analiza_regresions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analiza resultados generales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_files(files, name=\"version 3 drought 2_5\"):\n",
    "    files = [f for f in files if name in f]\n",
    "    return files\n",
    "\n",
    "\n",
    "def count_significative_vars(s):\n",
    "    \n",
    "    s = s.str.strip()\n",
    "    is_p01  = s.str.contains(\"*\", regex=False)\n",
    "    is_p005 = s.str.contains(\"**\", regex=False)\n",
    "    is_p001 = s.str.contains(\"***\", regex=False)\n",
    "    \n",
    "    is_negative  = s.str.contains(\"-.*\\.\", regex=True)\n",
    "\n",
    "    count_positive_p01  = (is_p01 & ~is_negative).sum()\n",
    "    count_positive_p005 = (is_p005 & ~is_negative).sum()\n",
    "    count_positive_p001 = (is_p001 & ~is_negative).sum()\n",
    "\n",
    "    count_negative_p01  = (is_p01 & is_negative).sum()\n",
    "    count_negative_p005 = (is_p005 & is_negative).sum()\n",
    "    count_negative_p001 = (is_p001 & is_negative).sum()\n",
    "    \n",
    "    count_estimates = s[s != \"\"].count() \n",
    "    return count_estimates, count_positive_p01, count_positive_p005, count_positive_p001, count_negative_p01, count_negative_p005, count_negative_p001\n",
    "\n",
    "def dict_to_df(dict):\n",
    "    results_df = pd.DataFrame().from_dict(dict, orient=\"index\", columns=[\"estimates\", \"p=0.1 (+)\", \"p=0.05 (+)\", \"p=0.01 (+)\", \"p=0.1 (-)\", \"p=0.05 (-)\", \"p=0.01 (-)\"]).reset_index(names=\"Specification\")\n",
    "    return results_df\n",
    "\n",
    "def process_significance(s):\n",
    "    # Replace all values that does not contain an asterisk with NaN and turn them into floats\n",
    "    s = s.where(s.str.contains(\"*\", regex=False))\n",
    "    return s\n",
    "\n",
    "def make_float(s):\n",
    "    # Remove asterisks and turn into floats\n",
    "    s = s.replace(r'[^0-9\\-.]', \"\", regex=True)\n",
    "    return s\n",
    "\n",
    "def parse_name(name):\n",
    "    print(name)\n",
    "    conditions = name.split(\" - \")\n",
    "\n",
    "    # # From 2003 or Last 10 years\n",
    "    # if (\"from 2003\" in conditions[-1]) or (\"last 10 years\" in conditions[-1]):\n",
    "    #     subset = conditions[-1].replace(\".tex\", \"\")\n",
    "    #     conditions = conditions[:-1]\n",
    "    # else:\n",
    "    subset = \"Last 10 years & from 2003\"\n",
    "    \n",
    "    # Control variables\n",
    "    if \"controls\" in conditions[-1]:\n",
    "        controls = conditions[-1].replace(\".tex\", \"\")\n",
    "        conditions = conditions[:-1]\n",
    "    else:\n",
    "        controls = \"Standard Controls\"\n",
    "    \n",
    "    # Fixed Effects\n",
    "    if any([kword in conditions[-1] for kword in [\"Decade\", \"No Trend\", \"Country FE\"]]):\n",
    "        fixed_effects = conditions[-1].replace(\".tex\", \"\")\n",
    "        conditions = conditions[:-1]\n",
    "    else:\n",
    "        fixed_effects = \"Standard FE\"\n",
    "    \n",
    "    assert len(conditions)==1\n",
    "    \n",
    "    words = conditions[0]\n",
    "    words = words.strip().split(\" \")\n",
    "    \n",
    "    version_nam, version_num = words[:2]\n",
    "\n",
    "    if version_num in [\"1\", \"2\"]:\n",
    "        months_num = words[2]\n",
    "        agg_timeframe = words[3]\n",
    "        temp_variable = words[4]\n",
    "        drought_num = \" - \"\n",
    "\n",
    "    elif version_num==\"3\":\n",
    "        months_num = words[3]\n",
    "        agg_timeframe = \"min-max\"\n",
    "        temp_variable = words[5]\n",
    "        drought_num = words[4]\n",
    "        drought_num = drought_num.replace(\"_\", \".\") + \" SD\"\n",
    "\n",
    "    elif version_num==\"4\":\n",
    "        months_num = words[3]\n",
    "        agg_timeframe = words[6]\n",
    "        temp_variable = words[5]\n",
    "        drought_num = words[4]\n",
    "        drought_num = drought_num.replace(\"_\", \".\") + \" SD\"\n",
    "\n",
    "    out_dict = {\n",
    "        \"Version\": version_num, \n",
    "        \"Variable Treshold\": drought_num, \n",
    "        \"SPI Timeframe\": months_num,\n",
    "        \"Agreggation\": agg_timeframe,\n",
    "        \"Temperature\": temp_variable,\n",
    "        \"Fixed Effects\": fixed_effects,\n",
    "        \"Controls set\": controls,\n",
    "        \"Subset\": subset\n",
    "    }\n",
    "    return out_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "version 4 drought SPI12 2_5 avg std_t  - controls1.tex\n",
      "['version 4 drought SPI12 2_5 avg std_t ']\n",
      "4\n",
      "{'Version': '4', 'Variable Treshold': '2.5 SD', 'SPI Timeframe': 'SPI12', 'Agreggation': 'std_t', 'Temperature': 'avg', 'Fixed Effects': 'Standard FE', 'Controls set': 'controls1', 'Subset': 'Last 10 years & from 2003'}\n",
      "version 4 drought SPI1 1_5 min-max stdm_t  - controls2.tex\n",
      "['version 4 drought SPI1 1_5 min-max stdm_t ']\n",
      "4\n",
      "{'Version': '4', 'Variable Treshold': '1.5 SD', 'SPI Timeframe': 'SPI1', 'Agreggation': 'stdm_t', 'Temperature': 'min-max', 'Fixed Effects': 'Standard FE', 'Controls set': 'controls2', 'Subset': 'Last 10 years & from 2003'}\n",
      "version 3 drought SPI6 2_0 min-max  - controls1.tex\n",
      "['version 3 drought SPI6 2_0 min-max ']\n",
      "3\n",
      "{'Version': '3', 'Variable Treshold': '2.0 SD', 'SPI Timeframe': 'SPI6', 'Agreggation': 'min-max', 'Temperature': 'min-max', 'Fixed Effects': 'Standard FE', 'Controls set': 'controls1', 'Subset': 'Last 10 years & from 2003'}\n",
      "version 1 SPI12 min-max t  - controls1.tex\n",
      "['version 1 SPI12 min-max t ']\n",
      "1\n",
      "{'Version': '1', 'Variable Treshold': ' - ', 'SPI Timeframe': 'SPI12', 'Agreggation': 'min-max', 'Temperature': 't', 'Fixed Effects': 'Standard FE', 'Controls set': 'controls1', 'Subset': 'Last 10 years & from 2003'}\n",
      "version 4 drought SPI1 2_0 min-max std_t  - controls2.tex\n",
      "['version 4 drought SPI1 2_0 min-max std_t ']\n",
      "4\n",
      "{'Version': '4', 'Variable Treshold': '2.0 SD', 'SPI Timeframe': 'SPI1', 'Agreggation': 'std_t', 'Temperature': 'min-max', 'Fixed Effects': 'Standard FE', 'Controls set': 'controls2', 'Subset': 'Last 10 years & from 2003'}\n",
      "version 3 drought SPI3 2_0 min-max  - controls2.tex\n",
      "['version 3 drought SPI3 2_0 min-max ']\n",
      "3\n",
      "{'Version': '3', 'Variable Treshold': '2.0 SD', 'SPI Timeframe': 'SPI3', 'Agreggation': 'min-max', 'Temperature': 'min-max', 'Fixed Effects': 'Standard FE', 'Controls set': 'controls2', 'Subset': 'Last 10 years & from 2003'}\n",
      "version 4 drought SPI1 1_5 min-max t  - controls3.tex\n",
      "['version 4 drought SPI1 1_5 min-max t ']\n",
      "4\n",
      "{'Version': '4', 'Variable Treshold': '1.5 SD', 'SPI Timeframe': 'SPI1', 'Agreggation': 't', 'Temperature': 'min-max', 'Fixed Effects': 'Standard FE', 'Controls set': 'controls3', 'Subset': 'Last 10 years & from 2003'}\n",
      "version 3 drought SPI6 2_0 avg  - controls3.tex\n",
      "['version 3 drought SPI6 2_0 avg ']\n",
      "3\n",
      "{'Version': '3', 'Variable Treshold': '2.0 SD', 'SPI Timeframe': 'SPI6', 'Agreggation': 'min-max', 'Temperature': 'avg', 'Fixed Effects': 'Standard FE', 'Controls set': 'controls3', 'Subset': 'Last 10 years & from 2003'}\n",
      "version 4 drought SPI1 2_0 min-max t  - controls2.tex\n",
      "['version 4 drought SPI1 2_0 min-max t ']\n",
      "4\n",
      "{'Version': '4', 'Variable Treshold': '2.0 SD', 'SPI Timeframe': 'SPI1', 'Agreggation': 't', 'Temperature': 'min-max', 'Fixed Effects': 'Standard FE', 'Controls set': 'controls2', 'Subset': 'Last 10 years & from 2003'}\n",
      "version 4 drought SPI12 2_5 avg stdm_t  - controls2.tex\n",
      "['version 4 drought SPI12 2_5 avg stdm_t ']\n",
      "4\n",
