User-friendly, type safe, runtime efficient tooling for working with tabular data deserialized from comma-separated values (CSV) files. The type of each row of data is inferred from data, which can then be streamed from disk, or worked with in memory.
We provide streaming and in-memory interfaces for efficiently working with datasets that can be safely indexed by column names found in the data files themselves. This type safety of column access and manipulation is checked at compile time.
For a running example, we will use variations of the
prestige.csv
data set. Each row includes 7 columns, but we just want to compute the average ratio of income to prestige.
If you have a CSV data where the values of each column may be classified by a single type, and ideally you have a header row giving each column a name, you may simply want to avoid writing out the Haskell type corresponding to each row. Frames provides TemplateHaskell machinery to infer a Haskell type for each row of your data set, thus preventing the situation where your code quietly diverges from your data.
We generate a collection of definitions generated by inspecting the data file at compile time (using tableTypes), then, at runtime, load that data into column-oriented storage in memory (an in-core array of structures (AoS)). We’re going to compute the average ratio of two columns, so we’ll use the foldl library. Our fold will project the columns we want, and apply a function that divides one by the other after appropriate numeric type conversions. Here is the entirety of that program.
Now consider a case where our data file lacks a header row (I deleted the first row from `prestige.csv`). We will provide our own name for the generated row type, our own column names, and, for the sake of demonstration, we will also specify a prefix to be added to every column-based identifier (particularly useful if the column names do come from a header row, and you want to work with multiple CSV files some of whose column names coincide). We customize behavior by updating whichever fields of the record produced by rowGen we care to change, passing the result to tableTypes'. Link to code.
Sometimes not every row has a value for every column. I went ahead and blanked the prestige column of every row whose type column was NA in prestige.csv. For example, the first such row now reads,
"athletes",11.44,8206,8.13,,3373,NA
We can no longer parse a Double for that row, so we will work with row types parameterized by a Maybe type constructor. We are substantially filtering our data, so we will perform this operation in a streaming fashion without ever loading the entire table into memory. Our process will be to check if the prestige column was parsed, only keeping those rows for which it was not, then project the income column from those rows, and finally throw away Nothing elements. Link to code.
For comparison to working with data frames in other languages, see the tutorial.
There are various
demos in the repository. Be sure to run the getdata build target to download the data files used by the demos! You can also download the data files manually and put them in a data directory in the directory from which you will be running the executables.
The benchmark shows several ways of dealing with data when you want to perform multiple traversals.
Another demo shows how to fuse multiple passes into one so that the full data set is never resident in memory. A Pandas version of a similar program is also provided for comparison.
This is a trivial program, but shows that performance is comparable to Pandas, and the memory savings of a compiled program are substantial.
