inital commit

README.md

@@ -1,12 +1,169 @@
----
-title: CS5130 FinalProject
-emoji: 📊
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 6.0.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Tesla Production & Deliveries Dashboard
+
+CS 5130 – Final Project (Gradio + pandas)
+
+📌 Project Overview
+
+This project is an interactive Business Intelligence Dashboard built using Gradio and pandas.
+It helps non-technical users explore and understand business data.
+The dashboard allows users to:
+
+Upload datasets (CSV or Excel)
+
+View basic statistics
+
+Apply interactive filters
+
+Create different types of visualizations
+
+Generate automated insights
+
+Export data and charts
+
+Two sample Tesla datasets (1K rows and 50K rows) are included for testing and demonstration.
+
+⭐ Key Features
+1. Data Upload & Validation
+
+Upload CSV or Excel files
+
+Built-in Tesla sample datasets
+
+Automatic detection of:
+
+Numeric columns
+
+Categorical columns
+
+Date columns
+
+Dataset preview
+
+Clear error handling and messages
+
+2. Summary Statistics
+
+Numeric summary (mean, median, std, min, max, quartiles)
+
+Categorical summary (unique values, mode, frequency)
+
+Missing value report
+
+Correlation heatmap for numeric columns
+
+3. Interactive Filtering
+
+Numeric range filters
+
+Categorical multi-select filters
+
+Date range filters
+
+Filtered data preview
+
+Export filtered result to CSV
+
+4. Visualizations
+
+Supports at least 4 required chart types:
+
+Time series plot
+
+Histogram
+
+Box plot
+
+Category bar chart
+
+Scatter plot
+
+Correlation heatmap
+
+Additional features:
+
+User selects columns
+
+Supports aggregation (sum, mean, count, median)
+
+Download charts as PNG
+
+5. Automated Insights
+
+Top and bottom performing models
+
+Region ranking by estimated deliveries
+
+Production vs. delivery comparison
+
+Overall trend summary
+
+📁 Project Structure
+
+project/
+
+│── app.py                  # Main Gradio application
+
+│── data_processor.py       # Data loading, cleaning, filtering
+
+│── visualizations.py       # Chart creation functions
+
+│── insights.py             # Insight generation functions
+
+│── utils.py                # Helper utilities
+
+│── prepare_tesla_data.py   # Synthetic dataset generator
+
+│── requirements.txt        # Dependencies
+
+│── README.md               # Documentation
+
+└── data/
+
+    ├── tesla_deliveries_1k.csv
+
+    └── tesla_deliveries_50k.csv
+
+▶ How to Run
+1. Install dependencies
+pip install -r requirements.txt
+
+2. Run the Gradio app
+python app.py
+
+3. Open the browser link
+
+Gradio will show a local URL such as:
+
+http://127.0.0.1:7860
+
+🧰 Technologies Used
+
+Python
+
+pandas
+
+NumPy
+
+Matplotlib / Seaborn
+
+Gradio
+
+🤖 Use of AI Tools
+
+AI tools (ChatGPT / Claude / GitHub Copilot) were used for:
+
+Code suggestions
+
+Debugging
+
+Improving documentation
+
+Refining design ideas
+
+All AI-generated code was reviewed, tested, and modified by me to ensure it works for this project.
+
+📌 Notes
+
+Sample Tesla datasets are synthetic and created for class demonstration.
+
+Dashboard is for educational use only.

app.py

@@ -0,0 +1,625 @@
+import gradio as gr
+import pandas as pd
+
+from data_processor import (
+    load_data,
+    get_basic_info,
+    detect_column_types,
+    numeric_summary,
+    categorical_summary,
+    missing_values_report,
+    correlation_matrix,
+    build_filter_metadata,
+    apply_filters,
+)
+
+from visualizations import (
+    create_time_series_plot,
+    create_distribution_plot,
+    create_category_bar_plot,
+    create_scatter_plot,
+    create_correlation_heatmap,
+)
+
+from insights import (
+    region_ranking,
+    top_bottom_groups,
+    model_production_vs_delivery,
+    overall_trend_summary,
+)
+
+from utils import figure_to_png_bytes, dict_to_text
+
+
+# -----------------------------
+# Helper functions for loading
+# -----------------------------
+def load_any_source(file_or_path):
+    """
+    Common loader for both uploaded files and sample CSV paths.
+    Returns:
+        df, meta, col_types, info_dict, preview_df, error_message
+    """
+    df, err = load_data(file_or_path)
+    if err:
+        return None, None, None, None, None, err
+
+    info = get_basic_info(df)
+    col_types = detect_column_types(df)
+    meta = build_filter_metadata(df, col_types)
+    preview = df.head(20)
+    return df, meta, col_types, info, preview, None
+
+
+# ============================================================
+# Gradio app
+# ============================================================
+def create_dashboard():
+    with gr.Blocks(theme=gr.themes.Soft()) as demo:
+        gr.Markdown("# 🚗 Tesla Production & Deliveries Dashboard")
+        gr.Markdown(
+            "Upload data or use the built-in Tesla samples to explore statistics, "
+            "filter data, visualize trends, and generate insights."
+        )
+
+        # Shared state across tabs
+        df_state = gr.State(None)
+        meta_state = gr.State(None)
+        col_types_state = gr.State(None)
+
+        # ----------------------------------------------------
+        # 1. DATA UPLOAD TAB
+        # ----------------------------------------------------
+        with gr.Tab("Data Upload"):
+            gr.Markdown("### Upload your own file or use one of the sample Tesla datasets")
+
+            file_input = gr.File(label="Upload CSV or Excel File")
+            load_btn = gr.Button("Load Uploaded Data")
+
+            sample_small_btn = gr.Button("Load Tesla Sample (1K rows)")
+            sample_large_btn = gr.Button("Load Tesla Sample (50K rows)")
+
+            info_box = gr.JSON(label="Dataset Info")
+            preview_box = gr.Dataframe(label="Preview (first 20 rows)", interactive=False)
+            error_box = gr.Markdown(label="Messages")
+
+        # ----------------------------------------------------
+        # 2. STATISTICS TAB
+        # ----------------------------------------------------
+        with gr.Tab("Statistics"):
+            gr.Markdown("### Summary Statistics")
+
+            num_stats = gr.Dataframe(label="Numeric Summary", interactive=False, visible=False)
+            cat_stats = gr.Dataframe(label="Categorical Summary", interactive=False, visible=False)
+            missing_stats = gr.Dataframe(label="Missing Values", interactive=False, visible=False)
+            corr_plot = gr.Plot(label="Correlation Heatmap", visible=False)
+
+            show_stats_btn = gr.Button("Generate Statistics")
+
+        # ----------------------------------------------------
+        # 3. FILTER & EXPLORE TAB
+        # ----------------------------------------------------
+        with gr.Tab("Filter & Explore"):
+            gr.Markdown("### Apply Filters to Explore the Dataset")
+
+            # Numeric filter
+            gr.Markdown("**Numeric Filter (optional)**")
+            with gr.Row():
+                filt_num_col = gr.Dropdown(label="Numeric Column", choices=[], interactive=True)
+                filt_num_min = gr.Number(label="Min value")
+                filt_num_max = gr.Number(label="Max value")
+
+            # Categorical filter
+            gr.Markdown("**Categorical Filter (optional)**")
+            with gr.Row():
+                filt_cat_col = gr.Dropdown(label="Categorical Column", choices=[], interactive=True)
+                filt_cat_vals = gr.Dropdown(
+                    label="Allowed Values", choices=[], multiselect=True, interactive=True
+                )
+
+            # Date filter
+            gr.Markdown("**Date Filter (optional)**")
+            with gr.Row():
+                filt_date_col = gr.Dropdown(label="Date Column", choices=[], interactive=True)
+                filt_date_start = gr.Textbox(label="Start date (YYYY-MM-DD)")
+                filt_date_end = gr.Textbox(label="End date (YYYY-MM-DD)")
+
+            apply_filters_btn = gr.Button("Apply Filters")
+
+            filtered_preview = gr.Dataframe(
+                label="Filtered Data Preview", interactive=False, visible=False
+            )
+
+            export_filtered_btn = gr.Button("Export Filtered Data")
+            filtered_download = gr.File(label="Download CSV")
+
+        # ----------------------------------------------------
+        # 4. VISUALIZATIONS TAB
+        # ----------------------------------------------------
+        with gr.Tab("Visualizations"):
+            gr.Markdown("### Create Charts")
+
+            chart_type = gr.Dropdown(
+                [
+                    "Time Series",
+                    "Histogram",
+                    "Box Plot",
+                    "Category Bar",
+                    "Scatter",
+                    "Correlation Heatmap",
+                ],
+                label="Chart Type",
+                value="Time Series",
+            )
+
+            x_col = gr.Dropdown(label="X Column (for Scatter)", choices=[], interactive=True)
+            y_col = gr.Dropdown(label="Y Column (for Scatter)", choices=[], interactive=True)
+            date_col = gr.Dropdown(label="Date Column (for Time Series)", choices=[], interactive=True)
+            value_col = gr.Dropdown(label="Value Column", choices=[], interactive=True)
+            category_col = gr.Dropdown(
+                label="Category Column (optional)", choices=[], interactive=True
+            )
+
+            agg_func = gr.Dropdown(
+                ["sum", "mean", "count", "median"],
+                label="Aggregation (for Time Series / Category Bar)",
+                value="sum",
+            )
+            freq = gr.Dropdown(
+                ["M", "Q", "Y"],
+                label="Resample Frequency (Time Series)",
+                value="Q",
+            )
+
+            viz_btn = gr.Button("Generate Chart")
+            viz_plot = gr.Plot(label="Visualization")
+            viz_download = gr.File(label="Download PNG")
+
+        # ----------------------------------------------------
+        # 5. INSIGHTS TAB
+        # ----------------------------------------------------
+        with gr.Tab("Insights"):
+            gr.Markdown("### Automatic Insights")
+
+            gr.Markdown("#### Region Ranking (by Estimated Deliveries)")
+            region_rank_tb = gr.Dataframe(
+                label="Regions ranked by total Estimated Deliveries",
+                interactive=False,
+                visible=False,
+            )
+
+            gr.Markdown("#### Top / Bottom Models (by Estimated Deliveries)")
+            model_top_tb = gr.Dataframe(
+                label="Top Models", interactive=False, visible=False
+            )
+            model_bottom_tb = gr.Dataframe(
+                label="Bottom Models", interactive=False, visible=False
+            )
+
+            gr.Markdown("#### Model Production vs Estimated Deliveries")
+            model_comp_tb = gr.Dataframe(
+                label="Production vs Deliveries by Model",
+                interactive=False,
+                visible=False,
+            )
+
+            trend_text = gr.Textbox(
+                label="Overall Trend Summary",
+                lines=10,
+                interactive=False,
+                value="Click 'Generate Insights' after loading a dataset.",
+            )
+
+            insights_btn = gr.Button("Generate Insights")
+
+        # =====================================================
+        # CALLBACK IMPLEMENTATIONS
+        # =====================================================
+
+        # ---------- Load handlers ----------
+        def handle_load(source):
+            df, meta, col_types, info, preview, err = load_any_source(source)
+            if err:
+                # Clear states and dropdowns, show error
+                empty_df_update = gr.update(value=None, visible=False)
+                empty_choices = gr.update(choices=[], value=None)
+
+                return (
+                    None,  # df_state
+                    None,  # meta_state
+                    None,  # col_types_state
+                    {"error": err},  # info_box
+                    None,  # preview_box
+                    f"❌ {err}",  # error_box
+                    empty_choices,  # filt_num_col
+                    empty_choices,  # filt_cat_col
+                    empty_choices,  # filt_date_col
+                    empty_choices,  # x_col
+                    empty_choices,  # y_col
+                    empty_choices,  # date_col
+                    empty_choices,  # value_col
+                    empty_choices,  # category_col
+                )
+
+            # Normal case
+            numeric_cols = col_types["numeric"]
+            categorical_cols = col_types["categorical"]
+            date_cols = col_types["date"]
+
+            num_choice_update = gr.update(choices=numeric_cols, value=None)
+            cat_choice_update = gr.update(choices=categorical_cols, value=None)
+            date_choice_update = gr.update(choices=date_cols, value=None)
+
+            return (
+                df,       # df_state
+                meta,     # meta_state
+                col_types,  # col_types_state
+                info,     # info_box
+                preview,  # preview_box
+                "✅ Data loaded successfully.",  # error_box
+                num_choice_update,   # filt_num_col
+                cat_choice_update,   # filt_cat_col
+                date_choice_update,  # filt_date_col
+                num_choice_update,   # x_col
+                num_choice_update,   # y_col
+                date_choice_update,  # date_col
+                num_choice_update,   # value_col
+                cat_choice_update,   # category_col
+            )
+
+        load_btn.click(
+            handle_load,
+            inputs=[file_input],
+            outputs=[
+                df_state,
+                meta_state,
+                col_types_state,
+                info_box,
+                preview_box,
+                error_box,
+                filt_num_col,
+                filt_cat_col,
+                filt_date_col,
+                x_col,
+                y_col,
+                date_col,
+                value_col,
+                category_col,
+            ],
+        )
+
+        def load_sample_small():
+            return handle_load("data/tesla_deliveries_1k.csv")
+
+        def load_sample_large():
+            return handle_load("data/tesla_deliveries_50k.csv")
+
+        sample_small_btn.click(
+            load_sample_small,
+            inputs=[],
+            outputs=[
+                df_state,
+                meta_state,
+                col_types_state,
+                info_box,
+                preview_box,
+                error_box,
+                filt_num_col,
+                filt_cat_col,
+                filt_date_col,
+                x_col,
+                y_col,
+                date_col,
+                value_col,
+                category_col,
+            ],
+        )
+
+        sample_large_btn.click(
+            load_sample_large,
+            inputs=[],
+            outputs=[
+                df_state,
+                meta_state,
+                col_types_state,
+                info_box,
+                preview_box,
+                error_box,
+                filt_num_col,
+                filt_cat_col,
+                filt_date_col,
+                x_col,
+                y_col,
+                date_col,
+                value_col,
+                category_col,
+            ],
+        )
+
+        # ---------- Statistics ----------
+        def show_stats(df, col_types):
+            if df is None or col_types is None:
+                empty_df = gr.update(value=None, visible=False)
+                empty_plot = gr.update(value=None, visible=False)
+                return empty_df, empty_df, empty_df, empty_plot
+
+            numeric_cols = col_types["numeric"]
+            categorical_cols = col_types["categorical"]
+
+            num_df = numeric_summary(df, numeric_cols)
+            cat_df = categorical_summary(df, categorical_cols)
+            miss_df = missing_values_report(df)
+            fig = create_correlation_heatmap(df, numeric_cols)
+
+            num_u = gr.update(value=num_df, visible=True)
+            cat_u = gr.update(value=cat_df, visible=True)
+            miss_u = gr.update(value=miss_df, visible=True)
+            corr_u = (
+                gr.update(value=fig, visible=True)
+                if fig is not None
+                else gr.update(value=None, visible=False)
+            )
+            return num_u, cat_u, miss_u, corr_u
+
+        show_stats_btn.click(
+            show_stats,
+            inputs=[df_state, col_types_state],
+            outputs=[num_stats, cat_stats, missing_stats, corr_plot],
+        )
+
+        # ---------- Filter metadata helpers ----------
+        def update_numeric_bounds(meta, col_name):
+            if meta is None or not col_name:
+                return gr.update(value=None), gr.update(value=None)
+            info = meta["numeric"].get(col_name)
+            if not info:
+                return gr.update(value=None), gr.update(value=None)
+            return gr.update(value=info["min"]), gr.update(value=info["max"])
+
+        def update_categorical_values(meta, col_name):
+            if meta is None or not col_name:
+                return gr.update(choices=[], value=[])
+            values = meta["categorical"].get(col_name, [])
+            return gr.update(choices=values, value=[])
+
+        def update_date_bounds(meta, col_name):
+            if meta is None or not col_name:
+                return gr.update(value=""), gr.update(value="")
+            info = meta["date"].get(col_name)
+            if not info:
+                return gr.update(value=""), gr.update(value="")
+            start = str(info["min"]).split(" ")[0]
+            end = str(info["max"]).split(" ")[0]
+            return gr.update(value=start), gr.update(value=end)
+
+        filt_num_col.change(
+            update_numeric_bounds,
+            inputs=[meta_state, filt_num_col],
+            outputs=[filt_num_min, filt_num_max],
+        )
+
+        filt_cat_col.change(
+            update_categorical_values,
+            inputs=[meta_state, filt_cat_col],
+            outputs=[filt_cat_vals],
+        )
+
+        filt_date_col.change(
+            update_date_bounds,
+            inputs=[meta_state, filt_date_col],
+            outputs=[filt_date_start, filt_date_end],
+        )
+
+        # ---------- Apply filters ----------
+        def apply_filters_wrapper(
+            df,
+            num_col,
+            num_min_val,
+            num_max_val,
+            cat_col,
+            cat_vals,
+            d_col,
+            d_start,
+            d_end,
+        ):
+            if df is None:
+                return gr.update(value=None, visible=False)
+
+            numeric_filters = {}
+            categorical_filters = {}
+            date_filters = {}
+
+            if num_col and num_min_val is not None and num_max_val is not None:
+                numeric_filters[num_col] = [num_min_val, num_max_val]
+
+            if cat_col and cat_vals:
+                categorical_filters[cat_col] = cat_vals
+
+            if d_col and d_start and d_end:
+                date_filters[d_col] = [d_start, d_end]
+
+            out_df = apply_filters(
+                df,
+                numeric_filters=numeric_filters or None,
+                categorical_filters=categorical_filters or None,
+                date_filters=date_filters or None,
+            )
+            return gr.update(value=out_df.head(50), visible=True)
+
+        apply_filters_btn.click(
+            apply_filters_wrapper,
+            inputs=[
+                df_state,
+                filt_num_col,
+                filt_num_min,
+                filt_num_max,
+                filt_cat_col,
+                filt_cat_vals,
+                filt_date_col,
+                filt_date_start,
+                filt_date_end,
+            ],
+            outputs=[filtered_preview],
+        )
+
+        # ---------- Export filtered data ----------
+        def export_filtered_data(
+            df,
+            num_col,
+            num_min_val,
+            num_max_val,
+            cat_col,
+            cat_vals,
+            d_col,
+            d_start,
+            d_end,
+        ):
+            if df is None:
+                return None
+
+            numeric_filters = {}
+            categorical_filters = {}
+            date_filters = {}
+
+            if num_col and num_min_val is not None and num_max_val is not None:
+                numeric_filters[num_col] = [num_min_val, num_max_val]
+
+            if cat_col and cat_vals:
+                categorical_filters[cat_col] = cat_vals
+
+            if d_col and d_start and d_end:
+                date_filters[d_col] = [d_start, d_end]
+
+            out_df = apply_filters(
+                df,
+                numeric_filters=numeric_filters or None,
+                categorical_filters=categorical_filters or None,
+                date_filters=date_filters or None,
+            )
+            path = "filtered_output.csv"
+            out_df.to_csv(path, index=False)
+            return path
+
+        export_filtered_btn.click(
+            export_filtered_data,
+            inputs=[
+                df_state,
+                filt_num_col,
+                filt_num_min,
+                filt_num_max,
+                filt_cat_col,
+                filt_cat_vals,
+                filt_date_col,
+                filt_date_start,
+                filt_date_end,
+            ],
+            outputs=[filtered_download],
+        )
+
+        # ---------- Visualizations ----------
+        def make_chart(df, ctype, x, y, date_c, val, cat, agg, freq_val, col_types):
+            if df is None:
+                return None, None
+
+            fig = None
+
+            if ctype == "Time Series" and date_c and val:
+                fig = create_time_series_plot(
+                    df,
+                    date_col=date_c,
+                    value_col=val,
+                    agg_func=agg,
+                    freq=freq_val,
+                    category_col=cat,
+                )
+            elif ctype == "Histogram" and val:
+                fig = create_distribution_plot(df, numeric_col=val, kind="hist")
+            elif ctype == "Box Plot" and val:
+                fig = create_distribution_plot(df, numeric_col=val, kind="box")
+            elif ctype == "Category Bar" and cat:
+                fig = create_category_bar_plot(
+                    df, category_col=cat, value_col=val, agg_func=agg
+                )
+            elif ctype == "Scatter" and x and y:
+                fig = create_scatter_plot(df, x_col=x, y_col=y, category_col=cat)
+            elif ctype == "Correlation Heatmap":
+                if col_types is None:
+                    col_types = detect_column_types(df)
+                numeric_cols = col_types["numeric"]
+                fig = create_correlation_heatmap(df, numeric_cols)
+
+            if fig is None:
+                return None, None
+
+            png_bytes = figure_to_png_bytes(fig)
+            path = "chart_output.png"
+            with open(path, "wb") as f:
+                f.write(png_bytes)
+
+            return fig, path
+
+        viz_btn.click(
+            make_chart,
+            inputs=[
+                df_state,
+                chart_type,
+                x_col,
+                y_col,
+                date_col,
+                value_col,
+                category_col,
+                agg_func,
+                freq,
+                col_types_state,
+            ],
+            outputs=[viz_plot, viz_download],
+        )
+
+        # ---------- Insights ----------
+        def get_insights(df):
+            if df is None:
+                empty_df = gr.update(value=None, visible=False)
+                msg = "No data loaded. Please load a dataset in the Data Upload tab first."
+                return empty_df, empty_df, empty_df, empty_df, msg
+
+            # 1) Region ranking
+            region_rank = region_ranking(df, value_col="Estimated_Deliveries")
+
+            # 2) Top / Bottom models (non-overlapping)
+            tb_models = top_bottom_groups(df, "Model", "Estimated_Deliveries", top_n=5)
+            model_top = tb_models["top"]
+            model_bottom = tb_models["bottom"]
+
+            # 3) Production vs deliveries by model
+            model_comp = model_production_vs_delivery(
+                df,
+                model_col="Model",
+                deliveries_col="Estimated_Deliveries",
+                prod_col="Production_Units",
+            )
+
+            # 4) Trend summary (quarterly)
+            summary_dict, _ = overall_trend_summary(
+                df, "Date", "Estimated_Deliveries", freq="Q"
+            )
+            summary_text = dict_to_text(summary_dict)
+
+            region_u = gr.update(value=region_rank, visible=True)
+            model_top_u = gr.update(value=model_top, visible=True)
+            model_bottom_u = gr.update(value=model_bottom, visible=True)
+            model_comp_u = gr.update(value=model_comp, visible=True)
+
+            return region_u, model_top_u, model_bottom_u, model_comp_u, summary_text
+
+        insights_btn.click(
+            get_insights,
+            inputs=[df_state],
+            outputs=[region_rank_tb, model_top_tb, model_bottom_tb, model_comp_tb, trend_text],
+        )
+
+    return demo
+
+
+if __name__ == "__main__":
+    demo = create_dashboard()
+    demo.launch()

data/tesla_deliveries_1k.csv

@@ -0,0 +1,1001 @@
+Date,Year,Month,Region,Model,Estimated_Deliveries,Production_Units,Avg_Price_USD,Battery_Capacity_kWh,Range_km,CO2_Saved_tons,Source_Type,Charging_Stations
+2023-05-03,2023,5,Europe,Cybertruck,17844,17422,113982.38,61,332,793.88,Interpolated (Month),5581
+2015-02-22,2015,2,Middle East,Model S,5099,5175,113540.95,61,362,272.18,Interpolated (Month),10416
+2019-01-19,2019,1,Asia,Model S,4664,4940,79954.44,123,669,502.56,Estimated (Region),4641
+2021-02-13,2021,2,Asia,Model 3,9810,11604,59600.56,83,455,713.25,Estimated (Region),8362
+2016-12-13,2016,12,North America,Model X,12678,13379,107499.56,123,705,1466.89,Estimated (Region),6279
+2020-04-25,2020,4,Middle East,Model S,4755,5689,106566.17,122,728,592.88,Estimated (Region),11849
+2015-11-03,2015,11,Middle East,Model 3,20540,21043,76237.93,121,678,1992.35,Interpolated (Month),9774
+2020-06-20,2020,6,Europe,Model Y,5025,5797,121896.82,75,437,312.52,Estimated (Region),3841
+2022-04-06,2022,4,Europe,Cybertruck,14328,15888,51091.58,82,484,958.38,Estimated (Region),12228
+2021-03-03,2021,3,North America,Model X,10727,10535,53433.47,73,468,665.35,Interpolated (Month),3315
+2020-11-15,2020,11,Middle East,Model 3,8819,8901,63363.37,74,402,435.43,Interpolated (Month),13471
+2025-05-28,2025,5,Middle East,Model Y,9179,10259,61193.25,60,338,475.27,Interpolated (Month),9563
+2020-10-21,2020,10,North America,Cybertruck,8394,8332,73138.62,75,412,499.29,Estimated (Region),14535
+2020-11-22,2020,11,Middle East,Cybertruck,10666,11530,70143.86,123,706,1207.06,Estimated (Region),4421
+2020-04-21,2020,4,Asia,Model Y,13499,13764,76407.96,82,459,952.7,Official (Quarter),2878
+2025-03-23,2025,3,Asia,Model Y,9284,10989,92574.76,102,544,805.55,Official (Quarter),9059
+2023-11-15,2023,11,Europe,Cybertruck,15112,15102,88570.32,102,543,1400.3,Interpolated (Month),5051
+2020-07-04,2020,7,Europe,Cybertruck,4410,5640,106179.74,100,592,456.91,Official (Quarter),3543
+2017-01-24,2017,1,Europe,Cybertruck,5774,6154,86430.92,121,663,591.2,Interpolated (Month),11876
+2021-01-13,2021,1,Middle East,Model 3,2283,2213,69523.25,119,639,205.99,Official (Quarter),11974
+2019-05-15,2019,5,North America,Model 3,7135,6979,77941.93,123,741,604.54,Official (Quarter),15502
+2016-09-11,2016,9,Middle East,Cybertruck,10374,12772,109561.43,122,635,973.86,Interpolated (Month),16590
+2020-01-06,2020,1,Middle East,Model X,18106,21563,103466.72,59,346,933.59,Interpolated (Month),10150
+2025-09-26,2025,9,Asia,Cybertruck,7895,9997,92176.79,120,670,956.64,Official (Quarter),8214
+2022-04-22,2022,4,Asia,Cybertruck,10439,11876,70539.88,103,623,883.82,Estimated (Region),14816
+2025-03-19,2025,3,North America,Model Y,10452,8994,109301.06,100,580,760.44,Estimated (Region),5000
+2016-12-12,2016,12,Asia,Model Y,8815,9205,119946.46,61,334,401.51,Estimated (Region),13434
+2022-05-24,2022,5,Europe,Model 3,12190,15286,57694.28,121,690,1353.9,Official (Quarter),11405
+2017-06-16,2017,6,Asia,Model S,14834,15005,96177.82,59,366,807.25,Interpolated (Month),4306
+2023-11-13,2023,11,Asia,Cybertruck,7951,9739,66195.66,61,339,456.65,Official (Quarter),10656
+2016-10-13,2016,10,Middle East,Cybertruck,6714,8044,62235.68,58,336,345.86,Estimated (Region),7468
+2024-01-07,2024,1,North America,Model S,12731,14821,59722.07,99,576,924.59,Estimated (Region),14356
+2020-09-03,2020,9,North America,Cybertruck,12986,13024,80667.2,77,426,810.65,Estimated (Region),11313
+2023-05-16,2023,5,North America,Model Y,14689,14672,52348.43,76,441,902.19,Official (Quarter),10938
+2024-01-25,2024,1,Asia,Model 3,14160,15133,76534.12,82,468,919.39,Interpolated (Month),7418
+2015-03-02,2015,3,North America,Model 3,6941,6568,75765.07,83,472,467.27,Estimated (Region),5587
+2023-03-25,2023,3,Europe,Cybertruck,7553,8493,93454.88,60,332,436.57,Estimated (Region),10951
+2019-08-24,2019,8,Middle East,Model X,8307,8359,87697.94,75,408,523.23,Interpolated (Month),8239
+2017-12-08,2017,12,Europe,Cybertruck,8843,7698,111405.6,61,313,372.78,Estimated (Region),11719
+2023-06-18,2023,6,Europe,Cybertruck,13345,14524,56575.92,118,691,1583.6,Interpolated (Month),5877
+2022-07-05,2022,7,Europe,Cybertruck,7643,7887,68508.62,82,477,595.27,Interpolated (Month),12277
+2019-04-22,2019,4,Middle East,Model S,10235,10113,89778.6,60,358,483.32,Estimated (Region),6215
+2023-01-20,2023,1,Asia,Cybertruck,7000,7947,102520.4,79,474,535.52,Interpolated (Month),10709
+2017-08-10,2017,8,Middle East,Model S,13563,13859,108450.36,59,365,664.31,Estimated (Region),9313
+2025-01-02,2025,1,Asia,Model S,12772,14076,110737.96,77,431,776.96,Interpolated (Month),4898
+2018-12-28,2018,12,Asia,Model Y,12711,13936,103664.07,83,487,902.61,Interpolated (Month),10336
+2024-05-13,2024,5,Asia,Model Y,13084,13495,116830.4,97,567,1118.81,Estimated (Region),13928
+2021-02-26,2021,2,North America,Model 3,6640,6531,92194.37,116,697,687.32,Official (Quarter),9926
+2025-09-19,2025,9,Asia,Model 3,14541,14359,95912.49,75,453,875.31,Estimated (Region),8505
+2025-11-22,2025,11,Europe,Model Y,8322,9567,99065.26,77,427,498.42,Interpolated (Month),6727
+2024-09-22,2024,9,Middle East,Model S,10647,11508,78053.44,60,361,629.28,Interpolated (Month),9920
+2016-09-06,2016,9,Europe,Model S,19901,21957,110880.37,103,622,1795.42,Estimated (Region),10036
+2024-02-11,2024,2,Middle East,Model Y,12703,12014,101129.07,60,338,668.39,Estimated (Region),12025
+2015-03-14,2015,3,Asia,Model Y,9519,11416,52338.2,76,443,705.84,Interpolated (Month),6791
+2015-04-14,2015,4,Middle East,Model X,12775,12974,105886.78,97,595,992.74,Interpolated (Month),13355
+2018-07-02,2018,7,Asia,Cybertruck,8960,10152,73098.55,61,337,444.99,Estimated (Region),9327
+2025-03-16,2025,3,Middle East,Model S,12276,11333,82159.52,119,703,999.23,Official (Quarter),11889
+2017-04-05,2017,4,Middle East,Model Y,6698,7898,98045.58,76,401,495.06,Estimated (Region),9421
+2018-08-21,2018,8,Asia,Model S,18458,22726,99381.69,59,364,929.13,Official (Quarter),14725
+2025-05-20,2025,5,Asia,Model S,9673,10568,81711.91,100,597,927.21,Official (Quarter),8542
+2022-12-26,2022,12,Europe,Model Y,8533,9521,52632.41,76,450,613.15,Estimated (Region),3199
+2018-02-21,2018,2,North America,Model Y,11426,12937,114291.57,82,461,865.95,Estimated (Region),10985
+2021-05-11,2021,5,Middle East,Cybertruck,16995,17605,58741.67,121,680,1627.2,Estimated (Region),11504
+2018-05-28,2018,5,Middle East,Cybertruck,6996,8558,90265.17,119,692,719.0,Official (Quarter),12362
+2015-07-12,2015,7,Asia,Model Y,8394,8680,96277.38,99,588,845.98,Official (Quarter),10127
+2021-08-10,2021,8,Asia,Cybertruck,4891,4849,108651.48,62,310,278.33,Official (Quarter),8884
+2024-08-26,2024,8,Europe,Model 3,11281,12549,66036.53,77,446,826.78,Estimated (Region),11691
+2020-03-11,2020,3,Asia,Model S,7180,7322,85681.03,81,459,488.23,Interpolated (Month),6243
+2019-01-03,2019,1,Asia,Model 3,12780,14198,67788.79,76,409,714.94,Official (Quarter),8625
+2015-04-14,2015,4,Middle East,Cybertruck,6533,6218,55263.23,81,470,463.65,Estimated (Region),12141
+2019-04-23,2019,4,North America,Model S,17911,18641,119290.91,117,663,1747.24,Estimated (Region),8945
+2017-08-06,2017,8,Asia,Cybertruck,12579,13024,53941.47,77,432,856.7,Official (Quarter),9875
+2018-08-13,2018,8,Middle East,Model Y,7340,6938,64535.23,58,339,317.82,Official (Quarter),12322
+2024-12-04,2024,12,Middle East,Cybertruck,12749,12647,98622.29,76,465,835.33,Official (Quarter),14413
+2022-10-20,2022,10,North America,Model 3,11158,12237,72875.63,82,480,788.73,Estimated (Region),8082
+2020-09-14,2020,9,Europe,Model 3,6251,6712,105019.95,75,431,423.27,Interpolated (Month),12373
+2017-01-10,2017,1,Asia,Cybertruck,9386,10717,60863.29,76,442,672.83,Estimated (Region),3608
+2021-10-07,2021,10,North America,Model S,13845,16403,83138.64,101,570,1176.37,Interpolated (Month),7932
+2016-01-16,2016,1,Middle East,Model Y,7164,8240,118712.07,74,403,420.55,Official (Quarter),5802
+2016-10-19,2016,10,Asia,Cybertruck,8253,9302,73982.5,101,585,805.77,Official (Quarter),5351
+2023-12-27,2023,12,Middle East,Cybertruck,8713,9854,60226.01,75,410,645.88,Estimated (Region),12190
+2015-12-13,2015,12,Middle East,Model X,15355,16445,87041.82,73,420,989.93,Interpolated (Month),4606
+2016-10-05,2016,10,Asia,Model S,15819,16881,104190.34,82,457,1014.76,Official (Quarter),8942
+2021-02-24,2021,2,Middle East,Model Y,9971,9930,58049.39,84,474,627.07,Estimated (Region),12288
+2022-09-18,2022,9,North America,Model X,14373,14337,96072.64,124,685,1477.9,Interpolated (Month),5063
+2024-07-20,2024,7,Europe,Model 3,1480,1564,88604.55,76,404,110.27,Interpolated (Month),10079
+2020-10-03,2020,10,North America,Model Y,8660,9999,82183.12,98,565,681.43,Official (Quarter),5017
+2017-05-09,2017,5,Europe,Model S,5753,6436,84494.69,83,498,406.61,Estimated (Region),8789
+2020-09-22,2020,9,Asia,Model 3,10280,10518,89161.25,61,337,516.66,Official (Quarter),11436
+2019-08-24,2019,8,Asia,Cybertruck,7995,8903,108445.27,76,423,586.08,Official (Quarter),8752
+2019-09-13,2019,9,Europe,Cybertruck,10108,10195,119866.43,76,440,619.9,Estimated (Region),11451
+2023-01-23,2023,1,Europe,Model X,6723,7650,104730.57,60,335,289.13,Estimated (Region),13809
+2024-03-24,2024,3,Asia,Model S,8724,9411,118534.35,100,558,792.53,Estimated (Region),11715
+2015-12-11,2015,12,Asia,Model 3,12955,13597,60441.0,81,483,981.94,Official (Quarter),13995
+2020-08-26,2020,8,Europe,Model Y,4272,4687,95700.58,79,478,324.71,Interpolated (Month),11814
+2021-11-09,2021,11,North America,Cybertruck,6618,6712,79447.13,101,557,549.56,Official (Quarter),4213
+2023-01-07,2023,1,North America,Model S,10449,10390,90176.85,82,445,681.24,Interpolated (Month),7296
+2023-11-20,2023,11,Asia,Model S,11913,10936,71167.3,61,350,598.99,Interpolated (Month),6370
+2025-08-18,2025,8,Middle East,Cybertruck,10068,11107,52648.93,117,713,1118.68,Interpolated (Month),4225
+2022-06-04,2022,6,Asia,Model X,7208,9293,53326.85,122,688,880.92,Estimated (Region),5571
+2016-03-24,2016,3,North America,Model X,5685,6201,78338.37,62,344,316.04,Estimated (Region),3791
+2025-12-06,2025,12,Europe,Model 3,16259,19404,107925.16,100,576,1588.69,Interpolated (Month),11945
+2019-03-23,2019,3,North America,Model X,10934,12312,56394.71,75,461,704.5,Estimated (Region),8948
+2021-03-01,2021,3,Europe,Model 3,7154,6905,59422.9,101,594,593.18,Official (Quarter),6734
+2021-04-23,2021,4,Europe,Model 3,12279,10867,86974.47,125,722,1207.38,Official (Quarter),7809
+2020-02-23,2020,2,Europe,Model X,9791,9945,58175.95,59,338,496.17,Official (Quarter),9749
+2020-05-22,2020,5,Middle East,Cybertruck,9321,10923,90078.75,120,750,1113.35,Interpolated (Month),14797
+2019-06-19,2019,6,Asia,Model 3,8163,7759,97853.51,74,420,528.08,Estimated (Region),5743
+2021-10-14,2021,10,Europe,Model X,9840,9440,99395.65,59,327,417.47,Official (Quarter),10143
+2016-10-20,2016,10,Middle East,Model X,6564,7416,75846.44,60,340,349.98,Official (Quarter),10603
+2020-03-08,2020,3,Middle East,Model X,8760,9152,96585.66,103,603,757.72,Interpolated (Month),11029
+2020-02-22,2020,2,Asia,Model 3,18347,19042,69732.5,62,357,959.7,Estimated (Region),3808
+2025-06-16,2025,6,Middle East,Model 3,7596,8383,72052.42,76,416,538.05,Interpolated (Month),6564
+2019-09-13,2019,9,North America,Model S,3575,4398,92176.25,119,702,407.58,Interpolated (Month),8584
+2016-12-15,2016,12,Asia,Cybertruck,2845,3100,62640.46,79,452,197.23,Official (Quarter),7438
+2025-10-16,2025,10,Middle East,Model S,7966,8422,72660.04,74,443,541.35,Official (Quarter),9588
+2021-08-02,2021,8,Middle East,Model 3,7129,7929,100957.89,119,714,684.92,Estimated (Region),11367
+2022-11-04,2022,11,North America,Model X,9470,11186,83736.07,79,485,672.04,Official (Quarter),9296
+2018-05-07,2018,5,North America,Model Y,9111,10093,96100.4,84,513,707.97,Estimated (Region),5693
+2016-04-04,2016,4,North America,Model X,12591,12744,54652.99,102,566,1157.95,Official (Quarter),14336
+2019-02-13,2019,2,North America,Model Y,8790,10007,66840.7,105,596,894.54,Estimated (Region),11939
+2023-01-19,2023,1,Asia,Model 3,7083,8061,77626.34,117,696,731.45,Estimated (Region),12753
+2023-05-19,2023,5,Asia,Model X,10263,10447,120089.08,84,478,769.57,Estimated (Region),9754
+2020-02-14,2020,2,Europe,Model Y,13385,15587,86862.17,120,661,1365.23,Official (Quarter),9966
+2019-06-24,2019,6,North America,Cybertruck,12272,13999,57396.35,61,351,562.42,Official (Quarter),13795
+2021-07-16,2021,7,Europe,Cybertruck,5356,6021,114965.99,97,533,418.26,Interpolated (Month),10396
+2019-07-03,2019,7,North America,Model Y,13318,14296,61291.8,103,608,1152.3,Official (Quarter),5095
+2023-12-22,2023,12,North America,Model Y,8687,9767,72949.99,76,420,630.32,Estimated (Region),8517
+2015-03-17,2015,3,Asia,Model X,8275,8455,84472.2,120,668,849.03,Official (Quarter),9772
+2020-01-18,2020,1,Middle East,Cybertruck,6399,7357,69410.77,76,430,466.24,Interpolated (Month),11623
+2015-09-16,2015,9,Middle East,Model X,3196,3579,88943.24,81,485,205.0,Interpolated (Month),5928
+2023-06-16,2023,6,Middle East,Cybertruck,6966,8055,110118.6,119,692,745.53,Official (Quarter),7711
+2018-05-03,2018,5,North America,Model S,5506,5515,93158.51,74,409,375.99,Interpolated (Month),9973
+2020-04-16,2020,4,Asia,Model 3,11198,11857,100604.48,116,663,1252.5,Official (Quarter),4075
+2015-10-23,2015,10,Middle East,Model Y,14775,16981,91901.5,83,511,1117.19,Estimated (Region),10693
+2015-09-09,2015,9,North America,Cybertruck,15519,17566,62720.73,61,325,791.06,Interpolated (Month),14412
+2017-09-17,2017,9,North America,Cybertruck,12538,11657,94229.9,59,327,621.06,Interpolated (Month),10925
+2021-12-01,2021,12,Middle East,Model Y,8266,9511,71340.7,121,739,875.9,Official (Quarter),6484
+2024-11-10,2024,11,Europe,Model S,8755,8055,108495.16,82,500,623.93,Interpolated (Month),11969
+2019-03-13,2019,3,Middle East,Model 3,10212,10876,59567.49,60,332,646.19,Estimated (Region),9061
+2018-07-28,2018,7,North America,Model S,7142,8208,95895.72,81,488,531.0,Official (Quarter),4453
+2024-08-07,2024,8,North America,Model 3,8238,8579,105232.78,98,564,722.88,Estimated (Region),13751
+2022-03-08,2022,3,North America,Model Y,14767,18213,66403.49,77,431,830.53,Official (Quarter),5520
+2018-12-12,2018,12,Asia,Model 3,11153,11002,51874.99,120,758,1127.98,Interpolated (Month),5699
+2021-07-28,2021,7,North America,Model 3,12883,14350,102772.61,125,668,1439.18,Estimated (Region),9638
+2023-11-24,2023,11,Europe,Model S,12282,11876,58292.06,100,582,1149.39,Interpolated (Month),11912
+2022-11-01,2022,11,Europe,Model Y,13423,13763,96515.14,98,586,699.64,Interpolated (Month),14082
+2025-10-07,2025,10,Middle East,Cybertruck,9646,11552,60285.51,77,427,701.32,Estimated (Region),3919
+2016-07-24,2016,7,Europe,Model S,13234,17389,54470.32,101,560,1505.06,Estimated (Region),9418
+2018-02-02,2018,2,Europe,Model X,11412,11172,84108.31,76,408,573.75,Interpolated (Month),6815
+2017-12-24,2017,12,Europe,Model S,14692,12613,49821.85,61,337,776.06,Official (Quarter),9130
+2019-04-08,2019,4,North America,Model 3,7908,8781,78264.21,99,543,525.8,Official (Quarter),14395
+2017-01-26,2017,1,North America,Cybertruck,8935,9999,78540.55,120,660,1091.19,Official (Quarter),3539
+2020-12-09,2020,12,Europe,Model Y,12986,15105,57381.57,120,670,1312.91,Estimated (Region),13254
+2016-08-13,2016,8,Middle East,Model X,6892,7596,89771.16,73,408,360.24,Estimated (Region),12401
+2020-07-19,2020,7,North America,Model X,2110,2178,109607.34,99,528,192.92,Interpolated (Month),3506
+2015-09-04,2015,9,Middle East,Cybertruck,5747,6579,50443.84,123,647,665.96,Official (Quarter),13937
+2016-09-16,2016,9,Asia,Model S,14398,14988,120899.65,60,344,753.85,Interpolated (Month),4684
+2018-08-15,2018,8,North America,Model Y,15166,16518,88811.2,80,478,1130.04,Official (Quarter),10520
+2021-10-22,2021,10,Middle East,Model S,9838,10943,89552.89,58,361,532.09,Official (Quarter),7371
+2021-10-28,2021,10,Asia,Cybertruck,18894,21094,50534.11,103,560,1328.46,Official (Quarter),13719
+2015-10-19,2015,10,Europe,Model X,10602,9378,82221.6,82,492,729.08,Official (Quarter),7984
+2025-02-12,2025,2,North America,Model S,10955,11509,73850.19,59,330,612.93,Official (Quarter),4402
+2015-10-12,2015,10,North America,Model 3,13695,13700,80154.58,119,690,1574.31,Estimated (Region),3457
+2020-08-12,2020,8,Asia,Model Y,8830,9267,54432.74,73,403,463.31,Official (Quarter),5942
+2018-11-23,2018,11,Middle East,Model 3,8774,9636,64336.15,123,716,804.52,Interpolated (Month),5254
+2021-09-09,2021,9,Middle East,Model X,9658,11662,116236.94,74,408,618.06,Estimated (Region),11856
+2025-01-05,2025,1,Europe,Model X,6972,7601,103238.46,123,686,618.16,Estimated (Region),5258
+2025-03-10,2025,3,North America,Model 3,6453,6670,69185.75,60,351,350.69,Interpolated (Month),9622
+2016-05-01,2016,5,Asia,Model 3,398,383,63000.19,123,641,37.17,Interpolated (Month),6135
+2015-12-03,2015,12,North America,Model Y,10096,12100,54215.56,75,454,596.65,Official (Quarter),9270
+2021-03-03,2021,3,Europe,Model S,9222,9693,51982.0,122,725,971.03,Interpolated (Month),4766
+2016-12-22,2016,12,Asia,Model 3,3768,4224,108147.1,122,658,418.19,Interpolated (Month),11803
+2022-05-21,2022,5,North America,Model Y,2242,2247,69566.15,101,559,173.99,Interpolated (Month),6607
+2017-05-20,2017,5,Middle East,Model S,11820,11900,76940.3,60,331,571.37,Official (Quarter),13258
+2019-08-13,2019,8,Middle East,Model Y,7829,9259,66844.8,119,690,733.66,Interpolated (Month),9858
+2022-12-21,2022,12,Europe,Model S,8128,7873,107445.22,125,662,777.62,Interpolated (Month),10610
+2024-08-05,2024,8,Middle East,Cybertruck,7857,8722,90204.17,98,556,630.42,Estimated (Region),6101
+2023-11-26,2023,11,Middle East,Model Y,10931,11495,86871.8,82,459,797.28,Estimated (Region),12291
+2022-03-15,2022,3,North America,Cybertruck,7221,9497,80007.56,82,439,561.99,Interpolated (Month),9951
+2023-08-27,2023,8,Europe,Model S,10064,10530,58162.42,60,350,545.33,Interpolated (Month),11528
+2018-03-05,2018,3,Europe,Cybertruck,12513,13841,66140.67,119,712,1346.82,Estimated (Region),11165
+2020-05-14,2020,5,Europe,Cybertruck,6677,8381,69849.7,75,407,433.58,Official (Quarter),13374
+2021-03-20,2021,3,Middle East,Model 3,7274,8314,78470.79,75,459,505.32,Estimated (Region),5568
+2017-11-14,2017,11,Asia,Cybertruck,6815,7685,74851.17,116,683,737.46,Interpolated (Month),8716
+2019-07-13,2019,7,Europe,Model X,7505,8811,77443.48,60,373,450.86,Official (Quarter),6370
+2015-09-05,2015,9,Europe,Model X,15450,15132,56821.2,98,603,1313.0,Official (Quarter),5351
+2019-10-11,2019,10,Europe,Model Y,10278,11579,56229.53,97,602,855.68,Official (Quarter),13164
+2023-12-07,2023,12,Middle East,Model Y,4662,5457,108829.29,100,555,474.71,Estimated (Region),13762
+2022-02-09,2022,2,North America,Model S,890,962,117433.44,121,626,83.19,Interpolated (Month),13252
+2020-04-20,2020,4,Europe,Model Y,8243,7764,75560.39,121,659,840.0,Official (Quarter),6723
+2018-06-18,2018,6,Middle East,Model 3,16183,19120,87810.55,76,456,1130.3,Interpolated (Month),3372
+2019-05-18,2019,5,Europe,Model S,13556,15570,99970.72,115,708,1497.23,Estimated (Region),6102
+2018-05-11,2018,5,Asia,Model S,6140,6417,77156.95,117,711,606.93,Estimated (Region),9292
+2024-10-27,2024,10,Europe,Model Y,5797,5889,75702.51,104,586,462.38,Official (Quarter),12670
+2025-02-03,2025,2,Europe,Model X,18349,18606,110906.66,98,557,1176.71,Official (Quarter),14706
+2019-02-10,2019,2,Middle East,Cybertruck,17555,19324,97763.42,76,423,1097.8,Interpolated (Month),7271
+2023-11-04,2023,11,Asia,Model Y,12608,11963,72774.82,81,443,861.64,Estimated (Region),11840
+2016-12-10,2016,12,Asia,Model X,10984,11372,112913.54,60,346,635.21,Official (Quarter),5209
+2023-02-27,2023,2,Middle East,Model Y,3093,3130,112020.5,74,436,177.57,Interpolated (Month),4422
+2021-04-11,2021,4,Middle East,Model S,11584,12085,86507.69,76,438,847.97,Estimated (Region),11993
+2018-12-26,2018,12,Asia,Model S,18512,17595,83698.33,120,655,1690.12,Official (Quarter),5092
+2016-10-14,2016,10,North America,Model Y,8558,10366,52265.06,119,701,875.21,Official (Quarter),12963
+2019-06-20,2019,6,Middle East,Model 3,14280,14168,51049.14,98,567,1165.04,Official (Quarter),12661
+2019-01-13,2019,1,Europe,Model Y,1648,1982,51055.52,121,678,151.13,Official (Quarter),10864
+2024-06-08,2024,6,Europe,Model X,8419,8230,105115.34,98,529,750.09,Estimated (Region),3385
+2022-10-22,2022,10,Europe,Model 3,22402,24029,94489.59,61,327,1186.41,Official (Quarter),13395
+2015-05-28,2015,5,Middle East,Model X,7937,9238,113154.3,126,706,906.68,Interpolated (Month),9268
+2024-10-08,2024,10,Europe,Model X,10523,12524,69395.56,103,571,869.78,Estimated (Region),3930
+2018-08-22,2018,8,Asia,Model 3,4146,4401,67414.49,83,503,341.4,Official (Quarter),3545
+2021-05-08,2021,5,Asia,Model Y,9428,8962,60368.28,120,723,997.68,Official (Quarter),11566
+2021-10-21,2021,10,Asia,Model Y,9723,9240,82765.16,96,598,807.77,Interpolated (Month),11404
+2020-12-23,2020,12,Asia,Model 3,9164,11314,118370.74,80,441,778.95,Official (Quarter),4490
+2015-07-13,2015,7,Middle East,Model Y,10698,11025,116080.12,84,465,822.9,Estimated (Region),12480
+2018-08-21,2018,8,Europe,Model 3,17775,20086,95744.36,81,459,1481.61,Estimated (Region),3833
+2021-01-08,2021,1,Asia,Model Y,12708,15915,82031.41,81,497,1071.07,Estimated (Region),11834
+2019-07-03,2019,7,North America,Model X,7762,9999,72305.86,75,447,530.61,Interpolated (Month),3322
+2022-03-03,2022,3,Europe,Model X,579,614,65074.02,73,439,36.81,Official (Quarter),8498
+2025-08-13,2025,8,Europe,Cybertruck,11320,11738,112469.75,59,338,494.09,Interpolated (Month),9686
+2022-08-26,2022,8,Middle East,Model S,11631,10561,99019.1,100,577,911.35,Official (Quarter),5175
+2020-04-04,2020,4,North America,Model X,8182,9058,95215.35,98,569,782.63,Estimated (Region),12382
+2025-04-13,2025,4,North America,Model X,7668,8216,68269.46,99,540,725.65,Official (Quarter),10986
+2021-04-20,2021,4,Asia,Model 3,11957,11014,60615.07,118,696,1243.09,Official (Quarter),10833
+2023-03-06,2023,3,Europe,Model X,7434,7910,91170.45,101,566,540.22,Official (Quarter),10751
+2022-02-21,2022,2,Middle East,Model X,8037,10036,57710.6,119,731,927.76,Estimated (Region),8106
+2021-09-09,2021,9,North America,Model 3,9395,10738,91028.03,75,447,703.87,Estimated (Region),4680
+2024-06-23,2024,6,Asia,Model 3,6548,7347,98580.62,76,421,420.08,Interpolated (Month),6161
+2021-11-17,2021,11,Asia,Model Y,5234,5750,63736.86,100,557,459.4,Interpolated (Month),11336
+2015-09-16,2015,9,North America,Model X,7954,7683,82762.29,82,482,512.9,Official (Quarter),4072
+2022-07-05,2022,7,North America,Model S,8701,9543,67834.65,83,472,650.84,Estimated (Region),14256
+2016-11-13,2016,11,North America,Model S,2885,2806,102101.51,122,684,218.06,Official (Quarter),6576
+2016-06-24,2016,6,Middle East,Model Y,8689,10591,64138.3,100,596,817.35,Estimated (Region),11905
+2017-05-01,2017,5,Asia,Model Y,12460,14082,49111.55,100,568,1314.53,Official (Quarter),3480
+2021-08-22,2021,8,Middle East,Model S,10125,11136,73015.86,118,683,938.06,Estimated (Region),3880
+2022-12-14,2022,12,Asia,Model S,14315,15351,119182.06,118,747,1596.42,Interpolated (Month),12953
+2021-06-21,2021,6,Asia,Model Y,7497,8341,75683.3,83,465,552.29,Interpolated (Month),10980
+2019-03-19,2019,3,Asia,Cybertruck,2100,2058,117689.95,83,451,133.71,Estimated (Region),10081
+2022-07-13,2022,7,Asia,Model X,10677,12244,65202.83,61,347,607.29,Estimated (Region),10530
+2021-07-09,2021,7,Europe,Model Y,14147,14362,88384.39,75,448,1035.11,Estimated (Region),12544
+2019-06-18,2019,6,North America,Model X,6366,7672,51997.75,82,440,465.22,Estimated (Region),12590
+2016-10-04,2016,10,North America,Model 3,15152,14748,91248.46,83,442,1039.06,Official (Quarter),6233
+2021-07-17,2021,7,Asia,Cybertruck,720,832,49153.03,84,463,56.66,Official (Quarter),14282
+2015-11-03,2015,11,Middle East,Cybertruck,11129,13499,112262.74,102,587,1342.17,Interpolated (Month),3116
+2024-05-19,2024,5,Asia,Model X,10801,13722,53456.81,60,360,613.69,Interpolated (Month),13062
+2024-11-18,2024,11,Middle East,Model 3,12099,12053,91258.72,103,571,948.15,Official (Quarter),11587
+2022-01-03,2022,1,Middle East,Cybertruck,7983,7600,109492.41,100,564,579.94,Interpolated (Month),7135
+2016-03-22,2016,3,Europe,Model Y,10816,10232,114823.48,83,450,694.93,Interpolated (Month),4172
+2023-11-12,2023,11,Europe,Model 3,9974,12112,93721.86,101,586,1004.67,Official (Quarter),16071
+2018-08-23,2018,8,Europe,Model Y,14121,15017,91835.58,100,577,1168.33,Official (Quarter),11496
+2025-12-02,2025,12,Asia,Model Y,15172,16506,69133.93,118,695,1285.57,Official (Quarter),14210
+2017-02-06,2017,2,Europe,Model 3,12524,13351,107251.66,76,416,788.77,Official (Quarter),11077
+2022-10-14,2022,10,Europe,Model Y,16106,17226,98623.94,79,479,1171.56,Estimated (Region),9247
+2019-12-06,2019,12,Europe,Cybertruck,15157,15004,107508.01,81,445,1098.5,Interpolated (Month),10068
+2022-04-10,2022,4,Middle East,Cybertruck,5854,6037,81455.86,60,331,282.34,Interpolated (Month),6890
+2016-03-19,2016,3,Asia,Model S,10804,11732,91602.94,119,702,960.89,Estimated (Region),6483
+2016-01-05,2016,1,Europe,Model 3,10636,10551,107823.82,74,429,698.56,Estimated (Region),13746
+2023-10-19,2023,10,Asia,Model X,7673,8109,56881.2,62,363,411.17,Official (Quarter),9860
+2019-06-03,2019,6,Middle East,Model X,10280,11253,79965.19,118,712,999.92,Interpolated (Month),6103
+2022-07-05,2022,7,Asia,Cybertruck,9502,10885,85682.8,99,593,1040.39,Estimated (Region),3926
+2022-10-17,2022,10,Middle East,Cybertruck,7168,7630,103698.04,116,695,761.72,Estimated (Region),2851
+2023-12-23,2023,12,Asia,Model Y,11343,13127,106604.16,98,578,1015.89,Official (Quarter),9082
+2022-04-05,2022,4,Europe,Model X,7048,8105,76592.41,72,415,485.07,Estimated (Region),10681
+2022-10-09,2022,10,North America,Model X,8445,9401,113284.29,101,568,836.25,Interpolated (Month),11153
+2023-10-26,2023,10,Middle East,Model Y,14807,16514,63232.67,106,562,1300.73,Interpolated (Month),14161
+2025-04-28,2025,4,Europe,Model 3,13336,13985,52184.08,81,474,960.8,Interpolated (Month),3349
+2017-12-17,2017,12,North America,Model Y,12335,12312,79876.94,79,492,697.78,Estimated (Region),4436
+2024-06-13,2024,6,North America,Model Y,11762,12871,101492.15,61,340,534.21,Estimated (Region),9023
+2019-07-10,2019,7,Middle East,Cybertruck,12821,14199,114954.32,99,578,1101.0,Interpolated (Month),5093
+2019-07-18,2019,7,Middle East,Model 3,11467,13807,121264.48,103,568,1067.27,Interpolated (Month),11033
+2015-06-17,2015,6,Asia,Model Y,6832,6766,76385.62,99,590,607.2,Interpolated (Month),11677
+2022-03-09,2022,3,North America,Model 3,6535,6811,88929.67,59,361,365.5,Estimated (Region),9134
+2019-01-01,2019,1,Asia,Model Y,10246,10510,76735.5,75,451,783.13,Estimated (Region),5903
+2021-07-05,2021,7,Europe,Model S,2083,1891,81909.7,122,677,218.74,Estimated (Region),10387
+2016-09-27,2016,9,Europe,Model X,15859,16497,111472.71,60,358,690.69,Official (Quarter),5069
+2017-06-12,2017,6,North America,Model 3,6661,6888,66038.32,59,330,323.73,Interpolated (Month),9011
+2022-07-14,2022,7,North America,Model X,9175,11714,119063.33,100,599,801.53,Official (Quarter),13595
+2015-12-14,2015,12,Europe,Model 3,10981,12333,94393.71,58,339,514.99,Official (Quarter),12644
+2015-12-22,2015,12,Middle East,Model 3,12972,12156,53453.29,121,694,1382.84,Estimated (Region),11301
+2021-02-02,2021,2,North America,Model S,4721,5082,101072.67,79,478,244.9,Official (Quarter),7658
+2016-12-03,2016,12,North America,Model Y,9205,9330,75654.02,119,687,1058.85,Interpolated (Month),7397
+2015-06-08,2015,6,Middle East,Model 3,9182,9346,72145.82,102,622,799.53,Estimated (Region),11737
+2015-03-14,2015,3,Middle East,Model 3,1969,2058,67854.54,60,346,93.04,Estimated (Region),3882
+2020-08-19,2020,8,North America,Model Y,12954,13456,89226.02,121,749,1554.02,Interpolated (Month),9414
+2023-03-14,2023,3,Asia,Model S,9594,9591,113050.99,60,357,474.59,Official (Quarter),8694
+2018-01-13,2018,1,Europe,Model S,17894,21244,99602.71,84,457,1346.12,Interpolated (Month),10559
+2019-09-27,2019,9,Europe,Model S,9120,9337,83197.15,102,540,727.92,Estimated (Region),8212
+2025-04-05,2025,4,Europe,Cybertruck,9113,11195,53894.29,83,505,778.45,Official (Quarter),7406
+2025-08-17,2025,8,Asia,Model Y,8376,9543,94003.72,84,465,648.23,Official (Quarter),5511
+2018-10-16,2018,10,Europe,Model S,11569,12855,95652.54,122,737,1138.82,Official (Quarter),4024
+2020-03-14,2020,3,Asia,Model Y,10932,11192,70949.53,81,469,771.06,Official (Quarter),10082
+2018-10-23,2018,10,Middle East,Model 3,12031,11515,55887.98,58,329,631.48,Interpolated (Month),5828
+2024-04-08,2024,4,North America,Model Y,3930,3902,68476.81,76,443,259.61,Interpolated (Month),8579
+2016-07-27,2016,7,Europe,Model X,15835,17301,55121.93,83,462,1097.43,Interpolated (Month),6917
+2024-10-10,2024,10,Europe,Cybertruck,11353,12071,76765.16,61,339,698.21,Official (Quarter),6042
+2016-03-16,2016,3,North America,Model 3,15659,16497,81632.04,98,581,1356.7,Estimated (Region),11959
+2022-05-15,2022,5,Middle East,Model S,8579,9422,65638.44,59,331,467.14,Estimated (Region),12226
+2015-11-24,2015,11,Middle East,Model S,7216,7883,54559.31,83,466,521.81,Interpolated (Month),8899
+2022-02-13,2022,2,North America,Model 3,6890,7244,116764.69,75,420,479.26,Estimated (Region),9311
+2020-08-08,2020,8,Asia,Cybertruck,8548,10873,110814.29,79,467,609.84,Official (Quarter),5707
+2023-02-01,2023,2,Middle East,Cybertruck,4911,5452,59474.48,76,430,314.14,Interpolated (Month),4997
+2025-05-11,2025,5,Europe,Model Y,7365,7652,102131.77,72,455,427.0,Interpolated (Month),10602
+2018-05-24,2018,5,Europe,Model Y,17829,15152,96293.69,80,458,1044.08,Official (Quarter),4974
+2017-02-01,2017,2,Europe,Model S,851,863,81686.64,123,716,83.09,Estimated (Region),14218
+2018-05-26,2018,5,Europe,Model S,6073,6378,92041.46,124,693,627.25,Interpolated (Month),5698
+2015-11-16,2015,11,North America,Model X,8319,9266,101918.79,80,439,647.21,Interpolated (Month),4086
+2020-03-04,2020,3,North America,Model Y,12944,15251,63103.95,99,531,1117.24,Official (Quarter),13662
+2020-02-09,2020,2,North America,Model S,12785,12251,110947.1,75,414,743.56,Interpolated (Month),13222
+2020-08-16,2020,8,Middle East,Model 3,11954,14574,57560.32,124,733,1235.42,Estimated (Region),12567
+2025-12-09,2025,12,North America,Model Y,7806,9073,57702.6,99,568,730.7,Interpolated (Month),5780
+2020-03-04,2020,3,Europe,Model S,8058,7762,70522.4,119,664,958.06,Estimated (Region),14167
+2024-12-19,2024,12,Asia,Model Y,16241,19448,81315.84,85,431,1237.11,Interpolated (Month),13488
+2016-11-19,2016,11,Middle East,Cybertruck,11404,12127,114711.73,81,476,749.77,Estimated (Region),3529
+2024-09-04,2024,9,Europe,Model 3,14777,15937,86100.57,101,587,1395.69,Official (Quarter),9782
+2018-04-08,2018,4,North America,Model X,6021,6430,118817.11,97,585,516.75,Interpolated (Month),8612
+2018-09-07,2018,9,Asia,Model 3,14825,15687,110198.79,75,431,1022.26,Official (Quarter),12618
+2021-07-19,2021,7,Middle East,Model S,11922,12635,74284.53,58,351,698.6,Estimated (Region),12938
+2020-11-21,2020,11,North America,Model Y,9526,9626,75536.41,73,441,593.12,Estimated (Region),10140
+2017-01-21,2017,1,Europe,Model 3,2549,2383,86506.53,102,581,200.96,Interpolated (Month),10586
+2021-04-27,2021,4,Middle East,Model X,12992,13202,51286.32,120,676,1376.1,Estimated (Region),14785
+2024-01-22,2024,1,Middle East,Model X,14737,16038,87464.04,99,561,1246.13,Official (Quarter),8128
+2016-11-09,2016,11,Asia,Model X,14725,17876,113230.57,99,605,1234.56,Interpolated (Month),8600
+2017-06-04,2017,6,North America,Model Y,6087,5959,119657.84,100,564,534.64,Interpolated (Month),4079
+2016-05-18,2016,5,Asia,Model Y,12551,12242,81697.26,82,470,919.84,Interpolated (Month),7107
+2018-06-26,2018,6,Asia,Model 3,16374,15965,91388.22,61,342,780.82,Official (Quarter),5393
+2017-11-28,2017,11,North America,Cybertruck,10328,9491,109030.53,57,326,484.22,Interpolated (Month),13016
+2020-06-07,2020,6,Middle East,Model X,14638,15747,65308.43,118,722,1391.43,Interpolated (Month),14691
+2019-02-07,2019,2,Europe,Model 3,7921,8703,97867.24,100,561,714.72,Estimated (Region),9413
+2024-06-02,2024,6,Asia,Model X,16666,19498,59704.35,60,325,803.04,Estimated (Region),11773
+2019-10-04,2019,10,Middle East,Model 3,3272,3642,86374.88,121,673,300.79,Official (Quarter),12283
+2024-06-16,2024,6,Middle East,Model 3,13315,12310,71074.72,82,467,1082.54,Interpolated (Month),10654
+2016-01-16,2016,1,North America,Model Y,5211,5073,85296.97,60,328,277.2,Interpolated (Month),7041
+2015-05-11,2015,5,North America,Model 3,11113,12302,54991.81,119,665,1144.79,Estimated (Region),14876
+2016-01-09,2016,1,Middle East,Model 3,13953,16241,78247.03,100,562,1294.25,Interpolated (Month),9565
+2023-10-24,2023,10,Asia,Model Y,14604,15872,95925.15,77,438,1046.86,Official (Quarter),5581
+2017-03-13,2017,3,Europe,Model X,18644,19280,96006.62,118,617,1768.03,Official (Quarter),6310
+2020-11-23,2020,11,Middle East,Model S,5963,6381,68529.61,60,369,325.5,Estimated (Region),10692
+2017-09-27,2017,9,Middle East,Model Y,11610,12386,122494.56,75,447,755.87,Estimated (Region),10764
+2024-09-09,2024,9,North America,Model Y,4433,4848,70546.99,124,708,472.55,Interpolated (Month),14825
+2023-03-06,2023,3,Europe,Model 3,7053,7839,67967.97,82,452,542.83,Official (Quarter),13054
+2024-12-27,2024,12,Middle East,Model Y,6196,6295,101812.55,122,674,631.47,Interpolated (Month),11880
+2023-06-18,2023,6,Europe,Model Y,10091,10639,60278.9,61,323,416.43,Estimated (Region),5644
+2022-10-09,2022,10,Europe,Cybertruck,10524,12056,110168.9,82,481,781.65,Estimated (Region),13773
+2019-04-18,2019,4,Middle East,Model Y,3633,4278,113120.29,119,652,398.06,Official (Quarter),5674
+2021-03-15,2021,3,Europe,Model X,5910,6337,57505.21,75,449,364.24,Official (Quarter),13435
+2025-05-26,2025,5,North America,Cybertruck,6910,7649,98784.6,75,413,453.43,Estimated (Region),3228
+2024-02-08,2024,2,North America,Model 3,7249,8259,77610.28,76,431,511.09,Interpolated (Month),3050
+2017-06-28,2017,6,Asia,Model 3,8612,9349,84989.84,60,330,518.68,Official (Quarter),10448
+2021-01-27,2021,1,North America,Cybertruck,6371,5744,55494.64,75,449,377.01,Interpolated (Month),10879
+2017-07-06,2017,7,North America,Model S,12381,14699,81461.97,117,673,1462.55,Official (Quarter),10239
+2018-04-05,2018,4,Middle East,Model Y,15984,18389,116403.52,98,556,1240.47,Estimated (Region),12598
+2016-10-15,2016,10,North America,Cybertruck,14878,15693,64825.79,86,427,955.54,Official (Quarter),5934
+2022-05-02,2022,5,Europe,Model Y,20544,22215,92162.56,60,346,1188.08,Official (Quarter),14462
+2025-07-09,2025,7,Asia,Model Y,7744,9064,51518.02,118,661,750.71,Interpolated (Month),10517
+2021-08-13,2021,8,Middle East,Model X,10406,12110,82699.84,62,338,567.73,Estimated (Region),14771
+2017-09-28,2017,9,Middle East,Cybertruck,1527,1799,99734.22,75,424,87.61,Official (Quarter),3643
+2019-03-28,2019,3,Asia,Model 3,7535,8287,93949.6,76,402,528.22,Official (Quarter),3383
+2023-12-05,2023,12,Middle East,Model 3,9860,10291,101783.6,60,324,432.64,Interpolated (Month),9535
+2016-09-25,2016,9,Middle East,Model Y,12368,12674,114034.85,75,438,716.71,Estimated (Region),14892
+2017-04-03,2017,4,Asia,Model Y,11089,10770,79338.67,100,583,863.2,Official (Quarter),3636
+2021-04-21,2021,4,Asia,Model X,9259,8652,94392.63,80,490,588.79,Interpolated (Month),5408
+2018-06-24,2018,6,Europe,Model Y,7583,8506,111389.97,75,421,481.79,Interpolated (Month),6951
+2015-02-25,2015,2,Middle East,Cybertruck,7768,8185,66842.79,60,335,375.69,Official (Quarter),6063
+2017-03-07,2017,3,Europe,Model S,5911,5881,114775.58,78,424,406.09,Official (Quarter),3735
+2022-09-26,2022,9,North America,Model Y,12901,12692,88791.45,120,715,1535.03,Official (Quarter),3762
+2025-02-04,2025,2,Asia,Model Y,8211,7245,55290.64,114,725,905.32,Estimated (Region),14997
+2025-06-15,2025,6,Asia,Model S,13052,12607,110883.05,100,586,1038.84,Estimated (Region),5721
+2021-01-14,2021,1,Europe,Model X,8783,8202,49878.0,79,457,633.0,Official (Quarter),5524
+2016-09-09,2016,9,Middle East,Model 3,9134,9927,113126.16,61,338,513.84,Estimated (Region),6406
+2022-04-01,2022,4,Europe,Model 3,6903,7037,108955.43,98,578,569.15,Interpolated (Month),3264
+2015-04-22,2015,4,Europe,Model S,10332,11190,61119.63,83,449,772.69,Estimated (Region),11368
+2025-04-10,2025,4,Asia,Model S,8322,9361,98672.77,118,700,978.2,Interpolated (Month),5660
+2017-11-19,2017,11,Middle East,Model X,9670,9777,83387.35,73,461,633.41,Interpolated (Month),14981
+2019-05-12,2019,5,Europe,Model X,13623,14534,97065.88,74,412,784.25,Interpolated (Month),13077
+2023-09-11,2023,9,North America,Model Y,5533,5730,101443.74,75,417,320.65,Official (Quarter),3887
+2016-08-25,2016,8,Europe,Model S,7889,7836,93806.46,79,487,543.53,Interpolated (Month),14672
+2015-12-03,2015,12,North America,Model S,9488,9717,101858.78,80,437,679.53,Estimated (Region),12170
+2025-06-21,2025,6,North America,Model Y,16334,17110,63338.98,60,338,826.28,Estimated (Region),14276
+2025-09-21,2025,9,Middle East,Cybertruck,6472,7317,65920.69,74,414,493.69,Official (Quarter),8967
+2015-06-11,2015,6,Europe,Model Y,3292,3167,115436.35,60,342,181.7,Official (Quarter),7111
+2017-07-08,2017,7,North America,Model X,7204,8343,91247.0,80,466,609.01,Interpolated (Month),3766
+2022-12-01,2022,12,North America,Model 3,8392,9008,93983.93,101,575,840.4,Estimated (Region),13016
+2022-02-27,2022,2,Asia,Model 3,7370,8110,115656.32,117,679,677.2,Official (Quarter),15280
+2015-09-27,2015,9,Asia,Model X,7942,8637,83607.3,85,482,585.28,Official (Quarter),6240
+2021-10-07,2021,10,Europe,Model S,9340,10401,83153.76,79,470,666.88,Official (Quarter),7375
+2018-11-01,2018,11,North America,Model S,9730,9817,67578.7,80,467,566.86,Official (Quarter),7905
+2016-01-04,2016,1,Middle East,Model S,15201,18222,91892.42,101,556,1363.57,Interpolated (Month),11360
+2024-12-02,2024,12,Asia,Cybertruck,6309,7566,99826.04,85,449,485.04,Official (Quarter),4658
+2020-05-24,2020,5,North America,Model Y,8696,10023,74265.38,101,594,777.42,Estimated (Region),9053
+2021-05-25,2021,5,Asia,Model 3,10312,11994,69960.07,61,332,596.02,Official (Quarter),12083
+2023-08-05,2023,8,Europe,Model Y,10844,12140,65809.08,75,430,725.64,Estimated (Region),6614
+2024-05-21,2024,5,North America,Model Y,13056,14224,92980.84,98,593,1455.07,Official (Quarter),3245
+2020-10-06,2020,10,Europe,Model 3,7982,8315,89444.23,119,666,813.27,Official (Quarter),3605
+2024-02-11,2024,2,Europe,Model Y,9505,9270,113133.94,98,545,804.45,Official (Quarter),13520
+2025-11-17,2025,11,Asia,Model Y,7864,8330,91792.19,117,683,824.53,Estimated (Region),4967
+2016-10-21,2016,10,Middle East,Model S,6522,7171,64961.59,101,553,661.28,Official (Quarter),9547
+2018-03-25,2018,3,Middle East,Model S,11742,13601,49105.75,117,729,1199.32,Official (Quarter),3329
+2020-01-02,2020,1,Asia,Model Y,11464,13503,89287.63,101,577,934.87,Official (Quarter),4850
+2018-04-06,2018,4,Asia,Model 3,7114,8497,70835.41,97,592,689.45,Interpolated (Month),9679
+2019-06-08,2019,6,Asia,Model Y,8897,8310,115131.52,82,468,706.72,Official (Quarter),12193
+2025-06-09,2025,6,Middle East,Model S,9200,9901,62451.71,59,341,516.38,Official (Quarter),8053
+2023-10-28,2023,10,North America,Model S,11332,12228,102591.01,77,417,652.39,Interpolated (Month),7548
+2023-10-22,2023,10,Middle East,Cybertruck,5044,5757,121704.62,60,346,281.66,Estimated (Region),14872
+2025-11-09,2025,11,Middle East,Model 3,4373,4851,67821.42,84,476,332.84,Interpolated (Month),3941
+2021-08-28,2021,8,North America,Model Y,11653,11432,80881.42,81,459,890.06,Interpolated (Month),12758
+2016-12-14,2016,12,Middle East,Model 3,9921,11140,104459.37,60,343,572.32,Estimated (Region),14590
+2020-12-15,2020,12,Middle East,Model S,3906,3525,59244.41,80,463,261.18,Estimated (Region),8945
+2023-05-13,2023,5,Middle East,Cybertruck,11282,12326,86155.28,60,374,715.05,Estimated (Region),12796
+2023-04-05,2023,4,Middle East,Model S,14762,16142,97451.8,60,351,957.56,Estimated (Region),5926
+2024-12-27,2024,12,Asia,Model X,16547,19351,82481.58,99,593,1525.46,Estimated (Region),8042
+2018-04-01,2018,4,Europe,Model 3,9630,10673,54910.74,98,590,932.22,Estimated (Region),6718
+2022-11-05,2022,11,Asia,Model X,7191,7611,98630.87,58,340,448.91,Estimated (Region),11765
+2025-03-07,2025,3,Asia,Model 3,12012,12561,106616.86,95,570,1256.54,Official (Quarter),12736
+2021-05-08,2021,5,North America,Cybertruck,2214,2195,66662.4,98,569,211.98,Interpolated (Month),3640
+2015-06-04,2015,6,Asia,Cybertruck,7089,8036,112187.03,76,403,448.1,Estimated (Region),6293
+2016-04-01,2016,4,North America,Cybertruck,10668,9940,102174.99,79,459,687.26,Official (Quarter),9077
+2017-01-19,2017,1,North America,Model X,12464,13221,61761.42,58,358,712.51,Interpolated (Month),12283
+2018-06-28,2018,6,North America,Model 3,9867,10772,60744.12,78,444,750.69,Estimated (Region),9537
+2021-01-04,2021,1,Europe,Model 3,12020,12722,109717.94,74,420,757.93,Estimated (Region),14113
+2019-07-09,2019,7,Asia,Model X,7690,8035,97345.36,75,413,542.58,Estimated (Region),3866
+2016-02-15,2016,2,Asia,Model 3,11607,11301,51222.2,76,398,727.07,Official (Quarter),10845
+2021-03-26,2021,3,North America,Model Y,13894,16626,80690.41,76,426,858.1,Official (Quarter),15046
+2024-03-20,2024,3,Europe,Model Y,14313,16970,109654.16,116,683,1455.93,Official (Quarter),12007
+2018-05-22,2018,5,Asia,Model 3,16105,15739,113571.16,75,423,952.41,Official (Quarter),4549
+2017-07-17,2017,7,Asia,Model S,9203,10312,84346.86,97,635,967.46,Estimated (Region),12950
+2023-10-12,2023,10,North America,Model X,15917,15606,74337.17,79,450,921.38,Official (Quarter),14786
+2015-09-06,2015,9,North America,Model Y,7724,8734,84531.74,97,590,677.8,Estimated (Region),7864
+2016-05-25,2016,5,Asia,Model S,7202,7772,68227.91,117,685,757.21,Official (Quarter),4654
+2020-01-23,2020,1,Europe,Model X,12310,12946,82529.75,80,453,894.95,Estimated (Region),11969
+2022-12-01,2022,12,North America,Model S,4465,5172,109876.41,80,488,383.98,Official (Quarter),11846
+2025-11-21,2025,11,Middle East,Model X,6214,6895,97106.09,103,549,615.07,Official (Quarter),6224
+2022-07-03,2022,7,Asia,Model 3,10605,11125,64689.08,86,480,749.81,Interpolated (Month),9109
+2017-10-21,2017,10,Asia,Model S,11318,12967,82547.9,74,449,767.1,Estimated (Region),3876
+2018-05-24,2018,5,Middle East,Model S,3149,3050,75515.07,97,563,280.23,Official (Quarter),4137
+2018-03-04,2018,3,Europe,Model S,8920,9778,107520.76,72,457,586.02,Interpolated (Month),4451
+2023-06-13,2023,6,Middle East,Model S,11403,9389,85741.59,75,419,692.82,Interpolated (Month),14161
+2022-09-04,2022,9,Middle East,Model Y,7451,7234,108699.82,81,436,481.93,Estimated (Region),6159
+2016-03-16,2016,3,Middle East,Model 3,5157,5900,73039.48,59,353,269.71,Estimated (Region),7915
+2015-08-26,2015,8,Asia,Model 3,11233,11430,111069.79,100,590,956.14,Interpolated (Month),4750
+2023-09-03,2023,9,Asia,Model 3,11953,13823,77217.9,83,502,943.07,Official (Quarter),10641
+2019-09-12,2019,9,North America,Cybertruck,3477,3797,71886.95,60,354,186.04,Interpolated (Month),10056
+2020-01-28,2020,1,Europe,Model S,15826,18816,75665.4,75,425,1170.46,Estimated (Region),4942
+2017-09-09,2017,9,North America,Model S,5675,5263,76694.42,73,449,459.21,Estimated (Region),8993
+2019-08-04,2019,8,Asia,Model X,8635,10512,89906.55,61,342,428.94,Estimated (Region),8346
+2025-07-14,2025,7,North America,Model X,4610,5023,114608.93,75,426,279.05,Estimated (Region),7842
+2021-12-15,2021,12,Europe,Model S,12909,11478,74895.58,83,474,750.16,Official (Quarter),8466
+2017-05-19,2017,5,Europe,Model X,8379,9237,52019.54,83,496,602.18,Interpolated (Month),13656
+2017-02-09,2017,2,Europe,Model Y,6656,6787,78304.09,97,591,613.23,Official (Quarter),5328
+2023-05-27,2023,5,North America,Cybertruck,4253,4712,54040.53,58,362,233.89,Official (Quarter),8417
+2023-11-15,2023,11,Europe,Model Y,11567,13147,118197.95,59,343,581.75,Interpolated (Month),11119
+2022-02-09,2022,2,Middle East,Model S,4372,4057,104512.08,73,421,282.24,Interpolated (Month),10143
+2017-07-27,2017,7,Europe,Cybertruck,6848,7378,107996.51,83,450,439.42,Interpolated (Month),14533
+2016-01-26,2016,1,Europe,Model S,9392,8704,59345.75,83,502,622.61,Official (Quarter),11543
+2019-07-14,2019,7,Europe,Model Y,6672,7118,69806.92,61,363,329.55,Estimated (Region),6247
+2025-10-01,2025,10,Europe,Model 3,7796,8117,117165.12,76,414,421.35,Official (Quarter),4764
+2024-12-17,2024,12,North America,Cybertruck,11194,13116,61869.38,121,686,1035.59,Estimated (Region),13523
+2021-11-16,2021,11,Middle East,Cybertruck,10392,11372,73105.05,123,716,1070.51,Official (Quarter),11269
+2019-11-05,2019,11,Asia,Model 3,9392,9029,116062.78,101,559,884.36,Official (Quarter),4880
+2016-06-18,2016,6,Europe,Model S,7300,7357,102969.74,120,713,744.3,Official (Quarter),9273
+2024-12-24,2024,12,Asia,Model 3,10949,10188,51026.32,77,425,723.23,Official (Quarter),8223
+2022-10-03,2022,10,Middle East,Model Y,10566,10276,63844.4,100,599,791.77,Estimated (Region),8874
+2015-02-21,2015,2,Europe,Model Y,10298,11372,69484.71,99,574,759.99,Official (Quarter),10911
+2025-01-04,2025,1,Asia,Model 3,16300,16027,68172.18,81,477,1208.81,Official (Quarter),11332
+2018-04-21,2018,4,Asia,Model S,6864,6341,93529.25,122,679,524.97,Official (Quarter),3251
+2015-07-12,2015,7,North America,Cybertruck,4469,4387,60459.58,61,335,244.49,Official (Quarter),4962
+2022-08-01,2022,8,Asia,Cybertruck,13728,12807,77507.56,75,442,748.53,Estimated (Region),12024
+2021-06-28,2021,6,North America,Model 3,14635,14024,66415.65,123,715,1580.96,Interpolated (Month),10996
+2023-04-05,2023,4,Europe,Model Y,11655,13806,98630.14,72,435,818.2,Interpolated (Month),15492
+2020-10-17,2020,10,North America,Model X,12027,11829,90594.83,60,331,635.09,Interpolated (Month),3770
+2024-08-06,2024,8,Middle East,Model 3,3510,5389,94415.29,100,579,457.21,Official (Quarter),8718
+2020-07-27,2020,7,Asia,Model S,9742,12189,104641.54,59,367,464.46,Interpolated (Month),7206
+2018-02-21,2018,2,Europe,Model 3,15136,15952,78265.98,86,476,1143.79,Estimated (Region),6571
+2015-08-23,2015,8,North America,Model S,10314,9497,111971.9,120,681,882.7,Interpolated (Month),9975
+2016-11-13,2016,11,Europe,Cybertruck,9828,10966,49056.49,119,631,1079.56,Estimated (Region),10046
+2022-11-14,2022,11,Asia,Model S,12182,13926,87049.63,120,714,1287.65,Interpolated (Month),4060
+2020-04-18,2020,4,Asia,Cybertruck,4400,4225,53408.85,96,625,367.61,Official (Quarter),5601
+2022-12-22,2022,12,Asia,Model Y,10311,12313,94842.35,98,562,824.36,Official (Quarter),2960
+2019-04-24,2019,4,Europe,Cybertruck,12058,11287,76302.66,122,623,1149.02,Estimated (Region),8758
+2023-09-01,2023,9,Europe,Model S,6917,7315,75186.98,118,726,654.15,Estimated (Region),4810
+2024-05-19,2024,5,North America,Model X,14265,14926,117450.84,98,577,1367.04,Interpolated (Month),9852
+2016-05-04,2016,5,Middle East,Model S,18208,19257,112423.74,62,346,862.8,Interpolated (Month),5985
+2024-05-09,2024,5,Europe,Cybertruck,8818,8690,52906.95,77,437,500.51,Interpolated (Month),13701
+2017-02-24,2017,2,North America,Model X,9835,10670,57899.55,83,487,845.87,Interpolated (Month),10611
+2023-05-08,2023,5,Middle East,Model S,11202,12566,96826.16,81,466,794.87,Estimated (Region),10549
+2021-11-23,2021,11,Middle East,Model X,3090,3343,70324.56,119,685,374.16,Official (Quarter),8711
+2015-08-13,2015,8,Europe,Model Y,6433,7264,108139.32,74,436,493.37,Estimated (Region),7927
+2017-03-07,2017,3,Middle East,Model S,9947,10574,98854.65,74,432,618.31,Estimated (Region),5310
+2018-11-10,2018,11,Asia,Model X,7608,7995,109570.56,80,450,547.34,Interpolated (Month),10510
+2022-03-15,2022,3,Asia,Model Y,6424,6208,106325.75,81,484,454.73,Official (Quarter),10452
+2020-11-22,2020,11,Europe,Model 3,5009,5969,89193.53,80,484,403.12,Interpolated (Month),7280
+2016-06-17,2016,6,Middle East,Model X,11361,11404,69342.16,79,477,858.77,Estimated (Region),15057
+2018-12-27,2018,12,Asia,Cybertruck,14918,14919,112637.4,100,531,1266.19,Interpolated (Month),7428
+2015-11-25,2015,11,Europe,Model X,12841,14570,95387.21,82,489,890.33,Official (Quarter),8617
+2017-03-22,2017,3,Asia,Model 3,12735,14832,116600.68,78,397,799.14,Official (Quarter),6026
+2016-01-17,2016,1,North America,Model S,7781,8329,70668.03,118,692,951.06,Estimated (Region),14283
+2017-05-09,2017,5,Europe,Cybertruck,8125,8969,86149.79,75,431,564.57,Interpolated (Month),12105
+2021-04-12,2021,4,Europe,Model X,9017,8359,52193.4,121,663,819.22,Interpolated (Month),6103
+2020-04-04,2020,4,Middle East,Model 3,5026,5817,70364.15,75,440,318.27,Interpolated (Month),12262
+2020-12-11,2020,12,Europe,Cybertruck,2328,2790,95040.77,83,489,169.92,Estimated (Region),14273
+2025-06-26,2025,6,Asia,Cybertruck,8830,9046,105982.33,83,470,552.18,Estimated (Region),10212
+2023-09-12,2023,9,North America,Model S,4797,5368,63695.36,75,420,272.99,Estimated (Region),3504
+2015-03-04,2015,3,North America,Model S,13514,15610,114519.21,60,351,857.92,Official (Quarter),9544
+2017-09-19,2017,9,Europe,Model Y,10500,10901,112192.04,60,338,545.19,Interpolated (Month),6749
+2017-07-27,2017,7,Middle East,Cybertruck,19028,20029,62500.99,58,365,1003.29,Estimated (Region),7458
+2022-02-25,2022,2,Europe,Model X,6360,6293,96449.21,81,456,358.11,Interpolated (Month),6613
+2024-01-08,2024,1,North America,Model 3,3940,4187,90060.25,81,488,288.66,Official (Quarter),8310
+2021-08-13,2021,8,Middle East,Model Y,12151,14353,88240.72,80,474,907.53,Official (Quarter),5204
+2015-10-15,2015,10,North America,Model Y,7214,7120,78460.7,101,590,598.77,Official (Quarter),6808
+2015-12-07,2015,12,Europe,Model Y,16016,17205,81754.03,77,416,1044.28,Official (Quarter),14469
+2024-04-08,2024,4,Europe,Model X,9503,9220,77270.43,118,685,937.05,Estimated (Region),9987
+2023-01-07,2023,1,Asia,Model Y,16688,16990,54751.01,82,460,961.76,Estimated (Region),7793
+2020-08-28,2020,8,North America,Model 3,9890,10572,81426.88,118,697,1035.32,Interpolated (Month),4888
+2024-03-21,2024,3,North America,Model Y,3465,3528,114017.5,119,688,340.39,Official (Quarter),12555
+2017-12-24,2017,12,Asia,Cybertruck,9860,10661,109938.24,59,345,476.24,Interpolated (Month),9951
+2019-10-23,2019,10,Europe,Model 3,6798,7511,66737.73,83,489,456.99,Estimated (Region),13259
+2015-04-27,2015,4,Europe,Model Y,11319,11461,70665.18,99,611,871.58,Official (Quarter),3456
+2023-07-03,2023,7,Asia,Model Y,15734,15783,71180.08,84,462,1080.48,Estimated (Region),7073
+2018-10-25,2018,10,Asia,Model S,6135,6076,83715.31,60,345,348.04,Estimated (Region),12156
+2018-06-02,2018,6,North America,Model X,12101,13156,56980.23,99,574,1050.08,Estimated (Region),10601
+2022-09-14,2022,9,Middle East,Cybertruck,9163,10131,118220.12,117,653,787.83,Interpolated (Month),10139
+2024-07-17,2024,7,Europe,Model X,10096,10250,113682.33,60,343,535.0,Official (Quarter),5082
+2019-11-11,2019,11,North America,Model Y,8019,8108,111940.73,61,332,416.4,Interpolated (Month),12891
+2022-10-05,2022,10,Middle East,Model 3,8280,9404,90293.35,60,342,473.22,Estimated (Region),14146
+2017-10-06,2017,10,North America,Model Y,8313,8716,92250.97,63,360,449.92,Interpolated (Month),4984
+2023-06-24,2023,6,Middle East,Model X,6596,6642,69546.79,62,361,269.56,Interpolated (Month),11682
+2016-02-09,2016,2,North America,Model Y,14892,16012,54379.6,99,531,1279.08,Estimated (Region),9564
+2025-03-09,2025,3,Asia,Model X,12357,13677,54714.55,119,675,1171.09,Estimated (Region),8275
+2024-10-26,2024,10,North America,Model 3,5628,6158,65647.95,121,705,530.73,Interpolated (Month),13656
+2022-06-05,2022,6,Middle East,Model X,14619,14924,99254.85,81,447,953.12,Official (Quarter),4182
+2024-12-26,2024,12,Europe,Model S,14134,15132,106856.86,61,354,754.32,Interpolated (Month),3681
+2019-12-26,2019,12,Europe,Model S,4806,5087,79827.3,76,451,294.35,Interpolated (Month),11719
+2024-12-17,2024,12,North America,Model 3,9793,10328,56457.51,82,457,687.48,Estimated (Region),10819
+2016-11-05,2016,11,Asia,Cybertruck,13776,15415,60900.91,83,448,1052.5,Estimated (Region),4617
+2017-02-05,2017,2,Europe,Model X,4993,5937,70922.68,85,479,373.77,Official (Quarter),10606
+2015-04-08,2015,4,North America,Model 3,13391,15285,59846.59,120,669,1305.63,Official (Quarter),3514
+2017-03-17,2017,3,Asia,Model S,7860,9551,62700.37,120,708,821.04,Official (Quarter),14443
+2025-04-05,2025,4,Asia,Model 3,8460,9637,92918.97,103,575,810.79,Interpolated (Month),10180
+2024-07-13,2024,7,Asia,Model Y,13771,15249,74916.02,76,422,789.83,Estimated (Region),8175
+2019-10-04,2019,10,North America,Model Y,10271,11492,85892.47,99,533,940.38,Interpolated (Month),10760
+2020-02-16,2020,2,Asia,Model S,4569,4565,69751.87,82,473,302.42,Estimated (Region),11286
+2024-02-01,2024,2,Europe,Model S,13118,13987,87050.98,100,545,1071.37,Official (Quarter),7881
+2020-01-20,2020,1,Asia,Cybertruck,15391,16003,77004.34,60,370,702.48,Interpolated (Month),14793
+2017-01-02,2017,1,Asia,Model S,10449,10858,96496.9,73,412,632.93,Official (Quarter),13253
+2022-03-06,2022,3,Europe,Model 3,12739,12997,99525.6,97,571,1145.42,Estimated (Region),12626
+2016-10-05,2016,10,North America,Model X,8876,9679,91415.28,118,668,899.86,Official (Quarter),6613
+2019-12-24,2019,12,North America,Cybertruck,6589,6591,53606.98,80,434,493.04,Estimated (Region),4813
+2025-01-02,2025,1,Middle East,Cybertruck,3779,4680,64451.32,99,592,380.56,Official (Quarter),6368
+2015-09-13,2015,9,Europe,Model 3,13767,12002,62867.51,76,419,823.12,Interpolated (Month),11171
+2019-03-17,2019,3,Middle East,Model Y,13188,15144,49160.53,120,726,1496.66,Official (Quarter),14191
+2017-03-15,2017,3,Middle East,Cybertruck,8619,8571,97159.53,82,429,460.55,Official (Quarter),6630
+2015-03-20,2015,3,Europe,Model Y,10840,12335,101548.74,98,604,921.18,Official (Quarter),11326
+2017-07-23,2017,7,Middle East,Model 3,7234,7095,66690.11,99,589,697.66,Estimated (Region),11158
+2021-12-12,2021,12,Europe,Model 3,13442,14592,102367.21,76,459,1019.38,Official (Quarter),4544
+2015-03-24,2015,3,Europe,Model X,4036,4404,99779.78,76,432,263.88,Interpolated (Month),4082
+2019-08-09,2019,8,Asia,Model S,13714,14535,77750.52,81,492,983.53,Official (Quarter),3760
+2025-09-28,2025,9,Middle East,Model Y,12554,13594,52249.31,60,342,639.91,Official (Quarter),4731
+2018-03-15,2018,3,Asia,Model 3,12684,12997,69232.81,86,464,803.24,Estimated (Region),15325
+2022-08-21,2022,8,Europe,Model Y,1349,1510,77671.07,60,322,71.7,Interpolated (Month),11766
+2015-02-25,2015,2,Europe,Cybertruck,5540,5240,98446.16,81,443,348.18,Interpolated (Month),11168
+2020-05-23,2020,5,Asia,Model 3,12108,13646,53715.87,82,449,866.34,Estimated (Region),8214
+2022-06-24,2022,6,Europe,Model Y,12501,14512,52478.58,120,660,1521.46,Estimated (Region),8580
+2025-04-16,2025,4,Asia,Model Y,10226,10901,50066.34,100,538,803.38,Interpolated (Month),5721
+2024-02-02,2024,2,Europe,Model 3,5937,7003,81541.32,75,452,343.3,Estimated (Region),5822
+2025-02-14,2025,2,Middle East,Model Y,7578,8180,88685.49,80,444,473.38,Estimated (Region),5279
+2016-07-06,2016,7,Asia,Model 3,12046,12772,105628.26,118,624,1330.77,Estimated (Region),7750
+2023-12-25,2023,12,North America,Cybertruck,11600,12682,63592.02,60,372,645.98,Interpolated (Month),6217
+2025-12-07,2025,12,North America,Model X,6124,7075,68784.74,82,473,465.68,Estimated (Region),9113
+2023-07-07,2023,7,Europe,Model X,6281,7014,95631.42,119,673,610.28,Official (Quarter),7505
+2020-12-07,2020,12,Middle East,Model 3,11399,13455,76556.99,60,342,654.92,Interpolated (Month),7830
+2018-12-18,2018,12,Europe,Model Y,14344,14208,67903.36,75,462,931.34,Estimated (Region),12159
+2024-08-16,2024,8,Middle East,Model Y,13232,14936,106476.83,83,459,902.46,Interpolated (Month),3233
+2019-04-27,2019,4,Asia,Model X,11049,11560,67205.33,82,483,779.57,Estimated (Region),3938
+2018-07-12,2018,7,Asia,Model 3,11966,13376,86004.98,76,441,689.26,Estimated (Region),9476
+2016-01-08,2016,1,Asia,Cybertruck,9053,9697,75800.01,74,439,629.78,Interpolated (Month),3823
+2023-01-02,2023,1,Europe,Model S,12004,13646,51791.76,83,479,919.15,Interpolated (Month),9306
+2022-12-01,2022,12,North America,Cybertruck,4980,5041,70800.54,60,335,245.13,Estimated (Region),9358
+2020-12-11,2020,12,North America,Model 3,15686,16569,77291.49,102,556,1253.86,Estimated (Region),6879
+2020-05-26,2020,5,North America,Cybertruck,14631,13971,117916.67,101,576,1148.18,Official (Quarter),3776
+2016-06-15,2016,6,Middle East,Cybertruck,6636,6989,61888.68,61,341,330.15,Interpolated (Month),13333
+2025-02-26,2025,2,Asia,Model S,7374,7391,53551.06,75,438,515.71,Estimated (Region),3638
+2019-11-03,2019,11,Middle East,Model Y,4982,4795,74873.88,82,460,342.85,Estimated (Region),5814
+2022-06-03,2022,6,Asia,Cybertruck,3086,3569,105027.04,61,346,135.7,Interpolated (Month),12678
+2017-10-24,2017,10,Middle East,Cybertruck,2791,2957,100626.65,99,547,244.19,Estimated (Region),9131
+2018-10-03,2018,10,North America,Model 3,777,815,120313.71,81,465,53.66,Official (Quarter),13870
+2018-04-02,2018,4,Europe,Model Y,10563,11871,112035.01,100,584,1091.08,Interpolated (Month),11994
+2024-09-12,2024,9,Middle East,Cybertruck,5682,5657,117918.64,101,577,501.93,Official (Quarter),14953
+2018-10-26,2018,10,Middle East,Model S,9138,10572,72985.41,59,313,508.64,Interpolated (Month),12977
+2024-01-20,2024,1,Middle East,Model S,14611,14675,95142.61,100,614,1355.19,Estimated (Region),7644
+2024-09-03,2024,9,Europe,Model X,13625,13659,94839.83,100,564,996.61,Estimated (Region),12826
+2025-05-24,2025,5,Europe,Model S,7839,8192,114832.28,78,497,521.82,Interpolated (Month),7975
+2025-01-24,2025,1,North America,Model S,5890,5992,65941.83,119,691,571.3,Estimated (Region),14161
+2024-09-23,2024,9,North America,Model S,10141,11468,119565.7,75,435,627.48,Estimated (Region),8714
+2019-11-26,2019,11,North America,Model S,11890,11451,64867.26,60,356,646.71,Interpolated (Month),8338
+2023-09-07,2023,9,Europe,Model 3,10112,11912,65094.38,84,463,787.5,Interpolated (Month),14393
+2024-06-28,2024,6,Middle East,Cybertruck,8584,9550,94719.1,80,479,627.31,Estimated (Region),5403
+2024-11-09,2024,11,Middle East,Model S,10585,10024,83928.58,81,484,600.98,Estimated (Region),7258
+2021-08-23,2021,8,Asia,Model Y,5637,5414,65860.94,114,703,641.0,Official (Quarter),8319
+2017-09-17,2017,9,Middle East,Model 3,12065,11811,76770.0,74,440,646.79,Estimated (Region),7542
+2021-07-22,2021,7,Asia,Model Y,15238,14151,87780.46,99,591,1578.94,Estimated (Region),11363
+2019-12-18,2019,12,Europe,Model Y,8495,8219,117371.71,59,339,363.67,Interpolated (Month),5179
+2015-07-18,2015,7,Asia,Model X,6638,6755,117627.95,102,549,520.58,Official (Quarter),13499
