“matplotlib”的搜索结果

Clean Excel data, summarize groups, and examine relationships between numeric columns. Produce charts such as scatter plots with trend lines, calculate measures of fit and statistical significance, and export cleaned data and statistical summaries.

中文简介正在准备中。当前显示英语简介。

• Clean missing group labels and numeric text
• Compare group averages and variation
• Analyze numeric relationships with scatter plots and trend lines

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.

中文简介正在准备中。当前显示英语简介。

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

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.

中文简介正在准备中。当前显示英语简介。

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

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.

中文简介正在准备中。当前显示英语简介。

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

Assess numeric Excel columns using IQR, a measure of the middle half of values, to flag outliers. Summarize counts and limits, create box plots, and examine distribution shape to inform data cleaning.

中文简介正在准备中。当前显示英语简介。

• Checking outliers before data analysis
• Comparing outlier rates across numeric columns
• Reviewing distribution shape with box plots

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.

中文简介正在准备中。当前显示英语简介。

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

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.

中文简介正在准备中。当前显示英语简介。

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

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.

中文简介正在准备中。当前显示英语简介。

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

Extract selected ranges from Excel workbooks with multiple sheets, check missing values, and clean numeric data. Fit a straight trend line, save charts and CSV results, and convert larger tables to Parquet for subsequent analysis.

中文简介正在准备中。当前显示英语简介。

• Checking missing values and category counts in a selected sheet
• Extracting numeric pairs from selected rows and columns
• Creating a scatter plot with a straight trend line

Inspect multi-sheet Excel files, use Parquet, a column-based file format, for larger datasets, and clean text before analysis. Produce category summaries, identify maximum values, and export formatted reports or scatter plots with fitted straight trend lines.

中文简介正在准备中。当前显示英语简介。

• Assess data volume in multi-sheet Excel files
• Clean unwanted characters from text columns
• Summarize categories and highlight the maximum

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.

中文简介正在准备中。当前显示英语简介。

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

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.

中文简介正在准备中。当前显示英语简介。

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

Find key figures in tabular data using column names or row keywords, calculate category averages and percentages, and export the results as a structured Excel report and a pie chart.

中文简介正在准备中。当前显示英语简介。

• Find figures using keywords in rows
• Calculate averages for a selected category
• Calculate one figure as a percentage of another

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.

中文简介正在准备中。当前显示英语简介。

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

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.

中文简介正在准备中。当前显示英语简介。

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

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.

中文简介正在准备中。当前显示英语简介。

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

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.

中文简介正在准备中。当前显示英语简介。

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

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.

中文简介正在准备中。当前显示英语简介。

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

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.

中文简介正在准备中。当前显示英语简介。

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

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.

中文简介正在准备中。当前显示英语简介。

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

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.

中文简介正在准备中。当前显示英语简介。

• 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

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。