0_DATAPREP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparación de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff = pd.read_excel(\"FISLAC_update.xlsx\", sheet_name = \"Sheet1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pob = pd.read_excel(\"POP_WB.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-59ca4d38292b>:4: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.\n",
      "To preserve the previous behavior, use\n",
      "\n",
      "\t>>> .groupby(..., group_keys=False)\n",
      "\n",
      "To adopt the future behavior and silence this warning, use \n",
      "\n",
      "\t>>> .groupby(..., group_keys=True)\n",
      "  pob['POP'] = pob.groupby('ISO3')['POP'].apply(lambda x: x.interpolate())\n",
      "<ipython-input-6-59ca4d38292b>:5: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.\n",
      "To preserve the previous behavior, use\n",
      "\n",
      "\t>>> .groupby(..., group_keys=False)\n",
      "\n",
      "To adopt the future behavior and silence this warning, use \n",
      "\n",
      "\t>>> .groupby(..., group_keys=True)\n",
      "  pob['POP'] = pob.groupby('ISO3')['POP'].apply(lambda x: x.interpolate().fillna(method='bfill').fillna(method='ffill'))\n"
     ]
    }
   ],
   "source": [
    "pob['DATE'] = pd.to_datetime(pob['DATE'], format='%Y')\n",
    "pob['POP'] = pd.to_numeric(pob['POP'], errors='coerce')\n",
    "pob.sort_values(by=['ISO3', 'DATE'], inplace=True)\n",
    "pob['POP'] = pob.groupby('ISO3')['POP'].apply(lambda x: x.interpolate())\n",
    "pob['POP'] = pob.groupby('ISO3')['POP'].apply(lambda x: x.interpolate().fillna(method='bfill').fillna(method='ffill'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pob['DATE'] = pob['DATE'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "explicativas = [\"INTR_EXPENSES_GDP\", \"DEBT_AFFORTABILITY\", \"PRIM_BAL_GDP\", \"CURR_EXP_GDP\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables = explicativas + [\"DEBT_GDP\", \"EXP_GDP\", \"REV_GDP\",\n",
    "             \"WGI_CONTR_CORR\", \"WGI_GOV_EFFECT\", \"WGI_POL_STAB\", \"WGI_REG_QUAL\", \"WGI_ROL\", \"WGI_VOICE_ACCOUNT\", \n",
    "             \"TRADE_OPEN\", \"CONSUMPTION_PC\", \"NET_FDI_GDP\", \"BROAD_MONEY_GDP\", \"LUR\", \"CAP_RETURN\", \"INT_RESERVES_CAP\", \"INFLATION\", \"BANK_CRISIS\", \"COMDEP\", \"INTRES_GDP\"]\n",
    "\n",
    "fr = [\"FISCAL_RULE\"]\n",
    "ids = [\"COUNTRY_NAME\", \"WEO_CODE\", \"ISO3\" , \"LAC1\", \"LAC2\", \"GROUP\", \"REGION\", \"DATE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dff[ids + variables + fr]\n",
    "\n",
    "fr = df[[\"ISO3\", \"DATE\", \"FISCAL_RULE\"]]\n",
    "fr = fr[df.DATE>2000]\n",
    "fr = fr.groupby([\"ISO3\"])[\"FISCAL_RULE\"].sum().reset_index()\n",
    "fr[\"FISCAL_RULE\"] = fr[\"FISCAL_RULE\"].apply(lambda x: 1 if x>0 else 0)\n",
    "\n",
    "df = df[ids + variables]\n",
    "\n",
    "df = df.merge(fr, \"left\", \"ISO3\")\n",
    "df= df[df.FISCAL_RULE==1]\n",
    "\n",
    "df = df[ids + variables]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.merge(pob, \"left\", [\"DATE\", \"ISO3\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df[df.LAC2==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "grupos = df[[\"ISO3\", \"REGION\", \"GROUP\", \"COUNTRY_NAME\"]].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster para seleccionar pool de paises"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-45-93b5fc6f98d6>:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  dfc = dfc.groupby(\"ISO3\").mean()\n"
     ]
    }
   ],
   "source": [
    "dfc = df[(df.DATE>=2000) & (df.DATE<=2005)]\n",
    "dfc = dfc.groupby(\"ISO3\").mean()\n",
    "dfc = dfc[variables]\n",
    "\n",
    "dfcm = dfc.copy()\n",
    "dfcm = dfcm.merge(grupos, \"left\", \"ISO3\")\n",
    "\n",
    "dfc = pd.DataFrame(scaler.fit_transform(dfc), columns=dfc.columns, index = dfc.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "peru = dfcm[dfcm.ISO3 == \"PER\"].T.reset_index(); peru.columns = [\"VARIABLE\", \"MEDIA\"]\n",
    "maximo_mimebros = 40"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering jerárquico aglomerativo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "linked = linkage(dfc, method='ward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-48-81ad07ee62b6>:14: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  jerarquico_media = dfcm[dfcm.cluster == cluster_peru].mean().reset_index(); jerarquico_media.columns = [\"VARIABLE\", \"JERARQUICO\"]\n"
     ]
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "\n",
    "\n",
    "for i in range(1,25):\n",
    "    num_clusters = i\n",
    "    clusters = fcluster(linked, num_clusters, criterion='maxclust')\n",
    "    dfcm[\"cluster\"] = clusters\n",
    "    cluster_peru = dfcm[dfcm[\"ISO3\"]==\"PER\"].cluster.values[0]\n",
    "    miembros = dfcm[dfcm.cluster == cluster_peru].shape[0]\n",
    "\n",
    "    if miembros<=maximo_mimebros:\n",
    "        break\n",
    "\n",
    "jerarquico_media = dfcm[dfcm.cluster == cluster_peru].mean().reset_index(); jerarquico_media.columns = [\"VARIABLE\", \"JERARQUICO\"]\n",
    "jerarquico_media.loc[-1] = [\"N\", miembros]\n",
    "peru = peru.merge(jerarquico_media, \"outer\", \"VARIABLE\")\n",
    "\n",
    "jerarquico_lista = list(dfcm[dfcm.cluster == cluster_peru].ISO3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# labels = dfc.index.tolist()\n",
    "# plt.figure(figsize=(15, 6), dpi=200)\n",
    "# dendrogram(linked, labels=labels, orientation='bottom', distance_sort='descending', show_leaf_counts=True)\n",
    "# plt.title('Dendrograma del Clustering Jerárquico Aglomerativo')\n",
    "# plt.ylabel('Índice de la muestra')\n",
    "# plt.xlabel('Distancia')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering espectral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-50-9fbf6a0b1dc8>:18: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  jerarquico_media = dfcm[dfcm.cluster == cluster_peru].mean().reset_index(); jerarquico_media.columns = [\"VARIABLE\", \"ESPECTRAL\"]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import SpectralClustering\n",
    "\n",
    "for i in range(1,25):\n",
    "\n",
    "    num_clusters = i\n",
    "    spectral = SpectralClustering(n_clusters= num_clusters, affinity='nearest_neighbors')\n",
    "    clusters = spectral.fit_predict(dfc)\n",
    "    dfcm[\"cluster\"] = clusters\n",
    "    cluster_peru = dfcm[dfcm[\"ISO3\"]==\"PER\"].cluster.values[0]\n",
    "    miembros = dfcm[dfcm.cluster == cluster_peru].shape[0]\n",
    "\n",
    "    if miembros<=maximo_mimebros:\n",
    "        break\n",
    "\n",
    "\n",
    "espectral_lista = list(dfcm[dfcm.cluster == cluster_peru].ISO3)\n",
    "\n",
    "jerarquico_media = dfcm[dfcm.cluster == cluster_peru].mean().reset_index(); jerarquico_media.columns = [\"VARIABLE\", \"ESPECTRAL\"]\n",
    "jerarquico_media.loc[-1] = [\"N\", miembros]\n",
    "peru = peru.merge(jerarquico_media, \"outer\", \"VARIABLE\")\n",
    "# peru = peru.merge(jerarquico_media, \"left\", \"VARIABLE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering OPTICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-51-bb3cfba93086>:19: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  jerarquico_media = dfcm[dfcm.cluster == cluster_peru].mean().reset_index(); jerarquico_media.columns = [\"VARIABLE\", \"OPTICS\"]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import OPTICS\n",
    "\n",
    "\n",
    "for i in range(1,25):\n",
    "\n",
    "    optics = OPTICS(min_samples=i)\n",
    "    clusters = optics.fit_predict(dfc)\n",
    "    clusters\n",
    "    dfcm[\"cluster\"] = clusters\n",
    "    cluster_peru = dfcm[dfcm[\"ISO3\"]==\"PER\"].cluster.values[0]\n",
    "    miembros = dfcm[dfcm.cluster == cluster_peru].shape[0]\n",
    "\n",
    "    if miembros<=maximo_mimebros:\n",
    "        break\n",
    "\n",
    "\n",
    "optics_lista = list(dfcm[dfcm.cluster == cluster_peru].ISO3)\n",
    "\n",
    "jerarquico_media = dfcm[dfcm.cluster == cluster_peru].mean().reset_index(); jerarquico_media.columns = [\"VARIABLE\", \"OPTICS\"]\n",
    "jerarquico_media.loc[-1] = [\"N\", miembros]\n",
    "peru = peru.merge(jerarquico_media, \"outer\", \"VARIABLE\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering BIRCH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-52-32cd93181d58>:19: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  jerarquico_media = dfcm[dfcm.cluster == cluster_peru].mean().reset_index(); jerarquico_media.columns = [\"VARIABLE\", \"BIRCH\"]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import Birch\n",
    "\n",
    "\n",
    "for i in range(1,25):\n",
    "\n",
    "    birch = Birch(n_clusters=i)\n",
    "    clusters = birch.fit_predict(dfc)\n",
    "    clusters\n",
    "    dfcm[\"cluster\"] = clusters\n",
    "    cluster_peru = dfcm[dfcm[\"ISO3\"]==\"PER\"].cluster.values[0]\n",
    "    miembros = dfcm[dfcm.cluster == cluster_peru].shape[0]\n",
    "\n",
    "    if miembros<=maximo_mimebros:\n",
    "        break\n",
    "\n",
    "\n",
    "birch_lista = list(dfcm[dfcm.cluster == cluster_peru].ISO3)\n",
    "\n",
    "jerarquico_media = dfcm[dfcm.cluster == cluster_peru].mean().reset_index(); jerarquico_media.columns = [\"VARIABLE\", \"BIRCH\"]\n",
    "jerarquico_media.loc[-1] = [\"N\", miembros]\n",
    "peru = peru.merge(jerarquico_media, \"outer\", \"VARIABLE\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering Gaussian Mixture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-53-bd3b65546759>:20: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  jerarquico_media = dfcm[dfcm.cluster == cluster_peru].mean().reset_index(); jerarquico_media.columns = [\"VARIABLE\", \"GMM\"]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "\n",
    "for i in range(1,25):\n",
    "\n",
    "    gmm = GaussianMixture(n_components=i)\n",
    "    gmm.fit(dfc)\n",
    "    clusters = gmm.predict(dfc)\n",
    "    clusters\n",
    "    dfcm[\"cluster\"] = clusters\n",
    "    cluster_peru = dfcm[dfcm[\"ISO3\"]==\"PER\"].cluster.values[0]\n",
    "    miembros = dfcm[dfcm.cluster == cluster_peru].shape[0]\n",
    "\n",
    "    if miembros<=maximo_mimebros:\n",
    "        break\n",
    "\n",
    "\n",
    "gmm_lista = list(dfcm[dfcm.cluster == cluster_peru].ISO3)\n",
    "\n",
    "jerarquico_media = dfcm[dfcm.cluster == cluster_peru].mean().reset_index(); jerarquico_media.columns = [\"VARIABLE\", \"GMM\"]\n",
    "jerarquico_media.loc[-1] = [\"N\", miembros]\n",
    "peru = peru.merge(jerarquico_media, \"outer\", \"VARIABLE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "lista_total = jerarquico_lista + espectral_lista + optics_lista + birch_lista + gmm_lista\n",
    "dfcont = pd.DataFrame(lista_total, columns=['ISO3'])\n",
    "dfcont = dfcont[dfcont[\"ISO3\"]!=\"PER\"]; dfcont = dfcont.merge(grupos, \"left\", \"ISO3\")\n",
    "\n",
    "# Contando la frecuencia de cada elemento y ordenándolos\n",
    "frecuencias = dfcont['COUNTRY_NAME'].value_counts()/5\n",
    "\n",
    "# # Creando un gráfico de barras\n",
    "# plt.figure(figsize=(8, 10), dpi=200)\n",
    "# frecuencias.plot(kind='barh')\n",
    "# plt.xlabel('Frecuencia')\n",
    "# plt.ylabel('País')\n",
    "# plt.title('Frecuencias por país')\n",
    "# # plt.xticks(rotation=70)\n",
    "# plt.gca().invert_yaxis()  # Invertir el eje y para mostrar el más frecuente en la parte superior\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster = dfcont.groupby(\"ISO3\").count()[\"COUNTRY_NAME\"].reset_index(); cluster.columns = [\"ISO3\", \"CLUSTER\"]\n",
    "df = df.merge(cluster, \"left\", \"ISO3\")\n",
    "df[\"CLUSTER\"] = df[\"CLUSTER\"].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Añadidura de variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp = ['gb_gdp_a', 'gb_p_gdp_a', 'ge_gdp_a', 'gei_gdp_a', 'ge_p_gdp_a', 'gr_gdp_a', 'gr_p_gdp_a']\n",
    "gdp_cap = [\"FIS_BAL\", \"PFISC_BAL\", \"EXP\", \"EXP_INT\", \"P_EXP\", \"REV\", \"P_REV\"]\n",
    "\n",
    "niv = [\"cpi_1990_e_a\", \"exr_a\", \"ir_a\", \"gdp_usd_a\"]\n",
    "niv_cap = [\"CPI\", \"EXR\", \"IR\", \"GDP_USD\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ugog = pd.read_excel(\"WEO_1990_2022_FR_20230810.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ugo = ugog.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ugo = ugo.rename(columns = {\"ifscode\":\"WEO_CODE\", \"year\": \"DATE\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ugo = ugo.drop_duplicates([\"WEO_CODE\", \"DATE\"], keep=\"last\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ugo[\"gdp_usd_a\"] = ugo[\"gdp_usd_a\"]*1000000000\n",
    "\n",
    "for i,e in zip(gdp, gdp_cap):\n",
    "    # ugo = ugo.rename(columns = {i:e})\n",
    "\n",
    "    ugo[e] = ugo[i]*ugo['gdp_usd_a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpi_1990_e_a CPI\n",
      "exr_a EXR\n",
      "ir_a IR\n",
      "gdp_usd_a GDP_USD\n"
     ]
    }
   ],
   "source": [
    "for i,e in zip(niv, niv_cap):\n",
    "    print(i,e)\n",
    "    ugo = ugo.rename(columns = {i:e})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ugo = ugo[[\"WEO_CODE\", \"DATE\"]+niv_cap+gdp_cap]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-35-ef5de885cb97>:1: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.\n",
      "To preserve the previous behavior, use\n",
      "\n",
      "\t>>> .groupby(..., group_keys=False)\n",
      "\n",
      "To adopt the future behavior and silence this warning, use \n",
      "\n",
      "\t>>> .groupby(..., group_keys=True)\n",
      "  ugo = ugo.groupby('WEO_CODE').apply(lambda group: group.interpolate(method='linear'))\n",
      "<ipython-input-35-ef5de885cb97>:2: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.\n",
      "To preserve the previous behavior, use\n",
      "\n",
      "\t>>> .groupby(..., group_keys=False)\n",
      "\n",
      "To adopt the future behavior and silence this warning, use \n",
      "\n",
      "\t>>> .groupby(..., group_keys=True)\n",
      "  ugo = ugo.groupby('WEO_CODE').apply(lambda group: group.interpolate(method='linear', limit_direction='backward'))\n",
      "<ipython-input-35-ef5de885cb97>:3: FutureWarning: Not prepending group keys to the result index of transform-like apply. In the future, the group keys will be included in the index, regardless of whether the applied function returns a like-indexed object.\n",
      "To preserve the previous behavior, use\n",
      "\n",
      "\t>>> .groupby(..., group_keys=False)\n",
      "\n",
      "To adopt the future behavior and silence this warning, use \n",
      "\n",
      "\t>>> .groupby(..., group_keys=True)\n",
      "  ugo = ugo.groupby('WEO_CODE').apply(lambda group: group.interpolate(method='linear', limit_direction='forward'))\n"
     ]
    }
   ],
   "source": [
    "ugo = ugo.groupby('WEO_CODE').apply(lambda group: group.interpolate(method='linear'))\n",
    "ugo = ugo.groupby('WEO_CODE').apply(lambda group: group.interpolate(method='linear', limit_direction='backward'))\n",
    "ugo = ugo.groupby('WEO_CODE').apply(lambda group: group.interpolate(method='linear', limit_direction='forward'))\n",
    "\n",
    "# df_interpolated = df.groupby('WEO_CODE').apply(lambda group: group.interpolate(method='linear', limit_direction='backward'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.merge(ugo, \"left\", [\"WEO_CODE\", \"DATE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_stata(\"dbec_vf.dta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       110.560883\n",
       "1       110.560883\n",
       "2       110.560883\n",
       "3       110.560883\n",
       "4       113.441681\n",
       "           ...    \n",
       "4047           NaN\n",
       "4048           NaN\n",
       "4049           NaN\n",
       "4050           NaN\n",
       "4051           NaN\n",
       "Name: CPI, Length: 4052, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"CPI\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>COUNTRY_NAME</th>\n",
       "      <th>WEO_CODE</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>LAC1</th>\n",
       "      <th>LAC2</th>\n",
       "      <th>GROUP</th>\n",
       "      <th>REGION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>DEBT_GDP</th>\n",
       "      <th>EXP_GDP</th>\n",
       "      <th>...</th>\n",
       "      <th>EXR</th>\n",
       "      <th>IR</th>\n",
       "      <th>GDP_USD</th>\n",
       "      <th>FIS_BAL</th>\n",
       "      <th>PFISC_BAL</th>\n",
       "      <th>EXP</th>\n",
       "      <th>EXP_INT</th>\n",
       "      <th>P_EXP</th>\n",
       "      <th>REV</th>\n",
       "      <th>P_REV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>1990</td>\n",
       "      <td>1.718502</td>\n",
       "      <td>0.220084</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>1991</td>\n",
       "      <td>1.610341</td>\n",
       "      <td>0.226332</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>1.502180</td>\n",
       "      <td>0.230916</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>1993</td>\n",
       "      <td>1.394019</td>\n",
       "      <td>0.206257</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>1994</td>\n",
       "      <td>1.285858</td>\n",
       "      <td>0.223522</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>1995</td>\n",
       "      <td>1.177697</td>\n",
       "      <td>0.205692</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>1996</td>\n",
       "      <td>1.069536</td>\n",
       "      <td>0.117118</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>0.961375</td>\n",
       "      <td>0.144603</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>1998</td>\n",
       "      <td>0.853214</td>\n",
       "      <td>0.180503</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>1999</td>\n",
       "      <td>0.841618</td>\n",
       "      <td>0.181956</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.946791</td>\n",
       "      <td>0.171639</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.031150</td>\n",
       "      <td>0.141463</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.985183</td>\n",
       "      <td>0.166357</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.958863</td>\n",
       "      <td>0.122783</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.997235</td>\n",
       "      <td>0.130567</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2005</td>\n",
       "      <td>1.029624</td>\n",
       "      <td>0.161561</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.467500</td>\n",
       "      <td>0.133699</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.478508</td>\n",
       "      <td>0.128719</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.357660</td>\n",
       "      <td>0.160267</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.203003</td>\n",
       "      <td>0.159987</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.198834</td>\n",
       "      <td>0.172594</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.196863</td>\n",
       "      <td>0.141580</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.315328</td>\n",
       "      <td>0.144975</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.517896</td>\n",
       "      <td>0.133990</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.622400</td>\n",
       "      <td>0.180163</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.597904</td>\n",
       "      <td>0.139638</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.538873</td>\n",
       "      <td>0.121188</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.502747</td>\n",
       "      <td>0.138559</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.500392</td>\n",
       "      <td>0.176084</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.471466</td>\n",
       "      <td>0.169121</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.434217</td>\n",
       "      <td>0.251106</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.475972</td>\n",
       "      <td>0.196812</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.506869</td>\n",
       "      <td>0.179159</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2023</td>\n",
       "      <td>0.490804</td>\n",
       "      <td>0.168229</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2024</td>\n",
       "      <td>0.485034</td>\n",
       "      <td>0.171423</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2025</td>\n",
       "      <td>0.480118</td>\n",
       "      <td>0.180064</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2026</td>\n",
       "      <td>0.461931</td>\n",
       "      <td>0.175718</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2027</td>\n",
       "      <td>0.434106</td>\n",
       "      <td>0.176244</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>Central African Republic (the)</td>\n",
       "      <td>626</td>\n",
       "      <td>CAF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>EMDEs</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2028</td>\n",
       "      <td>0.429581</td>\n",
       "      <td>0.179121</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        COUNTRY_NAME  WEO_CODE ISO3  LAC1  LAC2  GROUP  \\\n",
       "1911  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1912  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1913  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1914  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1915  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1916  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1917  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1918  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1919  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1920  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1921  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1922  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1923  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1924  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1925  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1926  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1927  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1928  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1929  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1930  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1931  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1932  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1933  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1934  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1935  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1936  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1937  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1938  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1939  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1940  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1941  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1942  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1943  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1944  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1945  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1946  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1947  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1948  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "1949  Central African Republic (the)       626  CAF     0     0  EMDEs   \n",
       "\n",
       "                  REGION  DATE  DEBT_GDP   EXP_GDP  ...  EXR  IR  GDP_USD  \\\n",
