,25862
+JH,13680
+JK,4690
+KA,95846
+KL,39617
+LA,40
+LD,1
+MH,136063
+ML,41
+MN,341
+MP,36818
+MZ,36
+NL,3
+OR,28446
+PB,14057
+PY,1481
+RJ,78221
+TN,66995
+TR,4177
+UK,15561
+UP,162868
+WB,11156
+Total,1024993

State_wise_total(EV)/2023.csv

@@ -0,0 +1,36 @@
+State,Total Consolidated
+AN,26
+AP,32877
+AR,21
+AS,60788
+BR,88213
+CG,38248
+CH,6415
+DD,161
+DL,73695
+GA,9482
+GJ,88613
+HP,1132
+HR,30505
+JH,21125
+JK,9747
+KA,152667
+KL,75810
+LA,28
+LD,6
+MH,194342
+ML,115
+MN,399
+MP,67988
+MZ,167
+NL,6
+OR,44543
+PB,25746
+PY,2638
+RJ,93766
+TN,90305
+TR,6311
+UK,16795
+UP,278305
+WB,21430
+Total,1532415

State_wise_total(EV)/2024.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,82
+AP,57489
+AR,99
+AS,68422
+BR,113144
+CG,49476
+CH,7071
+DD,492
+DL,85296
+GA,11755
+GJ,80344
+HP,2106
+HR,47801
+JH,26279
+JK,13366
+KA,185436
+KL,86898
+LA,20
+LD,5
+MH,250906
+ML,832
+MN,445
+MP,101438
+MZ,574
+NL,45
+OR,65973
+PB,47151
+PY,4740
+RJ,111227
+SK,17
+TN,135492
+TR,9624
+UK,20223
+UP,378403
+WB,46564
+Total,2009235

State_wise_total(EV)/2025.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,31
+AP,14490
+AR,60
+AS,17927
+BR,29460
+CG,13171
+CH,1843
+DD,175
+DL,32862
+GA,2911
+GJ,19613
+HP,784
+HR,12622
+JH,7521
+JK,3019
+KA,58623
+KL,30519
+LA,1
+LD,6
+MH,83852
+ML,158
+MN,204
+MP,34535
+MZ,274
+NL,26
+OR,19240
+PB,14097
+PY,1434
+RJ,29581
+SK,10
+TN,47008
+TR,2957
+UK,4545
+UP,112251
+WB,18335
+Total,614145

