{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.468508Z", "start_time": "2019-11-01T15:44:12.562054Z" } }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Creamos un Dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.478192Z", "start_time": "2019-11-01T15:44:14.471473Z" } }, "outputs": [], "source": [ "df = pd.DataFrame(data={\"Pais\":['Mexico','Argentina','Espana','Colombia'],\n", " \"Poblacion\":[127212000, 45167000, 47099000, 48922000]})" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.504353Z", "start_time": "2019-11-01T15:44:14.481401Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Pais Poblacion\n", "0 Mexico 127212000\n", "1 Argentina 45167000\n", "2 Espana 47099000\n", "3 Colombia 48922000" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ordenamos por columna" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.527018Z", "start_time": "2019-11-01T15:44:14.514223Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Pais Poblacion\n", "0 Mexico 127212000\n", "3 Colombia 48922000\n", "2 Espana 47099000\n", "1 Argentina 45167000" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_values([\"Poblacion\"], ascending=False)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.539098Z", "start_time": "2019-11-01T15:44:14.529889Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Pais Poblacion\n", "1 Argentina 45167000\n", "3 Colombia 48922000\n", "2 Espana 47099000\n", "0 Mexico 127212000" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.sort_values([\"Pais\"])\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Agregar una columna" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.550702Z", "start_time": "2019-11-01T15:44:14.541083Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficie
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "1 Argentina 45167000 1964375\n", "3 Colombia 48922000 2780400\n", "2 Espana 47099000 505944\n", "0 Mexico 127212000 1142748" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Superficie\"] = [1964375, 2780400, 505944, 1142748]\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Asigno mismo valor a todas las filas en una columna nueva" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.562537Z", "start_time": "2019-11-01T15:44:14.552807Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficieDeporte
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" ], "text/plain": [ " Pais Poblacion Superficie Deporte\n", "1 Argentina 45167000 1964375 Futbol\n", "3 Colombia 48922000 2780400 Futbol\n", "2 Espana 47099000 505944 Futbol\n", "0 Mexico 127212000 1142748 Futbol" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Deporte']= 'Futbol'\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Eliminar una columna" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.578164Z", "start_time": "2019-11-01T15:44:14.566159Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "1 Argentina 45167000 1964375\n", "3 Colombia 48922000 2780400\n", "2 Espana 47099000 505944\n", "0 Mexico 127212000 1142748" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.drop(['Deporte'],axis=1)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Eliminar multiples columnas" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.595086Z", "start_time": "2019-11-01T15:44:14.581744Z" }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Poblacion\n", "1 45167000\n", "3 48922000\n", "2 47099000\n", "0 127212000" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.drop(['Superficie','Pais'], axis=1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.612566Z", "start_time": "2019-11-01T15:44:14.598332Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "1 Argentina 45167000 1964375\n", "3 Colombia 48922000 2780400\n", "2 Espana 47099000 505944\n", "0 Mexico 127212000 1142748" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df # recuerda! como no lo asignamos, el df anterior sigue \"intacto\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Agregar una fila nueva al final" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.621927Z", "start_time": "2019-11-01T15:44:14.615114Z" } }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cantidad_filas = len(df) # obtengo la cantidad de filas\n", "cantidad_filas" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.638448Z", "start_time": "2019-11-01T15:44:14.624353Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficie
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "1 Argentina 45167000 1964375\n", "3 Colombia 48922000 2780400\n", "2 Espana 47099000 505944\n", "0 Mexico 127212000 1142748\n", "4 Benezuela 0 916445" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[cantidad_filas] = [\"Benezuela\", 0, 916445] # Está mal escrito el pais! (lo sé)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Actualizo la fila entera" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.654066Z", "start_time": "2019-11-01T15:44:14.640609Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficie
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "1 Argentina 45167000 1964375\n", "3 Colombia 48922000 2780400\n", "2 Espana 47099000 505944\n", "0 Mexico 127212000 1142748\n", "4 Venezuela 0 916445" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[4] = [\"Venezuela\", 0, 916445]\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Actualizo una celda" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.669129Z", "start_time": "2019-11-01T15:44:14.656468Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficie
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "1 Argentina 45167000 1964375\n", "3 Colombia 48922000 2780400\n", "2 Espana 47099000 505944\n", "0 Mexico 127212000 1142748\n", "4 Venezuela 32423000 916445" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.at[4,'Poblacion'] = 32423000\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Eliminar una fila" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.687725Z", "start_time": "2019-11-01T15:44:14.673498Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "1 Argentina 45167000 1964375\n", "2 Espana 47099000 505944\n", "0 Mexico 127212000 1142748\n", "4 Venezuela 32423000 916445" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.drop([3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Eliminar multiples filas" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.704514Z", "start_time": "2019-11-01T15:44:14.690302Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficie
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "2 Espana 47099000 505944\n", "0 Mexico 127212000 1142748\n", "4 Venezuela 32423000 916445" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.drop([3,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Filtrar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Paises con mas de 46 millones de habitantes" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.718480Z", "start_time": "2019-11-01T15:44:14.706795Z" } }, "outputs": [ { "data": { "text/html": [ "
\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", " \n", " \n", " \n", " \n", "
PaisPoblacionSuperficie
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "3 Colombia 48922000 2780400\n", "2 Espana 47099000 505944\n", "0 Mexico 127212000 1142748" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mas_de_46 = df[ df['Poblacion'] > 46000000 ]\n", "mas_de_46" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Más de 46 mill y superficie menor a 600.000 Km2" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.733229Z", "start_time": "2019-11-01T15:44:14.721385Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "2 Espana 47099000 505944" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "doble_filtro = df[ (df['Poblacion'] > 46000000) & (df['Superficie'] < 600000) ]\n", "doble_filtro" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Busco por un valor específico" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.750037Z", "start_time": "2019-11-01T15:44:14.735910Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficie
3Colombia489220002780400
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "3 Colombia 48922000 2780400" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "por_nombre = df[ df['Pais'] == 'Colombia' ]\n", "por_nombre" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Paises con nombre mayor a 6 letras" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.768810Z", "start_time": "2019-11-01T15:44:14.752716Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficie
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "1 Argentina 45167000 1964375\n", "3 Colombia 48922000 2780400\n", "4 Venezuela 32423000 916445" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nombre_largo = df[ df['Pais'].str.len() > 6 ]\n", "nombre_largo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filtrar por True/False" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.783588Z", "start_time": "2019-11-01T15:44:14.771849Z" } }, "outputs": [ { "data": { "text/html": [ "