       "1911  Sub-Saharan Africa  1990  1.718502  0.220084  ...  NaN NaN      NaN   \n",
       "1912  Sub-Saharan Africa  1991  1.610341  0.226332  ...  NaN NaN      NaN   \n",
       "1913  Sub-Saharan Africa  1992  1.502180  0.230916  ...  NaN NaN      NaN   \n",
       "1914  Sub-Saharan Africa  1993  1.394019  0.206257  ...  NaN NaN      NaN   \n",
       "1915  Sub-Saharan Africa  1994  1.285858  0.223522  ...  NaN NaN      NaN   \n",
       "1916  Sub-Saharan Africa  1995  1.177697  0.205692  ...  NaN NaN      NaN   \n",
       "1917  Sub-Saharan Africa  1996  1.069536  0.117118  ...  NaN NaN      NaN   \n",
       "1918  Sub-Saharan Africa  1997  0.961375  0.144603  ...  NaN NaN      NaN   \n",
       "1919  Sub-Saharan Africa  1998  0.853214  0.180503  ...  NaN NaN      NaN   \n",
       "1920  Sub-Saharan Africa  1999  0.841618  0.181956  ...  NaN NaN      NaN   \n",
       "1921  Sub-Saharan Africa  2000  0.946791  0.171639  ...  NaN NaN      NaN   \n",
       "1922  Sub-Saharan Africa  2001  1.031150  0.141463  ...  NaN NaN      NaN   \n",
       "1923  Sub-Saharan Africa  2002  0.985183  0.166357  ...  NaN NaN      NaN   \n",
       "1924  Sub-Saharan Africa  2003  0.958863  0.122783  ...  NaN NaN      NaN   \n",
       "1925  Sub-Saharan Africa  2004  0.997235  0.130567  ...  NaN NaN      NaN   \n",
       "1926  Sub-Saharan Africa  2005  1.029624  0.161561  ...  NaN NaN      NaN   \n",
       "1927  Sub-Saharan Africa  2006  0.467500  0.133699  ...  NaN NaN      NaN   \n",
       "1928  Sub-Saharan Africa  2007  0.478508  0.128719  ...  NaN NaN      NaN   \n",
       "1929  Sub-Saharan Africa  2008  0.357660  0.160267  ...  NaN NaN      NaN   \n",
       "1930  Sub-Saharan Africa  2009  0.203003  0.159987  ...  NaN NaN      NaN   \n",
       "1931  Sub-Saharan Africa  2010  0.198834  0.172594  ...  NaN NaN      NaN   \n",
       "1932  Sub-Saharan Africa  2011  0.196863  0.141580  ...  NaN NaN      NaN   \n",
       "1933  Sub-Saharan Africa  2012  0.315328  0.144975  ...  NaN NaN      NaN   \n",
       "1934  Sub-Saharan Africa  2013  0.517896  0.133990  ...  NaN NaN      NaN   \n",
       "1935  Sub-Saharan Africa  2014  0.622400  0.180163  ...  NaN NaN      NaN   \n",
       "1936  Sub-Saharan Africa  2015  0.597904  0.139638  ...  NaN NaN      NaN   \n",
       "1937  Sub-Saharan Africa  2016  0.538873  0.121188  ...  NaN NaN      NaN   \n",
       "1938  Sub-Saharan Africa  2017  0.502747  0.138559  ...  NaN NaN      NaN   \n",
       "1939  Sub-Saharan Africa  2018  0.500392  0.176084  ...  NaN NaN      NaN   \n",
       "1940  Sub-Saharan Africa  2019  0.471466  0.169121  ...  NaN NaN      NaN   \n",
       "1941  Sub-Saharan Africa  2020  0.434217  0.251106  ...  NaN NaN      NaN   \n",
       "1942  Sub-Saharan Africa  2021  0.475972  0.196812  ...  NaN NaN      NaN   \n",
       "1943  Sub-Saharan Africa  2022  0.506869  0.179159  ...  NaN NaN      NaN   \n",
       "1944  Sub-Saharan Africa  2023  0.490804  0.168229  ...  NaN NaN      NaN   \n",
       "1945  Sub-Saharan Africa  2024  0.485034  0.171423  ...  NaN NaN      NaN   \n",
       "1946  Sub-Saharan Africa  2025  0.480118  0.180064  ...  NaN NaN      NaN   \n",
       "1947  Sub-Saharan Africa  2026  0.461931  0.175718  ...  NaN NaN      NaN   \n",
       "1948  Sub-Saharan Africa  2027  0.434106  0.176244  ...  NaN NaN      NaN   \n",
       "1949  Sub-Saharan Africa  2028  0.429581  0.179121  ...  NaN NaN      NaN   \n",
       "\n",
       "      FIS_BAL  PFISC_BAL  EXP  EXP_INT  P_EXP  REV  P_REV  \n",
       "1911      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1912      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1913      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1914      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1915      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1916      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1917      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1918      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1919      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1920      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1921      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1922      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1923      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1924      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1925      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1926      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1927      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1928      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1929      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1930      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1931      