_David-s BQNT Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Fetch of some prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the BQL library\n",
    "import bql\n",
    "# Import the bqviz and bqplot libraries\n",
    "import bqviz as bqv\n",
    "from bqplot import pyplot as plt\n",
    "\n",
    "# Instantiate an object to interface with the BQL service\n",
    "bq = bql.Service()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>CURRENCY</th>\n",
       "      <th>#LAST_PRICE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>USD</td>\n",
       "      <td>36.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-15</td>\n",
       "      <td>USD</td>\n",
       "      <td>37.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-16</td>\n",
       "      <td>USD</td>\n",
       "      <td>38.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-17</td>\n",
       "      <td>USD</td>\n",
       "      <td>37.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-18</td>\n",
       "      <td>USD</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-19</td>\n",
       "      <td>USD</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>USD</td>\n",
       "      <td>37.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>USD</td>\n",
       "      <td>37.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-22</td>\n",
       "      <td>USD</td>\n",
       "      <td>38.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-23</td>\n",
       "      <td>USD</td>\n",
       "      <td>37.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-24</td>\n",
       "      <td>USD</td>\n",
       "      <td>37.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-25</td>\n",
       "      <td>USD</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-26</td>\n",
       "      <td>USD</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-27</td>\n",
       "      <td>USD</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-28</td>\n",
       "      <td>USD</td>\n",
       "      <td>37.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-29</td>\n",
       "      <td>USD</td>\n",
       "      <td>36.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-30</td>\n",
       "      <td>USD</td>\n",
       "      <td>37.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>USD</td>\n",
       "      <td>36.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>USD</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR US Equity</th>\n",
       "      <td>2019-06-02</td>\n",
       "      <td>USD</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DATE CURRENCY  #LAST_PRICE\n",
       "ID                                             \n",
       "TWTR US Equity 2019-05-14      USD        36.93\n",
       "TWTR US Equity 2019-05-15      USD        37.90\n",
       "TWTR US Equity 2019-05-16      USD        38.30\n",
       "TWTR US Equity 2019-05-17      USD        37.50\n",
       "TWTR US Equity 2019-05-18      USD          NaN\n",
       "TWTR US Equity 2019-05-19      USD          NaN\n",
       "TWTR US Equity 2019-05-20      USD        37.15\n",
       "TWTR US Equity 2019-05-21      USD        37.47\n",
       "TWTR US Equity 2019-05-22      USD        38.58\n",
       "TWTR US Equity 2019-05-23      USD        37.19\n",
       "TWTR US Equity 2019-05-24      USD        37.41\n",
       "TWTR US Equity 2019-05-25      USD          NaN\n",
       "TWTR US Equity 2019-05-26      USD          NaN\n",
       "TWTR US Equity 2019-05-27      USD          NaN\n",
       "TWTR US Equity 2019-05-28      USD        37.29\n",
       "TWTR US Equity 2019-05-29      USD        36.85\n",
       "TWTR US Equity 2019-05-30      USD        37.14\n",
       "TWTR US Equity 2019-05-31      USD        36.44\n",
       "TWTR US Equity 2019-06-01      USD          NaN\n",
       "TWTR US Equity 2019-06-02      USD          NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a request for the daily last price of Apple and IBM over the last year\n",
    "request = \"get(PX_LAST(dates=range(-1Y,-1D)) as #LAST_PRICE) for (['TWTR US Equity','MU US Equity'])\"\n",
    "\n",
    "# Execute the request to return a response object\n",
    "response = bq.execute(request)\n",
    "\n",
    "# Convert the response object to a DataFrame \n",
    "df = response[0].df()\n",
    "df.head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ID</th>\n",
       "      <th>DATE</th>\n",
       "      <th>CURRENCY</th>\n",
       "      <th>#LAST_PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>486</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>USD</td>\n",
       "      <td>45.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>487</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-04-17</td>\n",
       "      <td>USD</td>\n",
       "      <td>45.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>488</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>USD</td>\n",
       "      <td>43.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>489</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>USD</td>\n",
       "      <td>41.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>490</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>USD</td>\n",
       "      <td>43.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>491</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>USD</td>\n",
       "      <td>43.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>492</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>USD</td>\n",
       "      <td>44.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>493</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>USD</td>\n",
       "      <td>45.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>494</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-04-28</td>\n",
       "      <td>USD</td>\n",
       "      <td>45.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>495</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>USD</td>\n",
       "      <td>49.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>496</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>USD</td>\n",
       "      <td>47.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>497</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>USD</td>\n",
