diff --git "a/Inflation.ipynb" "b/Inflation.ipynb"
new file mode 100644--- /dev/null
+++ "b/Inflation.ipynb"
@@ -0,0 +1,2380 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "8d7051b2",
+ "metadata": {},
+ "source": [
+ "# Inflation, consumer prices (annual %)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "81cde157",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Country Name | \n",
+ " Country Code | \n",
+ " 1960 | \n",
+ " 1961 | \n",
+ " 1962 | \n",
+ " 1963 | \n",
+ " 1964 | \n",
+ " 1965 | \n",
+ " 1966 | \n",
+ " 1967 | \n",
+ " ... | \n",
+ " 2013 | \n",
+ " 2014 | \n",
+ " 2015 | \n",
+ " 2016 | \n",
+ " 2017 | \n",
+ " 2018 | \n",
+ " 2019 | \n",
+ " 2020 | \n",
+ " 2021 | \n",
+ " 2022 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " Aruba | \n",
+ " ABW | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " ... | \n",
+ " -2.372065 | \n",
+ " 0.421441 | \n",
+ " 0.474764 | \n",
+ " -0.931196 | \n",
+ " -1.028282 | \n",
+ " 3.626041 | \n",
+ " 4.257462 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " Africa Eastern and Southern | \n",
+ " AFE | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " ... | \n",
+ " 5.750981 | \n",
+ " 5.370290 | \n",
+ " 5.245878 | \n",
+ " 6.571396 | \n",
+ " 6.399343 | \n",
+ " 4.720811 | \n",
+ " 4.653665 | \n",
+ " 7.321106 | \n",
+ " 6.824727 | \n",
+ " 10.773751 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " Afghanistan | \n",
+ " AFG | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " ... | \n",
+ " 7.385772 | \n",
+ " 4.673996 | \n",
+ " -0.661709 | \n",
+ " 4.383892 | \n",
+ " 4.975952 | \n",
+ " 0.626149 | \n",
+ " 2.302373 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " Africa Western and Central | \n",
+ " AFW | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " ... | \n",
+ " 2.439201 | \n",
+ " 1.768436 | \n",
+ " 2.130817 | \n",
+ " 1.487416 | \n",
+ " 1.764635 | \n",
+ " 1.784050 | \n",
+ " 1.760112 | \n",
+ " 2.437609 | \n",
+ " 3.653533 | \n",
+ " 7.967574 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " Angola | \n",
+ " AGO | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " ... | \n",
+ " 8.777814 | \n",
+ " 7.280387 | \n",
+ " 9.353840 | \n",
+ " 30.698958 | \n",
+ " 29.842578 | \n",
+ " 19.630594 | \n",
+ " 17.079704 | \n",
+ " 22.271564 | \n",
+ " 25.754266 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 261 | \n",
+ " Kosovo | \n",
+ " XKX | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " ... | \n",
+ " 1.767324 | \n",
+ " 0.428958 | \n",
+ " -0.536929 | \n",
+ " 0.273169 | \n",
+ " 1.488234 | \n",
+ " 1.053798 | \n",
+ " 2.675992 | \n",
+ " 0.198228 | \n",
+ " 3.353691 | \n",
+ " 11.580510 | \n",
+ "
\n",
+ " \n",
+ " 262 | \n",
+ " Yemen, Rep. | \n",
+ " YEM | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " ... | \n",
+ " 10.968442 | \n",
+ " 8.104726 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 263 | \n",
+ " South Africa | \n",
+ " ZAF | \n",
+ " 1.288859 | \n",
+ " 2.102374 | \n",
+ " 1.246285 | \n",
+ " 1.33797 | \n",
+ " 2.534973 | \n",
+ " 4.069029 | \n",
+ " 3.489234 | \n",
+ " 3.538992 | \n",
+ " ... | \n",
+ " 5.784469 | \n",
+ " 6.129838 | \n",
+ " 4.540642 | \n",
+ " 6.571396 | \n",
+ " 5.184247 | \n",
+ " 4.517165 | \n",
+ " 4.120246 | \n",
+ " 3.210036 | \n",
+ " 4.611672 | \n",
+ " 7.039727 | \n",
+ "
\n",
+ " \n",
+ " 264 | \n",
+ " Zambia | \n",
+ " ZMB | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " ... | \n",
+ " 6.977676 | \n",
+ " 7.806876 | \n",
+ " 10.110593 | \n",
+ " 17.869730 | \n",
+ " 6.577312 | \n",
+ " 7.494572 | \n",
+ " 9.150316 | \n",
+ " 15.733060 | \n",
+ " 22.020768 | \n",
+ " 10.993204 | \n",
+ "
\n",
+ " \n",
+ " 265 | \n",
+ " Zimbabwe | \n",
+ " ZWE | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " ... | \n",
+ " 1.634950 | \n",
+ " -0.197785 | \n",
+ " -2.430968 | \n",
+ " -1.543670 | \n",
+ " 0.893962 | \n",
+ " 10.618866 | \n",
+ " 255.304991 | \n",
+ " 557.201817 | \n",
+ " 98.546105 | \n",
+ " 104.705171 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
266 rows × 65 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Country Name Country Code 1960 1961 1962 \\\n",
+ "0 Aruba ABW NaN NaN NaN \n",
+ "1 Africa Eastern and Southern AFE NaN NaN NaN \n",
+ "2 Afghanistan AFG NaN NaN NaN \n",
+ "3 Africa Western and Central AFW NaN NaN NaN \n",
+ "4 Angola AGO NaN NaN NaN \n",
+ ".. ... ... ... ... ... \n",
+ "261 Kosovo XKX NaN NaN NaN \n",
+ "262 Yemen, Rep. YEM NaN NaN NaN \n",
+ "263 South Africa ZAF 1.288859 2.102374 1.246285 \n",
+ "264 Zambia ZMB NaN NaN NaN \n",
+ "265 Zimbabwe ZWE NaN NaN NaN \n",
+ "\n",
+ " 1963 1964 1965 1966 1967 ... 2013 \\\n",
+ "0 NaN NaN NaN NaN NaN ... -2.372065 \n",
+ "1 NaN NaN NaN NaN NaN ... 5.750981 \n",
+ "2 NaN NaN NaN NaN NaN ... 7.385772 \n",
+ "3 NaN NaN NaN NaN NaN ... 2.439201 \n",
+ "4 NaN NaN NaN NaN NaN ... 8.777814 \n",
+ ".. ... ... ... ... ... ... ... \n",
+ "261 NaN NaN NaN NaN NaN ... 1.767324 \n",
+ "262 NaN NaN NaN NaN NaN ... 10.968442 \n",
+ "263 1.33797 2.534973 4.069029 3.489234 3.538992 ... 5.784469 \n",
+ "264 NaN NaN NaN NaN NaN ... 6.977676 \n",
+ "265 NaN NaN NaN NaN NaN ... 1.634950 \n",
+ "\n",
+ " 2014 2015 2016 2017 2018 2019 \\\n",
+ "0 0.421441 0.474764 -0.931196 -1.028282 3.626041 4.257462 \n",
+ "1 5.370290 5.245878 6.571396 6.399343 4.720811 4.653665 \n",
+ "2 4.673996 -0.661709 4.383892 4.975952 0.626149 2.302373 \n",
+ "3 1.768436 2.130817 1.487416 1.764635 1.784050 1.760112 \n",
+ "4 7.280387 9.353840 30.698958 29.842578 19.630594 17.079704 \n",
+ ".. ... ... ... ... ... ... \n",
+ "261 0.428958 -0.536929 0.273169 1.488234 1.053798 2.675992 \n",
+ "262 8.104726 NaN NaN NaN NaN NaN \n",
+ "263 6.129838 4.540642 6.571396 5.184247 4.517165 4.120246 \n",
+ "264 7.806876 10.110593 17.869730 6.577312 7.494572 9.150316 \n",
+ "265 -0.197785 -2.430968 -1.543670 0.893962 10.618866 255.304991 \n",
+ "\n",
+ " 2020 2021 2022 \n",
+ "0 NaN NaN NaN \n",
+ "1 7.321106 6.824727 10.773751 \n",
+ "2 NaN NaN NaN \n",
+ "3 2.437609 3.653533 7.967574 \n",
+ "4 22.271564 25.754266 NaN \n",
+ ".. ... ... ... \n",
+ "261 0.198228 3.353691 11.580510 \n",
+ "262 NaN NaN NaN \n",
+ "263 3.210036 4.611672 7.039727 \n",
+ "264 15.733060 22.020768 10.993204 \n",
+ "265 557.201817 98.546105 104.705171 \n",
+ "\n",
+ "[266 rows x 65 columns]"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "inflation = pd.read_excel('Inflation.xlsx')\n",
+ "inflation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "b450a0de",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 266 entries, 0 to 265\n",
+ "Data columns (total 65 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 Country Name 266 non-null object \n",
+ " 1 Country Code 266 non-null object \n",
+ " 2 1960 70 non-null float64\n",
+ " 3 1961 72 non-null float64\n",
+ " 4 1962 74 non-null float64\n",
+ " 5 1963 74 non-null float64\n",
+ " 6 1964 79 non-null float64\n",
+ " 7 1965 86 non-null float64\n",
+ " 8 1966 93 non-null float64\n",
+ " 9 1967 100 non-null float64\n",
+ " 10 1968 101 non-null float64\n",
+ " 11 1969 102 non-null float64\n",
+ " 12 1970 107 non-null float64\n",
+ " 13 1971 111 non-null float64\n",
+ " 14 1972 114 non-null float64\n",
+ " 15 1973 117 non-null float64\n",
+ " 16 1974 119 non-null float64\n",
+ " 17 1975 123 non-null float64\n",
+ " 18 1976 124 non-null float64\n",
+ " 19 1977 130 non-null float64\n",
+ " 20 1978 130 non-null float64\n",
+ " 21 1979 124 non-null float64\n",
+ " 22 1980 131 non-null float64\n",
+ " 23 1981 149 non-null float64\n",
+ " 24 1982 150 non-null float64\n",
+ " 25 1983 150 non-null float64\n",
+ " 26 1984 154 non-null float64\n",
+ " 27 1985 155 non-null float64\n",
+ " 28 1986 163 non-null float64\n",
+ " 29 1987 170 non-null float64\n",
+ " 30 1988 170 non-null float64\n",
+ " 31 1989 173 non-null float64\n",
+ " 32 1990 173 non-null float64\n",
+ " 33 1991 179 non-null float64\n",
+ " 34 1992 187 non-null float64\n",
+ " 35 1993 193 non-null float64\n",
+ " 36 1994 198 non-null float64\n",
+ " 37 1995 202 non-null float64\n",
+ " 38 1996 205 non-null float64\n",
+ " 39 1997 205 non-null float64\n",
+ " 40 1998 205 non-null float64\n",
+ " 41 1999 207 non-null float64\n",
+ " 42 2000 211 non-null float64\n",
+ " 43 2001 216 non-null float64\n",
+ " 44 2002 218 non-null float64\n",
+ " 45 2003 221 non-null float64\n",
+ " 46 2004 222 non-null float64\n",
+ " 47 2005 225 non-null float64\n",
+ " 48 2006 228 non-null float64\n",
+ " 49 2007 230 non-null float64\n",
+ " 50 2008 231 non-null float64\n",
+ " 51 2009 234 non-null float64\n",
+ " 52 2010 236 non-null float64\n",
+ " 53 2011 240 non-null float64\n",
+ " 54 2012 239 non-null float64\n",
+ " 55 2013 236 non-null float64\n",
+ " 56 2014 235 non-null float64\n",
+ " 57 2015 234 non-null float64\n",
+ " 58 2016 234 non-null float64\n",
+ " 59 2017 229 non-null float64\n",
+ " 60 2018 226 non-null float64\n",
+ " 61 2019 225 non-null float64\n",
+ " 62 2020 219 non-null float64\n",
+ " 63 2021 217 non-null float64\n",
+ " 64 2022 213 non-null float64\n",
+ "dtypes: float64(63), object(2)\n",
+ "memory usage: 135.2+ KB\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "((266, 65),\n",
+ " Index(['Country Name', 'Country Code', '1960', '1961', '1962', '1963', '1964',\n",
+ " '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973',\n",
+ " '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982',\n",
+ " '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n",
+ " '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000',\n",
+ " '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009',\n",
+ " '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018',\n",
+ " '2019', '2020', '2021', '2022'],\n",
+ " dtype='object'),\n",
+ " Country Name object\n",
+ " Country Code object\n",
+ " 1960 float64\n",
+ " 1961 float64\n",
+ " 1962 float64\n",
+ " ... \n",
+ " 2018 float64\n",
+ " 2019 float64\n",
+ " 2020 float64\n",
+ " 2021 float64\n",
+ " 2022 float64\n",
+ " Length: 65, dtype: object,\n",
+ " None,\n",
+ " 1960 1961 1962 1963 1964 1965 \\\n",
+ " count 70.000000 72.000000 74.000000 74.000000 79.000000 86.000000 \n",
+ " mean 3.554980 3.491289 4.630783 5.846612 6.263870 8.192841 \n",
+ " std 6.795697 4.508248 15.209955 17.152509 13.749261 33.376181 \n",
+ " min -5.030042 -3.900000 -3.846154 -2.694655 -4.535654 -3.878976 \n",
+ " 25% 0.871370 1.468978 1.147323 1.790965 1.870349 1.940405 \n",
+ " 50% 1.945749 2.102977 2.669962 2.898982 3.328408 3.410026 \n",
+ " 75% 4.037155 3.601606 4.614353 4.998460 4.822426 4.938170 \n",
+ " max 39.590444 22.747264 131.397849 145.910781 108.994709 306.763110 \n",
+ " \n",
+ " 1966 1967 1968 1969 ... 2013 \\\n",
+ " count 93.000000 100.000000 101.000000 102.000000 ... 236.000000 \n",
+ " mean 17.765764 5.469524 6.309715 4.446334 ... 4.087093 \n",
+ " std 117.536433 14.150802 18.309690 4.140570 ... 5.071277 \n",
+ " min -1.361868 -8.422486 -10.033895 -4.339051 ... -4.294873 \n",
+ " 25% 2.479008 1.564937 1.588785 2.347005 ... 1.463358 \n",
+ " 50% 3.815659 3.020244 3.161937 3.388412 ... 2.774232 \n",
+ " 75% 6.951872 4.500319 4.697428 5.811237 ... 5.363191 \n",
+ " max 1136.254112 106.000000 128.843042 21.763295 ... 40.639428 \n",
+ " \n",
+ " 2014 2015 2016 2017 2018 2019 \\\n",
+ " count 235.000000 234.000000 234.000000 229.000000 226.000000 225.000000 \n",
+ " mean 3.641227 3.580789 6.037178 4.745088 4.073728 4.625074 \n",
+ " std 5.580485 9.636531 30.096995 13.085162 7.532605 18.422995 \n",
+ " min -1.509245 -3.749145 -3.078218 -1.537100 -2.814698 -3.233389 \n",
+ " 25% 0.926777 0.309365 0.455380 1.429107 1.627863 1.108255 \n",
+ " 50% 2.626684 1.557907 1.675408 2.450534 2.597456 2.206073 \n",
+ " 75% 4.632431 4.055194 4.330767 4.520229 4.039775 3.322559 \n",
+ " max 62.168650 121.738085 379.999586 187.851630 83.501529 255.304991 \n",
+ " \n",
+ " 2020 2021 2022 \n",
+ " count 219.000000 217.000000 213.000000 \n",
+ " mean 6.807404 7.536486 11.577459 \n",
+ " std 39.599591 27.451909 17.605291 \n",
+ " min -2.595243 -0.772844 -6.687321 \n",
+ " 25% 0.602391 2.343284 5.821158 \n",
+ " 50% 2.002412 3.653533 8.160590 \n",
+ " 75% 3.723892 5.214049 10.773751 \n",
+ " max 557.201817 359.093041 171.205491 \n",
+ " \n",
+ " [8 rows x 63 columns])"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "inflation.shape, inflation.columns, inflation.dtypes, inflation.info(), inflation.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b929bfda",
+ "metadata": {},
+ "source": [
+ "### Nombres de las hojas Excel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "ea237b90",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['Data', 'Metadata - Countries', 'Metadata - Indicators'])"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "file_path = 'Inflation.xlsx'\n",
+ "data = pd.read_excel(file_path, sheet_name=None) \n",
+ "\n",
+ "sheet_names = data.keys()\n",
+ "sheet_names"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5cf6f308",
+ "metadata": {},
+ "source": [
+ "### Imputación con Mediana a valores NaN"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "b2207262",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Country Name 0\n",
+ "Country Code 0\n",
+ "1960 196\n",
+ "1961 194\n",
+ "1962 192\n",
+ " ... \n",
+ "2018 40\n",
+ "2019 41\n",
+ "2020 47\n",
+ "2021 49\n",
+ "2022 53\n",
+ "Length: 65, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(inflation.isna().sum())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "893edd8b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "# Crear una copia para trabajar con la imputación\n",
+ "inflation_imputed = inflation.copy()\n",
+ "\n",
+ "# Identificar las columnas problemáticas\n",
+ "non_positive_columns = inflation_imputed.loc[:, inflation_imputed.columns[2:]].apply(lambda x: (x <= 0).any())\n",
+ "problematic_columns = non_positive_columns[non_positive_columns].index.tolist()\n",
+ "\n",
+ "# Establecer un valor positivo mínimo para imputar\n",
+ "min_positive_value = 0.01\n",
+ "\n",
+ "# Imputar valores no positivos\n",
+ "for column in problematic_columns:\n",
+ " inflation_imputed[column] = inflation_imputed[column].apply(lambda x: min_positive_value if x <= 0 else x)\n",
+ "\n",
+ "# Comprobación que no hay valores no positivos en las columnas problemáticas\n",
+ "assert all(inflation_imputed[problematic_columns].min() > 0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "20c75b01",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "( Country Name Country Code 1960 1961 1962 \\\n",
+ " 0 Aruba ABW -0.137047 -0.172253 -0.073786 \n",
+ " 1 Africa Eastern and Southern AFE -0.137047 -0.172253 -0.073786 \n",
+ " 2 Afghanistan AFG -0.137047 -0.172253 -0.073786 \n",
+ " 3 Africa Western and Central AFW -0.137047 -0.172253 -0.073786 \n",
+ " 4 Angola AGO -0.137047 -0.172253 -0.073786 \n",
+ " \n",
+ " 1963 1964 1965 1966 1967 ... 2013 2014 \\\n",
+ " 0 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... -0.838328 -0.605012 \n",
+ " 1 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... 0.373381 0.345457 \n",
+ " 2 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... 0.718425 0.211728 \n",
+ " 3 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... -0.325614 -0.346310 \n",
+ " 4 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... 1.012234 0.712308 \n",
+ " \n",
+ " 2015 2016 2017 2018 2019 2020 2021 \\\n",
+ " 0 -0.335730 -0.199353 -0.365765 -0.035131 -0.004350 -0.113314 -0.128176 \n",
+ " 1 0.195933 0.033496 0.160902 0.122722 0.019088 0.034941 -0.000207 \n",
+ " 2 -0.387521 -0.044134 0.043573 -0.467679 -0.120006 -0.113314 -0.128176 \n",
+ " 3 -0.151190 -0.146923 -0.221133 -0.300723 -0.152084 -0.101183 -0.128176 \n",
+ " 4 0.653699 0.889729 2.093307 2.272532 0.754171 0.451674 0.763668 \n",
+ " \n",
+ " 2022 \n",
+ " 0 -0.175279 \n",
+ " 1 -0.009400 \n",
+ " 2 -0.175279 \n",
+ " 3 -0.187531 \n",
+ " 4 -0.175279 \n",
+ " \n",
+ " [5 rows x 65 columns],\n",
+ " (-14.788756107337168,\n",
+ " 2.187749182327035e-27,\n",
+ " 0,\n",
+ " 265,\n",
+ " {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " 726.0200966131579))"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from statsmodels.tsa.stattools import adfuller\n",
+ "\n",
+ "# Imputación con Medianas\n",
+ "for column in inflation_imputed.columns[2:]:\n",
+ " median_value = inflation_imputed[column].median()\n",
+ " inflation_imputed[column] = inflation_imputed[column].fillna(median_value)\n",
+ "\n",
+ "# Estandarización\n",
+ "scaler = StandardScaler()\n",
+ "inflation_imputed.iloc[:, 2:] = scaler.fit_transform(inflation_imputed.iloc[:, 2:])\n",
+ "\n",
+ "# Estacionaridad año 2022\n",
+ "adf_result = adfuller(inflation_imputed['2022'].dropna())\n",
+ "adf_result\n",
+ "\n",
+ "inflation_imputed.head(), adf_result"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "0fbbf1df",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'1960': {'ADF Statistic': -16.561879845209727,\n",
+ " 'p-value': 1.9039955232125e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1961': {'ADF Statistic': -17.08286394348471,\n",
+ " 'p-value': 7.685186221218232e-30,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1962': {'ADF Statistic': -16.221460811308305,\n",
+ " 'p-value': 3.893941858006242e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1963': {'ADF Statistic': -16.32643033993515,\n",
+ " 'p-value': 3.0912967888245425e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1964': {'ADF Statistic': -16.51036672525898,\n",
+ " 'p-value': 2.108656808532059e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1965': {'ADF Statistic': -16.361848532575053,\n",
+ " 'p-value': 2.865528406643489e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1966': {'ADF Statistic': -16.299805562432127,\n",
+ " 'p-value': 3.274880159793727e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1967': {'ADF Statistic': -16.48458878585416,\n",
+ " 'p-value': 2.2210308924998992e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1968': {'ADF Statistic': -16.374825059085243,\n",
+ " 'p-value': 2.787727076593123e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1969': {'ADF Statistic': -16.180101625979585,\n",
+ " 'p-value': 4.2753093419972246e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1970': {'ADF Statistic': -7.464263557919528,\n",
+ " 'p-value': 5.254887484198605e-11,\n",
+ " 'Critical Values': {'1%': -3.4554613060274972,\n",
+ " '5%': -2.8725931472675046,\n",
+ " '10%': -2.5726600403359887},\n",
+ " '1%': -3.4554613060274972,\n",
+ " '5%': -2.8725931472675046,\n",
+ " '10%': -2.5726600403359887},\n",
+ " '1971': {'ADF Statistic': -4.994899669419086,\n",
+ " 'p-value': 2.2714800240675204e-05,\n",
+ " 'Critical Values': {'1%': -3.4558530692911504,\n",
+ " '5%': -2.872764881778665,\n",
+ " '10%': -2.572751643088207},\n",
+ " '1%': -3.4558530692911504,\n",
+ " '5%': -2.872764881778665,\n",
+ " '10%': -2.572751643088207},\n",
+ " '1972': {'ADF Statistic': -16.736192322864053,\n",
+ " 'p-value': 1.3701655632929363e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1973': {'ADF Statistic': -16.402137820004523,\n",
+ " 'p-value': 2.63200266935777e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1974': {'ADF Statistic': -16.33505055301089,\n",
+ " 'p-value': 3.0344725792922094e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1975': {'ADF Statistic': -16.34288118163558,\n",
+ " 'p-value': 2.9839177807536584e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1976': {'ADF Statistic': -6.374196000252904,\n",
+ " 'p-value': 2.305320845352544e-08,\n",
+ " 'Critical Values': {'1%': -3.455558114028747,\n",
+ " '5%': -2.872635586277424,\n",
+ " '10%': -2.572682677000175},\n",
+ " '1%': -3.455558114028747,\n",
+ " '5%': -2.872635586277424,\n",
+ " '10%': -2.572682677000175},\n",
+ " '1977': {'ADF Statistic': -10.330841861826329,\n",
+ " 'p-value': 2.8452828214258223e-18,\n",
+ " 'Critical Values': {'1%': -3.455365238788105,\n",
+ " '5%': -2.8725510317187024,\n",
+ " '10%': -2.5726375763314966},\n",
+ " '1%': -3.455365238788105,\n",
+ " '5%': -2.8725510317187024,\n",
+ " '10%': -2.5726375763314966},\n",
+ " '1978': {'ADF Statistic': -15.738108643474378,\n",
+ " 'p-value': 1.2655208418815288e-28,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1979': {'ADF Statistic': -15.306574721050085,\n",
+ " 'p-value': 4.241990809413822e-28,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1980': {'ADF Statistic': -13.957800766357051,\n",
+ " 'p-value': 4.5776640309107405e-26,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1981': {'ADF Statistic': -14.356212648681561,\n",
+ " 'p-value': 1.0023968480552248e-26,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1982': {'ADF Statistic': -13.052001200406185,\n",
+ " 'p-value': 2.132496310980607e-24,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1983': {'ADF Statistic': -11.072887957884408,\n",
+ " 'p-value': 4.507114270266454e-20,\n",
+ " 'Critical Values': {'1%': -3.455365238788105,\n",
+ " '5%': -2.8725510317187024,\n",
+ " '10%': -2.5726375763314966},\n",
+ " '1%': -3.455365238788105,\n",
+ " '5%': -2.8725510317187024,\n",
+ " '10%': -2.5726375763314966},\n",
+ " '1984': {'ADF Statistic': -14.29116709770967,\n",
+ " 'p-value': 1.2748721779998471e-26,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1985': {'ADF Statistic': -15.988842165169272,\n",
+ " 'p-value': 6.70650496252454e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1986': {'ADF Statistic': -11.086777349067471,\n",
+ " 'p-value': 4.177712886273715e-20,\n",
+ " 'Critical Values': {'1%': -3.455365238788105,\n",
+ " '5%': -2.8725510317187024,\n",
+ " '10%': -2.5726375763314966},\n",
+ " '1%': -3.455365238788105,\n",
+ " '5%': -2.8725510317187024,\n",
+ " '10%': -2.5726375763314966},\n",
+ " '1987': {'ADF Statistic': -17.050028025288196,\n",
+ " 'p-value': 8.082404872641124e-30,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1988': {'ADF Statistic': -16.595085837481196,\n",
+ " 'p-value': 1.7848160300492385e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1989': {'ADF Statistic': -16.48187871436308,\n",
+ " 'p-value': 2.233259136184471e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1990': {'ADF Statistic': -16.374399872965665,\n",
+ " 'p-value': 2.790236354088407e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1991': {'ADF Statistic': -16.62734498857959,\n",
+ " 'p-value': 1.6776630492223644e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1992': {'ADF Statistic': -16.556277326657714,\n",
+ " 'p-value': 1.925048737995589e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1993': {'ADF Statistic': -16.623316945940946,\n",
+ " 'p-value': 1.6906026446076304e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1994': {'ADF Statistic': -16.348280054070734,\n",
+ " 'p-value': 2.949640805246965e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1995': {'ADF Statistic': -16.548360581562534,\n",
+ " 'p-value': 1.9552831704223722e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1996': {'ADF Statistic': -16.321597679782645,\n",
+ " 'p-value': 3.123701530649341e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1997': {'ADF Statistic': -16.423309965657825,\n",
+ " 'p-value': 2.5183696468028545e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1998': {'ADF Statistic': -16.121507393530393,\n",
+ " 'p-value': 4.892099890657972e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1999': {'ADF Statistic': -16.594482819346908,\n",
+ " 'p-value': 1.7868976809283985e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2000': {'ADF Statistic': -16.719671148310947,\n",
+ " 'p-value': 1.412022090399162e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2001': {'ADF Statistic': -16.654227885628206,\n",
+ " 'p-value': 1.594359356628338e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2002': {'ADF Statistic': -16.28359769314797,\n",
+ " 'p-value': 3.39290478799536e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2003': {'ADF Statistic': -17.170557316398565,\n",
+ " 'p-value': 6.747802662365729e-30,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2004': {'ADF Statistic': -16.849389078572607,\n",
