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@damianavila
Created April 10, 2015 11:07
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#Data Science con Python "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"##(o un breve raconto de las libs para laburar con datos)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## ¿Quién les habla?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### *de iure*:\n",
"\n",
"* Lic. en Bioquímica. \n",
"* Doctorando en Ciencias Biológicas (escribiendo... una tesis inmunológica)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### *de facto*:\n",
"\n",
"* Desarrollador Python/JS (ponele...).\n",
"* Científico de Datos (ponele...).\n",
"* Analista Cuantitativo en Finanzas (ponele...)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Curioso..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<p style=\"text-align: center;\"><i class=\"fa-github fa-2x fa\"> [damianavila](https://github.com/damianavila)</i></p>\n",
"\n",
"<p style=\"text-align: center;\"><i class=\"fa-twitter fa-2x fa\"> [@damian_avila](http://twitter.com/damian_avila)</i></p>\n",
"\n",
"<p style=\"text-align: center;\"><i class=\"fa-envelope fa-2x fa\"> <a href=\"mailto:[email protected]\">[email protected]</a></i></p>\n",
"\n",
"<p style=\"text-align: center;\"><i class=\"fa-user fa-2x fa\"> [www.damian.oquanta.info](http://www.damian.oquanta.info)</i></p>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## ¿Qué hace un científico?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* **Observa**"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* Genera una **hipótesis** sobre el/los fenómeno/s observado/s"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* Diseña y realiza un **experimento** (o una **simulación**) para testear la hipótesis"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* Obtiene **datos** del experimento (o la simulación)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"* **Procesa** y **visualiza** los datos obtenidos"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* Propone un **modelo** para explicar los datos procesados"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* Genera **predicciones** a partir del modelo propuesto"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* **Contrasta** (valida) las predicciones con los datos experimentales (o simulados)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* **Comunica** la hipótesis, los resultados obtenidos y el modelo propuesto"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"###The Cross Industry Standard Process for Data Mining"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![models1](https://upload.wikimedia.org/wikipedia/commons/thumb/b/b9/CRISP-DM_Process_Diagram.png/479px-CRISP-DM_Process_Diagram.png)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"###Data Science Workflow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![models2](http://cacm.acm.org/system/assets/0001/3678/rp-overview.jpg)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"##¿Qué necesita un científico de datos?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* Un lenguaje **fácil** de aprender."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* Un lenguaje **versátil** en su utilización."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* Un lenguaje con una sintaxis **simple** y de **alto nivel**."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"* Una colección de **herramientas** para el tratamiento de datos."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* Múltiples opciones para la **visualización** de datos."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* Un \"entorno\" **unificado** e **interactivo**."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#Python"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"* Un lenguaje **fácil** de aprender."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Un lenguaje con una sintaxis **simple** y de **alto nivel**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Un lenguaje **versátil** en su utilización."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"##y las baterías adicionales..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"* Una colección de **herramientas** para el tratamiento de datos."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"###Bajo nivel..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* [**Numpy**](http://www.numpy.org), objetos tipo arreglos y rutinas para manipularlos."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"* Python tiene listas, enteros, punto flotante, etc. Para cálculo numérico necesitamos más... allí aparece Numpy.\n",
"* Numpy es un paquete que provee a Python con arreglos multidimensionales de alta eficiencia y diseñados para cálculo científico.\n",
"* Un array puede contener:\n",
" * tiempos discretos de un experimento o simulación.\n",
