'matplotlib' 검색 결과

Clean data across multiple Excel sheets, compare totals between sheets, and summarize categories and proportions. Create charts and export a formatted workbook with unusual values highlighted for review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing totals across Excel sheets
• Cleaning inconsistent text and blank cells
• Charting category counts and proportions

Clean category and numeric fields in structured data, compare groups with counts and cross-tabulations, and produce charts, Excel results, and a Markdown report. Export visuals as high-resolution PNG and SVG files for use in reports.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare category distributions across groups
• Clean category labels and fill merged-cell gaps
• Export analysis results to Excel

Clean Excel data and group records with similar numerical features. Produce a combined view of indicator trends, value distributions, category shares, and grouped data, then export the charts as an image and cleaned records as Excel.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing trends across multiple indicators
• Checking value distributions and category shares
• Grouping records with similar numerical features

Combine data from multiple Excel sheets, clean inconsistent labels and merged cells, and summarize counts by category. Create Excel summary tables with percentages and totals, plus labeled bar charts for comparing categories.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Summarizing data across multiple Excel sheets
• Cleaning merged cells and inconsistent category labels
• Comparing category counts and percentages

Analyze Excel data by classifying items, calculating counts and percentages, and comparing categories across groups. Identify values above related limits, then produce a pie chart and an Excel statistics file with download links.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Summarizing category counts and percentages
• Comparing categories across regions or other groups
• Finding values that exceed related limits

Turn Excel or CSV data into an Excel report with category totals, percentage shares, and a doughnut chart. The workflow identifies likely category and numeric columns, cleans missing values, and highlights the largest share.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare spending shares by category
• Summarize quantities or scores by category
• Create an Excel report with a doughnut chart

Analyze Excel workbooks across multiple sheets, locate matching fields, clean values, and compare groups. Produce counts, cross-tabulations that compare categories, charts, and an Excel report with a download link.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Count matching fields across multiple sheets
• Clean numeric values and merged-cell groups
• Compare counts and shares by category

Analyze one Excel worksheet by cleaning column names, extracting numbers, and handling gaps from merged cells. Create category counts, percentages, cross-tabulations comparing groups, high-resolution charts, and CSV exports, with Parquet storage for the data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clean worksheets containing merged cells
• Summarize category counts and percentages
• Compare groups with cross-tabulations

Count categories in a chosen data column after removing missing values and placeholder labels. Calculate each category’s share and create high-resolution charts combining counts, percentages, pie charts, and horizontal bars for distribution reports.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare record counts across categories
• Show category shares with pie and bar charts
• Exclude placeholder labels before counting

Analyze numerical data in Excel with box plots and an outlier summary. Extract error values from text containing the ± symbol, chart their distribution, and export statistical results as a CSV report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare numerical distributions across spreadsheet columns
• Count outliers using the interquartile range
• Extract error values from text and chart their distribution

Group Excel figures by department or product line, assign performance tiers, and calculate projections using growth rates set for each tier. Compare current and projected values in charts and summarize overall growth for target setting.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting sales targets by department
• Estimating production targets by product line
• Comparing projections under tier-specific growth assumptions

Count rows across Excel sheets to assess data size, then analyze an existing Parquet file, a format for storing tabular data. Calculate category counts and percentages, and save the results as an Excel summary and a pie chart.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Counting rows across every sheet in an Excel workbook
• Calculating category counts and percentages from Parquet data
• Creating a pie chart of category proportions

Analyze Excel data using custom categories, count distributions, and compare groups with charts. Score text using its length, technical terms, numbers, and selected complex concepts, then visualize difficulty levels and the relationship between text length and scores.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Summarize Excel records using custom categories
• Compare category distributions across groups
• Score and grade text using adjustable rules

Extract text and data from images of charts, tables, interfaces, and diagrams using a vision model, an AI that reads images. Turn the results into structured tables, recreate charts, or export data to Excel and CSV.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert a table screenshot into Excel or CSV
• Extract values from a chart and recreate it
• Describe the layout and components of a UI screenshot

Analyze Excel files with 10,000 or more rows using reading methods suited to their size. Convert data to Parquet, a column-based storage format, reduce memory use, and prepare summaries, charts, or filtered exports.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Summarizing regional sales from 100,000 rows
• Exporting high-value transactions from a million-row log
• Speeding up repeated analysis with Parquet

Clean categorical Excel data and compare counts and percentage shares across groups. Create cross-tabulation tables, heatmaps that show differences through color, and category rankings, then export the analysis to a workbook with multiple sheets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare award distributions across organizations
• Visualize category shares with a heatmap
• Identify leading groups within each category

Analyze trends and group performance in Excel data, clean percentage values, and calculate projections using growth rates assigned by performance tier. Produce comparison charts and a summary of current totals, projected totals, and growth.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Track changes in business metrics over time
• Compare performance across departments or categories
• Estimate growth using rates assigned by performance tier

Extract numbers from text values containing a specified unit, remove unusable rows, and visualize the distribution. Create a histogram with mean and median markers, plus a dashboard showing frequencies, proportions, and cumulative percentages.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extract numbers from spreadsheet values containing a unit
• Check a distribution against its mean and median
• Compare value frequencies and proportions

Compare counts and shares for two categories in Excel data. The skill cleans the selected columns, calculates differences and percentages, and produces a summary spreadsheet plus bar and pie charts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing counts for two categories
• Visualizing category shares
• Cleaning merged-cell data before counting

Clean category data in Excel, group entries, and calculate counts, totals, and shares. Create a summary spreadsheet with a total row and highlighted maximum values, alongside a bar chart for comparing groups.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Cleaning category labels before analysis
• Combining labels into defined groups
• Calculating group counts, totals, and shares

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.