My example pull request title

utils.py

@@ -0,0 +1,61 @@
+import pandas as pd
+import numpy as np
+
+
+def summarize_dataframe(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Generates a summary of a pandas DataFrame.
+
+    This function provides a quick overview of the DataFrame, including:
+    - Basic statistics for numeric columns
+    - Unique value counts for categorical columns
+    - Missing value counts for all columns
+    - Sample values for each column
+
+    Parameters:
+    df (pd.DataFrame): The input DataFrame to summarize
+
+    Returns:
+    pd.DataFrame: A summary DataFrame with statistics for each column
+    """
+    # Initialize lists to store summary information
+    columns = []
+    dtypes = []
+    non_null_counts = []
+    null_counts = []
+    unique_counts = []
+    numeric_stats = []
+    sample_values = []
+
+    for col in df.columns:
+        columns.append(col)
+        dtypes.append(str(df[col].dtype))
+        non_null_counts.append(df[col].count())
+        null_counts.append(df[col].isnull().sum())
+        unique_counts.append(df[col].nunique())
+
+        if np.issubdtype(df[col].dtype, np.number):
+            numeric_stats.append(
+                f"min: {df[col].min():.2f}, max: {df[col].max():.2f}, mean: {df[col].mean():.2f}, median: {df[col].median():.2f}"
+            )
+        else:
+            numeric_stats.append("N/A")
+
+        # Add sample values
+        sample = df[col].dropna().sample(n=min(3, df[col].count())).tolist()
+        sample_values.append(str(sample))
+
+    # Create summary DataFrame
+    summary_df = pd.DataFrame(
+        {
+            "Column": columns,
+            "Data Type": dtypes,
+            "Non-Null Count": non_null_counts,
+            "Null Count": null_counts,
+            "Unique Values": unique_counts,
+            "Numeric Stats": numeric_stats,
+            "Sample Values": sample_values,
+        }
+    )
+
+    return summary_df