      "{'Version': '4', 'Variable Treshold': '2.5 SD', 'SPI Timeframe': 'SPI12', 'Agreggation': 'stdm_t', 'Temperature': 'avg', 'Fixed Effects': 'Standard FE', 'Controls set': 'controls2', 'Subset': 'Last 10 years & from 2003'}\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "for name in all_results_df.file.sample(10).values:\n",
    "    print(parse_name(name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genera tabla de regresiones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 432/432 [00:12<00:00, 35.35it/s]\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 58\u001b[0m\n\u001b[0;32m     55\u001b[0m     all_results_df[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSet \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpval\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (%)\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m all_results_df[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSet \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpval\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m/\u001b[39m all_results_df[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSet estimates\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m     57\u001b[0m \u001b[38;5;66;03m# Parse the names of the\u001b[39;00m\n\u001b[1;32m---> 58\u001b[0m all_results_df \u001b[38;5;241m=\u001b[39m \u001b[43mall_results_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparse_name\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mapply(pd\u001b[38;5;241m.\u001b[39mSeries)\u001b[38;5;241m.\u001b[39mjoin(all_results_df)\n\u001b[0;32m     60\u001b[0m all_results_df\u001b[38;5;241m.\u001b[39mto_excel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mregs_significance_all_models.xlsx\u001b[39m\u001b[38;5;124m\"\u001b[39m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)    \n\u001b[0;32m     61\u001b[0m os\u001b[38;5;241m.\u001b[39mremove(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mregs_significance_all_models.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\ofici\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\series.py:4924\u001b[0m, in \u001b[0;36mSeries.apply\u001b[1;34m(self, func, convert_dtype, args, by_row, **kwargs)\u001b[0m\n\u001b[0;32m   4789\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\n\u001b[0;32m   4790\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   4791\u001b[0m     func: AggFuncType,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   4796\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   4797\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[0;32m   4798\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   4799\u001b[0m \u001b[38;5;124;03m    Invoke function on values of Series.\u001b[39;00m\n\u001b[0;32m   4800\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   4915\u001b[0m \u001b[38;5;124;03m    dtype: float64\u001b[39;00m\n\u001b[0;32m   4916\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m   4917\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSeriesApply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   4918\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4919\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4920\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconvert_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4921\u001b[0m \u001b[43m        \u001b[49m\u001b[43mby_row\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby_row\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4922\u001b[0m \u001b[43m        \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   4923\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m-> 4924\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ofici\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\apply.py:1427\u001b[0m, in \u001b[0;36mSeriesApply.apply\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1424\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_compat()\n\u001b[0;32m   1426\u001b[0m \u001b[38;5;66;03m# self.func is Callable\u001b[39;00m\n\u001b[1;32m-> 1427\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ofici\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\apply.py:1507\u001b[0m, in \u001b[0;36mSeriesApply.apply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1501\u001b[0m \u001b[38;5;66;03m# row-wise access\u001b[39;00m\n\u001b[0;32m   1502\u001b[0m \u001b[38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons\u001b[39;00m\n\u001b[0;32m   1503\u001b[0m \u001b[38;5;66;03m# we need to give `na_action=\"ignore\"` for categorical data.\u001b[39;00m\n\u001b[0;32m   1504\u001b[0m \u001b[38;5;66;03m# TODO: remove the `na_action=\"ignore\"` when that default has been changed in\u001b[39;00m\n\u001b[0;32m   1505\u001b[0m \u001b[38;5;66;03m#  Categorical (GH51645).\u001b[39;00m\n\u001b[0;32m   1506\u001b[0m action \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mdtype, CategoricalDtype) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1507\u001b[0m mapped \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_values\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1508\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmapper\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcurried\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\n\u001b[0;32m   1509\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1511\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(mapped) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mapped[\u001b[38;5;241m0\u001b[39m], ABCSeries):\n\u001b[0;32m   1512\u001b[0m     \u001b[38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[0;32m   1513\u001b[0m     \u001b[38;5;66;03m#  See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[0;32m   1514\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39m_constructor_expanddim(\u001b[38;5;28mlist\u001b[39m(mapped), index\u001b[38;5;241m=\u001b[39mobj\u001b[38;5;241m.\u001b[39mindex)\n",
      "File \u001b[1;32mc:\\Users\\ofici\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\base.py:921\u001b[0m, in \u001b[0;36mIndexOpsMixin._map_values\u001b[1;34m(self, mapper, na_action, convert)\u001b[0m\n\u001b[0;32m    918\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arr, ExtensionArray):\n\u001b[0;32m    919\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mmap(mapper, na_action\u001b[38;5;241m=\u001b[39mna_action)\n\u001b[1;32m--> 921\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_action\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ofici\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\algorithms.py:1743\u001b[0m, in \u001b[0;36mmap_array\u001b[1;34m(arr, mapper, na_action, convert)\u001b[0m\n\u001b[0;32m   1741\u001b[0m values \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m   1742\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_action \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1743\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_infer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1744\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1745\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mmap_infer_mask(\n\u001b[0;32m   1746\u001b[0m         values, mapper, mask\u001b[38;5;241m=\u001b[39misna(values)\u001b[38;5;241m.\u001b[39mview(np\u001b[38;5;241m.\u001b[39muint8), convert\u001b[38;5;241m=\u001b[39mconvert\n\u001b[0;32m   1747\u001b[0m     )\n",
      "File \u001b[1;32mlib.pyx:2972\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[1;34m()\u001b[0m\n",
      "Cell \u001b[1;32mIn[4], line 64\u001b[0m, in \u001b[0;36mparse_name\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m     61\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     62\u001b[0m     fixed_effects \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStandard FE\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 64\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(conditions)\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[0;32m     66\u001b[0m words \u001b[38;5;241m=\u001b[39m conditions[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m     67\u001b[0m words \u001b[38;5;241m=\u001b[39m words\u001b[38;5;241m.\u001b[39mstrip()\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import re\n",
    "from tqdm import tqdm\n",
    "\n",
    "folder = r\"D:\\World Bank\\Paper - Child mortality and Climate Shocks\\Outputs\"\n",
    "files = os.listdir(folder)\n",
    "files = [f for f in files if \".tex\" in f]\n",
    "assert len(files)!=0\n",
    "\n",
    "\n",
    "for i, file in tqdm(enumerate(files), total=len(files)):\n",
    "\n",
    "    filename = rf\"{folder}\\{file}\"\n",
    "    df = pd.read_csv(filename,\n",
    "                    sep='&',\n",
    "                    header=0,\n",
    "                    skiprows=4,\n",
    "                    skipfooter=3,\n",
    "                    engine='python')\n",
    "\n",
    "    keywords = \"drought|excessiverain|t_|std_t|stdm_t|spi\" # Regex allowed\n",
    "    \n",
    "    df = df.set_index(df.columns[0]) # Set variable names in index\n",
    "    df = df[df.index != ' '] # Remove standard errors\n",
    "    df = df[df.index.str.contains(f'{keywords}|VARIABLES')]\n",
    "\n",
    "    results = {}\n",
    "    for specification in df.columns:\n",
    "        counts = count_significative_vars(df[specification])\n",
    "        specification = re.sub(r'[^0-9a-zA-Z]+', '', specification)\n",
    "        results[specification] = counts\n",
    "\n",
    "    results_df = dict_to_df(results)    \n",
    "    results_df[\"path\"] = filename\n",
    "    results_df[\"file\"] = file\n",
    "    results_df = results_df[['file', 'path', 'Specification', 'estimates', 'p=0.1 (+)', 'p=0.05 (+)', 'p=0.01 (+)', 'p=0.1 (-)', 'p=0.05 (-)', 'p=0.01 (-)']]\n",
    "\n",
    "    if i==0:\n",
    "        results_df.to_csv(\"regs_significance_all_models.csv\", index=False)    \n",
    "    else:\n",
    "        results_df.to_csv(\"regs_significance_all_models.csv\", mode=\"a\", index=False, header=False)\n",
    "        \n",
    "# ### Process data\n",
    "all_results_df = pd.read_csv(\"regs_significance_all_models.csv\")\n",
    "\n",
    "# Consistency in specification\n",
    "all_results_df[\"Consistent (p=0.1)\"]  = (all_results_df[\"p=0.1 (+)\"]  >= 0) & (all_results_df[\"p=0.1 (-)\"]  == 0)\n",
    "all_results_df[\"Consistent (p=0.05)\"] = (all_results_df[\"p=0.05 (+)\"] >= 0) & (all_results_df[\"p=0.05 (-)\"] == 0)\n",
    "all_results_df[\"Consistent (p=0.01)\"] = (all_results_df[\"p=0.01 (+)\"] >= 0) & (all_results_df[\"p=0.01 (-)\"] == 0)\n",
    "\n",
    "# Consistency in regression set\n",
    "regressions_set = all_results_df.groupby(\"path\")\n",
    "for pval in [\"0.1 (+)\", \"0.05 (+)\", \"0.01 (+)\", \"0.1 (-)\", \"0.05 (-)\", \"0.01 (-)\"]:\n",
    "    all_results_df[f\"Set {pval}\"] = regressions_set[f\"p={pval}\"].transform(\"sum\")\n",
    "    all_results_df[f\"Set estimates\"] = regressions_set[f\"estimates\"].transform(\"sum\")\n",
    "    all_results_df[f\"Set {pval} (%)\"] = all_results_df[f\"Set {pval}\"] / all_results_df[f\"Set estimates\"]\n",
    "       \n",
    "# Parse the names of the\n",
    "all_results_df = all_results_df.file.apply(parse_name).apply(pd.Series).join(all_results_df)\n",
    "\n",
    "all_results_df.to_excel(\"regs_significance_all_models.xlsx\", index=False)    \n",
    "os.remove(\"regs_significance_all_models.csv\")\n",
    "print(\"Done! File saved as regs_significance_all_models.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('version', '4', 'drought', '2_5', '6', 'months.tex')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parse_name(results_df.file.values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['        (1) ', '        (2) ', '        (3) ', '        (4) ',\n",
       "       '       (5) ', '       (6) ', '       (7) ', '       (8) ',\n",
