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data-analyst

Analyze missing values in CSV datasets and recommend imputation strategies.

Updated Jun 22, 2026
Search Tags:#data quality#dashboard#csv#plotly dash#missing values#imputation
One-click install
npx skills add https://github.com/PaneriVatsal/FRIDAY --skill data-analyst-panerivatsal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analyst
Source: https://github.com/PaneriVatsal/FRIDAY/tree/main/.agents/skills/data-analyst
Command: npx skills add https://github.com/PaneriVatsal/FRIDAY --skill data-analyst-panerivatsal

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scikit-learn, plotly, dash, dash-bootstrap-components, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill removes the manual effort of finding missing data, choosing imputation methods, and turning CSV datasets into useful visual analyses.

Core Features & Use Cases

  • Missing Value Analysis: Detect missing-value patterns, summarize data quality, and recommend column-specific treatment.
  • Intelligent Imputation: Fill numeric, categorical, datetime, and ID-like fields with methods suited to the data shape and missingness rate.
  • Interactive Dashboard Creation: Build Plotly Dash dashboards with summary tables, distributions, correlations, categorical charts, and time-series views.
  • Use Case: If a sales CSV has gaps in revenue, region, and date columns, this Skill can assess the file, impute the missing entries, and generate a dashboard for exploration.

Quick Start

Use the data-analyst skill to analyze the attached CSV, impute missing values, and generate an interactive dashboard.

Frequently Asked Questions about data-analyst

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I handle missing values in a CSV dataset and choose the right imputation strategy?▼

Handling missing values in a CSV requires analyzing missingness patterns and rates per column to recommend appropriate imputation strategies for numeric, categorical, datetime, and ID-like fields.

What's the best way to turn a CSV with gaps into an interactive dashboard for exploratory analysis?▼

Build a Plotly Dash dashboard from a CSV by imputing missing entries and rendering visual analytics like summary tables, distributions, correlations, and time-series views for exploratory analysis.

Do I need pandas and scikit-learn to automatically assess data quality and clean tabular files?▼

Assessing data quality and cleaning tabular files requires pandas, numpy, and scikit-learn to detect missing-value patterns, summarize datasets, and apply intelligent imputation methods.

Does Plotly Dash support time-series visualization and categorical charts for cleaned CSV data?▼

Plotly Dash supports interactive dashboard creation for cleaned CSV data by rendering summary tables, distributions, correlations, categorical charts, and time-series views using dash-bootstrap-components.

Can I impute missing dates and categorical regions in a sales CSV automatically?▼

Imputing missing dates and categorical regions in a sales CSV involves assessing the file and filling datetime, ID-like, and categorical fields with methods suited to the data shape and missingness rate.

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