app.py

@@ -0,0 +1,741 @@
+import streamlit as st
+import pandas as pd
+import sqlite3
+import plotly.express as px
+from io import BytesIO
+import json
+import os
+import sqlite3
+import pandas as pd
+import streamlit as st
+import re
+import plotly.express as px
+import json
+from difflib import get_close_matches
+
+
+# ---------------------- Config ----------------------
+st.set_page_config(page_title="Vehicle Dashboard India", layout="wide")
+
+# ---------------------- DB Connection ----------------------
+@st.cache_data
+def load_data(query):
+    with sqlite3.connect(r"vehicle_analysis_1.db") as conn:
+        df = pd.read_sql_query(query, conn)
+        for col in ['row_value', 'column_value']:
+            if col in df.columns:
+                df = df[df[col] != col]
+        return df
+
+# ---------------------- Setup: Load State Mapping ----------------------
+@st.cache_data
+def load_state_rto_mapping():
+    with open("state_rto_data.json", "r") as f:
+        data = json.load(f)
+        return {
+            entry["State Code"]: {
+                "state_name": entry["Full State Name"].strip(),
+                "rtos": [rto.strip() for rto in entry["RTO List"].split(",")]
+            }
+            for entry in data
+        }
+
+state_rto_map = load_state_rto_mapping()
+abbr_to_full = {abbr: info["state_name"] for abbr, info in state_rto_map.items()}
+
+# Utility function
+def load_csv_data(folder_path):
+    all_data = []
+    for filename in os.listdir(folder_path):
+        if filename.endswith(".csv"):
+            file_path = os.path.join(folder_path, filename)
+            try:
+                df = pd.read_csv(file_path)
+                df = df.iloc[:-1]
+                year = filename.split('_')[-1].replace('.csv', '')
+                df['year'] = year
+                all_data.append(df)
+            except Exception:
+                pass
+    return pd.concat(all_data, ignore_index=True) if all_data else None
+
+
+def dashboard_overview(): 
+    st.title("📊 Vehicle Registration Summary")
+
+    # Load data
+    df_total = load_csv_data(r"state_wise_total")
+    df_ev = load_csv_data(r"State_wise_total(EV)")
+    df_fuel = load_data("SELECT * FROM fuel_vs_state")
+    df_class = load_data("SELECT * FROM vehicle_class_vs_state")
+    df_cat = load_data("SELECT * FROM vehicle_category_group_vs_state")
+
+    # Clean the data
+    df_total['count'] = pd.to_numeric(df_total['Total Consolidated'], errors='coerce').fillna(0)
+    df_ev['count'] = pd.to_numeric(df_ev['Total Consolidated'], errors='coerce').fillna(0)
+    df_fuel['count'] = pd.to_numeric(df_fuel['count'], errors='coerce').fillna(0)
+    df_class['count'] = pd.to_numeric(df_class['count'], errors='coerce').fillna(0)
+    df_cat['count'] = pd.to_numeric(df_cat['count'], errors='coerce').fillna(0)
+
+    df_total['year'] = pd.to_numeric(df_total['year'], errors='coerce')
+    df_ev['year'] = pd.to_numeric(df_ev['year'], errors='coerce')
+
+    # National totals
+    total_count = df_total['count'].sum()
+    total_ev = df_ev['count'].sum()
+    ev_share = round((total_ev / total_count) * 100, 2) if total_count else 0
+
+    col1, col2, col3 = st.columns(3)
+    col1.metric("Total Vehicles", f"{int(total_count):,}")
+    col2.metric("EV Registered", f"{int(total_ev):,}")
+    col3.metric("EV Share", f"{ev_share}%")
+
+    # 1. Year-wise state-wise registration graph
+    if not df_total.empty:
+        min_year_total, max_year_total = int(df_total["year"].min()), int(df_total["year"].max())
+        selected_years_total = st.slider("Select Year Range for Registration", min_year_total, max_year_total, (min_year_total, max_year_total))
+        df_total_filtered = df_total[(df_total["year"] >= selected_years_total[0]) & (df_total["year"] <= selected_years_total[1])]
+        fig1 = px.line(df_total_filtered, x="year", y="count", color="State", title=f"Year-wise Registration by State ({selected_years_total[0]} - {selected_years_total[1]})")
+        st.plotly_chart(fig1, use_container_width=True)
+    else:
+        st.warning("No data available for year-wise registration.")
+
+    # 2. Fuel type trend
+    fuel_types = sorted(df_fuel['row_value'].dropna().unique())
+    fuel_options = ["Select All"] + fuel_types
+    selected_fuel_types = st.multiselect("Select Fuel Type(s)", fuel_options, help="Select 'Select All' to include all fuel types")
+    if "Select All" in selected_fuel_types:
+        selected_fuel_types = fuel_types
+    if selected_fuel_types:
+        df_fuel_filtered = df_fuel[df_fuel['row_value'].isin(selected_fuel_types)]
+        df_fuel_grouped = df_fuel_filtered.groupby(['year', 'row_value'])['count'].sum().reset_index()
+        fig2 = px.area(df_fuel_grouped, x="year", y="count", color="row_value", title="National Fuel Type Trend Over Years")
+        st.plotly_chart(fig2, use_container_width=True)
+
+    # 3. Vehicle class trend
+    vehicle_classes = sorted(df_class['row_value'].dropna().unique())
+    class_options = ["Select All"] + vehicle_classes
+    selected_vehicle_classes = st.multiselect("Select Vehicle Class(es)", class_options, help="Select 'Select All' to include all classes")
+    if "Select All" in selected_vehicle_classes:
+        selected_vehicle_classes = vehicle_classes
+    if selected_vehicle_classes:
+        df_class_filtered = df_class[df_class['row_value'].isin(selected_vehicle_classes)]
+        df_class_grouped = df_class_filtered.groupby(['year', 'row_value'])['count'].sum().reset_index()
+        fig3 = px.area(df_class_grouped, x="year", y="count", color="row_value", title="Vehicle Class Trend Across India")
+        st.plotly_chart(fig3, use_container_width=True)
+
+    # 4. EV adoption graph
+    ev_states = sorted(df_ev['State'].dropna().unique())
+    ev_options = ["Select All"] + ev_states
+    selected_ev_states = st.multiselect("Select States for EV Adoption", ev_options, help="Select 'Select All' to include all states")
+    if "Select All" in selected_ev_states:
+        selected_ev_states = ev_states
+    if selected_ev_states:
+        df_ev_filtered = df_ev[df_ev['State'].isin(selected_ev_states)]
+        df_ev_statewise = df_ev_filtered.groupby("State")["count"].sum().reset_index().sort_values(by="count", ascending=False).head(10)
+        fig5 = px.bar(df_ev_statewise, x="State", y="count", title="Top 10 States by EV Registration")
+        st.plotly_chart(fig5, use_container_width=True)
+
+        # 5. EV vs. Total Vehicle Registrations Over Time
+        df_ev_vs_total = df_ev_filtered.groupby("year")["count"].sum().reset_index()
+        df_total_vs_ev = pd.merge(df_total.groupby("year")['count'].sum().reset_index(), df_ev_vs_total, on="year", suffixes=('_total', '_ev'))
+        fig6 = px.line(df_total_vs_ev, x="year", y=["count_total", "count_ev"], title="EV vs Total Vehicle Registrations Over Time")
+        st.plotly_chart(fig6, use_container_width=True)
+
+
+def ev_insights():
+    st.title("⚡ Electric Vehicle (EV) Analysis")
+
+    # Load State Abbreviation Mapping from JSON
+    with open("state_rto_data.json", "r") as f:
+        state_data = json.load(f)
+
+    state_code_to_name = {
+        entry["State Code"].strip().lower(): entry["Full State Name"].strip()
+        for entry in state_data
+    }
+    state_name_to_code = {
+        entry["Full State Name"].strip(): entry["State Code"].strip().lower()
+        for entry in state_data
+    }
+
+    # 1️⃣ EV Registrations by Fuel Type (State-wise)
+    df_ev_state = load_data("SELECT * FROM fuel_vs_stateev")
+    unique_states = df_ev_state["column_value"].dropna().unique()
+    state_full_names = [state_code_to_name.get(state.strip().lower(), state) for state in unique_states]
+    
+    state_selection = st.selectbox("Select a State", sorted(state_full_names))
+    selected_abbr = state_name_to_code.get(state_selection.strip(), state_selection.strip().lower())
+    filtered = df_ev_state[df_ev_state["column_value"].str.lower() == selected_abbr]
+
+    fig1 = px.bar(
+        filtered, x="year", y="count", color="row_value", barmode="group",
+        title=f"EV Registrations by Fuel Type in {state_selection}"
+    )
+    st.plotly_chart(fig1, use_container_width=True)
+
+    # 2️⃣ EV Growth Over Years (National) with Year Range Filter
+    st.subheader("📈 National EV Growth Over Years")
+    ev_growth = df_ev_state.groupby("year")["count"].sum().reset_index()
+
+    if not ev_growth.empty:
+        min_year_nat, max_year_nat = int(ev_growth["year"].min()), int(ev_growth["year"].max())
+        selected_years_nat = st.slider("Select Year Range (National)", min_year_nat, max_year_nat, (min_year_nat, max_year_nat))
+        ev_growth = ev_growth[(ev_growth["year"] >= selected_years_nat[0]) & (ev_growth["year"] <= selected_years_nat[1])]
+    else:
+        st.warning("No national EV data available for selected range.")
+
+    fig2 = px.line(ev_growth, x="year", y="count", title="Total EVs Registered per Year in India")
+    st.plotly_chart(fig2, use_container_width=True)
+
+
+def fuel_norm_distribution_dashboard(root_dir, state_mapping_file, start_year=2009, end_year=2025):
+
+    # Folder paths
+    folders = {
+        "statewise": os.path.join(root_dir, "Fuel_vs_state"),
+        "norms": os.path.join(root_dir, "Norm_vs_state"),
+        "fuelwise": os.path.join(root_dir, "Fuel_vs_Norm")
+    }
+
+    # Load the state mapping JSON file
+    with open(state_mapping_file, 'r') as f:
+        state_mapping = json.load(f)
+
+    # Create a dictionary mapping state codes to full state names (case-insensitive key mapping)
+    state_dict = {item["State Code"].upper(): item["Full State Name"].strip() for item in state_mapping}
+
+    # Function to load data
+    def load_data(folder_path, year_file):
+        file_path = os.path.join(folder_path, year_file)
+        if os.path.exists(file_path):
+            df = pd.read_csv(file_path)
+            # Remove 'Total' row/column if present
+            if "Total" in df.columns:
+                df = df.drop(columns=["Total"])
+            if df.iloc[:, 0].str.contains("Total", na=False).any():
+                df = df[~df.iloc[:, 0].str.contains("Total", na=False)]
+            return df
+        else:
+            st.warning(f"File not found: {file_path}")
+            return pd.DataFrame()
+
+    # Function to load data for multiple years
+    def load_data_for_year_range(folder_path, start_year, end_year):
+        combined_df = pd.DataFrame()
+        for year in range(start_year, end_year + 1):
+            year_file = f"{year}.csv"
+            df = load_data(folder_path, year_file)
+            combined_df = pd.concat([combined_df, df], ignore_index=True)
+        return combined_df
+
+    # Load data for selected year range
+    statewise_df = load_data_for_year_range(folders["statewise"], start_year, end_year)
+    norms_df = load_data_for_year_range(folders["norms"], start_year, end_year)
+    fuelwise_df = load_data_for_year_range(folders["fuelwise"], start_year, end_year)
+
+    # Set up Streamlit app title
+    st.title("🛢 Fuel Allocation & Emission Norms Dashboard (2009–2025)")
+
+    # Tabs for different visualizations
+    tab1, tab2, tab3 = st.tabs([
+        "📍 Fuel Distribution over States",
+        "📊 Norm Distribution by State",
+        "🔥 Emission by Fuel Type"
+    ])
+
+    # Tab 1: Statewise Fuel Allocation
+    with tab1:
+        st.subheader("Fuel Distribution Across States")
+        if not statewise_df.empty:
+            # Convert from wide to long format
+            melted_df = pd.melt(statewise_df, id_vars=["Fuel"], var_name="State", value_name="Fuel_Amount")
+
+            # Replace state codes with full state names (case-insensitive matching)
+            melted_df["State"] = melted_df["State"].apply(lambda x: state_dict.get(x.upper(), x))
+
+            # Allow users to select states (categories)
+            selected_states = st.multiselect("Select States", ["Select All"] + list(melted_df["State"].unique()), default=[], key="state_selection")
+
+            # If "Select All" is chosen, select all states
+            if "Select All" in selected_states:
+                selected_states = list(melted_df["State"].unique())
+
+            filtered_df = melted_df[melted_df["State"].isin(selected_states)]
+
+            # Create and display the plot
+            fig1 = px.bar(filtered_df, x="State", y="Fuel_Amount", color="Fuel",
+                          title=f"Fuel Distribution from {start_year} to {end_year}",
+                          labels={"Fuel_Amount": "Amount (in units)"}, barmode="group")
+            st.plotly_chart(fig1, use_container_width=True)
+
+    # Tab 2: Norm Distribution by State
+    with tab2:
+        st.subheader("Emission Norm Distribution by State")
+        if not norms_df.empty:
+            # Convert from wide to long format
+            melted_norms = pd.melt(norms_df, id_vars=["Norms"], var_name="State", value_name="Count")
+
+            # Replace state codes with full state names (case-insensitive matching)
+            melted_norms["State"] = melted_norms["State"].apply(lambda x: state_dict.get(x.upper(), x))
+
+            # Allow users to select a state
+            selected_states = st.multiselect("Select States", ["Select All"] + list(melted_norms["State"].unique()), default=[], key="norm_state_selection")
+
+            # If "Select All" is chosen, select all states
+            if "Select All" in selected_states:
+                selected_states = list(melted_norms["State"].unique())
+
+            filtered_norms = melted_norms[melted_norms["State"].isin(selected_states)]
+
+            # Create and display the plot
+            fig2 = px.bar(filtered_norms, x="State", y="Count", color="Norms",
+                          title=f"Emission Norms Distribution from {start_year} to {end_year}",
+                          labels={"Count": "Count of Norms"}, barmode="group")
+            st.plotly_chart(fig2, use_container_width=True)
+
+    # Tab 3: Fuelwise Emissions
+    with tab3:
+        st.subheader("Norm Emissions by Fuel Type")
+        if not fuelwise_df.empty:
+            # Check if 'Fuel' exists and others are norms
+            norm_columns = [col for col in fuelwise_df.columns if col != "Fuel"]
+            if "Fuel" in fuelwise_df.columns and len(norm_columns) > 0:
+                # Melt the DataFrame to long format
+                melted_fuelwise = pd.melt(fuelwise_df, id_vars=["Fuel"], 
+                                          var_name="Norm_Type", value_name="Emission")
+
+                # Drop missing or non-numeric values if necessary
+                melted_fuelwise = melted_fuelwise.dropna()
+                melted_fuelwise["Emission"] = pd.to_numeric(melted_fuelwise["Emission"], errors="coerce")
+                melted_fuelwise = melted_fuelwise.dropna(subset=["Emission"])
+
+                # Allow users to select fuel categories
+                selected_fuels = st.multiselect("Select Fuel Types", ["Select All"] + list(melted_fuelwise["Fuel"].unique()), default=[], key="fuel_selection")
+
+                # If "Select All" is chosen, select all fuels
+                if "Select All" in selected_fuels:
+                    selected_fuels = list(melted_fuelwise["Fuel"].unique())
+
+                filtered_fuelwise = melted_fuelwise[melted_fuelwise["Fuel"].isin(selected_fuels)]
+
+                # Create and display the plot
+                fig3 = px.bar(filtered_fuelwise, x="Fuel", y="Emission", color="Norm_Type",
+                              title=f"Emission per Fuel Type from {start_year} to {end_year}",
+                              labels={"Emission": "Emission Amount"}, barmode="stack")
+                st.plotly_chart(fig3, use_container_width=True)
+
+                # Additional visualization for Top Fuels emitting most pollution
+                top_fuels = melted_fuelwise.groupby("Fuel")["Emission"].sum().reset_index()
+                top_fuels_sorted = top_fuels.sort_values(by="Emission", ascending=False).head(10)
+
+                st.subheader("Top Fuels Emitting Most Pollution")
+                st.write(top_fuels_sorted)
+
+                fig4 = px.bar(top_fuels_sorted, x="Fuel", y="Emission", title="Top Fuels by Emission",
+                              labels={"Emission": "Total Emission"}, color="Fuel")
+                st.plotly_chart(fig4, use_container_width=True)
+            else:
+                st.error("The required format was not found in 'fuelwise_df'. Ensure the file includes a 'Fuel' column and multiple norm-type columns.")
+
+
+def state_analysis():
