Descriptive Statistics & Summary Tables Skill

Descriptive Statistics & Summary Tables Skill

This skill generates publication-quality summary statistics tables, balance tables, and correlation matrices — the essential "Table 1" found in every empirical economics paper.

When to Use

• Before any regression: Summarize your sample to understand distributions and detect issues
• For "Data" section of papers: Standard Table 1 with means, SDs, and sample sizes
• Treatment/control comparison: Balance tables with t-tests or normalized differences
• Variable relationships: Correlation matrices for initial exploration

Summary Statistics Table (Table 1)

Python

# Python — publication-quality summary stats
import pandas as pd

# Basic summary stats
desc = df[['income', 'age', 'education', 'hours_worked']].describe().T
desc = desc[['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max']]
desc.columns = ['N', 'Mean', 'SD', 'Min', 'P25', 'Median', 'P75', 'Max']
print(desc.round(3).to_string())

# Using tableone for clinical/econ style Table 1
# pip install tableone
from tableone import TableOne
table1 = TableOne(df, columns=['income', 'age', 'education', 'hours_worked'],
                  categorical=['female', 'race'],
                  groupby='treatment', pval=True)
print(table1.tabulate(tablefmt="github"))
table1.to_excel("table1.xlsx")

R

# R — modelsummary::datasummary
library(modelsummary)