Python Data Visualization Guide

Objective IMPORTANT UPDATE AS OF JULY 11TH: The upload script has been updated. Thank you for your patience. This is a Python evaluation focused on data visualization and storytelling: given a type of plot (dashboard, Sankey diagram, etc.), your task is to recreate a similar visual that tells the same story using a dummy dataset and Python scripts for data generation and visualization. You will also write an input prompt that would naturally produce the visual you created. Walkthrough Video: [Evaluation Labeling Instructions] Python Visualization - Jul Update Labeling Steps 1. Find and Analyze the Dashboard/Graph The data row begins with searching the web for a reference image of a business-related dashboard or graph that matches the chart description you are given. For example, you may search for something such as “dashboard for business software”. When you find a reference image, you will need to obtain the direct URL to the image itself. You can do this by right-clicking on the image and selecting “Copy Image Address”. Carefully inspect your reference image. Your objective is to creatively expand on this reference image, capturing the core features while developing datasets and visualizations that reflect real-world situations. 2. Generate a Prompt Write a simple user-style question or instruction that the reference image would answer. This prompt frames the data story and helps guide your synthetic data generation: The prompt should not be very specific and can be open-ended. The prompt should not specify a lot of formatting requirements. The prompt must be natural and practical, reflecting the kind of questions a user might realistically ask in a real-world scenario. Prompt Examples Below are some examples of good prompts along with the story and visuals related to them. These are for inspiration only and you should not directly use them: Prompt Topic Story & Visuals “Show how global electric-vehicle (EV) adoption has evolved since 2015 and predict the next five years.” • Multi-line time-series of unit sales by region • Stacked area of battery chemistries • Sankey of supply-chain flows • Heat-map of EV market-share by country “Analyze hospital network capacity vs. infectious-disease outbreaks during winter seasons.” • Dual-axis line (ICU beds vs. cases) • Correlation heat-map of symptoms & test positivity • Box-whisker of LOS by diagnosis group “Contrast same-day vs. two-day e-commerce delivery performance during holiday peaks.” • Violin plot of delivery times • Pareto of top delay causes • Time-series forecast of warehouse backlog “Track sustainable-aviation-fuel (SAF) usage across the airline industry and project carbon savings.” • Waterfall of CO₂ reductions • Treemap of SAF feedstocks • Monte Carlo projection of carbon offset targets “Visualise smart-city energy flows between residential, commercial, and EV charging nodes.” • Chord diagram of kWh transfers • Area chart of renewables vs. grid demand • Animated map of substation loads by hour “Evaluate multi-modal public-transport punctuality and rider sentiment in megacities.” • Box-plot of lateness by mode (bus, metro, rail) • Word-cloud & sentiment drill-down • Gantt of headways over 24 h “Benchmark fintech fraud-detection algorithms across geographies and transaction types.” • ROC curves for each model • Confusion-matrix heat-maps • KPI bullet charts for latency & cost “Map food-delivery fleet efficiency vs. weather impacts in dense urban zones.” • Scatter of drop-offs vs. travel km • Histogram of idle minutes per driver • Isochrone map overlaying rainfall intensity “Identify semiconductor-fab yield losses and correlate with equipment maintenance logs.” • Stacked bar of defect classes • Control chart of daily yields • Network graph of tool dependencies “Forecast coastal-city real-estate risk under sea-level-rise scenarios to 2100.” • Scenario fan-chart of property values • Choropleth of flood exposure zones • Animated slider of shoreline retreat 3. Generate Data To Tell the Business Story Write a data creation script within the scripts folder (../scripts/data_gen.py) that: Uses only pandas and numpy. Generates at least two datasets (as DataFrames or Numpy arrays) into the data folder, such as (../data/sales_data.csv and ../data/local_sales_data.csv) Tells a similar story based on your reference image, reflects real-world situations, and contains enough detail to fully recreate the image. 