+    st.title("🗺 State Analysis")
+
+    # Load data
+    df_state = load_data("SELECT * FROM fuel_vs_state")
+    df_norm = load_data("SELECT * FROM norm_vs_state")
+
+    # State selection
+    state_options = sorted(df_state["column_value"].dropna().unique())
+    selected_state = st.selectbox("Select a State", state_options)
+
+    # Filter based on state
+    filtered = df_state[df_state["column_value"] == selected_state]
+    norm_filtered = df_norm[df_norm["column_value"] == selected_state]
+
+    # Tabs for Absolute and Normalized
+    tab1, tab2 = st.tabs(["⛽ Fuel analysis(State Wise)", "📊 Norm analysis(State Wise)"])
+
+    with tab1:
+        st.subheader(f"{selected_state} - Fuel Distribution")
+
+        # Fuel type filter with "Select All" option
+        fuel_types = sorted(filtered["row_value"].dropna().unique())
+        fuel_options = ["Select All"] + fuel_types
+        selected_fuels = st.multiselect(
+            "Select Fuel Types", 
+            fuel_options,
+            default=[]
+        )
+
+        # Apply fuel filter
+        if "Select All" in selected_fuels or not selected_fuels:
+            selected_fuels = fuel_types
+        filtered = filtered[filtered["row_value"].isin(selected_fuels)]
+
+        if not filtered.empty:
+            # Year range filter
+            min_year, max_year = int(filtered["year"].min()), int(filtered["year"].max())
+            selected_years = st.slider("Select Year Range", min_year, max_year, (min_year, max_year))
+            filtered = filtered[(filtered["year"] >= selected_years[0]) & (filtered["year"] <= selected_years[1])]
+
+            # Plot
+            fig = px.bar(
+                filtered, x="year", y="count", color="row_value", barmode="group",
+                title=f"{selected_state} Vehicle Distribution by Fuel Type "
+            )
+            st.plotly_chart(fig, use_container_width=True)
+
+            # Download
+            csv = filtered.to_csv(index=False).encode("utf-8")
+            file_label = f"{selected_state.replace(' ', '_')}_fuel_data.csv"
+            st.download_button(
+                label="📥 Download Absolute Data as CSV",
+                data=csv,
+                file_name=file_label,
+                mime="text/csv"
+            )
+        else:
+            st.warning("No absolute data available for selected filters.")
+
+    with tab2:
+        st.subheader(f"{selected_state} - Norm Distribution")
+
+        # Norm fuel type filter with "Select All" option
+        norm_fuels = sorted(norm_filtered["row_value"].dropna().unique())
+        norm_fuel_options = ["Select All"] + norm_fuels
+        selected_norm_fuels = st.multiselect(
+            "Select Norm Types", 
+            norm_fuel_options,
+            default=[],
+            key="norm_fuel"
+        )
+
+        # Apply norm fuel filter
+        if "Select All" in selected_norm_fuels or not selected_norm_fuels:
+            selected_norm_fuels = norm_fuels
+        norm_filtered = norm_filtered[norm_filtered["row_value"].isin(selected_norm_fuels)]
+
+        if not norm_filtered.empty:
+            min_year_norm, max_year_norm = int(norm_filtered["year"].min()), int(norm_filtered["year"].max())
+            selected_years_norm = st.slider(
+                "Select Year Range for Norm Data", 
+                min_year_norm, max_year_norm, 
+                (min_year_norm, max_year_norm),
+                key="norm_slider"
+            )
+            norm_filtered = norm_filtered[
+                (norm_filtered["year"] >= selected_years_norm[0]) & 
+                (norm_filtered["year"] <= selected_years_norm[1])
+            ]
+
+            # Plot
+            fig_norm = px.bar(
+                norm_filtered, x="year", y="count", color="row_value", barmode="stack",
+                title=f"{selected_state} Norm Vehicle Distribution"
+            )
+            st.plotly_chart(fig_norm, use_container_width=True)
+
+            # Download
+            csv_norm = norm_filtered.to_csv(index=False).encode("utf-8")
+            file_label_norm = f"{selected_state.replace(' ', '_')}_norm_data.csv"
+            st.download_button(
+                label="📥 Download Norm Data as CSV",
+                data=csv_norm,
+                file_name=file_label_norm,
+                mime="text/csv"
+            )
+        else:
+            st.warning("No norm data available for selected filters.")
+
+
+def vehicle_class_category():
+    st.title("🚗 Vehicle Class & Category")
+     # Load data
+    df_class = load_data("SELECT * FROM vehicle_class_vs_state")
+    df_cat = load_data("SELECT * FROM vehicle_category_group_vs_state")
+
+    # Unique states
+    unique_states = df_class["column_value"].dropna().unique()
+    selected_state = st.selectbox("Select a State", sorted(unique_states), key="state_select")
+
+    # Filter state-wise
+    df_class_filtered = df_class[df_class["column_value"] == selected_state]
+    df_cat_filtered = df_cat[df_cat["column_value"] == selected_state]
+    
+
+    # Tabs for Class and Category
+    tab1, tab2 = st.tabs(["🚘 Vehicle Class", "🚛 Vehicle Category"])
+
+    with tab1:
+        st.subheader(f"Vehicle Class Trend - {selected_state}")
+
+        # Row_value filter for class
+        row_class_values = sorted(df_class_filtered["row_value"].dropna().unique())
+        selected_class_types = st.multiselect(
+            "Select Vehicle Class Types",
+            options=row_class_values,
+            default=[],
+            key="class_select",
+            help="Leave empty to show all vehicle classes"
+        )
+        if selected_class_types:
+            df_class_filtered = df_class_filtered[df_class_filtered["row_value"].isin(selected_class_types)]
+
+        # Graph
+        fig1 = px.line(
+            df_class_filtered,
+            x="year",
+            y="count",
+            color="row_value",
+            title=f"{selected_state} - Vehicle Class Trend"
+        )
+        st.plotly_chart(fig1, use_container_width=True, key="vehicle_class_trend")
+
+        # Download button for vehicle class
+        csv_class = df_class_filtered.to_csv(index=False).encode('utf-8')
+        st.download_button(
+            label="📥 Download Vehicle Class Data",
+            data=csv_class,
+            file_name=f"{selected_state}_vehicle_class_data.csv",
+            mime="text/csv"
+        )
+
+    with tab2:
+        st.subheader(f"Vehicle Category Trend - {selected_state}")
+
+        # Row_value filter for category
+        row_cat_values = sorted(df_cat_filtered["row_value"].dropna().unique())
+        selected_cat_types = st.multiselect(
+            "Select Vehicle Category Types",
+            options=row_cat_values,
+            default=[],
+            key="category_select",
+            help="Leave empty to show all vehicle categories"
+        )
+        if selected_cat_types:
+            df_cat_filtered = df_cat_filtered[df_cat_filtered["row_value"].isin(selected_cat_types)]
+
+        # Graph
+        fig2 = px.line(
+            df_cat_filtered,
+            x="year",
+            y="count",
+            color="row_value",
+            title=f"{selected_state} - Vehicle Category Trend"
+        )
+        st.plotly_chart(fig2, use_container_width=True, key="vehicle_category_trend")
+
+        # Download button for vehicle category
+        csv_category = df_cat_filtered.to_csv(index=False).encode('utf-8')
+        st.download_button(
+            label="📥 Download Vehicle Category Data",
+            data=csv_category,
+            file_name=f"{selected_state}_vehicle_category_data.csv",
+            mime="text/csv")
+
+
+def ask_with_llm():
+    """
+    Single function to run the entire Streamlit dashboard:
+    - Loads databases and mappings
+    - Parses natural-language queries
+    - Executes queries and renders results
+    """
+    # ---------- CONFIG ----------
+    DATABASE_PATH  = r"vehicle_analysis_1.db"
+    STATE_RTO_JSON = r"state_rto_data.json"
+
+    TABLE_MAPPINGS = {
+        'fuel':    {'state': 'fuel_vs_state',                   'state_ev': 'fuel_vs_stateev',                   'rto': 'fuel_vs_rto',                   'rto_ev': 'fuel_vs_rtoev'},
+        'norm':    {'state': 'norm_vs_state',                   'state_ev': 'norm_vs_stateev',                   'rto': 'norm_vs_rto',                   'rto_ev': 'norm_vs_rtoev'},
+        'category':{'state': 'vehicle_category_group_vs_state', 'state_ev': 'vehicle_category_group_vs_stateev', 'rto': 'vehicle_category_group_vs_rto', 'rto_ev': 'vehicle_category_group_vs_rtoev'},
+        'class':   {'state': 'vehicle_class_vs_state',          'state_ev': 'vehicle_class_vs_stateev',          'rto': None,                             'rto_ev': 'vehicle_class_vs_rtoev'}
+    }
+    TABLE_TOTALS = {'state': 'state_wise_total', 'state_ev': 'state_wise_totalev'}
+
+    @st.cache_data
+    def load_all():
+        # Load DB tables
+        conn = sqlite3.connect(DATABASE_PATH)
+        df_map = {}
+        for dims in TABLE_MAPPINGS.values():
+            for tbl in dims.values():
+                if tbl and tbl not in df_map:
+                    df_map[tbl] = pd.read_sql(f"SELECT * FROM {tbl}", conn)
+        for tbl in TABLE_TOTALS.values():
+            if tbl not in df_map:
+                df_map[tbl] = pd.read_sql(f"SELECT * FROM {tbl}", conn)
+        conn.close()
+
+        # Load JSON mapping
+        with open(STATE_RTO_JSON, 'r', encoding='utf-8') as f:
+            raw = json.load(f)
+        state_rto_map = {}
+        if isinstance(raw, dict):
+            for state, rtos in raw.items():
+                state_rto_map[state.lower()] = rtos
+        elif isinstance(raw, list):
+            for entry in raw:
+                if isinstance(entry, dict):
+                    for state, rtos in entry.items():
+                        state_rto_map[state.lower()] = rtos
+        else:
+            st.error("Invalid JSON format for state→RTO mapping.")
+
+        return df_map, state_rto_map
+
+    # Load data and mappings
+    df_map, state_rto_map = load_all()
+    STATES = df_map['fuel_vs_state']['column_value'].unique().tolist()
+    RTOS   = df_map['fuel_vs_rto']['column_value'].unique().tolist()
+
+    # Parsers
+    def parse_ev(q: str) -> bool:
+        return bool(re.search(r"\bEV\b|electric|evs?\b", q, re.IGNORECASE))
+
+    def parse_dimension(q: str) -> str | None:
+        ql = q.lower()
+        for d in TABLE_MAPPINGS:
+            if re.search(rf"\b{d}\b", ql):
+                return d
+        for tok in re.findall(r"\w+", ql):
+            match = get_close_matches(tok, TABLE_MAPPINGS.keys(), n=1, cutoff=0.8)
+            if match:
+                return match[0]
+        return None
+
+    def parse_year_filter(q: str) -> tuple[str|None,int|None]:
+        ql = q.lower()
+        if m := re.search(r"after\s*(20\d{2})", ql):   return ('>',  int(m.group(1)))
+        if m := re.search(r"before\s*(20\d{2})", ql):  return ('<',  int(m.group(1)))
+        if m := re.search(r"in\s*(20\d{2})", ql):      return ('==', int(m.group(1)))
+        if m := re.search(r"\b(20\d{2})\b", ql):     return ('==', int(m.group(1)))
+        return (None, None)
+
+    def parse_top_n(q: str) -> int|None:
+        ql = q.lower()
+        if m := re.search(r"top\s*(\d+)\s*states?\b", ql):
+            return int(m.group(1))
+        return None
+
+    def parse_location(q: str) -> tuple[str|None,str|None]:
+        ql = q.lower()
+        for r in RTOS:
+            if r.lower() in ql:
+                return ('rto', r)
+        for s in STATES:
+            if s.lower() in ql:
+                return ('state', s)
+        for tok in re.findall(r"\w+", ql):
+            m = get_close_matches(tok, [s.lower() for s in STATES], n=1, cutoff=0.8)
+            if m:
+                return ('state', STATES[[s.lower() for s in STATES].index(m[0])])
+        for tok in re.findall(r"\w+", ql):
+            m = get_close_matches(tok, [r.lower() for r in RTOS], n=1, cutoff=0.8)
+            if m:
+                return ('rto', RTOS[[r.lower() for r in RTOS].index(m[0])])
+        return (None, None)
+
+    # Streamlit UI
+    st.title("🚗 Vehicle Data Dashboard (Ask Anything)")
+    query = st.text_input("🔍 Ask your vehicle data question:", placeholder="e.g., Norm-wise registrations in Maharashtra in 2024")
+    if not query:
+        return
+
+    ev       = parse_ev(query)
+    dim      = parse_dimension(query)
+    op, year = parse_year_filter(query)
+    top_n    = parse_top_n(query)
+    loc_type, loc = parse_location(query)
+
+    # Determine table
+    if dim:
+        opts   = TABLE_MAPPINGS[dim]
+        suffix = (f"{loc_type}{'_ev' if ev else ''}" if loc_type in ('state','rto') else ('state_ev' if ev else 'state'))
+        tbl_name = opts.get(suffix) or (opts['state_ev'] if ev else opts['state'])
+    else:
+        tbl_name = TABLE_TOTALS['state_ev' if ev else 'state']
+
+    df = df_map[tbl_name]
+    if op and year:
+        df = df.query(f"year {op} @year")
+    if loc_type == 'rto':
+        df = df[df['column_value']==loc]
+    elif loc_type == 'state':
+        if 'rto' in tbl_name:
+            rto_list = state_rto_map.get(loc.lower(), [])
+            df = df[df['column_value'].isin(rto_list)]
+        else:
+            df = df[df['column_value']==loc]
+
+    # Render
+    if dim:
+        if top_n and loc_type is None:
+            agg    = df.groupby('column_value', as_index=False)['count'].sum()
+            result = agg.nlargest(top_n, 'count')
+            title  = f"Top {top_n} states by {dim}{' (EV)' if ev else ''}" + (f" in {year}" if op=='==' else "")
+            fig    = px.bar(result, x='column_value', y='count', labels={'column_value':'State','count':'Total'})
+        elif loc_type:
+            brk    = df.groupby('row_value', as_index=False)['count'].sum().sort_values('count', ascending=False)
+            title  = f"{dim.capitalize()} breakdown for {loc_type.upper()}: {loc}" + (f" after {year}" if op=='>' else "")
+            result = brk.rename(columns={'row_value':dim.capitalize(),'count':'Total'})
+            fig    = px.bar(brk, x='row_value', y='count', labels={'row_value':dim.capitalize(),'count':'Total'})
+        else:
+            agg    = df.groupby('column_value', as_index=False)['count'].sum()
+            title  = f"State-wise totals by {dim}{' (EV)' if ev else ''}"
+            result = agg.rename(columns={'column_value':'State','count':'Total'})
+            fig    = px.bar(agg, x='column_value', y='count', labels={'column_value':'State','count':'Total'})
+    else:
+        if loc_type == 'state':
+            ts     = df[df['column_value']==loc].groupby('year', as_index=False)['count'].sum()
+            title  = f"Total registrations for STATE: {loc}{' (EV)' if ev else ''}"
+            result = ts.rename(columns={'count':'Total'})
+            fig    = px.line(ts, x='year', y='count', labels={'count':'Total'})
+        else:
+            ts     = df.groupby('year', as_index=False)['count'].sum()
+            title  = f"Overall total registrations{' (EV)' if ev else ''}"
+            result = ts.rename(columns={'count':'Total'})
+            fig    = px.line(ts, x='year', y='count', labels={'count':'Total'})
+    st.dataframe(result)
+    st.plotly_chart(fig)
+    st.markdown("#### 💬 Try asking:")
+    st.markdown("- Top 5 states by fuel in 2022  ")
+    st.markdown("- Norm-wise registrations in Maharashtra in 2024 ")
+    st.markdown("- Electric (EV) norm-wise registrations in Karnataka after 2021 ")
+    st.markdown("- Fuel-wise registrations in Delhi in 2020")
+    st.markdown("- vehicle classes by registrations in Tamil Nadu in 2022 ")
+   
+    
+
+# Run the entire app
+# ask_with_llm()
+        
+# ---------------------- Sidebar ----------------------
+st.sidebar.title("🔍 Navigation")
+section = st.sidebar.radio("Go to", [
+    "Dashboard Overview",
+    "EV Insights",
+    "Fuel Norm Analysis(StateWise)",
+    "State Analysis",
+    "Vehicle Class & Category",
+    "Ask with Text (LLM)"
+])
+
+# ---------------------- Page Routing ----------------------
+if section == "Dashboard Overview":
+    dashboard_overview()
+elif section == "EV Insights":
+    ev_insights()
+elif section == "Fuel Norm Analysis(StateWise)":
+    fuel_norm_distribution_dashboard(
+    root_dir=r"D:\Data_for_query_tool\data", 
+    state_mapping_file=r'state_rto_data.json'
+)
+elif section == "State Analysis":
+    state_analysis()
+elif section == "Vehicle Class & Category":
+    vehicle_class_category()
+elif section == "Ask with Text (LLM)":
+    ask_with_llm()