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4Venezuela32423000916445
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "1 Argentina 45167000 1964375\n", "4 Venezuela 32423000 916445" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arreglo = [True,False,False,False,True] # Esto es lo que realmente hacemos en los casos anteriores!\n", "df[arreglo]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Obtener el indice de una fila" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T16:06:11.395330Z", "start_time": "2019-11-01T16:06:11.388462Z" } }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "por_nombre = df[ df['Pais'] == 'Colombia' ]\n", "por_nombre.index.tolist()[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Aplicar operaciones entre columnas" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.802908Z", "start_time": "2019-11-01T15:44:14.786471Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficieHabit_x_km2
3Colombia48922000278040017
1Argentina45167000196437522
4Venezuela3242300091644535
2Espana4709900050594493
0Mexico1272120001142748111
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" ], "text/plain": [ " Pais Poblacion Superficie Habit_x_km2\n", "3 Colombia 48922000 2780400 17\n", "1 Argentina 45167000 1964375 22\n", "4 Venezuela 32423000 916445 35\n", "2 Espana 47099000 505944 93\n", "0 Mexico 127212000 1142748 111" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# agregamos en una nueva columna el ratio de habitantes por superficie\n", "df['Habit_x_km2'] = (df['Poblacion'] / df['Superficie']).astype(int)\n", "df.sort_values(['Habit_x_km2'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aplicar una operacion definida" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.813306Z", "start_time": "2019-11-01T15:44:14.808267Z" } }, "outputs": [], "source": [ "def crear_codigo(name):\n", " name = name.upper() # paso a mayusculas\n", " name = name[0:4] # primeros 4 char\n", " return name\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### aplicamos usando 1 columna" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.836817Z", "start_time": "2019-11-01T15:44:14.819827Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficieHabit_x_km2Codigo
1Argentina45167000196437522ARGE
3Colombia48922000278040017COLO
2Espana4709900050594493ESPA
0Mexico1272120001142748111MEXI
4Venezuela3242300091644535VENE
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" ], "text/plain": [ " Pais Poblacion Superficie Habit_x_km2 Codigo\n", "1 Argentina 45167000 1964375 22 ARGE\n", "3 Colombia 48922000 2780400 17 COLO\n", "2 Espana 47099000 505944 93 ESPA\n", "0 Mexico 127212000 1142748 111 MEXI\n", "4 Venezuela 32423000 916445 35 VENE" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Codigo'] = df['Pais'].apply(crear_codigo)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aplicamos una función enviando cada fila (todas las columnas)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.865479Z", "start_time": "2019-11-01T15:44:14.839881Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficieHabit_x_km2CodigoCategoria
1Argentina45167000196437522ARGEC
3Colombia48922000278040017COLOA
2Espana4709900050594493ESPAB
0Mexico1272120001142748111MEXIB
4Venezuela3242300091644535VENEC
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" ], "text/plain": [ " Pais Poblacion Superficie Habit_x_km2 Codigo Categoria\n", "1 Argentina 45167000 1964375 22 ARGE C\n", "3 Colombia 48922000 2780400 17 COLO A\n", "2 Espana 47099000 505944 93 ESPA B\n", "0 Mexico 127212000 1142748 111 MEXI B\n", "4 Venezuela 32423000 916445 35 VENE C" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def categoria(fila):\n", " pob = fila['Poblacion']\n", " habit = fila['Habit_x_km2']\n", " if pob > 46000000:\n", " if habit <50:\n", " return 'A'\n", " else:\n", " return 'B'\n", " \n", " return 'C'\n", "\n", "df['Categoria'] = df.apply(categoria, axis=1)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aplicar enviando algunas columnas como parámetros" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.893419Z", "start_time": "2019-11-01T15:44:14.867583Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficieHabit_x_km2CodigoCategoriacolor
1Argentina45167000196437522ARGECazul
3Colombia48922000278040017COLOArojo
2Espana4709900050594493ESPABverde
0Mexico1272120001142748111MEXIBazul
4Venezuela3242300091644535VENECazul
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" ], "text/plain": [ " Pais Poblacion Superficie Habit_x_km2 Codigo Categoria color\n", "1 Argentina 45167000 1964375 22 ARGE C azul\n", "3 Colombia 48922000 2780400 17 COLO A rojo\n", "2 Espana 47099000 505944 93 ESPA B verde\n", "0 Mexico 127212000 1142748 111 MEXI B azul\n", "4 Venezuela 32423000 916445 35 VENE C azul" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def asigna_color(codigo, categoria):\n", " if categoria=='A':\n", " return 'rojo'\n", " if codigo == 'ESPA':\n", " return 'verde'\n", " return 'azul'\n", "\n", "df['color'] = df.apply(lambda x: asigna_color(x['Codigo'],x['Categoria']), axis=1);\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mapeo" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.912983Z", "start_time": "2019-11-01T15:44:14.896382Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficieHabit_x_km2CodigoCategoriacolormapeo_color
1Argentina45167000196437522ARGECazul0
3Colombia48922000278040017COLOArojo1
2Espana4709900050594493ESPABverde2
0Mexico1272120001142748111MEXIBazul0
4Venezuela3242300091644535VENECazul0
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" ], "text/plain": [ " Pais Poblacion Superficie Habit_x_km2 Codigo Categoria color \\\n", "1 Argentina 45167000 1964375 22 ARGE C azul \n", "3 Colombia 48922000 2780400 17 COLO A rojo \n", "2 Espana 47099000 505944 93 ESPA B verde \n", "0 Mexico 127212000 1142748 111 MEXI B azul \n", "4 Venezuela 32423000 916445 35 VENE C azul \n", "\n", " mapeo_color \n", "1 0 \n", "3 1 \n", "2 2 \n", "0 0 \n", "4 0 " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['mapeo_color'] = df['color'].map( {'azul': 0, 'rojo': 1, 'verde': 2} ).astype(int)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Reordenamos columnas" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.934899Z", "start_time": "2019-11-01T15:44:14.916339Z" } }, "outputs": [ { "data": { "text/html": [ "
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CodigoPaisPoblacionCategoriaSuperficieHabit_x_km2
1ARGEArgentina45167000C196437522
3COLOColombia48922000A278040017
2ESPAEspana47099000B50594493
0MEXIMexico127212000B1142748111
4VENEVenezuela32423000C91644535
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" ], "text/plain": [ " Codigo Pais Poblacion Categoria Superficie Habit_x_km2\n", "1 ARGE Argentina 45167000 C 1964375 22\n", "3 COLO Colombia 48922000 A 2780400 17\n", "2 ESPA Espana 47099000 B 505944 93\n", "0 MEXI Mexico 127212000 B 1142748 111\n", "4 VENE Venezuela 32423000 C 916445 35" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df[ ['Codigo','Pais','Poblacion','Categoria','Superficie','Habit_x_km2'] ]\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Join entre tablas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Concat usando indice" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.952745Z", "start_time": "2019-11-01T15:44:14.937961Z" } }, "outputs": [ { "data": { "text/html": [ "
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Comida
MEXIBurritos
ARGEMilanesa
ESPATortilla
COLOSancocho
VENEArepas
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" ], "text/plain": [ " Comida\n", "MEXI Burritos\n", "ARGE Milanesa\n", "ESPA Tortilla\n", "COLO Sancocho\n", "VENE Arepas" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Creamos un DF nuevo, le asignamos el Codigo como identificador único\n", "df_comida = pd.DataFrame(data={\n", " \"Comida\":['Burritos', 'Milanesa', 'Tortilla', 'Sancocho','Arepas']},\n", " index = ['MEXI','ARGE','ESPA','COLO','VENE'])\n", "df_comida" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.973063Z", "start_time": "2019-11-01T15:44:14.955356Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionCategoriaSuperficieHabit_x_km2
Codigo
ARGEArgentina45167000C196437522
COLOColombia48922000A278040017
ESPAEspana47099000B50594493
MEXIMexico127212000B1142748111
VENEVenezuela32423000C91644535
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" ], "text/plain": [ " Pais Poblacion Categoria Superficie Habit_x_km2\n", "Codigo \n", "ARGE Argentina 45167000 C 1964375 22\n", "COLO Colombia 48922000 A 2780400 17\n", "ESPA Espana 47099000 B 505944 93\n", "MEXI Mexico 127212000 B 1142748 111\n", "VENE Venezuela 32423000 C 916445 35" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#asignamos indice en nuestro DF inicial\n", "df_index = df.set_index('Codigo')\n", "df_index" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:14.995530Z", "start_time": "2019-11-01T15:44:14.979854Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionCategoriaSuperficieHabit_x_km2Comida
ARGEArgentina45167000C196437522Milanesa
COLOColombia48922000A278040017Sancocho
ESPAEspana47099000B50594493Tortilla
MEXIMexico127212000B1142748111Burritos
VENEVenezuela32423000C91644535Arepas
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" ], "text/plain": [ " Pais Poblacion Categoria Superficie Habit_x_km2 Comida\n", "ARGE Argentina 45167000 C 1964375 22 Milanesa\n", "COLO Colombia 48922000 A 2780400 17 Sancocho\n", "ESPA Espana 47099000 B 505944 93 Tortilla\n", "MEXI Mexico 127212000 B 1142748 111 Burritos\n", "VENE Venezuela 32423000 C 916445 35 Arepas" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#hacemos el join por indice\n", "result1 = pd.concat([df_index, df_comida], axis=1,sort=True)\n", "result1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Left join por columna clave (merge)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:15.010248Z", "start_time": "2019-11-01T15:44:14.998373Z" } }, "outputs": [ { "data": { "text/html": [ "
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CategoriaFactor
0A12.500
1B103.000
2C0.001
\n", "
" ], "text/plain": [ " Categoria Factor\n", "0 A 12.500\n", "1 B 103.000\n", "2 C 0.001" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Imaginemos que tenemos un DF nuevo, le asignamos el Codigo como identificador único\n", "df_factor = pd.DataFrame(data={\"Categoria\" : [\"A\",\"B\",\"C\"],\n", " \"Factor\":[12.5,103,0.001]}\n", " )\n", "df_factor" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:15.041154Z", "start_time": "2019-11-01T15:44:15.012815Z" } }, "outputs": [ { "data": { "text/html": [ "
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CodigoPaisPoblacionCategoriaSuperficieHabit_x_km2Factor
0ARGEArgentina45167000C1964375220.001
1COLOColombia48922000A27804001712.500
2ESPAEspana47099000B50594493103.000
3MEXIMexico127212000B1142748111103.000
4VENEVenezuela32423000C916445350.001
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" ], "text/plain": [ " Codigo Pais Poblacion Categoria Superficie Habit_x_km2 Factor\n", "0 ARGE Argentina 45167000 C 1964375 22 0.001\n", "1 COLO Colombia 48922000 A 2780400 17 12.500\n", "2 ESPA Espana 47099000 B 505944 93 103.000\n", "3 MEXI Mexico 127212000 B 1142748 111 103.000\n", "4 VENE Venezuela 32423000 C 916445 35 0.001" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result2 = pd.merge(df, df_factor, how='left', on=['Categoria'])\n", "result2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adicionar multiples filas desde otra Tabla con Append" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:44:15.055827Z", "start_time": "2019-11-01T15:44:15.044875Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisPoblacionSuperficie
0Brasil2106880008515770
1Chile1924100056102
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" ], "text/plain": [ " Pais Poblacion Superficie\n", "0 Brasil 210688000 8515770\n", "1 Chile 19241000 56102" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#supongamos que tenemos otra tabla:\n", "df_otros = pd.DataFrame(data={\"Pais\":['Brasil','Chile'],\n", " \"Poblacion\":[210688000, 19241000],\n", " \"Superficie\":[8515770,56102]})\n", "df_otros" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:46:06.559771Z", "start_time": "2019-11-01T15:46:06.537879Z" } }, "outputs": [ { "data": { "text/html": [ "
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CategoriaCodigoHabit_x_km2PaisPoblacionSuperficie
0CARGE22.0Argentina451670001964375
1ACOLO17.0Colombia489220002780400
2BESPA93.0Espana47099000505944
3BMEXI111.0Mexico1272120001142748
4CVENE35.0Venezuela32423000916445
5NaNNaNNaNBrasil2106880008515770
6NaNNaNNaNChile1924100056102
