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https://github.com/juliadata/indexedtables.jl
Flexible tables with ordered indices
https://github.com/juliadata/indexedtables.jl
data-analysis data-manipulation indexedtables julia juliadb
Last synced: 5 days ago
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Flexible tables with ordered indices
- Host: GitHub
- URL: https://github.com/juliadata/indexedtables.jl
- Owner: JuliaData
- License: mit
- Created: 2016-03-10T23:09:06.000Z (almost 9 years ago)
- Default Branch: main
- Last Pushed: 2024-08-04T14:56:04.000Z (5 months ago)
- Last Synced: 2024-12-18T01:07:47.448Z (5 days ago)
- Topics: data-analysis, data-manipulation, indexedtables, julia, juliadb
- Language: Julia
- Homepage: https://juliadb.org
- Size: 920 KB
- Stars: 121
- Watchers: 21
- Forks: 37
- Open Issues: 53
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE
Awesome Lists containing this project
README
# IndexedTables.jl
[](https://juliadata.github.io/JuliaDB.jl/stable/)
[](https://juliadata.github.io/JuliaDB.jl/latest/)
[](https://github.com/JuliaData/IndexedTables.jl/actions?query=workflow%3ACI)
[](https://codecov.io/gh/JuliaData/IndexedTables.jl)
[](https://juliahub.com/ui/Packages/IndexedTables/YIs33?t=2)
[](https://juliahub.com/ui/Packages/IndexedTables/YIs33)
**IndexedTables** provides tabular data structures where some of the columns form a sorted index.
It provides the backend to [JuliaDB](https://github.com/JuliaData/JuliaDB.jl), 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.
```julia
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.
```julia
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)
```
### NDSparse
- Sorted by index variables (first argument).
- Data is accessed as an N-dimensional sparse array with arbitrary indexes.
```julia
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)
```
## Get started
For more information, check out the [JuliaDB Documentation](https://juliadata.github.io/JuliaDB.jl/latest/).