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+pandas
+plotly

state_rto_data.json

@@ -0,0 +1,182 @@
+[
+    {
+        "State Code": "an",
+        "Full State Name": "Andaman & Nicobar Island  ",
+        "RTO List": "Baratang, Campbell Bay, Car Nicobar, Diglipur, Ferrargunj, Kamorta, Katchal, Little Andaman, Mayabunder, Port Blair DTO, Rangat, Shaheed Dweep, Swaraj Dweep, Teressa"
+    },
+    {
+        "State Code": "ap",
+        "Full State Name": "Andhra Pradesh  ",
+        "RTO List": "Adoni RTO, Amalapuram RTA, Anakapalli RTA, Anantapur RTA, Atmakur MVI Office, Atmakur-Kurnool MVI Office, Badvel MVI Office, Bapatla MVI Office, Bhimavaram RTA, Chilakaluripeta MVI Office, Chintoor, Chirala UO, Chittoor RTA, Cuddapah RTA, Darsi UO, Dhone MVI Office, Gajuwaka RTA, Gudiwada RTA, Gudur RTA, Guntakal UO, Guntur RTA, Hindupur RTA, Itchapuram MVI Office, Jaggayyapet UO, Jangareddy Gudem UO, Kadiri MVI Office, Kandukur MVI Office, Kavali UO, Kovvuru UO, Kurnool RTA, Macherla MVI Office, Madanapalle UO, Mandapeta UO, Mangalagiri MVI Office, Markapur UO, Nandigama RTA, Nandyal RTA, Narasaraopet RTA, Narsipatnam MVI Office, Nellore RTA, Nuzvid UO, Paderu RTA, Palakole UO, Palakonda MVI Office, Palamaner MVI Office, Palasa MVI Office, PARVATHIPURAM RTO OFFICE, Peddapuram MVI Office, Piduguralla UO, Piler MVI Office, Prakasam RTA, Proddutur RTA, Pulivendula MVI Office, Punganur UO, Puttur MVI Office, Rajahmundry RTA, Rajampet MVI Office, Ramachandrapuram UO, Ravulapalem UO, Rayachoti MVI Office, REGIONAL TRANSPORT OFFICE RAMPACHODAVARAM, RTA Eluru, RTA Kakinada, RTA MACHILIPATNAM, Salur MVI Office, Srikakulam RTA, Srikalahasthi MVI Office, Sullurpet UO, Tadepalli Gudem UO, Tadipatri UO, Tanuku UO, Tekkali MVI Office, Tenali UO, Tirupati RTA, UNIT OFFICE KATHIPUDI, UNIT OFFICE RAYADURG, Vijayawada RTA, Vishakapatnam RTA, Vizianagaram RTA, Vuyyuru UO"
+    },
+    {
+        "State Code": "ar",
+        "Full State Name": "Arunachal Pradesh  ",
+        "RTO List": "ANJAW, CHANGLANG, DIBANG VALLEY, EAST KAMENG, EAST SIANG, ITANAGAR CAPITAL REGION, KAMLE, KRA-DAADI, KURUNG KUMEY, LEPARADA, LOHIT, LONGDING, LOWER DIBANG VALLEY, LOWER SIANG, LOWER SUBANSIRI, NAMSAI, PAKKE-KESANG, SHI-YOMI, SIANG, STATE TRANSPORT AUTHORITY, TAWANG, TIRAP, UPPER SIANG, UPPER SUBANSIRI, WEST KAMENG, WEST SIANG, YUPIA"
+    },
+    {
+        "State Code": "as",
+        "Full State Name": "Assam  ",
+        "RTO List": "NIAIMT,CACHAR, NIAIMT,HAILAKANDI, NIAIMT,KARIMGANJ, BARPETA, BASKA, BISWANATH CHARIALI, BONGAIGAON, CACHAR, CHARAIDEO, CHIRANG, DARRANG, DHEMAJI, DHUBRI, DIBRUGARH, DIMA HASAO, GOALPARA, GOLAGHAT, HAILAKANDI, HOJAI, JORHAT, KAMRUP, KAMRUP(RURAL), KARBI ANGLONG, KARIMGANJ, KOKRAJHAR, LAKHIMPUR, MAJULI, MORIGAON, NAGAON, NALBARI, SIVASAGAR, SONITPUR, SOUTH SALMARA, STATE TRANSPORT AUTHORITY, TINSUKIA, UDALGURI"
+    },
+    {
+        "State Code": "br",
+        "Full State Name": "Bihar  ",
+        "RTO List": "M/S MURARI AUTO, Patna, ARARIA, ARAWAL, AURANGABAD, BANKA, BEGUSARAI, BETTIAH, BHABHUA, BHAGALPUR, BHAGALPUR RTA, BHOJPUR, BUXUR, CHAPARA, CHAPRA RTA, DARBHANGA, DARBHANGA RTA, GAYA, GAYA RTA, GOPALGANJ, JAMUI, JEHANABAD, KATIHAR, KHAGARIA, KISHANGANJ, LAKHISARAI, MADHEPURA, MADHUBANI, MOTIHARI, MUNGER, MUNGER RTA, MUZAFFARPUR, MUZAFFARPUR RTA, NALANDA, NAWADA, PATNA, PATNA RTA, PURNEA, PURNEA RTA, ROHTAS, SAHARSA, SAHARSA RTA, SAMASTIPUR, SHEIKHPURA, SHEOHAR, SITAMARHI, SIWAN, STA BIHAR, SUPAUL, VAISHALI"
+    },
+    {
+        "State Code": "ch",
+        "Full State Name": "Chandigarh  ",
+        "RTO List": "CHANDIGARH UT (RLA AND STA)"
+    },
+    {
+        "State Code": "cg",
+        "Full State Name": "Chhattisgarh  ",
+        "RTO List": "Ambikapur RTO, BAIKUNTHPUR DTO, Balod DTO, Baloda Bazar DTO, Balrampur DTO, Bemetara DTO, Bijapur DTO, Bilaspur RTO, Dantewada DTO, Dhamtari DTO, DURG RTO, Gariyaband DTO, Gaurela-Pendra-Marwahi DTO, JAGDALPUR RTO, Janjgir Champa DTO, Jashpur DTO, KANKER DTO, KAWARDHA DTO, KONDAGAON DTO, Korba DTO, Mahasamund DTO, Mungeli DTO, Narayanpur DTO, Raigarh DTO, Raipur RTO, Rajnandgaon ARTO, RTA TC NAWA RAIPUR, State Transport Authority, Sukma DTO, Surajpur DTO"
+    },
+    {
+        "State Code": "dl",
+        "Full State Name": "Delhi  ",
+        "RTO List": "BURARI AUTO UNIT, BURARI TAXI UNIT, DWARKA, I P ESTATE, JANAKPURI, JHULJHULI FITNESS CENTER, KAIR CLUSTER BUS FITNESS CENTER, KUSHAKNALA CLUSTER BUS FITNESS CENTER, LADO SARAI FITNESS CENTER, LONI ROAD, MALL ROAD, MAYUR VIHAR, RAJA GARDEN FITNESS CENTER, RAJOURI GARDEN, RAJPUR ROAD/VIU BURARI, ROHINI, SARAI KALE KHAN, SHAKUR BASTI FITNESS CENTER, SOUTH DELHI, SURAJMAL VIHAR, VASANT VIHAR, VISHWAS NAGAR FITNESS CENTER, WAZIRPUR"
+    },
+    {
+        "State Code": "ga",
+        "Full State Name": "Goa  ",
+        "RTO List": "STATE TRANSPORT AUTHORITY, GOA, ASSISTANT DIRECTOR OF TRANSPORT ENF. (NORTH) PANAJI, ASSISTANT DIRECTOR OF TRANSPORT ENF. (SOUTH) MARGAO, BICHOLIM RTO, CANACONA RTO, DHARBANDORA RTO, MAPUSA RTO, MARGAO RTO, PANAJI RTO, PERNEM RTO, PONDA RTO, QUEPEM RTO, VASCO RTO"
+    },
+    {
+        "State Code": "gj",
+        "Full State Name": "Gujarat  ",
+        "RTO List": "Ahmedabad (Rural), Bawla ARTO, AAHWA, AHMEDABAD, AHMEDABAD EAST, AMRELI, ANAND, BANASKANTHA, BARDOLI, BHARUCH, BHAVNAGAR, BOTAD, CHHOTAUDAIPUR, DAHOD, GANDHINAGAR, JAMNAGAR, JUNAGADH, KACHCHH, KACHCHH EAST, KHAMBHALIYA, KHEDA, LUNAVADA, MEHSANA, MODASA, MORBI, NARMADA, NAVSARI, PANCHMAHAL, PATAN, PORBANDAR, RAJKOT, SABARKANTHA, SURAT, SURENDRANAGAR, TAPI, VADODARA, VALSAD, VERAVAL"
+    },
+    {
+        "State Code": "hr",
+        "Full State Name": "Haryana  ",
+        "RTO List": "M.G. MOTORS, M/S DAISY MOTORS PVT LTD, M/S GLOBAL AUTOMART PVT LTD, M/S KARNAL MOTORS PVT LTD, M/S MAHABALAJI AUTO SERVICE PVT LTD, M/S METRO MOTORS PVT LTD, M/S MOHAN FOURWHEEL PVT LTD, M/S PARAS TRUCKS, M/S PASCO AUTOMOBILES, M/S PASCO MOTORS LLP, M/S SHREE MOTORS PVT LTD, M/S YASHODHA TRANSWHEELS LLP, RA(MV) PANCHKULA, RTA BHIWANI, RTA FARIDABAD, RTA FATEHABAD, RTA GURGAON, RTA HISAR, RTA JHAJJAR, RTA JIND, RTA KAITHAL, RTA KARNAL, RTA KURUKSHETRA, RTA MOHINDERGARH, RTA NUH, RTA PALWAL, RTA PANCHKULA, RTA PANIPAT, RTA REWARI, RTA ROHTAK, RTA SIRSA, RTA SONEPAT, RTA YNR, AMBALA CITY, ASSANDH, BAHADURGARH, BALLABGARH, BARARA, BAWAL, BHIWANI, CHARKHI DADRI, DABWALI, ELLNABAD, FARIDABAD, FATEHABAD, FEROZEPUR ZIRKHA, GANAUR, GOHANA, GULHA, GURUGRAM SOUTH, HANSI, HARYANA HEAD OFFICE CHD, HATHIN, HISAR, HODEL, JAGADHARI, JHAJHAR, JIND, KAITHAL, KALAYAT, KANINA, KARNAL, KOSLI, LADWA, LOHARU, M/S ABC MOTORS, M/S AMBA MOTORS, M/S COMPETENT AUTOMOBILES, M/S CSG AUTOMOBILES, M/S DAKSH MARKETING, M/S DHINGRA TRUCKING PVT LTD, M/S DINCO FOUR WHEELS LLP, M/S EAKANSH MOTORS PVT LTD, M/S EAKANSH WHEELS, M/S G.N.G AUTO AIDS PVT.LTD, M/S GARG MOTORS, M/S GLOBAL AUTMOBOLINES, M/S H.S MOTORS, M/S HARCHAND MOTORS, M/S HARYANA MOTOR, M/S HIMGIRI AUTOMOBILES PVT.LTD, M/S HIND MOTORS, M/S HISAR AUTOMOBILES, M/S JAGMOHAN AUTOMOBILES PVT LTD, M/S JOHAR AUTOMOBILES, M/S KARAN AUTOMOTIVE, M/S KARNAL MOTORS PVT, M/S KBS MOTORS PVT LTD, M/S KHANNA CAR PLAZA PVT LTD, M/S LEKHRAJ AUTO PLAZA PVT LTD, M/S SATGURU MOTORS, M/S MANGALAM MOTORS, M/S MANGLA MOTORS, M/S MG MOTORS, M/S PALTINUM MOTOCOPR, M/S PALUCK TECHNOLOGIES PVT.LTD, M/S PANDIT AUTOMOBILES PVT LTD, M/S PASCO AUTOMOBILES (x3), M/S PASCO AUTOMOBILES COMPLEX, M/S PASCO MOTORS LLP, M/S PASCO MOTORS LLP SHOP, M/S PASCO MOTORS, M/S PREM MOTORS PVT LTD, M/S PREM MOTORS PVT LTD IDC, M/S R.K.H AUTOMOBILES PVT LTD, M/S RAMA MOTORS, M/S RANA MOTORS PVT LTD, M/S RUDRA MOTORS, M/S SAHIL MOTORS, M/S SANJAY AUTOMOTIVE LLP, M/S SHAKTI MOTORS PVT LTD, M/S SHIVA MOTORS, M/S SHREE KRISHANA MOTORS, M/S SHREE SALASAR MOTORS, M/S SISOTHYA AUTOMOBILES, M/S STAR AUTOMOBILE, M/S SUPREME MOBILES PVT LTD (x2), M/S TAYAL MOTORS PVT LTD, M/S TCS AUTOWORLD, M/S THADESHRI MOTORS, M/S TIRUPATI MOTORS, M/S UNIQUE MOTORS PVT LTD, M/S UNITED MOTORS, M/S VIPIN MOTORS, M/S VMT MOTORS, MAHENDERGARH, MEHAM, NARAINGARH, NARNAUL, NARWANA, NUH, PALWAL, PANCHKULA, PANIPAT, PATAUDI, PEHOWA, RATIA, REWARI, RLA TAURU, ROHTAK, RTA AMBALA, RTA CHARKHI DADRI, SAFIDON, SAMALKHA, SDM AMBALA CANTONMENT, SDM BADHRA, SDM BADKHAL, SDM BADLI, SDM BADSHAHPUR, SDM BARWALA, SDM BERI, SDM BILASPUR, SDM GHARAUNDA, SDM GURUGRAM, SDM INDIRI, SDM KALANWALI, SDM KHARKHONDA, SDM NARNAUND, SDM OFFICE KALKA, SDM PUNHANA, SDM RADAUR, SDM SAMPLA, SDM SHAHABAD, SDM UCHANA, SIRSA, SIWANI, SONEPAT, THANESAR, TOHANA, TOSHAM"
