Aula_1 (2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bql \n",
    "import bqviz as bqv\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mpl_dates\n",
    "bq = bql.Service()\n",
    "\n",
    "entities = ['MSFT US Equity', 'GE US Equity', 'BAC US Equity']\n",
    "interval = bq.func.range('2019-01-01', '2019-12-31')\n",
    "price = bq.data.px_last(dates=interval, frq='D', fill='prev')\n",
    "\n",
    "request = bql.Request(entities, price)\n",
    "result = bq.execute(request)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bql.request.response.Response object at 0x000002D59F0F79E8>\n"
     ]
    }
   ],
   "source": [
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting Response object to a DataFrame:\n",
    "\n",
    "df_main = result[0].df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1095 entries, MSFT US Equity to BAC US Equity\n",
      "Data columns (total 3 columns):\n",
      "DATE                                                                    1095 non-null datetime64[ns]\n",
      "CURRENCY                                                                1095 non-null object\n",
      "PX_LAST(frq=PER.D,fill=FILL.prev,dates=RANGE(2019-01-01,2019-12-31))    1095 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 34.2+ KB\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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(frq=PER.D,fill=FILL.prev,dates=RANGE(2019-01-01,2019-12-31))</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>MSFT US Equity</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>USD</td>\n",
       "      <td>101.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT US Equity</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>USD</td>\n",
       "      <td>101.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT US Equity</th>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>USD</td>\n",
       "      <td>97.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT US Equity</th>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>USD</td>\n",
       "      <td>101.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT US Equity</th>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>USD</td>\n",
       "      <td>101.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     DATE CURRENCY  \\\n",
       "ID                                   \n",
       "MSFT US Equity 2019-01-01      USD   \n",
       "MSFT US Equity 2019-01-02      USD   \n",
       "MSFT US Equity 2019-01-03      USD   \n",
       "MSFT US Equity 2019-01-04      USD   \n",
       "MSFT US Equity 2019-01-05      USD   \n",
       "\n",
       "                PX_LAST(frq=PER.D,fill=FILL.prev,dates=RANGE(2019-01-01,2019-12-31))  \n",
       "ID                                                                                    \n",
       "MSFT US Equity                                             101.57                     \n",
       "MSFT US Equity                                             101.12                     \n",
       "MSFT US Equity                                              97.40                     \n",
       "MSFT US Equity                                             101.93                     \n",
       "MSFT US Equity                                             101.93                     "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspecting DataFrame:\n",
    "\n",
    "df_main.info()\n",
    "\n",
    "print('\\n')\n",
    "\n",
    "df_main.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MSFT US Equity</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>USD</td>\n",
       "      <td>101.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT US Equity</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>USD</td>\n",
       "      <td>101.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT US Equity</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>USD</td>\n",
       "      <td>97.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT US Equity</td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>USD</td>\n",
       "      <td>101.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSFT US Equity</td>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>USD</td>\n",
       "      <td>101.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ID       Date Currency   Close\n",
       "0  MSFT US Equity 2019-01-01      USD  101.57\n",
       "1  MSFT US Equity 2019-01-02      USD  101.12\n",
       "2  MSFT US Equity 2019-01-03      USD   97.40\n",
       "3  MSFT US Equity 2019-01-04      USD  101.93\n",
       "4  MSFT US Equity 2019-01-05      USD  101.93"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming columns and resetting the index column:\n",
    "\n",
    "col_names = ['Date', 'Currency', 'Close']\n",
    "df_main.columns = col_names\n",
    "df_main.reset_index(inplace=True)\n",
    "                    \n",
    "df_main.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ID</th>\n",
       "      <th>BAC US Equity</th>\n",
       "      <th>GE US Equity</th>\n",
       "      <th>MSFT US Equity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>35.35</td>\n",
       "      <td>11.18</td>\n",
       "      <td>158.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-28</th>\n",
       "      <td>35.35</td>\n",
       "      <td>11.18</td>\n",
       "      <td>158.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29</th>\n",
       "      <td>35.35</td>\n",
       "      <td>11.18</td>\n",
       "      <td>158.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>35.15</td>\n",
       "      <td>11.08</td>\n",
       "      <td>157.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>35.22</td>\n",
       "      <td>11.16</td>\n",
       "      <td>157.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ID          BAC US Equity  GE US Equity  MSFT US Equity\n",
       "Date                                                   \n",
       "2019-12-27          35.35         11.18          158.96\n",
       "2019-12-28          35.35         11.18          158.96\n",
       "2019-12-29          35.35         11.18          158.96\n",
       "2019-12-30          35.15         11.08          157.59\n",
       "2019-12-31          35.22         11.16          157.70"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building final DataFrame:\n",
    "\n",
    "df_final = df_main.pivot('Date', 'ID', 'Close')\n",
    "df_final.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning DataFrame (dealing with missing values):\n",
    "\n",
    "# Removing missing values (rows, axis=0):\n",
    "\n",
    "df_final.dropna(inplace=True, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "131765a142d548e593dc13f59401e1f7",
       "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": [
    "# Plotting our data using bqv: \n",
    "\n",
    "line_plot = bqv.LinePlot(df_final).set_style()\n",
    "line_plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting our data using matplotlib:\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "format = mpl_dates.DateFormatter('%B %Y')\n",
    "plt.gca().xaxis.set_major_formatter(format)\n",
    "plt.plot(df_final.index, df_final)\n",
    "plt.ylabel('Price (USD)')\n",
    "\n",
    "labels = df_final.columns\n",
    "\n",
    "plt.grid(linewidth=0.5)\n",
    "plt.legend(labels)\n",
    "plt.tick_params(axis='x', rotation=30)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (sandboxed)",
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}