+ " 'p-value': 1.1218086174694089e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2005': {'ADF Statistic': -15.72469912635325,\n",
+ " 'p-value': 1.3110927913494982e-28,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2006': {'ADF Statistic': -15.420107459480043,\n",
+ " 'p-value': 3.0425490294431915e-28,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2007': {'ADF Statistic': -16.904483241143566,\n",
+ " 'p-value': 1.0217532614308852e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2008': {'ADF Statistic': -15.908256208542031,\n",
+ " 'p-value': 8.179710051210137e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2009': {'ADF Statistic': -16.585954138858384,\n",
+ " 'p-value': 1.8166595879779965e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2010': {'ADF Statistic': -17.648430931536318,\n",
+ " 'p-value': 3.730096901392275e-30,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2011': {'ADF Statistic': -17.054153400890982,\n",
+ " 'p-value': 8.030988221744877e-30,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2012': {'ADF Statistic': -17.215469638122105,\n",
+ " 'p-value': 6.329006220300376e-30,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2013': {'ADF Statistic': -17.671748730791403,\n",
+ " 'p-value': 3.642086195933934e-30,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2014': {'ADF Statistic': -17.368902882611234,\n",
+ " 'p-value': 5.151558669736277e-30,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2015': {'ADF Statistic': -5.11182570923261,\n",
+ " 'p-value': 1.3271379205319513e-05,\n",
+ " 'Critical Values': {'1%': -3.4557539868570775,\n",
+ " '5%': -2.8727214497041422,\n",
+ " '10%': -2.572728476331361},\n",
+ " '1%': -3.4557539868570775,\n",
+ " '5%': -2.8727214497041422,\n",
+ " '10%': -2.572728476331361},\n",
+ " '2016': {'ADF Statistic': -16.318552340008786,\n",
+ " 'p-value': 3.144326986957438e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2017': {'ADF Statistic': -16.095486770964694,\n",
+ " 'p-value': 5.198481634434111e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2018': {'ADF Statistic': -3.4775173981887857,\n",
+ " 'p-value': 0.00858362079101299,\n",
+ " 'Critical Values': {'1%': -3.4561550092339512,\n",
+ " '5%': -2.8728972266578676,\n",
+ " '10%': -2.5728222369384763},\n",
+ " '1%': -3.4561550092339512,\n",
+ " '5%': -2.8728972266578676,\n",
+ " '10%': -2.5728222369384763},\n",
+ " '2019': {'ADF Statistic': -6.178785210601108,\n",
+ " 'p-value': 6.53168255712498e-08,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2020': {'ADF Statistic': -4.458795721908584,\n",
+ " 'p-value': 0.00023324438861709374,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2021': {'ADF Statistic': -15.896638099336487,\n",
+ " 'p-value': 8.42090787410305e-29,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '2022': {'ADF Statistic': -14.788756107337168,\n",
+ " 'p-value': 2.187749182327035e-27,\n",
+ " 'Critical Values': {'1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678},\n",
+ " '1%': -3.4552699038400827,\n",
+ " '5%': -2.8725092359464526,\n",
+ " '10%': -2.5726152830188678}}"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "years_columns = inflation_imputed.columns[2:]\n",
+ "adf_results = {}\n",
+ "\n",
+ "# ADF por año\n",
+ "for column in years_columns:\n",
+ " adf_result = adfuller(inflation_imputed[column].dropna())\n",
+ " adf_results[column] = {\n",
+ " 'ADF Statistic': adf_result[0],\n",
+ " 'p-value': adf_result[1],\n",
+ " 'Critical Values': adf_result[4],\n",
+ " '1%': adf_result[4]['1%'], # Critical value for a 99% confidence level\n",
+ " '5%': adf_result[4]['5%'], # Critical value for a 95% confidence level\n",
+ " '10%': adf_result[4]['10%'] # Critical value for a 90% confidence level\n",
+ " }\n",
+ "\n",
+ "{year: adf_results[year] for year in list(years_columns)[:63]} # Ver los primeros 5 años."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "e30859b6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Country Name | \n",
+ " Country Code | \n",
+ " 1960 | \n",
+ " 1961 | \n",
+ " 1962 | \n",
+ " 1963 | \n",
+ " 1964 | \n",
+ " 1965 | \n",
+ " 1966 | \n",
+ " 1967 | \n",
+ " ... | \n",
+ " 2013 | \n",
+ " 2014 | \n",
+ " 2015 | \n",
+ " 2016 | \n",
+ " 2017 | \n",
+ " 2018 | \n",
+ " 2019 | \n",
+ " 2020 | \n",
+ " 2021 | \n",
+ " 2022 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " Aruba | \n",
+ " ABW | \n",
+ " -0.137047 | \n",
+ " -0.172253 | \n",
+ " -0.073786 | \n",
+ " -0.092948 | \n",
+ " -0.119010 | \n",
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+ " -0.118318 | \n",
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+ " -0.335730 | \n",
+ " -0.199353 | \n",
+ " -0.365765 | \n",
+ " -0.035131 | \n",
+ " -0.004350 | \n",
+ " -0.113314 | \n",
+ " -0.128176 | \n",
+ " -0.175279 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " Africa Eastern and Southern | \n",
+ " AFE | \n",
+ " -0.137047 | \n",
+ " -0.172253 | \n",
+ " -0.073786 | \n",
+ " -0.092948 | \n",
+ " -0.119010 | \n",
+ " -0.084192 | \n",
+ " -0.070462 | \n",
+ " -0.118318 | \n",
+ " ... | \n",
+ " 0.373381 | \n",
+ " 0.345457 | \n",
+ " 0.195933 | \n",
+ " 0.033496 | \n",
+ " 0.160902 | \n",
+ " 0.122722 | \n",
+ " 0.019088 | \n",
+ " 0.034941 | \n",
+ " -0.000207 | \n",
+ " -0.009400 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " Afghanistan | \n",
+ " AFG | \n",
+ " -0.137047 | \n",
+ " -0.172253 | \n",
+ " -0.073786 | \n",
+ " -0.092948 | \n",
+ " -0.119010 | \n",
+ " -0.084192 | \n",
+ " -0.070462 | \n",
+ " -0.118318 | \n",
+ " ... | \n",
+ " 0.718425 | \n",
+ " 0.211728 | \n",
+ " -0.387521 | \n",
+ " -0.044134 | \n",
+ " 0.043573 | \n",
+ " -0.467679 | \n",
+ " -0.120006 | \n",
+ " -0.113314 | \n",
+ " -0.128176 | \n",
+ " -0.175279 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " Africa Western and Central | \n",
+ " AFW | \n",
+ " -0.137047 | \n",
+ " -0.172253 | \n",
+ " -0.073786 | \n",
+ " -0.092948 | \n",
+ " -0.119010 | \n",
+ " -0.084192 | \n",
+ " -0.070462 | \n",
+ " -0.118318 | \n",
+ " ... | \n",
+ " -0.325614 | \n",
+ " -0.346310 | \n",
+ " -0.151190 | \n",
+ " -0.146923 | \n",
+ " -0.221133 | \n",
+ " -0.300723 | \n",
+ " -0.152084 | \n",
+ " -0.101183 | \n",
+ " -0.128176 | \n",
+ " -0.187531 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " Angola | \n",
+ " AGO | \n",
+ " -0.137047 | \n",
+ " -0.172253 | \n",
+ " -0.073786 | \n",
+ " -0.092948 | \n",
+ " -0.119010 | \n",
+ " -0.084192 | \n",
+ " -0.070462 | \n",
+ " -0.118318 | \n",
+ " ... | \n",
+ " 1.012234 | \n",
+ " 0.712308 | \n",
+ " 0.653699 | \n",
+ " 0.889729 | \n",
+ " 2.093307 | \n",
+ " 2.272532 | \n",
+ " 0.754171 | \n",
+ " 0.451674 | \n",
+ " 0.763668 | \n",
+ " -0.175279 | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 261 | \n",
+ " Kosovo | \n",
+ " XKX | \n",
+ " -0.137047 | \n",
+ " -0.172253 | \n",
+ " -0.073786 | \n",
+ " -0.092948 | \n",
+ " -0.119010 | \n",
+ " -0.084192 | \n",
+ " -0.070462 | \n",
+ " -0.118318 | \n",
+ " ... | \n",
+ " -0.467422 | \n",
+ " -0.603569 | \n",
+ " -0.387521 | \n",
+ " -0.190014 | \n",
+ " -0.243916 | \n",
+ " -0.406017 | \n",
+ " -0.097904 | \n",
+ " -0.163604 | \n",
+ " -0.140276 | \n",
+ " 0.041812 | \n",
+ "
\n",
+ " \n",
+ " 262 | \n",
+ " Yemen, Rep. | \n",
+ " YEM | \n",
+ " -0.137047 | \n",
+ " -0.172253 | \n",
+ " -0.073786 | \n",
+ " -0.092948 | \n",
+ " -0.119010 | \n",
+ " -0.084192 | \n",
+ " -0.070462 | \n",
+ " -0.118318 | \n",
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+ " 1.474595 | \n",
+ " 0.870629 | \n",
+ " -0.215031 | \n",
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+ " -0.125703 | \n",
+ " -0.113314 | \n",
+ " -0.128176 | \n",
+ " -0.175279 | \n",
+ "
\n",
+ " \n",
+ " 263 | \n",
+ " South Africa | \n",
+ " ZAF | \n",
+ " -0.326206 | \n",
+ " -0.172508 | \n",
+ " -0.251933 | \n",
+ " -0.265061 | \n",
+ " -0.224168 | \n",
+ " -0.049476 | \n",
+ " -0.075163 | \n",
+ " -0.058277 | \n",
+ " ... | \n",
+ " 0.380449 | \n",
+ " 0.491335 | \n",
+ " 0.117346 | \n",
+ " 0.033496 | \n",
+ " 0.060743 | \n",
+ " 0.093359 | \n",
+ " -0.012467 | \n",
+ " -0.079652 | \n",
+ " -0.089512 | \n",
+ " -0.246429 | \n",
+ "
\n",
+ " \n",
+ " 264 | \n",
+ " Zambia | \n",
+ " ZMB | \n",
+ " -0.137047 | \n",
+ " -0.172253 | \n",
+ " -0.073786 | \n",
+ " -0.092948 | \n",
+ " -0.119010 | \n",
+ " -0.084192 | \n",
+ " -0.070462 | \n",
+ " -0.118318 | \n",
+ " ... | \n",
+ " 0.632291 | \n",
+ " 0.813425 | \n",
+ " 0.738027 | \n",
+ " 0.434448 | \n",
+ " 0.175572 | \n",
+ " 0.522665 | \n",
+ " 0.285095 | \n",
+ " 0.269418 | \n",
+ " 0.613008 | \n",
+ " 0.004531 | \n",
+ "
\n",
+ " \n",
+ " 265 | \n",
+ " Zimbabwe | \n",
+ " ZWE | \n",
+ " -0.137047 | \n",
+ " -0.172253 | \n",
+ " -0.073786 | \n",
+ " -0.092948 | \n",
+ " -0.119010 | \n",
+ " -0.084192 | \n",
+ " -0.070462 | \n",
+ " -0.118318 | \n",
+ " ... | \n",
+ " -0.495361 | \n",
+ " -0.684033 | \n",
+ " -0.387521 | \n",
+ " -0.199353 | \n",
+ " -0.292901 | \n",
+ " 0.973150 | \n",
+ " 14.846775 | \n",
+ " 15.362467 | \n",
+ " 3.701080 | \n",
+ " 5.953207 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
266 rows × 65 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Country Name Country Code 1960 1961 1962 \\\n",
+ "0 Aruba ABW -0.137047 -0.172253 -0.073786 \n",
+ "1 Africa Eastern and Southern AFE -0.137047 -0.172253 -0.073786 \n",
+ "2 Afghanistan AFG -0.137047 -0.172253 -0.073786 \n",
+ "3 Africa Western and Central AFW -0.137047 -0.172253 -0.073786 \n",
+ "4 Angola AGO -0.137047 -0.172253 -0.073786 \n",
+ ".. ... ... ... ... ... \n",
+ "261 Kosovo XKX -0.137047 -0.172253 -0.073786 \n",
+ "262 Yemen, Rep. YEM -0.137047 -0.172253 -0.073786 \n",
+ "263 South Africa ZAF -0.326206 -0.172508 -0.251933 \n",
+ "264 Zambia ZMB -0.137047 -0.172253 -0.073786 \n",
+ "265 Zimbabwe ZWE -0.137047 -0.172253 -0.073786 \n",
+ "\n",
+ " 1963 1964 1965 1966 1967 ... 2013 \\\n",
+ "0 -0.092948 -0.119010 -0.084192 -0.070462 -0.118318 ... -0.838328 \n",
+ "1 -0.092948 -0.119010 -0.084192 -0.070462 -0.118318 ... 0.373381 \n",
+ "2 -0.092948 -0.119010 -0.084192 -0.070462 -0.118318 ... 0.718425 \n",
+ "3 -0.092948 -0.119010 -0.084192 -0.070462 -0.118318 ... -0.325614 \n",
+ "4 -0.092948 -0.119010 -0.084192 -0.070462 -0.118318 ... 1.012234 \n",
+ ".. ... ... ... ... ... ... ... \n",
+ "261 -0.092948 -0.119010 -0.084192 -0.070462 -0.118318 ... -0.467422 \n",
+ "262 -0.092948 -0.119010 -0.084192 -0.070462 -0.118318 ... 1.474595 \n",
+ "263 -0.265061 -0.224168 -0.049476 -0.075163 -0.058277 ... 0.380449 \n",
+ "264 -0.092948 -0.119010 -0.084192 -0.070462 -0.118318 ... 0.632291 \n",
+ "265 -0.092948 -0.119010 -0.084192 -0.070462 -0.118318 ... -0.495361 \n",
+ "\n",
+ " 2014 2015 2016 2017 2018 2019 2020 \\\n",
+ "0 -0.605012 -0.335730 -0.199353 -0.365765 -0.035131 -0.004350 -0.113314 \n",
+ "1 0.345457 0.195933 0.033496 0.160902 0.122722 0.019088 0.034941 \n",
+ "2 0.211728 -0.387521 -0.044134 0.043573 -0.467679 -0.120006 -0.113314 \n",
+ "3 -0.346310 -0.151190 -0.146923 -0.221133 -0.300723 -0.152084 -0.101183 \n",
+ "4 0.712308 0.653699 0.889729 2.093307 2.272532 0.754171 0.451674 \n",
+ ".. ... ... ... ... ... ... ... \n",
+ "261 -0.603569 -0.387521 -0.190014 -0.243916 -0.406017 -0.097904 -0.163604 \n",
+ "262 0.870629 -0.215031 -0.140252 -0.164594 -0.183440 -0.125703 -0.113314 \n",
+ "263 0.491335 0.117346 0.033496 0.060743 0.093359 -0.012467 -0.079652 \n",
+ "264 0.813425 0.738027 0.434448 0.175572 0.522665 0.285095 0.269418 \n",
+ "265 -0.684033 -0.387521 -0.199353 -0.292901 0.973150 14.846775 15.362467 \n",
+ "\n",
+ " 2021 2022 \n",
+ "0 -0.128176 -0.175279 \n",
+ "1 -0.000207 -0.009400 \n",
+ "2 -0.128176 -0.175279 \n",
+ "3 -0.128176 -0.187531 \n",
+ "4 0.763668 -0.175279 \n",
+ ".. ... ... \n",
+ "261 -0.140276 0.041812 \n",
+ "262 -0.128176 -0.175279 \n",
+ "263 -0.089512 -0.246429 \n",
+ "264 0.613008 0.004531 \n",
+ "265 3.701080 5.953207 \n",
+ "\n",
