vamseeachanta 7. Export to HTML and Notebooks
7. Export to HTML and Notebooks
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.*