This article shows three examples of using NumPy array operations — known as vectorized operations — to run simulations like Monte Carlo or Bootstrap models on large datasets nearly instantly.

Learn how to use Python’s statsmodels library to calculate the ideal sample size before collecting data.

Learn how to build a reusable Python script that automatically runs hypothesis tests across all variables in a dataset.

Learn how DoWhy automatically identifies causal estimands using the Lalonde dataset in three steps.

Estimate the Average Treatment Effect in Python using DoWhy with regression and propensity score matching on real data.

Learn how to stress-test causal effect estimates in DoWhy using refutation methods on the Lalonde dataset.

Learn how to build and test causal inference models in Python using the DoWhy library.

Learn how to count word frequencies across text datasets with TextBlob, Counter, and stop word filtering in Python.

Use TextBlob to score text subjectivity and separate opinion-based entries from factual statements in Python.

Learn how to score text sentiment from -1.0 to 1.0 with TextBlob and label entries automatically.