{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Universidad de Los Andes - Facultad de Economía**
\n", "**Econometría y el Aprendizaje de las Máquinas**
\n", "**Junio 25 del 2019**\n", "\n", "\n", "# Introducción a Python para la Ciencia de Datos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Por qué Python?\n", "\n", "**From _Top 10 IEEE Spectrum Ranking - Google Search Evolution_**\n", "\n", "Why is Python continuing to have such a hold on programmer mindshare? \n", "\n", "Python is now listed as an **embedded language**. Previously, writing for embedded applications tilted heavily toward compiled languages, to avoid the overhead of evaluating code on the fly on machines with limited processing power and memory. But while Moore’s Law may be fading, it’s not dead yet. Many modern microcontrollers now have more than enough power to host a Python interpreter. A nice aspect of using Python this way is that it is **very handy in certain applications** to play with attached hardware via an interactive prompt or dynamically reload scripts on the fly. Growing into a new domain can only help boost Python’s popularity.\n", "\n", "\n", "_(Taken from [IEEE Spectrum](https://spectrum.ieee.org/at-work/innovation/the-2018-top-programming-languages))_ " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is one of the syntaxes used for importing modules to the working environment\n", "from IPython.display import IFrame \n", "\n", "# display an Inline Frame\n", "IFrame('https://spectrum.ieee.org/static/interactive-the-top-programming\\\n", "-languages-2018', width=900, height=500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Sobre Jupyter Notebook\n", "\n", "- ¿Qué es el número a la izquierda de las celdas ([In \\[n\\]]())? \n", "- ¿Cómo veo que el Jupyter notebook aún está funcionando?\n", "- ¿Cómo cambiar entre modos 'Command' y 'Edit'?\n", " - `` for 'Command' mode\n", " - `` for 'Edit' mode\n", "- ¿Cuáles son los comandos más utiles? \n", " - (Command/Edit mode) ``: run cell\n", " - (Command/Edit mode) ``: run cell and advance to the next one\n", " - (Command mode) ``: insert cell above\n", " - (Command mode) ``: insert cell bellow\n", " - (Edit mode) `++<->`: split cell at text cursor" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Introducción a NumPy (_Numerical Python_)\n", "\n", "- el contenedors multiobjeto estándar de Python es la list (list)\n", "- listas de tipo dinámicos vs vectores de tipo fijo\n", "- los NumPy arrays tienen poca flexibilidad pero con mucho más eficientes para guardar y manipular datos\n", "- los Numpy arrays pueden ser multidimensionales" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# importar el paquete de numpy" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Crear un numpy array\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[2, 3, 4],\n", " [4, 5, 6],\n", " [6, 7, 8]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# nested lists result in multidimensinal arrays" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creación desde _built-in functions_" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# array de ceros\n", "\n", "# matriz de unos y otro numero\n", "\n", "# array de 'rangos'\n", "\n", "# array igualmente espaciado\n", "\n", "# array de valores aleatorias\n", "\n", "# matriz identidad\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Atributos de NumPy" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# seed for reproducibility\n", "\n", "# One-dimensional array\n", "# Two-dimensional array\n", "# Three-dimensional array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cada array tiene atributos\n", "\n", "- ``ndim``: dimensión\n", "- ``shape``: tamaño de cada dimension\n", "- ``size``: el tamaño total del array\n", "- ``dtype``: tipo de dato en el array" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x3 ndim: 3\n", "x3 shape: (3, 4, 5)\n", "x3 size: 60\n", "x3 dtype: int64\n" ] } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Otros atributos son ``itemsize``, que da el tamaño (en bytes) de cada elemento del array, y ``nbytes``, que da el tamaño (en bytes) total del array." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "itemsize: 8 bytes\n", "nbytes: 480 bytes\n" ] } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Indexing y Slicing" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# diferencias de Python y R\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[12, 5, 2, 4],\n", " [ 7, 6, 8, 8],\n", " [ 1, 6, 7, 7]])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# los valores tambien se pueden modificar utilizando los indices\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recuerden que, a diferencia de las listas de Python, los NumPy arrays tienen tipo de variables fijas. por ejemplo, si agregamos un _floating point_ a un array de enteros, el valor será redondeado. " ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3, 0, 3, 3, 7, 9])" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3, 0, 3])" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# tomar los primeros 3 elementos de x1\n" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([7, 9])" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# tomar los ultimos 2 elementos de x1\n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3, 7, 9])" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# tomar todos los elementos despues de la posicion 3\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 3, 3])" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# tomar los elemtos entre la posicion 1 y 3 (notar que el intervalo es abierto en la derecha)\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[2, 3, 4, 5, 6, 7, 8, 9]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# los intervalos abiertos a la derecha tambien se dan con range\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sub vectores multidimensionales" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[12, 5, 2, 4],\n", " [ 7, 6, 8, 8],\n", " [ 1, 6, 7, 7]])" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[12, 5, 2],\n", " [ 7, 6, 8]])" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# dos filas, tres columnas" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[12, 2],\n", " [ 7, 8],\n", " [ 1, 7]])" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# todas las filas, columnas pares" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Otras operaciones" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0., 0., 0.],\n", " [1., 2., 3.],\n", " [4., 5., 6.],\n", " [7., 8., 9.]])" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Manipulación de Datos con Pandas (y un poco de diccionarios)\n", "\n", "Pandas is a package built on top of NumPy, and provides an efficient implementation of a ``DataFrame``.\n", "As well as offering a convenient storage interface for labeled data, Pandas implements a number of powerful data operations familiar to users of both database frameworks and spreadsheet programs.\n", "\n", "Pandas, and in particular its ``Series`` and ``DataFrame`` objects, builds on the NumPy array structure and provides efficient access to these sorts of \"data munging\" tasks that occupy much of a data scientist's time.\n", "\n", "\n", "Data Frames can be created from scratch or from given lists and dictionaries as it is shown in the following diagram.\n", "\n", "\"data\n", "\n", "_(Image taken from [Practical Business Python](https://pbpython.com/pandas-list-dict.html))_" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "# importar modelo de pandas" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "#definir diccionarios de sexo y estatura\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# definir indice del data frame" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nombresexoaltura
0PaulaF1.70
1JuanM1.72
2AlvaroM1.90
3HamadysF1.65
\n", "
" ], "text/plain": [ " nombre sexo altura\n", "0 Paula F 1.70\n", "1 Juan M 1.72\n", "2 Alvaro M 1.90\n", "3 Hamadys F 1.65" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#cambiar el nombre de columna estatura por altura" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# juan mide 1.70 no 1.85" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora llamamos el **Boston Housing Data Set** que consiste del precio de varias casas en Boston. Junto con el precio, la base provee información tal como la tasa de crimen (CRIM), la edad de la persona dueña de la casa (AGE), entre otras." ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "# importar funcion load_boston del modulo de sklearn" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#cargar base Boston en su fromato original (diccionario)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Crear un Pandas DF con los datos de Boston" ] }, { "cell_type": "code", "execution_count": 163, "metadata": {}, "outputs": [], "source": [ "# guardar base de datos como un CSV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matplotlib para visualizar datos\n", "\n", "_Reiniciemos el Kernel_" ] }, { "cell_type": "code", "execution_count": 164, "metadata": {}, "outputs": [], "source": [ "#leer datos de boston" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [], "source": [ "#importar modelo de matplotlib" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# histograma de precios" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Separar un Data Frame en dos (entrenamiento-prueba)\n", "\n", "Cuando se entrena un modelo de Aprendizaje de Máquinas debemos partir/dividir nuestra base de datos en una **base de entrenamiento** y una **base de prueba**. La primera se utiliza para entrenar el modelo y la segunda se utiliza después para probar la capacidad predictiva del modelo. " ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [], "source": [ "# Primero separamos las variables predictivas" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [], "source": [ "# OPCION 1\n", "# con lo que hemos visto hasta el momento. cómo escogerian los indices de entrenamiento?\n" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [], "source": [ "# OPCION 2\n", "# importar funcion para separar el dataset\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regresión Lineal" ] }, { "cell_type": "code", "execution_count": 153, "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", "\n", "# crear modelo de RL\n", "\n", "# entrenar el modelo con los datos de entrenamiento\n", "\n", "# predecir los precios para la base de prueba\n" ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# graficar los precios predecidos vs los precios reales\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Crear variables y seleccionar elementos" ] }, { "cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPRICEAGE_50
100.2248912.57.870.00.5246.37794.36.34675.0311.015.2392.5220.4515.01
110.1174712.57.870.00.5246.00982.96.22675.0311.015.2396.9013.2718.91
120.0937812.57.870.00.5245.88939.05.45095.0311.015.2390.5015.7121.70
130.629760.08.140.00.5385.94961.84.70754.0307.021.0396.908.2620.41
140.637960.08.140.00.5386.09684.54.46194.0307.021.0380.0210.2618.21
150.627390.08.140.00.5385.83456.54.49864.0307.021.0395.628.4719.91
161.053930.08.140.00.5385.93529.34.49864.0307.021.0386.856.5823.10
170.784200.08.140.00.5385.99081.74.25794.0307.021.0386.7514.6717.51
180.802710.08.140.00.5385.45636.63.79654.0307.021.0288.9911.6920.20
190.725800.08.140.00.5385.72769.53.79654.0307.021.0390.9511.2818.21
201.251790.08.140.00.5385.57098.13.79794.0307.021.0376.5721.0213.61
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" ], "text/plain": [ " CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n", "10 0.22489 12.5 7.87 0.0 0.524 6.377 94.3 6.3467 5.0 311.0 \n", "11 0.11747 12.5 7.87 0.0 0.524 6.009 82.9 6.2267 5.0 311.0 \n", "12 0.09378 12.5 7.87 0.0 0.524 5.889 39.0 5.4509 5.0 311.0 \n", "13 0.62976 0.0 8.14 0.0 0.538 5.949 61.8 4.7075 4.0 307.0 \n", "14 0.63796 0.0 8.14 0.0 0.538 6.096 84.5 4.4619 4.0 307.0 \n", "15 0.62739 0.0 8.14 0.0 0.538 5.834 56.5 4.4986 4.0 307.0 \n", "16 1.05393 0.0 8.14 0.0 0.538 5.935 29.3 4.4986 4.0 307.0 \n", "17 0.78420 0.0 8.14 0.0 0.538 5.990 81.7 4.2579 4.0 307.0 \n", "18 0.80271 0.0 8.14 0.0 0.538 5.456 36.6 3.7965 4.0 307.0 \n", "19 0.72580 0.0 8.14 0.0 0.538 5.727 69.5 3.7965 4.0 307.0 \n", "20 1.25179 0.0 8.14 0.0 0.538 5.570 98.1 3.7979 4.0 307.0 \n", "\n", " PTRATIO B LSTAT PRICE AGE_50 \n", "10 15.2 392.52 20.45 15.0 1 \n", "11 15.2 396.90 13.27 18.9 1 \n", "12 15.2 390.50 15.71 21.7 0 \n", "13 21.0 396.90 8.26 20.4 1 \n", "14 21.0 380.02 10.26 18.2 1 \n", "15 21.0 395.62 8.47 19.9 1 \n", "16 21.0 386.85 6.58 23.1 0 \n", "17 21.0 386.75 14.67 17.5 1 \n", "18 21.0 288.99 11.69 20.2 0 \n", "19 21.0 390.95 11.28 18.2 1 \n", "20 21.0 376.57 21.02 13.6 1 " ] }, "execution_count": 175, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# seleccionar las observaciones del indice 10 al 20\n" ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AGEPRICE
065.224.0
261.134.7
454.236.2
666.622.9
8100.016.5
1094.315.0
1239.021.7
1484.518.2
1629.323.1
1836.620.2
2098.113.6
2291.715.2
2494.115.6
2690.316.6
2894.418.4
3094.112.7
3282.013.2
3496.913.5
3661.420.0
3830.224.7
4015.834.9
426.625.3
4440.021.2
4633.320.0
4895.314.4
5045.719.7
5221.125.0
5447.618.9
5635.724.7
5829.223.3
.........
44696.414.9
44898.714.1
45092.613.4
45291.816.1
45494.114.9
45687.912.7
45883.714.9
46090.016.4
46283.019.5
46465.421.4
46684.719.0
46871.019.1
47084.019.9
47275.023.2
47495.413.8
47693.616.7
47896.714.6
48064.723.0
48277.025.0
48441.920.6
48679.819.1
48892.715.2
49098.08.1
49283.520.1
49442.624.5
49672.919.7
49865.321.2
50079.716.8
50276.720.6
50489.322.0
\n", "

253 rows × 2 columns

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" ], "text/plain": [ " AGE PRICE\n", "0 65.2 24.0\n", "2 61.1 34.7\n", "4 54.2 36.2\n", "6 66.6 22.9\n", "8 100.0 16.5\n", "10 94.3 15.0\n", "12 39.0 21.7\n", "14 84.5 18.2\n", "16 29.3 23.1\n", "18 36.6 20.2\n", "20 98.1 13.6\n", "22 91.7 15.2\n", "24 94.1 15.6\n", "26 90.3 16.6\n", "28 94.4 18.4\n", "30 94.1 12.7\n", "32 82.0 13.2\n", "34 96.9 13.5\n", "36 61.4 20.0\n", "38 30.2 24.7\n", "40 15.8 34.9\n", "42 6.6 25.3\n", "44 40.0 21.2\n", "46 33.3 20.0\n", "48 95.3 14.4\n", "50 45.7 19.7\n", "52 21.1 25.0\n", "54 47.6 18.9\n", "56 35.7 24.7\n", "58 29.2 23.3\n", ".. ... ...\n", "446 96.4 14.9\n", "448 98.7 14.1\n", "450 92.6 13.4\n", "452 91.8 16.1\n", "454 94.1 14.9\n", "456 87.9 12.7\n", "458 83.7 14.9\n", "460 90.0 16.4\n", "462 83.0 19.5\n", "464 65.4 21.4\n", "466 84.7 19.0\n", "468 71.0 19.1\n", "470 84.0 19.9\n", "472 75.0 23.2\n", "474 95.4 13.8\n", "476 93.6 16.7\n", "478 96.7 14.6\n", "480 64.7 23.0\n", "482 77.0 25.0\n", "484 41.9 20.6\n", "486 79.8 19.1\n", "488 92.7 15.2\n", "490 98.0 8.1\n", "492 83.5 20.1\n", "494 42.6 24.5\n", "496 72.9 19.7\n", "498 65.3 21.2\n", "500 79.7 16.8\n", "502 76.7 20.6\n", "504 89.3 22.0\n", "\n", "[253 rows x 2 columns]" ] }, "execution_count": 183, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# seleccionar la edad y el precio de las viviendas con indice par\n" ] }, { "cell_type": "code", "execution_count": 161, "metadata": {}, "outputs": [], "source": [ "# crear variable indicadora de si la edad es mayor o menor a 50\n" ] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPRICE
00.0063218.02.310.00.5386.57565.24.09001.0296.015.3396.904.9824.0
10.027310.07.070.00.4696.42178.94.96712.0242.017.8396.909.1421.6
20.027290.07.070.00.4697.18561.14.96712.0242.017.8392.834.0334.7
40.069050.02.180.00.4587.14754.26.06223.0222.018.7396.905.3336.2
50.029850.02.180.00.4586.43058.76.06223.0222.018.7394.125.2128.7
60.0882912.57.870.00.5246.01266.65.56055.0311.015.2395.6012.4322.9
70.1445512.57.870.00.5246.17296.15.95055.0311.015.2396.9019.1527.1
80.2112412.57.870.00.5245.631100.06.08215.0311.015.2386.6329.9316.5
90.1700412.57.870.00.5246.00485.96.59215.0311.015.2386.7117.1018.9
100.2248912.57.870.00.5246.37794.36.34675.0311.015.2392.5220.4515.0
110.1174712.57.870.00.5246.00982.96.22675.0311.015.2396.9013.2718.9
130.629760.08.140.00.5385.94961.84.70754.0307.021.0396.908.2620.4
140.637960.08.140.00.5386.09684.54.46194.0307.021.0380.0210.2618.2
150.627390.08.140.00.5385.83456.54.49864.0307.021.0395.628.4719.9
170.784200.08.140.00.5385.99081.74.25794.0307.021.0386.7514.6717.5
190.725800.08.140.00.5385.72769.53.79654.0307.021.0390.9511.2818.2
201.251790.08.140.00.5385.57098.13.79794.0307.021.0376.5721.0213.6
210.852040.08.140.00.5385.96589.24.01234.0307.021.0392.5313.8319.6
221.232470.08.140.00.5386.14291.73.97694.0307.021.0396.9018.7215.2
230.988430.08.140.00.5385.813100.04.09524.0307.021.0394.5419.8814.5
240.750260.08.140.00.5385.92494.14.39964.0307.021.0394.3316.3015.6
250.840540.08.140.00.5385.59985.74.45464.0307.021.0303.4216.5113.9
260.671910.08.140.00.5385.81390.34.68204.0307.021.0376.8814.8116.6
270.955770.08.140.00.5386.04788.84.45344.0307.021.0306.3817.2814.8
280.772990.08.140.00.5386.49594.44.45474.0307.021.0387.9412.8018.4
291.002450.08.140.00.5386.67487.34.23904.0307.021.0380.2311.9821.0
301.130810.08.140.00.5385.71394.14.23304.0307.021.0360.1722.6012.7
311.354720.08.140.00.5386.072100.04.17504.0307.021.0376.7313.0414.5
321.387990.08.140.00.5385.95082.03.99004.0307.021.0232.6027.7113.2
331.151720.08.140.00.5385.70195.03.78724.0307.021.0358.7718.3513.1
.............................................
4723.568680.018.100.00.5806.43775.02.896524.0666.020.2393.3714.3623.2
4734.646890.018.100.00.6146.98067.62.532924.0666.020.2374.6811.6629.8
4748.055790.018.100.00.5845.42795.42.429824.0666.020.2352.5818.1413.8
4756.393120.018.100.00.5846.16297.42.206024.0666.020.2302.7624.1013.3
4764.871410.018.100.00.6146.48493.62.305324.0666.020.2396.2118.6816.7
47715.023400.018.100.00.6145.30497.32.100724.0666.020.2349.4824.9112.0
47810.233000.018.100.00.6146.18596.72.170524.0666.020.2379.7018.0314.6
47914.333700.018.100.00.6146.22988.01.951224.0666.020.2383.3213.1121.4
4805.824010.018.100.00.5326.24264.73.424224.0666.020.2396.9010.7423.0
4815.708180.018.100.00.5326.75074.93.331724.0666.020.2393.077.7423.7
4825.731160.018.100.00.5327.06177.03.410624.0666.020.2395.287.0125.0
4853.673670.018.100.00.5836.31251.93.991724.0666.020.2388.6210.5821.2
4865.691750.018.100.00.5836.11479.83.545924.0666.020.2392.6814.9819.1
4874.835670.018.100.00.5835.90553.23.152324.0666.020.2388.2211.4520.6
4880.150860.027.740.00.6095.45492.71.82094.0711.020.1395.0918.0615.2
4890.183370.027.740.00.6095.41498.31.75544.0711.020.1344.0523.977.0
4900.207460.027.740.00.6095.09398.01.82264.0711.020.1318.4329.688.1
4910.105740.027.740.00.6095.98398.81.86814.0711.020.1390.1118.0713.6
4920.111320.027.740.00.6095.98383.52.10994.0711.020.1396.9013.3520.1
4930.173310.09.690.00.5855.70754.02.38176.0391.019.2396.9012.0121.8
4960.289600.09.690.00.5855.39072.92.79866.0391.019.2396.9021.1419.7
4970.268380.09.690.00.5855.79470.62.89276.0391.019.2396.9014.1018.3
4980.239120.09.690.00.5856.01965.32.40916.0391.019.2396.9012.9221.2
4990.177830.09.690.00.5855.56973.52.39996.0391.019.2395.7715.1017.5
5000.224380.09.690.00.5856.02779.72.49826.0391.019.2396.9014.3316.8
5010.062630.011.930.00.5736.59369.12.47861.0273.021.0391.999.6722.4
5020.045270.011.930.00.5736.12076.72.28751.0273.021.0396.909.0820.6
5030.060760.011.930.00.5736.97691.02.16751.0273.021.0396.905.6423.9
5040.109590.011.930.00.5736.79489.32.38891.0273.021.0393.456.4822.0
5050.047410.011.930.00.5736.03080.82.50501.0273.021.0396.907.8811.9
\n", "

359 rows × 14 columns

\n", "
" ], "text/plain": [ " CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n", "0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 \n", "1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 \n", "2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0 \n", "4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 \n", "5 0.02985 0.0 2.18 0.0 0.458 6.430 58.7 6.0622 3.0 222.0 \n", "6 0.08829 12.5 7.87 0.0 0.524 6.012 66.6 5.5605 5.0 311.0 \n", "7 0.14455 12.5 7.87 0.0 0.524 6.172 96.1 5.9505 5.0 311.0 \n", "8 0.21124 12.5 7.87 0.0 0.524 5.631 100.0 6.0821 5.0 311.0 \n", "9 0.17004 12.5 7.87 0.0 0.524 6.004 85.9 6.5921 5.0 311.0 \n", "10 0.22489 12.5 7.87 0.0 0.524 6.377 94.3 6.3467 5.0 311.0 \n", "11 0.11747 12.5 7.87 0.0 0.524 6.009 82.9 6.2267 5.0 311.0 \n", "13 0.62976 0.0 8.14 0.0 0.538 5.949 61.8 4.7075 4.0 307.0 \n", "14 0.63796 0.0 8.14 0.0 0.538 6.096 84.5 4.4619 4.0 307.0 \n", "15 0.62739 0.0 8.14 0.0 0.538 5.834 56.5 4.4986 4.0 307.0 \n", "17 0.78420 0.0 8.14 0.0 0.538 5.990 81.7 4.2579 4.0 307.0 \n", "19 0.72580 0.0 8.14 0.0 0.538 5.727 69.5 3.7965 4.0 307.0 \n", "20 1.25179 0.0 8.14 0.0 0.538 5.570 98.1 3.7979 4.0 307.0 \n", "21 0.85204 0.0 8.14 0.0 0.538 5.965 89.2 4.0123 4.0 307.0 \n", "22 1.23247 0.0 8.14 0.0 0.538 6.142 91.7 3.9769 4.0 307.0 \n", "23 0.98843 0.0 8.14 0.0 0.538 5.813 100.0 4.0952 4.0 307.0 \n", "24 0.75026 0.0 8.14 0.0 0.538 5.924 94.1 4.3996 4.0 307.0 \n", "25 0.84054 0.0 8.14 0.0 0.538 5.599 85.7 4.4546 4.0 307.0 \n", "26 0.67191 0.0 8.14 0.0 0.538 5.813 90.3 4.6820 4.0 307.0 \n", "27 0.95577 0.0 8.14 0.0 0.538 6.047 88.8 4.4534 4.0 307.0 \n", "28 0.77299 0.0 8.14 0.0 0.538 6.495 94.4 4.4547 4.0 307.0 \n", "29 1.00245 0.0 8.14 0.0 0.538 6.674 87.3 4.2390 4.0 307.0 \n", "30 1.13081 0.0 8.14 0.0 0.538 5.713 94.1 4.2330 4.0 307.0 \n", "31 1.35472 0.0 8.14 0.0 0.538 6.072 100.0 4.1750 4.0 307.0 \n", "32 1.38799 0.0 8.14 0.0 0.538 5.950 82.0 3.9900 4.0 307.0 \n", "33 1.15172 0.0 8.14 0.0 0.538 5.701 95.0 3.7872 4.0 307.0 \n", ".. ... ... ... ... ... ... ... ... ... ... \n", "472 3.56868 0.0 18.10 0.0 0.580 6.437 75.0 2.8965 24.0 666.0 \n", "473 4.64689 0.0 18.10 0.0 0.614 6.980 67.6 2.5329 24.0 666.0 \n", "474 8.05579 0.0 18.10 0.0 0.584 5.427 95.4 2.4298 24.0 666.0 \n", "475 6.39312 0.0 18.10 0.0 0.584 6.162 97.4 2.2060 24.0 666.0 \n", "476 4.87141 0.0 18.10 0.0 0.614 6.484 93.6 2.3053 24.0 666.0 \n", "477 15.02340 0.0 18.10 0.0 0.614 5.304 97.3 2.1007 24.0 666.0 \n", "478 10.23300 0.0 18.10 0.0 0.614 6.185 96.7 2.1705 24.0 666.0 \n", "479 14.33370 0.0 18.10 0.0 0.614 6.229 88.0 1.9512 24.0 666.0 \n", "480 5.82401 0.0 18.10 0.0 0.532 6.242 64.7 3.4242 24.0 666.0 \n", "481 5.70818 0.0 18.10 0.0 0.532 6.750 74.9 3.3317 24.0 666.0 \n", "482 5.73116 0.0 18.10 0.0 0.532 7.061 77.0 3.4106 24.0 666.0 \n", "485 3.67367 0.0 18.10 0.0 0.583 6.312 51.9 3.9917 24.0 666.0 \n", "486 5.69175 0.0 18.10 0.0 0.583 6.114 79.8 3.5459 24.0 666.0 \n", "487 4.83567 0.0 18.10 0.0 0.583 5.905 53.2 3.1523 24.0 666.0 \n", "488 0.15086 0.0 27.74 0.0 0.609 5.454 92.7 1.8209 4.0 711.0 \n", "489 0.18337 0.0 27.74 0.0 0.609 5.414 98.3 1.7554 4.0 711.0 \n", "490 0.20746 0.0 27.74 0.0 0.609 5.093 98.0 1.8226 4.0 711.0 \n", "491 0.10574 0.0 27.74 0.0 0.609 5.983 98.8 1.8681 4.0 711.0 \n", "492 0.11132 0.0 27.74 0.0 0.609 5.983 83.5 2.1099 4.0 711.0 \n", "493 0.17331 0.0 9.69 0.0 0.585 5.707 54.0 2.3817 6.0 391.0 \n", "496 0.28960 0.0 9.69 0.0 0.585 5.390 72.9 2.7986 6.0 391.0 \n", "497 0.26838 0.0 9.69 0.0 0.585 5.794 70.6 2.8927 6.0 391.0 \n", "498 0.23912 0.0 9.69 0.0 0.585 6.019 65.3 2.4091 6.0 391.0 \n", "499 0.17783 0.0 9.69 0.0 0.585 5.569 73.5 2.3999 6.0 391.0 \n", "500 0.22438 0.0 9.69 0.0 0.585 6.027 79.7 2.4982 6.0 391.0 \n", "501 0.06263 0.0 11.93 0.0 0.573 6.593 69.1 2.4786 1.0 273.0 \n", "502 0.04527 0.0 11.93 0.0 0.573 6.120 76.7 2.2875 1.0 273.0 \n", "503 0.06076 0.0 11.93 0.0 0.573 6.976 91.0 2.1675 1.0 273.0 \n", "504 0.10959 0.0 11.93 0.0 0.573 6.794 89.3 2.3889 1.0 273.0 \n", "505 0.04741 0.0 11.93 0.0 0.573 6.030 80.8 2.5050 1.0 273.0 \n", "\n", " PTRATIO B LSTAT PRICE \n", "0 15.3 396.90 4.98 24.0 \n", "1 17.8 396.90 9.14 21.6 \n", "2 17.8 392.83 4.03 34.7 \n", "4 18.7 396.90 5.33 36.2 \n", "5 18.7 394.12 5.21 28.7 \n", "6 15.2 395.60 12.43 22.9 \n", "7 15.2 396.90 19.15 27.1 \n", "8 15.2 386.63 29.93 16.5 \n", "9 15.2 386.71 17.10 18.9 \n", "10 15.2 392.52 20.45 15.0 \n", "11 15.2 396.90 13.27 18.9 \n", "13 21.0 396.90 8.26 20.4 \n", "14 21.0 380.02 10.26 18.2 \n", "15 21.0 395.62 8.47 19.9 \n", "17 21.0 386.75 14.67 17.5 \n", "19 21.0 390.95 11.28 18.2 \n", "20 21.0 376.57 21.02 13.6 \n", "21 21.0 392.53 13.83 19.6 \n", "22 21.0 396.90 18.72 15.2 \n", "23 21.0 394.54 19.88 14.5 \n", "24 21.0 394.33 16.30 15.6 \n", "25 21.0 303.42 16.51 13.9 \n", "26 21.0 376.88 14.81 16.6 \n", "27 21.0 306.38 17.28 14.8 \n", "28 21.0 387.94 12.80 18.4 \n", "29 21.0 380.23 11.98 21.0 \n", "30 21.0 360.17 22.60 12.7 \n", "31 21.0 376.73 13.04 14.5 \n", "32 21.0 232.60 27.71 13.2 \n", "33 21.0 358.77 18.35 13.1 \n", ".. ... ... ... ... \n", "472 20.2 393.37 14.36 23.2 \n", "473 20.2 374.68 11.66 29.8 \n", "474 20.2 352.58 18.14 13.8 \n", "475 20.2 302.76 24.10 13.3 \n", "476 20.2 396.21 18.68 16.7 \n", "477 20.2 349.48 24.91 12.0 \n", "478 20.2 379.70 18.03 14.6 \n", "479 20.2 383.32 13.11 21.4 \n", "480 20.2 396.90 10.74 23.0 \n", "481 20.2 393.07 7.74 23.7 \n", "482 20.2 395.28 7.01 25.0 \n", "485 20.2 388.62 10.58 21.2 \n", "486 20.2 392.68 14.98 19.1 \n", "487 20.2 388.22 11.45 20.6 \n", "488 20.1 395.09 18.06 15.2 \n", "489 20.1 344.05 23.97 7.0 \n", "490 20.1 318.43 29.68 8.1 \n", "491 20.1 390.11 18.07 13.6 \n", "492 20.1 396.90 13.35 20.1 \n", "493 19.2 396.90 12.01 21.8 \n", "496 19.2 396.90 21.14 19.7 \n", "497 19.2 396.90 14.10 18.3 \n", "498 19.2 396.90 12.92 21.2 \n", "499 19.2 395.77 15.10 17.5 \n", "500 19.2 396.90 14.33 16.8 \n", "501 21.0 391.99 9.67 22.4 \n", "502 21.0 396.90 9.08 20.6 \n", "503 21.0 396.90 5.64 23.9 \n", "504 21.0 393.45 6.48 22.0 \n", "505 21.0 396.90 7.88 11.9 \n", "\n", "[359 rows x 14 columns]" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# crear dataframe con las observaciones donde la edad es mayor a 50\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Funciones, loops y condicionales\n", "\n", "Vamos a crear una función que, dado un precio, indique en qué cuartil está frente a los precios de la base de datos de boston" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "boston.PRICE.describe()" ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [], "source": [ "# crear una funcion que dado un precio retorne el cuartil al que pertenece\n" ] }, { "cell_type": "code", "execution_count": 217, "metadata": {}, "outputs": [], "source": [ "# como lo harian con un loop?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Creamos las variable cuartil_1 y cuartil_2 utilizando las funciones creadas y luego verificamos que sean iguales\n", "Usamos las funciones ``map`` y ``apply`` que se pueden aplicar a Pandas Series" ] }, { "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }