descriptive-statistics

To generate descriptive statistics and a Table 1 summary, run the Python script on your CSV file. It calculates central tendency, dispersion, and normality, then outputs publication-ready tables using pandas and tabulate.

To check data normality and visualize distributions, this approach computes skewness, kurtosis, and normality tests using scipy. It then generates distribution plots via matplotlib to help you assess data shape visually.

Yes, you can use pandas and scipy to automate central tendency and dispersion calculations for clinical research. The process computes mean, median, SD, and IQR, then formats these baseline characteristics into manuscript-ready reports.

You need matplotlib and tabulate installed to produce publication-ready descriptive statistics outputs. While numpy, scipy, and pandas handle core calculations, these optional dependencies enable plot generation and formatted table exports.

Data inspection handles missingness and data types by evaluating the dataset's structure before computing summary statistics. It checks for missing values and validates column types to ensure accurate downstream analytical results.

Limitations when running summary statistics scripts on extremely large datasets depend on memory constraints. Because the process relies on pandas and numpy, available system memory dictates the maximum dataset size you can process locally.