Pandas Profiling in Python Comparison Column Infographic

Data visualization infographic titled "Radar Spider Chart" using COMPARISON COLUMNS as the dominant visual element to explain the concept visually for the search intent "pandas profiling in python". Create a large, central grouped comparison column chart with sharp axis labels and tick marks in English, no truncated axis, honest zero baseline, and one clearly surprising outlier category highlighted. X-axis label: "Profiling Dimensions". Y-axis label: "Coverage Score (Illustrative)". Categories on the x-axis: "Completeness", "Missing Values Detection", "Distribution Summary", "Correlation Scan", "Type Inference", "Outlier Detection". Show two series in the legend: "Basic EDA" and "Pandas Profiling". Use realistic illustrative values: Basic EDA = 62, 55, 68, 34, 71, 29; Pandas Profiling = 88, 91, 86, 82, 89, 57. Make "Correlation Scan" the standout surprise / outlier with a visibly larger gap between the two columns, and add a subtle annotation arrow pointing to it. Include 4 key insight callouts around the chart, each with a small icon, a headline number, and a short interpretation in English: 1) icon magnifying glass, headline "48 pts", text "Largest gap appears in Correlation Scan, where automated profiling surfaces relationships much faster." 2) icon warning triangle, headline "57", text "Outlier Detection remains moderate, suggesting profiling helps but still needs analyst review." 3) icon checklist, headline "91", text "Missing Values Detection is the strongest area, making data quality issues easier to spot early." 4) icon lightning bolt, headline "+24 avg", text "Across all dimensions, Pandas Profiling scores about 24 points higher than Basic EDA in this illustrative example." Add a small side mini-panel labeled "Why the outlier matters" with 3 short bullets: "Faster first-pass diagnostics", "Better visibility into hidden structure", "Useful for quick dataset audits". Add a small SOURCE / DATA-NOTE strip at the bottom in English: "Data note: Illustrative example for visual explanation of pandas profiling in Python. Numbers are not sourced measurements and are shown for infographic demonstration only." Visual style: NYT graphics desk, Economist warm beige palette, muted sand background, charcoal text, deep brick red highlight for the outlier, dusty blue-gray secondary series, thin rules, restrained editorial typography, subtle paper texture, calm analytical mood. Include editorial data journalism illustration, FT / Bloomberg-grade chart aesthetics, vector-clean infographic layout. All text MUST be written in English (array). Every heading, label, caption, legend and metric name in the image must be in English — not English. Spell each English word correctly using English characters and diacritics. Numbers stay as digits, no fake authoritative sources cited, no watermarks Numbers labeled "illustrative" unless the user supplied specific sourced data. No fake authoritative sources cited (do not invent "Source: Reuters 2025" — use "Illustrative example" instead). No misleading axis truncation or scale manipulation.