+    },
+    {
+        "State Code": "hp",
+        "Full State Name": "Himachal Pradesh  ",
+        "RTO List": "ADDITIONAL/JOINT/DY. DIRECTOR-CUM-RTO (HQ), ARTO BADDI, ARTO BAROTIWALA, ARTO DAMTAL (TOKI), ARTO GAGRET, ARTO KALA AMB, ARTO KANDWAL, ARTO MEHATPUR, ARTO PAONTA SAHIB, ARTO PARWANOO, ARTO SWARGHAT, ARTO TIPRA, ARTO TUNUHATTI, HRTC BAIJNATH, HRTC BILASPUR, HRTC CHAMBA, HRTC DEHRA, HRTC DHARAMSHALA, HRTC HAMIRPUR, HRTC JASSUR, HRTC KULLU, HRTC MANDI, HRTC NAHAN, HRTC NALAGARH, HRTC PALAMPUR, HRTC RAMPUR, HRTC SARKAGHAT, HRTC SHIMLA(RURAL), HRTC SUNDERNAGAR, HRTC TARADEVI, HRTC UNA, RLA AMB, RLA ANI, RLA ARKI, RLA BAIJNATH, RLA BALH, RLA BALICHOWKI, RLA BANGANA, RLA BANJAR, RLA BARSAR, RLA BHARMOUR, RLA BHORANJ, RLA BILASPUR, RLA CHAMBA, RLA CHOPAL, RLA CHURAH, RLA CHUWARI, RLA DALHOUSIE, RLA DEHRA, RLA DHARAMPUR, RLA DHARAMSHALA, RLA DHEERA, RLA DODRA KAWAR, RLA FATHEPUR, RLA GAGRET, RLA GHUMARVI, RLA GOHAR, RLA HAMIRPUR, RLA HAROLI, RLA INDORA, RLA JAISINGPUR, RLA JAWALAJI, RLA JAWALI, RLA JHANDUTTA, RLA JOGENDRANAGAR, RLA JUBBAL, RLA KAFFOTA, RLA KALPA, RLA KANDAGHAT, RLA KANGRA, RLA KARSOG, RLA KASAULI, RLA KAZA, RLA KELANG, RLA KOTKHAI, RLA KOTLI, RLA KULU, RLA KUMARSAIN, RLA KUPVI, RLA MANALI, RLA MANDI, RLA NADAUN, RLA NAGROTA BAGWAN, RLA NAHAN, RLA NALAGARH, RLA NICHAR, RLA NIRMAND, RLA NURPUR, RLA PACHHAD, RLA PADDHAR, RLA PALAMPUR, RLA PANGI, RLA PAONTA SAHIB, RLA PARWANOO, RLA POOH, RLA RAJGARH, RLA RAMPUR BUSHAR, RLA ROHRU, RLA SALOONI, RLA SANGRAH, RLA SARKAGHAT, RLA SHAHPUR, RLA SHILLAI, RLA SHIMLA HP-03/HP-07(URBAN), RLA SHIMLA(RURAL), RLA SHRI NAINA DEVI JI SWARGHAT, RLA SOLAN, RLA SUJANPUR, RLA SUNDARNAGAR, RLA THEOG, RLA THUNAG, RLA UDAIPUR, RLA UNA, RTO (FLYING SQUAD) KANGRA AT DHARAMSHALA, RTO (FLYING SQUAD) KULLU, RTO BADDI(NALAGARH), RTO BILASPUR, RTO CHAMBA, RTO DHARAMSHALA, RTO HAMIRPUR, RTO KULLU, RTO MANDI, RTO NAHAN, RTO RAMPUR, RTO SHIMLA, RTO SOLAN, RTO UNA, STA SHIMLA"
+    },
+    {
+        "State Code": "jk",
+        "Full State Name": "Jammu & Kashmir  ",
+        "RTO List": "ANANTNAG ARTO, BANDIPORA ARTO, BARAMULLA ARTO, BUDGAM ARTO, DODA ARTO, GANDERBAL ARTO, JAMMU RTO, KATHUA RTO, KISHTWAR ARTO, KULGAM ARTO, KUPWARA ARTO, POONCH ARTO, PULWAMA ARTO, RAJOURI ARTO, RAMBAN ARTO, REASI ARTO, SAMBA ARTO, SHOPIAN ARTO, SRINAGAR RTO, STATE TRANSPORT AUTHORITY, UDHAMPUR ARTO"
+    },
+    {
+        "State Code": "jh",
+        "Full State Name": "Jharkhand  ",
+        "RTO List": "Authorised Testing Centre (TUV SUD), Ranchi, Authorized Fitness Centre(VAHAN), Dhanbad, M/s Auto Fitness Centre, Ranchi, M/s Auto Tech Vehicle Fitness, Ranchi, M/S Global Automated Fitness, Hazaribag, M/s Universal Automated Fitness Centre, East Singhbhum, BOKARO, CHATRA, DEOGHAR, DHANBAD, DTO OFFICE DUMKA, EAST SINGHBHUM (JAMSHEDPUR), GARHWA, GIRIDIH, GODDA, GUMLA, HAZARIBAG, JAMTARA, KHUNTI, KODERMA, LATEHAR, LOHARDAGA, PAKUR, PALAMU, RAMGARH, RANCHI, SAHEBGANJ, SARAIKELA-KHARSAWAN, SIMDEGA, STATE TRANSPORT AUTHORITY, WEST SINGHBHUM (CHAIBASA)"
+    },
+    {
+        "State Code": "ka",
+        "Full State Name": "Karnataka  ",
+        "RTO List": "Chandapura, Bengaluru RTO, Madhugiri, Tumakuru ARTO, R.T. Nagar, Bengaluru, STU AND AUTORIKSHAW, SHANTHINAGAR RTO, Surathkal, Mangaluru, TARIKERE, CHIKKAMAGALURU ARTO, ATHANI ARTO, Bagalkot RTO, Bailhongal RTO, Banashankari, Bantwala ARTO, Baswakalyan RTO, Bellary RTO, BENGALURU CENTRAL RTO, BENGALURU EAST RTO, BENGALURU NORTH RTO, BENGALURU SOUTH RTO, BENGALURU WEST RTO, BHALKI ARTO, Bidar RTO, Bijapur RTO, Chamarajanagar RTO, Chickaballapur RTO, CHIKAMANGLUR RTO, Chikkodi RTO, Chintamani ARTO, CHITRADURGA RTO, Dandeli ARTO, DAVANAGERE RTO, Devanahalli ARTO, DHARWAD EAST RTO, DHARWAD WEST RTO, Electronic City RTO, Gadag RTO, Gokak ARTO, Hassan RTO, Haveri RTO, Honnavar ARTO, Hospet RTO, Hunsur ARTO, JAMKHANDI ARTO, Jnanabharathi RTO, K G F ARTO, KALABURAGI RTO, Karwar RTO, Kolar RTO, Koppal RTO, Krishnarajapuram RTO, MADIKERI RTO, Mandya RTO, Mangalore RTO, Marathahalli, MYSURU EAST RTO, MYSURU WEST RTO, Nagamangala RTO, Nelamangala RTO, Puttur RTO, Raichur RTO, Ramanagar RTO, Ramdurga ARTO, Ranibennur ARTO, REGIONAL TRANSPORT OFFICE BELAGAVI, Sagar ARTO, SAKALESHPURA ARTO, Shimoga RTO, Sirsi RTO, STATE TRANSPORT AUTHORITY, Tiptur ARTO, Tumkur RTO, Udupi RTO, Yadgiri RTO, YALAHANKA RTO"
+    },
+    {
+        "State Code": "kl",
+        "Full State Name": "Kerala  ",
+        "RTO List": "ADOOR SRTO, ALAPPUZHA RTO, ALATHUR SRTO, ALUVA SRTO, ANGAMALI SRTO, ATTINGAL RTO, CHADAYAMANGALA SRTO, CHALAKKUDY SRTO, CHANGANACHERRY SRTO, CHENGANNUR SRTO, CHERTHALA SRTO, CHITTUR SRTO, DEVIKULAM SRTO, ERNAKULAM RTO, GURUVAYUR SRTO, IDUKKI RTO, IRINJALAKUDA SRTO, IRTTY SRTO, KANHANGAD SRTO, KANJIRAPPALLY SRTO, KANNUR RTO, KARUNAGAPPALLY SRTO, KASARGODE RTO, KATTAKADA SRTO, KAYAMKULAM SRTO, KAZHAKUTTOM SRTO, KODUNGALLUR SRTO, KODUVALLY SRTO, KOILANDY SRTO, KOLLAM RTO, KONDOTTY SRTO, KONNI SRTO, KOTHAMANGALAM SRTO, KOTTARAKKARA SRTO, KOTTAYAM RTO, KOZHIKODE RTO, KUNNATHUR SRTO, KUTTANADU SRTO, MALAPPURAM RTO, MALLAPPALLY SRTO, MANANTHAVADY SRTO, MANNARGHAT SRTO, MATTANCHERRY SRTO, MAVELIKKARA SRTO, MUVATTUPUZHA RTO, NANMANDA SRTO, NATIONALISED SECTOR(TVPM) RTO, NEDUMANGADU SRTO, NEYYATTINKARA SRTO, NILAMBUR SRTO, NORTH PARUR SRTO, OTTAPPALAM SRTO, PALAI SRTO, PALAKKAD RTO, PARASSALA SRTO, PATHANAMTHITTA RTO, PATHANAPURAM SRTO, PATTAMBI SRTO, PAYYANNUR SRTO, PERAMBRA SRTO, PERINTHALMANNA SRTO, PERUMBAVUR SRTO, PONNANI SRTO, PUNALUR SRTO, RAMANATTUKARA (FEROKE) SRTO, RANNI SRTO, SULTHANBATHERY SRTO, TC OFFICE - STA OFFICE, THALASSERY SRTO, THALIPARAMBA SRTO, THIRUR SRTO, THIRURANGADI SRTO, THIRUVALLA SRTO, THODUPUZHA SRTO, THRIPRAYAR SRTO, THRISSUR RTO, TRIPUNITHURA SRTO, TRIVANDRUM RTO, UDUMBANCHOLA SRTO, UZHAVOOR SRTO, VADAKARA RTO, VAIKOM SRTO, VANDIPERIYAR SRTO, VARKALA SRTO, VELLARIKUNDU SRTO, WADAKKANCHERRY SRTO, WAYANAD RTO"
+    },
+    {
+        "State Code": "la",
+        "Full State Name": "Ladakh  ",
+        "RTO List": "KARGIL ARTO, LEH ARTO, STATE TRANSPORT AUTHORITY"
+    },
+    {
+        "State Code": "ld",
+        "Full State Name": "Lakshadweep  ",
+        "RTO List": "AGATTI, AMINI, ANDROTH, CHETLAT, KADMAT, KALPENI, KAVARATTI, KILTAN, MINICOY"
+    },
+    {
+        "State Code": "mp",
+        "Full State Name": "Madhya Pradesh  ",
+        "RTO List": "AGAR MALWA RTO, ALIRAJPUR DTO, ANUPPUR DTO, ASHOKNAGAR DTO, BADWANI DTO, BALAGHAT DTO, BETUL DTO, BHIND DTO, BHOPAL RTO, BURHANPUR DTO, CHATTARPUR ARTO, CHHINDWARA ARTO, DAMOH DTO, DATIA DTO, DEWAS DTO, DHAR ARTO, DINDORI DTO, GUNA DTO, GWALIOR RTO, HARDA DTO, HOSANGABAD DTO, INDORE RTO, JABALPUR RTO, JHABUA DTO, KATNI ARTO, KHANDWA ARTO, KHARGONE ARTO, MANDLA DTO, MANDSAUR ARTO, MORENA DTO, NARSINGHPUR DTO, NEEMUCH DTO, PANNA DTO, RAISEN DTO, RAJGARH DTO, RATLAM DTO, REWA RTO, SAGAR RTO, SATNA ARTO, SEHORE DTO, SEONI ARTO, SHAHDOL RTO, SHAJAPUR DTO, SHEOPUR DTO, SHIVPURI DTO, SIDHI DTO, SINGROLI DTO, STATE TRANSPORT AUTHORITY, TIKAMGARH DTO, UJJAIN RTO, UMARIA DTO, VIDISHA DTO"
+    },
+    {
+        "State Code": "mh",
+        "Full State Name": "Maharashtra  ",
+        "RTO List": "DY REGIONAL TRANSPORT OFFICE, HINGOLI, AHEMEDNAGAR, AKLUJ, AKOLA, AMBEJOGAI, AMRAWATI, BARAMATI, BEED, BHANDARA, BULDHANA, CHANDRAPUR, CHHATRAPATI SAMBHAJINAGAR, Chiplun Chiplun Track, DHARASHIV, DHULE, DY RTO PIMPRI CHINCHWAD, DY RTO RATNAGIRI, DY.R.T.O.BORIVALI, GADCHIROLI, GONDHIA, JALANA, JALGAON, KALYAN, KARAD, KOLHAPUR, LATUR, MALEGAON, MUMBAI (CENTRAL), MUMBAI (EAST), MUMBAI (WEST), NAGPUR (EAST), NAGPUR (RURAL), NAGPUR (U), NANDED, NANDURBAR, NASHIK, PANVEL, PARBHANI, PEN (RAIGAD), PUNE, RTO MH04-Mira Bhayander FitnessTrack, SANGLI, SATARA, SINDHUDURG(KUDAL), SOLAPUR, SRIRAMPUR, TC OFFICE, THANE, VASAI, VASHI (NEW MUMBAI), WARDHA, WASHIM, YAWATMAL"
+    },
+    {
+        "State Code": "mn",
+        "Full State Name": "Manipur  ",
+        "RTO List": "DISTRICT TRANSPORT OFFICE, TENGNOUPAL, BISHNUPUR, Chandel, CHURACHANDPUR, IMPHAL EAST, IMPHAL WEST, KANGPOKPI, SENAPATI, STA MANIPUR, Tamenglong, THOUBAL, UKHRUL"
+    },
+    {
+        "State Code": "ml",
+        "Full State Name": "Meghalaya  ",
+        "RTO List": "AMPATI, BAGHMARA, DTO JOWAI, DTO MAIRANG, KHLIEHRIAT, MAWKYRWAT, NONGPOH, NONGSTOIN, RESUBELPARA, SHILLONG, STATE TRANSPORT AUTHORITY, TURA, WILLIAMNAGAR"
+    },
+    {
+        "State Code": "mz",
+        "Full State Name": "Mizoram  ",
+        "RTO List": "AIZAWL DTO, AIZAWL RURAL DTO, CHAMPHAI, KOLASIB, LAWNGTLAI, LUNGLEI, MAMIT, SAIHA, SERCHHIP, STATE TRANSPORT AUTHORITY"
+    },
+    {
+        "State Code": "nl",
+        "Full State Name": "Nagaland  ",
+        "RTO List": "DIMAPUR DTO, KOHIMA RTO, MOKOKCHUNG RTO, MON DTO, PHEK DTO, TRANSPORT COMMISSIONERATE, TUENSANG DTO, WOKHA DTO, ZUNHEBOTO DTO"
+    },
+    {
+        "State Code": "or",
+        "Full State Name": "Odisha  ",
+        "RTO List": "ANGUL RTO, ARTO BARBIL, BALASORE RTO, BARGARH RTO, BHADRAK RTO, BHANJANAGAR RTO, BHUBANESWAR RTO, BHUBANESWAR-II RTO, BOLANGIR RTO, BOUDH RTO, CHANDIKHOLE RTO, CUTTACK RTO, DEOGARH RTO, DHENKANAL RTO, GAJAPATI RTO, GANJAM RTO, JAGATSINGHPUR RTO, JAJPUR RTO, JHARSUGUDA RTO, KALAHANDI RTO, KENDRAPARA RTO, KEONJHAR RTO, KHORDHA ARTO, KORAPUT RTO, MALKANAGIRI RTO, MAYURBHANJ RTO, NAWARANGPUR RTO, NAYAGARH RTO, NUAPADA RTO, PHULBANI RTO, PURI RTO, RAIRANGPUR ARTO, RAYGADA RTO, ROURKELA RTO, SAMBALPUR RTO, SONEPUR RTO, STATE TRANSPORT AUTHORITY, SUNDERGARH RTO, TALCHER RTO"
+    },
+    {
+        "State Code": "py",
+        "Full State Name": "Puducherry  ",
+        "RTO List": "BAHOUR, CHECK POST, KARAIKAL, MAHE, OULGARET, PUDUCHERRY, VILLIANUR, YANAM"
+    },
+    {
+        "State Code": "pb",
+        "Full State Name": "Punjab  ",
+        "RTO List": "ABOHAR SDM, AHMEDGARH SDM, AJNALA SDM, AMARGARH SDM, AMLOH SDM, AMRITSAR RTA, AMRITSAR-2 SDM, ANANDPUR SAHIB SDM, BABA BAKALA SDM, BAGHA PURANA SDM, BALACHAUR SDM, BANGA SDM, BARNALA SDM, BASSI PATHANA SDM, BATALA SDM, BATHINDA RTA, BHAWNIGARH SDM, BHIKHIWIND SDM, BHOLATH SDM, Budhlada SDM, CHAMKAUR SAHIB SDM, DASUYA SDM, DERA BABA NANAK SDM, DERA BASSI SDM, DHAR KALAN SDM, DHARAMKOT SDM, DHURI SDM, DINANAGAR SDM, DIRBA SDM, DUDHAN SADHAN SDM, FARIDKOT RTA, FATEHGARH SAHIB SDM, FAZILKA SDM, FEROZPUR RTA, GARSHANKAR SDM, GIDDARBAHA SDM, GURDASPUR RTA, GURU HAR SAHAI SDM, HOSHIARPUR RTA, JAGRAON SDM, JAITO SDM, JALALABAD SDM, JALANDHAR RTA, JALANDHAR-11 SDM, KALANAUR SDM, KAPURTHALA SDM, KHADUR SAHIB SDM, KHAMANO SDM, KHANNA SDM, KHARAR SDM, KOTKAPURA SDM, LEHRAGAGA SDM, LUDHIANA EAST SDM, LUDHIANA RTA, MAJITHA SDM, MALERKOTLA SDM, MALOUT SDM, MANSA SDM, MAUR MANDI SDM, MOGA SDM, MOONAK SDM, MORINDA SDM, MUKERIAN SDM, MUKTSAR SDM, NABHA SDM, NAKODAR SDM, NANGAL SDM, NIHAL SINGH WALA SDM, PATHANKOT SDM, PATIALA RTA, PATRAN SDM, PATTI SDM, PAYAL SDM, PHAGWARA SDM, PHILLOUR SDM, PUNJAB STA(RAC)/(AITP), RAIKOT SDM, RAJPURA SDM, RAMPURA PHUL SDM, ROPAR SDM, SAHIBZADA AJIT SINGH NAGAR RTA, SAMANA SDM, SAMRALA SDM, SANGRUR RTA, SARDULGARH SDM, SHAHD BHAGAT SINGH NAGAR SDM, SHAHKOT SDM, SULTANPUR SDM, SUNAM SDM, TALWANDI SABO SDM, TAPA SDM, TARN TARAN SDM, ZIRA SDM"
+    },
+    {
+        "State Code": "rj",
+        "Full State Name": "Rajasthan  ",
+        "RTO List": "JAGATPURA, JAIPUR ARTO, VIDHYADHAR NAGAR,JAIPUR DTO, A&L Company, ABU ROAD DTO, Adinath Fitness Center, Agarwal Fitness Center, AJMER RTO, Alwar Auto Mobile Fitness Center, Alwar Fitness Center, ALWAR RTO, ARAVALI FITNESS TESTING CENTER, Arihant Vehicle Fitness Center, Atharva Enterprises, BALAJI ALLIANCE, BALAJI FITNESS CENTER (BHILWARA), BALAJI FITNESS CENTER (HANUMANGARH), BALOTRA DTO, BANSWARA DTO, BANSWARA VEHICLE FITNESS CENTER, BARAN DTO, BARMER DTO, BEAWAR DTO, Bharat Vahan Fitness Center, BHARATPUR RTO, BHILWARA DTO, BHINMAL DTO, BHIWARI DTO, BIKANER RTO, BUNDI DTO, CHITTORGARH RTO, CHOMU DTO, CHURU DTO, DAUSA RTO, DHOLPUR DTO, DIDWANA DTO, DUDU DTO, DUDU FITNESS CENTER, DUNGARPUR DTO, EXPLORE IT SERVICES PVT. LTD., FREEDOM MOTORS, G.Y. Fitness Center, Ganesh Ji Fitness Center, HANUMANGARH DTO, Hindustan Automobiles, Indira Vehicle Fitness Centre, INFINITY FITNESS CENTER, Jai Bhawani Fitness Center, JAIPUR (FIRST) RTO, JAIPUR (SECOND) RTO, Jaipur Vehicle Fitness and Maintenance Center, JAISALMER DTO, JALORE DTO, JALORE FITNESS CENTRE, JHALAWAR DTO, JHUNJHUNU DTO, Jodhpur Parivahan Fitness Centre, JODHPUR RTO, KAROLI DTO, KEKRI DTO, KHETRI DTO, KISHANGARH DTO, KOTA RTO, Kota Vehicle Fitness Center, KOTPUTALI DTO, Laxmi Parivahan Fitness Center, M & D Automobile Fitness Center, M.K. Fitness Center, M/S Dholpur Fitness Center, M/S Jagdamba Fitness Center, M/S Nandan Fitness Testing Center, M/S OM Fitness & Service Center, Mahadev Fitness Center, MAHADEV FITNESS CENTER BHILWARA, MAHADEV FITNESS CENTER JODHPUR, Mahaveer Jain Fitness Centre, MAHAVEER PRASAD RAM KISHAN, Marudhara Transport Company, Marwar Fitness Center, Matsya Fitness Center, Meel Motors, Meera Fitness Center, MEERA FITNESS TESTING CENTER CHITTORGARH, Naganaray Fitness Center, NAGAUR DTO, Navdeep Fitness Test Center, Navdurga Vahan Fitness Center, Navkar Shri Fitness Testing Center, NOHAR DTO, NOKHA DTO, Nokha Vehicle Fitness Center, PALI RTO, Parasvnath Fitness Center, PAWAN VEHICLE FITNESS CENTER PVT LTD, PHALODI DTO, PIPAR CITY DTO, POKHRAN DTO, PRATAPGARH DTO, Preksha Parivahan Fitness Center, Prerna Parivahan Fitness Center, R.K. Fitness Center, RAJSAMAND DTO, RAMGANJMANDI DTO, Royal Motors, SADULSHAHAR DTO, SAHAPURA (BHILWARA) DTO, SAHAPURA (JAIPUR) DTO, SALUMBAR DTO, SAWAI MADHOPUR DTO, Schoolnet India Limited, SHAHPURA BHILWARA FITNESS CENTER, SHAHPURA VEHICLE FITNESS CENTER (JAIPUR), Shanti Vehicle Fitness Testing Center, Shashank Automobiles, SHIV KRIPA FITNESS CENTER PVT LTD, SHREE BALAJI FITNESS CENTER PALI, Shree Fitness Center, Shree Kamdhenu Fitness Center, SHREE SHYAM VEHICLE FITNESS CENTER, SHRI BALAJI FITNESS CENTER BIKANER, Shri Bikaner Fitness Center, Shri Fitness Center, Shri Force Fitness Center, Shri Karni Fitness Center, SHRI MAHALAXMI FITNESS CENTER, Shri Vinayak Auto Fitness Center, SIKAR RTO, Sikar Vehicle Fitness Center, SIROHI DTO, Speedline Auto Fitness Private Limited, SRI GANGANAGAR DTO, SUJANGARH DTO, SUMERPUR DTO, Swarna Shri Fitness Testing Center, Tirupati Associates, TIRUPATI ASSOCIATES MORIJA CHOMU, TIRUPATI FITNESS CENTER, TONK DTO, Tonk Fitness Center, Udaipur Fitness Center, UDAIPUR RTO, Vaahan Fitness Center, Vatsal Enterprises"
+    },
+    {
+        "State Code": "sk",
+        "Full State Name": "Sikkim  ",
+        "RTO List": "Singtam, East Sikkim, GYALSING, JORETHANG, MANGAN, NAMCHI, Pakyong, RTO GANGTOK, SORENG"
+    },
+    {
+        "State Code": "tn",
+        "Full State Name": "Tamil Nadu  ",
+        "RTO List": "ALANGUDI UO, AMBASAMUTHIRAM UO, AMBATTUR RTO, AMBUR UO, ARAKKONAM UO, ARANI RTO, ARANTHANGI UO, ARAVAKURICHI UO, ARIYALUR RTO, ARUPPUKOTTAI UO, ATTUR RTO, AVINASHI UO, BATLAGUNDU UO, BHAVANI UO, CHENGALPATTU RTO, CHENNAI (CENTRAL) RTO, CHENNAI (EAST) RTO, CHENNAI (NORTH) RTO, CHENNAI (NORTH-EAST) RTO, CHENNAI (SOUTH) RTO, CHENNAI (SOUTH-EAST) RTO, CHENNAI (SOUTH-WEST) RTO, CHENNAI (WEST) RTO, CHEYYAR UO, CHIDAMBARAM RTO, COIMBATORE (CENTRAL) RTO, COIMBATORE (NORTH) RTO, COIMBATORE (SOUTH) RTO, COIMBATORE (WEST) RTO, CUDDALORE RTO, DHARAPURAM RTO, DHARMAPURI RTO, DINDIGUL RTO, ERODE (WEST) RTO, ERODE RTO, GINGEE UO, GOPICHETTIPALAYAM RTO, GUDALORE UO, GUDIYATHAM UO, GUMMIDIPOONDI UO, HARUR UO, HOSUR RTO, ILLUPPUR UO, KALLAKURICHI RTO, KANCHEEPURAM RTO, KANGEYAM UO, KARAIKUDI UO, KARUR RTO, KOVILPATTI RTO, KRISHNAGIRI RTO, KULITHALI UO, KUMARAPALAYAM RTO, KUMBAKONAM RTO, KUNDRATHUR RTO, LALKUDI UO, MADURAI (CENTRAL) RTO, MADURAI (NORTH) RTO, MADURAI (SOUTH) RTO, MADURANTAGAM UO, MANAPARAI UO, MANMANGALAM UO, MANNARGUDI UO, MARTHANDAM RTO, MAYILADUTHURAI RTO, MEENAMBAKKAM RTO, MELUR UO, METTUPALAYAM RTO, METTUR RTO, MUSURI UO, NAGAPATTINAM RTO, NAGERCOIL RTO, NAMAKKAL (NORTH) RTO, NAMAKKAL (SOUTH) RTO, NATHAM UO, NEYVELI UO, ODDANCHATRAM UO, OMALURE UO, OOTY RTO, PALACODE UO, PALANI RTO, PANRUTI UO, PARAMAKUDI UO, PARAMATHI VELLURE UO, PATTUKOTTAI UO, PERAMBALUR RTO, PERUNDURAI RTO, POLLACHI RTO, POONAMALLEE RTO, PUDUKOTTAI RTO, RAMANATHAPURAM RTO, RANIPET RTO, RASIPURAM UO, REDHILLS RTO, RTO CHENNAI (NORTH WEST), SALEM (EAST) RTO, SALEM (SOUTH) RTO, SALEM (WEST) RTO, SANKAGIRI RTO, SANKARANKOVIL RTO, SATHYAMANGALAM UO, SHOLINGANALLUR RTO, SIRKALI UO, SIVAGANGAI RTO, SIVAKASI RTO, SRIPERUMBUDUR RTO, SRIRANGAM RTO, SRIVILLIPUTHUR RTO, STATE TRANSPORT AUTHORITY, SULUR UO, TAMBARAM RTO, TENKASI RTO, THANJAVUR RTO, THENI RTO, THIRUCHENDUR RTO, THIRUKALUKUNTRAM UO, THIRUMANGALAM UO, THIRUPATTUR RTO, THIRUTHANI UO, THIRUTHURAIPOONDI UO, THOOTHUKUDI RTO, THURAIYUR UO, TINDIVANAM RTO, TIRUCHENGODE RTO, TIRUCHI RTO, TIRUCHI(EAST) RTO, TIRUNELVELI RTO, TIRUPPUR (NORTH) RTO, TIRUPPUR (SOUTH) RTO, TIRUVALLUR RTO, TIRUVANNAMALAI RTO, TIRUVARUR RTO, TIRUVERANBUR UO, UDUMALPET RTO, ULUNDURPET RTO, USILAMPATTI UO, UTHAMAPALAYAM UO, VADIPATTI UO, VALAPPADI UO, VALLIYUR UO, VALPARAI UO, VANIYAMBADI RTO, VEDACHANDUR UO, VELLORE RTO, VILUPPURAM RTO, VIRUDHACHALAM UO, VIRUDHUNAGAR RTO"
+    },
+    {
+        "State Code": "ts",
+        "Full State Name": "Telangana  ",
+        "RTO List": "MULUGU, NARAYANPET, RTA ADILABAD, RTA BHADRADRI, RTA IBRAHIMPATNAM, RTA JAGITYAL, RTA JANGOAN, RTA JAYASHANKAR, RTA JOGULAMBA, RTA KAMAREDDY, RTA KARIMNAGAR, RTA KHAMMAM, RTA KOMRAMBHEEM, RTA MAHABOOBNAGAR, RTA MAHABUBABAD, RTA MANCHERIAL, RTA MEDAK, RTA MEDCHAL, RTA NAGARKURNOOL, RTA NALGONDA, RTA NIRMAL, RTA NIZAMABAD, RTA PEDDAPALLI, RTA RAJANNA, RTA RANGAREDDY, RTA SANGAREDDY, RTA SIDDIPET, RTA SURYAPET, RTA UPPAL, RTA VIKARABAD, RTA WANAPARTHY, RTA WARANGAL RURAL, RTA WARANGAL URBAN, RTA YADADRI, RTA-HYDERABAD-CZ, RTA-HYDERABAD-EZ, RTA-HYDERABAD-NZ, RTA-HYDERABAD-SZ, RTA-HYDERABAD-WZ, UNIT OFFICE ARMOOR, UNIT OFFICE BHADRACHALAM, UNIT OFFICE BHODAN, UNIT OFFICE HUZURABAD, UNIT OFFICE KALWAKURTHY, UNIT OFFICE KODAD, UNIT OFFICE KORUTLA, UNIT OFFICE KUKATPALLY, UNIT OFFICE MIRYALAGUDA, UNIT OFFICE PARGI, UNIT OFFICE PATANCHERUVU, UNIT OFFICE PEBBAIR, UNIT OFFICE RAMAGUNDAM, UNIT OFFICE SATTUPALLI, UNIT OFFICE SHADNAGAR, UNIT OFFICE WYRA, UNIT OFFICE ZAHIRABAD"
+    },
+    {
+        "State Code": "tr",
+        "Full State Name": "Tripura  ",
+        "RTO List": "DHALAI DTO, GOMATI DTO, KHOWAI DTO, NORTH TRIPURA DHM DTO, NORTH TRIPURA DTO, SEPAHIJALA DTO, SOUTH TRIPURA DTO, STA TRIPURA, UNAKOTI DTO, WEST TRIPURA JTC"
+    },
+    {
+        "State Code": "up",
+        "Full State Name": "Uttar Pradesh  ",
+        "RTO List": "Agra RTO, AKBARPUR(AMBEDKAR NAGAR), ALIGARH RTO, Amethi ARTO, ARTO OFFICE RAMPUR, AURAIYA, AYODHYA RTO, Azamgarh RTO, Badaun, Baghpat, Bahraich, Ballia, Balrampur, BANDARTO, Barabanki ARTO, BAREILLY, BASTI RTO, Bhadohi(SANT RAVIDAS NAGAR), Bijnor, Bulandshahar, Chandauli, Chitrakoot, DEORIA, Etah, Etawah, Farrukhabad, FATHEHPUR, FEROZABAD, GHAZIABAD, Ghazipur, GONDA, Gorakhpur RTO, HAMIRPUR(UP), Hapur, HARDOI, HATHRAS, JAUNPUR, JhansiRTO, JPNAGAR, Kannauj, Kanpur Dehat, KANPUR NAGAR, Kasganj(kashi ram nagar), Kaushambi, LAKHIMPUR KHERI, Lalitpur, M/S Sai Dham Super Srv Soln Pvt Ltd Ghaziabad, MAHANAGAR ARTO LUCKNOW (UP321), Maharajganj, Mahoba, Mainpuri, MATHURA, Mau, MEERUT RTO, MIRZAPUR RTO, MORADABAD, MuzaffarNagar, Noida, Orai, PADRAUNA(KUSHI NAGAR), Pilibhit, PRATAPGARH, Prayagraj RTO, Raibareilly, SAHARANPUR RTO, SAHJAHANPUR, Sambhal ARTO, Sant Kabir Nagar, SHAMLI ARTO, Shravasti, Siddharth Nagar(naugarh), Sitapur, SONBHADRA, STATE TRANSPORT AUTHORITY, Sultanpur, TRANSPORT NAGAR RTO LUCKNOW (UP32), Unnao, VARANASI RTO"
+    },
+    {
+        "State Code": "uk",
+        "Full State Name": "Uttarakhand  ",
+        "RTO List": "ALMORA RTO, BAGESHWAR ARTO, DEHRADUN RTO, HALDWANI RTO, HARIDWAR ARTO, KARANPRAYAG ARTO, KASHIPUR ARTO, KOTDWAR ARTO, PAURI RTO, PITHORAGARH ARTO, RAMNAGAR ARTO, RANIKHET ARTO, RISHIKESH ARTO, ROORKEE ARTO, RUDRAPRAYAG ARTO, STATE TRANSPORT AUTHORITY, TANAKPUR ARTO, TEHRI ARTO, UDHAM SINGH NAGAR ARTO, UTTARKASHI ARTO, VIKAS NAGAR ARTO"
+    },
+    {
+        "State Code": "wb",
+        "Full State Name": "West Bengal  ",
+        "RTO List": "ALIPORE RTO, Alipurduar RTO, Arambag ARTO, Bangaon ARTO, Bankura RTO, Barasat RTO, Barrackpore ARTO, Baruipur ARTO, Basirhat ARTO, BEHALA ARTO, Birbhum RTO, Bishnupur ARTO, Bolpur ARTO, Chanchol ARTO, Contai ARTO, Coochbehar RTO, Dakshin Dinajpur RTO, Darjeeling RTO, Diamond Harbour ARTO, Durgapore ARTO, GHATAL ARTO, HALDIA ARTO, Hooghly RTO, Howrah RTO, Islampur ARTO, Jalpaiguri RTO, JANGIPUR ARTO, Jhargram RTO, KALIMPONG RTO, Kalna ARTO, Kalyani ARTO, Kandi, KASBA ARTO, Katwa ARTO, Kharagpur ARTO, MALDA RTO, Manbazar, Mathabhanga ARTO, Murshidabad RTO, Nadia RTO, Paschim Burdwan RTO, Paschim Midnapore RTO, Purba Burdwan RTO, Purulia RTO, PVD KOLKATA, Raghunathpur ARTO, Rampurhat ARTO, SALTLAKE ARTO, SILIGURI ARTO, Srirampur ARTO, STA WEST BENGAL, STA-Durgapur, STA-North Bengal (Siliguri), TAMLUK RTO, Tehatta ARTO, Uluberia ARTO, UTTAR DINAJPUR RTO"
+    },
+    {
+        "State Code": "dd",
+        "Full State Name": "UT of DNH and DD  ",
+        "RTO List": "DAMAN, DIU, SILVASSA"
+    }
+]

