Concatenating multiple data frames

The concatenate_rows and concatenate_columns functions are not really verbs in the data grammar, but are more like conjunctions. They are the only operations that take an arbitrary number of data frames as arguments. As such, they are genuine Python functions importable from tabeline, not methods of DataFrame.

concatenate_rows

Concatenate the rows of the given data frames into a single data frame. The data frames must have the same columns, in the same order. Typically, only data frames without grouping are concatenated, but concatenation will be allowed if all group levels are identical. In other libraries, this operation may be called "row binding" or "vertical stacking".

from tabeline import DataFrame, concatenate_rows

study1 = DataFrame(study=[1, 1, 1], subject=[10, 11, 12])
study2 = DataFrame(study=[2, 2, 2], subject=[20, 21, 22])

concatenate_rows(study1, study2)
# shape: (6, 2)
# ┌───────┬─────────┐
# │ study ┆ subject │
# │ ---   ┆ ---     │
# │ i64   ┆ i64     │
# ╞═══════╪═════════╡
# │ 1     ┆ 10      │
# │ 1     ┆ 11      │
# │ 1     ┆ 12      │
# │ 2     ┆ 20      │
# │ 2     ┆ 21      │
# │ 2     ┆ 22      │
# └───────┴─────────┘

concatenate_columns

Concatenate the columns of the given data frames into a single data frame. All data frames must have the same number of rows. No columns may be duplicated. No group levels may be present. In other libraries, this operation may be called "column binding" or "horizontal stacking".

This is one of the most dangerous operations in Tabeline. Often, inner_join is a safer choice than concatenate_columns. Joining on key columns ensures that the data is correctly matched even if the row order is mixed up.

from tabeline import DataFrame, concatenate_columns

study1 = DataFrame(study=[1, 1, 1], subject=[10, 10, 11])
study2 = DataFrame(measurements=[12.5, 12.0, 5.6])

concatenate_columns(study1, study2)
# shape: (3, 3)
# ┌───────┬─────────┬──────────────┐
# │ study ┆ subject ┆ measurements │
# │ ---   ┆ ---     ┆ ---          │
# │ i64   ┆ i64     ┆ f64          │
# ╞═══════╪═════════╪══════════════╡
# │ 1     ┆ 10      ┆ 12.5         │
# │ 1     ┆ 10      ┆ 12.0         │
# │ 1     ┆ 11      ┆ 5.6          │
# └───────┴─────────┴──────────────┘