dbnomics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dbnomics\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp = dbnomics.fetch_series( \"OECD\", \"MEI\", \"USA.NAEXCP01.STSA.A\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "inflation = dbnomics.fetch_series(\"OECD\", \"KEI\", \"CPALTT01.USA.GP.A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# growth = dbnomics.fetch_series(\"OECD\", \"CSPCUBE\", \"EVOGDP_T1.USA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemployment = dbnomics.fetch_series(\"OECD\", \"CSPCUBE\", \"UNEMPLRT_T1C.USA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3be5e70690>]"
      ]
     },
     "execution_count": 136,
     "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": [
    "plt.plot(unemployment['period'], unemployment['value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3be5ec55d0>]"
      ]
     },
     "execution_count": 137,
     "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": [
    "plt.plot(growth['period'], growth['value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unemployment, growth, inflation, gdp = [e[['period','value']]for e in [unemployment, growth, inflation, gdp]]\n",
    "unemployment, inflation, gdp = [e[['period','value']].set_index('period') for e in [unemployment, inflation, gdp]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "growth = gdp.diff()/gdp.shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1955-01-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1956-01-01</td>\n",
       "      <td>0.056108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1957-01-01</td>\n",
       "      <td>0.054939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1958-01-01</td>\n",
       "      <td>0.015165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1959-01-01</td>\n",
       "      <td>0.084005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>0.044231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>0.039796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>0.026901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>0.042981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.054346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               value\n",
       "period              \n",
       "1955-01-01       NaN\n",
       "1956-01-01  0.056108\n",
       "1957-01-01  0.054939\n",
       "1958-01-01  0.015165\n",
       "1959-01-01  0.084005\n",
       "...              ...\n",
       "2014-01-01  0.044231\n",
       "2015-01-01  0.039796\n",
       "2016-01-01  0.026901\n",
       "2017-01-01  0.042981\n",
       "2018-01-01  0.054346\n",
       "\n",
       "[64 rows x 1 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = unemployment.merge(growth, on='period').merge(inflation, on='period').merge(gdp, on='period', suffixes=('_z', '_t')).rename(columns=\n",
    "    {'value_x': \"unemployment\", 'value_y': \"growth\", 'value_z': 'inflation', 'value_t': 'gdp'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['period'] = df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "ch = alt.Chart(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "alt.Chart(...)"
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     "execution_count": 185,
     "metadata": {},
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   "source": [
    "ch.mark_point().encode(x='inflation', y='unemployment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
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542.38225, \"period\": \"1960-01-01T00:00:00\"}, {\"unemployment\": 6.683333, \"growth\": 0.036556321671662347, \"inflation\": 1.0707241476, \"gdp\": 562.20975, \"period\": \"1961-01-01T00:00:00\"}, {\"unemployment\": 5.541667, \"growth\": 0.07419250555508874, \"inflation\": 1.1987733482, \"gdp\": 603.9215, \"period\": \"1962-01-01T00:00:00\"}, {\"unemployment\": 5.666667, \"growth\": 0.055520460854597775, \"inflation\": 1.2396694215, \"gdp\": 637.4515, \"period\": \"1963-01-01T00:00:00\"}, {\"unemployment\": 5.191667, \"growth\": 0.07374678701046274, \"inflation\": 1.2789115646, \"gdp\": 684.4615, \"period\": \"1964-01-01T00:00:00\"}, {\"unemployment\": 4.525, \"growth\": 0.08448795147718317, \"inflation\": 1.5851692638, \"gdp\": 742.29025, \"period\": \"1965-01-01T00:00:00\"}, {\"unemployment\": 3.783333, \"growth\": 0.09581629288543132, \"inflation\": 3.0150753769, \"gdp\": 813.41375, \"period\": \"1966-01-01T00:00:00\"}, {\"unemployment\": 3.841667, \"growth\": 0.05721872786143571, 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3.6645632175, \"gdp\": 4855.21625, \"period\": \"1987-01-01T00:00:00\"}, {\"unemployment\": 5.491667, \"growth\": 0.07851797538369165, \"inflation\": 4.0777411074, \"gdp\": 5236.438, \"period\": \"1988-01-01T00:00:00\"}, {\"unemployment\": 5.266667, \"growth\": 0.07736967381261838, \"inflation\": 4.8270030301, \"gdp\": 5641.5795, \"period\": \"1989-01-01T00:00:00\"}, {\"unemployment\": 5.616667, \"growth\": 0.0569991081398393, \"inflation\": 5.3979564399, \"gdp\": 5963.1445, \"period\": \"1990-01-01T00:00:00\"}, {\"unemployment\": 6.816667, \"growth\": 0.0326983104300088, \"inflation\": 4.2349639645, \"gdp\": 6158.12925, \"period\": \"1991-01-01T00:00:00\"}, {\"unemployment\": 7.508333, \"growth\": 0.05881623871405432, \"inflation\": 3.0288196781, \"gdp\": 6520.32725, \"period\": \"1992-01-01T00:00:00\"}, {\"unemployment\": 6.9, \"growth\": 0.05187335497616317, \"inflation\": 2.9516569664, \"gdp\": 6858.5585, \"period\": \"1993-01-01T00:00:00\"}, {\"unemployment\": 6.083333, 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\"2000-01-01T00:00:00\"}, {\"unemployment\": 4.733333, \"growth\": 0.03213661867553926, \"inflation\": 2.8261711189, \"gdp\": 10581.82225, \"period\": \"2001-01-01T00:00:00\"}, {\"unemployment\": 5.775, \"growth\": 0.033509918388583915, \"inflation\": 1.5860316265, \"gdp\": 10936.41825, \"period\": \"2002-01-01T00:00:00\"}, {\"unemployment\": 5.991667, \"growth\": 0.04771466197353959, \"inflation\": 2.2700949734, \"gdp\": 11458.24575, \"period\": \"2003-01-01T00:00:00\"}, {\"unemployment\": 5.533333, \"growth\": 0.06593369670047446, \"inflation\": 2.6772366931, \"gdp\": 12213.73025, \"period\": \"2004-01-01T00:00:00\"}, {\"unemployment\": 5.066667, \"growth\": 0.06737554646746845, \"inflation\": 3.3927468455, \"gdp\": 13036.637, \"period\": \"2005-01-01T00:00:00\"}, {\"unemployment\": 4.616667, \"growth\": 0.05967581976854918, \"inflation\": 3.2259441007, \"gdp\": 13814.609, \"period\": \"2006-01-01T00:00:00\"}, {\"unemployment\": 4.616667, \"growth\": 0.04612870331690169, 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6.166667, \"growth\": 0.044230822907835754, \"inflation\": 1.6222229774, \"gdp\": 17527.25825, \"period\": \"2014-01-01T00:00:00\"}, {\"unemployment\": 5.291667, \"growth\": 0.03979641253930864, \"inflation\": 0.1186271356, \"gdp\": 18224.78025, \"period\": \"2015-01-01T00:00:00\"}, {\"unemployment\": 4.866667, \"growth\": 0.026900749598887448, \"inflation\": 1.2615832057, \"gdp\": 18715.0405, \"period\": \"2016-01-01T00:00:00\"}, {\"unemployment\": 4.35, \"growth\": 0.04298056421518306, \"inflation\": 2.1301100037, \"gdp\": 19519.4235, \"period\": \"2017-01-01T00:00:00\"}, {\"unemployment\": 3.9, \"growth\": 0.054345841720171754, \"inflation\": 2.4425832969, \"gdp\": 20580.223, \"period\": \"2018-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brush = alt.selection_interval(encodings=['x'])\n",
    "timeline = ch.mark_point().encode(x='period', y='gdp').add_selection(brush)\n",
    "reg_philips = ch.transform_filter(brush).encode(x='unemployment', y='inflation').transform_regression('unemployment', 'inflation').mark_line()\n",
    "philips = ch.mark_point().encode(x='unemployment', y='inflation', color=alt.condition(brush, alt.value(\"lightblue\"), alt.value('lightgray')))\n",
    "\n",
    "reg_okun = ch.transform_filter(brush).encode(x='growth', y='unemployment').transform_regression('growth', 'unemployment').mark_line()\n",
    "okun = ch.mark_point().encode(x='growth', y='unemployment', color=alt.condition(brush, alt.value(\"default\"), alt.value('lightgray')))\n",
    "\n",
    "reg_gg = ch.transform_filter(brush).encode(x='growth', y='inflation').transform_regression('growth', 'inflation').mark_line()\n",
    "gg = ch.mark_point().encode(x='growth', y='inflation', color=alt.condition(brush, alt.value(\"default\"), alt.value('lightgray')))\n",
    "\n",
    "timeline & (philips+reg_philips) | (okun + reg_okun) & (reg_gg+gg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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