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" ], "text/plain": [ " Categoria Codigo Habit_x_km2 Pais Poblacion Superficie\n", "0 C ARGE 22.0 Argentina 45167000 1964375\n", "1 A COLO 17.0 Colombia 48922000 2780400\n", "2 B ESPA 93.0 Espana 47099000 505944\n", "3 B MEXI 111.0 Mexico 127212000 1142748\n", "4 C VENE 35.0 Venezuela 32423000 916445\n", "5 NaN NaN NaN Brasil 210688000 8515770\n", "6 NaN NaN NaN Chile 19241000 56102" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Queremos agregar estas filas al final\n", "df.append(df_otros, ignore_index=True, sort=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Agrupar" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:47:36.567983Z", "start_time": "2019-11-01T15:47:36.557822Z" } }, "outputs": [ { "data": { "text/plain": [ "Categoria\n", "A 1\n", "B 2\n", "C 2\n", "dtype: int64" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# agrupo por categoria y sumo cuantos hay de cada una\n", "grupo2 = df.groupby(['Categoria']).size()\n", "grupo2" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:47:26.846898Z", "start_time": "2019-11-01T15:47:26.831749Z" } }, "outputs": [ { "data": { "text/html": [ "
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PoblacionSuperficieHabit_x_km2
Categoria
A48922000278040017
B1743110001648692204
C77590000288082057
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" ], "text/plain": [ " Poblacion Superficie Habit_x_km2\n", "Categoria \n", "A 48922000 2780400 17\n", "B 174311000 1648692 204\n", "C 77590000 2880820 57" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# agrupo por cateroría y sumo\n", "grupo1 = df.groupby(['Categoria']).sum()\n", "grupo1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Agrupamos por 2 variables y sumarizamos" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:57:48.494522Z", "start_time": "2019-11-01T15:57:48.471806Z" } }, "outputs": [ { "data": { "text/html": [ "
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Factor
meancountsum
Categoria
A12.500112.500
B103.0002206.000
C0.00120.002
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" ], "text/plain": [ " Factor \n", " mean count sum\n", "Categoria \n", "A 12.500 1 12.500\n", "B 103.000 2 206.000\n", "C 0.001 2 0.002" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tabla = result2[['Categoria', 'Factor']].groupby(['Categoria'], as_index=False).agg(['mean', 'count', 'sum'])\n", "tabla" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pivotar una Tabla" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T16:01:28.469318Z", "start_time": "2019-11-01T16:01:28.440453Z" } }, "outputs": [ { "data": { "text/html": [ "
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PaisArgentinaColombiaEspanaMexicoVenezuela
Categoria
A0.00012.50.00.00.000
B0.0000.0103.0103.00.000
C0.0010.00.00.00.001
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" ], "text/plain": [ "Pais Argentina Colombia Espana Mexico Venezuela\n", "Categoria \n", "A 0.000 12.5 0.0 0.0 0.000\n", "B 0.000 0.0 103.0 103.0 0.000\n", "C 0.001 0.0 0.0 0.0 0.001" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tabla_t = pd.pivot_table(result2, index='Categoria', columns='Pais', values='Factor').fillna(0)\n", "tabla_t" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transponer una tabla" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T16:08:34.699663Z", "start_time": "2019-11-01T16:08:34.685897Z" } }, "outputs": [ { "data": { "text/html": [ "
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13204
CodigoARGECOLOESPAMEXIVENE
PaisArgentinaColombiaEspanaMexicoVenezuela
Poblacion45167000489220004709900012721200032423000
CategoriaCABBC
Superficie196437527804005059441142748916445
Habit_x_km222179311135
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" ], "text/plain": [ " 1 3 2 0 4\n", "Codigo ARGE COLO ESPA MEXI VENE\n", "Pais Argentina Colombia Espana Mexico Venezuela\n", "Poblacion 45167000 48922000 47099000 127212000 32423000\n", "Categoria C A B B C\n", "Superficie 1964375 2780400 505944 1142748 916445\n", "Habit_x_km2 22 17 93 111 35" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualizacion" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:35.805982Z", "start_time": "2019-11-01T15:48:35.411642Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "df[['Poblacion','Superficie']].plot.hist(bins=5,alpha=0.5)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:36.336928Z", "start_time": "2019-11-01T15:48:36.045568Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.set_index('Pais')['Poblacion'].plot(kind='bar');" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:36.884370Z", "start_time": "2019-11-01T15:48:36.687776Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.set_index('Pais