state_wise_total/2009.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,6503
+AP,503981
+AR,9424
+AS,92677
+BR,103370
+CG,187306
+CH,35878
+DD,10264
+DL,374533
+GA,38262
+GJ,536408
+HP,68663
+HR,411264
+JH,201508
+JK,62490
+KA,851961
+KL,500486
+LA,1408
+LD,958
+MH,1094395
+ML,18019
+MN,18093
+MP,646015
+MZ,10617
+NL,13901
+OR,299395
+PB,390311
+PY,62486
+RJ,410523
+SK,2712
+TN,1121725
+TR,17666
+UK,115724
+UP,1255684
+WB,378535
+Total,9853145

state_wise_total/2010.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,7517
+AP,635716
+AR,10037
+AS,161928
+BR,237608
+CG,314126
+CH,53825
+DD,12626
+DL,450296
+GA,54216
+GJ,971747
+HP,88414
+HR,552332
+JH,256009
+JK,74102
+KA,1106966
+KL,637365
+LA,1501
+LD,834
+MH,1601586
+ML,21956
+MN,20500
+MP,770145
+MZ,12764
+NL,20770
+OR,391691
+PB,487439
+PY,69884
+RJ,776856
+SK,5528
+TN,1482447
+TR,26387
+UK,152073
+UP,1548447
+WB,555264
+Total,13570902

state_wise_total/2011.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,7172
+AP,721945
+AR,10840
+AS,209870
+BR,381148
+CG,332714
+CH,50572
+DD,14389
+DL,480781
+GA,75025
+GJ,1400530
+HP,88542
+HR,629140
+JH,277245
+JK,95283
+KA,1278803
+KL,781836
+LA,1683
+LD,1071
+MH,1961079
+ML,23745
+MN,23263
+MP,880452
+MZ,14400
+NL,22864
+OR,418846
+PB,574060
+PY,82767
+RJ,962544
+SK,6172
+TN,1728652
+TR,28311
+UK,173013
+UP,1720751
+WB,599068
+Total,16058576

state_wise_total/2012.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,8441
+AP,762657
+AR,12461
+AS,216210
+BR,504682
+CG,364224
+CH,52068
+DD,15299
+DL,484542
+GA,75681
+GJ,1431639
+HP,90305
+HR,626629
+JH,302507
+JK,102481
+KA,1373993
+KL,976200
+LA,2246
+LD,1110
+MH,2044973
+ML,26194
+MN,28856
+MP,909386
+MZ,15643
+NL,24749
+OR,431274
+PB,837505
+PY,65881
+RJ,1100656
+SK,9697
+TN,1890230
+TR,29884
+UK,186572
+UP,1910655
+WB,627007
+Total,17542537

state_wise_total/2013.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,8108
+AP,702497
+AR,14303
+AS,236810
+BR,604281
+CG,453738
+CH,45910
+DD,15677
+DL,486691
+GA,69699
+GJ,1340061
+HP,97564
+HR,640572
+JH,324878
+JK,110857
+KA,1388531
+KL,941144
+LA,1851
+LD,887
+MH,2029793
+ML,25257
+MN,29147
+MP,995290
+MZ,16158
+NL,23030
+OR,474624
+PB,770122
+PY,53050
+RJ,1212101
+SK,5484
+TN,1676825
+TR,29558
+UK,181840
+UP,2063364
+WB,691890
+Total,17761592

state_wise_total/2014.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,8454
+AP,879720
+AR,15059
+AS,261022
+BR,674775
+CG,490947
+CH,47268
+DD,15647
+DL,525582
+GA,75246
+GJ,1504738
+HP,106815
+HR,656586
+JH,349352
+JK,109414
+KA,1549205
+KL,902902
+LA,2357
+LD,959
+MH,2258081
+ML,21909
+MN,29632
+MP,1048456
+MZ,14507
+NL,20348
+OR,533795
+PB,813429
+PY,53681
+RJ,1323772
+SK,4828
+TN,1649191
+TR,30694
+UK,200125
+UP,2364093
+WB,743877
+Total,19286466

state_wise_total/2015.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,8463
+AP,857293
+AR,13848
+AS,293956
+BR,732097
+CG,463564
+CH,46754
+DD,15566
+DL,623900
+GA,74083
+GJ,1492664
+HP,112535
+HR,684339
+JH,403490
+JK,118086
+KA,1563735
+KL,894124
+LA,2052
+LD,1033
+MH,2275190
+ML,20670
+MN,28385
+MP,982785
+MZ,13445
+NL,22974
+OR,591470
+PB,816249
+PY,54755
+RJ,1311577
+SK,5591
+TN,1626113
+TR,35429
+UK,217306
+UP,2436132
+WB,822604
+Total,19662257

state_wise_total/2016.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,9484
+AP,1043293
+AR,18135
+AS,329997
+BR,870620
+CG,467901
+CH,47445
+DD,18237
+DL,681707
+GA,78520
+GJ,1595893
+HP,123652
+HR,690868
+JH,447470
+JK,113994
+KA,1663785
+KL,946258
+LA,2282
+LD,846
+MH,2420195
+ML,23560
+MN,29411
+MP,1093730
+MZ,14798
+NL,26166
+OR,647743
+PB,775204
+PY,66244
+RJ,1317611
+SK,5429
+TN,1903740
+TR,39792
+UK,223661
+UP,2636230
+WB,950144
+Total,21324045

state_wise_total/2017.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,9979
+AP,1144793
+AR,23191
+AS,427626
+BR,913311
+CG,556447
+CH,48466
+DD,19193
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+UP,3080898
+WB,1037163
+Total,23302965

state_wise_total/2018.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,10141
+AP,1245102
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+AS,518751
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+UK,282748
+UP,3354521
+WB,1128771
+Total,25420108

state_wise_total/2019.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,9138
+AP,1147673
+AR,34981
+AS,516908
+BR,1266653
+CG,668447
+CH,46126
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+TN,1970231
+TR,56254
+UK,245876
+UP,3362766
+WB,1176609
+Total,24157369

state_wise_total/2020.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,6224
+AP,987883
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+AS,407082
+BR,1036877
+CG,481833
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+RJ,1107629
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+TR,51040
+UK,185313
+UP,2626140
+WB,1055869
+Total,18632934

state_wise_total/2021.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,5735
+AP,951774
+AR,23491
+AS,457668
+BR,1009965
+CG,455865
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+Total,18913477

state_wise_total/2022.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,7059
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+AS,516652
+BR,1129644
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+UK,231037
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+WB,972475
+Total,21580679

state_wise_total/2023.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,8069
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+AS,653450
+BR,1288309
+CG,564645
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+WB,1002303
+Total,24011346

state_wise_total/2024.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,8463
+AP,913738
+AR,36744
+AS,629669
+BR,1395065
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+UK,281767
+UP,3758113
+WB,1184917
+Total,26211754

state_wise_total/2025.csv

@@ -0,0 +1,37 @@
+State,Total Consolidated
+AN,2342
+AP,232534
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+BR,351702
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+TR,16557
+UK,69924
+UP,1076554
+WB,317905
+Total,7355447

vehicle_analysis_1.db

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e9c7921c36ee9c1a58bdfb41ee3e858d3a582f7a250d151eedfdc4dcf5a5a93
+size 92508160