1-iris-data-analysis-rust.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f4e8c1d9-c986-44cf-96cf-52328f5af66d",
   "metadata": {},
   "source": [
    "## Google Colab Rust Setup\n",
    "\n",
    "The following cell is used to set up a Rust environment on Colab. Don't execute it locally!\n",
    "\n",
    "Many thanks to [`mateusvmv`](https://github.com/mateusvmv) for this hack in [`gist.github.com/korakot/ae95315ea6a3a3b33ee26203998a59a3`](https://gist.github.com/korakot/ae95315ea6a3a3b33ee26203998a59a3?permalink_comment_id=4715636#gistcomment-4715636)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbb265f4-a05b-4e51-8320-adf9b83a78fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This script sets up and spins up a Jupyter Notebook environment with a Rust kernel using Nix and IPC Proxy. \n",
    "!wget -qO- https://gist.github.com/wiseaidev/2af6bef753d48565d11bcd478728c979/archive/3f6df40db09f3517ade41997b541b81f0976c12e.tar.gz | tar xvz --strip-components=1\n",
    "!bash setup_evcxr_kernel.sh"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "856eb37e-5c28-41eb-b8c6-f7ef62e9edb7",
   "metadata": {},
   "source": [
    "## Available Commands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a247bf97-5f02-43d4-aaff-5e478dcc597a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr><td>:allow_static_linking</td><td>Set whether to allow static linking of dependencies (0/1)</td></tr>\n",
       "<tr><td>:build_env</td><td>Set environment variables when building code (key=value)</td></tr>\n",
       "<tr><td>:cache</td><td>Set cache size in MiB, or 0 to disable.</td></tr>\n",
       "<tr><td>:clear</td><td>Clear all state, keeping compilation cache</td></tr>\n",
       "<tr><td>:clear_cache</td><td>Clear the cache used by the :cache command</td></tr>\n",
       "<tr><td>:codegen_backend</td><td>Set/print the codegen backend. Requires nightly</td></tr>\n",
       "<tr><td>:dep</td><td>Add dependency. e.g. :dep regex = \"1.0\"</td></tr>\n",
       "<tr><td>:doc</td><td>show the documentation of a variable, keyword, type or module</td></tr>\n",
       "<tr><td>:efmt</td><td>Set the formatter for errors returned by ?</td></tr>\n",
       "<tr><td>:env</td><td>Set an environment variable (key=value)</td></tr>\n",
       "<tr><td>:explain</td><td>Print explanation of last error</td></tr>\n",
       "<tr><td>:fmt</td><td>Set output formatter (default: {:?})</td></tr>\n",
       "<tr><td>:help</td><td>Print command help</td></tr>\n",
       "<tr><td>:internal_debug</td><td>Toggle various internal debugging code</td></tr>\n",
       "<tr><td>:last_compile_dir</td><td>Print the directory in which we last compiled</td></tr>\n",
       "<tr><td>:last_error_json</td><td>Print the last compilation error as JSON (for debugging)</td></tr>\n",
       "<tr><td>:linker</td><td>Set/print linker. Supported: system, lld, mold</td></tr>\n",
       "<tr><td>:load_config</td><td>Reloads startup configuration files. Accepts optional flag `--quiet` to suppress logging.</td></tr>\n",
       "<tr><td>:offline</td><td>Set offline mode when invoking cargo (0/1)</td></tr>\n",
       "<tr><td>:opt</td><td>Set optimization level (0/1/2)</td></tr>\n",
       "<tr><td>:preserve_vars_on_panic</td><td>Try to keep vars on panic (0/1)</td></tr>\n",
       "<tr><td>:quit</td><td>Quit evaluation and exit</td></tr>\n",
       "<tr><td>:restart</td><td>Restart child process</td></tr>\n",
       "<tr><td>:sccache</td><td>Set whether to use sccache (0/1).</td></tr>\n",
       "<tr><td>:show_deps</td><td>Show the current dependencies</td></tr>\n",
       "<tr><td>:t</td><td>Short version of :type</td></tr>\n",
       "<tr><td>:time_passes</td><td>Toggle printing of rustc pass times (requires nightly)</td></tr>\n",
       "<tr><td>:timing</td><td>Toggle printing of how long evaluations take</td></tr>\n",
       "<tr><td>:toolchain</td><td>Set which toolchain to use (e.g. nightly)</td></tr>\n",
       "<tr><td>:type</td><td>Show variable type</td></tr>\n",
       "<tr><td>:types</td><td>Toggle printing of types</td></tr>\n",
       "<tr><td>:vars</td><td>List bound variables and their types</td></tr>\n",
       "<tr><td>:version</td><td>Print Evcxr version</td></tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       ":allow_static_linking Set whether to allow static linking of dependencies (0/1)\n",
       ":build_env        Set environment variables when building code (key=value)\n",
       ":cache            Set cache size in MiB, or 0 to disable.\n",
       ":clear            Clear all state, keeping compilation cache\n",
       ":clear_cache      Clear the cache used by the :cache command\n",
       ":codegen_backend  Set/print the codegen backend. Requires nightly\n",
       ":dep              Add dependency. e.g. :dep regex = \"1.0\"\n",
       ":doc              show the documentation of a variable, keyword, type or module\n",
       ":efmt             Set the formatter for errors returned by ?\n",
       ":env              Set an environment variable (key=value)\n",
       ":explain          Print explanation of last error\n",
       ":fmt              Set output formatter (default: {:?})\n",
       ":help             Print command help\n",
       ":internal_debug   Toggle various internal debugging code\n",
       ":last_compile_dir Print the directory in which we last compiled\n",
       ":last_error_json  Print the last compilation error as JSON (for debugging)\n",
       ":linker           Set/print linker. Supported: system, lld, mold\n",
       ":load_config      Reloads startup configuration files. Accepts optional flag `--quiet` to suppress logging.\n",
       ":offline          Set offline mode when invoking cargo (0/1)\n",
       ":opt              Set optimization level (0/1/2)\n",
       ":preserve_vars_on_panic Try to keep vars on panic (0/1)\n",
       ":quit             Quit evaluation and exit\n",
       ":restart          Restart child process\n",
       ":sccache          Set whether to use sccache (0/1).\n",
       ":show_deps        Show the current dependencies\n",
       ":t                Short version of :type\n",
       ":time_passes      Toggle printing of rustc pass times (requires nightly)\n",
       ":timing           Toggle printing of how long evaluations take\n",
       ":toolchain        Set which toolchain to use (e.g. nightly)\n",
       ":type             Show variable type\n",
       ":types            Toggle printing of types\n",
       ":vars             List bound variables and their types\n",
       ":version          Print Evcxr version\n"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ":help"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c03eb46d-3ddf-44fa-9ae0-16db877b01c7",
   "metadata": {},
   "source": [
    "## Set Toolchain cause of Polars `static_assertions` dep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb306673-d888-463a-be37-c608efe48f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Toolchain: nightly\n"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ":toolchain nightly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d075a4a0-c8e2-4331-a40c-62e5ca1a1a10",
