DataFusion Python API: https://arrow.apache.org/datafusion-python/
The main purpose of this repo is to see if the savvy framework has enough features for mildly complex usages. Especially,
- to check if savvy can convert an external pointer or ALTREP that an other package defines. e.g. https://arrow.apache.org/datafusion-python/
- to figure out what async support is supposed to be e.g. <https://github.com/yutannihilation/savvy-async-poc?
library(datafusionr)
x <- arrow::as_record_batch(mtcars)
# TODO: currently, this relies on nanoarrow to export the data as Arrow C Stream interface
x <- nanoarrow::as_nanoarrow_array_stream(x)
ctx <- session_context()
df <- ctx$create_data_frame(x, table_name = "mtcars")
df$print()
#> DataFrame()
#> +------+-----+-------+-------+------+-------+-------+-----+-----+------+------+
#> | mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb |
#> +------+-----+-------+-------+------+-------+-------+-----+-----+------+------+
#> | 21.0 | 6.0 | 160.0 | 110.0 | 3.9 | 2.62 | 16.46 | 0.0 | 1.0 | 4.0 | 4.0 |
#> | 21.0 | 6.0 | 160.0 | 110.0 | 3.9 | 2.875 | 17.02 | 0.0 | 1.0 | 4.0 | 4.0 |
#> | 22.8 | 4.0 | 108.0 | 93.0 | 3.85 | 2.32 | 18.61 | 1.0 | 1.0 | 4.0 | 1.0 |
#> | 21.4 | 6.0 | 258.0 | 110.0 | 3.08 | 3.215 | 19.44 | 1.0 | 0.0 | 3.0 | 1.0 |
#> | 18.7 | 8.0 | 360.0 | 175.0 | 3.15 | 3.44 | 17.02 | 0.0 | 0.0 | 3.0 | 2.0 |
#> | 18.1 | 6.0 | 225.0 | 105.0 | 2.76 | 3.46 | 20.22 | 1.0 | 0.0 | 3.0 | 1.0 |
#> | 14.3 | 8.0 | 360.0 | 245.0 | 3.21 | 3.57 | 15.84 | 0.0 | 0.0 | 3.0 | 4.0 |
#> | 24.4 | 4.0 | 146.7 | 62.0 | 3.69 | 3.19 | 20.0 | 1.0 | 0.0 | 4.0 | 2.0 |
#> | 22.8 | 4.0 | 140.8 | 95.0 | 3.92 | 3.15 | 22.9 | 1.0 | 0.0 | 4.0 | 2.0 |
#> | 19.2 | 6.0 | 167.6 | 123.0 | 3.92 | 3.44 | 18.3 | 1.0 | 0.0 | 4.0 | 4.0 |
#> | 17.8 | 6.0 | 167.6 | 123.0 | 3.92 | 3.44 | 18.9 | 1.0 | 0.0 | 4.0 | 4.0 |
#> | 16.4 | 8.0 | 275.8 | 180.0 | 3.07 | 4.07 | 17.4 | 0.0 | 0.0 | 3.0 | 3.0 |
#> | 17.3 | 8.0 | 275.8 | 180.0 | 3.07 | 3.73 | 17.6 | 0.0 | 0.0 | 3.0 | 3.0 |
#> | 15.2 | 8.0 | 275.8 | 180.0 | 3.07 | 3.78 | 18.0 | 0.0 | 0.0 | 3.0 | 3.0 |
#> | 10.4 | 8.0 | 472.0 | 205.0 | 2.93 | 5.25 | 17.98 | 0.0 | 0.0 | 3.0 | 4.0 |
#> | 10.4 | 8.0 | 460.0 | 215.0 | 3.0 | 5.424 | 17.82 | 0.0 | 0.0 | 3.0 | 4.0 |
#> | 14.7 | 8.0 | 440.0 | 230.0 | 3.23 | 5.345 | 17.42 | 0.0 | 0.0 | 3.0 | 4.0 |
#> | 32.4 | 4.0 | 78.7 | 66.0 | 4.08 | 2.2 | 19.47 | 1.0 | 1.0 | 4.0 | 1.0 |
#> | 30.4 | 4.0 | 75.7 | 52.0 | 4.93 | 1.615 | 18.52 | 1.0 | 1.0 | 4.0 | 2.0 |
#> | 33.9 | 4.0 | 71.1 | 65.0 | 4.22 | 1.835 | 19.9 | 1.0 | 1.0 | 4.0 | 1.0 |
#> | 21.5 | 4.0 | 120.1 | 97.0 | 3.7 | 2.465 | 20.01 | 1.0 | 0.0 | 3.0 | 1.0 |
#> | 15.5 | 8.0 | 318.0 | 150.0 | 2.76 | 3.52 | 16.87 | 0.0 | 0.0 | 3.0 | 2.0 |
#> | 15.2 | 8.0 | 304.0 | 150.0 | 3.15 | 3.435 | 17.3 | 0.0 | 0.0 | 3.0 | 2.0 |
#> | 13.3 | 8.0 | 350.0 | 245.0 | 3.73 | 3.84 | 15.41 | 0.0 | 0.0 | 3.0 | 4.0 |
#> | 19.2 | 8.0 | 400.0 | 175.0 | 3.08 | 3.845 | 17.05 | 0.0 | 0.0 | 3.0 | 2.0 |
#> | 27.3 | 4.0 | 79.0 | 66.0 | 4.08 | 1.935 | 18.9 | 1.0 | 1.0 | 4.0 | 1.0 |
#> | 26.0 | 4.0 | 120.3 | 91.0 | 4.43 | 2.14 | 16.7 | 0.0 | 1.0 | 5.0 | 2.0 |
#> | 30.4 | 4.0 | 95.1 | 113.0 | 3.77 | 1.513 | 16.9 | 1.0 | 1.0 | 5.0 | 2.0 |
#> | 15.8 | 8.0 | 351.0 | 264.0 | 4.22 | 3.17 | 14.5 | 0.0 | 1.0 | 5.0 | 4.0 |
#> | 19.7 | 6.0 | 145.0 | 175.0 | 3.62 | 2.77 | 15.5 | 0.0 | 1.0 | 5.0 | 6.0 |
#> | 15.0 | 8.0 | 301.0 | 335.0 | 3.54 | 3.57 | 14.6 | 0.0 | 1.0 | 5.0 | 8.0 |
#> | 21.4 | 4.0 | 121.0 | 109.0 | 4.11 | 2.78 | 18.6 | 1.0 | 1.0 | 4.0 | 2.0 |
#> +------+-----+-------+-------+------+-------+-------+-----+-----+------+------+