pablodiegoo/data-viz
src/datapro/data/skills/data-viz/SKILL.md
Generates professional statistical charts (Bar, Pie, Grouped) using Matplotlib and Seaborn. Use this skill to visualize survey data, trends, and distributions for reports.
$ install --global
skillsauthnpx skillsauth add pablodiegoo/data-pro-skill data-vizInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- data-viz
- description:
- Generates professional statistical charts (Bar, Pie, Grouped) using Matplotlib and Seaborn. Use this skill to visualize survey data, trends, and distributions for reports.
Data Viz Skill
This skill provides a standardized way to generate high-quality statistical charts for reports. It handles styling (using Sebrae-compatible colors), layout, and saving to files.
Capabilities
1. Bar Charts (plot_bar)
Best for comparing categories or counts. Supports vertical and horizontal orientation.
2. Pie Charts (plot_pie)
Best for showing composition (shares) of a whole. Limit to Top 5-7 categories for readability.
3. Grouped Bar Charts (plot_grouped_bar)
Best for comparing distributions across segments.
4. Evolution Line Charts (Survey Specific)
Best for comparing means of domains across points in time (e.g., Start vs End). Use plot_evolution_line.
5. Word Clouds
For qualitative text analysis visualization. Use in conjunction with survey-qual-analyzer frequencies.
6. Multivariate Analysis (principal_component_plotting, correlation_ellipse_plot, multivariate_normal_contours)
- PCA: Scree plots, loadings, and biplots.
- Correlation: Ellipse plots for correlation matrices (publication style).
- Probability: Bivariate normal distribution contours.
7. Performance Visualization (performance_curve_builder)
- Performance Curves: Cumulative results over time for any strategy.
- Drawdown: Visualizing risk periods and recovery.
Usage
import pandas as pd
# Import from skill scripts directory
from scripts.plotter import plot_bar, plot_pie, plot_grouped_bar
from scripts.evolution_plotter import plot_evolution_line
from scripts.visuals import * # Additional visualization utilities
from scripts.advanced_plots import * # Advanced chart types
from scripts.principal_component_plotting import plot_feature_importance, plot_biplot
from scripts.correlation_ellipse_plot import plot_corr_ellipses
from scripts.multivariate_normal_contours import plot_contours
from scripts.performance_curve_builder import calculate_drawdown, extend_series_to_date
# 1. Simple Bar Chart (Top 10 Cities)
plot_bar(df, x_col="City", title="Respondents by City", filename="output/city_dist.png", orientation='h')
# 2. Evolution of Domains (Survey Pre vs Post)
# Expected columns: 'Cycle', 'Domain', 'Mean'
plot_evolution_line(df_evo, x="Cycle", y="Mean", hue="Domain", title="Evolution of Domains", filename="output/evolution.png")
Aesthetic Standards & Surveys
- Palette: Dark blue, cyan, and neutral grays for contrast.
- Labels: Always include sample size (n) if available.
- Premium: High DPI (300) and clean backgrounds for publication-ready reports.
Dependencies
Requires matplotlib, seaborn, and pandas.
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