data-visualization

name data-visualization description Use for creating publication-quality charts and multi-panel analysis summaries. Triggers when tasks involve visualizing data, plotting results, creating charts, or producing visual reports from analysis output. Data Visualization Skill Create publication-quality analytical charts using matplotlib and seaborn in a headless GPU sandbox. Charts are saved as PNG files to /workspace/ for retrieval. When to Use This Skill Use this skill when: Visualizing results from cuDF analysis or cuML models Creating charts (bar, line, scatter, heatmap, histogram, box plot) Building multi-panel analysis summaries The user asks for visual output, plots, graphs, or charts Presenting statistical findings with figures Initialization (REQUIRED) MUST call matplotlib.use('Agg') BEFORE importing pyplot. This enables headless rendering. import matplotlib matplotlib.use( 'Agg' ) # Headless backend — MUST be before pyplot import import matplotlib.pyplot as plt import numpy as np # Publication-quality defaults plt.rcParams.update({ 'figure.dpi' : 100 , 'savefig.dpi' : 300 , 'font.size' : 11 , 'axes.labelsize' : 12 , 'axes.titlesize' : 14 , 'xtick.labelsize' : 10 , 'ytick.labelsize' : 10 , 'legend.fontsize' : 10 , 'figure.constrained_layout.use' : True , }) # Colorblind-safe palette (Okabe-Ito) COLORS = [ '#0173B2' , '#DE8F05' , '#029E73' , '#D55E00' , '#CC78BC' , '#CA9161' , '#FBAFE4' , '#949494' , '#ECE133' , '#56B4E9' ] Saving Charts Always save to /workspace/ with these settings: plt.savefig( '/workspace/chart_name.png' , dpi= 300 , bbox_inches= 'tight' , facecolor= 'white' , edgecolor= 'none' ) plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline dpi=300 for print quality bbox_inches='tight' removes excess whitespace facecolor='white' ensures white background Always call plt.close() after saving to free memory Displaying Charts (REQUIRED) After saving any chart, you MUST call read_file on it to display it inline in the conversation: read_file("/workspace/chart_name.png") Users cannot see charts unless you do this. Every chart you save MUST be followed by a read_file call. Quick Reference Bar Chart (from groupby results) # After: result = to_pd(df.groupby("category")["value"].mean()) fig, ax = plt.subplots(figsize=( 8 , 5 )) bars = ax.bar(result.index, result.values, color=COLORS[: len (result)], edgecolor= 'black' , linewidth= 0.8 ) for bar in bars: height = bar.get_height() ax.text(bar.get_x() + bar.get_width()/ 2. , height, f' {height: .1 f} ' , ha= 'center' , va= 'bottom' , fontsize= 9 ) ax.set_ylabel( 'Mean Value' , fontweight= 'bold' ) ax.set_xlabel( 'Category' , fontweight= 'bold' ) ax.set_title( 'Average Value by Category' , fontweight= 'bold' ) ax.grid(axis= 'y' , alpha= 0.3 , linestyle= '--' ) ax.set_axisbelow( True ) plt.savefig( '/workspace/bar_chart.png' , dpi= 300 , bbox_inches= 'tight' , facecolor= 'white' , edgecolor= 'none' ) plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline Line Chart (trends over time) fig, ax = plt.subplots(figsize=( 10 , 5 )) for i, col in enumerate (columns_to_plot): ax.plot(df[ "date" ], df[col], label=col, color=COLORS[i], linewidth= 2 , marker= 'o' , markersize= 3 , markevery= max ( 1 , len (df)// 20 )) ax.set_ylabel( 'Values' , fontweight= 'bold' ) ax.set_xlabel( 'Date' , fontweight= 'bold' ) ax.set_title( 'Trends Over Time' , fontweight= 'bold' ) ax.legend(frameon= True , shadow= False ) ax.grid( True , alpha= 0.3 , linestyle= '--' ) ax.set_axisbelow( True ) plt.xticks(rotation= 45 , ha= 'right' ) plt.savefig( '/workspace/line_chart.png' , dpi= 300 , bbox_inches= 'tight' , facecolor= 'white' , edgecolor= 'none' ) plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline Scatter Plot — Continuous Color (correlations) fig, ax = plt.subplots(figsize=( 8 , 6 )) scatter = ax.scatter(df[ "x" ], df[ "y" ], c=df[ "value" ], cmap= 'viridis' , s= 40 , alpha= 0.7 , edgecolors= 'black' , linewidth= 0.3 ) plt.colorbar(scatter, ax=ax, label= 'Value' ) # Optional: trend line z = np.polyfit(df[ "x" ], df[ "y" ], 1 ) ax.plot(df[ "x" ].sort_values(), np.poly1d(z)(df[ "x" ].sort_values()), "r--" , linewidth= 2 , label= f'y= {z[ 0 ]: .2 f} x+ {z[ 1 ]: .2 f} ' ) ax.set_xlabel( 'X' , fontweight= 'bold' ) ax.set_ylabel( 'Y' , fontweight= 'bold' ) ax.set_title( 'Correlation Analysis' , fontweight= 'bold' ) ax.legend() ax.grid( True , alpha= 0.3 , linestyle= '--' ) plt.savefig( '/workspace/scatter_correlation.png' , dpi= 300 , bbox_inches= 'tight' , facecolor= 'white' , edgecolor= 'none' ) plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline Scatter Plot — Categorical Color (clusters) fig, ax = plt.subplots(figsize=( 8 , 6 )) for i, label in enumerate ( sorted (df[ "cluster" ].unique())): mask = df[ "cluster" ] == label ax.scatter(df.loc[mask, "x" ], df.loc[mask, "y" ], c=COLORS[i], label= f'Cluster {label} ' , s= 40 , alpha= 0.7 ) ax.set_xlabel( 'X' , fontweight= 'bold' ) ax.set_ylabel( 'Y' , fontweight= 'bold' ) ax.set_title( 'Cluster Visualization' , fontweight= 'bold' ) ax.legend() ax.grid( True , alpha= 0.3 , linestyle= '--' ) plt.savefig( '/workspace/scatter_clusters.png' , dpi= 300 , bbox_inches= 'tight' , facecolor= 'white' , edgecolor= 'none' ) plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline Heatmap (correlation matrix or confusion matrix) import seaborn as sns fig, ax = plt.subplots(figsize=( 8 , 7 )) # corr_matrix = to_pd(df[numeric_cols].corr()) sns.heatmap(corr_matrix, annot= True , fmt= '.2f' , cmap= 'RdBu_r' , center= 0 , square= True , linewidths= 1 , vmin=- 1 , vmax= 1 , cbar_kws={ 'label' : 'Correlation' }, ax=ax) ax.set_title( 'Correlation Matrix' , fontweight= 'bold' ) plt.savefig( '/workspace/heatmap.png' , dpi= 300 , bbox_inches= 'tight' , facecolor= 'white' , edgecolor= 'none' ) plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline Histogram with KDE fig, ax = plt.subplots(figsize=( 8 , 5 )) ax.hist(df[ "value" ], bins= 30 , color=COLORS[ 0 ], alpha= 0.7 , edgecolor= 'black' , linewidth= 0.5 , density= True , label= 'Distribution' ) # Add KDE curve from scipy.stats import gaussian_kde kde = gaussian_kde(df[ "value" ].dropna()) x_range = np.linspace(df[ "value" ]. min (), df[ "value" ]. max (), 200 ) ax.plot(x_range, kde(x_range), color=COLORS[ 1 ], linewidth= 2 , label= 'KDE' ) ax.set_xlabel( 'Value' , fontweight= 'bold' ) ax.set_ylabel( 'Density' , fontweight= 'bold' ) ax.set_title( 'Value Distribution' , fontweight= 'bold' ) ax.legend() ax.grid(axis= 'y' , alpha= 0.3 , linestyle= '--' ) plt.savefig( '/workspace/histogram.png' , dpi= 300 , bbox_inches= 'tight' , facecolor= 'white' , edgecolor= 'none' ) plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline Box Plot (compare groups) fig, ax = plt.subplots(figsize=( 8 , 5 )) groups = [df[df[ "group" ] == g][ "value" ].values for g in group_names] bp = ax.boxplot(groups, labels=group_names, patch_artist= True , widths= 0.6 , showmeans= True , meanprops= dict (marker= 'D' , markerfacecolor= 'red' , markersize= 6 )) for i, patch in enumerate (bp[ 'boxes' ]): patch.set_facecolor(COLORS[i % len (COLORS)]) patch.set_alpha( 0.7 ) ax.set_ylabel( 'Value' , fontweight= 'bold' ) ax.set_title( 'Distribution by Group' , fontweight= 'bold' ) ax.grid(axis= 'y' , alpha= 0.3 , linestyle= '--' ) ax.set_axisbelow( True ) plt.savefig( '/workspace/boxplot.png' , dpi= 300 , bbox_inches= 'tight' , facecolor= 'white' , edgecolor= 'none' ) plt.close() # IMPORTANT: call read_file("/workspace/<chart>.png") to display inline Multi-Panel Analysis Summary Use this to create a single image with multiple charts — the most effective way to present a complete analysis. fig, axes = plt.subplots( 2 , 2 , figsize=( 14 , 10 )) # Top-left: Distribution axes[ 0 , 0 ].hist(df[ "value" ], bins= 30 , color=COLORS[ 0 ], alpha= 0.7 , edgecolor= 'black' , linewidth= 0.5 ) axes[ 0 , 0 ].set_title( 'Value Distribution' , fontweight= 'bold' ) axes[ 0 , 0 ].set_xlabel( 'Value' ) axes[ 0 , 0 ].grid(axis= 'y' , alpha= 0.3 , linestyle= '--' ) # Top-right: Scatter axes[ 0 , 1 ].scatter(df[ "x" ], df[ "y" ], c=COLORS[ 0 ], s= 30 , alpha= 0.5 ) axes[ 0 , 1 ].set_title( 'X vs Y' , fontweight= 'bold' ) axes[ 0 , 1 ].set_xlabel( 'X' ) axes[ 0 , 1 ].set_ylabel( 'Y' ) axes[ 0 , 1 ].grid( True , alpha= 0.3 , linestyle= '--' ) # Bottom-left: Bar chart group_means = df.groupby( "category" )[ "value" ].mean() axes[ 1 , 0 ].bar(group_means.index, group_means.values, color=COLORS[: len (group_means)]) axes[ 1 , 0 ].set_title( 'Mean by Category' , fontweight= 'bold' ) axes[ 1 , 0 ].set_xlabel( 'Category' ) axes[ 1 , 0 ].grid(axis= 'y' , alpha= 0.3 , linestyle= '--' ) # Bottom-right: Box plot axes[ 1 , 1 ].boxplot([df[df[ "category" ] == c][ "value" ].values for c in categories], labels=categories, patch_artist= True ) axes[ 1 , 1 ].set_title( 'Distribution by Category' , fontweight= 'bold' )