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1932      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1933      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1934      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1935      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1936      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1937      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1938      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1939      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1940      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1941      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1942      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1943      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1944      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1945      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1946      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1947      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1948      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "1949      NaN        NaN  NaN      NaN    NaN  NaN    NaN  \n",
       "\n",
       "[39 rows x 41 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.WEO_CODE==626\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv(\"FISLAC_update.csv\", sep = \";\")\n",
    "\n",
    "df = dff[, \"DATE\", \"DEBT_GDP\",\"DEMOCRACY\",\"EXP_GDP\",\"REV_GDP\", \n",
    "         \"WGI_CONTR_CORR\", \"WGI_GOV_EFFECT\", \"WGI_GOV_IND\", \"WGI_POL_STAB\", \"WGI_REG_QUAL\", \"WGI_ROL\", \"WGI_VOICE_ACCOUNT\"]]\n",
    "df = df[(df.DATE>=2000) & (df.DATE<=2023)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "dffr = pd.read_excel(\"Application_EC.xlsx\", sheet_name=\"DDBB_CS\") #LECTURA BASE DE CLAUSULAS\n",
    "dffr = dffr.rename(columns = {\"year\": \"DATE\"})\n",
    "ec = dffr.groupby([\"ISO3\", \"DATE\"])[\"EC\"].sum().reset_index(); ec[\"EC\"] = ec[\"EC\"].apply(lambda x: 1 if x>0 else 0) #EXISTENCIA DE CLAUSULA\n",
    "act = dffr.groupby([\"ISO3\", \"DATE\"])[\"ACT\"].sum().reset_index(); act[\"ACT\"] = act[\"ACT\"].apply(lambda x: 1 if x>0 else 0) #ACTIVACIÓN CLASULA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.merge(ec, \"left\", [\"ISO3\", \"DATE\"])\n",
    "df = df.merge(act, \"left\", [\"ISO3\", \"DATE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-52-11a7026dd533>:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df1 = df1[df.LAC1 == 1]\n"
     ]
    }
   ],
   "source": [
    "# df1 = df.dropna()\n",
    "# df1 = df1[df.LAC1 == 1]\n",
    "# df1.to_stata(\"dbec1.dta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2 = df[df.LAC1 == 1]\n",
    "df.to_stata(\"dbec3.dta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COUNTRY_NAME            0\n",
       "WEO_CODE                0\n",
       "ISO3                    0\n",
       "LAC1                    0\n",
       "LAC2                    0\n",
       "DATE                    0\n",
       "DEBT_GDP                0\n",
       "DEMOCRACY               0\n",
       "EXP_GDP                 0\n",
       "REV_GDP                 0\n",
       "WGI_CONTR_CORR          0\n",
       "WGI_GOV_EFFECT          0\n",
       "WGI_GOV_IND             0\n",
       "WGI_POL_STAB            0\n",
       "WGI_REG_QUAL            0\n",
       "WGI_ROL                 0\n",
       "WGI_VOICE_ACCOUNT       0\n",
       "EC                   3266\n",
       "ACT                  3266\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CONTRAS DE CONSERVAR EC E INDICE --> PERDIDA DE DATA\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COUNTRY_NAME\n",
       "Argentina                           24\n",
       "Bahamas (the)                       24\n",
       "Barbados                            24\n",
       "Belize                              24\n",
       "Bolivia (Plurinational State of)    24\n",
       "Brazil                              24\n",
       "Chile                               24\n",
       "Colombia                            24\n",
       "Costa Rica                          24\n",
       "Dominican Republic (the)            24\n",
       "Ecuador                             24\n",
       "El Salvador                         24\n",
       "Guatemala                           24\n",
       "Guyana                              24\n",
       "Haiti                               24\n",
       "Honduras                            24\n",
       "Jamaica                             24\n",
       "Mexico                              24\n",
       "Nicaragua                           24\n",
       "Panama                              24\n",
       "Paraguay                            24\n",
