{ "cells": [ { "cell_type": "markdown", "id": "3dc9150a", "metadata": {}, "source": [ "# Joint plot" ] }, { "cell_type": "markdown", "id": "9e289998", "metadata": {}, "source": [ "## Table of Contents\n", "\n", "1. [Default Presentation of Joint Plot](#default)\n", "\n", "2. [Change Geom Types](#change-geom)\n", "\n", "3. [Geometries Customization](#geom-customization)\n", "\n", "4. [Marginal Layers Customization](#marginal-customization)\n", "\n", "5. [Grouping](#grouping)\n", "\n", "6. [Additional Layer](#additional_layer)" ] }, { "cell_type": "code", "execution_count": 1, "id": "2cb4b712", "metadata": { "execution": { "iopub.execute_input": "2024-04-26T11:54:51.847585Z", "iopub.status.busy": "2024-04-26T11:54:51.847585Z", "iopub.status.idle": "2024-04-26T11:54:52.694762Z", "shell.execute_reply": "2024-04-26T11:54:52.694762Z" } }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "from lets_plot.bistro import *\n", "from lets_plot import *" ] }, { "cell_type": "code", "execution_count": 2, "id": "411b248e", "metadata": { "execution": { "iopub.execute_input": "2024-04-26T11:54:52.694762Z", "iopub.status.busy": "2024-04-26T11:54:52.694762Z", "iopub.status.idle": "2024-04-26T11:54:52.710511Z", "shell.execute_reply": "2024-04-26T11:54:52.710511Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "LetsPlot.setup_html()" ] }, { "cell_type": "code", "execution_count": 3, "id": "df4a9ae6", "metadata": { "execution": { "iopub.execute_input": "2024-04-26T11:54:52.710511Z", "iopub.status.busy": "2024-04-26T11:54:52.710511Z", "iopub.status.idle": "2024-04-26T11:54:52.993888Z", "shell.execute_reply": "2024-04-26T11:54:52.993888Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(150, 5)\n" ] }, { "data": { "text/html": [ "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/iris.csv\")\n", "print(df.shape)\n", "df.head()" ] }, { "cell_type": "markdown", "id": "75f00ac0", "metadata": {}, "source": [ "\n", "\n", "## 1. Default Presentation of Joint Plot\n", "\n", "In the simplest case, assign `x` and `y` to create a scatterplot (using `geom_point()`) with marginal histograms (using `geom_histogram()`)." ] }, { "cell_type": "code", "execution_count": 4, "id": "76bddb5b", "metadata": { "execution": { "iopub.execute_input": "2024-04-26T11:54:52.993888Z", "iopub.status.busy": "2024-04-26T11:54:52.993888Z", "iopub.status.idle": "2024-04-26T11:54:53.104747Z", "shell.execute_reply": "2024-04-26T11:54:53.104747Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joint_plot(df, \"petal_length\", \"petal_width\")" ] }, { "cell_type": "markdown", "id": "5da4eb92", "metadata": {}, "source": [ "\n", "\n", "## 2. Change Geom Types\n", "\n", "Besides the points there are another two types of geoms: `tile` and `density2d(f)`." ] }, { "cell_type": "code", "execution_count": 5, "id": "20fec1dc", "metadata": { "execution": { "iopub.execute_input": "2024-04-26T11:54:53.104747Z", "iopub.status.busy": "2024-04-26T11:54:53.104747Z", "iopub.status.idle": "2024-04-26T11:54:53.137374Z", "shell.execute_reply": "2024-04-26T11:54:53.136233Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joint_plot(df, \"petal_length\", \"petal_width\", geom='tile')" ] }, { "cell_type": "code", "execution_count": 6, "id": "61ea146c", "metadata": { "execution": { "iopub.execute_input": "2024-04-26T11:54:53.137374Z", "iopub.status.busy": "2024-04-26T11:54:53.137374Z", "iopub.status.idle": "2024-04-26T11:54:53.418595Z", "shell.execute_reply": "2024-04-26T11:54:53.418595Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joint_plot(df.dropna(), \"petal_length\", \"petal_width\", color_by=\"species\", geom='density2d')" ] }, { "cell_type": "markdown", "id": "2e6a14f3", "metadata": {}, "source": [ "\n", "\n", "## 3. Change Geom Parameters\n", "\n", "Use additional parameters for better customization: `color`, `size`, `alpha`, etc." ] }, { "cell_type": "code", "execution_count": 7, "id": "6c9ef64c", "metadata": { "execution": { "iopub.execute_input": "2024-04-26T11:54:53.418595Z", "iopub.status.busy": "2024-04-26T11:54:53.418595Z", "iopub.status.idle": "2024-04-26T11:54:53.434115Z", "shell.execute_reply": "2024-04-26T11:54:53.434115Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joint_plot(df, \"petal_length\", \"petal_width\", color=\"#756bb1\", size=8, alpha=.5, se=False)" ] }, { "cell_type": "markdown", "id": "953dfc07", "metadata": {}, "source": [ "\n", "\n", "## 4. Marginal Layers Customization\n", "\n", "`marginal` parameter is a shortcut for the `ggmarginal()` layer." ] }, { "cell_type": "code", "execution_count": 8, "id": "ffa9a0db", "metadata": { "execution": { "iopub.execute_input": "2024-04-26T11:54:53.434115Z", "iopub.status.busy": "2024-04-26T11:54:53.434115Z", "iopub.status.idle": "2024-04-26T11:54:53.496936Z", "shell.execute_reply": "2024-04-26T11:54:53.496936Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joint_plot(df, \"petal_length\", \"petal_width\", color=\"black\", marginal=\"box:lb:.03,hist:t:.4,hist:r\") + \\\n", " ggmarginal(\"tr\", layer=geom_area(stat='density', color=\"magenta\", fill=\"magenta\", alpha=.1)) + \\\n", " theme(axis_line_x='blank', axis_line_y='blank')" ] }, { "cell_type": "markdown", "id": "b57000df", "metadata": {}, "source": [ "\n", "\n", "## 5. Grouping\n", "\n", "The `color_by` parameter sets the mapping to the fill and color aesthetics." ] }, { "cell_type": "code", "execution_count": 9, "id": "922e790f", "metadata": { "execution": { "iopub.execute_input": "2024-04-26T11:54:53.496936Z", "iopub.status.busy": "2024-04-26T11:54:53.496936Z", "iopub.status.idle": "2024-04-26T11:54:53.528177Z", "shell.execute_reply": "2024-04-26T11:54:53.528177Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joint_plot(df, \"petal_length\", \"petal_width\", color_by=\"species\", marginal=\"hist:tr\")" ] }, { "cell_type": "markdown", "id": "20225054", "metadata": {}, "source": [ "\n", "\n", "## 6. Additional Layer\n", "\n", "Add any other layer that supports `x` and `y` aesthetics (e.g. points layer with the `geom_point()` function)." ] }, { "cell_type": "code", "execution_count": 10, "id": "20dbcf35", "metadata": { "execution": { "iopub.execute_input": "2024-04-26T11:54:53.528177Z", "iopub.status.busy": "2024-04-26T11:54:53.528177Z", "iopub.status.idle": "2024-04-26T11:54:53.732271Z", "shell.execute_reply": "2024-04-26T11:54:53.732271Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joint_plot(df, \"petal_length\", \"petal_width\", geom='density2df', \\\n", " color=\"#993404\", alpha=1/3, reg_line=False) + \\\n", " geom_point(size=5, shape=21, color=\"#993404\", fill=\"#ffffd4\") + \\\n", " scale_fill_gradient(low=\"#d95f0e\", high=\"#fff7bc\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 5 }