" * señales grabadas por un instrumento de medida.\n",
" * pixeles de una imagen, etc."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### El objeto *arreglo*.\n",
"\n",
"* Los arreglos de NumPy son de tipado estático y homogéneo.\n",
"* Multidimensionales.\n",
"* Son más eficientes en el uso de la memoria. La funciones matemáticas complejas y computacionalmente costosas (pj: la multiplicación de matrices) son implementadas en lenguajes compilados como C o Fortran."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 4, 5]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lista = [1, 2, 3, 4 , 5]\n",
"lista"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array(lista)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(a)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dtype('int64')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.dtype"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.ndim"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5,)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"###Indexado y cortes"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.arange(10)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(0, 2, 9)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0], a[2], a[-1]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mis_indices = [0, 2, -1]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 2, 9])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[mis_indices]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 3, 4, 5, 6, 7, 8])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[2:9] "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 5, 8])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[2:9:3] "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 2, 4, 6, 8])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[::2]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([3, 5, 7, 9])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[3::2]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([8, 9])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[-2:]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"###Copias y Vistas"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"####Plain Python"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = [1, 2, 3]\n",
"b = a[:]\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[100, 2, 3]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b[0] = 100\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"####Numpy"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([1, 2, 3])\n",
"b = a[:]\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"b[0] = 100"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([100, 2, 3])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"###¿Qué más tenemos en Numpy?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Operaciones por elementos:\n",
" * Escalares\n",
" * Aritméticas\n",
" * Lógicas\n",
" \n",
"* Reducciones básicas:\n",
" * Aritmeticas\n",
" * Estadísticos\n",
" * Lógicas\n",
" \n",
"* Broadcasting:\n",
"\n",
" En NumPy es posible hacer operaciones entre arreglos de diferente tamaño a través del broadcasting. NumPy transforma (propaga) los arreglos involucrados para que tengan el mismo tamaño y, por tanto, puedan someterse a las operaciones por elementos sin generar excepciones.\n",
"\n",
"* Manipulación de arreglos\n",
"\n",
"* I/O\n",
"\n",
"* Algebra lineal básica"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* [**Scipy**](http://www.scipy.org), biblioteca para procesamiento de datos de alto nivel: optimización, regresión, interpolación, etc."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"SciPy se desarrolla sobre NumPy y provee de un gran número de algoritmos científicos de alto nivel.\n",
"\n",
"* Functiones especiales (scipy.special)\n",
"* Integración (scipy.integrate)\n",
"* Optimización (scipy.optimize)\n",
"* Interpolación (scipy.interpolate)\n",
"* Transformadas de Fourier (scipy.fftpack)\n",
"* Procesamiento de señales (scipy.signal)\n",
"* Algebra lineal(scipy.linalg)\n",
"* Problemas de matrices dispersas (scipy.sparse)\n",
"* Estadística (scipy.stats)\n",
"* Procesamiento de imágenes multidimensional (scipy.ndimage)\n",
"* IO (scipy.io)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"###Buscando mínimos y máximos de una función."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from scipy import optimize"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def f(x):\n",
" return 4*x**3 + (x-2)**2 + x**4"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"gg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8751c738d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"x = np.linspace(-5, 3, 100)\n",
"ax.plot(x, f(x));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podemos usar la función fmin_bfgs para buscar los mínimos locales:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: -3.506641\n",
