diff --git a/vis-layers.ipynb b/vis-layers.ipynb
index 6a92e84..cf6a92d 100644
--- a/vis-layers.ipynb
+++ b/vis-layers.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "95f0a171",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Layers {#sec-vis-layers}\n"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9fdd3b8a",
+   "id": "1",
    "metadata": {},
    "source": [
     "## Introduction\n",
@@ -30,12 +30,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17575f3a",
+   "id": "2",
    "metadata": {},
    "source": [
     "### Prerequisites\n",
     "\n",
-    "You will need to install the **letsplot** package for this chapter, as well as **pandas**.\n",
+    "You will need to install the **letsplot** package for this chapter, as well as **polars**.\n",
     "\n",
     "In your Python session, import the libraries we'll be using:"
    ]
@@ -43,11 +43,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a86fb211",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
+    "import polars as pl\n",
     "from lets_plot import *\n",
     "\n",
     "LetsPlot.setup_html()"
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55b00fde",
+   "id": "4",
    "metadata": {},
    "source": [
     "## Aesthetic mappings\n",
@@ -68,35 +68,29 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "39a6d993",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "mpg = pd.read_csv(\n",
-    "    \"https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2/mpg.csv\", index_col=0\n",
+    "mpg = pl.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2/mpg.csv\")\n",
+    "\n",
+    "mpg = mpg.with_columns(\n",
+    "    [\n",
+    "        pl.col(\"manufacturer\").cast(pl.Categorical),\n",
+    "        pl.col(\"model\").cast(pl.Categorical),\n",
+    "        pl.col(\"trans\").cast(pl.Categorical),\n",
+    "        pl.col(\"drv\").cast(pl.Categorical),\n",
+    "        pl.col(\"fl\").cast(pl.Categorical),\n",
+    "        pl.col(\"class\").cast(pl.Categorical),\n",
+    "    ]\n",
     ")\n",
     "\n",
-    "mpg = mpg.astype(\n",
-    "    {\n",
-    "        \"manufacturer\": \"category\",\n",
-    "        \"model\": \"category\",\n",
-    "        \"displ\": \"double\",\n",
-    "        \"year\": \"int64\",\n",
-    "        \"cyl\": \"int64\",\n",
-    "        \"trans\": \"category\",\n",
-    "        \"drv\": \"category\",\n",
-    "        \"cty\": \"double\",\n",
-    "        \"hwy\": \"double\",\n",
-    "        \"fl\": \"category\",\n",
-    "        \"class\": \"category\",\n",
-    "    }\n",
-    ")\n",
     "mpg.head()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6d6f1307",
+   "id": "6",
    "metadata": {},
    "source": [
     "Among the variables in `mpg` are:\n",
@@ -118,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fe77349a",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +122,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e77b5640",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +131,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53e51510",
+   "id": "9",
    "metadata": {},
    "source": [
     "Similarly, we can map `class` to `size` or `alpha` aesthetics as well, which control the shape and the transparency of the points, respectively."
@@ -146,7 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ef221330",
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,7 +150,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d042255e",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec07349f",
+   "id": "12",
    "metadata": {},
    "source": [
     "While we are able to do it, mapping an unordered discrete (categorical) variable (`class`) to an ordered aesthetic variable (`size` or `alpha`) is generally not a good idea because it implies a ranking that does not in fact exist.\n",
@@ -182,7 +176,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "618edcb4",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,7 +185,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "52611640",
+   "id": "14",
    "metadata": {},
    "source": [
     "Here, the colour doesn't convey information about a variable, but only changes the appearance of the plot.\n",
@@ -206,7 +200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37d49ff8",
+   "id": "15",
    "metadata": {},
    "source": [
     "So far we have discussed aesthetics that we can map or set in a scatterplot, when using a point geom.\n",
@@ -217,7 +211,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f1da019",
+   "id": "16",
    "metadata": {},
    "source": [
     "1.  Create a scatterplot of `hwy` vs. `displ` where the points are pink filled in triangles.\n",
@@ -240,7 +234,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83aa98f0",
+   "id": "17",
    "metadata": {},
    "source": [
     "## Geometric objects\n",
@@ -251,7 +245,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "277a4c0f",
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +255,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "07247ba9",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,7 +264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58824a10",
+   "id": "20",
    "metadata": {},
    "source": [
     "Both plots contain the same x variable, the same y variable, and both describe the same data.\n",
@@ -283,7 +277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0db26c6c",
+   "id": "21",
    "metadata": {},
    "source": [