   "metadata": {},
   "source": [
    "## Enable Cache (20000 MB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bbaba2ad-db6a-4746-91f5-ef8f91a09832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Deleted 403 MiB from cache\n"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ":clear_cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72dd690c-7199-4166-88b6-4372906824fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cache: 20000 MiB\n"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ":cache 20000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc8e31fd",
   "metadata": {},
   "source": [
    "## Install Required Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "05fa3594",
   "metadata": {},
   "outputs": [],
   "source": [
    ":dep plotters = { version = \"^0.3.6\", default_features = false, features = [\"evcxr\", \"all_series\", \"all_elements\"] }\n",
    "// or\n",
    "// :dep plotters = { git = \"https://github.com/plotters-rs/plotters\"}\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a09fc16e-7590-468b-9178-de8c9e6f7d03",
   "metadata": {},
   "outputs": [],
   "source": [
    ":dep polars = {version = \"0.41.3\", default-features = false, features = [\"describe\", \"lazy\", \"ndarray\", \"polars-io\", \"fmt\"]}\n",
    "// or\n",
    "// :dep polars = { git = \"https://github.com/pola-rs/polars\"}\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f567d472-f706-4623-8ea4-d471e0e1ee3d",
   "metadata": {},
   "outputs": [],
   "source": [
    ":dep ndarray = {version = \"0.15.6\"}\n",
    "// or\n",
    "// :dep ndarray = { git = \"https://github.com/rust-ndarray/ndarray\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "733b4b69-2815-4346-b9a2-e7b5aeaea3b5",
   "metadata": {},
   "outputs": [],
   "source": [
    ":dep smartcore = {version = \"0.3.2\"}\n",
    "// or\n",
    "// :dep smartcore = { git = \"https://github.com/smartcorelib/smartcore\"}\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49178047-1ca8-445f-98f4-7ce02d1bfeb5",
   "metadata": {},
   "source": [
    "## Show Installed Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5f51099a-6475-4368-bb7d-13cb371f468f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ndarray = {version = \"0.15.6\"}\n",
       "plotters = { version = \"^0.3.6\", default_features = false, features = [\"evcxr\", \"all_series\", \"all_elements\"] }\n",
       "polars = {version = \"0.41.3\", default-features = false, features = [\"describe\", \"lazy\", \"ndarray\", \"polars-io\", \"fmt\"]}\n",
       "smartcore = {version = \"0.3.2\"}\n"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ":show_deps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d74e942",
   "metadata": {},
   "source": [
    "## Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "140ecc92",
   "metadata": {},
   "outputs": [],
   "source": [
    "use polars::prelude::*;\n",
    "use polars::frame::DataFrame;\n",
    "use std::path::Path;\n",
    "use ndarray::{ArrayBase, DataMut, Dimension, concatenate, Axis};\n",
    "use plotters::prelude::*;\n",
    "use smartcore::linalg::basic::matrix::DenseMatrix;\n",
    "use ndarray::prelude::*;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf1f06a-d9a0-4d85-b8e9-33d2ca40ba53",
   "metadata": {},
   "source": [
    "### Display Documentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48da5ae7-de73-41dc-9d35-68a36b97597e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre><code class=\"language-rust\">polars_core::frame\n",
       "</code></pre>\n",
       "<pre><code class=\"language-rust\">pub struct DataFrame {\n",
       "    pub(crate) columns: Vec&lt;Series, Global&gt;,\n",
       "}\n",
       "</code></pre>\n",
       "<hr />\n",
       "<p>A contiguous growable collection of <code>Series</code> that have the same length.</p>\n",
       "<h2>Use declarations</h2>\n",
       "<p>All the common tools can be found in <a href=\"https://docs.rs/polars-core/0.41.3/polars_core/prelude/index.html\"><code>crate::prelude</code></a> (or in <code>polars::prelude</code>).</p>\n",
       "<pre><code class=\"language-rust\">use polars_core::prelude::*; // if the crate polars-core is used directly\n",
       "// use polars::prelude::*;      if the crate polars is used\n",
       "</code></pre>\n",
       "<h1>Initialization</h1>\n",
       "<h2>Default</h2>\n",
       "<p>A <code>DataFrame</code> can be initialized empty:</p>\n",
       "<pre><code class=\"language-rust\"># use polars_core::prelude::*;\n",
       "let df = DataFrame::default();\n",
       "assert!(df.is_empty());\n",
       "</code></pre>\n",
       "<h2>Wrapping a <code>Vec&lt;Series&gt;</code></h2>\n",
       "<p>A <code>DataFrame</code> is built upon a <code>Vec&lt;Series&gt;</code> where the <code>Series</code> have the same length.</p>\n",
       "<pre><code class=\"language-rust\"># use polars_core::prelude::*;\n",
       "let s1 = Series::new(&quot;Fruit&quot;, &amp;[&quot;Apple&quot;, &quot;Apple&quot;, &quot;Pear&quot;]);\n",
       "let s2 = Series::new(&quot;Color&quot;, &amp;[&quot;Red&quot;, &quot;Yellow&quot;, &quot;Green&quot;]);\n",
       "\n",
       "let df: PolarsResult&lt;DataFrame&gt; = DataFrame::new(vec![s1, s2]);\n",
       "</code></pre>\n",
       "<h2>Using a macro</h2>\n",
       "<p>The <a href=\"https://docs.rs/polars-core/0.41.3/polars_core/utils/macro.df.html\"><code>df</code></a> macro is a convenient method:</p>\n",
       "<pre><code class=\"language-rust\"># use polars_core::prelude::*;\n",
       "let df: PolarsResult&lt;DataFrame&gt; = df!(&quot;Fruit&quot; =&gt; &amp;[&quot;Apple&quot;, &quot;Apple&quot;, &quot;Pear&quot;],\n",
       "                                      &quot;Color&quot; =&gt; &amp;[&quot;Red&quot;, &quot;Yellow&quot;, &quot;Green&quot;]);\n",
       "</code></pre>\n",
       "<h2>Using a CSV file</h2>\n",
       "<p>See the <code>polars_io::csv::CsvReader</code>.</p>\n",
       "<h1>Indexing</h1>\n",
       "<h2>By a number</h2>\n",
       "<p>The <code>Index&lt;usize&gt;</code> is implemented for the <code>DataFrame</code>.</p>\n",
       "<pre><code class=\"language-rust\"># use polars_core::prelude::*;\n",
       "let df = df!(&quot;Fruit&quot; =&gt; &amp;[&quot;Apple&quot;, &quot;Apple&quot;, &quot;Pear&quot;],\n",
       "             &quot;Color&quot; =&gt; &amp;[&quot;Red&quot;, &quot;Yellow&quot;, &quot;Green&quot;])?;\n",
       "\n",
       "assert_eq!(df[0], Series::new(&quot;Fruit&quot;, &amp;[&quot;Apple&quot;, &quot;Apple&quot;, &quot;Pear&quot;]));\n",
       "assert_eq!(df[1], Series::new(&quot;Color&quot;, &amp;[&quot;Red&quot;, &quot;Yellow&quot;, &quot;Green&quot;]));\n",
       "# Ok::&lt;(), PolarsError&gt;(())\n",
       "</code></pre>\n",
       "<h2>By a <code>Series</code> name</h2>\n",
       "<pre><code class=\"language-rust\"># use polars_core::prelude::*;\n",
       "let df = df!(&quot;Fruit&quot; =&gt; &amp;[&quot;Apple&quot;, &quot;Apple&quot;, &quot;Pear&quot;],\n",
       "             &quot;Color&quot; =&gt; &amp;[&quot;Red&quot;, &quot;Yellow&quot;, &quot;Green&quot;])?;\n",
       "\n",
       "assert_eq!(df[&quot;Fruit&quot;], Series::new(&quot;Fruit&quot;, &amp;[&quot;Apple&quot;, &quot;Apple&quot;, &quot;Pear&quot;]));\n",
       "assert_eq!(df[&quot;Color&quot;], Series::new(&quot;Color&quot;, &amp;[&quot;Red&quot;, &quot;Yellow&quot;, &quot;Green&quot;]));\n",
       "# Ok::&lt;(), PolarsError&gt;(())\n",
       "</code></pre>\n"
      ],
      "text/plain": [
       "polars_core::frame\n",
       "\n",
       "pub struct DataFrame {\n",
       "    pub(crate) columns: Vec<Series, Global>,\n",
       "}\n",
       "\n",
       "\n",
       "A contiguous growable collection of Series that have the same length.\n",
       "\n",
       "Use declarationsAll the common tools can be found in crate::prelude (or in polars::prelude).\n",
       "\n",
       "use polars_core::prelude::*; // if the crate polars-core is used directly\n",
       "// use polars::prelude::*;      if the crate polars is used\n",
       "\n",
       "InitializationDefaultA DataFrame can be initialized empty:\n",
       "\n",
       "# use polars_core::prelude::*;\n",
       "let df = DataFrame::default();\n",
       "assert!(df.is_empty());\n",
       "\n",
       "Wrapping a Vec<Series>A DataFrame is built upon a Vec<Series> where the Series have the same length.\n",
       "\n",
       "# use polars_core::prelude::*;\n",
       "let s1 = Series::new(\"Fruit\", &[\"Apple\", \"Apple\", \"Pear\"]);\n",
       "let s2 = Series::new(\"Color\", &[\"Red\", \"Yellow\", \"Green\"]);\n",
       "\n",
       "let df: PolarsResult<DataFrame> = DataFrame::new(vec![s1, s2]);\n",
       "\n",
       "Using a macroThe df macro is a convenient method:\n",
       "\n",
       "# use polars_core::prelude::*;\n",
       "let df: PolarsResult<DataFrame> = df!(\"Fruit\" => &[\"Apple\", \"Apple\", \"Pear\"],\n",
       "                                      \"Color\" => &[\"Red\", \"Yellow\", \"Green\"]);\n",
       "\n",
       "Using a CSV fileSee the polars_io::csv::CsvReader.\n",
       "\n",
       "IndexingBy a numberThe Index<usize> is implemented for the DataFrame.\n",
       "\n",
       "# use polars_core::prelude::*;\n",
       "let df = df!(\"Fruit\" => &[\"Apple\", \"Apple\", \"Pear\"],\n",
       "             \"Color\" => &[\"Red\", \"Yellow\", \"Green\"])?;\n",
       "\n",
       "assert_eq!(df[0], Series::new(\"Fruit\", &[\"Apple\", \"Apple\", \"Pear\"]));\n",
       "assert_eq!(df[1], Series::new(\"Color\", &[\"Red\", \"Yellow\", \"Green\"]));\n",
       "# Ok::<(), PolarsError>(())\n",
       "\n",
       "By a Series name# use polars_core::prelude::*;\n",
       "let df = df!(\"Fruit\" => &[\"Apple\", \"Apple\", \"Pear\"],\n",
       "             \"Color\" => &[\"Red\", \"Yellow\", \"Green\"])?;\n",
       "\n",
       "assert_eq!(df[\"Fruit\"], Series::new(\"Fruit\", &[\"Apple\", \"Apple\", \"Pear\"]));\n",
       "assert_eq!(df[\"Color\"], Series::new(\"Color\", &[\"Red\", \"Yellow\", \"Green\"]));\n",
       "# Ok::<(), PolarsError>(())"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ":doc DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9bfcd32",
   "metadata": {},
   "source": [
    "\n",
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0bf4c133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shape: (150, 6)\n",
       "┌─────┬───────────────┬──────────────┬───────────────┬──────────────┬────────────────┐\n",
       "│ Id  ┆ SepalLengthCm ┆ SepalWidthCm ┆ PetalLengthCm ┆ PetalWidthCm ┆ Species        │\n",
       "│ --- ┆ ---           ┆ ---          ┆ ---           ┆ ---          ┆ ---            │\n",
       "│ i64 ┆ f64           ┆ f64          ┆ f64           ┆ f64          ┆ str            │\n",
       "╞═════╪═══════════════╪══════════════╪═══════════════╪══════════════╪════════════════╡\n",
       "│ 1   ┆ 5.1           ┆ 3.5          ┆ 1.4           ┆ 0.2          ┆ Iris-setosa    │\n",
       "│ 2   ┆ 4.9           ┆ 3.0          ┆ 1.4           ┆ 0.2          ┆ Iris-setosa    │\n",
       "│ 3   ┆ 4.7           ┆ 3.2          ┆ 1.3           ┆ 0.2          ┆ Iris-setosa    │\n",
       "│ 4   ┆ 4.6           ┆ 3.1          ┆ 1.5           ┆ 0.2          ┆ Iris-setosa    │\n",
       "│ 5   ┆ 5.0           ┆ 3.6          ┆ 1.4           ┆ 0.2          ┆ Iris-setosa    │\n",
       "│ …   ┆ …             ┆ …            ┆ …             ┆ …            ┆ …              │\n",
       "│ 146 ┆ 6.7           ┆ 3.0          ┆ 5.2           ┆ 2.3          ┆ Iris-virginica │\n",
       "│ 147 ┆ 6.3           ┆ 2.5          ┆ 5.0           ┆ 1.9          ┆ Iris-virginica │\n",
       "│ 148 ┆ 6.5           ┆ 3.0          ┆ 5.2           ┆ 2.0          ┆ Iris-virginica │\n",
       "│ 149 ┆ 6.2           ┆ 3.4          ┆ 5.4           ┆ 2.3          ┆ Iris-virginica │\n",
       "│ 150 ┆ 5.9           ┆ 3.0          ┆ 5.1           ┆ 1.8          ┆ Iris-virginica │\n",
       "└─────┴───────────────┴──────────────┴───────────────┴──────────────┴────────────────┘"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn read_data_frame_from_csv(\n",
    "    csv_file_path: &Path,\n",
    ") -> DataFrame {\n",
    "    CsvReadOptions::default()\n",
    "        .with_has_header(true)\n",
    "        .try_into_reader_with_file_path(Some(csv_file_path.to_path_buf())).unwrap()\n",
    "        .finish()\n",
    "        .unwrap()\n",
    "}\n",
    "\n",
    "let iris_file_path: &Path = Path::new(\"dataset/Iris.csv\");\n",
    "let iris_df: DataFrame = read_data_frame_from_csv(iris_file_path);\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "444bd562-1594-4861-9688-639082aa3079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "iris_df: DataFrame"
      ],
      "text/plain": [
       "iris_df: DataFrame"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ":type iris_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a92b8ae8-d177-452a-9e99-b594162b01a5",
   "metadata": {},
   "source": [
    "## Convert To LazyFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8e0ecfb9-08a3-4cc6-9e28-66ff1860eaa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "// This function will consume `iris_df`\n",
    "let iris_df_lazy = iris_df.lazy();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5d567742-ec6c-452d-a345-805918be9aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ok(\"DF [\\\"Id\\\", \\\"SepalLengthCm\\\", \\\"SepalWidthCm\\\", \\\"PetalLengthCm\\\"]; PROJECT */6 COLUMNS; SELECTION: None\")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df_lazy.describe_plan()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "80a41af4-4c65-47d6-bee8-8def9d275d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ok(\"DF [\\\"Id\\\", \\\"SepalLengthCm\\\", \\\"SepalWidthCm\\\", \\\"PetalLengthCm\\\"]; PROJECT */6 COLUMNS; SELECTION: None\")"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df_lazy.explain(true)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861a1871",
   "metadata": {},
   "source": [
    "## About The Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9914bb3e",
   "metadata": {},
   "source": [
    "### Shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d6f0eb1-85bf-4627-98ea-2b1c615bce64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shape: (150, 6)\n",
       "┌─────┬───────────────┬──────────────┬───────────────┬──────────────┬────────────────┐\n",
       "│ Id  ┆ SepalLengthCm ┆ SepalWidthCm ┆ PetalLengthCm ┆ PetalWidthCm ┆ Species        │\n",
       "│ --- ┆ ---           ┆ ---          ┆ ---           ┆ ---          ┆ ---            │\n",
       "│ i64 ┆ f64           ┆ f64          ┆ f64           ┆ f64          ┆ str            │\n",
       "╞═════╪═══════════════╪══════════════╪═══════════════╪══════════════╪════════════════╡\n",
       "│ 1   ┆ 5.1           ┆ 3.5          ┆ 1.4           ┆ 0.2          ┆ Iris-setosa    │\n",
       "│ 2   ┆ 4.9           ┆ 3.0          ┆ 1.4           ┆ 0.2          ┆ Iris-setosa    │\n",
       "│ 3   ┆ 4.7           ┆ 3.2          ┆ 1.3           ┆ 0.2          ┆ Iris-setosa    │\n",
       "│ 4   ┆ 4.6           ┆ 3.1          ┆ 1.5           ┆ 0.2          ┆ Iris-setosa    │\n",
       "│ 5   ┆ 5.0           ┆ 3.6          ┆ 1.4           ┆ 0.2          ┆ Iris-setosa    │\n",
       "│ …   ┆ …             ┆ …            ┆ …             ┆ …            ┆ …              │\n",
       "│ 146 ┆ 6.7           ┆ 3.0          ┆ 5.2           ┆ 2.3          ┆ Iris-virginica │\n",
       "│ 147 ┆ 6.3           ┆ 2.5          ┆ 5.0           ┆ 1.9          ┆ Iris-virginica │\n",
       "│ 148 ┆ 6.5           ┆ 3.0          ┆ 5.2           ┆ 2.0          ┆ Iris-virginica │\n",
       "│ 149 ┆ 6.2           ┆ 3.4          ┆ 5.4           ┆ 2.3          ┆ Iris-virginica │\n",
       "│ 150 ┆ 5.9           ┆ 3.0          ┆ 5.1           ┆ 1.8          ┆ Iris-virginica │\n",
       "└─────┴───────────────┴──────────────┴───────────────┴──────────────┴────────────────┘"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "let iris_df: DataFrame = read_data_frame_from_csv(iris_file_path);\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d8879960",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.shape()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c087622a",
   "metadata": {},
   "source": [
    "### Head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7955f71c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shape: (5, 6)\n",
       "┌─────┬───────────────┬──────────────┬───────────────┬──────────────┬─────────────┐\n",
       "│ Id  ┆ SepalLengthCm ┆ SepalWidthCm ┆ PetalLengthCm ┆ PetalWidthCm ┆ Species     │\n",
       "│ --- ┆ ---           ┆ ---          ┆ ---           ┆ ---          ┆ ---         │\n",
       "│ i64 ┆ f64           ┆ f64          ┆ f64           ┆ f64          ┆ str         │\n",
       "╞═════╪═══════════════╪══════════════╪═══════════════╪══════════════╪═════════════╡\n",
       "│ 1   ┆ 5.1           ┆ 3.5          ┆ 1.4           ┆ 0.2          ┆ Iris-setosa │\n",
       "│ 2   ┆ 4.9           ┆ 3.0          ┆ 1.4           ┆ 0.2          ┆ Iris-setosa │\n",
       "│ 3   ┆ 4.7           ┆ 3.2          ┆ 1.3           ┆ 0.2          ┆ Iris-setosa │\n",
       "│ 4   ┆ 4.6           ┆ 3.1          ┆ 1.5           ┆ 0.2          ┆ Iris-setosa │\n",
       "│ 5   ┆ 5.0           ┆ 3.6          ┆ 1.4           ┆ 0.2          ┆ Iris-setosa │\n",
       "└─────┴───────────────┴──────────────┴───────────────┴──────────────┴─────────────┘"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head(Some(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "456622d0",
   "metadata": {},
   "source": [
    "### Tail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2ee10ba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shape: (5, 6)\n",
       "┌─────┬───────────────┬──────────────┬───────────────┬──────────────┬────────────────┐\n",
       "│ Id  ┆ SepalLengthCm ┆ SepalWidthCm ┆ PetalLengthCm ┆ PetalWidthCm ┆ Species        │\n",
       "│ --- ┆ ---           ┆ ---          ┆ ---           ┆ ---          ┆ ---            │\n",
       "│ i64 ┆ f64           ┆ f64          ┆ f64           ┆ f64          ┆ str            │\n",
       "╞═════╪═══════════════╪══════════════╪═══════════════╪══════════════╪════════════════╡\n",
       "│ 146 ┆ 6.7           ┆ 3.0          ┆ 5.2           ┆ 2.3          ┆ Iris-virginica │\n",
       "│ 147 ┆ 6.3           ┆ 2.5          ┆ 5.0           ┆ 1.9          ┆ Iris-virginica │\n",
       "│ 148 ┆ 6.5           ┆ 3.0          ┆ 5.2           ┆ 2.0          ┆ Iris-virginica │\n",
       "│ 149 ┆ 6.2           ┆ 3.4          ┆ 5.4           ┆ 2.3          ┆ Iris-virginica │\n",
       "│ 150 ┆ 5.9           ┆ 3.0          ┆ 5.1           ┆ 1.8          ┆ Iris-virginica │\n",
       "└─────┴───────────────┴──────────────┴───────────────┴──────────────┴────────────────┘"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.tail(Some(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17825991",
   "metadata": {},
   "source": [
    "### Schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "49093475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Schema:\n",
       "name: Id, data type: Int64\n",
       "name: SepalLengthCm, data type: Float64\n",
       "name: SepalWidthCm, data type: Float64\n",
       "name: PetalLengthCm, data type: Float64\n",
       "name: PetalWidthCm, data type: Float64\n",
       "name: Species, data type: String\n"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.schema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11450a74-c2c2-4d4f-b554-8d83ddc984f5",
   "metadata": {},
   "source": [
    "### Describe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11b16e4-dcef-4f98-9dc7-310a9bbbcb2b",
   "metadata": {},
   "source": [
    "What happened to the `describe` function, ma G?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7ceed7e3-d007-43ae-af56-709efe14f086",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ok(\"DF [\\\"Id\\\", \\\"SepalLengthCm\\\", \\\"SepalWidthCm\\\", \\\"PetalLengthCm\\\"]; PROJECT */6 COLUMNS; SELECTION: None\")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "// Where is iris_df.describe(None)?\n",
    "iris_df_lazy.logical_plan.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "13aee88b-f60a-46c2-a143-7c6c51824706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ok(\"                                     0\\n   ┌─────────────────────────────────────────────────────────────────\\n   │\\n   │  ╭─────────────────────────────────────────────────────────────╮\\n   │  │ DF [\\\"Id\\\", \\\"SepalLengthCm\\\", \\\"SepalWidthCm\\\", \\\"PetalLengthCm\\\"] │\\n 0 │  │ PROJECT */6 COLUMNS                                         │\\n   │  ╰─────────────────────────────────────────────────────────────╯\\n\")"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df_lazy.logical_plan.describe_tree_format()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ba36cc8",
   "metadata": {},
   "source": [
    "### Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e35feace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Id\", \"SepalLengthCm\", \"SepalWidthCm\", \"PetalLengthCm\", \"PetalWidthCm\", \"Species\"]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "let column_names = iris_df.get_column_names(); \n",
    "\n",
    "{\n",
    "    column_names\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "000ccc93",
   "metadata": {},
   "source": [
    "### Drop Species Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "194ade74",
   "metadata": {},
   "outputs": [],
   "source": [
    "let numeric_iris_df: DataFrame = iris_df.drop(\"Species\")?;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e84dd09",
   "metadata": {},
   "source": [
    "### Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cd5af63c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ok(shape: (1, 5)\n",
       "┌─────┬───────────────┬──────────────┬───────────────┬──────────────┐\n",
       "│ Id  ┆ SepalLengthCm ┆ SepalWidthCm ┆ PetalLengthCm ┆ PetalWidthCm │\n",
       "│ --- ┆ ---           ┆ ---          ┆ ---           ┆ ---          │\n",
       "│ f64 ┆ f64           ┆ f64          ┆ f64           ┆ f64          │\n",
       "╞═════╪═══════════════╪══════════════╪═══════════════╪══════════════╡\n",
       "│ 1.0 ┆ 5.1           ┆ 3.5          ┆ 1.4           ┆ 0.2          │\n",
       "└─────┴───────────────┴──────────────┴───────────────┴──────────────┘)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_df.lazy().mean().fetch(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8915589",
   "metadata": {},
   "source": [
    "### Max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "69f715b9-e5c4-440a-af5b-571915c361a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "let numeric_iris_df: DataFrame = iris_df.drop(\"Species\")?;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4933def4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ok(shape: (1, 5)\n",
       "┌─────┬───────────────┬──────────────┬───────────────┬──────────────┐\n",
       "│ Id  ┆ SepalLengthCm ┆ SepalWidthCm ┆ PetalLengthCm ┆ PetalWidthCm │\n",
       "│ --- ┆ ---           ┆ ---          ┆ ---           ┆ ---          │\n",
       "│ i64 ┆ f64           ┆ f64          ┆ f64           ┆ f64          │\n",
       "╞═════╪═══════════════╪══════════════╪═══════════════╪══════════════╡\n",
       "│ 1   ┆ 5.1           ┆ 3.5          ┆ 1.4           ┆ 0.2          │\n",
       "└─────┴───────────────┴──────────────┴───────────────┴──────────────┘)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_df.lazy().max().fetch(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f13f5e",
   "metadata": {},
   "source": [
    "### Comvert To ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "27884612-3a82-4959-9fdd-6c5990f7a133",
   "metadata": {},
   "outputs": [],
   "source": [
    "let numeric_iris_df: DataFrame = iris_df.drop(\"Species\")?;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ac33887d",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1.0, 5.1, 3.5, 1.4, 0.2],\n",
       " [2.0, 4.9, 3.0, 1.4, 0.2],\n",
       " [3.0, 4.7, 3.2, 1.3, 0.2],\n",
       " [4.0, 4.6, 3.1, 1.5, 0.2],\n",
       " [5.0, 5.0, 3.6, 1.4, 0.2],\n",
       " ...,\n",
       " [146.0, 6.7, 3.0, 5.2, 2.3],\n",
       " [147.0, 6.3, 2.5, 5.0, 1.9],\n",
       " [148.0, 6.5, 3.0, 5.2, 2.0],\n",
       " [149.0, 6.2, 3.4, 5.4, 2.3],\n",
       " [150.0, 5.9, 3.0, 5.1, 1.8]], shape=[150, 5], strides=[1, 150], layout=Ff (0xa), const ndim=2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "let numeric_iris_ndarray: ArrayBase<_, _> = numeric_iris_df.to_ndarray::<Float64Type>(IndexOrder::Fortran).unwrap();\n",
    "numeric_iris_ndarray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca1f17c5",
   "metadata": {},
   "source": [
    "<hr />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d47a54",
   "metadata": {},
   "source": [
    "## ndarray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd4af7e",
   "metadata": {},
   "source": [
    "### First Element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "caa142b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_ndarray[[0, 0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4921527",
   "metadata": {},
   "source": [
    "### First Row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "59383cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 5.1, 3.5, 1.4, 0.2], shape=[5], strides=[150], layout=Custom (0x0), const ndim=1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_ndarray.slice(s![0, ..])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9880b197",
   "metadata": {},
   "source": [
    "### Shape (rows, cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4d286e5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_ndarray.dim()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8df7233e",
   "metadata": {},
   "source": [
    "### Last Row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3421e427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[150.0, 5.9, 3.0, 5.1, 1.8], shape=[5], strides=[150], layout=Custom (0x0), const ndim=1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "let numeric_iris_ndarray_shape = numeric_iris_ndarray.dim(); // (rows, cols)\n",
    "// numeric_iris_ndarray.slice(s![numeric_iris_ndarray_shape.1 -1, ..])\n",
    "// or\n",
    "numeric_iris_ndarray.slice(s![-1, ..])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f0580d",
   "metadata": {},
   "source": [
    "### First Five Row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0d36256d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1.0, 5.1, 3.5, 1.4, 0.2],\n",
       " [2.0, 4.9, 3.0, 1.4, 0.2],\n",
       " [3.0, 4.7, 3.2, 1.3, 0.2],\n",
       " [4.0, 4.6, 3.1, 1.5, 0.2],\n",
       " [5.0, 5.0, 3.6, 1.4, 0.2]], shape=[5, 5], strides=[1, 150], layout=f (0x8), const ndim=2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_ndarray.slice(s![0..5, ..])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae53953",
   "metadata": {},
   "source": [
    "### Last Five Row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bbb35d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[146.0, 6.7, 3.0, 5.2, 2.3],\n",
       " [147.0, 6.3, 2.5, 5.0, 1.9],\n",
       " [148.0, 6.5, 3.0, 5.2, 2.0],\n",
       " [149.0, 6.2, 3.4, 5.4, 2.3],\n",
       " [150.0, 5.9, 3.0, 5.1, 1.8]], shape=[5, 5], strides=[1, 150], layout=f (0x8), const ndim=2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_ndarray.slice(s![-5.., ..])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b42d99",
   "metadata": {},
   "source": [
    "### Check for zero elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e4ff5189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "false"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_ndarray.is_empty()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be5fc1e",