       "Peru                                24\n",
       "Suriname                            24\n",
       "Trinidad and Tobago                 24\n",
       "Uruguay                             24\n",
       "Name: DATE, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"COUNTRY_NAME\"])[\"DATE\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pd.read_csv(\"pvalues.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WEO_CODE</th>\n",
       "      <th>SYNTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>233</td>\n",
       "      <td>0.379722</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>233</td>\n",
       "      <td>0.410785</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>233</td>\n",
       "      <td>0.475261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>233</td>\n",
       "      <td>0.450213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>233</td>\n",
       "      <td>0.414655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>343</td>\n",
       "      <td>1.086235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>343</td>\n",
       "      <td>1.002725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>343</td>\n",
       "      <td>0.947355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>343</td>\n",
       "      <td>1.086761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>343</td>\n",
       "      <td>0.989765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>220 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     WEO_CODE     SYNTH\n",
       "0         233  0.379722\n",
       "1         233  0.410785\n",
       "2         233  0.475261\n",
       "3         233  0.450213\n",
       "4         233  0.414655\n",
       "..        ...       ...\n",
       "215       343  1.086235\n",
       "216       343  1.002725\n",
       "217       343  0.947355\n",
       "218       343  1.086761\n",
       "219       343  0.989765\n",
       "\n",
       "[220 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>WEO_CODE</th>\n",
       "      <th>233</th>\n",
       "      <th>238</th>\n",
       "      <th>248</th>\n",
       "      <th>273</th>\n",
       "      <th>283</th>\n",
       "      <th>288</th>\n",
       "      <th>293</th>\n",
       "      <th>298</th>\n",
       "      <th>313</th>\n",
       "      <th>343</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SYNTH</th>\n",
       "      <td>0.436617</td>\n",
       "      <td>0.403087</td>\n",
       "      <td>0.391452</td>\n",
       "      <td>0.462354</td>\n",
       "      <td>0.474082</td>\n",
       "      <td>0.244354</td>\n",
       "      <td>0.318912</td>\n",
       "      <td>0.57216</td>\n",
       "      <td>0.395633</td>\n",
       "      <td>1.078448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "WEO_CODE       233       238       248       273       283       288  \\\n",
       "SYNTH     0.436617  0.403087  0.391452  0.462354  0.474082  0.244354   \n",
       "\n",
       "WEO_CODE       293      298       313       343  \n",
       "SYNTH     0.318912  0.57216  0.395633  1.078448  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(p, values = \"SYNTH\", columns = \"WEO_CODE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.002725</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>343</td>\n",
       "      <td>0.989765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>220 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     WEO_CODE     SYNTH\n",
       "0         233  0.379722\n",
       "1         233  0.410785\n",
       "2         233  0.475261\n",
       "3         233  0.450213\n",
       "4         233  0.414655\n",
       "..        ...       ...\n",
       "215       343  1.086235\n",
       "216       343  1.002725\n",
       "217       343  0.947355\n",
       "218       343  1.086761\n",
       "219       343  0.989765\n",
       "\n",
       "[220 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Simulando un DataFrame con datos aleatorios\n",
    "np.random.seed(42)  # Para reproducibilidad\n",
    "data = np.random.rand(100, 6)  # 100 muestras, 4 características\n",
    "df = pd.DataFrame(data, columns=['Feature_1', 'Feature_2', 'Feature_3', 'Feature_4', 'Feature_5', 'Feature_6'])\n",
    "\n",
    "# Realizando el clustering jerárquico aglomerativo\n",
    "linked = linkage(df, method='ward')\n",
    "\n",
    "# Creando el dendrograma\n",
    "plt.figure(figsize=(10, 7))\n",
    "dendrogram(linked, orientation='top', distance_sort='descending', show_leaf_counts=True)\n",
    "plt.title('Dendrograma del Clustering Jerárquico Aglomerativo')\n",
    "plt.xlabel('Índice de la muestra')\n",
    "plt.ylabel('Distancia')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}