     "Every geom function in **lets-plot** takes a `mapping` argument, either defined locally in the geom layer or globally in the `ggplot()` layer.\n",
@@ -299,7 +293,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4b20c825",
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -309,7 +303,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "84df3e78",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,7 +312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b114911",
+   "id": "24",
    "metadata": {},
    "source": [
     "Here, `geom_smooth()` separates the cars into three lines based on their `drv` value, which describes a car's drive train.\n",
@@ -331,7 +325,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c9e8d92f",
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,7 +338,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b6392da1",
+   "id": "26",
    "metadata": {},
    "source": [
     "Notice that this plot contains two geoms in the same graph.\n",
@@ -363,7 +357,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b3916558",
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -372,7 +366,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "88708546",
+   "id": "28",
    "metadata": {},
    "source": [
     "You can use the same idea to specify different data for each layer.\n",
@@ -383,23 +377,23 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "38870eb5",
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": [
     "(\n",
     "    ggplot(mpg, aes(x=\"displ\", y=\"hwy\"))\n",
     "    + geom_point()\n",
-    "    + geom_point(data=mpg.loc[mpg[\"class\"] == \"2seater\", :], color=\"red\", size=2)\n",
+    "    + geom_point(data=mpg.filter(pl.col(\"class\") == \"2seater\"), color=\"red\", size=2)\n",
     "    + geom_point(\n",
-    "        data=mpg.loc[mpg[\"class\"] == \"2seater\", :], shape=1, size=3, color=\"red\"\n",
+    "        data=mpg.filter(pl.col(\"class\") == \"2seater\"), shape=1, size=3, color=\"red\"\n",
     "    )\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "35a3d017",
+   "id": "30",
    "metadata": {},
    "source": [
     "Geoms are the fundamental building blocks of **lets-plot**.\n",
@@ -415,7 +409,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39a12f36",
+   "id": "31",
    "metadata": {},
    "source": [
     "### Exercises\n",
@@ -443,7 +437,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ae75c5c1",
+   "id": "32",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -460,7 +454,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c1d45ab",
+   "id": "33",
    "metadata": {},
    "source": [
     "## Facets\n",
@@ -471,7 +465,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cb651300",
+   "id": "34",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -480,7 +474,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0cd9ef67",
+   "id": "35",
    "metadata": {},
    "source": [
     "To facet your plot with the combination of two variables, switch from `facet_wrap()` to `facet_grid()`."
@@ -489,7 +483,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "61481052",
+   "id": "36",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -498,7 +492,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f502141",
+   "id": "37",
    "metadata": {},
    "source": [
     "By default each of the facets share the same scale and range for x and y axes.\n",
@@ -509,7 +503,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "adcd9079",
+   "id": "38",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -523,7 +517,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ceb2a354",
+   "id": "39",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -532,7 +526,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e3a949f",
+   "id": "40",
    "metadata": {},
    "source": [
     "### Exercises\n",
@@ -602,7 +596,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cacf1fb5",
+   "id": "41",
    "metadata": {},
    "source": [
     "## Statistical transformations\n",
@@ -616,34 +610,34 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f379e31b",
+   "id": "42",
    "metadata": {},
    "outputs": [],
    "source": [
-    "diamonds = pd.read_csv(\n",
-    "    \"https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2/diamonds.csv\",\n",
-    "    index_col=0,\n",
+    "diamonds = pl.read_csv(\n",
+    "    \"https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2/diamonds.csv\"\n",
     ")\n",
     "diamonds_cut_order = [\"Fair\", \"Good\", \"Very Good\", \"Premium\", \"Ideal\"]\n",
-    "diamonds[\"cut\"] = diamonds[\"cut\"].astype(\n",
-    "    pd.CategoricalDtype(categories=diamonds_cut_order, ordered=True)\n",
-    ")\n",
+    "diamonds = diamonds.with_columns(pl.col(\"cut\").cast(pl.Enum(diamonds_cut_order)))\n",
+    "\n",
     "diamonds.head()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d8faf1ab",
+   "id": "43",
    "metadata": {},
    "outputs": [],
    "source": [
-    "(ggplot(diamonds, aes(x=\"cut\")) + geom_bar())"
+    "diamonds_sorted = diamonds.sort(\"cut\")\n",
+    "\n",
+    "(ggplot(diamonds_sorted, aes(x=\"cut\")) + geom_bar())"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a27666cf",
+   "id": "44",
    "metadata": {},
    "source": [
     "On the x-axis, the chart displays `cut`, a variable from `diamonds`.\n",
@@ -666,7 +660,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62519f73",
+   "id": "45",
