{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from lets_plot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "LetsPlot.setup_html()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "economics_url = 'https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/economics.csv'\n",
    "economics = pd.read_csv(economics_url)\n",
    "economics['date'] = pd.to_datetime(economics['date'])\n",
    "start = datetime(2000, 1, 1)\n",
    "economics = economics.loc[economics['date'] >= start]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_dt = ggplot(economics, aes('date', 'unemploy')) + geom_line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Default\n",
    "p_dt + scale_x_datetime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define format of the axis labels\n",
    "p_dt + scale_x_datetime(format=\"%b %Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(economics, aes('date', 'date')) + geom_line()\\\n",
    "+ scale_x_datetime(format=\"%B\")+ scale_y_datetime(format=\"%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar_dict = {\n",
    "  'time': ['Lunch', 'Dinner', 'Night'],\n",
    "  'total_bill': [14.89, 17.23, 40]\n",
    "}\n",
    "ggplot(bar_dict, aes('time', 'total_bill', fill='time')) + geom_bar(stat='identity') + scale_x_discrete(format='time is {}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 21\n",
    "x = [v for v in range(N)]\n",
    "y0 = [pow(10, v / 10.) for v in range(N)]\n",
    "y1 = [v * 5 for v in range(N)]\n",
    "formula = ['10^(x/10)'] * N + ['5*x'] * N\n",
    "data = dict(x=x * 2, y=y0 + y1, formula=formula)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = ggplot(data) + geom_point(aes('x', 'y', color='formula', size='formula')) + scale_size_manual(values=[7, 3])\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p + scale_x_continuous(format='x={.1f}') + scale_y_continuous(format='y={.1f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Skip 'format' if labels are specified \n",
    "\n",
    "breaks = [0, 10, 20]\n",
    "labels = ['0', '10', '20']\n",
    "\n",
    "p + scale_x_continuous(breaks=breaks, labels=labels, format='is {.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p + scale_x_log10(format='x={.2f}') + scale_y_log10(format='y={.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p + scale_x_reverse(format='.2f')"
   ]
  }
 ],
 "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}