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Dplyr (R), Pandas (Python) & Nim data frame comparison

This comparison is inspired by the comparison here: https://gist.github.com/conormm/fd8b1980c28dd21cfaf6975c86c74d07

The Nim data frame implementation we use here is the Datamancer data frame.

Note that due to UFCS in Nim we can write the commands we present either similar to the Python notation as if the function were a method of the

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import std / [strutils, strformat, tables, dynlib, os]
import noise, shell


import compiler/[llstream, renderer, types, magicsys, ast,
                 transf, # for code transformation (for -> while etc)
                 injectdestructors, # destructor injection
                 pathutils, # AbsoluteDir
                 modulegraphs] # getBody
import ./nimeval_dynlib_clean

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import std / [strutils, strformat, tables, dynlib, os]
import noise, shell


import compiler/[llstream, renderer, types, magicsys, ast,
                 transf, # for code transformation (for -> while etc)
                 injectdestructors, # destructor injection
                 pathutils, # AbsoluteDir
                 modulegraphs] # getBody
import ./nimeval_dynlib

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import noise, strutils, strformat, shell, tables, dynlib

proc printHelp() = echo ""

const procTmpl = """
{.push cdecl, exportc, dynlib.}
$#
{.pop.}
"""
const exprTmpl = """

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import datamancer
import std / [math, complex]

const xn = 960
const yn = 960
const xmin = -2.0
const xmax = 0.6
const ymin = -1.5
const ymax = 1.5
const MAX_ITERS = 200

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{"type":"Feature","geometry":{"type":"Point","coordinates":[18.9276,69.69,100]},"properties":{"meta":{"updated_at":"2022-12-22T14:42:02Z","units":{"air_pressure_at_sea_level":"hPa","air_temperature":"celsius","cloud_area_fraction":"%","precipitation_amount":"mm","relative_humidity":"%","wind_from_direction":"degrees","wind_speed":"m/s"}},"timeseries":[{"time":"2022-12-22T14:00:00Z","data":{"instant":{"details":{"air_pressure_at_sea_level":986.2,"air_temperature":-2.1,"cloud_area_fraction":86.7,"relative_humidity":81.6,"wind_from_direction":184.4,"wind_speed":6.1}},"next_12_hours":{"summary":{"symbol_code":"lightsnowshowers_day"}},"next_1_hours":{"summary":{"symbol_code":"lightsnow"},"details":{"precipitation_amount":0.2}},"next_6_hours":{"summary":{"symbol_code":"snow"},"details":{"precipitation_amount":2.2}}}},{"time":"2022-12-22T15:00:00Z","data":{"instant":{"details":{"air_pressure_at_sea_level":985.9,"air_temperature":-1.9,"cloud_area_fraction":84.9,"relative_humidity":80.7,"wind_from_direction":187.0,"wi

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import ggplotnim, math
import arraymancer

const ε = 3
proc φ(r: float): float =
  result = exp(-pow((ε.float * r), 2.0))

proc toMatrix(n: int, start, stop: float): Tensor[float] =
  result = zeros[float]([n, n])
  let xs = linspace(start, stop, n)

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#[
This code solves the unit conversion from system A to system B generically.
If natural units are involed, the remaining units (e.g. Energy in particle physics
natural units) are combined with the constants set to 1 that replace real units.

It solves the following system of linear equations which arises from:

`Π_i,α A_i^α = Π_i^β B_i^β`

where `A_i` is the i-th unit of unit system `A` and `α` the power needed to raise

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Calling Nim from R

A super short introduction how to call Nim code from R using the .C interface. I’m not an R user normally, so I googled and used this post as a reference: https://www.r-bloggers.com/three-ways-to-call-cc-from-r/

Writing our Nim procedure

Let’s define a simple procedure, which we want Nim to do:

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# Note by @Vindaar: this is just a modified version from here:
# https://forum.nim-lang.org/t/7102
# that uses ggplotnim

# Generate random pairs of non-equal numbers and test the routine's distribution
# License: MIT
# Usage:
# ./randpairs [1-6;c]
import os, random, hashes, tables, strutils, stats, ggplotnim