{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# BOOTSTRAPING" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FECHACHUQUIBAMBILLA
01956-01-01142.8
11956-02-0175.1
21956-03-0142.6
31956-04-018.1
41956-05-013.6
.........
6912013-08-017.4
6922013-09-015.3
6932013-10-0157.3
6942013-11-0144.6
6952013-12-01215.8
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696 rows × 2 columns

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" ], "text/plain": [ " FECHA CHUQUIBAMBILLA\n", "0 1956-01-01 142.8\n", "1 1956-02-01 75.1\n", "2 1956-03-01 42.6\n", "3 1956-04-01 8.1\n", "4 1956-05-01 3.6\n", ".. ... ...\n", "691 2013-08-01 7.4\n", "692 2013-09-01 5.3\n", "693 2013-10-01 57.3\n", "694 2013-11-01 44.6\n", "695 2013-12-01 215.8\n", "\n", "[696 rows x 2 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# chuquibambilla\n", "\n", "%reset -sf\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "file=\"Chuquibambilla_Puno.xlsx\"\n", "data=pd.read_excel(file)\n", "data" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "data_idx=data.set_index('FECHA')\n", "#seleccionar mes =2, FEB\n", "data_feb=data_idx.loc[data_idx.index.month==2]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CHUQUIBAMBILLA
FECHA
1956-02-0175.1
1957-02-01140.3
1958-02-01137.6
1959-02-01106.4
1960-02-01115.3
1961-02-01110.7
1962-02-01166.8
1963-02-01137.6
1964-02-0178.7
1965-02-01132.1
1966-02-01109.5
1967-02-01165.9
1968-02-01233.2
1969-02-01125.5
1970-02-0197.4
1971-02-01183.2
1972-02-01131.1
1973-02-0184.8
1974-02-01118.3
1975-02-01116.8
1976-02-0188.9
1977-02-01138.3
1978-02-01127.7
1979-02-0153.6
1980-02-01103.7
1981-02-01176.0
1982-02-0191.7
1983-02-0158.1
1984-02-01175.4
1985-02-01123.5
1986-02-01114.8
1987-02-0175.1
1988-02-0172.6
1989-02-0184.7
1990-02-0197.4
1991-02-0161.4
1992-02-0171.1
1993-02-0129.0
1994-02-01133.1
1995-02-01119.7
1996-02-01112.0
1997-02-01204.3
1998-02-01131.5
1999-02-01162.6
2000-02-01180.6
2001-02-01127.3
2002-02-01175.5
2003-02-01154.8
2004-02-01137.0
2005-02-01213.7
2006-02-01115.9
2007-02-0196.9
2008-02-0196.6
2009-02-0190.4
2010-02-01111.4
2011-02-01180.7
2012-02-01149.6
2013-02-01174.4
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" ], "text/plain": [ " CHUQUIBAMBILLA\n", "FECHA \n", "1956-02-01 75.1\n", "1957-02-01 140.3\n", "1958-02-01 137.6\n", "1959-02-01 106.4\n", "1960-02-01 115.3\n", "1961-02-01 110.7\n", "1962-02-01 166.8\n", "1963-02-01 137.6\n", "1964-02-01 78.7\n", "1965-02-01 132.1\n", "1966-02-01 109.5\n", "1967-02-01 165.9\n", "1968-02-01 233.2\n", "1969-02-01 125.5\n", "1970-02-01 97.4\n", "1971-02-01 183.2\n", "1972-02-01 131.1\n", "1973-02-01 84.8\n", "1974-02-01 118.3\n", "1975-02-01 116.8\n", "1976-02-01 88.9\n", "1977-02-01 138.3\n", "1978-02-01 127.7\n", "1979-02-01 53.6\n", "1980-02-01 103.7\n", "1981-02-01 176.0\n", "1982-02-01 91.7\n", "1983-02-01 58.1\n", "1984-02-01 175.4\n", "1985-02-01 123.5\n", "1986-02-01 114.8\n", "1987-02-01 75.1\n", "1988-02-01 72.6\n", "1989-02-01 84.7\n", "1990-02-01 97.4\n", "1991-02-01 61.4\n", "1992-02-01 71.1\n", "1993-02-01 29.0\n", "1994-02-01 133.1\n", "1995-02-01 119.7\n", "1996-02-01 112.0\n", "1997-02-01 204.3\n", "1998-02-01 131.5\n", "1999-02-01 162.6\n", "2000-02-01 180.6\n", "2001-02-01 127.3\n", "2002-02-01 175.5\n", "2003-02-01 154.8\n", "2004-02-01 137.0\n", "2005-02-01 213.7\n", "2006-02-01 115.9\n", "2007-02-01 96.9\n", "2008-02-01 96.6\n", "2009-02-01 90.4\n", "2010-02-01 111.4\n", "2011-02-01 180.7\n", "2012-02-01 149.6\n", "2013-02-01 174.4" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_feb" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#selecting 1981-2010" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CHUQUIBAMBILLA
