{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "c4bf7edd-4cc8-4c22-96c0-10431374aef2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import PIL\n", "import os\n", "import pandas as pd\n", "import numpy as np\n", "import IPython.display as ip\n", "import requests\n", "from io import BytesIO\n", "\n", "from lets_plot import *\n", "LetsPlot.setup_html()\n", "LetsPlot.set({'magick_export': True})\n", "\n", "mpg = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv')\n", "airquality_df = pd.read_csv(\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/datasets/airquality.csv\")\n", "pie_df = pd.read_csv(\"https://raw.githubusercontent.com/JetBrains/lets-plot/refs/heads/master/docs/f-25a/data/gdp_forecast_2025_trillion_usd.csv\", encoding ='utf-8')\n", "markdown_df = pd.read_csv(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv\")\n", "markdown_df.drop(columns=[\"Unnamed: 0\"], inplace=True)\n", "\n", "boat_response = requests.get('https://github.com/JetBrains/lets-plot-docs/raw/master/source/examples/cookbook/images/fisher_boat.png')\n", "boat_raster = PIL.Image.open(BytesIO(boat_response.content))\n", "boat = np.asarray(boat_raster)\n", "\n", "def show(p, name, w=None, h=None, dpi=None, unit=None, scale=1, show_svg=True, out=None):\n", " if (out is None) or isinstance(out, str):\n", " out = name + \".png\"\n", " png_img = p.to_png(out, w=w, h=h, dpi=dpi, unit=unit, scale=scale)\n", " out_dir = os.path.dirname(png_img)\n", " \n", " if show_svg:\n", " svg_img = os.path.join(out_dir, name + '.svg')\n", " svg = p.to_svg(svg_img)\n", " ip.display(ip.HTML(\"