       "       '       (9) ', '      (10) ', '      (11) ', '      (12) ',\n",
       "       '      (13) ', '      (14) ', '      (15) ', '      (16) \\\\ '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                                            \n",
       "drought6\\_2\\_5\\_q1                              False\n",
       "drought6\\_2\\_5\\_q2                              False\n",
       "drought6\\_2\\_5\\_q3                              False\n",
       "drought6\\_2\\_5\\_30d                             False\n",
       "excessiverain6\\_2\\_5\\_q1                        False\n",
       "excessiverain6\\_2\\_5\\_q2                        False\n",
       "excessiverain6\\_2\\_5\\_q3                        False\n",
       "excessiverain6\\_2\\_5\\_30d                       False\n",
       "drought6\\_2\\_5\\_30d3m                            True\n",
       "excessiverain6\\_2\\_5\\_30d3m                      True\n",
       "drought6\\_2\\_5\\_3m6m                             True\n",
       "excessiverain6\\_2\\_5\\_3m6m                       True\n",
       "drought6\\_2\\_5\\_6m12m                            True\n",
       "excessiverain6\\_2\\_5\\_6m12m                      True\n",
       "Name:         (1) , dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['        (1) '].str.strip() == \"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resultados por país"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "def dot_plot(all_specs, varname, quarter):\n",
    "    \n",
    "    fig = go.Figure()\n",
    "\n",
    "    colors = [\n",
    "        'rgba(156, 165, 196, 0.95)','rgba(156, 165, 196, 0.95)','rgba(156, 165, 196, 0.95)','rgba(156, 165, 196, 0.95)',\n",
    "        'rgba(204, 204, 204, 0.95)','rgba(204, 204, 204, 0.95)','rgba(204, 204, 204, 0.95)','rgba(204, 204, 204, 0.95)',\n",
    "        'rgba(255, 166, 86, 0.95)','rgba(255, 166, 86, 0.95)','rgba(255, 166, 86, 0.95)','rgba(255, 166, 86, 0.95)',\n",
    "        'rgba(240, 230, 140, 0.95)','rgba(240, 230, 140, 0.95)','rgba(240, 230, 140, 0.95)','rgba(240, 230, 140, 0.95)',\n",
    "    ]\n",
    "    data = all_specs[\n",
    "        all_specs.variable.str.contains(\"drought\") &\n",
    "        all_specs.variable.str.contains(quarter)\n",
    "    ]\n",
    "    for i, spec in enumerate(data.specification.unique()):\n",
    "        data_plot = data[data.specification == spec]\n",
    "        fig.add_trace(go.Scatter(\n",
    "            x=data_plot[\"value\"].astype(\"float\"),\n",
    "            y=data_plot[\"Country\"],\n",
    "            marker=dict(\n",
    "                color=colors[i],\n",
    "                size=16\n",
    "            ),\n",
    "            mode='markers',\n",
    "            name=spec,\n",
    "        ))\n",
    "\n",
    "    fig.update_traces(mode='markers', marker=dict(line_width=1, symbol='circle', size=16))\n",
    "\n",
    "    fig.update_layout(\n",
    "        title=varname,\n",
    "        xaxis=dict(\n",
    "            showgrid=False,\n",
    "            showline=True,\n",
    "            linecolor='rgb(102, 102, 102)',\n",
    "            tickfont_color='rgb(102, 102, 102)',\n",
    "            showticklabels=True,\n",
    "            ticks='outside',\n",
    "            tickcolor='rgb(102, 102, 102)',\n",
    "        ),\n",
    "        margin=dict(l=140, r=40, b=50, t=80),\n",
    "        legend=dict(\n",
    "            font_size=10,\n",
    "            yanchor='top',\n",
    "            xanchor='center',\n",
    "        ),\n",
    "        width=1600,\n",
    "        height=600,\n",
    "        paper_bgcolor='white',\n",
    "        plot_bgcolor='white',\n",
    "        hovermode='closest',\n",
    "    )\n",
    "    \n",
    "    # Add a vertical line at zero\n",
    "    fig.update_layout(shapes=[\n",
    "        dict(\n",
    "            type=\"line\",\n",
    "            x0=0,\n",