   "metadata": {},
   "source": [
    "<hr />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6fea35",
   "metadata": {},
   "source": [
    "## Mathematics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "163db148",
   "metadata": {},
   "source": [
    "### Sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d4188560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13403.199999999997"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_ndarray.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b753bd",
   "metadata": {},
   "source": [
    "### Sum Along Axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5b33caa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[11325.0, 876.5000000000001, 458.1, 563.8000000000002, 179.80000000000004], shape=[5], strides=[1], layout=CFcf (0xf), const ndim=1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_ndarray.sum_axis(Axis(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "832d3033",
   "metadata": {},
   "source": [
    "### Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5dddf68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.87093333333333"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_ndarray.mean().unwrap()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7733043",
   "metadata": {},
   "source": [
    "### Transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "71f6c578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1.0, 2.0, 3.0, 4.0, 5.0, ..., 146.0, 147.0, 148.0, 149.0, 150.0],\n",
       " [5.1, 4.9, 4.7, 4.6, 5.0, ..., 6.7, 6.3, 6.5, 6.2, 5.9],\n",
       " [3.5, 3.0, 3.2, 3.1, 3.6, ..., 3.0, 2.5, 3.0, 3.4, 3.0],\n",
       " [1.4, 1.4, 1.3, 1.5, 1.4, ..., 5.2, 5.0, 5.2, 5.4, 5.1],\n",
       " [0.2, 0.2, 0.2, 0.2, 0.2, ..., 2.3, 1.9, 2.0, 2.3, 1.8]], shape=[5, 150], strides=[150, 1], layout=Cc (0x5), const ndim=2"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_ndarray.t()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a8af17",
   "metadata": {},
   "source": [
    "### 2-D matrix multiply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c3d3450e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[41.26, 39.489999999999995, 40.03, 40.449999999999996, 45.1, ..., 198.41, 195.26, 199.33, 200.54000000000002, 198.09],\n",
       " [39.489999999999995, 39.010000000000005, 40.49, 41.98, 47.3, ..., 341.56999999999994, 339.75, 344.53, 346.59999999999997, 345.41],\n",
       " [40.03, 40.49, 43.06, 45.53, 51.88, ..., 486.31, 485.49, 491.31, 494.49999999999994, 494.32000000000005],\n",
       " [40.449999999999996, 41.98, 45.53, 49.059999999999995, 56.300000000000004, ..., 632.38, 632.61, 639.3999999999999, 643.62, 644.4499999999999],\n",
       " [45.1, 47.3, 51.88, 56.300000000000004, 64.96000000000001, ..., 782.04, 782.88, 790.9799999999999, 796.26, 797.8],\n",
       " ...,\n",
       " [198.41, 341.56999999999994, 486.31, 632.38, 782.04, ..., 21402.22, 21542.079999999998, 21692.19, 21839.110000000004, 21979.19],\n",
       " [195.26, 339.75, 485.49, 632.61, 782.88, ..., 21542.079999999998, 21683.55, 21834.25, 21981.93, 22123.589999999997],\n",
       " [199.33, 344.53, 491.31, 639.3999999999999, 790.9799999999999, ..., 21692.19, 21834.25, 21986.29, 22135.18, 22277.469999999998],\n",
       " [200.54000000000002, 346.59999999999997, 494.49999999999994, 643.62, 796.26, ..., 21839.110000000004, 21981.93, 22135.18, 22285.45, 22428.460000000003],\n",
       " [198.09, 345.41, 494.32000000000005, 644.4499999999999, 797.8, ..., 21979.19, 22123.589999999997, 22277.469999999998, 22428.460000000003, 22573.06]], shape=[150, 150], strides=[150, 1], layout=Cc (0x5), const ndim=2"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_ndarray.dot(&numeric_iris_ndarray.t())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aac50180",
   "metadata": {},
   "source": [
    "<hr />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007882a8",
   "metadata": {},
   "source": [
    "## Arithmetics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "66d1926f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2.0, 6.1, 4.5, 2.4, 1.2],\n",
       " [3.0, 5.9, 4.0, 2.4, 1.2],\n",
       " [4.0, 5.7, 4.2, 2.3, 1.2],\n",
       " [5.0, 5.6, 4.1, 2.5, 1.2],\n",
       " [6.0, 6.0, 4.6, 2.4, 1.2],\n",
       " ...,\n",
       " [147.0, 7.7, 4.0, 6.2, 3.3],\n",
       " [148.0, 7.3, 3.5, 6.0, 2.9],\n",
       " [149.0, 7.5, 4.0, 6.2, 3.0],\n",
       " [150.0, 7.2, 4.4, 6.4, 3.3],\n",
       " [151.0, 6.9, 4.0, 6.1, 2.8]], shape=[150, 5], strides=[1, 150], layout=Ff (0xa), const ndim=2"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "&numeric_iris_ndarray + 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e531193c",
   "metadata": {},
   "source": [
    "<hr />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "534cb70e",
   "metadata": {},
   "source": [
    "## Sepal Length Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e0ab8d7e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9], shape=[150], strides=[1], layout=CFcf (0xf), const ndim=1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_ndarray.column(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff9aa48a-7f29-46c2-a9cc-23c295a7aad6",
   "metadata": {},
   "source": [
    "Many thanks to [Brendan A R Sechter](https://github.com/sgeos) for fixing this bug in [#17](https://github.com/wiseaidev/rust-data-analysis/issues/17)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "98276f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[5.1, 3.5],\n",
       " [4.9, 3.0],\n",
       " [4.7, 3.2],\n",
       " [4.6, 3.1],\n",
       " [5.0, 3.6],\n",
       " [5.4, 3.9],\n",
       " [4.6, 3.4],\n",
       " [5.0, 3.4],\n",
       " [4.4, 2.9],\n",
       " [4.9, 3.1],\n",
       " [5.4, 3.7],\n",
       " [4.8, 3.4],\n",
       " [4.8, 3.0],\n",
       " [4.3, 3.0],\n",
       " [5.8, 4.0],\n",
       " [5.7, 4.4],\n",
       " [5.4, 3.9],\n",
       " [5.1, 3.5],\n",
       " [5.7, 3.8],\n",
       " [5.1, 3.8],\n",
       " [5.4, 3.4],\n",
       " [5.1, 3.7],\n",
       " [4.6, 3.6],\n",
       " [5.1, 3.3],\n",
       " [4.8, 3.4],\n",
       " [5.0, 3.0],\n",
       " [5.0, 3.4],\n",
       " [5.2, 3.5],\n",
       " [5.2, 3.4],\n",
       " [4.7, 3.2],\n",
       " [4.8, 3.1],\n",
       " [5.4, 3.4],\n",
       " [5.2, 4.1],\n",
       " [5.5, 4.2],\n",
       " [4.9, 3.1],\n",
       " [5.0, 3.2],\n",
       " [5.5, 3.5],\n",
       " [4.9, 3.1],\n",
       " [4.4, 3.0],\n",
       " [5.1, 3.4],\n",
       " [5.0, 3.5],\n",
       " [4.5, 2.3],\n",
       " [4.4, 3.2],\n",
       " [5.0, 3.5],\n",
       " [5.1, 3.8],\n",
       " [4.8, 3.0],\n",
       " [5.1, 3.8],\n",
       " [4.6, 3.2],\n",
       " [5.3, 3.7],\n",
       " [5.0, 3.3],\n",
       " [7.0, 3.2],\n",
       " [6.4, 3.2],\n",
       " [6.9, 3.1],\n",
       " [5.5, 2.3],\n",
       " [6.5, 2.8],\n",
       " [5.7, 2.8],\n",
       " [6.3, 3.3],\n",
       " [4.9, 2.4],\n",
       " [6.6, 2.9],\n",
       " [5.2, 2.7],\n",
       " [5.0, 2.0],\n",
       " [5.9, 3.0],\n",
       " [6.0, 2.2],\n",
       " [6.1, 2.9],\n",
       " [5.6, 2.9],\n",
       " [6.7, 3.1],\n",
       " [5.6, 3.0],\n",
       " [5.8, 2.7],\n",
       " [6.2, 2.2],\n",
       " [5.6, 2.5],\n",
       " [5.9, 3.2],\n",
       " [6.1, 2.8],\n",
       " [6.3, 2.5],\n",
       " [6.1, 2.8],\n",
       " [6.4, 2.9],\n",
       " [6.6, 3.0],\n",
       " [6.8, 2.8],\n",
       " [6.7, 3.0],\n",
       " [6.0, 2.9],\n",
       " [5.7, 2.6],\n",
       " [5.5, 2.4],\n",
       " [5.5, 2.4],\n",
       " [5.8, 2.7],\n",
       " [6.0, 2.7],\n",
       " [5.4, 3.0],\n",
       " [6.0, 3.4],\n",
       " [6.7, 3.1],\n",
       " [6.3, 2.3],\n",
       " [5.6, 3.0],\n",
       " [5.5, 2.5],\n",
       " [5.5, 2.6],\n",
       " [6.1, 3.0],\n",
       " [5.8, 2.6],\n",
       " [5.0, 2.3],\n",
       " [5.6, 2.7],\n",
       " [5.7, 3.0],\n",
       " [5.7, 2.9],\n",
       " [6.2, 2.9],\n",
       " [5.1, 2.5],\n",
       " [5.7, 2.8],\n",
       " [6.3, 3.3],\n",
       " [5.8, 2.7],\n",
       " [7.1, 3.0],\n",
       " [6.3, 2.9],\n",
       " [6.5, 3.0],\n",
       " [7.6, 3.0],\n",
       " [4.9, 2.5],\n",
       " [7.3, 2.9],\n",
       " [6.7, 2.5],\n",
       " [7.2, 3.6],\n",
       " [6.5, 3.2],\n",
       " [6.4, 2.7],\n",
       " [6.8, 3.0],\n",
       " [5.7, 2.5],\n",
       " [5.8, 2.8],\n",
       " [6.4, 3.2],\n",
       " [6.5, 3.0],\n",
       " [7.7, 3.8],\n",
       " [7.7, 2.6],\n",
       " [6.0, 2.2],\n",
       " [6.9, 3.2],\n",
       " [5.6, 2.8],\n",
       " [7.7, 2.8],\n",
       " [6.3, 2.7],\n",
       " [6.7, 3.3],\n",
       " [7.2, 3.2],\n",
       " [6.2, 2.8],\n",
       " [6.1, 3.0],\n",
       " [6.4, 2.8],\n",
       " [7.2, 3.0],\n",
       " [7.4, 2.8],\n",
       " [7.9, 3.8],\n",
       " [6.4, 2.8],\n",
       " [6.3, 2.8],\n",
       " [6.1, 2.6],\n",
       " [7.7, 3.0],\n",
       " [6.3, 3.4],\n",
       " [6.4, 3.1],\n",
       " [6.0, 3.0],\n",
       " [6.9, 3.1],\n",
       " [6.7, 3.1],\n",
       " [6.9, 3.1],\n",
       " [5.8, 2.7],\n",
       " [6.8, 3.2],\n",
       " [6.7, 3.3],\n",
       " [6.7, 3.0],\n",
       " [6.3, 2.5],\n",
       " [6.5, 3.0],\n",
       " [6.2, 3.4],\n",
       " [5.9, 3.0]], shape=[150, 2], strides=[1, 150], layout=Ff (0xa), const ndim=2"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "  let axis = Axis(1); // dropped at end of block\n",
    "  let array_data_a = numeric_iris_ndarray.column(1).insert_axis(axis); // dropped at end of block\n",
    "  let array_data_b = numeric_iris_ndarray.column(2).insert_axis(axis); // dropped at end of block\n",
    "  concatenate(axis, &[array_data_a, array_data_b])? // returned by block\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f95db658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Id\", \"SepalLengthCm\", \"SepalWidthCm\", \"PetalLengthCm\", \"PetalWidthCm\"]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_iris_df.get_column_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "123683c0-5c81-408d-a23a-9a1d89ec8d18",
   "metadata": {},
   "source": [
    "## Filter one label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a019756d-87ca-41c4-9e38-4884a8493f75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ok(shape: (50, 6)\n",
       "┌─────┬───────────────┬──────────────┬───────────────┬──────────────┬────────────────┐\n",
       "│ Id  ┆ SepalLengthCm ┆ SepalWidthCm ┆ PetalLengthCm ┆ PetalWidthCm ┆ Species        │\n",
       "│ --- ┆ ---           ┆ ---          ┆ ---           ┆ ---          ┆ ---            │\n",
       "│ i64 ┆ f64           ┆ f64          ┆ f64           ┆ f64          ┆ str            │\n",
       "╞═════╪═══════════════╪══════════════╪═══════════════╪══════════════╪════════════════╡\n",
       "│ 101 ┆ 6.3           ┆ 3.3          ┆ 6.0           ┆ 2.5          ┆ Iris-virginica │\n",
       "│ 102 ┆ 5.8           ┆ 2.7          ┆ 5.1           ┆ 1.9          ┆ Iris-virginica │\n",
       "│ 103 ┆ 7.1           ┆ 3.0          ┆ 5.9           ┆ 2.1          ┆ Iris-virginica │\n",
       "│ 104 ┆ 6.3           ┆ 2.9          ┆ 5.6           ┆ 1.8          ┆ Iris-virginica │\n",
       "│ 105 ┆ 6.5           ┆ 3.0          ┆ 5.8           ┆ 2.2          ┆ Iris-virginica │\n",
       "│ …   ┆ …             ┆ …            ┆ …             ┆ …            ┆ …              │\n",
       "│ 146 ┆ 6.7           ┆ 3.0          ┆ 5.2           ┆ 2.3          ┆ Iris-virginica │\n",
       "│ 147 ┆ 6.3           ┆ 2.5          ┆ 5.0           ┆ 1.9          ┆ Iris-virginica │\n",
       "│ 148 ┆ 6.5           ┆ 3.0          ┆ 5.2           ┆ 2.0          ┆ Iris-virginica │\n",
       "│ 149 ┆ 6.2           ┆ 3.4          ┆ 5.4           ┆ 2.3          ┆ Iris-virginica │\n",
       "│ 150 ┆ 5.9           ┆ 3.0          ┆ 5.1           ┆ 1.8          ┆ Iris-virginica │\n",
       "└─────┴───────────────┴──────────────┴───────────────┴──────────────┴────────────────┘)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "let column_name = \"Species\";\n",
    "let column_filter_value = \"Iris-virginica\";\n",
    "let mask = iris_df.column(column_name)?.equal(column_filter_value)?;\n",
    "let filtered_dataset = iris_df.filter(&mask);\n",
    "filtered_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "183aa9f1",
   "metadata": {},
   "source": [
    "<hr />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "523bfbbf",
   "metadata": {},
   "source": [
    "## Scatter Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "331a7852",
   "metadata": {},
   "source": [
    "### Sepal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "da5e36a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<circle cx=\"339\" cy=\"204\" r=\"3\" opacity=\"1\" fill=\"#0000FF\" stroke=\"none\" stroke-width=\"1\"/>\n",
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       "<circle cx=\"339\" cy=\"244\" r=\"3\" opacity=\"1\" fill=\"#0000FF\" stroke=\"none\" stroke-width=\"1\"/>\n",
       "</svg>\n",
       "</div>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "let sepal_samples:Vec<(f64,f64)> = {\n",
    "    let sepal_length_cm: DataFrame = iris_df.select(vec![\"SepalLengthCm\"]).unwrap();\n",
    "    let mut sepal_length = sepal_length_cm.to_ndarray::<Float64Type>(IndexOrder::Fortran).unwrap().into_raw_vec().into_iter();\n",
    "    let sepal_width_cm: DataFrame = iris_df.select(vec![\"SepalWidthCm\"]).unwrap();\n",
    "    let mut sepal_width = sepal_width_cm.to_ndarray::<Float64Type>(IndexOrder::Fortran).unwrap().into_raw_vec().into_iter();\n",
    "    sepal_width.zip(sepal_length).collect()\n",
    "};\n",
    "\n",
    "evcxr_figure((640, 480), |root| {\n",
    "    let mut chart = ChartBuilder::on(&root)\n",
    "        .caption(\"Iris Dataset\", (\"Arial\", 30).into_font())\n",
    "        .x_label_area_size(40)\n",
    "        .y_label_area_size(40)\n",
    "        .build_cartesian_2d(1f64..5f64, 3f64..9f64)?;\n",
    "    \n",
    "    chart.configure_mesh()\n",
    "        .x_desc(\"Sepal Length (cm)\")\n",