')['Habit_x_km2'].plot(kind='area');" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:37.676445Z", "start_time": "2019-11-01T15:48:37.280687Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.set_index('Pais').plot.barh(stacked=True);" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:38.052042Z", "start_time": "2019-11-01T15:48:37.912306Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.set_index('Pais')['Superficie'].plot.pie();" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:38.810494Z", "start_time": "2019-11-01T15:48:38.586759Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.plot.scatter(x='Habit_x_km2', y='Superficie');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Basicos!" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:42.272449Z", "start_time": "2019-11-01T15:48:42.269550Z" } }, "outputs": [], "source": [ "# Leer un archivo csv\n", "#df_csv = pd.read_csv(\"nombre_archivo.csv\")" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:42.479909Z", "start_time": "2019-11-01T15:48:42.471503Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 5 entries, 1 to 4\n", "Data columns (total 6 columns):\n", "Codigo 5 non-null object\n", "Pais 5 non-null object\n", "Poblacion 5 non-null int64\n", "Categoria 5 non-null object\n", "Superficie 5 non-null int64\n", "Habit_x_km2 5 non-null int64\n", "dtypes: int64(3), object(3)\n", "memory usage: 440.0+ bytes\n" ] } ], "source": [ "# Informacion de columnas, tipos de datos y nulos\n", "df.info()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:42.660761Z", "start_time": "2019-11-01T15:48:42.627756Z" } }, "outputs": [ { "data": { "text/html": [ "
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PoblacionSuperficieHabit_x_km2
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max1.272120e+082780400.000111.00000
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" ], "text/plain": [ " Poblacion Superficie Habit_x_km2\n", "count 5.000000e+00 5.000 5.00000\n", "mean 6.016460e+07 1461982.400 55.60000\n", "std 3.803612e+07 908866.653 43.33359\n", "min 3.242300e+07 505944.000 17.00000\n", "25% 4.516700e+07 916445.000 22.00000\n", "50% 4.709900e+07 1142748.000 35.00000\n", "75% 4.892200e+07 1964375.000 93.00000\n", "max 1.272120e+08 2780400.000 111.00000" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# estadísticas sobre las columnas numéricas\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:42.784227Z", "start_time": "2019-11-01T15:48:42.772163Z" } }, "outputs": [ { "data": { "text/html": [ "
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CodigoPaisPoblacionCategoriaSuperficieHabit_x_km2
0MEXIMexico127212000B1142748111
4VENEVenezuela32423000C91644535
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" ], "text/plain": [ " Codigo Pais Poblacion Categoria Superficie Habit_x_km2\n", "0 MEXI Mexico 127212000 B 1142748 111\n", "4 VENE Venezuela 32423000 C 916445 35" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#ver primeras n filas (ó ultimas)\n", "df.head(2)\n", "df.tail(2)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:42.927525Z", "start_time": "2019-11-01T15:48:42.921997Z" } }, "outputs": [ { "data": { "text/plain": [ "(5, 6)" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# dimensiones del dataframe (filas, columnas)\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:43.063888Z", "start_time": "2019-11-01T15:48:43.058500Z" } }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#cantidad de filas\n", "len(df)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:45.118320Z", "start_time": "2019-11-01T15:48:45.111800Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Argentina\n", "Colombia\n", "Espana\n", "Mexico\n", "Venezuela\n" ] } ], "source": [ "#Iterar un dataframe\n", "for index, row in df.iterrows(): \n", " print(row['Pais'])\n" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T16:14:18.686615Z", "start_time": "2019-11-01T16:14:18.676083Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "55.6\n", "278\n", "5\n", "17\n", "111\n" ] } ], "source": [ "# Obtener sumatoria, media y cantidad de una columna\n", "print(df['Habit_x_km2'].mean())\n", "print(df['Habit_x_km2'].sum())\n", "print(df['Habit_x_km2'].count())\n", "print(df['Habit_x_km2'].min())\n", "print(df['Habit_x_km2'].max())" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:48:45.404594Z", "start_time": "2019-11-01T15:48:45.396695Z" } }, "outputs": [ { "data": { "text/plain": [ "Codigo 0\n", "Pais 0\n", "Poblacion 0\n", "Categoria 0\n", "Superficie 0\n", "Habit_x_km2 0\n", "dtype: int64" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Revisar si tenemos nulos en la tabla\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:53:42.527742Z", "start_time": "2019-11-01T15:53:42.520437Z" } }, "outputs": [ { "data": { "text/plain": [ "1 1964375\n", "3 2780400\n", "2 505944\n", "0 1142748\n", "4 916445\n", "Name: Superficie, dtype: int64" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#acceso a una columna\n", "df.Superficie" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:54:15.168930Z", "start_time": "2019-11-01T15:54:15.162817Z" } }, "outputs": [ { "data": { "text/plain": [ "array(['C', 'A', 'B'], dtype=object)" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# unicos\n", "df.Categoria.unique()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T15:54:38.800040Z", "start_time": "2019-11-01T15:54:38.794408Z" } }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# cantidad de unicos\n", "len(df.Categoria.unique())" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T16:21:27.013185Z", "start_time": "2019-11-01T16:21:27.005734Z" } }, "outputs": [ { "data": { "text/plain": [ "B 2\n", "C 2\n", "A 1\n", "Name: Categoria, dtype: int64" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# contabilizar por una columna\n", "pd.value_counts(df['Categoria'], sort = True)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T16:24:48.876826Z", "start_time": "2019-11-01T16:24:48.869436Z" } }, "outputs": [ { "data": { "text/plain": [ "Codigo VENE\n", "Pais Venezuela\n", "Poblacion 32423000\n", "Categoria C\n", "Superficie 916445\n", "Habit_x_km2 35\n", "Name: 4, dtype: object" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Obtener ultima fila\n", "df.iloc[-1]" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T16:25:29.673946Z", "start_time": "2019-11-01T16:25:29.667260Z" } }, "outputs": [ { "data": { "text/plain": [ "1 ARGE\n", "3 COLO\n", "2 ESPA\n", "0 MEXI\n", "4 VENE\n", "Name: Codigo, dtype: object" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# obtener primera columna\n", "df.iloc[:,0]" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T16:26:27.040151Z", "start_time": "2019-11-01T16:26:27.032513Z" } }, "outputs": [ { "data": { "text/plain": [ "Codigo ESPA\n", "Pais Espana\n", "Poblacion 47099000\n", "Categoria B\n", "Superficie 505944\n", "Habit_x_km2 93\n", "Name: 2, dtype: object" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Busco por indice\n", "df.loc[2]" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T16:30:21.541499Z", "start_time": "2019-11-01T16:30:21.535748Z" } }, "outputs": [ { "data": { "text/plain": [ "Index(['Codigo', 'Pais', 'Poblacion', 'Categoria', 'Superficie',\n", " 'Habit_x_km2'],\n", " dtype='object')" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T16:29:00.459256Z", "start_time": "2019-11-01T16:29:00.449058Z" } }, "outputs": [ { "data": { "text/html": [ "
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Pais
2Espana
4Venezuela
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" ], "text/plain": [ " Pais\n", "2 Espana\n", "4 Venezuela" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# obtner una columna dentro de una condicion\n", "df.loc[ df['Superficie'] < 1000000 ,[\"Pais\"]]" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "ExecuteTime": { "end_time": "2019-11-01T16:31:45.314846Z", "start_time": "2019-11-01T16:31:45.299595Z" } }, "outputs": [ { "data": { "text/html": [ "
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CodigoPaisPoblacionCategoriaSuperficieHabit_x_km2
1ARGEArgentina45167000C196437522
3COLOColombia48922000A278040017
2ESPAEspana47099000D50594493
0MEXIMexico127212000B1142748111
4VENEVenezuela32423000D91644535
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" ], "text/plain": [ " Codigo Pais Poblacion Categoria Superficie Habit_x_km2\n", "1 ARGE Argentina 45167000 C 1964375 22\n", "3 COLO Colombia 48922000 A 2780400 17\n", "2 ESPA Espana 47099000 D 505944 93\n", "0 MEXI Mexico 127212000 B 1142748 111\n", "4 VENE Venezuela 32423000 D 916445 35" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Modificar Valor con loc\n", "df.loc[ df['Superficie'] < 1000000 ,\"Categoria\"] = 'D'\n", "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 2 }