charty_new_bar_plot_api.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ":pyplot"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "require 'charty'\n",
    "Charty::Backends::Pyplot.activate_iruby_integration\n",
    "Charty.use_backend(:pyplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{:x=>[\"a\", \"b\", \"c\", \"d\", \"e\"], :y=>[10, 20, 30, 25, 15]}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "  x: [\"a\", \"b\", \"c\", \"d\", \"e\"],\n",
    "  y: [10, 20, 30, 25, 15]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = Charty.bar_plot(:x, :y, data: data)\n",
    "fig.render"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = Charty.bar_plot do |pl|\n",
    "  pl.data = data\n",
    "  pl.x = :x\n",
    "  pl.y = :y\n",
    "end\n",
    "fig.render"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "require 'daru'\n",
    "df = Daru::DataFrame.new({\n",
    "  foo: [\"a\", \"b\", \"c\", \"d\", \"e\"],\n",
    "  bar: [10, 20, 30, 25, 15]\n",
    "})\n",
    "fig = Charty.bar_plot(:foo, :bar, data: df)\n",
    "fig.render"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Ruby 2.6.2",
   "language": "ruby",
   "name": "ruby"
  },
  "language_info": {
   "file_extension": ".rb",
   "mimetype": "application/x-ruby",
   "name": "ruby",
   "version": "2.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}