descriptive-stats

To calculate descriptive statistics for CSV or Excel datasets, use pandas to compute mean, median, standard deviation, quartiles, range, and coefficient of variation. This process reveals core data characteristics by applying numeric column analysis directly to your imported files.

The best way to detect outliers in Excel data using Python involves applying IQR, Z-score, and consensus detection methods. These techniques identify statistical anomalies for data cleaning by flagging values that deviate significantly from the distribution.

Distribution analysis for numeric columns works by calculating skewness and kurtosis, performing normality tests, and generating visual outputs like histograms and Q-Q plots. This mechanism reveals the shape and spread of your data using scipy and seaborn.

Yes, you can generate an HTML report for descriptive statistics and group comparisons. The process uses jinja2 to format terminal tables and charts into a shareable HTML file, allowing you to present category-level significance testing results directly.

Yes, this statistical analysis approach requires Python dependencies including pandas, numpy, scipy, seaborn, matplotlib, plotly, and openpyxl. These libraries provide the foundational data manipulation, statistical computing, and visualization capabilities needed to process your files.

Limitations of using descriptive statistics for data quality control include its focus on summary metrics rather than predictive modeling. While it detects outliers and assesses distribution shapes, it cannot explain causal relationships or forecast future trends from the historical data.