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Sub-skill of autoviz: 7. Export to HTML and Notebooks.
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npx skills add https://github.com/vamseeachanta/workspace-hub --skill autoviz-7-export-to-html-and-notebooksコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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SKILL.md を表示中
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
| name | autoviz-7-export-to-html-and-notebooks |
| description | Sub-skill of autoviz: 7. Export to HTML and Notebooks. |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
HTML Report Generation:
from autoviz import AutoViz_Class
import pandas as pd
import os
def generate_html_report(
df: pd.DataFrame,
output_dir: str,
report_name: str = "eda_report",
target: str = ""
) -> str:
"""
Generate comprehensive HTML report with AutoViz.
Args:
df: Input DataFrame
output_dir: Directory for output files
report_name: Name for the report
target: Target variable (optional)
Returns:
Path to generated report
"""
os.makedirs(output_dir, exist_ok=True)
AV = AutoViz_Class()
# Generate HTML charts
df_analyzed = AV.AutoViz(
filename="",
dfte=df,
depVar=target,
chart_format="html",
save_plot_dir=output_dir,
verbose=1
)
# Create summary HTML
html_content = f"""
<!DOCTYPE html>
<html>
<head>
<title>{report_name} - AutoViz EDA Report</title>
<style>
body {{ font-family: Arial, sans-serif; margin: 20px; }}
h1 {{ color: #333; }}
.summary {{ background: #f5f5f5; padding: 15px; border-radius: 5px; }}
.chart-container {{ margin: 20px 0; }}
</style>
</head>
<body>
<h1>{report_name}</h1>
<div class="summary">
<h2>Dataset Summary</h2>
<p>Rows: {len(df):,}</p>
<p>Columns: {len(df.columns)}</p>
<p>Numeric columns: {len(df.select_dtypes(include=['number']).columns)}</p>
<p>Categorical columns: {len(df.select_dtypes(include=['object', 'category']).columns)}</p>
<p>Target variable: {target if target else 'Not specified'}</p>
</div>
<h2>Column Information</h2>
<table border="1" style="border-collapse: collapse;">
<tr><th>Column</th><th>Type</th><th>Non-Null</th><th>Unique</th></tr>
"""
for col in df.columns:
html_content += f"""
<tr>
<td>{col}</td>
<td>{df[col].dtype}</td>
<td>{df[col].notna().sum()}</td>
<td>{df[col].nunique()}</td>
</tr>
"""
html_content += """
</table>
<h2>Generated Charts</h2>
<p>Charts have been saved to the output directory.</p>
</body>
</html>
"""
report_path = os.path.join(output_dir, f"{report_name}.html")
with open(report_path, "w") as f:
f.write(html_content)
return report_path
# Usage
# report_path = generate_html_report(df, "output/eda", "sales_analysis", "revenue")
# print(f"Report saved to: {report_path}")
Jupyter Notebook Integration:
# In Jupyter Notebook
from autoviz import AutoViz_Class
import pandas as pd
# Load data
df = pd.read_csv("data.csv")
# Initialize AutoViz
AV = AutoViz_Class()
# Use 'server' format for inline display in notebooks
%matplotlib inline
df_analyzed = AV.AutoViz(
filename="",
dfte=df,
depVar="target",
chart_format="server", # Display inline in notebook
verbose=1
)
# Alternative: Use bokeh for interactive plots in notebooks
df_analyzed = AV.AutoViz(
filename="",
dfte=df,
depVar="target",
chart_format="bokeh", # Interactive Bokeh plots
verbose=1
)
Export to Notebook File:
from autoviz import AutoViz_Class
import pandas as pd
import nbformat as nbf
import os
def create_eda_notebook(
df: pd.DataFrame,
output_path: str,
dataset_name: str = "dataset"
) -> str:
"""
Create a Jupyter notebook with AutoViz EDA.
Args:
df: Input DataFrame
output_path: Path for output notebook
dataset_name: Name for the dataset
Returns:
Path to created notebook
"""
nb = nbf.v4.new_notebook()
cells = [
nbf.v4.new_markdown_cell(f"# Exploratory Data Analysis: {dataset_name}"),
nbf.v4.new_code_cell("""
from autoviz import AutoViz_Class
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
"""),
nbf.v4.new_markdown_cell("## Load Data"),
nbf.v4.new_code_cell(f"""
# Data is pre-loaded
df = pd.read_csv("{dataset_name}.csv") # Update path as needed
print(f"Dataset shape: {{df.shape}}")
df.head()
"""),
nbf.v4.new_markdown_cell("## AutoViz Analysis"),
nbf.v4.new_code_cell("""
AV = AutoViz_Class()
df_analyzed = AV.AutoViz(
filename="",
dfte=df,
chart_format="server",
*Content truncated — see parent skill for full reference.*