    "        .y_desc(\"Sepal Width (cm)\")\n",
    "        .draw()?;\n",
    "    \n",
    "    chart.draw_series(sepal_samples.iter().map(|(x, y)| Circle::new((*x,*y), 3, BLUE.filled())));\n",
    "\n",
    "    Ok(())\n",
    "}).style(\"width:60%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9182ebce",
   "metadata": {},
   "source": [
    "### Petal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b2d5f0b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "let petal_samples: Vec<(f64,f64)> = {\n",
    "    let petal_length_cm: DataFrame = iris_df.select(vec![\"PetalLengthCm\"]).unwrap();\n",
    "    let mut petal_length = petal_length_cm.to_ndarray::<Float64Type>(IndexOrder::Fortran).unwrap().into_raw_vec().into_iter();\n",
    "    let petal_width_cm: DataFrame = iris_df.select(vec![\"PetalWidthCm\"]).unwrap();\n",
    "    let mut petal_width = petal_width_cm.to_ndarray::<Float64Type>(IndexOrder::Fortran).unwrap().into_raw_vec().into_iter();\n",
    "    petal_width.zip(petal_length).collect()\n",
    "};\n",
    "\n",
    "evcxr_figure((640, 480), |root| {\n",
    "    let mut chart = ChartBuilder::on(&root)\n",
    "        .caption(\"Iris Dataset\", (\"Arial\", 30).into_font())\n",
    "        .x_label_area_size(40)\n",
    "        .y_label_area_size(40)\n",
    "        .build_cartesian_2d(0f64..3f64, 0f64..8f64)?;\n",
    "    \n",
    "    chart.configure_mesh()\n",
    "        .x_desc(\"Petal Length (cm)\")\n",
    "        .y_desc(\"Petal Width (cm)\")\n",
    "        .draw()?;\n",
    "    \n",
    "    chart.draw_series(petal_samples.iter().map(|(x, y)| Circle::new((*x,*y), 3, GREEN.filled())));\n",
    "\n",
    "    Ok(())\n",
    "}).style(\"width:60%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1942870b",
   "metadata": {},
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3fedc85a-94a7-49aa-9233-b6230a1ba111",
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   "outputs": [
    {
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   ],
   "source": [
    "evcxr_figure((640, 480), |root| {\n",
    "    _ = root.fill(&WHITE);\n",
    "\n",
    "    let mut chart = ChartBuilder::on(&root)\n",
    "        .caption(\"Histogram\", (\"Arial\", 20).into_font())\n",
    "        .x_label_area_size(50)\n",
    "        .y_label_area_size(50)\n",
    "        .build_cartesian_2d(1u32..5u32, 0f64..1f64)?;\n",
    "\n",
    "    chart.configure_mesh()\n",
    "        .disable_x_mesh()\n",
    "        .disable_y_mesh()\n",
    "        .y_labels(5)\n",
    "        .y_label_formatter(&|y| format!(\"{}%\", (*y * 100.0) as u32))\n",
    "        .draw()?;\n",
    "\n",
    "    let hist = Histogram::vertical(&chart)\n",
    "        .style(RED.filled())\n",
    "        .margin(0)\n",
    "        .data(sepal_samples.iter().map(|(x,_)| (*x as u32, 0.01)));\n",
    "\n",
    "    let _ = chart.draw_series(hist);\n",
    "    \n",
    "    Ok(())\n",
    "}).style(\"width:100%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9acf4c06",
   "metadata": {},
   "source": [
    "## Combination of Histogram and Scatter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab1a62dc-4a3b-4a93-b6a4-dac3041441e8",
   "metadata": {},
   "source": [
    "Many thanks to [Brendan A R Sechter](https://github.com/sgeos) for implementing this feature in [#17](https://github.com/wiseaidev/rust-data-analysis/issues/17)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5ae2d0cb",
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      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evcxr_figure((640, 480), |root| {\n",
    "    let root = root.titled(\"Scatter with Histogram Example\", (\"Arial\", 20).into_font())?;\n",
    "    \n",
    "    let areas = root.split_by_breakpoints([560], [80]);\n",
    "\n",
    "    let mut x_hist_ctx = ChartBuilder::on(&areas[0])\n",
    "     .x_label_area_size(40)\n",
    "        .y_label_area_size(40)\n",
    "        .build_cartesian_2d(1..5, 3..9)?;\n",
    "    let mut y_hist_ctx = ChartBuilder::on(&areas[3])\n",
    "        .x_label_area_size(40)\n",
    "     .y_label_area_size(40)\n",
    "        .build_cartesian_2d(1..5, 3..9)?;\n",
    "    let mut scatter_ctx = ChartBuilder::on(&areas[2])\n",
    "        .x_label_area_size(40)\n",
    "        .y_label_area_size(40)\n",
    "        .build_cartesian_2d(1f64..5f64, 3f64..9f64)?;\n",
    "    scatter_ctx.configure_mesh()\n",
    "        .disable_x_mesh()\n",
    "        .disable_y_mesh()\n",
    "        .draw()?;\n",
    "    scatter_ctx.draw_series(sepal_samples.iter().map(|(x,y)| Circle::new((*x,*y), 3, BLUE.filled())))?;\n",
    "\n",
    "    let x_hist = Histogram::vertical(&x_hist_ctx)\n",
    "        .style(RED.filled())\n",
    "        .margin(0)\n",
    "        .data(sepal_samples.iter().map(|(x,_)| (*x as i32, 1)));\n",
    "    x_hist_ctx.draw_series(x_hist)?;\n",
    "\n",
    "    let y_hist = Histogram::horizontal(&y_hist_ctx)\n",
    "        .style(GREEN.filled())\n",
    "        .margin(0)\n",
    "        .data(sepal_samples.iter().map(|(_,y)| (*y as i32, 1)));\n",
    "    y_hist_ctx.draw_series(y_hist)?;\n",
    "\n",
    "    Ok(())\n",
    "}).style(\"width:60%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "235733b3",
   "metadata": {},
   "source": [
    "## ML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "662024b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "let features: DataFrame = iris_df.select(vec![\"SepalLengthCm\", \"SepalWidthCm\", \"PetalLengthCm\", \"PetalWidthCm\"]).unwrap();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "712540c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "let features: DataFrame = iris_df.select(vec![\"SepalLengthCm\"]).unwrap();\n",
    "let array = features.to_ndarray::<Float64Type>(IndexOrder::Fortran).unwrap().into_raw_vec();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2cb2a939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2fa9338a",
   "metadata": {},
   "outputs": [],
   "source": [
    "let features: DataFrame = iris_df.select(vec![\"SepalWidthCm\"]).unwrap();\n",
    "let array = features.to_ndarray::<Float64Type>(IndexOrder::Fortran).unwrap().into_raw_vec();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "76727c69-124d-471f-a239-1c7543ee620a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.1, 3.0, 3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1, 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8, 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3, 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3.0, 2.5, 2.8, 3.2, 3.0, 3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3.0, 2.8, 3.0, 2.8, 3.8, 2.8, 2.8, 2.6, 3.0, 3.4, 3.1, 3.0, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be8bb354-20dc-4cfa-aa7d-71b0020d6ecf",
   "metadata": {},
   "source": [
    "---\n",
    "---"
   ]
  }
 ],
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   "display_name": "Rust",
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