+ "[266 rows x 65 columns]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "inflation_imputed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f421abf9",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7b1c0eae",
+ "metadata": {},
+ "source": [
+ "### Remove rows\n",
+ "Indicadores que no se usarán"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "72f60c11",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Country Name Country Code 1960 1961 1962 \\\n",
+ "0 Aruba ABW -0.137047 -0.172253 -0.073786 \n",
+ "1 Africa Eastern and Southern AFE -0.137047 -0.172253 -0.073786 \n",
+ "2 Afghanistan AFG -0.137047 -0.172253 -0.073786 \n",
+ "3 Africa Western and Central AFW -0.137047 -0.172253 -0.073786 \n",
+ "4 Angola AGO -0.137047 -0.172253 -0.073786 \n",
+ "\n",
+ " 1963 1964 1965 1966 1967 ... 2013 2014 \\\n",
+ "0 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... -0.838328 -0.605012 \n",
+ "1 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... 0.373381 0.345457 \n",
+ "2 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... 0.718425 0.211728 \n",
+ "3 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... -0.325614 -0.346310 \n",
+ "4 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... 1.012234 0.712308 \n",
+ "\n",
+ " 2015 2016 2017 2018 2019 2020 2021 \\\n",
+ "0 -0.335730 -0.199353 -0.365765 -0.035131 -0.004350 -0.113314 -0.128176 \n",
+ "1 0.195933 0.033496 0.160902 0.122722 0.019088 0.034941 -0.000207 \n",
+ "2 -0.387521 -0.044134 0.043573 -0.467679 -0.120006 -0.113314 -0.128176 \n",
+ "3 -0.151190 -0.146923 -0.221133 -0.300723 -0.152084 -0.101183 -0.128176 \n",
+ "4 0.653699 0.889729 2.093307 2.272532 0.754171 0.451674 0.763668 \n",
+ "\n",
+ " 2022 \n",
+ "0 -0.175279 \n",
+ "1 -0.009400 \n",
+ "2 -0.175279 \n",
+ "3 -0.187531 \n",
+ "4 -0.175279 \n",
+ "\n",
+ "[5 rows x 65 columns]\n",
+ "['Aruba' 'Africa Eastern and Southern' 'Afghanistan'\n",
+ " 'Africa Western and Central' 'Angola' 'Albania' 'Andorra' 'Arab World'\n",
+ " 'United Arab Emirates' 'Argentina' 'Armenia' 'American Samoa'\n",
+ " 'Antigua and Barbuda' 'Australia' 'Austria' 'Azerbaijan' 'Burundi'\n",
+ " 'Belgium' 'Benin' 'Burkina Faso' 'Bangladesh' 'Bulgaria' 'Bahrain'\n",
+ " 'Bahamas, The' 'Bosnia and Herzegovina' 'Belarus' 'Belize' 'Bermuda'\n",
+ " 'Bolivia' 'Brazil' 'Barbados' 'Brunei Darussalam' 'Bhutan' 'Botswana'\n",
+ " 'Central African Republic' 'Canada' 'Central Europe and the Baltics'\n",
+ " 'Switzerland' 'Channel Islands' 'Chile' 'China' \"Cote d'Ivoire\"\n",
+ " 'Cameroon' 'Congo, Dem. Rep.' 'Congo, Rep.' 'Colombia' 'Comoros'\n",
+ " 'Cabo Verde' 'Costa Rica' 'Caribbean small states' 'Cuba' 'Curacao'\n",
+ " 'Cayman Islands' 'Cyprus' 'Czechia' 'Germany' 'Djibouti' 'Dominica'\n",
+ " 'Denmark' 'Dominican Republic' 'Algeria' 'East Asia & Pacific'\n",
+ " 'Europe & Central Asia' 'Ecuador' 'Egypt, Arab Rep.' 'Euro area'\n",
+ " 'Eritrea' 'Spain' 'Estonia' 'Ethiopia' 'European Union' 'Finland' 'Fiji'\n",
+ " 'France' 'Faroe Islands' 'Micronesia, Fed. Sts.' 'Gabon' 'United Kingdom'\n",
+ " 'Georgia' 'Ghana' 'Gibraltar' 'Guinea' 'Gambia, The' 'Guinea-Bissau'\n",
+ " 'Equatorial Guinea' 'Greece' 'Grenada' 'Greenland' 'Guatemala' 'Guam'\n",
+ " 'Guyana' 'Hong Kong SAR, China' 'Honduras' 'Croatia' 'Haiti' 'Hungary'\n",
+ " 'Indonesia' 'Isle of Man' 'India' 'Ireland' 'Iran, Islamic Rep.' 'Iraq'\n",
+ " 'Iceland' 'Israel' 'Italy' 'Jamaica' 'Jordan' 'Japan' 'Kazakhstan'\n",
+ " 'Kenya' 'Kyrgyz Republic' 'Cambodia' 'Kiribati' 'St. Kitts and Nevis'\n",
+ " 'Korea, Rep.' 'Kuwait' 'Lao PDR' 'Lebanon' 'Liberia' 'Libya' 'St. Lucia'\n",
+ " 'Latin America & Caribbean' 'Liechtenstein' 'Sri Lanka' 'Lesotho'\n",
+ " 'Lithuania' 'Luxembourg' 'Latvia' 'Macao SAR, China'\n",
+ " 'St. Martin (French part)' 'Morocco' 'Monaco' 'Moldova' 'Madagascar'\n",
+ " 'Maldives' 'Middle East & North Africa' 'Mexico' 'Marshall Islands'\n",
+ " 'Middle income' 'North Macedonia' 'Mali' 'Malta' 'Myanmar' 'Montenegro'\n",
+ " 'Mongolia' 'Northern Mariana Islands' 'Mozambique' 'Mauritania'\n",
+ " 'Mauritius' 'Malawi' 'Malaysia' 'North America' 'Namibia' 'New Caledonia'\n",
+ " 'Niger' 'Nigeria' 'Nicaragua' 'Netherlands' 'Norway' 'Nepal' 'Nauru'\n",
+ " 'New Zealand' 'OECD members' 'Oman' 'Other small states' 'Pakistan'\n",
+ " 'Panama' 'Peru' 'Philippines' 'Palau' 'Papua New Guinea' 'Poland'\n",
+ " 'Puerto Rico' \"Korea, Dem. People's Rep.\" 'Portugal' 'Paraguay'\n",
+ " 'West Bank and Gaza' 'Pacific island small states' 'French Polynesia'\n",
+ " 'Qatar' 'Romania' 'Russian Federation' 'Rwanda' 'South Asia'\n",
+ " 'Saudi Arabia' 'Sudan' 'Senegal' 'Singapore' 'Solomon Islands'\n",
+ " 'Sierra Leone' 'El Salvador' 'San Marino' 'Somalia' 'Serbia'\n",
+ " 'Sub-Saharan Africa (excluding high income)' 'South Sudan'\n",
+ " 'Sub-Saharan Africa' 'Small states' 'Sao Tome and Principe' 'Suriname'\n",
+ " 'Slovak Republic' 'Slovenia' 'Sweden' 'Eswatini'\n",
+ " 'Sint Maarten (Dutch part)' 'Seychelles' 'Syrian Arab Republic'\n",
+ " 'Turks and Caicos Islands' 'Chad' 'Togo' 'Thailand' 'Tajikistan'\n",
+ " 'Turkmenistan' 'Timor-Leste' 'Tonga' 'South Asia (IDA & IBRD)'\n",
+ " 'Trinidad and Tobago' 'Tunisia' 'Turkiye' 'Tuvalu' 'Tanzania' 'Uganda'\n",
+ " 'Ukraine' 'Uruguay' 'United States' 'Uzbekistan'\n",
+ " 'St. Vincent and the Grenadines' 'Venezuela, RB' 'British Virgin Islands'\n",
+ " 'Virgin Islands (U.S.)' 'Viet Nam' 'Vanuatu' 'World' 'Samoa' 'Kosovo'\n",
+ " 'Yemen, Rep.' 'South Africa' 'Zambia' 'Zimbabwe']\n",
+ "Original size: (266, 65)\n",
+ "Filtered size: (239, 65)\n"
+ ]
+ }
+ ],
+ "source": [
+ "rows_to_remove = [\n",
+ " \"East Asia & Pacific (excluding high income)\", \n",
+ " \"Early-demographic dividend\", \n",
+ " \"Europe & Central Asia (excluding high income)\", \n",
+ " \"Fragile and conflict affected situations\", \n",
+ " \"High income\", \n",
+ " \"Heavily indebted poor countries (HIPC)\", \n",
+ " \"IBRD only\", \n",
+ " \"IDA & IBRD total\", \n",
+ " \"IDA total\", \n",
+ " \"IDA blend\", \n",
+ " \"IDA only\", \n",
+ " \"Not classified\", \n",
+ " \"Latin America & Caribbean (excluding high income)\", \n",
+ " \"Least developed countries: UN classification\", \n",
+ " \"Low income\", \n",
+ " \"Lower middle income\", \n",
+ " \"Low & middle income\", \n",
+ " \"Late-demographic dividend\", \n",
+ " \"Middle East & North Africa (excluding high income)\", \n",
+ " \"Pre-demographic dividend\", \n",
+ " \"Post-demographic dividend\", \n",
+ " \"East Asia & Pacific (IDA & IBRD countries)\", \n",
+ " \"Europe & Central Asia (IDA & IBRD countries)\", \n",
+ " \"Latin America & the Caribbean (IDA & IBRD countries)\", \n",
+ " \"Middle East & North Africa (IDA & IBRD countries)\", \n",
+ " \"Sub-Saharan Africa (IDA & IBRD countries)\", \n",
+ " \"Upper middle income\"\n",
+ "]\n",
+ "\n",
+ "inflation_filtered = inflation_imputed[~inflation_imputed['Country Name'].isin(rows_to_remove)]\n",
+ "\n",
+ "print(inflation_filtered.head())\n",
+ "print(inflation_filtered['Country Name'].unique()) \n",
+ "print(\"Original size:\", inflation_imputed.shape)\n",
+ "print(\"Filtered size:\", inflation_filtered.shape)\n",
+ "\n",
+ "inflation_filtered.to_csv('main_inflation.csv', index=False) # DataFrame principal"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d0680e68",
+ "metadata": {},
+ "source": [
+ "### World | Regions | Countries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "1d9edee7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Country Name Country Code 1960 1961 1962 1963 1964 \\\n",
+ "0 Aruba ABW -0.137047 -0.172253 -0.073786 -0.092948 -0.11901 \n",
+ "2 Afghanistan AFG -0.137047 -0.172253 -0.073786 -0.092948 -0.11901 \n",
+ "4 Angola AGO -0.137047 -0.172253 -0.073786 -0.092948 -0.11901 \n",
+ "5 Albania ALB -0.137047 -0.172253 -0.073786 -0.092948 -0.11901 \n",
+ "6 Andorra AND -0.137047 -0.172253 -0.073786 -0.092948 -0.11901 \n",
+ "\n",
+ " 1965 1966 1967 ... 2013 2014 2015 2016 \\\n",
+ "0 -0.084192 -0.070462 -0.118318 ... -0.838328 -0.605012 -0.335730 -0.199353 \n",
+ "2 -0.084192 -0.070462 -0.118318 ... 0.718425 0.211728 -0.387521 -0.044134 \n",
+ "4 -0.084192 -0.070462 -0.118318 ... 1.012234 0.712308 0.653699 0.889729 \n",
+ "5 -0.084192 -0.070462 -0.118318 ... -0.431479 -0.373692 -0.177337 -0.154446 \n",
+ "6 -0.084192 -0.070462 -0.118318 ... -0.254901 -0.181476 -0.215031 -0.140252 \n",
+ "\n",
+ " 2017 2018 2019 2020 2021 2022 \n",
+ "0 -0.365765 -0.035131 -0.004350 -0.113314 -0.128176 -0.175279 \n",
+ "2 0.043573 -0.467679 -0.120006 -0.113314 -0.128176 -0.175279 \n",
+ "4 2.093307 2.272532 0.754171 0.451674 0.763668 -0.175279 \n",
+ "5 -0.202831 -0.265540 -0.172731 -0.123948 -0.193228 -0.266395 \n",
+ "6 -0.164594 -0.183440 -0.125703 -0.113314 -0.128176 -0.175279 \n",
+ "\n",
+ "[5 rows x 65 columns]\n",
+ " Country Name Country Code 1960 1961 1962 \\\n",
+ "1 Africa Eastern and Southern AFE -0.137047 -0.172253 -0.073786 \n",
+ "3 Africa Western and Central AFW -0.137047 -0.172253 -0.073786 \n",
+ "36 Central Europe and the Baltics CEB -0.137047 -0.172253 -0.073786 \n",
+ "61 East Asia & Pacific EAS -0.137047 -0.172253 -0.073786 \n",
+ "62 Europe & Central Asia ECS -0.137047 -0.172253 -0.073786 \n",
+ "\n",
+ " 1963 1964 1965 1966 1967 ... 2013 2014 \\\n",
+ "1 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... 0.373381 0.345457 \n",
+ "3 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... -0.325614 -0.346310 \n",
+ "36 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... -0.536867 -0.675617 \n",
+ "61 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... -0.295329 -0.203718 \n",
+ "62 -0.092948 -0.11901 -0.084192 -0.070462 -0.118318 ... -0.470935 -0.566010 \n",
+ "\n",
+ " 2015 2016 2017 2018 2019 2020 2021 \\\n",
+ "1 0.195933 0.033496 0.160902 0.122722 0.019088 0.034941 -0.000207 \n",
+ "3 -0.151190 -0.146923 -0.221133 -0.300723 -0.152084 -0.101183 -0.128176 \n",
+ "36 -0.387521 -0.199353 -0.195472 -0.192524 -0.098580 -0.122511 -0.120658 \n",
+ "61 -0.300014 -0.154391 -0.218189 -0.224512 -0.155405 -0.133430 -0.178349 \n",
+ "62 -0.342943 -0.185804 -0.205352 -0.263730 -0.148737 -0.135520 -0.144327 \n",
+ "\n",
+ " 2022 \n",
+ "1 -0.009400 \n",
+ "3 -0.187531 \n",
+ "36 0.234001 \n",
+ "61 -0.349961 \n",
+ "62 -0.033703 \n",
+ "\n",
+ "[5 rows x 65 columns]\n",
+ " Country Name Country Code 1960 1961 1962 1963 \\\n",
+ "232 World WLD -0.137047 -0.172253 -0.073786 -0.092948 \n",
+ "\n",
+ " 1964 1965 1966 1967 ... 2013 2014 2015 \\\n",
+ "232 -0.11901 -0.084192 -0.070462 -0.118318 ... -0.280768 -0.233753 -0.22774 \n",
+ "\n",
+ " 2016 2017 2018 2019 2020 2021 2022 \n",
+ "232 -0.142731 -0.180772 -0.204649 -0.125703 -0.115139 -0.135706 -0.187531 \n",
+ "\n",
+ "[1 rows x 65 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "# Cargar el DataFrame principal\n",
+ "main_inflation = pd.read_csv('main_inflation.csv')\n",
+ "\n",
+ "regions_list = [\n",
+ " \"Africa Eastern and Southern\", \"Africa Western and Central\",\n",
+ " \"Central Europe and the Baltics\", \"East Asia & Pacific\",\n",
+ " \"Europe & Central Asia\", \"European Union\", \"Latin America & Caribbean\",\n",
+ " \"Middle East & North Africa\", \"North America\", \"OECD members\",\n",
+ " \"Sub-Saharan Africa (excluding high income)\", \"South Asia (IDA & IBRD)\"\n",
+ "]\n",
+ "\n",
+ "# 1) DataFrame por países\n",
+ "inflation_countries = main_inflation[~main_inflation['Country Name'].isin(regions_list + ['World'])]\n",
+ "\n",
+ "# 2) DataFrame por regiones\n",
+ "inflation_regions = main_inflation[main_inflation['Country Name'].isin(regions_list)]\n",
+ "\n",
+ "# 3) DataFrame global\n",
+ "inflation_world = main_inflation[main_inflation['Country Name'] == 'World']\n",
+ "\n",
+ "print(inflation_countries.head())\n",
+ "print(inflation_regions.head())\n",
+ "print(inflation_world.head())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c92df2",
+ "metadata": {},
+ "source": [
+ "### Visualización de Tendencias Temporales | Countries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "ea1c9d7d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
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