DataAuditKit Report

{{ summary.rows }} rows × {{ summary.columns }} columns — {{ summary.memory_mb }} MB

{% if alerts %}

Data Quality Alerts

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{% set triggered = alerts|selectattr('triggered')|list %} {% if triggered %} {% for alert in triggered %}
{{ alert.severity }} {{ alert.name }}: {{ alert.description }}
{% endfor %} {% else %}

All quality checks passed.

{% endif %}
{% endif %}

Overview

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{{ summary.rows }}
Rows
{{ summary.columns }}
Columns
{{ summary.memory_mb }}
Memory (MB)
{{ summary.duplicates }}
Duplicates
{% if summary.type_summary %}

Column Types

{% for tname, tcount in summary.type_summary.items() %} {{ tname }}: {{ tcount }} {% endfor %}
{% for col, ctype in summary.column_types.items() %} {{ col }} {% endfor %}
{% endif %}
{% if fix_log %}

Automated Fixes

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{% if fix_log.dropped_columns %}
Dropped {{ fix_log.dropped_columns|length }} constant column(s): {{ fix_log.dropped_columns|join(', ') }}
{% endif %} {% if fix_log.imputed_columns %}
Imputed missing values in {{ fix_log.imputed_columns|length }} column(s): {{ fix_log.imputed_columns|join(', ') }}
{% endif %} {% if not fix_log.dropped_columns and not fix_log.imputed_columns %}

No fixes were applied.

{% endif %}
{% endif %}

Missing Values

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{% set has_missing = missing.missing_count and missing.missing_count.values()|select('>',0)|list %} {% if has_missing %} {% if missing_plot %} Missing values plot {% endif %} {% for col in missing.missing_count.keys() %} {% if missing.missing_count[col] > 0 %} {% endif %} {% endfor %}
ColumnMissing CountPercent
{{ col }} {{ missing.missing_count[col] }} {{ missing.missing_percent[col] }}%
{% else %}

No missing values detected.

{% endif %} {% if missing_patterns %}

Missing Value Patterns

{{ missing_patterns.rows_complete }}
Complete Rows
{{ missing_patterns.rows_with_any_null }}
Rows with Nulls
{{ missing_patterns.rows_with_all_null }}
All-Null Rows
{{ missing_patterns.pct_complete }}%
Completeness
{% if missing_patterns.nullity_correlation %}

Nullity Correlation

{% for col1, row in missing_patterns.nullity_correlation.items() %} {% for col2, val in row.items() %} {% endfor %} {% endfor %}
Column PairCorrelation
{{ col1 }} ↔ {{ col2 }}{{ val }}
{% endif %} {% endif %}

Duplicate Rows

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{% if duplicates.duplicates > 0 %}
Found {{ duplicates.duplicates }} duplicate row(s).
{% else %}

No duplicate rows found.

{% endif %}

Numeric Statistics

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{% if statistics %} {% for col, stats in statistics.items() %}

{{ col }}

{% for stat, val in stats.items() %} {% endfor %}
StatisticValue
{{ stat }}{{ val }}
{% endfor %} {% else %}

No numeric columns to analyze.

{% endif %}
{% if categorical_statistics %}

Categorical Statistics

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{% for col, stats in categorical_statistics.items() %}

{{ col }}

{{ stats.count }}
Count
{{ stats.nunique }}
Unique
{{ stats.missing }}
Missing
{{ stats.missing_pct }}%
Missing %
{% if stats.top_values %}

Top Values

{% for val, freq in stats.top_values.items() %} {% endfor %}
ValueFrequency
{{ val }}{{ freq }}
{% endif %} {% endfor %}
{% endif %}

Correlation

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{% if correlation %} {% if correlation_plot %} Correlation matrix {% endif %} {% for col1, row in correlation.items() %}

{{ col1 }}

{% for col2, val in row.items() %} {% if col1 != col2 %} {% endif %} {% endfor %}
ColumnCorrelation
{{ col2 }}{{ "%.4f"|format(val) }}
{% endfor %} {% else %}

No correlation data available.

{% endif %}

Outliers

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{% if outliers %} {% set has_outliers = namespace(value=false) %} {% for col, count in outliers.items() %} {% if count > 0 %}{% set has_outliers.value = true %}{% endif %} {% endfor %} {% if has_outliers.value %} {% for col, count in outliers.items() %} {% if count > 0 %} {% endif %} {% endfor %}
ColumnOutlier Count
{{ col }}{{ count }}
{% else %}

No outliers detected.

{% endif %} {% else %}

Outlier detection disabled.

{% endif %}
{% if imbalance %}

Target Imbalance

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{{ imbalance.imbalance_ratio }}
Imbalance Ratio
{% if imbalance.imbalance_ratio < 0.5 %}
Dataset is imbalanced (ratio = {{ imbalance.imbalance_ratio }}). Consider resampling techniques.
{% endif %} {% for cls, count in imbalance.class_counts.items() %} {% endfor %}
ClassCount
{{ cls }}{{ count }}
{% endif %} {% if leakage %}

Leakage Detection

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Potential data leakage detected! Features with near-perfect correlation to target.
{% for col, val in leakage.items() %} {% endfor %}
Suspicious ColumnCorrelation with Target
{{ col }}{{ val }}
{% endif %}