{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "flex_title = \"Plotly plots\"\n",
    "flex_orientation = \"rows\"\n",
    "flex_subtitle = \"built using jupyter-flex\"\n",
    "flex_source_link = \"https://github.com/danielfrg/jupyter-flex/blob/master/examples/plots/plotly.ipynb\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Row 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "margin = go.layout.Margin(l=20, r=20, b=20, t=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "cls = np.repeat([\"A\", \"B\"], 10)\n",
    "x = np.arange(0, 20) + norm.rvs(20, 3, size=20)\n",
    "y = np.arange(0, 20) + norm.rvs(20, 3, size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"cls\": cls, \"x\": x, \"y\": y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "fig = px.scatter(df, x=\"x\", y=\"y\")\n",
    "\n",
    "fig.update_layout(margin=margin)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "fig = px.scatter(df, x=\"x\", y=\"y\", trendline=\"ols\")\n",
    "\n",
    "fig.update_layout(margin=margin)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Row 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lowess Smoothed Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "fig = px.scatter(df, x=\"x\", y=\"y\", trendline=\"lowess\")\n",
    "\n",
    "fig.update_layout(margin=margin)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "fig = px.scatter(df, x=\"x\", y=\"y\", color=\"cls\", trendline=\"ols\")\n",
    "\n",
    "fig.update_layout(margin=margin)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Density"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Row 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gamma density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "import scipy.special as sc\n",
    "import plotly.figure_factory as ff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    \"nu075\": np.random.gamma(1, 0.75, size=100),\n",
    "    \"nu1\": np.random.gamma(1, 1, size=100),\n",
    "    \"nu2\": np.random.gamma(1, 2, size=100),\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "fig = ff.create_distplot([df[c] for c in df.columns], df.columns, show_hist=False)\n",
    "\n",
    "fig.update_layout(margin=margin)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iris density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "df = px.data.iris()\n",
    "fig = px.scatter(df, x=\"sepal_width\", y=\"sepal_length\", color=\"species\", marginal_y=\"rug\", marginal_x=\"histogram\")\n",
    "\n",
    "\n",
    "fig.update_layout(margin=margin)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Row 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iris density countour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "df = px.data.iris()\n",
    "fig = px.density_contour(df, x=\"sepal_width\", y=\"sepal_length\", color=\"species\", marginal_x=\"rug\", marginal_y=\"histogram\")\n",
    "\n",
    "fig.update_layout(margin=margin)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "df = px.data.tips()\n",
    "fig = px.histogram(df, x=\"sex\", y=\"tip\", histfunc=\"avg\", color=\"smoker\", barmode=\"group\",\n",
    "             facet_row=\"time\", facet_col=\"day\", category_orders={\"day\": [\"Thur\", \"Fri\", \"Sat\", \"Sun\"],\n",
    "                                                                \"time\": [\"Lunch\", \"Dinner\"]})\n",
    "\n",
    "fig.update_layout(margin=margin)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "orientation=columns"
    ]
   },
   "source": [
    "# Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "start = datetime.datetime(2015, 1, 1)\n",
    "end = datetime.datetime(2019, 12, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "df_1 = web.DataReader(\"AAPL\", \"yahoo\", start, end).reset_index()\n",
    "df_2 = web.DataReader(\"MSFT\", \"yahoo\", start, end).reset_index()\n",
    "df_3 = web.DataReader(\"AMZN\", \"yahoo\", start, end).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "margin = go.layout.Margin(l=20, r=20, b=20, t=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "fig = go.Figure([go.Scatter(x=df_1['Date'], y=df_1['Close'])])\n",
    "fig.update_layout(margin=margin)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Microsoft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "fig = go.Figure([go.Scatter(x=df_2['Date'], y=df_2['Close'])])\n",
    "fig.update_layout(margin=margin)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Amazon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "fig = go.Figure([go.Scatter(x=df_3['Date'], y=df_3['Close'])])\n",
    "fig.update_layout(margin=margin)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tabs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "tabs"
    ]
   },
   "source": [
    "## Tabs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tab 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "cls = np.repeat([\"A\", \"B\"], 10)\n",
    "x = np.arange(0, 20) + norm.rvs(20, 3, size=20)\n",
    "y = np.arange(0, 20) + norm.rvs(20, 3, size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"cls\": cls, \"x\": x, \"y\": y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "fig = px.scatter(df, x=\"x\", y=\"y\")\n",
    "\n",
    "fig.update_layout(margin=margin)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tab 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "fig = px.scatter(df, x=\"x\", y=\"y\", trendline=\"ols\")\n",
    "\n",
    "fig.update_layout(margin=margin)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tab 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "fig = px.scatter(df, x=\"x\", y=\"y\", trendline=\"lowess\")\n",
    "\n",
    "fig.update_layout(margin=margin)\n",
    "fig"
   ]
  }
 ],
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   "codemirror_mode": {
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