dataframe: An intuitive, dynamically-typed DataFrame library.
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| Versions [RSS] | 0.1.0.0, 0.1.0.1, 0.1.0.2, 0.1.0.3, 0.2.0.0, 0.2.0.1, 0.2.0.2, 0.3.0.0, 0.3.0.1, 0.3.0.2, 0.3.0.3, 0.3.0.4, 0.3.1.1, 0.3.1.2, 0.3.2.0, 0.3.3.0, 0.3.3.1, 0.3.3.2, 0.3.3.3, 0.3.3.4, 0.3.3.5, 0.3.3.6, 0.3.3.7, 0.3.3.8, 0.3.3.9, 0.3.4.0, 0.3.4.1 |
|---|---|
| Change log | CHANGELOG.md |
| Dependencies | array (>=0.5 && <0.6), attoparsec (>=0.12 && <=0.14.4), base (>=4.17.2.0 && <4.21), bytestring (>=0.11 && <=0.12.2.0), containers (>=0.6.7 && <0.8), directory (>=1.3.0.0 && <=1.3.9.0), hashable (>=1.2 && <=1.5.0.0), statistics (>=0.16.2.1), text (>=2.0 && <=2.1.2), time (>=1.12 && <=1.14), vector (>=0.13 && <0.14), vector-algorithms (>=0.9 && <0.10) [details] |
| Tested with | ghc ==9.8.3 || ==9.6.6 || ==9.4.8 |
| License | GPL-3.0-or-later |
| Copyright | (c) 2024-2024 Michael Chavinda |
| Author | Michael Chavinda |
| Maintainer | [email protected] |
| Category | Data |
| Bug tracker | https://github.com/mchav/dataframe/issues |
| Source repo | head: git clone https://github.com/mchav/dataframe |
| Uploaded | by mchav at 2025-05-05T03:57:56Z |
| Distributions | NixOS:0.3.3.4, Stackage:0.3.4.1 |
| Reverse Dependencies | 3 direct, 0 indirect [details] |
| Executables | dataframe |
| Downloads | 235 total (64 in the last 30 days) |
| Rating | (no votes yet) [estimated by Bayesian average] |
| Your Rating | |
| Status | Docs available [build log] Last success reported on 2025-05-05 [all 1 reports] |
Readme for dataframe-0.1.0.3
[back to package description]DataFrame
An intuitive, dynamically-typed DataFrame library.
A tool for exploratory data analysis.
Installing
- Install Haskell (ghc + cabal) via ghcup selecting all the default options.
- To install dataframe run
cabal update && cabal install dataframe - Open a Haskell repl with dataframe loaded by running
cabal repl --build-depends dataframe. - Follow along any one of the tutorials below.
What is exploratory data analysis?
We provide a primer here and show how to do some common analyses.
Coming from other dataframe libraries
Familiar with another dataframe library? Get started:
Example usage
Code example
import qualified DataFrame as D
import DataFrame ((|>))
main :: IO ()
df <- D.readTsv "./data/chipotle.tsv"
print $ df
|> D.select ["item_name", "quantity"]
|> D.groupBy ["item_name"]
|> D.aggregate (zip (repeat "quantity") [D.Maximum, D.Mean, D.Sum])
|> D.sortBy D.Descending ["Sum_quantity"]
Output:
----------------------------------------------------------------------------------------------------
index | item_name | Sum_quantity | Mean_quantity | Maximum_quantity
------|---------------------------------------|--------------|--------------------|-----------------
Int | Text | Int | Double | Int
------|---------------------------------------|--------------|--------------------|-----------------
0 | Chips and Fresh Tomato Salsa | 130 | 1.1818181818181819 | 15
1 | Izze | 22 | 1.1 | 3
2 | Nantucket Nectar | 31 | 1.1481481481481481 | 3
3 | Chips and Tomatillo-Green Chili Salsa | 35 | 1.1290322580645162 | 3
4 | Chicken Bowl | 761 | 1.0482093663911847 | 3
5 | Side of Chips | 110 | 1.0891089108910892 | 8
6 | Steak Burrito | 386 | 1.048913043478261 | 3
7 | Steak Soft Tacos | 56 | 1.018181818181818 | 2
8 | Chips and Guacamole | 506 | 1.0563674321503131 | 4
9 | Chicken Crispy Tacos | 50 | 1.0638297872340425 | 2
Full example in ./app folder using many of the constructs in the API.
Visual example
Future work
- Apache arrow and Parquet compatability
- Integration with common data formats (currently only supports CSV)
- Support windowed plotting (currently only supports ASCII plots)
- Create a lazy API that builds an execution graph instead of running eagerly (will be used to compute on files larger than RAM)
Contributing
- Please first submit an issue and we can discuss there.