FECHA
1981-02-01176.0
1982-02-0191.7
1983-02-0158.1
1984-02-01175.4
1985-02-01123.5
1986-02-01114.8
1987-02-0175.1
1988-02-0172.6
1989-02-0184.7
1990-02-0197.4
1991-02-0161.4
1992-02-0171.1
1993-02-0129.0
1994-02-01133.1
1995-02-01119.7
1996-02-01112.0
1997-02-01204.3
1998-02-01131.5
1999-02-01162.6
2000-02-01180.6
2001-02-01127.3
2002-02-01175.5
2003-02-01154.8
2004-02-01137.0
2005-02-01213.7
2006-02-01115.9
2007-02-0196.9
2008-02-0196.6
2009-02-0190.4
2010-02-01111.4
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" ], "text/plain": [ " CHUQUIBAMBILLA\n", "FECHA \n", "1981-02-01 176.0\n", "1982-02-01 91.7\n", "1983-02-01 58.1\n", "1984-02-01 175.4\n", "1985-02-01 123.5\n", "1986-02-01 114.8\n", "1987-02-01 75.1\n", "1988-02-01 72.6\n", "1989-02-01 84.7\n", "1990-02-01 97.4\n", "1991-02-01 61.4\n", "1992-02-01 71.1\n", "1993-02-01 29.0\n", "1994-02-01 133.1\n", "1995-02-01 119.7\n", "1996-02-01 112.0\n", "1997-02-01 204.3\n", "1998-02-01 131.5\n", "1999-02-01 162.6\n", "2000-02-01 180.6\n", "2001-02-01 127.3\n", "2002-02-01 175.5\n", "2003-02-01 154.8\n", "2004-02-01 137.0\n", "2005-02-01 213.7\n", "2006-02-01 115.9\n", "2007-02-01 96.9\n", "2008-02-01 96.6\n", "2009-02-01 90.4\n", "2010-02-01 111.4" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_feb_c=data_feb.loc[\"1981\":\"2010\"]\n", "data_feb_c" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CHUQUIBAMBILLA
FECHA
1981-02-01176.0
1982-02-0191.7
1983-02-0158.1
1984-02-01175.4
1985-02-01123.5
1986-02-01114.8
1987-02-0175.1
1988-02-0172.6
1989-02-0184.7
1990-02-0197.4
1991-02-0161.4
1992-02-0171.1
1993-02-0129.0
1994-02-01133.1
1995-02-01119.7
1996-02-01112.0
1997-02-01204.3
1998-02-01131.5
1999-02-01162.6
2000-02-01180.6
2001-02-01127.3
2002-02-01175.5
2003-02-01154.8
2004-02-01137.0
2005-02-01213.7
2006-02-01115.9
2007-02-0196.9
2008-02-0196.6
2009-02-0190.4
2010-02-01111.4
\n", "
" ], "text/plain": [ " CHUQUIBAMBILLA\n", "FECHA \n", "1981-02-01 176.0\n", "1982-02-01 91.7\n", "1983-02-01 58.1\n", "1984-02-01 175.4\n", "1985-02-01 123.5\n", "1986-02-01 114.8\n", "1987-02-01 75.1\n", "1988-02-01 72.6\n", "1989-02-01 84.7\n", "1990-02-01 97.4\n", "1991-02-01 61.4\n", "1992-02-01 71.1\n", "1993-02-01 29.0\n", "1994-02-01 133.1\n", "1995-02-01 119.7\n", "1996-02-01 112.0\n", "1997-02-01 204.3\n", "1998-02-01 131.5\n", "1999-02-01 162.6\n", "2000-02-01 180.6\n", "2001-02-01 127.3\n", "2002-02-01 175.5\n", "2003-02-01 154.8\n", "2004-02-01 137.0\n", "2005-02-01 213.7\n", "2006-02-01 115.9\n", "2007-02-01 96.9\n", "2008-02-01 96.6\n", "2009-02-01 90.4\n", "2010-02-01 111.4" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_feb_c" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data_feb_c.sample(n=30,replace=True).plot(marker=\"o\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "promedios=[]\n", "series=[]\n", "for i in range(1,1000): \n", " a=data_feb_c.sample(n=30,replace=True).mean()[0]\n", " promedios.append(a)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "list" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(promedios)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "promedio sin bstrp: 119.80333333333334\n", "promedio con bstrp: 119.2157057057057\n", "Int. Confianza al 95%: 104.09049999999999 - 135.36149999999995\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "plt.hist(promedios)\n", "plt.axvline(np.percentile(promedios,2.5), color='g', linestyle='dashed', linewidth=2)\n", "plt.axvline(np.percentile(promedios,97.5), color='g', linestyle='dashed', linewidth=2)\n", "\n", "\n", "print(\"promedio sin bstrp:\",data_feb_c.CHUQUIBAMBILLA.mean())\n", "print(\"promedio con bstrp:\",np.mean(promedios))\n", "print(\"Int. Confianza al 95%:\",np.percentile(promedios,2.5),\"-\",np.percentile(promedios,97.5))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# EJERCICIO BOOTSTRAPING DE LAS ESTACIONES: mediana, STD,MES=4" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "%reset -sf\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "file=\"otrasestaciones.xlsx\"\n", "data=pd.read_excel(file)\n", "data_idx=data.set_index('FECHA')\n", "\n", "data_idx=data_idx[\"HUANUCO\"]\n", "data_idx\n", "\n", "print(type(data_idx))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/plain": [ "FECHA\n", "1981-04-01 15.281020\n", "1982-04-01 14.958634\n", "1983-04-01 10.822447\n", "1984-04-01 35.598234\n", "1985-04-01 28.964100\n", "1986-04-01 37.861343\n", "1987-04-01 25.842864\n", "1988-04-01 36.337568\n", "1989-04-01 22.100000\n", "1990-04-01 35.400000\n", "1991-04-01 18.900000\n", "1992-04-01 15.500000\n", "1993-04-01 42.500000\n", "1994-04-01 50.300000\n", "1995-04-01 24.000000\n", "1996-04-01 74.100000\n", "1997-04-01 17.100000\n", "1998-04-01 10.700000\n", "1999-04-01 20.900000\n", "2000-04-01 14.200000\n", "2001-04-01 38.200000\n", "2002-04-01 46.500000\n", "2003-04-01 31.700000\n", "2004-04-01 18.200000\n", "2005-04-01 14.100000\n", "2006-04-01 33.800000\n", "2007-04-01 22.600000\n", "2008-04-01 48.200000\n", "2009-04-01 71.000000\n", "2010-04-01 29.900000\n", "Name: HUANUCO, dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#seleccionar mes =4\n", "data_apr=data_idx.loc[data_idx.index.month==4]\n", "data_apr_c=data_apr.loc[\"1981\":\"2010\"]\n", "\n", "print(type(data_apr_c))\n", "\n", "data_apr_c" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "promedios=[]\n", "series=[]\n", "for i in range(1,1000): # \n", " a=data_apr_c.sample(n=30,replace=True).mean()\n", " promedios.append(a)\n", " series.append(a) \n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "promedio sin bstrp: 30.185540316786295\n", "promedio con bstrp: 30.494627418456027\n", "Int. Confianza al 95%: 24.958243876365856 - 36.12019293703962\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(promedios)\n", "plt.axvline(np.percentile(promedios,2.5), color='g', linestyle='dashed', linewidth=2)\n", "plt.axvline(np.percentile(promedios,97.5), color='g', linestyle='dashed', linewidth=2)\n", "\n", "print(\"promedio sin bstrp:\",data_apr_c.mean())\n", "print(\"promedio con bstrp:\",np.mean(promedios))\n", "print(\"Int. Confianza al 95%:\",np.percentile(promedios,2.5),\"-\",np.percentile(promedios,97.5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "####################################################\n", "####################################################\n", "####################################################\n", "####################################################" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }