muni nav_discount.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Welcome to BQuant\n",
    "<img style=\"float:left;\" src=\"workflow.png\" width=\"500\" height=\"500\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the BQL library\n",
    "import bql\n",
    "import pandas as pd\n",
    "\n",
    "# Instantiate an object to interface with the BQL service\n",
    "bq = bql.Service()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BQL (String) Query Here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the request string\n",
    "request = \"\"\"\n",
    "let(\n",
    "#NAV=fund_net_asset_val().Value;\n",
    "#px=PX_LAST().Value;\n",
    "#premium=(100)*(#px -#NAV)/#NAV;\n",
    "#max_discount=MIN(#premium);\n",
    "#rank=grouprank(-#max_discount).Value;\n",
    "#AUM=last(dropNA(FUND_TOTAL_ASSETS().Value));\n",
    "#current_=LAST(dropna(#premium)).Value;\n",
    "#volume=matches(px_volume,#premium==min(#premium));\n",
    "#name =groupsort(long_comp_name,sortby=#max_discount,order=asc).Value;\n",
    ")\n",
    "\n",
    "get(\n",
    "   #name,\n",
    "   #current_,\n",
    "   #max_discount,\n",
    "   #volume().date+0d as #Max_Discount_Date,\n",
    "   #volume().Value as #volume_, \n",
    "   #AUM,\n",
    "   PARENT_COMP_NAME as #Issuer\n",
    ")\n",
    "\n",
    "\n",
    "for(filter(\n",
    "   filter(\n",
    "   fundsuniv(['active','primary']), \n",
    "   FUND_TYP == 'ETF' AND EXCH_CODE == 'US' AND \n",
    "   #aum > 75M AND \n",
    "   FUND_STRATEGY == 'Municipal'), \n",
    "#rank <= '50')\n",
    ")\n",
    "\n",
    "with(dates=range(-6M,0D), currency=USD)\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Execute Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute the request\n",
    "response = bq.execute(request)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display the response in a DataFrame\n",
    "newdf1 = bql.combined_df(response)\n",
    "#newdf1.sample(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.write_excel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display as a sortable Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bqwidgets import DataGrid\n",
    "\n",
    "newdf1.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ea275ecc852485994371ea18d2bfab3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DataGrid(column_defs=[{'headerName': 'ID', 'field': 'ID', 'filter': 'text', 'width': 100}, {'headerName': '#na…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dg = DataGrid(data=newdf1)\n",
    "dg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Scatter Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6303254c6860407b92f4ce9c7ac3f417",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Box(children=(Figure(axes=[Axis(label='ID', scale=OrdinalScale(), side='bottom'), Axis(label='I…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import bqviz as bqv\n",
    "\n",
    "bqv.InteractiveScatterPlot(newdf1).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pairplot (seaborn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a12e429550>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(newdf1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Historgram (pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21d037004e0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "newdf1['#max_discount'].plot.hist(title='Peak Discount to NAV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box and Whiskers (seaborn) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a12f68dda0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='#Max_Discount_Date', y='#max_discount',data=newdf1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression (seaborn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1a12f787908>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot('#AUM', '#max_discount', data=newdf1, kind='reg', truncate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bloomberg-style boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "styles = {\n",
    "        'axes.edgecolor': '.8',\n",
    "        'axes.facecolor': '#111111',\n",
    "        'axes.labelcolor': 'white',\n",
    "        'figure.facecolor': '#111111',\n",
    "        'grid.color': '#444444',\n",
    "        'grid.linestyle': '--',\n",
    "        'patch.linewidth': 1.0,\n",
    "        'figure.facecolor': '#111111',\n",
    "        'font.family': ['Arial', 'Helvetica', 'sans-serif'],\n",
    "        'patch.force_edgecolor': True,\n",
    "        'text.color': 'white',\n",
    "        'xtick.color': 'white',\n",
    "        'ytick.color': 'white',\n",
    "        'lines.color': 'r'}\n",
    "\n",
    "sns.set_style(styles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.pairplot(data=newdf1, dropna=True, kind='reg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from bqplot import *\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create some random data to work with\n",
    "np.random.seed(0)\n",
    "rows = ['Y Category {}'.format(i+1) for i in range(6)]\n",
    "columns = ['X Category {}'.format(i+1) for i in range(15)]\n",
    "dataframe = pd.DataFrame(np.random.randn(6,15), index=rows, columns=columns)\n",
    "\n",
    "# Create the scales\n",
    "scale_color = ColorScale(colors=['#FF1E3E', '#1a1a1a', '#30C030'])\n",
    "scale_x = OrdinalScale(allow_padding=False)\n",
    "scale_y = OrdinalScale(allow_padding=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the grid heat map\n",
    "mark_grid_map = GridHeatMap(color=dataframe,\n",
    "                            scales={'color': scale_color,\n",
    "                                    'row':scale_y,\n",
    "                                    'column':scale_x},\n",
    "                            row=dataframe.index,\n",
    "                            column=dataframe.columns)\n",
    "\n",
    "# Add the text labels\n",
    "label_df = pd.melt(dataframe.reset_index(), id_vars='index')\n",
    "mark_label = Label(x=label_df[label_df.columns[1]],\n",
    "                  y=label_df[label_df.columns[0]],\n",
    "                  text=['{0:.2f}'.format(val)\n",
    "                        for val in label_df[label_df.columns[2]]],\n",
    "                  align='middle',\n",
    "                  font_weight='normal',\n",
    "                  default_size=12,\n",
    "                  colors=['white'],\n",
    "                  scales={'x': scale_x, 'y':scale_y})\n",
    "\n",
    "# Create the axes\n",
    "axis_y = Axis(scale=scale_y,orientation='vertical')\n",
    "axis_x = Axis(scale=scale_x,\n",
    "              tick_style={'text-anchor': 'start'},\n",
    "              tick_rotate=25)\n",
    "\n",
    "# Create the figure\n",
    "visualization = Figure(marks=[mark_grid_map, mark_label],\n",
    "                       axes=[axis_y, axis_x],\n",
    "                       padding_y=0.0,\n",
    "                       fig_margin={'top': 50, 'bottom':60,\n",
    "                                   'left':100, 'right':80},\n",
    "                       layout={'width': 'auto', 'height': '400px'},\n",
    "                       title_style={'fill': 'white', 'font-size': '18'},\n",
    "                       title='Basic heatmap with values')\n",
    "\n",
    "def update_plot(new_df):\n",
    "    \"\"\"This function will update the plot when provided a new dataframe.\"\"\"\n",
    "\n",
    "    mark_grid_map.color = new_df\n",
    "    mark_grid_map.row = new_df.index\n",
    "    mark_grid_map.column = new_df.columns\n",
    "    new_label_df = pd.melt(new_df.reset_index(), id_vars='index')\n",
    "    mark_label.x = new_label_df[new_label_df.columns[1]]\n",
    "    mark_label.y = new_label_df[new_label_df.columns[0]]\n",
    "    mark_label.text = ['{0:.2f}'.format(val)\n",
    "                       for val in new_label_df[new_label_df.columns[2]]]\n",
    "\n",
    "visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Interactive Chart via `bqplot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = newdf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code copied from Visualizaiton Gallery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bqplot as bqp\n",
    "from bqplot.interacts import BrushSelector\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from ipywidgets import Dropdown, HBox, VBox, HTML\n",
    "import bqwidgets as bqw\n",
    "\n",
    "\n",
    "# Create scales\n",
    "scale_x = bqp.LinearScale()\n",
    "scale_y = bqp.LinearScale()\n",
    "\n",
    "# Create marks\n",
    "mark_scatter = bqp.Scatter(x=dataframe[dataframe.columns[3]],\n",
    "                           y=dataframe[dataframe.columns[4]],\n",
    "                           scales={'x': scale_x, 'y': scale_y},\n",
    "                           default_size=48,\n",
    "                           colors=['#1B84ED'])\n",
    "\n",
    "# Create Axes\n",
    "axis_x = bqp.Axis(scale=scale_x, label=dataframe.columns[3])\n",
    "axis_y = bqp.Axis(scale=scale_y,\n",
    "                  orientation='vertical',\n",
    "                  tick_format='0.0f',\n",
    "                  label=dataframe.columns[4])\n",
    "\n",
    "# Create selector\n",
    "selector = BrushSelector(x_scale=scale_x,\n",
    "                         y_scale=scale_y,\n",
    "                         marks=[mark_scatter])\n",
    "\n",
    "# Create Figure\n",
    "figure = bqp.Figure(marks=[mark_scatter],\n",
    "                    axes=[axis_x, axis_y],\n",
    "                    animation_duration=500,\n",
    "                    layout={'width':'99%', 'height':'400px'},\n",
    "                    padding_x=0.05,\n",
    "                    title='Interactive scatter plot with linked datagrid',\n",
    "                    title_style={'font-size': '22px'},\n",
    "                    padding_y=0.05,\n",
    "                    interaction=selector,\n",
    "                    fig_margin={'top': 50, 'bottom': 60,\n",
    "                                'left': 50, 'right':30})\n",
    "\n",
    "# Create dropown widgets\n",
    "dropdown_x = Dropdown(description='X axis',\n",
    "                      options=dataframe.columns,\n",
    "                      value=dataframe.columns[3])\n",
    "dropdown_y = Dropdown(description='Y axis',\n",
    "                      options=dataframe.columns,\n",
    "                      value=dataframe.columns[4])\n",
    "\n",
    "# Define callback function for dropdown widgets\n",
    "def update_plot(evt):\n",
    "    if evt is not None:\n",
    "        new_value = evt['new']\n",
    "        if evt['owner'] == dropdown_x:\n",
    "            mark_scatter.x = dataframe[new_value]\n",
    "            axis_x.label = new_value\n",
    "        elif evt['owner'] == dropdown_y:\n",
    "            mark_scatter.y = dataframe[new_value]\n",
    "            axis_y.label = new_value\n",
    "\n",
    "\n",
    "# Define callback function for selections\n",
    "def on_select(evt):\n",
    "    if evt is not None and evt['new'] is not None:\n",
    "        indices = evt['new']\n",
    "        datagrid.data = dataframe.iloc[indices].reset_index()\n",
    "\n",
    "# Bind callback to the dropdown widgets\n",
    "dropdown_x.observe(update_plot, names=['value'])\n",
    "dropdown_y.observe(update_plot, names=['value'])\n",
    "mark_scatter.observe(on_select, names=['selected'])\n",
    "\n",
    "# Create datagrid\n",
    "datagrid = bqw.DataGrid(data=dataframe, layout={'height': '300px'})\n",
    "\n",
    "# Create Box containers\n",
    "widget_box = HBox([dropdown_x, dropdown_y], layout={'margin': '10px'})\n",
    "app_container = VBox([figure, widget_box, datagrid],\n",
    "                     layout={'width':'100%'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# **Display the visualization**\n",
    "app_container"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Picker\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "issuer_picker = widgets.Dropdown(options=newdf1['#Issuer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "issuer_picker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as ipw\n",
    "import bqplot as bqp\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate some data to work with\n",
    "np.random.seed(0)\n",
    "id1 = ['Security 1']*9 + ['Security 2']*9 + ['Security 3']*9\n",
    "id2 = ['Index C']*9 + ['Index B']*9 + ['Index A']*9\n",
    "cat1 = ['Period {}'.format(n+1) for n in range(9)]*3\n",
    "cat2 = ['Category {}'.format(chr(n)) for n in range(65+9, 65,-1)]*3\n",
    "dataframe = pd.DataFrame(data={'ID 1': id1,\n",
    "                               'ID 2': id2,\n",
    "                               'Category 1': cat1,\n",
    "                               'Category 2': cat2,\n",
    "                               'Value 1': np.random.randn(27),\n",
    "                               'Value 2': np.random.randn(27)})\n",
    "\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demo = dataframe\n",
    "newdf1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Columns that will appear in the dropdown widgets\n",
    "X_COLUMNS = ['ID 1', 'ID 2']\n",
    "Y_COLUMNS = ['Category 1', 'Category 2']\n",
    "VALUE_COLUMNS = ['Value 1', 'Value 2']\n",
    "\n",
    "# We need to reshape the data to create our plot\n",
    "processed_dataframe = dataframe.pivot(index=Y_COLUMNS[0],\n",
    "                                      columns=X_COLUMNS[0],\n",
    "                                      values=VALUE_COLUMNS[0])\n",
    "processed_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_df = pd.melt(processed_dataframe.reset_index(),\n",
    "                   id_vars=[Y_COLUMNS[0]],\n",
    "                   value_name=VALUE_COLUMNS[0])\n",
    "\n",
    "# Create scales\n",
    "scale_color = bqp.ColorScale(colors=['#FF1E3E', '#1a1a1a', '#30C030'])\n",
    "scale_x = bqp.OrdinalScale()\n",
    "scale_y = bqp.OrdinalScale()\n",
    "\n",
    "# Create axes\n",
    "axis_x = bqp.Axis(orientation='horizontal',\n",
    "                  scale=scale_x,\n",
    "                  label=X_COLUMNS[0],\n",
    "                  grid_lines='none',\n",
    "                  label_offset='3em')\n",