+2020-07-18,2020,7,North America,Model S,13799,14854,54665.45,102,560,981.01,Estimated (Region),4781
+2018-07-22,2018,7,North America,Model 3,4966,4843,89442.73,100,555,414.9,Estimated (Region),3029
+2016-09-21,2016,9,Europe,Model 3,5991,6446,105829.57,60,341,280.24,Official (Quarter),7612
+2018-11-04,2018,11,Europe,Model 3,7359,8417,62545.05,84,466,457.65,Interpolated (Month),14875
+2017-10-24,2017,10,North America,Model X,6886,7999,93990.61,82,476,429.93,Official (Quarter),11411
+2023-11-16,2023,11,Asia,Model X,14584,17715,110107.09,98,571,1468.99,Official (Quarter),5540
+2017-01-10,2017,1,North America,Model S,5641,6690,67625.15,58,329,325.64,Estimated (Region),14029
+2022-04-15,2022,4,Asia,Model X,11923,12707,116475.32,99,529,1096.58,Interpolated (Month),8156
+2016-06-28,2016,6,Asia,Model S,13255,15532,112433.42,83,518,929.91,Interpolated (Month),6013
+2023-01-25,2023,1,Middle East,Model Y,8091,9212,104680.61,105,588,742.1,Estimated (Region),8194
+2016-03-24,2016,3,North America,Model Y,1387,1559,94844.06,100,588,108.14,Estimated (Region),7840
+2018-01-13,2018,1,Europe,Model Y,2462,2401,55203.5,98,561,201.88,Official (Quarter),8848
+2021-11-22,2021,11,North America,Model S,14941,15460,90142.61,80,462,983.54,Interpolated (Month),12983
+2023-11-11,2023,11,Europe,Model X,10952,12472,55012.44,121,714,1238.37,Interpolated (Month),14094
+2024-01-20,2024,1,North America,Cybertruck,9309,10369,78736.33,116,699,1001.58,Official (Quarter),9136
+2022-04-18,2022,4,Europe,Model Y,15349,16467,51996.57,76,462,1031.79,Estimated (Region),11934
+2015-12-07,2015,12,Asia,Cybertruck,5116,5413,81487.58,102,639,417.67,Official (Quarter),5214
+2025-11-08,2025,11,Asia,Model X,10434,9126,125594.16,101,572,949.3,Interpolated (Month),8805
+2016-11-02,2016,11,Europe,Model Y,5762,5853,80953.45,60,328,274.08,Official (Quarter),9000
+2019-09-04,2019,9,Middle East,Model Y,14602,15904,54408.64,76,435,902.64,Interpolated (Month),13706
+2015-05-13,2015,5,Asia,Model Y,8138,7974,86155.45,100,592,631.26,Interpolated (Month),11916
+2025-01-14,2025,1,Europe,Model S,7393,6996,57925.75,59,358,356.87,Official (Quarter),3722
+2017-08-05,2017,8,Asia,Model 3,8241,8617,76890.25,78,424,584.78,Official (Quarter),4021
+2016-02-12,2016,2,Europe,Model 3,8235,9679,106171.19,74,434,581.17,Official (Quarter),13294
+2025-05-17,2025,5,Europe,Cybertruck,6342,6316,113785.11,61,350,334.55,Estimated (Region),5048
+2016-03-07,2016,3,Middle East,Cybertruck,10477,12068,97097.91,78,488,783.98,Official (Quarter),9760
+2024-06-10,2024,6,Middle East,Model X,6414,6479,110678.2,77,424,513.89,Estimated (Region),14245
+2018-02-11,2018,2,Asia,Model 3,13138,13601,49793.61,84,471,970.55,Interpolated (Month),11232
+2016-09-15,2016,9,North America,Model 3,8737,9032,90800.93,100,564,656.6,Interpolated (Month),13969
+2016-12-11,2016,12,Middle East,Model S,16166,16516,85058.55,61,345,766.06,Estimated (Region),12729
+2018-02-21,2018,2,North America,Model 3,10670,11155,82235.99,83,454,728.8,Estimated (Region),14222
+2019-09-10,2019,9,Middle East,Model X,7655,9002,121938.91,99,570,702.62,Interpolated (Month),12977
+2024-01-13,2024,1,North America,Model X,7274,7708,103393.64,83,479,487.88,Official (Quarter),14913
+2025-10-11,2025,10,Europe,Model Y,8764,9754,99026.12,98,603,778.5,Official (Quarter),8057
+2021-12-06,2021,12,Middle East,Model S,10000,9891,59920.28,74,427,700.44,Interpolated (Month),7778
+2022-11-20,2022,11,Europe,Cybertruck,9424,9109,63544.95,61,338,395.05,Interpolated (Month),8287
+2019-09-07,2019,9,Europe,Model X,6667,7242,114332.97,122,698,650.46,Official (Quarter),7568
+2025-12-09,2025,12,Middle East,Model 3,11353,12410,49681.62,81,449,738.47,Interpolated (Month),10992
+2019-07-23,2019,7,Asia,Model Y,10814,12110,110365.73,74,452,729.52,Estimated (Region),6737
+2016-02-27,2016,2,North America,Model X,4930,5529,59753.15,81,493,410.99,Estimated (Region),8701
+2019-06-19,2019,6,Europe,Model X,13506,15418,95404.05,83,443,1013.01,Estimated (Region),9380
+2023-04-26,2023,4,North America,Model Y,12095,13319,56906.56,59,339,693.34,Interpolated (Month),8912
+2020-08-24,2020,8,Middle East,Model S,13717,15008,115426.45,121,706,1442.98,Interpolated (Month),3856
+2023-09-14,2023,9,North America,Model X,14959,15208,115875.54,83,453,1017.9,Interpolated (Month),14954
+2015-01-22,2015,1,North America,Model X,8012,8424,63887.63,75,442,455.49,Official (Quarter),9537
+2017-07-10,2017,7,Middle East,Model X,5405,5715,53972.05,84,474,369.55,Estimated (Region),5696
+2017-09-13,2017,9,Asia,Model X,5904,5796,76161.73,82,503,446.74,Estimated (Region),9402
+2020-09-15,2020,9,Middle East,Model Y,10108,10979,81329.29,75,438,736.39,Interpolated (Month),12908
+2018-12-27,2018,12,Middle East,Model S,4178,5519,92830.17,98,570,391.69,Estimated (Region),8447
+2018-04-24,2018,4,Asia,Model X,6196,6609,110376.09,77,433,428.99,Estimated (Region),10807
+2024-04-14,2024,4,Middle East,Model X,10288,13058,106553.91,118,714,1131.71,Interpolated (Month),9177
+2019-12-19,2019,12,North America,Model 3,13395,15206,115803.47,60,350,780.19,Estimated (Region),4824
+2020-04-06,2020,4,Middle East,Model X,16521,16596,69861.83,82,450,959.26,Estimated (Region),12544
+2018-06-23,2018,6,Middle East,Model Y,8283,9156,107230.67,117,666,898.09,Interpolated (Month),3857
+2021-02-16,2021,2,Asia,Model X,10951,10906,57858.05,59,327,508.42,Estimated (Region),14316
+2024-10-15,2024,10,North America,Model S,11069,11508,109303.97,77,446,668.15,Interpolated (Month),4183
+2021-12-22,2021,12,Middle East,Cybertruck,12834,14039,81266.68,60,356,561.97,Estimated (Region),8883
+2023-09-18,2023,9,Europe,Model Y,10227,11153,91302.17,73,430,667.57,Interpolated (Month),11770
+2019-12-03,2019,12,Middle East,Cybertruck,4443,4823,81831.61,83,497,364.3,Estimated (Region),6980
+2025-11-09,2025,11,Europe,Model 3,8474,8162,71858.98,60,356,475.16,Interpolated (Month),11608
+2015-11-05,2015,11,North America,Model Y,13979,16377,76483.14,107,515,1375.81,Interpolated (Month),6048
+2017-07-21,2017,7,Asia,Model 3,10179,13038,76962.2,101,569,1132.63,Official (Quarter),8626
+2018-02-01,2018,2,North America,Cybertruck,4788,5585,87858.09,60,361,257.51,Interpolated (Month),8511
+2020-06-06,2020,6,North America,Model S,12443,12340,64172.58,103,554,1170.28,Interpolated (Month),7777
+2016-04-17,2016,4,Asia,Model X,10897,11360,100724.77,79,454,688.43,Interpolated (Month),6491
+2023-01-20,2023,1,North America,Model Y,8680,8371,63318.36,75,469,546.33,Interpolated (Month),11248
+2022-03-15,2022,3,Middle East,Model 3,12739,14398,116922.42,78,418,788.69,Interpolated (Month),10501
+2023-03-25,2023,3,North America,Model 3,10554,11151,79139.76,75,414,520.81,Estimated (Region),13787
+2018-11-26,2018,11,Europe,Model Y,9662,9682,95173.12,116,654,1035.31,Official (Quarter),4886
+2019-12-04,2019,12,Europe,Model 3,8510,8876,61090.46,74,464,530.34,Interpolated (Month),11231
+2016-06-27,2016,6,Asia,Model Y,15400,15329,59858.93,77,416,860.13,Interpolated (Month),12069
+2019-01-18,2019,1,North America,Model Y,5666,5965,92567.97,76,420,318.17,Interpolated (Month),11665
+2018-03-18,2018,3,Asia,Model Y,16832,17397,79602.69,118,707,1735.0,Estimated (Region),9836
+2017-09-03,2017,9,Europe,Model S,6423,7042,102555.64,74,450,468.45,Interpolated (Month),5725
+2024-09-18,2024,9,Asia,Cybertruck,9982,10618,109768.11,81,459,818.82,Interpolated (Month),10623
+2024-03-21,2024,3,Middle East,Cybertruck,13547,15544,129513.36,101,570,1023.67,Estimated (Region),12674
+2021-02-17,2021,2,Europe,Model X,12681,13027,101744.69,84,484,821.12,Interpolated (Month),7354
+2019-07-03,2019,7,Asia,Cybertruck,10516,13649,93512.75,61,341,573.85,Estimated (Region),6235
+2025-10-15,2025,10,Asia,Cybertruck,2816,3310,107998.89,101,523,273.75,Official (Quarter),3104
+2019-11-27,2019,11,Middle East,Cybertruck,10282,10831,117725.77,80,450,756.43,Estimated (Region),12575
+2019-01-21,2019,1,North America,Model 3,12838,13023,88704.24,117,662,1289.93,Estimated (Region),10319
+2020-09-04,2020,9,North America,Model S,6670,8216,60266.84,121,740,719.61,Interpolated (Month),6048
+2015-10-27,2015,10,Middle East,Model S,13141,16557,111333.39,97,568,1158.47,Official (Quarter),13165
+2015-09-27,2015,9,Middle East,Model Y,10252,11566,87074.7,82,464,871.15,Estimated (Region),12970
+2020-09-05,2020,9,Asia,Model Y,8260,9518,79265.94,60,334,460.85,Estimated (Region),13020
+2019-05-23,2019,5,North America,Model X,8398,8617,70286.18,80,435,544.88,Interpolated (Month),9372
+2021-10-12,2021,10,Asia,Model S,3441,3057,75709.46,98,545,210.1,Official (Quarter),6274
+2025-08-17,2025,8,Asia,Model S,11604,11984,101621.24,120,733,1256.84,Estimated (Region),7665
+2019-04-13,2019,4,Middle East,Model X,13639,14668,98142.39,101,597,1361.41,Interpolated (Month),14272
+2017-10-22,2017,10,Europe,Model X,8654,8833,82175.86,123,679,1013.07,Interpolated (Month),10209
+2015-07-28,2015,7,Europe,Model X,6039,7234,112870.01,101,588,503.52,Estimated (Region),6069
+2016-06-23,2016,6,Asia,Cybertruck,7839,8356,112038.49,82,475,554.64,Estimated (Region),9343
+2022-10-27,2022,10,Middle East,Model S,11188,11547,99834.64,59,323,531.67,Estimated (Region),7689
+2019-01-27,2019,1,Europe,Cybertruck,8681,9509,57282.74,73,476,666.05,Estimated (Region),9928
+2019-08-03,2019,8,North America,Model S,10561,11946,51819.12,81,458,809.35,Interpolated (Month),14912
+2016-04-08,2016,4,Europe,Model S,6656,8021,98035.76,83,502,459.85,Estimated (Region),7045
+2017-08-21,2017,8,North America,Model S,11395,12524,58386.22,60,363,592.04,Estimated (Region),4382
+2016-08-17,2016,8,Asia,Model X,7552,7828,74490.58,96,556,516.78,Estimated (Region),9518
+2020-12-02,2020,12,Asia,Cybertruck,15219,15914,89340.19,74,427,890.35,Interpolated (Month),6136
+2025-09-03,2025,9,North America,Model Y,6027,7267,111690.66,123,679,672.75,Estimated (Region),12437
+2022-02-15,2022,2,Europe,Model 3,11412,11529,53077.55,60,341,599.41,Estimated (Region),9814
+2015-09-18,2015,9,North America,Model S,8567,8196,117222.48,117,689,780.19,Official (Quarter),9920
+2016-10-05,2016,10,Europe,Model 3,10605,9858,110029.81,105,631,865.6,Estimated (Region),4434
+2025-07-05,2025,7,Middle East,Cybertruck,15446,15078,91558.59,82,471,1220.1,Official (Quarter),14788
+2023-02-04,2023,2,Europe,Model Y,7008,8111,70140.44,76,447,443.03,Estimated (Region),8947
+2018-10-16,2018,10,Asia,Cybertruck,1670,1714,113302.25,60,337,85.8,Estimated (Region),6368
+2020-04-01,2020,4,Middle East,Cybertruck,13273,14083,105326.16,119,711,1397.52,Interpolated (Month),8264
+2015-10-17,2015,10,Asia,Model Y,3459,4084,113537.17,99,561,319.99,Interpolated (Month),9370
+2025-06-14,2025,6,Middle East,Cybertruck,14530,15083,97549.84,104,578,1286.26,Official (Quarter),12965
+2023-12-14,2023,12,Asia,Cybertruck,12817,13452,86672.71,102,563,988.06,Official (Quarter),3466
+2022-08-20,2022,8,Europe,Model X,5357,5401,75864.44,76,428,344.91,Official (Quarter),7103
+2025-12-15,2025,12,North America,Cybertruck,9577,10044,67081.13,83,476,612.66,Official (Quarter),4086
+2022-02-21,2022,2,Asia,Model S,7140,7246,80407.34,118,689,686.52,Interpolated (Month),8734
+2018-11-22,2018,11,Middle East,Cybertruck,11659,11944,73461.54,77,467,688.35,Interpolated (Month),11441
+2019-02-26,2019,2,North America,Model X,15204,13784,116534.24,74,447,893.48,Official (Quarter),4862
+2023-06-23,2023,6,Europe,Model X,13759,15498,86040.55,101,574,1345.46,Interpolated (Month),3473
+2017-01-15,2017,1,Europe,Model Y,6602,7762,114333.44,73,412,463.08,Official (Quarter),13742
+2018-10-14,2018,10,Europe,Model X,8816,9167,114023.33,115,648,763.83,Estimated (Region),11712
+2021-01-07,2021,1,Middle East,Model S,8428,8954,92457.11,101,548,744.9,Official (Quarter),3423
+2019-05-17,2019,5,Middle East,Model X,11752,13215,81284.85,59,336,703.52,Interpolated (Month),11251
+2024-12-04,2024,12,Europe,Model 3,5738,6674,110526.32,115,656,548.44,Estimated (Region),4626
+2025-07-27,2025,7,North America,Cybertruck,5767,6499,99980.02,61,330,291.4,Interpolated (Month),4225
+2024-08-24,2024,8,North America,Model Y,9448,10657,122091.58,78,409,594.68,Estimated (Region),14129
+2025-01-04,2025,1,Asia,Cybertruck,10153,12242,54786.25,62,329,541.89,Official (Quarter),3911
+2024-04-23,2024,4,Middle East,Cybertruck,10963,11609,53101.11,99,546,813.86,Interpolated (Month),3132
+2015-06-04,2015,6,North America,Model X,10925,11934,105559.82,82,451,892.61,Estimated (Region),14079
+2022-06-21,2022,6,Europe,Model X,14072,16083,89290.3,76,435,927.85,Interpolated (Month),15556
+2016-03-03,2016,3,Asia,Model 3,17344,17038,60324.66,61,331,846.5,Estimated (Region),10786
+2024-08-14,2024,8,Middle East,Model S,8832,9411,91453.44,119,662,855.98,Interpolated (Month),8221
+2018-04-19,2018,4,Europe,Model S,18427,20997,53595.34,100,601,1493.5,Official (Quarter),9773
+2025-01-21,2025,1,North America,Cybertruck,1957,2368,102900.97,121,694,224.14,Official (Quarter),3997
+2025-04-12,2025,4,Europe,Model X,8868,10828,88111.33,101,544,801.53,Estimated (Region),4361
+2018-04-26,2018,4,Middle East,Cybertruck,46,54,69206.08,75,421,3.12,Interpolated (Month),10840
+2024-07-22,2024,7,North America,Model Y,7975,9964,112668.47,120,683,983.19,Official (Quarter),9991
+2021-02-06,2021,2,Europe,Model S,8294,9010,113239.71,84,451,601.77,Interpolated (Month),9367
+2022-10-19,2022,10,Europe,Model X,10985,13509,61472.1,61,325,602.95,Official (Quarter),10240
+2016-07-21,2016,7,Europe,Model 3,16194,19998,110204.16,76,437,1093.84,Estimated (Region),12660
+2021-04-10,2021,4,Europe,Model Y,6559,6865,84046.44,85,490,535.23,Interpolated (Month),11495
+2017-04-21,2017,4,North America,Cybertruck,10723,9618,54309.75,119,674,943.26,Estimated (Region),4879
+2020-10-26,2020,10,Europe,Model Y,2917,3353,113637.55,77,451,215.35,Estimated (Region),4063
+2022-09-23,2022,9,Asia,Model Y,7214,6651,117933.73,121,667,702.52,Interpolated (Month),5435
+2020-12-22,2020,12,Europe,Model S,14662,16315,77220.77,107,590,1223.91,Estimated (Region),4792
+2021-06-18,2021,6,North America,Model X,8250,8540,87199.91,59,339,423.14,Official (Quarter),8855
+2015-11-08,2015,11,Asia,Model X,7208,8084,117790.14,81,501,551.3,Interpolated (Month),12214
+2019-05-23,2019,5,North America,Model S,6220,6328,77381.02,61,355,311.5,Estimated (Region),5641
+2022-11-11,2022,11,Asia,Cybertruck,6216,6341,66995.68,76,432,386.78,Official (Quarter),7758
+2015-07-15,2015,7,Asia,Cybertruck,110,121,114850.44,125,703,12.88,Interpolated (Month),14973
+2016-03-09,2016,3,Asia,Model X,10713,12789,58542.92,118,702,1027.3,Official (Quarter),5793
+2017-05-09,2017,5,North America,Cybertruck,7986,8269,113332.37,61,352,409.36,Estimated (Region),11938
+2021-01-05,2021,1,Middle East,Cybertruck,12904,13669,92243.37,61,362,703.17,Estimated (Region),8852
+2022-01-27,2022,1,Middle East,Model S,12202,13174,112936.64,83,447,972.39,Estimated (Region),10373
+2021-07-05,2021,7,North America,Model X,9894,10670,69615.19,118,666,1059.47,Official (Quarter),11138
+2016-02-03,2016,2,North America,Cybertruck,14045,16066,69168.92,103,616,1392.7,Interpolated (Month),11092
+2023-03-27,2023,3,Middle East,Model 3,10382,10933,102862.33,84,474,730.81,Estimated (Region),11715
+2016-01-16,2016,1,North America,Model 3,292,321,53637.42,83,467,21.02,Interpolated (Month),4508
+2016-07-13,2016,7,Asia,Cybertruck,10577,11014,119661.23,120,657,1054.87,Estimated (Region),8835
+2019-03-21,2019,3,Middle East,Cybertruck,2635,2585,62664.59,60,362,123.09,Estimated (Region),9470
+2025-01-11,2025,1,Middle East,Model 3,12196,12261,73372.22,100,548,911.3,Official (Quarter),7562
+2022-01-04,2022,1,Asia,Model Y,9768,9781,90762.64,83,470,615.86,Estimated (Region),14621
+2017-08-21,2017,8,Europe,Model Y,10456,10536,115136.87,60,350,548.38,Interpolated (Month),10023
+2020-11-05,2020,11,Europe,Model S,14978,16328,76194.7,59,340,818.5,Official (Quarter),3878
+2022-03-16,2022,3,Asia,Model 3,10347,10126,77069.18,59,355,477.51,Interpolated (Month),12767
+2019-01-27,2019,1,Middle East,Cybertruck,7052,7976,110492.84,81,485,564.39,Estimated (Region),8102
+2016-03-27,2016,3,Europe,Model 3,16349,17860,101727.9,100,616,1564.0,Interpolated (Month),3038
+2019-04-11,2019,4,Europe,Model Y,14350,14147,90752.66,120,715,1680.74,Interpolated (Month),10230
+2018-08-22,2018,8,Europe,Model S,14933,14606,62287.41,121,740,1436.27,Official (Quarter),6238
+2023-03-18,2023,3,Middle East,Model X,4270,4691,105877.32,83,502,329.31,Official (Quarter),5443
+2025-06-14,2025,6,Asia,Model Y,7601,7917,101133.66,59,362,472.78,Interpolated (Month),15206
+2016-10-04,2016,10,Asia,Model X,9674,12342,85914.94,77,425,699.42,Interpolated (Month),3754
+2023-05-11,2023,5,North America,Model S,7826,7765,87505.53,119,695,763.76,Interpolated (Month),9068
+2022-04-19,2022,4,Europe,Model S,16294,15693,69899.93,99,587,1352.5,Interpolated (Month),12950
+2023-01-28,2023,1,North America,Cybertruck,5807,6496,82214.86,61,356,332.32,Official (Quarter),4769
+2015-09-01,2015,9,Asia,Model S,12559,10920,57608.28,96,585,991.74,Interpolated (Month),3301
+2015-04-21,2015,4,Europe,Cybertruck,7237,8862,68955.05,74,426,476.79,Official (Quarter),11418
+2022-08-24,2022,8,North America,Cybertruck,3747,4032,103044.02,84,436,282.39,Official (Quarter),5567
+2021-11-27,2021,11,North America,Model 3,10424,10635,96167.95,79,485,652.72,Estimated (Region),6131
+2018-02-12,2018,2,Asia,Model S,14139,16377,81863.26,102,567,1252.81,Official (Quarter),5968
+2020-07-04,2020,7,North America,Model Y,10777,11340,100710.73,77,398,564.89,Interpolated (Month),6144
+2023-09-08,2023,9,Middle East,Model 3,11829,13767,52404.49,119,719,1196.01,Estimated (Region),8354
+2015-12-24,2015,12,Middle East,Cybertruck,18063,15770,106170.28,101,574,1392.27,Interpolated (Month),8493
+2022-04-25,2022,4,North America,Model 3,12315,12497,85206.21,123,711,1320.62,Estimated (Region),9495
+2023-01-18,2023,1,Europe,Model Y,15426,16284,98697.34,74,456,973.46,Interpolated (Month),6115
+2020-07-04,2020,7,Asia,Model 3,4371,4713,86960.63,102,566,354.17,Interpolated (Month),3301
+2021-07-04,2021,7,Asia,Model X,5094,5708,97269.61,118,660,523.06,Official (Quarter),4414
+2025-06-14,2025,6,Asia,Model X,9595,11276,52339.64,74,436,574.57,Official (Quarter),14488
+2018-09-17,2018,9,Europe,Model Y,5200,6324,56720.98,118,633,558.23,Interpolated (Month),11296
+2020-03-01,2020,3,North America,Cybertruck,14603,15146,112982.09,76,420,1033.53,Interpolated (Month),3205
+2020-10-20,2020,10,Middle East,Model X,6871,6894,85484.1,102,580,562.42,Estimated (Region),6349
+2016-07-16,2016,7,Middle East,Model Y,6184,6530,93629.91,60,350,317.29,Interpolated (Month),3917
+2023-08-01,2023,8,North America,Cybertruck,627,649,108975.84,58,347,30.39,Official (Quarter),9955
+2025-08-03,2025,8,Europe,Model X,14308,14380,64768.88,117,734,1190.25,Official (Quarter),11658
+2020-10-13,2020,10,Asia,Model S,9634,10681,94252.53,60,327,511.47,Interpolated (Month),7567
+2016-03-21,2016,3,North America,Model S,8973,10338,57753.62,62,352,570.64,Interpolated (Month),3140
+2017-06-24,2017,6,North America,Model X,6115,6977,111023.72,76,426,407.82,Interpolated (Month),3724
+2021-11-13,2021,11,Middle East,Model 3,10140,9533,82331.12,119,627,999.14,Official (Quarter),11337
+2018-02-09,2018,2,Middle East,Model 3,7823,7576,111573.11,83,492,628.04,Official (Quarter),5125
+2015-05-28,2015,5,North America,Model Y,4724,5051,52310.14,119,696,523.69,Official (Quarter),9577
+2021-06-13,2021,6,Middle East,Cybertruck,6091,5985,76713.28,74,441,432.37,Interpolated (Month),7248
+2020-07-09,2020,7,Middle East,Model Y,9256,8480,117476.71,118,723,793.4,Interpolated (Month),5104