    "            y0=0,\n",
    "            x1=0,\n",
    "            y1=all_specs.Country.drop_duplicates().shape[0],\n",
    "            line=dict(\n",
    "                color=\"red\",\n",
    "                width=2,\n",
    "                dash=\"dashdot\",\n",
    "            )\n",
    "        )\n",
    "    ])\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|█▋        | 9/54 [00:00<00:00, 83.24it/s]C:\\Users\\ofici\\AppData\\Local\\Temp\\ipykernel_24444\\3964789092.py:30: FutureWarning:\n",
      "\n",
      "Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\ofici\\AppData\\Local\\Temp\\ipykernel_24444\\3964789092.py:30: FutureWarning:\n",
      "\n",
      "Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      " 54%|█████▎    | 29/54 [00:00<00:00, 85.37it/s]C:\\Users\\ofici\\AppData\\Local\\Temp\\ipykernel_24444\\3964789092.py:30: FutureWarning:\n",
      "\n",
      "Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      " 74%|███████▍  | 40/54 [00:00<00:00, 92.30it/s]C:\\Users\\ofici\\AppData\\Local\\Temp\\ipykernel_24444\\3964789092.py:30: FutureWarning:\n",
      "\n",
      "Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "100%|██████████| 54/54 [00:00<00:00, 75.46it/s]\n"
     ]
    },
    {
     "data": {
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   "source": [
    "import re\n",
    "from tqdm import tqdm\n",
    "\n",
    "folder = r\"Z:\\Laboral\\World Bank\\Paper - Child mortality and Climate Shocks\\Outputs\\countries\"\n",
    "name = \"version 3 drought 2_5 12 months\"\n",
    "version_nam, version_num, drought_nam, drought_num, months_num, months_nam = parse_name(name)\n",
    "\n",
    "files = os.listdir(folder)\n",
    "files = [f for f in files if \".tex\" in f]\n",
    "files = select_files(files, name=name)\n",
    "\n",
    "all_specs = []\n",
    "for file in tqdm(files):\n",
    "\n",
    "    filename = rf\"{folder}\\{file}\"\n",
    "    country = re.search(r\"([A-Z]{3})\", file).group(0)\n",
    "    \n",
    "    df = pd.read_csv(filename,\n",
    "                    sep='&',\n",
    "                    header=0,\n",
    "                    skiprows=4,\n",
    "                    skipfooter=3,\n",
    "                    engine='python')\n",
    "\n",
    "    keywords = \"temp|spi|drought|excessiverain|prec\" # Regex allowed\n",
    "    \n",
    "    # Process data\n",
    "    df = df.set_index(df.columns[0]) # Set variable names in index\n",
    "    df = df[df.index != ' '] # Remove standard errors\n",
    "    df = df[df.index.str.contains(keywords)]\n",
    "    df.index = df.index.str.strip()\n",
    "    df.index.name = \"variable\"\n",
    "    df = df.reset_index()\n",
    "    results = {}\n",
    "    df.columns = [re.sub(r'[^0-9a-zA-Z]+', '', col) for col in df.columns]\n",
    "\n",
    "    # Format dataframe\n",
    "    drought_num_str = drought_num.replace(\"_\", r\"\\_\")\n",
    "    months_num_str = fr\"\\_{months_num}\\_\"\n",
    "    country_specs = df.melt(id_vars=[\"variable\"], var_name=\"specification\", value_name=\"value\")\n",
    "    country_specs[\"value\"] = process_significance(country_specs.value)    \n",
    "    country_specs[\"value\"] = make_float(country_specs.value)    \n",
    "    country_specs[\"Country\"] = country\n",
    "    country_specs[\"variable\"] = country_specs[\"variable\"].str.replace(months_num_str, \" \").str.replace(drought_num_str, drought_num)\n",
    "    all_specs += [country_specs]\n",
    "    \n",
    "all_specs = pd.concat(all_specs)\n",
    "\n",
    "dot_plot(all_specs, f\"drought\", \"q1\")\n",
    "dot_plot(all_specs, f\"drought\", \"q2\")\n",
    "dot_plot(all_specs, f\"drought\", \"q3\")\n",
    "dot_plot(all_specs, f\"drought\", \"30d\")\n",
    "dot_plot(all_specs, f\"drought\", \"30d3m\")\n",
    "dot_plot(all_specs, f\"drought\", \"3m6m\")\n",
    "dot_plot(all_specs, f\"drought\", \"6m12m\")"
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