IndexedTables provides tabular data structures where some of the columns form a sorted index. It provides the backend to JuliaDB, but can be used on its own for efficient in-memory data processing and analytics.
IndexedTables offers two data structures: IndexedTable and NDSparse.
- Both types store data in columns.
IndexedTableandNDSparsediffer mainly in how data is accessed.- Both types have equal performance for Table operations (
select,filter, etc.).
using Pkg
Pkg.add("IndexedTables")
using IndexedTables
t = table((x = 1:100, y = randn(100)))
select(t, :x)
filter(row -> row.y > 0, t)
First let's create some data to work with.
using Dates
city = vcat(fill("New York", 3), fill("Boston", 3))
dates = repeat(Date(2016,7,6):Day(1):Date(2016,7,8), 2)
vals = [91, 89, 91, 95, 83, 76]- (Optionally) Sorted by primary key(s),
pkey. - Data is accessed as a Vector of NamedTuples.
using IndexedTables
julia> t1 = table((city = city, dates = dates, values = vals); pkey = [:city, :dates])
Table with 6 rows, 3 columns:
city dates values
──────────────────────────────
"Boston" 2016-07-06 95
"Boston" 2016-07-07 83
"Boston" 2016-07-08 76
"New York" 2016-07-06 91
"New York" 2016-07-07 89
"New York" 2016-07-08 91
julia> t1[1]
(city = "Boston", dates = 2016-07-06, values = 95)- Sorted by index variables (first argument).
- Data is accessed as an N-dimensional sparse array with arbitrary indexes.
julia> t2 = ndsparse((city=city, dates=dates), (value=vals,))
2-d NDSparse with 6 values (1 field named tuples):
city dates │ value
───────────────────────┼──────
"Boston" 2016-07-06 │ 95
"Boston" 2016-07-07 │ 83
"Boston" 2016-07-08 │ 76
"New York" 2016-07-06 │ 91
"New York" 2016-07-07 │ 89
"New York" 2016-07-08 │ 91
julia> t2["Boston", Date(2016, 7, 6)]
(value = 95)For more information, check out the JuliaDB Documentation.