+2018-01-13,2018,1,North America,Model 3,19020,20041,106609.4,77,417,1229.59,Official (Quarter),13506
+2019-05-01,2019,5,Middle East,Cybertruck,9542,11124,60868.17,95,569,1089.94,Interpolated (Month),7450
+2020-10-09,2020,10,Asia,Cybertruck,9378,11734,114478.1,83,447,757.01,Interpolated (Month),9971
+2017-03-15,2017,3,Europe,Model Y,9647,10177,83439.58,59,340,418.1,Interpolated (Month),4999
+2020-09-26,2020,9,North America,Model Y,5986,6352,105202.99,60,348,315.8,Interpolated (Month),7874
+2018-07-21,2018,7,Europe,Cybertruck,4566,4822,104223.36,124,693,415.92,Interpolated (Month),10210
+2024-09-22,2024,9,Asia,Model 3,10787,13159,74092.67,118,669,1311.76,Official (Quarter),14583
+2017-08-16,2017,8,North America,Model 3,13681,16027,61930.77,81,468,908.87,Estimated (Region),5897
+2024-09-04,2024,9,Asia,Model X,9251,9203,102296.06,100,604,746.08,Official (Quarter),6312
+2015-10-11,2015,10,Europe,Cybertruck,8069,7524,54670.19,76,470,482.89,Estimated (Region),4885
+2023-08-28,2023,8,Asia,Cybertruck,12559,13894,78311.25,59,349,614.48,Interpolated (Month),8682
+2021-09-02,2021,9,Europe,Model S,4647,5360,51978.32,73,424,347.66,Estimated (Region),7628
+2024-05-25,2024,5,Middle East,Cybertruck,15039,16678,49268.75,74,443,1018.63,Interpolated (Month),4291
+2018-10-20,2018,10,Asia,Model X,6140,6793,64147.31,103,619,568.09,Estimated (Region),6622
+2022-09-08,2022,9,Asia,Model S,13165,14142,108249.62,60,338,596.85,Estimated (Region),5472
+2023-01-22,2023,1,Europe,Cybertruck,10321,11330,83752.76,61,346,418.02,Estimated (Region),3216
+2024-08-24,2024,8,Asia,Model Y,8063,9933,93612.72,75,420,507.99,Interpolated (Month),4221
+2020-02-02,2020,2,North America,Model 3,11027,11517,71889.5,99,574,992.4,Interpolated (Month),14113
+2017-02-03,2017,2,Middle East,Model 3,9805,10672,75015.11,85,459,759.24,Official (Quarter),6613
+2023-07-19,2023,7,Middle East,Model 3,11345,12872,103494.07,60,337,568.71,Official (Quarter),13044
+2020-04-28,2020,4,Europe,Cybertruck,4079,4309,82265.72,74,415,291.91,Interpolated (Month),12055
+2021-06-01,2021,6,Middle East,Model Y,3559,4181,74617.32,102,564,324.69,Estimated (Region),3677
+2024-03-19,2024,3,Europe,Model 3,14177,12452,72592.28,74,458,900.67,Official (Quarter),12214
+2017-07-04,2017,7,Europe,Model S,2408,2531,80980.61,119,724,222.34,Interpolated (Month),6086
+2025-08-19,2025,8,Middle East,Model 3,13551,15087,119144.05,82,481,1021.94,Official (Quarter),8290
+2019-03-18,2019,3,Asia,Model Y,9683,12216,114317.88,100,597,1095.96,Official (Quarter),10152
+2015-08-03,2015,8,Middle East,Model 3,12630,15335,75366.8,121,707,1205.8,Interpolated (Month),8562
+2022-03-20,2022,3,North America,Model S,8765,9470,69413.94,78,425,516.56,Interpolated (Month),3055
+2016-08-26,2016,8,Asia,Model 3,11724,14629,83275.84,101,614,1326.85,Estimated (Region),8199
+2023-05-25,2023,5,Middle East,Model Y,12061,13434,101264.59,71,426,678.87,Estimated (Region),7079
+2018-01-01,2018,1,Middle East,Model 3,8120,9548,57262.15,121,675,941.71,Official (Quarter),13651
+2019-05-07,2019,5,Asia,Model 3,10516,11025,74007.13,82,447,767.35,Official (Quarter),9265
+2020-03-07,2020,3,Asia,Model 3,5302,6475,88741.99,60,346,282.42,Interpolated (Month),8050
+2025-01-13,2025,1,Europe,Model 3,15087,15483,103860.47,60,350,815.18,Official (Quarter),6002
+2015-02-05,2015,2,Asia,Model X,3904,4267,64235.02,75,438,284.6,Official (Quarter),7455
+2015-08-24,2015,8,North America,Model X,4460,4246,108315.75,60,359,246.48,Estimated (Region),9394
+2022-01-22,2022,1,Asia,Model 3,8719,8693,59513.0,76,434,557.41,Estimated (Region),9339
+2023-12-23,2023,12,Asia,Model S,5755,6337,63677.51,119,712,555.71,Estimated (Region),5326
+2016-05-06,2016,5,Middle East,Model 3,7014,8745,112568.62,59,312,348.51,Official (Quarter),13173
+2024-08-01,2024,8,Europe,Model S,19106,18093,76062.88,64,355,1026.56,Official (Quarter),10152
+2025-07-26,2025,7,Asia,Model X,15548,18622,68246.03,81,477,1007.97,Estimated (Region),6346
+2023-05-09,2023,5,Asia,Model S,9939,12255,79629.79,75,427,671.8,Official (Quarter),9514
+2016-11-19,2016,11,Asia,Model Y,8989,9967,91204.55,80,457,634.2,Official (Quarter),10959
+2025-10-26,2025,10,Europe,Model S,5747,7028,94303.89,60,334,307.83,Official (Quarter),8223
+2021-09-02,2021,9,Asia,Model S,13484,13497,69534.46,80,468,956.86,Official (Quarter),9852
+2018-11-01,2018,11,Middle East,Model S,12963,12855,114726.74,59,346,737.64,Official (Quarter),9417
+2017-09-01,2017,9,Asia,Model S,12051,10665,57638.79,77,476,792.14,Estimated (Region),7152
+2024-10-05,2024,10,Middle East,Cybertruck,10587,13195,91728.64,61,330,562.79,Estimated (Region),10507
+2016-04-02,2016,4,Middle East,Model 3,10263,11008,109205.01,121,723,1192.51,Estimated (Region),12014
+2016-06-23,2016,6,Middle East,Model 3,13496,14968,102469.02,116,680,1330.88,Interpolated (Month),9028
+2017-04-17,2017,4,Europe,Model X,3392,4053,116318.19,85,493,304.91,Estimated (Region),8059
+2021-04-28,2021,4,Middle East,Model 3,8091,8038,82706.15,117,703,903.08,Estimated (Region),3791
+2023-01-23,2023,1,Middle East,Model S,7347,7637,73037.42,62,360,369.45,Interpolated (Month),4425
+2023-09-27,2023,9,North America,Model 3,8964,9654,95168.71,84,486,526.63,Estimated (Region),7515
+2023-11-07,2023,11,North America,Model S,12979,12442,97116.68,61,355,571.17,Estimated (Region),7105
+2016-04-09,2016,4,Europe,Model 3,16296,17206,95515.62,98,559,1100.95,Estimated (Region),12553
+2016-03-24,2016,3,Europe,Model S,8904,10468,77340.63,60,355,466.57,Official (Quarter),10201
+2017-11-07,2017,11,Middle East,Model Y,8155,9245,62856.52,74,424,435.8,Official (Quarter),5174
+2023-01-02,2023,1,North America,Model X,16034,16706,74311.85,120,702,1619.87,Estimated (Region),11367
+2017-08-19,2017,8,Middle East,Cybertruck,14133,15947,95256.45,75,436,906.49,Official (Quarter),5147
+2025-04-23,2025,4,Middle East,Cybertruck,11083,12476,90121.58,72,424,716.47,Official (Quarter),3181
+2023-06-28,2023,6,Asia,Model 3,8129,8105,71573.4,84,473,523.23,Interpolated (Month),14934
+2019-07-26,2019,7,Middle East,Model Y,9324,8961,84165.69,120,688,985.6,Interpolated (Month),6658
+2022-09-04,2022,9,Europe,Model S,10084,11111,83454.03,81,434,671.55,Interpolated (Month),6593
+2022-02-22,2022,2,Middle East,Model Y,14468,16741,103557.52,59,342,723.04,Estimated (Region),11958
+2020-11-24,2020,11,North America,Model X,12471,11762,81199.25,101,565,1059.62,Interpolated (Month),11970
+2023-12-20,2023,12,Middle East,Model S,9539,10622,72515.5,98,552,828.17,Interpolated (Month),4656
+2024-05-23,2024,5,Europe,Model S,9301,9568,75855.94,78,486,662.2,Official (Quarter),14658
+2019-01-24,2019,1,North America,Model X,8796,9879,119794.64,83,487,636.67,Interpolated (Month),14446
+2023-03-13,2023,3,North America,Model S,9021,9856,94723.64,101,573,781.49,Estimated (Region),15196
+2025-10-02,2025,10,Asia,Model X,4950,5332,55044.43,75,434,341.77,Official (Quarter),6407
+2021-01-13,2021,1,North America,Model S,10424,11282,108088.26,59,355,500.36,Estimated (Region),10908
+2021-09-06,2021,9,Middle East,Model 3,9414,10301,73913.94,75,441,753.13,Official (Quarter),5998
+2015-01-04,2015,1,Middle East,Model X,7550,8079,77995.89,73,403,438.3,Official (Quarter),4306
+2018-08-04,2018,8,Middle East,Model X,9711,8968,71109.26,118,685,1024.03,Official (Quarter),10850
+2019-07-20,2019,7,Europe,Model S,3704,3466,104592.1,59,343,178.62,Interpolated (Month),10195
+2018-05-15,2018,5,Europe,Model X,11676,12542,91221.62,122,672,1354.88,Interpolated (Month),12813
+2024-07-16,2024,7,Asia,Model S,6135,7021,101369.35,74,439,439.87,Estimated (Region),8953
+2017-02-21,2017,2,Middle East,Model Y,10474,12666,79549.7,74,422,747.73,Official (Quarter),6189
+2016-09-16,2016,9,Europe,Model Y,13192,14629,67123.03,78,415,779.58,Interpolated (Month),7416
+2020-01-18,2020,1,North America,Model 3,8886,10479,104278.35,60,336,503.13,Interpolated (Month),8083
+2016-04-03,2016,4,Middle East,Model X,10044,10206,66493.21,83,456,750.64,Estimated (Region),7676
+2023-08-24,2023,8,Middle East,Model 3,9932,12059,55728.91,60,352,531.92,Official (Quarter),8735
+2019-03-13,2019,3,North America,Model S,10197,10764,110528.73,81,475,705.82,Interpolated (Month),10601
+2018-09-05,2018,9,Middle East,Cybertruck,9392,12056,64628.03,96,596,813.2,Interpolated (Month),10762
+2019-05-23,2019,5,Asia,Model Y,13105,14050,91417.46,84,514,909.84,Interpolated (Month),9967
+2019-08-21,2019,8,Europe,Cybertruck,12575,16032,84848.35,83,477,1102.2,Estimated (Region),8166
+2016-08-02,2016,8,Asia,Model S,12526,12316,78153.99,120,737,1132.63,Official (Quarter),9766
+2024-07-06,2024,7,Middle East,Model 3,9770,9618,90448.06,75,435,554.87,Interpolated (Month),10238
+2022-07-01,2022,7,Middle East,Model X,8788,8760,91863.29,76,441,594.98,Official (Quarter),7383
+2018-09-08,2018,9,Middle East,Model 3,10810,11664,74429.31,58,348,626.3,Official (Quarter),9564
+2019-04-03,2019,4,Europe,Model S,12515,12735,100714.97,99,583,1218.6,Interpolated (Month),7319
+2019-09-20,2019,9,Asia,Model Y,7348,7602,63460.03,98,549,572.75,Estimated (Region),12617
+2018-07-15,2018,7,North America,Model Y,14297,17627,61302.8,104,544,1304.84,Estimated (Region),13846
+2024-11-28,2024,11,North America,Model X,9455,9467,50413.6,83,472,633.42,Estimated (Region),14825
+2018-06-21,2018,6,North America,Model Y,16247,15751,102929.76,72,433,1021.59,Estimated (Region),13771
+2016-02-18,2016,2,Asia,Model X,12797,14622,57634.15,116,700,1121.8,Interpolated (Month),7882
+2019-05-15,2019,5,North America,Cybertruck,5539,5346,98279.37,75,408,357.87,Official (Quarter),7170
+2024-07-02,2024,7,Europe,Cybertruck,15401,16115,115386.82,99,586,1378.2,Interpolated (Month),4588
+2018-12-02,2018,12,Europe,Cybertruck,7108,7537,79230.78,117,708,680.03,Estimated (Region),13598
+2016-09-18,2016,9,North America,Cybertruck,15653,16116,113079.1,78,430,889.46,Interpolated (Month),11736
+2015-12-01,2015,12,Asia,Model S,5067,6225,98586.55,83,467,392.93,Official (Quarter),6030
+2016-06-02,2016,6,North America,Model X,5720,5506,60093.4,101,571,537.85,Official (Quarter),12220
+2020-06-07,2020,6,Europe,Model S,1277,1425,102589.5,74,418,72.31,Interpolated (Month),6774
+2024-07-25,2024,7,Middle East,Cybertruck,10736,12912,68187.65,83,514,902.13,Estimated (Region),13251
+2015-01-13,2015,1,Europe,Model X,8875,8605,115105.33,81,477,712.17,Official (Quarter),10862
+2022-03-20,2022,3,Asia,Model X,15669,13837,70341.1,60,321,826.68,Interpolated (Month),13177
+2023-09-12,2023,9,Asia,Model Y,3386,3730,114657.68,101,577,274.98,Estimated (Region),13660
+2016-04-05,2016,4,North America,Model 3,4596,5271,74663.0,79,455,303.68,Interpolated (Month),10797
+2022-01-11,2022,1,Europe,Model X,14539,17311,56988.35,77,413,982.85,Estimated (Region),6964
+2016-04-03,2016,4,Asia,Model Y,9075,8803,72786.97,75,435,581.24,Interpolated (Month),13363
+2016-10-22,2016,10,Europe,Model S,18086,19514,49711.3,123,664,1556.97,Estimated (Region),14864
+2021-11-06,2021,11,Asia,Model 3,9680,9854,58634.56,73,392,633.86,Estimated (Region),12224
+2015-06-11,2015,6,Europe,Cybertruck,10003,10870,64711.5,80,443,665.28,Estimated (Region),4633
+2021-11-28,2021,11,North America,Model Y,7418,8532,93914.64,80,488,685.15,Estimated (Region),7556
+2015-07-17,2015,7,Asia,Model 3,11141,9822,85188.79,121,704,1035.0,Estimated (Region),3624
+2019-03-13,2019,3,Europe,Cybertruck,11565,12598,52061.21,100,561,1020.14,Interpolated (Month),11978
+2022-03-16,2022,3,Middle East,Model S,18317,20893,109489.22,99,615,1480.14,Estimated (Region),9005
+2025-07-22,2025,7,Europe,Cybertruck,10288,10667,69621.64,73,410,596.08,Estimated (Region),8048
+2018-12-08,2018,12,North America,Model X,9056,10765,73508.93,97,616,919.76,Estimated (Region),9836
+2021-02-18,2021,2,Middle East,Model S,11568,11420,93060.85,82,454,815.39,Official (Quarter),12009
+2023-03-25,2023,3,North America,Model Y,6113,7473,71412.85,119,733,617.13,Interpolated (Month),3924
+2024-03-17,2024,3,North America,Model S,3867,4478,78525.26,81,465,284.2,Estimated (Region),5704
+2021-01-13,2021,1,Middle East,Model X,7463,7954,66030.69,77,424,432.82,Interpolated (Month),9641
+2015-06-05,2015,6,North America,Cybertruck,12216,13307,61956.1,100,560,1034.05,Estimated (Region),3619
+2019-07-07,2019,7,Middle East,Model X,6711,6169,101935.24,84,471,472.31,Estimated (Region),12199
+2018-02-27,2018,2,Asia,Model Y,11591,12146,90130.97,59,326,566.7,Estimated (Region),4457
+2023-12-16,2023,12,North America,Model S,9186,8983,109351.76,75,430,562.36,Estimated (Region),7003
+2022-03-09,2022,3,North America,Model X,10768,9553,73117.14,75,420,637.93,Estimated (Region),12412
+2025-04-12,2025,4,Europe,Model Y,12681,13283,110599.3,79,473,819.41,Official (Quarter),3560
+2021-10-17,2021,10,Asia,Model 3,8251,9086,51275.5,84,489,689.2,Official (Quarter),5278
+2025-10-16,2025,10,North America,Cybertruck,10307,10029,49172.94,82,488,657.56,Official (Quarter),6278
+2018-11-20,2018,11,Europe,Model S,8760,9691,57831.43,61,353,470.8,Estimated (Region),7401
+2020-10-08,2020,10,Middle East,Model 3,13596,12204,89176.1,98,576,1047.31,Interpolated (Month),13955
+2017-10-19,2017,10,Middle East,Model S,12732,13016,57117.63,60,351,525.42,Official (Quarter),6627
+2025-03-13,2025,3,Asia,Cybertruck,8383,9948,115991.25,59,325,412.74,Estimated (Region),6818
+2019-08-27,2019,8,North America,Model 3,13712,13005,100820.86,61,343,691.51,Interpolated (Month),11499
+2022-08-05,2022,8,Middle East,Cybertruck,12151,12082,91920.8,101,614,1024.72,Official (Quarter),5145
+2016-04-05,2016,4,Europe,Model Y,12745,12907,87866.55,101,550,1162.86,Estimated (Region),8281
+2017-06-13,2017,6,Middle East,Model 3,9745,9543,95878.95,84,461,692.39,Official (Quarter),3722
+2016-12-15,2016,12,Asia,Model S,20354,19299,117510.19,123,734,2197.46,Official (Quarter),11790
+2019-07-20,2019,7,North America,Model S,10320,10566,57196.93,83,467,795.97,Official (Quarter),9871
+2015-03-12,2015,3,Europe,Cybertruck,5534,6163,72804.09,117,704,453.2,Interpolated (Month),13070
+2018-09-26,2018,9,Europe,Model X,5316,5485,57479.84,81,478,362.8,Interpolated (Month),12022
+2020-08-14,2020,8,North America,Cybertruck,5870,6550,72968.9,59,364,309.41,Interpolated (Month),14073
+2021-12-04,2021,12,Middle East,Model 3,7906,9613,100773.85,101,568,805.0,Interpolated (Month),6759
+2017-04-04,2017,4,Middle East,Model S,5889,6462,97514.81,120,690,643.36,Estimated (Region),13506
+2015-05-28,2015,5,Europe,Model S,9791,9167,59235.91,117,669,851.54,Interpolated (Month),10611
+2017-11-04,2017,11,Middle East,Cybertruck,16062,16333,90569.06,100,598,1487.57,Official (Quarter),13811
+2024-12-27,2024,12,Middle East,Model S,6385,6873,96367.47,58,376,318.22,Interpolated (Month),14013
+2021-06-08,2021,6,North America,Model Y,9364,9747,98924.48,82,450,622.15,Official (Quarter),6422
+2025-08-03,2025,8,Europe,Model Y,10634,12334,108595.48,73,426,717.05,Estimated (Region),7412
+2022-07-09,2022,7,Asia,Model Y,7787,8339,76889.77,121,657,815.61,Estimated (Region),11070
+2020-05-18,2020,5,Middle East,Model X,3682,4232,72384.76,121,728,476.93,Official (Quarter),3760
+2018-05-12,2018,5,Asia,Cybertruck,5450,5413,107537.41,62,332,283.83,Estimated (Region),7272
+2024-02-22,2024,2,Asia,Model Y,11743,12187,104578.67,83,446,818.04,Official (Quarter),11632
+2016-04-18,2016,4,Asia,Cybertruck,2597,2675,79715.4,119,684,297.15,Official (Quarter),9684
+2021-09-12,2021,9,Middle East,Cybertruck,10112,12605,67857.48,122,715,1387.65,Estimated (Region),14157
+2017-04-18,2017,4,Asia,Model X,2705,3222,52181.42,117,673,306.73,Estimated (Region),11732
+2023-03-28,2023,3,Asia,Model X,7379,7759,80797.85,80,462,531.01,Interpolated (Month),11227
+2024-10-12,2024,10,Asia,Model Y,8846,10122,122739.56,83,478,688.0,Official (Quarter),12875
+2017-01-05,2017,1,Middle East,Model Y,8241,9311,112587.94,96,548,702.64,Estimated (Region),13624
+2016-07-12,2016,7,Europe,Cybertruck,12860,13797,80535.64,100,566,1262.39,Estimated (Region),5795
+2024-12-24,2024,12,Europe,Model Y,7141,7739,112777.57,80,481,478.16,Official (Quarter),12152
+2022-03-07,2022,3,Middle East,Cybertruck,10550,11861,96307.68,75,442,682.47,Official (Quarter),5351
+2016-02-27,2016,2,Middle East,Model X,5506,7052,52848.16,80,481,454.75,Official (Quarter),8311
+2018-04-17,2018,4,Europe,Model X,23970,23968,77469.58,61,318,1215.39,Interpolated (Month),11698
+2015-04-17,2015,4,Middle East,Model 3,11175,11111,83780.66,120,676,1054.11,Estimated (Region),7376
+2020-10-09,2020,10,Europe,Cybertruck,11290,12096,83130.19,62,347,553.61,Official (Quarter),15574
+2023-06-26,2023,6,Asia,Cybertruck,10315,12672,83357.83,120,706,1252.14,Official (Quarter),7451
+2015-04-02,2015,4,Asia,Model X,14864,15457,109690.23,98,588,1427.69,Estimated (Region),7195
+2016-08-09,2016,8,Middle East,Model Y,17939,19191,79359.78,60,348,960.4,Interpolated (Month),8877
+2025-10-12,2025,10,North America,Model 3,5537,5876,67608.0,97,603,466.08,Estimated (Region),9239
+2016-06-14,2016,6,Asia,Model X,12401,12450,90059.2,76,435,874.65,Official (Quarter),13144
+2021-05-14,2021,5,Middle East,Model S,16197,16181,70980.3,101,584,1348.96,Interpolated (Month),4955
+2021-05-05,2021,5,North America,Model X,8563,8922,79794.82,79,454,545.52,Estimated (Region),4325
+2017-02-07,2017,2,Asia,Cybertruck,14340,14556,119159.83,74,394,812.76,Interpolated (Month),3438
+2022-10-03,2022,10,Asia,Model Y,9923,10167,109490.66,75,434,632.5,Interpolated (Month),14516
+2021-02-17,2021,2,Europe,Cybertruck,12287,10605,80753.53,119,676,1089.75,Official (Quarter),13984
+2025-12-10,2025,12,Asia,Model 3,8556,7953,72438.34,82,475,626.01,Official (Quarter),13581
+2023-02-16,2023,2,Europe,Model X,10860,9735,88461.98,83,487,821.2,Estimated (Region),12703
+2017-10-17,2017,10,North America,Model S,17171,18337,72892.73,82,487,1408.2,Interpolated (Month),4969
+2023-10-20,2023,10,Middle East,Model 3,15063,15655,96604.63,102,572,1364.08,Estimated (Region),8198
+2023-08-16,2023,8,North America,Model X,8787,9115,79556.85,81,484,664.07,Estimated (Region),6238
+2024-04-19,2024,4,Europe,Model 3,13776,14377,57121.42,61,323,659.78,Official (Quarter),9000
+2025-01-17,2025,1,Asia,Model Y,7850,8278,94011.64,76,423,441.58,Interpolated (Month),4449
+2020-02-19,2020,2,Europe,Cybertruck,18057,21253,124675.28,100,585,1741.82,Interpolated (Month),6706
+2016-02-18,2016,2,Middle East,Model S,10680,12272,108615.8,119,732,1219.0,Official (Quarter),11585
+2022-05-09,2022,5,Europe,Cybertruck,11977,15074,110622.43,79,452,1044.16,Estimated (Region),4212
+2015-11-07,2015,11,Europe,Cybertruck,7180,7950,121147.46,98,610,783.56,Interpolated (Month),14690
+2018-01-23,2018,1,Middle East,Model X,10739,11470,52809.02,82,461,787.38,Official (Quarter),9700
+2015-08-10,2015,8,Middle East,Model S,15185,15758,49866.49,59,335,747.79,Interpolated (Month),7924
+2017-03-16,2017,3,Europe,Model 3,6655,6904,120902.49,62,350,349.11,Estimated (Region),5715
+2017-04-06,2017,4,North America,Model S,11509,15344,54021.35,61,348,630.12,Estimated (Region),7225
+2021-04-13,2021,4,Europe,Model S,17352,17399,102047.78,59,334,748.81,Official (Quarter),15817
+2022-09-15,2022,9,Europe,Cybertruck,10482,11136,63220.13,101,532,811.0,Interpolated (Month),8677
+2016-07-18,2016,7,North America,Model S,10455,9472,49107.04,75,428,622.18,Official (Quarter),5151
+2025-09-15,2025,9,North America,Model X,14590,16441,95307.53,99,569,1095.24,Interpolated (Month),3704
+2017-03-11,2017,3,North America,Cybertruck,16003,17604,76695.29,83,501,1142.74,Estimated (Region),5716
+2020-07-27,2020,7,North America,Model 3,16927,17565,83307.91,76,411,972.32,Estimated (Region),11845
+2023-09-15,2023,9,Middle East,Model X,9735,11102,88310.73,82,466,641.41,Official (Quarter),9044

data_processor.py

@@ -0,0 +1,302 @@
+import pandas as pd
+
+
+def load_data(file):
+    """
+    Load a CSV or Excel file into a pandas DataFrame.
+
+    This function should work both with:
+    - a file path string
+    - a Gradio UploadedFile object (has .name)
+    """
+    try:
+        # If "file" is a Gradio upload, it has a .name attribute.
+        if hasattr(file, "name"):
+            path = file.name
+        else:
+            path = file
+
+        if path.endswith(".csv"):
+            df = pd.read_csv(path)
+        elif path.endswith(".xlsx") or path.endswith(".xls"):
+            df = pd.read_excel(path)
+        else:
+            raise ValueError("Only .csv, .xlsx, or .xls files are supported.")
+
+        # Try to parse any column that already looks like a date.
+        # For your Tesla data, "Date" will be parsed correctly.
+        for col in df.columns:
+            if "date" in col.lower():
+                try:
+                    df[col] = pd.to_datetime(df[col])
+                except Exception:
+                    # If parsing fails, just keep it as is.
+                    pass
+
+        return df, None
+
+    except Exception as e:
+        # Return None and an error message so Gradio can display it.
+        return None, f"Error loading data: {e}"
+
+
+def get_basic_info(df):
+    """
+    Return basic information about the dataset:
+    - number of rows and columns
+    - column names
+    - data types as strings
+    """
+    shape = df.shape
+    columns = list(df.columns)
+    dtypes = df.dtypes.astype(str).to_dict()
+
+    info = {
+        "n_rows": shape[0],
+        "n_cols": shape[1],
+        "columns": columns,
+        "dtypes": dtypes,
+    }
+    return info
+
+
+def detect_column_types(df):
+    """
+    Split columns into:
+    - numeric_cols
+    - categorical_cols
+    - date_cols
+
+    This will be used for:
+    - summary statistics
+    - filters
+    - visualizations
+    """
+    numeric_cols = df.select_dtypes(include=["number"]).columns.tolist()
+    date_cols = df.select_dtypes(include=["datetime64[ns]", "datetime64[ns, UTC]"]).columns.tolist()
+
+    # Everything else is treated as categorical for this project.
+    categorical_cols = [col for col in df.columns if col not in numeric_cols + date_cols]
+
+    col_types = {
+        "numeric": numeric_cols,
+        "categorical": categorical_cols,
+        "date": date_cols,
+    }
+    return col_types
+
+
+def numeric_summary(df, numeric_cols):
+    """
+    Calculate summary statistics for numeric columns.
+
+    Returns a DataFrame where each row is a column and
+    columns include: count, mean, std, min, 25%, 50%, 75%, max
+    """
+    if not numeric_cols:
+        return pd.DataFrame()
+
+    summary = df[numeric_cols].describe().T  # transpose so each row is a column
+    summary = summary.reset_index().rename(columns={"index": "column"})
+    return summary
+
+
+def categorical_summary(df, categorical_cols, max_unique_to_show=20):
+    """
+    Create a summary for categorical columns.
+
+    For each categorical column we will show:
+    - number of unique values
+    - the most frequent value (mode)
+    - frequency of the mode
+    - up to 'max_unique_to_show' value counts (for display if needed)
+    """
+    rows = []
+
+    for col in categorical_cols:
+        series = df[col].astype("object")
+
+        n_unique = series.nunique(dropna=False)
+
+        # Mode (most common value)
+        if not series.mode(dropna=False).empty:
+            mode_value = series.mode(dropna=False).iloc[0]
+        else:
+            mode_value = None
+
+        value_counts = series.value_counts(dropna=False)
+        mode_freq = int(value_counts.iloc[0]) if len(value_counts) > 0 else 0
+
+        # We keep the top value counts as a JSON-like string to show in a table if needed.
+        top_values = value_counts.head(max_unique_to_show).to_dict()
+
+        rows.append(
+            {
+                "column": col,
+                "unique_values": int(n_unique),
+                "mode": mode_value,
+                "mode_freq": mode_freq,
+                "top_values": str(top_values),
+            }
+        )
+
+    if not rows:
+        return pd.DataFrame()
+
+    summary_df = pd.DataFrame(rows)
+    return summary_df
+
+
+def missing_values_report(df):
+    """
+    Return a DataFrame with:
+    - column name
+    - number of missing values
+    - percentage of missing values
+    """
+    total_rows = len(df)
+    missing_counts = df.isna().sum()
+
+    rows = []
+    for col, count in missing_counts.items():
+        if total_rows > 0:
+            pct = (count / total_rows) * 100
+        else:
+            pct = 0.0
+        rows.append(
+            {
+                "column": col,
+                "missing_count": int(count),
+                "missing_pct": round(pct, 2),
+            }
+        )
+
+    report_df = pd.DataFrame(rows)
+    return report_df
+
+
+def correlation_matrix(df, numeric_cols):
+    """
+    Compute the correlation matrix for numeric columns.
+    """
+    if len(numeric_cols) < 2:
+        return pd.DataFrame()
+    corr = df[numeric_cols].corr()
+    return corr
+
+
+def build_filter_metadata(df, col_types):
+    """
+    Prepare simple metadata that the Gradio UI can use to build filters.
+
+    For numeric columns:
+        min and max value
+
+    For categorical columns:
+        sorted list of unique values
+
+    For date columns:
+        min and max date
+    """
+    meta = {
+        "numeric": {},
+        "categorical": {},
+        "date": {},
+    }
+
+    # Numeric ranges
+    for col in col_types["numeric"]:
+        col_series = df[col].dropna()
+        if col_series.empty:
+            continue
+        meta["numeric"][col] = {
+            "min": float(col_series.min()),
+            "max": float(col_series.max()),
+        }
+
+    # Categorical unique values
+    for col in col_types["categorical"]:
+        unique_vals = df[col].dropna().unique().tolist()
+        # Convert numpy types to plain Python for safety
+        unique_vals = [str(v) for v in unique_vals]
+        meta["categorical"][col] = sorted(unique_vals)
+
+    # Date min/max
+    for col in col_types["date"]:
+        col_series = df[col].dropna()
+        if col_series.empty:
+            continue
+        meta["date"][col] = {
+            "min": col_series.min(),
+            "max": col_series.max(),
+        }
+
+    return meta
+
+
+def apply_filters(df, numeric_filters=None, categorical_filters=None, date_filters=None):
+    """
+    Apply simple filters to the DataFrame.
+
+    numeric_filters: dict like
+        {
+            "Estimated_Deliveries": [min_val, max_val],
+            "Production_Units": [min_val, max_val],
+        }
+
+    categorical_filters: dict like
+        {
+            "Region": ["Europe", "Asia"],
+            "Model": ["Model 3", "Model Y"],
+        }
+
+    date_filters: dict like
+        {
+            "Date": ["2018-01-01", "2023-12-31"]
+        }
+
+    All arguments are optional. If a filter dict is None, it is ignored.
+    """
+    filtered = df.copy()
+
+    # Numeric ranges
+    if numeric_filters:
+        for col, bounds in numeric_filters.items():
+            if col not in filtered.columns:
+                continue
+            try:
+                min_val, max_val = bounds
+                filtered = filtered[
+                    (filtered[col] >= min_val) & (filtered[col] <= max_val)
+                ]
+            except Exception:
+                # If something goes wrong, just skip this column filter.
+                continue
+
+    # Categorical selections (multi-select)
+    if categorical_filters:
+        for col, allowed_values in categorical_filters.items():
+            if col not in filtered.columns:
+                continue
+            if not allowed_values:
+                # If list is empty, skip this filter.
+                continue
+            filtered = filtered[filtered[col].astype(str).isin(allowed_values)]
+
+    # Date range filters
+    if date_filters:
+        for col, bounds in date_filters.items():
+            if col not in filtered.columns:
+                continue
+            try:
+                start, end = bounds
+                # Convert to datetime just in case inputs are strings.
+                start = pd.to_datetime(start)
+                end = pd.to_datetime(end)
+                filtered = filtered[
+                    (filtered[col] >= start) & (filtered[col] <= end)
+                ]
+            except Exception:
+                continue
+
+    return filtered

insights.py

@@ -0,0 +1,240 @@
+import pandas as pd
+import numpy as np
+
+
+def top_bottom_groups(df, group_col, value_col, top_n=3):
+    """
+    Group by `group_col`, sum `value_col`, and return NON-overlapping
+    top and bottom groups.
+
+    This is used for things like:
+        - Top / bottom models by Estimated_Deliveries
+
+    Returns a dict:
+        {
+            "top":    DataFrame,
+            "bottom": DataFrame
+        }
+    """
+    grouped = (
+        df.groupby(group_col)[value_col]
+        .sum()
+        .reset_index()
+        .rename(columns={value_col: f"total_{value_col}"})
+    )
+
+    if grouped.empty:
+        return {"top": pd.DataFrame(), "bottom": pd.DataFrame()}
+
+    # Sort descending for potential "top" list
+    grouped_desc = grouped.sort_values(by=f"total_{value_col}", ascending=False)
+
+    # We only want up to half of the unique groups in each list to avoid overlap
+    max_pairs = max(1, len(grouped_desc) // 2)
+    n = min(top_n, max_pairs)
+
+    # Top n
+    top = grouped_desc.head(n).reset_index(drop=True)
+
+    # Bottom n from the remaining (no overlap with top)
+    grouped_asc = grouped.sort_values(by=f"total_{value_col}", ascending=True)
+    bottom = grouped_asc[~grouped_asc[group_col].isin(top[group_col])].head(n)
+    bottom = bottom.reset_index(drop=True)
+
+    return {"top": top, "bottom": bottom}
+
+
+def region_ranking(df, value_col="Estimated_Deliveries"):
+    """
+    Rank regions by total value_col (for this project: Estimated_Deliveries).
+
+    Returns a DataFrame with columns like:
+        Region, total_Estimated_Deliveries, rank
+    """
+    if "Region" not in df.columns or value_col not in df.columns:
+        return pd.DataFrame()
+
+    grouped = (
+        df.groupby("Region")[value_col]
+        .sum()
+        .reset_index()
+        .rename(columns={value_col: f"total_{value_col}"})
+    )
+
+    if grouped.empty:
+        return grouped
+
+    grouped = grouped.sort_values(by=f"total_{value_col}", ascending=False)
+    grouped["rank"] = range(1, len(grouped) + 1)
+    return grouped.reset_index(drop=True)
+
+
+def model_production_vs_delivery(
+    df,
+    model_col="Model",
+    deliveries_col="Estimated_Deliveries",
+    prod_col="Production_Units",
+):
+    """
+    Compare total production vs total estimated deliveries by model.
+
+    Returns a DataFrame with:
+        Model,
+        total_estimated_deliveries,
+        total_production_units,
+        delivery_rate_percent,
+        inventory_gap
+    """
+    needed_cols = [model_col, deliveries_col, prod_col]
+    for c in needed_cols:
+        if c not in df.columns:
+            return pd.DataFrame()
+
+    tmp = df[[model_col, deliveries_col, prod_col]].copy()
+
+    grouped = (
+        tmp.groupby(model_col)[[deliveries_col, prod_col]]
+        .sum()
+        .reset_index()
+        .rename(
+            columns={
+                deliveries_col: "total_estimated_deliveries",
+                prod_col: "total_production_units",
+            }
+        )
+    )
+
+    if grouped.empty:
+        return grouped
+
+    # Delivery rate = deliveries / production * 100
+    grouped["delivery_rate_percent"] = grouped.apply(
+        lambda row: (row["total_estimated_deliveries"] / row["total_production_units"] * 100.0)
+        if row["total_production_units"] != 0
+        else None,
+        axis=1,
+    )
+
+    # Inventory gap = produced but not (yet) delivered
+    grouped["inventory_gap"] = (
+        grouped["total_production_units"] - grouped["total_estimated_deliveries"]
+    )
+
+    # Round for nicer display
+    grouped["delivery_rate_percent"] = grouped["delivery_rate_percent"].round(2)
+
+    return grouped
+
+
+def overall_trend_summary(df, date_col, value_col, freq="Q"):
+    """
+    Build a simple trend summary using time resampling.
+
+    For this project we use it for:
+        - Estimated_Deliveries over time (quarterly)
+
+    Returns:
+        summary_dict, quarterly_series
+
+    The summary_dict is already written in human-friendly sentences so that
+    utils.dict_to_text() will show something nice like:
+
+        start: On 2015-03-31, estimated deliveries were 9,883,795.
+        end: On 2025-12-31, estimated deliveries were 11,087,134.
+        ...
+
+    quarterly_series is the resampled pandas Series (for debugging or extension).
+    """
+    summary = {}
+
+    if date_col not in df.columns or value_col not in df.columns:
+        summary["info"] = "Trend summary unavailable — missing date or value column."
+        return summary, pd.Series(dtype="float64")
+
+    tmp = df[[date_col, value_col]].dropna().copy()
+    if tmp.empty:
+        summary["info"] = "Trend summary unavailable — no valid rows after dropping missing values."
+        return summary, pd.Series(dtype="float64")
+
+    tmp[date_col] = pd.to_datetime(tmp[date_col])
+    tmp = tmp.sort_values(by=date_col)
+
+    # Resample (e.g. quarterly) and sum
+    series = tmp.set_index(date_col)[value_col].resample(freq).sum()
+
+    if series.empty:
+        summary["info"] = "Trend summary unavailable — no data after resampling."
+        return summary, series
+
+    start_period = series.index[0]
+    end_period = series.index[-1]
+
+    start_value = float(series.iloc[0])
+    end_value = float(series.iloc[-1])
+
+    absolute_change = end_value - start_value
+    percent_change = (absolute_change / start_value * 100.0) if start_value != 0 else None
+
+    best_period = series.idxmax()
+    best_value = float(series.max())
+
+    # These strings already contain date + number as you requested
+    summary["start"] = (
+        f"On {start_period.date()}, estimated deliveries were {start_value:,.0f}."
+    )
+    summary["end"] = (
+        f"On {end_period.date()}, estimated deliveries were {end_value:,.0f}."
+    )
+    summary["change"] = (
+        f"From {start_period.date()} to {end_period.date()}, deliveries changed by "
+        f"{absolute_change:,.0f} units."
+    )
+    if percent_change is not None:
+        summary["growth"] = (
+            f"Overall growth between the first and last period is {percent_change:.2f}%."
+        )
+    else:
+        summary["growth"] = "Overall growth percentage could not be computed (start value is 0)."
+
+    summary["best_quarter"] = (
+        f"The highest quarter in this dataset is {best_period.date()} "
+        f"with {best_value:,.0f} estimated deliveries."
+    )
+
+    return summary, series
+
+
+# Optional: kept here in case you want to experiment later.
+# Not used by app.py in the current design.
+def simple_anomaly_detection(df, date_col, value_col, freq="Q", z_threshold=2.0):
+    """
+    Simple anomaly detection based on z-scores of resampled values.
+
+    NOT used in the current dashboard, but left here for potential extensions.
+    """
+    summary, series = overall_trend_summary(df, date_col, value_col, freq=freq)
+
+    if series is None or series.empty:
+        return pd.DataFrame()
+
+    values = series.values.astype(float)
+    mean = values.mean()
+    std = values.std()
+
+    if std == 0:
+        return pd.DataFrame()
+
+    z_scores = (values - mean) / std
+    mask = np.abs(z_scores) >= z_threshold
+
+    if not mask.any():
+        return pd.DataFrame()
+
+    out = pd.DataFrame(
+        {
+            "period": series.index[mask].astype(str),
+            "value": values[mask],
+            "z_score": z_scores[mask],
+        }
+    )
+    return out

prepare_tesla_data.py

@@ -0,0 +1,194 @@
+"""
+prepare_tesla_data.py
+
+This script takes the cleaned Tesla dataset in:
+    data/tesla_clean_full.csv
+
+and generates two synthetic datasets:
+
+    data/tesla_deliveries_1k.csv   ~ 1,000 rows
+    data/tesla_deliveries_50k.csv  ~ 50,000 rows
+
+It also makes sure there is a proper Date column built
+from Year and Month so the dashboard can use time series
+charts and trend insights.
+"""
+
+import os
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+# -------------------------------------------------------------------
+# CONFIG
+# -------------------------------------------------------------------
+
+# Cleaned base dataset (already downloaded + cleaned from Kaggle)
+CLEAN_FILE = Path("data") / "tesla_clean_full.csv"
+
+# Output files (synthetic samples)
+OUT_DIR = Path("data")
+OUT_1K = OUT_DIR / "tesla_deliveries_1k.csv"
+OUT_50K = OUT_DIR / "tesla_deliveries_50k.csv"
+
+RANDOM_SEED = 42
+
+
+# -------------------------------------------------------------------
+# Helper functions
+# -------------------------------------------------------------------
+
+def load_clean_data(path: Path) -> pd.DataFrame:
+    """
+    Load the cleaned Tesla dataset and make sure it has:
+      - Date column (datetime)
+      - Year and Month columns in sync with Date
+
+    If there is no Date column but we have Year and Month,
+    we create Date as the first day of that month.
+    """
+    if not path.exists():
+        raise FileNotFoundError(
+            f"Could not find cleaned file at: {path}\n"
+            "Make sure data/tesla_clean_full.csv exists."
+        )
+
+    df = pd.read_csv(path)
+
+    # If Date is missing but Year + Month exist, create it
+    if "Date" not in df.columns:
+        if "Year" in df.columns and "Month" in df.columns:
+            # Make sure they are integers
+            df["Year"] = df["Year"].astype(int)
+            df["Month"] = df["Month"].astype(int)
+
+            df["Date"] = pd.to_datetime(
+                df[["Year", "Month"]].assign(DAY=1)
+            )
+        else:
+            raise ValueError(
+                "Data does not have a Date column or Year/Month columns. "
+                "Cannot construct a proper Date."
+            )
+    else:
+        # Parse Date if it exists
+        df["Date"] = pd.to_datetime(df["Date"])
+
+    # Make Year / Month match Date (in case they were inconsistent)
+    df["Year"] = df["Date"].dt.year
+    df["Month"] = df["Date"].dt.month
+
+    # Sort for nicer behavior
+    df = df.sort_values(["Date", "Region", "Model"]).reset_index(drop=True)
+    return df
+
+
+def make_synthetic_from_clean(
+    df: pd.DataFrame,
+    target_rows: int,
+    seed: int = RANDOM_SEED,
+) -> pd.DataFrame:
+    """
+    Create a synthetic dataset with around target_rows rows.
+
+    Steps:
+      1. Repeat the base dataset enough times.
+      2. Sample down to exactly target_rows rows (with replacement).
+      3. Add small random noise to numeric columns.
+      4. Jitter Date by a few days, and re-sync Year / Month.
+    """
+    rng = np.random.default_rng(seed)
+
+    base_n = len(df)
+    repeats = int(np.ceil(target_rows / base_n))
+
+    # Repeat the dataset and then sample rows
+    df_rep = pd.concat([df] * repeats, ignore_index=True)
+    df_rep = df_rep.sample(n=target_rows, random_state=seed).reset_index(drop=True)
+
+    # ---- Jitter Date slightly (0–27 days) ----
+    # This keeps the general time pattern but avoids exact duplicates.
+    date_jitter_days = rng.integers(0, 28, size=len(df_rep))
+    jitter = pd.to_timedelta(date_jitter_days, unit="D")
+    df_rep["Date"] = df_rep["Date"] + jitter
+
+    # Recompute Year / Month so they match the new Date
+    df_rep["Year"] = df_rep["Date"].dt.year
+    df_rep["Month"] = df_rep["Date"].dt.month
+
+    # ---- Add noise to numeric columns ----
+    # Columns we expect from the Tesla dataset. If some are missing,
+    # we just skip them.
+    noise_specs = {
+        "Estimated_Deliveries": 0.05,   # ±5%
+        "Production_Units": 0.05,       # ±5%
+        "Avg_Price_USD": 0.03,          # ±3%
+        "Battery_Capacity_kWh": 0.02,   # ±2%
+        "Range_km": 0.03,               # ±3%
+        "CO2_Saved_tons": 0.08,         # ±8%
+        "Charging_Stations": 0.05,      # ±5%
+    }
+
+    for col, pct in noise_specs.items():
+        if col not in df_rep.columns:
+            continue  # skip if this column doesn't exist
+
+        # 1 + N(0, pct) multiplier
+        factors = 1.0 + rng.normal(loc=0.0, scale=pct, size=len(df_rep))
+        df_rep[col] = df_rep[col].astype(float) * factors
+
+        # Integer-like columns
+        if col in ["Estimated_Deliveries", "Production_Units", "Charging_Stations"]:
+            df_rep[col] = df_rep[col].round().astype(int)
+            df_rep[col] = df_rep[col].clip(lower=0)
+
+        # Capacity and range can also be integers
+        if col in ["Battery_Capacity_kWh", "Range_km"]:
+            df_rep[col] = df_rep[col].round().astype(int)
+            df_rep[col] = df_rep[col].clip(lower=0)
+
+        # Price and CO2 can stay as floats but nicely rounded
+        if col in ["Avg_Price_USD", "CO2_Saved_tons"]:
+            df_rep[col] = df_rep[col].round(2)
+
+    # Make sure columns are in the same order as the original df
+    df_rep = df_rep[df.columns]
+    return df_rep
+
+
+# -------------------------------------------------------------------
+# Main script
+# -------------------------------------------------------------------
+
+def main():
+    # Ensure output directory exists
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+
+    print(f"Loading cleaned Tesla data from: {CLEAN_FILE}")
+    df_clean = load_clean_data(CLEAN_FILE)
+    print(f"Base cleaned data shape: {df_clean.shape}")
+
+    # (Optional) re-save the cleaned full dataset so we are sure it
+    # includes the Date column and synced Year/Month.
+    clean_out = OUT_DIR / "tesla_clean_full.csv"
+    df_clean.to_csv(clean_out, index=False)
+    print(f"Re-saved cleaned full dataset to: {clean_out}")
+
+    # ---- Create 1K synthetic sample ----
+    df_1k = make_synthetic_from_clean(df_clean, target_rows=1000, seed=RANDOM_SEED)
+    df_1k.to_csv(OUT_1K, index=False)
+    print(f"Saved synthetic 1K dataset to: {OUT_1K} (rows={len(df_1k)})")
+
+    # ---- Create 50K synthetic sample ----
+    df_50k = make_synthetic_from_clean(df_clean, target_rows=50000, seed=RANDOM_SEED + 1)
+    df_50k.to_csv(OUT_50K, index=False)
+    print(f"Saved synthetic 50K dataset to: {OUT_50K} (rows={len(df_50k)})")
+
+    # Show a small preview of the 50K dataset so we can eyeball it
+    print("\nSample of 50K synthetic dataset (first 5 rows):")
+    print(df_50k.head())
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,6 @@
+pandas==2.2.1
+matplotlib==3.8.2
+seaborn==0.13.1
+plotly==5.18.0
+gradio==4.19.2
+numpy==1.26.2

utils.py

@@ -0,0 +1,37 @@
+import io
+import base64
+import matplotlib.pyplot as plt
+
+
+def figure_to_png_bytes(fig):
+    """
+    Convert a matplotlib figure to PNG bytes.
+    Useful for exporting images in Gradio.
+    """
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", dpi=150, bbox_inches="tight")
+    buf.seek(0)
+    return buf.getvalue()
+
+
+def safe_get(d, key, default=None):
+    """
+    Helper to safely get values from dictionaries.
+    """
+    if d is None:
+        return default
+    return d.get(key, default)
+
+
+def dict_to_text(d):
+    """
+    Simple helper to display a dictionary as multi-line text.
+    Useful for showing the trend summary in the Insights tab.
+    """
+    if not isinstance(d, dict) or len(d) == 0:
+        return "No insights available."
+
+    lines = []
+    for k, v in d.items():
+        lines.append(f"{k}: {v}")
+    return "\n".join(lines)

visualizations.py

@@ -0,0 +1,330 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def _safe_title(text):
+    """Small helper to avoid None or empty titles."""
+    if text is None:
+        return ""
+    return str(text)
+
+
+def create_time_series_plot(df, date_col, value_col, agg_func="sum", freq="M", category_col=None):
+    """
+    Create a time series line plot.
+
+    If category_col is provided (for example "Region" or "Model"),
+    it will draw one line per category with a legend.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Input data.
+    date_col : str
+        Column name with datetime values (e.g. "Date").
+    value_col : str
+        Numeric column to aggregate (e.g. "Estimated_Deliveries").
+    agg_func : str
+        Aggregation method: "sum", "mean", "count", "median".
+    freq : str
+        Resampling frequency ("M" = month, "Q" = quarter).
+    category_col : str or None
+        Optional column to group by (e.g. "Region" or "Model").
+
+    Returns
+    -------
+    fig : matplotlib.figure.Figure or None
+        The figure object with the plot, or None if something fails.
+    """
+    if date_col not in df.columns or value_col not in df.columns:
+        return None
+
+    data = df.copy()
+
+    # Make sure date column is datetime
+    if not pd.api.types.is_datetime64_any_dtype(data[date_col]):
+        try:
+            data[date_col] = pd.to_datetime(data[date_col])
+        except Exception:
+            return None
+
+    # If no category, just aggregate the whole series over time (old behavior)
+    if category_col is None or category_col not in df.columns:
+        data = data.set_index(date_col)
+
+        if agg_func == "mean":
+            grouped = data[value_col].resample(freq).mean()
+        elif agg_func == "count":
+            grouped = data[value_col].resample(freq).count()
+        elif agg_func == "median":
+            grouped = data[value_col].resample(freq).median()
+        else:
+            # default to sum
+            grouped = data[value_col].resample(freq).sum()
+
+        fig, ax = plt.subplots()
+        ax.plot(grouped.index, grouped.values)
+        ax.set_xlabel("Date")
+        ax.set_ylabel(_safe_title(value_col))
+        ax.set_title(f"Time Series of {value_col} ({agg_func})")
+        ax.grid(True)
+        fig.tight_layout()
+        return fig
+
+    # If category_col is given (e.g. Region / Model), draw a line per category.
+    data = data[[date_col, value_col, category_col]].dropna()
+    if data.empty:
+        return None
+
+    # Group by category and date (using Grouper for frequency)
+    grouped = data.groupby(
+        [category_col, pd.Grouper(key=date_col, freq=freq)]
+    )[value_col]
+
+    if agg_func == "mean":
+        grouped = grouped.mean()
+    elif agg_func == "count":
+        grouped = grouped.count()
+    elif agg_func == "median":
+        grouped = grouped.median()
+    else:
+        grouped = grouped.sum()
+
+    # Now grouped is a Series with MultiIndex: (category, date)
+    # We unstack so that the index is date and columns are categories.
+    table = grouped.unstack(0)
+
+    fig, ax = plt.subplots()
+
+    # Plot one line per category
+    for col in table.columns:
+        # Some categories might be all NaN if data is weird; skip those
+        series = table[col].dropna()
+        if series.empty:
+            continue
+        ax.plot(series.index, series.values, label=str(col))
+
+    ax.set_xlabel("Date")
+    ax.set_ylabel(_safe_title(value_col))
+    ax.set_title(f"Time Series of {value_col} by {category_col} ({agg_func})")
+    ax.grid(True)
+    ax.legend(title=_safe_title(category_col))
+
+    fig.tight_layout()
+    return fig
+
+
+def create_distribution_plot(df, numeric_col, kind="hist", bins=30):
+    """
+    Create a distribution plot for a numeric column.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Input data.
+    numeric_col : str
+        Numeric column to visualize.
+    kind : str
+        "hist" for histogram, "box" for box plot.
+    bins : int
+        Number of bins for histogram.
+
+    Returns
+    -------
+    fig : matplotlib.figure.Figure or None
+        The figure object with the plot, or None if something fails.
+    """
+    if numeric_col not in df.columns:
+        return None
+
+    series = df[numeric_col].dropna()
+    if series.empty:
+        return None
+
+    fig, ax = plt.subplots()
+
+    if kind == "box":
+        ax.boxplot(series.values, vert=True)
+        ax.set_xticks([1])
+        ax.set_xticklabels([_safe_title(numeric_col)])
+        ax.set_ylabel(_safe_title(numeric_col))
+        ax.set_title(f"Box Plot of {numeric_col}")
+    else:
+        # Default to histogram
+        ax.hist(series.values, bins=bins)
+        ax.set_xlabel(_safe_title(numeric_col))
+        ax.set_ylabel("Frequency")
+        ax.set_title(f"Histogram of {numeric_col}")
+
+    fig.tight_layout()
+    return fig
+
+
+def create_category_bar_plot(df, category_col, value_col=None, agg_func="count", top_n=10):
+    """
+    Create a bar chart for a categorical column.
+
+    - If value_col is None or agg_func == "count":
+        Show counts of each category.
+    - If value_col is numeric:
+        Aggregate using sum / mean / median.
+
+    This works nicely with columns like "Region" or "Model"
+    for the Tesla dataset.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Input data.
+    category_col : str
+        Name of the categorical column (e.g. "Region").
+    value_col : str or None
+        Numeric column to aggregate (e.g. "Estimated_Deliveries"), or None.
+    agg_func : str
+        "sum", "mean", "median", or "count".
+    top_n : int
+        Show only the top N categories.
+
+    Returns
+    -------
+    fig : matplotlib.figure.Figure or None
+        The figure object with the plot, or None if something fails.
+    """
+    if category_col not in df.columns:
+        return None
+
+    data = df.copy()
+
+    # Pure counts mode
+    if value_col is None or agg_func == "count":
+        counts = data[category_col].value_counts().head(top_n)
+        y_values = counts.values
+        x_labels = counts.index.astype(str).tolist()
+        title = f"Top {top_n} {category_col} by Count"
+        y_label = "Count"
+    else:
+        if value_col not in df.columns:
+            return None
+
+        data = data[[category_col, value_col]].dropna()
+        if data.empty:
+            return None
+
+        if agg_func == "mean":
+            grouped = data.groupby(category_col)[value_col].mean()
+        elif agg_func == "median":
+            grouped = data.groupby(category_col)[value_col].median()
+        else:
+            # default to sum
+            grouped = data.groupby(category_col)[value_col].sum()
+
+        grouped = grouped.sort_values(ascending=False).head(top_n)
+        y_values = grouped.values
+        x_labels = grouped.index.astype(str).tolist()
+        title = f"Top {top_n} {category_col} by {agg_func} of {value_col}"
+        y_label = _safe_title(value_col)
+
+    fig, ax = plt.subplots()
+    ax.bar(x_labels, y_values)
+    ax.set_xlabel(_safe_title(category_col))
+    ax.set_ylabel(y_label)
+    ax.set_title(title)
+    ax.tick_params(axis="x", rotation=45)
+
+    fig.tight_layout()
+    return fig
+
+
+def create_scatter_plot(df, x_col, y_col, category_col=None):
+    """
+    Create a scatter plot for two numeric columns.
+
+    If category_col is given (e.g. "Region" or "Model"),
+    points will be split by that category and a legend will be shown.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Input data.
+    x_col : str
+        Column for x-axis.
+    y_col : str
+        Column for y-axis.
+    category_col : str or None
+        Optional column to group points by.
+
+    Returns
+    -------
+    fig : matplotlib.figure.Figure or None
+        The figure object with the plot, or None if something fails.
+    """
+    if x_col not in df.columns or y_col not in df.columns:
+        return None
+
+    data = df[[x_col, y_col] + ([category_col] if category_col and category_col in df.columns else [])].dropna()
+    if data.empty:
+        return None
+
+    fig, ax = plt.subplots()
+
+    if category_col is None or category_col not in data.columns:
+        # Simple scatter, no categories
+        ax.scatter(data[x_col], data[y_col])
+        ax.set_title(f"Scatter Plot: {x_col} vs {y_col}")
+    else:
+        # One scatter per category, with legend
+        for cat_value, group_df in data.groupby(category_col):
+            ax.scatter(group_df[x_col], group_df[y_col], label=str(cat_value))
+
+        ax.set_title(f"Scatter Plot: {x_col} vs {y_col} by {category_col}")
+        ax.legend(title=_safe_title(category_col))
+
+    ax.set_xlabel(_safe_title(x_col))
+    ax.set_ylabel(_safe_title(y_col))
+    ax.grid(True)
+
+    fig.tight_layout()
+    return fig
+
+
+def create_correlation_heatmap(df, numeric_cols):
+    """
+    Create a correlation heatmap for numeric columns,
+    with the numeric values displayed on the cells.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Input data.
+    numeric_cols : list of str
+        List of numeric column names to include in the correlation matrix.
+
+    Returns
+    -------
+    fig : matplotlib.figure.Figure or None
+        The figure object with the heatmap, or None if something fails.
+    """
+    cols = [c for c in numeric_cols if c in df.columns]
+    if len(cols) < 2:
+        return None
+
+    corr = df[cols].corr()
+
+    fig, ax = plt.subplots()
+    cax = ax.matshow(corr.values)
+    fig.colorbar(cax)
+
+    ax.set_xticks(range(len(cols)))
+    ax.set_yticks(range(len(cols)))
+    ax.set_xticklabels(cols, rotation=45, ha="left")
+    ax.set_yticklabels(cols)
+    ax.set_title("Correlation Heatmap", pad=20)
+
+    # Add the correlation values on top of each cell
+    for i in range(len(cols)):
+        for j in range(len(cols)):
+            value = corr.values[i, j]
+            ax.text(j, i, f"{value:.2f}", va="center", ha="center", fontsize=8)
+
+    fig.tight_layout()
+    return fig