       "      <td>45.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>498</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>USD</td>\n",
       "      <td>44.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>499</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-05-05</td>\n",
       "      <td>USD</td>\n",
       "      <td>45.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>500</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>USD</td>\n",
       "      <td>46.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>501</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>USD</td>\n",
       "      <td>46.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>502</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-05-08</td>\n",
       "      <td>USD</td>\n",
       "      <td>48.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>503</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>USD</td>\n",
       "      <td>48.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>504</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-05-12</td>\n",
       "      <td>USD</td>\n",
       "      <td>45.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>505</td>\n",
       "      <td>MU US Equity</td>\n",
       "      <td>2020-05-13</td>\n",
       "      <td>USD</td>\n",
       "      <td>43.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index            ID       DATE CURRENCY  #LAST_PRICE\n",
       "486    486  MU US Equity 2020-04-16      USD        45.53\n",
       "487    487  MU US Equity 2020-04-17      USD        45.70\n",
       "488    488  MU US Equity 2020-04-20      USD        43.40\n",
       "489    489  MU US Equity 2020-04-21      USD        41.41\n",
       "490    490  MU US Equity 2020-04-22      USD        43.91\n",
       "491    491  MU US Equity 2020-04-23      USD        43.77\n",
       "492    492  MU US Equity 2020-04-24      USD        44.16\n",
       "493    493  MU US Equity 2020-04-27      USD        45.28\n",
       "494    494  MU US Equity 2020-04-28      USD        45.31\n",
       "495    495  MU US Equity 2020-04-29      USD        49.83\n",
       "496    496  MU US Equity 2020-04-30      USD        47.89\n",
       "497    497  MU US Equity 2020-05-01      USD        45.06\n",
       "498    498  MU US Equity 2020-05-04      USD        44.46\n",
       "499    499  MU US Equity 2020-05-05      USD        45.41\n",
       "500    500  MU US Equity 2020-05-06      USD        46.08\n",
       "501    501  MU US Equity 2020-05-07      USD        46.59\n",
       "502    502  MU US Equity 2020-05-08      USD        48.25\n",
       "503    503  MU US Equity 2020-05-11      USD        48.20\n",
       "504    504  MU US Equity 2020-05-12      USD        45.69\n",
       "505    505  MU US Equity 2020-05-13      USD        43.53"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop N/A values and reset the dataframe index\n",
    "df = df.dropna().reset_index()\n",
    "df.tail(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ID</th>\n",
       "      <th>MU US Equity</th>\n",
       "      <th>TWTR US Equity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-14</th>\n",
       "      <td>38.51</td>\n",
       "      <td>36.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-15</th>\n",
       "      <td>38.41</td>\n",
       "      <td>37.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-16</th>\n",
       "      <td>37.31</td>\n",
       "      <td>38.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-17</th>\n",
       "      <td>36.06</td>\n",
       "      <td>37.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-20</th>\n",
       "      <td>34.62</td>\n",
       "      <td>37.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ID          MU US Equity  TWTR US Equity\n",
       "DATE                                    \n",
       "2019-05-14         38.51           36.93\n",
       "2019-05-15         38.41           37.90\n",
       "2019-05-16         37.31           38.30\n",
       "2019-05-17         36.06           37.50\n",
       "2019-05-20         34.62           37.15"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pivot the DataFrame on the columns to plot\n",
    "df = df.pivot(\"DATE\",\"ID\",\"#LAST_PRICE\")\n",
    "\n",
    "# Check the DataFrame columns\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 'Pythonic' Fetch (BQL Object Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate an object to interface with the BQL service\n",
    "bq = bql.Service()\n",
    "\n",
    "# Define the date range for the request\n",
    "date_range = bq.func.range('2019-06-05','2020-02-03')\n",
    "\n",
    "# Define data items for the pricing fields\n",
    "# Pass the defined date range\n",
    "last = bq.data.px_last(dates=date_range)\n",
    "high = bq.data.px_high(dates=date_range)\n",
    "low = bq.data.px_low(dates=date_range)\n",
    "\n",
    "# Generate the request using the security ticker and a list of the data items\n",
    "request = bql.Request(\"GILD US Equity\", [last, high, low])\n",
    "\n",
    "# Execute the request\n",
    "response = bq.execute(request)\n",
    "\n",
    "# Display the response in a data frame\n",
    "# Use the combined_df function to display \n",
    "# the three returned values in a single data frame \n",
    "newdf = bql.combined_df(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>CURRENCY</th>\n",
       "      <th>PX_LAST(dates=RANGE(2019-06-05,2020-02-03))</th>\n",
       "      <th>PX_HIGH(dates=RANGE(2019-06-05,2020-02-03))</th>\n",
       "      <th>PX_LOW(dates=RANGE(2019-06-05,2020-02-03))</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GILD US Equity</th>\n",
       "      <td>2019-06-18</td>\n",
       "      <td>USD</td>\n",
       "      <td>68.20</td>\n",
       "      <td>68.900</td>\n",
       "      <td>67.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GILD US Equity</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>USD</td>\n",
       "      <td>63.15</td>\n",
       "      <td>64.200</td>\n",
       "      <td>62.8400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GILD US Equity</th>\n",
       "      <td>2019-06-20</td>\n",
       "      <td>USD</td>\n",
       "      <td>68.57</td>\n",
       "      <td>69.320</td>\n",
       "      <td>67.7100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GILD US Equity</th>\n",
       "      <td>2019-12-21</td>\n",
       "      <td>USD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GILD US Equity</th>\n",
       "      <td>2019-09-13</td>\n",
       "      <td>USD</td>\n",
       "      <td>66.52</td>\n",
       "      <td>67.490</td>\n",
       "      <td>65.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GILD US Equity</th>\n",
       "      <td>2019-07-10</td>\n",
       "      <td>USD</td>\n",
       "      <td>67.46</td>\n",
       "      <td>68.530</td>\n",
       "      <td>66.9002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GILD US Equity</th>\n",
       "      <td>2019-10-13</td>\n",
       "      <td>USD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GILD US Equity</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>USD</td>\n",
       "      <td>63.94</td>\n",
       "      <td>64.055</td>\n",
       "      <td>62.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GILD US Equity</th>\n",
       "      <td>2019-07-06</td>\n",
       "      <td>USD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GILD US Equity</th>\n",
       "      <td>2019-06-11</td>\n",
       "      <td>USD</td>\n",
       "      <td>66.10</td>\n",
       "      <td>66.835</td>\n",
       "      <td>65.9500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DATE CURRENCY  \\\n",
       "ID                                   \n",
       "GILD US Equity 2019-06-18      USD   \n",
       "GILD US Equity 2020-01-24      USD   \n",
       "GILD US Equity 2019-06-20      USD   \n",
       "GILD US Equity 2019-12-21      USD   \n",
       "GILD US Equity 2019-09-13      USD   \n",
       "GILD US Equity 2019-07-10      USD   \n",
       "GILD US Equity 2019-10-13      USD   \n",
       "GILD US Equity 2020-01-23      USD   \n",
       "GILD US Equity 2019-07-06      USD   \n",
       "GILD US Equity 2019-06-11      USD   \n",
       "\n",
       "                PX_LAST(dates=RANGE(2019-06-05,2020-02-03))  \\\n",
       "ID                                                            \n",
       "GILD US Equity                                        68.20   \n",
       "GILD US Equity                                        63.15   \n",
       "GILD US Equity                                        68.57   \n",
       "GILD US Equity                                          NaN   \n",
       "GILD US Equity                                        66.52   \n",
       "GILD US Equity                                        67.46   \n",
       "GILD US Equity                                          NaN   \n",
       "GILD US Equity                                        63.94   \n",
       "GILD US Equity                                          NaN   \n",
       "GILD US Equity                                        66.10   \n",
       "\n",
       "                PX_HIGH(dates=RANGE(2019-06-05,2020-02-03))  \\\n",
       "ID                                                            \n",
       "GILD US Equity                                       68.900   \n",
       "GILD US Equity                                       64.200   \n",
       "GILD US Equity                                       69.320   \n",
       "GILD US Equity                                          NaN   \n",
       "GILD US Equity                                       67.490   \n",
       "GILD US Equity                                       68.530   \n",
       "GILD US Equity                                          NaN   \n",
       "GILD US Equity                                       64.055   \n",
       "GILD US Equity                                          NaN   \n",
       "GILD US Equity                                       66.835   \n",
       "\n",
       "                PX_LOW(dates=RANGE(2019-06-05,2020-02-03))  \n",
       "ID                                                          \n",
       "GILD US Equity                                     67.3800  \n",
       "GILD US Equity                                     62.8400  \n",
       "GILD US Equity                                     67.7100  \n",
       "GILD US Equity                                         NaN  \n",
       "GILD US Equity                                     65.9600  \n",
       "GILD US Equity                                     66.9002  \n",
       "GILD US Equity                                         NaN  \n",
       "GILD US Equity                                     62.7400  \n",
       "GILD US Equity                                         NaN  \n",
       "GILD US Equity                                     65.9500  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.sample(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chart a time series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With BQVIZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "615823a52bba4af6a7dca4a42e3d91b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "GridBox(children=(Figure(animation_duration=500, axes=[Axis(color='white', grid_color='#3c3c3c', grid_lines='d…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pass the example DataFrame to the bqviz LinePlot object and apply Bloomberg styles\n",
    "bqv_plot = bqv.LinePlot(df).set_style()\n",
    "\n",
    "# Display the plot\n",
    "bqv_plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With BQPLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55975ee1fedb4d7696d5f4bad97643a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Figure(axes=[Axis(scale=DateScale()), Axis(orientation='vertical', scale=LinearScale())], fig_m…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define series objects for the price lines\n",
    "nvda_price = df['TWTR US Equity']\n",
    "mu_price = df['MU US Equity']\n",
    "# Define a series object for the date index\n",
    "plot_dates = df.index\n",
    "\n",
    "# Create the figure canvas\n",
    "plt.figure()\n",
    "# Define the plot using the price date and price series objects\n",
    "nvda_line = plt.plot(plot_dates, [nvda_price, mu_price])\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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