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IndexedTables.jl

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.

Data Structures

IndexedTables offers two data structures: IndexedTable and NDSparse.

• Both types store data in columns.
• IndexedTable and NDSparse differ mainly in how data is accessed.
• Both types have equal performance for Table operations (select, filter, etc.).

Quickstart

using Pkg
Pkg.add("IndexedTables")
using IndexedTables

t = table((x = 1:100, y = randn(100)))

select(t, :x)

filter(row -> row.y > 0, t)

IndexedTable vs. NDSparse

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]

IndexedTable

• (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> t11

### NDSparse

- 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)

## Get started

For more information, check out the [JuliaDB Documentation](http://juliadb.org/latest/index.html).