Statistical Summary Agents
Distribution analysis, correlation, outlier detection in agent-generated reports.
Why Statistical Summaries Matter
Raw data is rarely useful on its own. A data analysis agent becomes genuinely valuable when it can automatically compute statistical summaries and translate numbers into plain-language insights.
This lesson covers the core statistical operations: describe, correlation, outlier detection, distribution identification, and automated insight generation.
df.describe(): The Starting Point
df.describe() computes count, mean, std, min, quartiles, and max for all numeric columns in one call. It's the standard first step in any exploratory data analysis.
import pandas as pd
df = pd.read_csv('sales_data.csv')
# Basic numeric summary
print(df.describe())
# Also describe categorical columns
print(df.describe(include='object'))
# Custom percentiles
print(df.describe(percentiles=[0.05, 0.25, 0.5, 0.75, 0.95]))
# Convert to clean dict for agent use
def get_numeric_summary(df):
desc = df.describe().round(2)
return {
col: desc[col].to_dict()
for col in desc.columns
}