To convert a wide DataFrame (one column per metric) to long format (one row per observation):

A data warehouse is best characterized as:

In a Bag-of-Words representation, each entry of the vector for a document is:

In NLP, “tokens” vs “types”:

A key limitation shared by Bag-of-Words AND TF-IDF (without further engineering):

In a MapReduce-style computation, the Map step:

To create an animated chart with a play button for a time-series:

TF-IDF up-weights words that:

Forward fill (ffill) is most appropriate for missing values in:

For a sentiment-analysis task, why might lower-casing the text hurt accuracy?