Menu
Sub-skill of autoviz: 7. Export to HTML and Notebooks.
The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
npx skills add https://github.com/vamseeachanta/workspace-hub --skill autoviz-7-export-to-html-and-notebooksThe command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
Based on SOC occupation classification
Showing SKILL.md
| 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.*