4. Recreate the Visualization Write a visualization script within the scripts folder (../scripts/viz.py) that: Uses only pandas, numpy, and plotly (library is known as dash) You can use the pip install dash command to install Plotly Dash. Reads the generated files from your data creation script. Generates one HTML file of an interactive dashboard into the outputs folder (../outputs/golden_image.html) using Plotly's HTML export method. Works and can be interacted with properly. Contains visuals that adhere to the following style guidelines: Typography: Titles MUST be bold, and properly formatted legends and labels. Aesthetics: Organize layout using visual containers (e.g., cards, sections). Use depth thoughtfully via shadows or gradients to create visual hierarchy. We do not want flat images. Storytelling: Establish a clear narrative flow: start with high-level KPIs, then drill into details. Data elements should feel connected and purposeful, not isolated or random. Complexity: Dashboards must follow the level of complexity found here. Dashboard complexity should match the visual density and insight variety shown in the provided reference. Avoid oversimplification. Layout: No overlapping elements or cut-off text. Ensure consistent padding, margin, and spacing between plots. Legends: If a legend is present, ensure it is clearly displayed and boxed if appropriate. Use well-organized legend placement with appropriate spacing. Color Palette: Use a professional and aesthetically pleasing color scheme. The color palette should complement the data and enhance readability. Overall Quality: The final plot should be polished and suitable for a presentation or publication. 5. Upload Files This step is very important to save your work. Download this .zip file, which contains an updated version of the upload script, as of July 8th: https://drive.google.com/file/d/1MjOgZaXfzaIPfVXgwxtllmYH4rpOrqh8/view?usp=sharing Open the file and enter the password, which is scatter. Place the upload.py script into the root directory of your project. Install the required libraries with pip install google-cloud-storage google-auth Run the script and follow the instructions that it provides within your terminal. IMPORTANT UPDATE: You will have to enter in a unique UUID instead of a data row ID. You can use https://www.uuidtools.com/v4 to generate a UUID. For example: a6dc08ba-b7f8-4964-af1b-5d6fc983b8ba Please confirm that all files in your 'data', 'outputs', and 'scripts' folders are correct. Once uploaded, you will NOT be able to update or replace them. Folder Structure Overview <data_row_id>/ ├── data/ │ ├── sample.npy │ ├── dataframe2.csv │ └── dataframe.csv # Generated .csv and/or npy files │ ├── scripts/ │ ├── data_gen.py # Data generation script │ └── viz.py # Visualization script │ ├── outputs/ │ └── dashboard.html # Interactive html generated using viz.py ├── upload.py # Script to upload your work Once uploaded, submit all the paths in the appropriate sections in the Labelbox editor. Best Practices Please refer to this section to see examples of the level of complexity that is required for dashboards. Refer to this section for examples of the charts that you may encounter. While searching for a reference image, here are some examples of charts you should be pursuing versus not: ✅Positive examples ❌Negative examples A full example is available here.

Great! Here's a clear breakdown of how you can proceed with this Python data visualization evaluation step by step, with examples and file structure suggestions so you can start building immediately. --- ## ✅ Step-by-Step Breakdown --- ### 🔍 Step 1: Find and Analyze a Reference Image You’ll need to pick a **business dashboard or complex chart** with elements like: - Sankey Diagrams - Sunburst/TreeMap - Multi-layered Heatmaps - Predictive Time Series - KPI + Detail Drilldowns **Example Search Queries** (use Google Images): - `"supply chain dashboard sankey site:plotly.com"` - `"retail analytics dashboard tableau"` - `"smart city energy flow chord diagram"` Then, **copy the image address** of the most useful one. --- ### 💬 Step 2: Write a Natural Prompt Think about what kind of **real-world question** your chart answers. #### Example: **Prompt:** > “Show how inventory levels and sales performance vary across different store locations, and how supply chain inefficiencies impact delivery timelines.” This could lead to: - KPI tiles: sales, inventory, delays - Sankey: Supplier → Warehouse → Store - Treemap: Inventory by category - Line: Sales over time **Avoid prompts like:** ❌ “Make a Sankey with 4 categories in green” ✅ Instead: “Track how different product categories move through a multi-region distribution system.” --- ### 📊 Step 3: Generate Synthetic Data (`data_gen.py`) Use **only `pandas` and `numpy`** to create **at least 2 datasets** (CSV or NPY). **Examples of useful datasets**: - `supply_chain_flows.csv`: with columns like `source`, `target`, `value` - `store_sales.csv`: with columns like `store`, `month`, `category`, `sales`, `inventory`, `delay_days` ```python # Example snippet import pandas as pd import numpy as np np.random.seed(42) categories = ['Electronics', 'Apparel', 'Grocery'] regions = ['North', 'South', 'East', 'West'] df_sales = pd.DataFrame({ 'store': np.random.choice(regions, 100), 'category': np.random.choice(categories, 100), 'month': pd.date_range(start='2023-01-01', periods=100, freq='D'), 'sales': np.random.randint(1000, 5000, 100), 'inventory': np.random.randint(2000, 8000, 100), 'delay_days': np.random.poisson(2, 100) }) df_sales.to_csv('../data/store_sales.csv', index=False) ``` --- ### 📈 Step 4: Create Dash Visualizations (`viz.py`) - Use `dash` from Plotly (install with `pip install dash`) - Read data from `../data/` - Output an **interactive dashboard** (`../outputs/golden_image.html`) **Use Plotly Dash to include multiple complex charts:** - Sankey diagram for supply flow - Heatmap or Treemap for inventory - Line or Bar chart for sales ```python import dash from dash import html, dcc import plotly.express as px import pandas as pd app = dash.Dash(__name__) df = pd.read_csv('../data/store_sales.csv') fig_sales = px.line(df, x='month', y='sales', color='store', title='Sales Over Time') app.layout = html.Div([ html.H1("Retail Analytics Dashboard", style={'fontWeight': 'bold'}), dcc.Graph(figure=fig_sales), # Add Sankey or other complex chart next ]) app.run_server(debug=False) ``` Use `plotly.io.write_html(fig, "outputs/golden_image.html")` if you want to export the chart outside the app for inspection. --- ### 📤 Step 5: Upload Work 1. Download `upload.py` script: https://drive.google.com/file/d/1MjOgZaXfzaIPfVXgwxtllmYH4rpOrqh8/view?usp=sharing Password: `scatter` 2. Generate a UUID: https://www.uuidtools.com/v4 3. Follow upload steps via terminal: ```bash pip install google-cloud-storage google-auth python upload.py ``` Make sure your folder structure looks like this: ``` a6dc08ba-b7f8-4964-af1b-5d6fc983b8ba/ ├── data/ │ └── store_sales.csv ├── scripts/ │ ├── data_gen.py │ └── viz.py ├── outputs/ │ └── golden_image.html └── upload.py ``` --- ## ✅ Final Tips - Use **realistic column names** (e.g., `region`, `order_id`, `ship_time`) to make the visuals intuitive. - Prioritize visual **density + clarity** (3–4 charts per dashboard). - Each plot should tie back to the original **prompt’s narrative**. - Use colors that match industry standards (e.g., red for delays, green for sales). --- If you'd like help finding a strong reference image or want a pre-made prompt/data idea with plots, let me know your theme (e.g., healthcare, fintech, logistics), and I can help build out the entire folder for you.

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iturn0image2 It looks like the image you shared is a **Sankey diagram**, showing flows from one set of source categories (on the left) to destination categories (on the right). The relative width of each link represents the magnitude of flow, with softer pastel colors and clear node separation—typical of BI dashboards used to trace flows such as: - **Supply chain logistics** (supplier → warehouse → store), - **Customer journey funnels** (entry → browse → purchase), - **Internal transitions** (departments → roles → locations) citeturn0image2turn0image9. --- ## ✅ Proposal: Synthetic Dashboard Recreation ### 🎯 Prompt You might generate this visualization with a prompt like: > “Visualize the flow of customer interactions from initial website visit through product discovery to final purchase by channel.” --- ## 🔄 How We’d Recreate It: High-Level Plan ### 1. **Data Generation (`data_gen.py`)** - Create a synthetic dataset that mimics customer journey stages (e.g., `"Initial Visit" → "Product Page" → "Add to Cart" → "Purchase"`). - Include channel dimensions like `channel = ["Organic Search", "Email", "Paid Ads", "Referral"]`. - Include flow counts that simulate realistic behavior, e.g., more organic visits, drop-off between stages, and conversion splits. Generate at least two data files: - `journey_flows.csv` with columns: `source_stage`, `target_stage`, `channel`, `count` - `conversion_summary.csv` with aggregated KPI info: total visits, drop-off rates, conversion rate per channel ### 2. **Visualization (`viz.py`)** Use `pandas`, `numpy`, and Plotly (via Dash) to build: - A **Sankey diagram** illustrating flows between journey stages, colored by channel. - KPI cards at top summarizing: total visits, overall conversion rate, drop-off percentage. - Additional visual: maybe a **bar chart** showing drop-off by stage or channel. Layout should emphasize: - Clean, bold title - Visual containers (cards + graphs) - Legend boxed and positioned clearly - Professional color palette (e.g., muted blues, greens, oranges) Export final dashboard to `outputs/golden_image.html`. --- ## 📂 Example Folder Structure ``` <your_uuid>/ ├── data/ │ ├── journey_flows.csv │ └── conversion_summary.csv ├── scripts/ │ ├── data_gen.py │ └── viz.py ├── outputs/ │ └── golden_image.html └── upload.py ``` --- ## ✍️ Example Prompt (for generating dataset/visualization) > **Prompt:** > “Show the customer journey from first website interaction to purchase, segmented by marketing channel, including drop-off between stages and conversion rates.” --- Would you like help writing the actual Python `data_gen.py` and `viz.py` code for this Sankey-based dashboard? Or if your target is a different domain (e.g. supply chain, internal transfers, budgets), I can tailor it accordingly—just share your preference!