1

    csv-analyzer-de

    by Ikerg

    Deterministic CSV profiling with automated data quality audits, interactive Plotly reports, and Pandas fix snippets.

    Updated Jun 2026
    Security scanned

    $5

    · or 25 credits

    30-day refund guarantee

    Secure checkout via Stripe

    Included in download

    • Detect hidden data corruption and mixed-type columns automatically
    • Generate interactive Plotly dashboards for any CSV file
    • terminal, file_write, file_read automation included
    • Instant install

    Sample input

    Analyze customers.csv and tell me if the data is clean enough for an import.

    Sample output

    Analysis found 3 high-severity issues in 'customers.csv':

    1. [HIGH] 142 duplicate rows found. Fix: df.drop_duplicates(inplace=True)
    2. [HIGH] Column 'Phone' has mixed types (int and str).
    3. [MEDIUM] 'City' has case-duplicates (e.g., 'London' vs 'london'). Interactive report saved to report.html.

    About This Skill

    The Ultimate CSV Profiling Tool

    The CSV Analyzer provides deterministic, professional-grade profiling for any tabular dataset. Instead of manually inspecting rows or guessing data types, this skill automates the discovery of data structures, quality issues, and statistical distributions. It solves the common developer pain point of "dirty data" by identifying hidden corruption—like mixed numeric/text types or case-sensitivity issues—before they break your production pipelines.

    What it does

    • Auto-Detection: Instantly sniffs out delimiters (comma, semicolon, tab, pipe) and file encodings.
    • Deep Quality Audit: Scans for high-severity issues like duplicate rows, polluted numeric columns, and hidden whitespace.
    • Actionable Fixes: Every finding includes a copy-pasteable Pandas snippet to resolve the specific data issue.
    • Interactive Visuals: Generates a self-contained Plotly HTML report with histograms, correlation heatmaps, and missingness bars.
    • Scalable Analysis: Supports sampling modes for large datasets to keep performance snappy.

    Why it's better than manual prompting

    Generic AI prompts often hallucinate data distributions or fail to see the "tail" of a CSV file. This skill uses deterministic Python execution to provide ground-truth statistics. You get precise row counts, exact null percentages, and verified candidate keys that an LLM cannot accurately calculate just by "looking" at a file snippet.

    Use Cases

    • Detect hidden data corruption and mixed-type columns automatically
    • Generate interactive Plotly dashboards for any CSV file
    • Identify primary key candidates and verify data integrity
    • Get instant Pandas code snippets to fix identified data quality issues

    Reviews

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    Security Scanned

    Passed automated security review

    Permissions

    Terminal / Shell
    Write Files
    Read Files

    File Scopes

    scripts/**
    examples/**

    Tags

    data-analysis
    pandas
    csv-tools
    data-cleaning
    python
    data-science

    Creator

    Ikerg

    Lead Data Engineer with 11 years of experience designing and delivering scalable data platforms across Databricks, AWS, and Azure ecosystems. Proven track record of building high-performance data solutions for large-scale, data-intensive organizations in industries including healthcare and robotics. Extensive experience working in highly regulated environments, managing complex data pipelines and large volumes of structured and unstructured data.

    Frequently Asked Questions

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