    "metadata": {},
    "source": [
     "You can learn which stat a geom uses by inspecting the default value for the `stat` argument.\n",
@@ -680,22 +674,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ca772dd5",
+   "id": "46",
    "metadata": {},
    "outputs": [],
    "source": [
-    "(\n",
-    "    ggplot(\n",
-    "        diamonds.value_counts(\"cut\").reset_index(name=\"counts\"),\n",
-    "        aes(x=\"cut\", y=\"counts\"),\n",
-    "    )\n",
-    "    + geom_bar(stat=\"identity\")\n",
-    ")"
+    "diamonds_counts = diamonds.group_by(\"cut\").agg(pl.len().alias(\"counts\")).sort(\"cut\")\n",
+    "\n",
+    "(ggplot(diamonds_counts, aes(x=\"cut\", y=\"counts\")) + geom_bar(stat=\"identity\"))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e365aaaf",
+   "id": "47",
    "metadata": {},
    "source": [
     "## Position adjustments\n",
@@ -707,7 +697,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f8da7d91",
+   "id": "48",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -717,7 +707,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "088e7550",
+   "id": "49",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -726,7 +716,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3f7a0a3",
+   "id": "50",
    "metadata": {},
    "source": [
     "Note what happens if you map the fill aesthetic to another variable, like `class`: the bars are automatically stacked.\n",
@@ -736,7 +726,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "181c70d2",
+   "id": "51",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -745,7 +735,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3f74621",
+   "id": "52",
    "metadata": {},
    "source": [
     "The stacking is performed automatically using the **position adjustment** specified by the `position` argument.\n",
@@ -759,7 +749,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a8e9c378",
+   "id": "53",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -768,7 +758,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2aeffbf7",
+   "id": "54",
    "metadata": {},
    "source": [
     "The identity position adjustment is more useful for 2d geoms, like points, where it is the default.\n",
@@ -780,7 +770,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "14205000",
+   "id": "55",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -789,7 +779,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a8e72bd",
+   "id": "56",
    "metadata": {},
    "source": [
     "-   `position = \"dodge\"` places overlapping objects directly *beside* one another.\n",
@@ -799,7 +789,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c33c4a03",
+   "id": "57",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -808,7 +798,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd46f22b",
+   "id": "58",
    "metadata": {},
    "source": [
     "There's one other type of adjustment that's not useful for bar charts, but can be very useful for scatterplots.\n",
@@ -819,7 +809,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ba4161de",
+   "id": "59",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -828,7 +818,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28a3bfc7",
+   "id": "60",
    "metadata": {},
    "source": [
     "The underlying values of `hwy` and `displ` are rounded so the points appear on a grid and many points overlap each other.\n",
@@ -844,7 +834,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "414ce7af",
+   "id": "61",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -853,7 +843,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96277926",
+   "id": "62",
    "metadata": {},
    "source": [
     "Adding randomness seems like a strange way to improve your plot, but while it makes your graph less accurate at small scales, it makes your graph *more* revealing at large scales.\n",
@@ -867,7 +857,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "90d09736",
+   "id": "63",
    "metadata": {},
    "source": [
     "### Exercises\n",
@@ -879,7 +869,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9bc38aef",
+   "id": "64",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -888,7 +878,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57b6523f",
+   "id": "65",
    "metadata": {},
    "source": [
     "2.  What, if anything, is the difference between the two plots?\n",
@@ -908,7 +898,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3138f75",
+   "id": "66",
    "metadata": {},
    "source": [
     "3.  What parameters to `geom_jitter()` control the amount of jittering?\n",
@@ -919,7 +909,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d302c31",
+   "id": "67",
    "metadata": {},
    "source": [
     "## The layered grammar of graphics\n",
@@ -954,7 +944,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0f7a7ff",
+   "id": "68",
    "metadata": {},
    "source": [
     "At this point, you would have a complete graph, but you could further adjust the positions of the geoms within the coordinate system (a position adjustment) or split the graph into subplots (faceting).\n",
@@ -971,9 +961,6 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "9d7534ecd9fbc7d385378f8400cf4d6cb9c6175408a574f1c99c5269f08771cc"
-  },
   "jupytext": {
    "cell_metadata_filter": "-all",
    "encoding": "# -*- coding: utf-8 -*-",
@@ -981,7 +968,7 @@
    "main_language": "python"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },