itself: the host stays out of layout, `class` goes to the root.\n host: { '[attr.class]': '\"contents\"' },\n imports: [\n LucideAngularModule,\n UiBadgeComponent,\n UiButtonComponent,\n UiCardComponent,\n UiCardHeaderComponent,\n UiCardTitleComponent,\n UiCardDescriptionComponent,\n UiCardContentComponent,\n UiProgressComponent,\n UiTableComponent,\n UiTableHeaderComponent,\n UiTableBodyComponent,\n UiTableRowComponent,\n UiTableHeadComponent,\n UiTableCellComponent,\n ],\n template: `\n
\n \n
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\n \n
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\n Table: {{ tableName }}\n
\n
\n \n Apache Parquet\n \n
{{ datasetVersion }}\n
\n
\n Profiled: 2026-08-21 14:30 UTC\n •\n Snappy Compressed\n •\n EDA Engine: ydata-profiler v4.8\n
\n
\n\n
\n \n
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\n Rows: \n {{ totalRows }}\n
\n
\n Cols: \n {{ totalColumns }}\n
\n
\n Memory: \n {{ memoryFootprint }}\n
\n
\n\n \n
\n @if (isExporting()) {\n \n } @else {\n \n }\n {{ isExporting() ? 'Generating HTML Report...' : 'Export EDA Report' }}\n \n \n
\n\n \n @if (showExportSuccess()) {\n
\n \n \n \n Exploratory Data Analysis report exported. HTML summary & JSON schema bundle ready for download.\n \n
\n \n Dismiss\n \n \n }\n\n \n
\n \n
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Numeric Columns
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55.6%\n
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\n 10\n Columns\n
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\n Types Breakdown:\n 8 Float64 · 2 Int32\n
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\n\n \n
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Categorical Columns
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33.3%\n
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\n 6\n Columns\n
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\n High Cardinality:\n 1 (customer_id)\n
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\n\n \n
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Missing Values
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\n
Clean\n
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\n 0.4%\n Missing\n
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\n Total Null Cells:\n 593 / 2,669,220\n
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Duplicate Rows
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100% Unique\n
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\n 0\n Duplicates\n
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\n Primary Key Integrity:\n 100.0% Valid\n
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\n\n \n
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\n \n
\n @for (f of typeFilters; track f.value) {\n \n }\n
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\n \n \n \n \n \n \n
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\n\n @if (viewMode() === 'table') {\n \n
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\n
Dataset Schema & Profiling Matrix
\n
\n Tabular view of all columns with inferred types, missingness, cardinality, and distribution summary.\n
\n
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\n {{ filteredColumns().length }} of 18 columns\n \n
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\n \n | Column Name | \n Data Type | \n Missing Values | \n Distinct Values | \n Distribution / Quantiles | \n Health Status | \n
\n \n
\n @for (col of filteredColumns(); track col.name) {\n \n | \n \n {{ col.name }}\n \n @if (copiedColumn() === col.name) {\n \n } @else {\n \n }\n \n \n | \n \n {{ col.type }}\n | \n \n {{ col.missing }}\n | \n {{ col.distinct }} | \n {{ col.summary }} | \n \n {{\n col.status\n }}\n | \n
\n }\n \n
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\n } @else {\n \n
\n \n @if (activeTypeFilter() === 'all' || activeTypeFilter() === 'numeric') {\n
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\n \n
\n
{{ spend.name }}
\n
{{ spend.type }}\n
\n @if (copiedColumn() === spend.name) {\n \n } @else {\n \n }\n \n \n
\n Monthly billed subscription and add-on expenditure in USD. Right-skewed distribution with long\n tail.\n
\n
\n\n \n
\n \n Missing:\n {{\n spend.missingPct\n }}\n \n \n Distinct:\n {{ spend.distinctCount }}\n \n \n Memory:\n {{ spend.memory }}\n \n
\n
\n
\n\n
\n \n
\n @for (m of spendMetrics; track m.label) {\n
\n
{{ m.label }}\n
{{ m.value }}
\n
\n }\n
\n\n \n
\n
\n
\n \n 10-Bin Distribution Frequency Histogram\n
\n @if (hoveredBin(); as b) {\n
\n {{ b.bin }}: {{ fmt(b.count) }} rows ({{ b.pct }}%)\n \n } @else {\n
Hover bar to inspect bin interval\n }\n
\n\n \n
\n
\n \n \n \n \n \n\n \n @for (bin of histogramBins; track $index; let idx = $index) {\n \n \n @if (bin.pct >= 5) {\n \n {{ bin.pct }}%\n \n }\n \n }\n \n
\n\n \n
\n $0.00\n $490\n $980\n $1,715\n $2,450\n
\n
\n
\n
\n
\n }\n\n \n @if (activeTypeFilter() === 'all' || activeTypeFilter() === 'categorical') {\n
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\n \n
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{{ tier.name }}
\n
{{ tier.type }}\n
\n @if (copiedColumn() === tier.name) {\n \n } @else {\n \n }\n \n \n
\n Active customer plan classification tier. Clean categorical distribution across 4 discrete levels.\n
\n
\n\n
\n \n Missing:\n {{\n tier.missingPct\n }}\n \n \n Distinct:\n {{ tier.distinctCount }} values\n \n \n Memory:\n {{ tier.memory }}\n \n
\n
\n
\n\n
\n \n
\n
\n Category Proportions\n 148,290 total instances\n
\n\n
\n @for (t of subscriptionTiers; track t.name) {\n
\n }\n
\n
\n\n \n
\n @for (t of subscriptionTiers; track t.name) {\n
\n
\n
\n {{ t.name }}\n {{ t.pct.toFixed(1) }}%\n
\n
{{ fmt(t.count) }} rows
\n
\n\n
\n
\n }\n
\n
\n
\n }\n\n \n @if (activeTypeFilter() === 'all' || activeTypeFilter() === 'numeric') {\n
\n
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\n
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\n
\n \n
\n
{{ age.name }}
\n
{{ age.type }}\n
\n @if (copiedColumn() === age.name) {\n \n } @else {\n \n }\n \n \n
\n Total account tenure in days since initial signup. Box plot shows median at 180d with Q1-Q3\n interquartile band.\n
\n
\n\n
\n \n Missing:\n {{ age.missingPct }} (42 rows)\n \n \n Distinct:\n {{ age.distinctCount }}\n \n \n Memory:\n {{ age.memory }}\n \n
\n
\n
\n\n
\n \n
\n
\n
Min (0%)\n
{{ age.min }}
\n
\n
\n
Q1 (P25)\n
{{ age.p25 }}
\n
\n
\n
Median (P50)\n
{{ age.p50 }}
\n
\n
\n
Q3 (P75)\n
{{ age.p75 }}
\n
\n
\n
\n
\n
\n\n \n
\n
\n Quantile Box & Whisker Plot\n IQR = 323 days (P75 - P25)\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 1d (Min)\n \n \n 42d\n \n \n 180d (Med)\n \n \n 365d\n \n \n 720d (P99)\n \n \n 850d (Max)\n \n \n
\n
\n
\n
\n
\n }\n\n \n @if (activeTypeFilter() === 'all' || activeTypeFilter() === 'boolean') {\n
\n
\n
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\n
\n \n
\n
{{ cancelled.name }}
\n
{{\n cancelled.type\n }}\n
\n @if (copiedColumn() === cancelled.name) {\n \n } @else {\n \n }\n \n \n
\n Binary churn indicator flag. 14.2% positive cancellation rate against 85.8% retained base.\n
\n
\n\n
\n \n Missing:\n {{\n cancelled.missingPct\n }}\n \n \n Distinct:\n {{ cancelled.distinctCount }} values\n \n \n Memory:\n {{ cancelled.memory }}\n \n
\n
\n
\n\n
\n \n
\n
\n Boolean Ratio Distribution\n Class Imbalance: 1 : 6.04\n
\n\n
\n
\n
\n
\n\n \n
\n \n
\n
\n
\n
\n \n False (Active / Retained)\n
\n
\n {{ cancelled.falsePct }}%\n \n \n
\n {{ fmt(cancelled.falseCount) }} instances\n
\n
\n
\n \n
\n
\n\n \n
\n
\n
\n
\n \n True (Cancelled / Churned)\n
\n
\n {{ cancelled.truePct }}%\n \n \n
\n {{ fmt(cancelled.trueCount) }} instances\n
\n
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\n \n
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\n