    "\n",
    "axis_y = bqp.Axis(orientation='vertical',\n",
    "                  scale=scale_y,\n",
    "                  label=Y_COLUMNS[0],\n",
    "                  grid_lines='none',\n",
    "                  label_offset='6em')\n",
    "\n",
    "# Create mark\n",
    "grid_map = bqp.GridHeatMap(color=processed_dataframe,\n",
    "                           row=processed_dataframe.index.astype(str),\n",
    "                           column=processed_dataframe.columns,\n",
    "                           scales={'color': scale_color,\n",
    "                                   'row': scale_y,\n",
    "                                   'column': scale_x},\n",
    "                           font_style={'font-size': '12px',\n",
    "                                       'fill':'black',\n",
    "                                       'font-weight': 'bold'},\n",
    "                           interactions={'click': 'select'},\n",
    "                           selected_style={'stroke': '#ffffff',\n",
    "                                           'stroke-width': 2},\n",
    "                           opacity=1,\n",
    "                           stroke='#626262')\n",
    "\n",
    "mark_label = bqp.Label(x=label_df[X_COLUMNS[0]],\n",
    "                       y=label_df[Y_COLUMNS[0]],\n",
    "                       text=['{0:.3f}'.format(val)\n",
    "                            for val in label_df[VALUE_COLUMNS[0]]],\n",
    "                       scales={'x': scale_x, 'y': scale_y},\n",
    "                       colors=['white'],\n",
    "                       default_size=14,\n",
    "                       font_weight='normal',\n",
    "                       align='middle')\n",
    "\n",
    "# Create figure\n",
    "figure = bqp.Figure(marks=[grid_map, mark_label],\n",
    "                    axes=[axis_x, axis_y],\n",
    "                    title='Interactive heat map with labels',\n",
    "                    title_style={'font-size': '22px'},\n",
    "                    layout={'width':'auto', 'height': '500px'},\n",
    "                    fig_margin={'top': 50, 'bottom': 75,\n",
    "                                'left': 120, 'right': 50},\n",
    "                    padding_y=0.0,\n",
    "                    padding_x=0.0)\n",
    "\n",
    "# HTML widget to display selection\n",
    "html = ipw.HTML()\n",
    "\n",
    "# Dropdown widgets\n",
    "dropdown_x = ipw.Dropdown(description='X axis',\n",
    "                          options=X_COLUMNS,\n",
    "                          value=X_COLUMNS[0])\n",
    "dropdown_y = ipw.Dropdown(description='Y axis',\n",
    "                          options=Y_COLUMNS,\n",
    "                          value=Y_COLUMNS[0])\n",
    "dropdown_value = ipw.Dropdown(description='Value',\n",
    "                              options=VALUE_COLUMNS,\n",
    "                              value=VALUE_COLUMNS[0])\n",
    "\n",
    "# Callback functions\n",
    "def handle_selection(self, evt):\n",
    "    html.value = 'Clicked on: {}'.format(evt['data'])\n",
    "\n",
    "def update_plot(evt=None):\n",
    "    if evt['new'] is not None:\n",
    "        processed_dataframe = dataframe.pivot(index=dropdown_y.value,\n",
    "                                              columns=dropdown_x.value,\n",
    "                                              values=dropdown_value.value)\n",
    "\n",
    "        label_df = pd.melt(processed_dataframe.reset_index(),\n",
    "                   id_vars=[dropdown_y.value],\n",
    "                   value_name=dropdown_value.value)\n",
    "\n",
    "        grid_map.color = processed_dataframe\n",
    "        grid_map.column = processed_dataframe.columns\n",
    "        grid_map.row = processed_dataframe.index\n",
    "        axis_x.label = dropdown_x.value\n",
    "        axis_y.label = dropdown_y.value\n",
    "        mark_label.x = label_df[dropdown_x.value]\n",
    "        mark_label.y = label_df[dropdown_y.value]\n",
    "        mark_label.text = ['{0:.3f}'.format(val)\n",
    "                          for val in label_df[dropdown_value.value]]\n",
    "\n",
    "# Event handlers for heatmap clicks and dropdown updates\n",
    "grid_map.on_element_click(handle_selection)\n",
    "dropdown_x.observe(update_plot, names=['value'])\n",
    "dropdown_y.observe(update_plot, names=['value'])\n",
    "dropdown_value.observe(update_plot, names=['value'])\n",
    "\n",
    "# Display the figure\n",
    "ipw.VBox([figure,\n",
    "          ipw.HBox([dropdown_x, dropdown_y, dropdown_value]),\n",
    "          html])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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