" Iterations: 6\n",
" Function evaluations: 30\n",
" Gradient evaluations: 10\n"
]
},
{
"data": {
"text/plain": [
"array([-2.67298167])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_min = optimize.fmin_bfgs(f, -2)\n",
"x_min"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 2.804988\n",
" Iterations: 3\n",
" Function evaluations: 15\n",
" Gradient evaluations: 5\n"
]
},
{
"data": {
"text/plain": [
"array([ 0.46961745])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimize.fmin_bfgs(f, 0.5) "
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.46961743402759754"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimize.brent(f)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-2.6729822917513886"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimize.fminbound(f, -4, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"###Alto nivel..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* [**pandas**](http://pandas.pydata.org/), \"el cinturón de Batman para el analista de datos\". El actor principal es el objeto DataFrame, una estructura de datos bidimensional con filas y columnas indexables, inspirado en el dataframe de R."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Algunas de sus principales características son:\n",
"\n",
"* DataFrame\n",
"* Indexado por filas y columnas\n",
"* Indexado booleano\n",
"* Tratamiento de valores NA\n",
"* Broadcasting\n",
"* Excelente métodos de \"split\", \"apply\", \"combine\", \"groupby\"\n",
"* Estadística (media, std, corr, etc.)\n",
"* Mergeado, unión y concatenación de datasets"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td>2013-01-02</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> foo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 1</td>\n",
" <td>2013-01-02</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> foo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 1</td>\n",
" <td>2013-01-02</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> foo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 1</td>\n",
" <td>2013-01-02</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> foo</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E\n",
"0 1 2013-01-02 1 3 foo\n",
"1 1 2013-01-02 1 3 foo\n",
"2 1 2013-01-02 1 3 foo\n",
"3 1 2013-01-02 1 3 foo"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df2 = pd.DataFrame({ 'A' : 1.,\n",
" 'B' : pd.Timestamp('20130102'),\n",
" 'C' : pd.Series(1,index=range(4),dtype='float32'),\n",
" 'D' : np.array([3] * 4,dtype='int32'),\n",
" 'E' : 'foo' })\n",
"df2"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"A float64\n",
"B datetime64[ns]\n",
"C float32\n",
"D int32\n",
"E object\n",
"dtype: object"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td> 0.328238</td>\n",
" <td> 2.150125</td>\n",
" <td>-0.431673</td>\n",
" <td> 1.136437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02</th>\n",
" <td>-0.125009</td>\n",
" <td>-0.924556</td>\n",
" <td> 1.959384</td>\n",
" <td> 0.012627</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-03</th>\n",
" <td>-1.614760</td>\n",
" <td> 0.151343</td>\n",
" <td>-0.508515</td>\n",
" <td> 0.531091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-04</th>\n",
" <td> 0.279493</td>\n",
" <td>-0.374569</td>\n",
" <td> 0.216071</td>\n",
" <td>-0.776623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-05</th>\n",
" <td> 0.111009</td>\n",
" <td> 1.987822</td>\n",
" <td>-0.300183</td>\n",
" <td> 0.049401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-06</th>\n",
" <td> 1.508283</td>\n",
" <td> 1.887487</td>\n",
" <td> 0.576780</td>\n",
" <td> 0.351069</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2013-01-01 0.328238 2.150125 -0.431673 1.136437\n",
"2013-01-02 -0.125009 -0.924556 1.959384 0.012627\n",
"2013-01-03 -1.614760 0.151343 -0.508515 0.531091\n",
"2013-01-04 0.279493 -0.374569 0.216071 -0.776623\n",
"2013-01-05 0.111009 1.987822 -0.300183 0.049401\n",
"2013-01-06 1.508283 1.887487 0.576780 0.351069"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates = pd.date_range('20130101', periods=6)\n",
"\n",
"df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td> 0.328238</td>\n",
" <td> 2.150125</td>\n",
" <td>-0.431673</td>\n",
" <td> 1.136437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02</th>\n",
" <td>-0.125009</td>\n",
" <td>-0.924556</td>\n",
" <td> 1.959384</td>\n",
" <td> 0.012627</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2013-01-01 0.328238 2.150125 -0.431673 1.136437\n",
"2013-01-02 -0.125009 -0.924556 1.959384 0.012627"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-04</th>\n",
" <td> 0.279493</td>\n",
" <td>-0.374569</td>\n",
" <td> 0.216071</td>\n",
" <td>-0.776623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-05</th>\n",
" <td> 0.111009</td>\n",
" <td> 1.987822</td>\n",
" <td>-0.300183</td>\n",
" <td> 0.049401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-06</th>\n",
" <td> 1.508283</td>\n",
" <td> 1.887487</td>\n",
" <td> 0.576780</td>\n",
" <td> 0.351069</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2013-01-04 0.279493 -0.374569 0.216071 -0.776623\n",
"2013-01-05 0.111009 1.987822 -0.300183 0.049401\n",
"2013-01-06 1.508283 1.887487 0.576780 0.351069"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail(3)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<class 'pandas.tseries.index.DatetimeIndex'>\n",
"[2013-01-01, ..., 2013-01-06]\n",
"Length: 6, Freq: D, Timezone: None"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.index"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['A', 'B', 'C', 'D'], dtype='object')"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.32823837, 2.15012542, -0.43167317, 1.13643701],\n",
" [-0.12500919, -0.92455611, 1.95938424, 0.01262702],\n",
" [-1.61476048, 0.15134312, -0.50851502, 0.5310914 ],\n",
" [ 0.27949297, -0.37456868, 0.21607081, -0.77662322],\n",
" [ 0.11100919, 1.98782224, -0.30018303, 0.0494015 ],\n",
" [ 1.50828286, 1.88748657, 0.57677982, 0.35106879]])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.values"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 6.000000</td>\n",
" <td> 6.000000</td>\n",
" <td> 6.000000</td>\n",
" <td> 6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 0.081209</td>\n",
" <td> 0.812942</td>\n",
" <td> 0.251977</td>\n",
" <td> 0.217334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 1.005645</td>\n",
" <td> 1.355717</td>\n",
" <td> 0.935161</td>\n",
" <td> 0.635474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-1.614760</td>\n",
" <td>-0.924556</td>\n",
" <td>-0.508515</td>\n",
" <td>-0.776623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-0.066005</td>\n",
" <td>-0.243091</td>\n",
" <td>-0.398801</td>\n",
" <td> 0.021821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 0.195251</td>\n",
" <td> 1.019415</td>\n",
" <td>-0.042056</td>\n",
" <td> 0.200235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 0.316052</td>\n",
" <td> 1.962738</td>\n",
" <td> 0.486603</td>\n",
" <td> 0.486086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 1.508283</td>\n",
" <td> 2.150125</td>\n",
" <td> 1.959384</td>\n",
" <td> 1.136437</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"count 6.000000 6.000000 6.000000 6.000000\n",
"mean 0.081209 0.812942 0.251977 0.217334\n",
"std 1.005645 1.355717 0.935161 0.635474\n",
"min -1.614760 -0.924556 -0.508515 -0.776623\n",
"25% -0.066005 -0.243091 -0.398801 0.021821\n",
"50% 0.195251 1.019415 -0.042056 0.200235\n",
"75% 0.316052 1.962738 0.486603 0.486086\n",
"max 1.508283 2.150125 1.959384 1.136437"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>2013-01-01 00:00:00</th>\n",
" <th>2013-01-02 00:00:00</th>\n",
" <th>2013-01-03 00:00:00</th>\n",
" <th>2013-01-04 00:00:00</th>\n",
" <th>2013-01-05 00:00:00</th>\n",
" <th>2013-01-06 00:00:00</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>A</th>\n",
" <td> 0.328238</td>\n",
" <td>-0.125009</td>\n",
" <td>-1.614760</td>\n",
" <td> 0.279493</td>\n",
" <td> 0.111009</td>\n",
" <td> 1.508283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td> 2.150125</td>\n",
" <td>-0.924556</td>\n",
" <td> 0.151343</td>\n",
" <td>-0.374569</td>\n",
" <td> 1.987822</td>\n",
" <td> 1.887487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C</th>\n",
" <td>-0.431673</td>\n",
" <td> 1.959384</td>\n",
" <td>-0.508515</td>\n",
" <td> 0.216071</td>\n",
" <td>-0.300183</td>\n",
" <td> 0.576780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D</th>\n",
" <td> 1.136437</td>\n",
" <td> 0.012627</td>\n",
" <td> 0.531091</td>\n",
" <td>-0.776623</td>\n",
" <td> 0.049401</td>\n",
" <td> 0.351069</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06\n",
"A 0.328238 -0.125009 -1.614760 0.279493 0.111009 1.508283\n",
"B 2.150125 -0.924556 0.151343 -0.374569 1.987822 1.887487\n",
"C -0.431673 1.959384 -0.508515 0.216071 -0.300183 0.576780\n",
"D 1.136437 0.012627 0.531091 -0.776623 0.049401 0.351069"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.T"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>D</th>\n",
" <th>C</th>\n",
" <th>B</th>\n",
" <th>A</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td> 1.136437</td>\n",
" <td>-0.431673</td>\n",
" <td> 2.150125</td>\n",
" <td> 0.328238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02</th>\n",
" <td> 0.012627</td>\n",
" <td> 1.959384</td>\n",
" <td>-0.924556</td>\n",
" <td>-0.125009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-03</th>\n",
" <td> 0.531091</td>\n",
" <td>-0.508515</td>\n",
" <td> 0.151343</td>\n",
" <td>-1.614760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-04</th>\n",
" <td>-0.776623</td>\n",
" <td> 0.216071</td>\n",
" <td>-0.374569</td>\n",
" <td> 0.279493</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-05</th>\n",
" <td> 0.049401</td>\n",
" <td>-0.300183</td>\n",
" <td> 1.987822</td>\n",
" <td> 0.111009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-06</th>\n",
" <td> 0.351069</td>\n",
" <td> 0.576780</td>\n",
" <td> 1.887487</td>\n",
" <td> 1.508283</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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" D C B A\n",
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]
},
"execution_count": 41,
"metadata": {},
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"df.sort_index(axis=1, ascending=False)"
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"metadata": {
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{
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" <td> 0.531091</td>\n",
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"execution_count": 42,
"metadata": {},
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{
"cell_type": "code",
"execution_count": 44,
"metadata": {
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{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 44,
"metadata": {},
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}
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"source": [
"type(df['A'])"
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{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
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"outputs": [
{
"data": {
"text/plain": [
"A 0.081209\n",
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"D 0.217334\n",
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"execution_count": 47,
"metadata": {},
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"df.mean()"
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" <th>2013-01-06</th>\n",
" <td> 0.487254</td>\n",
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" A B C D\n",
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"df.apply(np.cumsum)"
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{
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{
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"source": [
"df = pd.DataFrame(np.random.randn(10, 4))\n",
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]
},
{
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"execution_count": 51,
"metadata": {
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"pieces = [df[:3], df[3:7], df[7:]]"
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{
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{
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" <th>7</th>\n",
" <td> foo</td>\n",
" <td> three</td>\n",
" <td>-1.456959</td>\n",
" <td> 0.423885</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"0 foo one 2.198971 0.580460\n",
"1 bar one 2.296764 -0.457668\n",
"2 foo two 0.268690 -0.137156\n",
"3 bar three 1.228435 2.440996\n",
"4 foo two -2.387769 -0.768026\n",
"5 bar two 1.034477 -0.701858\n",
"6 foo one -0.372800 -0.474752\n",
"7 foo three -1.456959 0.423885"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',\n",
" 'foo', 'bar', 'foo', 'foo'],\n",
" 'B' : ['one', 'one', 'two', 'three',\n",
" 'two', 'two', 'one', 'three'],\n",
" 'C' : np.random.randn(8), \n",
" 'D' : np.random.randn(8)})\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" <th></th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" <tr>\n",
" <th>A</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>bar</th>\n",
" <td> 4.559676</td>\n",
" <td> 1.281469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>foo</th>\n",
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" <td>-0.375589</td>\n",
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"text/plain": [
" C D\n",
"A \n",
"bar 4.559676 1.281469\n",
"foo -1.749868 -0.375589"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('A').sum()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" <th></th>\n",
" <th></th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" <tr>\n",
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" <th>B</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">bar</th>\n",
" <th>one</th>\n",
" <td> 2.296764</td>\n",
" <td>-0.457668</td>\n",
" </tr>\n",
" <tr>\n",
" <th>three</th>\n",
" <td> 1.228435</td>\n",
" <td> 2.440996</td>\n",
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" <tr>\n",
" <th>two</th>\n",
" <td> 1.034477</td>\n",
" <td>-0.701858</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">foo</th>\n",
" <th>one</th>\n",
" <td> 1.826171</td>\n",
" <td> 0.105708</td>\n",
" </tr>\n",
" <tr>\n",
" <th>three</th>\n",
" <td>-1.456959</td>\n",
" <td> 0.423885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>two</th>\n",
" <td>-2.119079</td>\n",
" <td>-0.905182</td>\n",
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"text/plain": [
" C D\n",
"A B \n",
"bar one 2.296764 -0.457668\n",
" three 1.228435 2.440996\n",
" two 1.034477 -0.701858\n",
"foo one 1.826171 0.105708\n",
" three -1.456959 0.423885\n",
" two -2.119079 -0.905182"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(['A','B']).sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* [**statsmodels**](http://statsmodels.sourceforge.net/), modelos estadísticos y econometría"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"* Modelos de regresión lineal\n",
"* Modelos lineas generalizados\n",
"* Modelos lineares robustos\n",
"* Modelos de series de tiempo\n",
"* Estimadores no paramétricos\n",
"* Tests estadísticos\n",
"* Integración con pandas"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>prestige_1</th>\n",
" <th>prestige_2</th>\n",
" <th>prestige_3</th>\n",
" <th>prestige_4</th>\n",
" </tr>\n",
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" <td> 0</td>\n",
" <td> 1</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" prestige_1 prestige_2 prestige_3 prestige_4\n",
"0 0 0 1 0\n",
"1 0 0 1 0\n",
"2 1 0 0 0\n",
"3 0 0 0 1\n",
"4 0 0 0 1"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import statsmodels.api as sm\n",
"import numpy as np\n",
" \n",
"# read the data in\n",
"df = pd.read_csv(\"http://www.ats.ucla.edu/stat/data/binary.csv\")\n",
" \n",
"# rename the 'rank' column because there is also a DataFrame method called 'rank'\n",
"df.columns = [\"admit\", \"gre\", \"gpa\", \"prestige\"]\n",
"dummy_ranks = pd.get_dummies(df['prestige'], prefix='prestige')\n",
"dummy_ranks.head()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>admit</th>\n",
" <th>gre</th>\n",
" <th>gpa</th>\n",
" <th>prestige_2</th>\n",
" <th>prestige_3</th>\n",
" <th>prestige_4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 0</td>\n",
" <td> 380</td>\n",
" <td> 3.61</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
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" <th>1</th>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td> 1</td>\n",
" <td> 800</td>\n",
" <td> 4.00</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 1</td>\n",
" <td> 640</td>\n",
" <td> 3.19</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 0</td>\n",
" <td> 520</td>\n",
" <td> 2.93</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" admit gre gpa prestige_2 prestige_3 prestige_4\n",
"0 0 380 3.61 0 1 0\n",
"1 1 660 3.67 0 1 0\n",
"2 1 800 4.00 0 0 0\n",
"3 1 640 3.19 0 0 1\n",
"4 0 520 2.93 0 0 1"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a clean data frame for the regression\n",
"cols_to_keep = ['admit', 'gre', 'gpa']\n",
"data = df[cols_to_keep].join(dummy_ranks.ix[:, 'prestige_2':])\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 0.573147\n",
" Iterations 6\n"
]
},
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Logit Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>admit</td> <th> No. Observations: </th> <td> 400</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>Logit</td> <th> Df Residuals: </th> <td> 394</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>MLE</td> <th> Df Model: </th> <td> 5</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 01 Apr 2015</td> <th> Pseudo R-squ.: </th> <td>0.08292</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>21:15:07</td> <th> Log-Likelihood: </th> <td> -229.26</td> \n",
"</tr>\n",
"<tr>\n",
" <th>converged:</th> <td>True</td> <th> LL-Null: </th> <td> -249.99</td> \n",
"</tr>\n",
"<tr>\n",
" <th> </th> <td> </td> <th> LLR p-value: </th> <td>7.578e-08</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>gre</th> <td> 0.0023</td> <td> 0.001</td> <td> 2.070</td> <td> 0.038</td> <td> 0.000 0.004</td>\n",
"</tr>\n",
"<tr>\n",
" <th>gpa</th> <td> 0.8040</td> <td> 0.332</td> <td> 2.423</td> <td> 0.015</td> <td> 0.154 1.454</td>\n",
"</tr>\n",
"<tr>\n",
" <th>prestige_2</th> <td> -0.6754</td> <td> 0.316</td> <td> -2.134</td> <td> 0.033</td> <td> -1.296 -0.055</td>\n",
"</tr>\n",
"<tr>\n",
" <th>prestige_3</th> <td> -1.3402</td> <td> 0.345</td> <td> -3.881</td> <td> 0.000</td> <td> -2.017 -0.663</td>\n",
"</tr>\n",
"<tr>\n",
" <th>prestige_4</th> <td> -1.5515</td> <td> 0.418</td> <td> -3.713</td> <td> 0.000</td> <td> -2.370 -0.733</td>\n",
"</tr>\n",
"<tr>\n",
" <th>intercept</th> <td> -3.9900</td> <td> 1.140</td> <td> -3.500</td> <td> 0.000</td> <td> -6.224 -1.756</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Logit Regression Results \n",
"==============================================================================\n",
"Dep. Variable: admit No. Observations: 400\n",
"Model: Logit Df Residuals: 394\n",
"Method: MLE Df Model: 5\n",
"Date: Wed, 01 Apr 2015 Pseudo R-squ.: 0.08292\n",
"Time: 21:15:07 Log-Likelihood: -229.26\n",
"converged: True LL-Null: -249.99\n",
" LLR p-value: 7.578e-08\n",
"==============================================================================\n",
" coef std err z P>|z| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"gre 0.0023 0.001 2.070 0.038 0.000 0.004\n",
"gpa 0.8040 0.332 2.423 0.015 0.154 1.454\n",
"prestige_2 -0.6754 0.316 -2.134 0.033 -1.296 -0.055\n",
"prestige_3 -1.3402 0.345 -3.881 0.000 -2.017 -0.663\n",
"prestige_4 -1.5515 0.418 -3.713 0.000 -2.370 -0.733\n",
"intercept -3.9900 1.140 -3.500 0.000 -6.224 -1.756\n",
"==============================================================================\n",
"\"\"\""
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# manually add the intercept\n",
"data['intercept'] = 1.0\n",
"\n",
"train_cols = data.columns[1:]\n",
"# Index([gre, gpa, prestige_2, prestige_3, prestige_4], dtype=object)\n",
" \n",
"logit = sm.Logit(data['admit'], data[train_cols])\n",
" \n",
"# fit the model\n",
"result = logit.fit()\n",
"\n",
"result.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* Múltiples opciones para la **visualización** de datos."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"###Fuera del browser...\n",
"\n",
"* matplotlib\n",
"\n",
" * Matplotlib es una biblioteca para generar figuras en 2 y 3 dimensiones.\n",
" * Es fácil de aprender.\n",
" * Soporta LATEX para etiquetas y texto.\n",
" * Cada uno de los elementos de la figura pueden controlarse programaticamente (reproducibilidad).\n",
" * Varios formatos para la exportación de las figuras: PNG, PDF, SVG y EPS.\n",
" * GUI para la exploraración interactiva las figuras.\n",
" * [Galería](http://matplotlib.org/gallery.html)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"* ggplot, \"me quiero parecer al ggplot de R\"\n",
"* prettyplotlib, \"no quiero ser tan feo como matplotlib\"\n",
"* seaborn\n",
" * \"quiero ser lindo e inteligente\"\n",
" * Interfase de alto nivel para plotear gráficos estadísticos \"lindos\".\n",
" * [Galería](http://web.stanford.edu/~mwaskom/software/seaborn/examples/index.html)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"###En el browser...\n",
"\n",
"* Bokeh\n",
" * Laburo en Bokeh así que ojo con lo que dicen...\n",
" * [Galería](http://bokeh.pydata.org/en/latest/docs/gallery.html)\n",
"\n",
"* plotly\n",
" * Parecido a Bokeh pero hay que pagar..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* Un \"entorno\" **unificado** e **interactivo**."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"* [**IPython**/**Jupyter**](http://ipython.org), un interprete interactivo avanzado, un web-notebook, una infraestructura para *computación* en paralelo."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"##Y además..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* scikit-learn (a.k.a, machine learning en python)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Incluye (entre otras cosas):\n",
"\n",
"* Biclustering\n",
"* Clustering\n",
"* Covariance estimation\n",
"* Cross decomposition\n",
"* Dataset examples\n",
"* Decomposition\n",
"* Ensemble methods\n",
"* Gaussian Process for Machine Learning\n",
"* Generalized Linear Models\n",
"* Manifold learning\n",
"* Gaussian Mixture Models\n",
"* Nearest Neighbors\n",
"* Semi Supervised Classification\n",
"* Support Vector Machines\n",
"* Decision Trees"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f8743907828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import pylab as pl\n",
"from matplotlib.colors import ListedColormap\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.datasets import make_moons, make_circles, make_classification\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.lda import LDA\n",
"from sklearn.qda import QDA\n",
"\n",
"h = .02 # step size in the mesh\n",
"\n",
"names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Decision Tree\",\n",
" \"Random Forest\", \"Naive Bayes\", \"LDA\", \"QDA\"]\n",
"classifiers = [\n",
" KNeighborsClassifier(3),\n",
" SVC(kernel=\"linear\", C=0.025),\n",
" SVC(gamma=2, C=1),\n",
" DecisionTreeClassifier(max_depth=5),\n",
" RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
" GaussianNB(),\n",
" LDA(),\n",
" QDA()]\n",
"\n",
"X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\n",
" random_state=1, n_clusters_per_class=1)\n",
"rng = np.random.RandomState(2)\n",
"X += 2 * rng.uniform(size=X.shape)\n",
"linearly_separable = (X, y)\n",
"\n",
"datasets = [make_moons(noise=0.3, random_state=0),\n",
" make_circles(noise=0.2, factor=0.5, random_state=1),\n",
" linearly_separable\n",
" ]\n",
"\n",
"figure = pl.figure(figsize=(24, 8))\n",
"i = 1\n",
"# iterate over datasets\n",
"for ds in datasets:\n",
" # preprocess dataset, split into training and test part\n",
" X, y = ds\n",
" X = StandardScaler().fit_transform(X)\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)\n",
"\n",
" x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
" y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
" xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
" np.arange(y_min, y_max, h))\n",
"\n",
" # just plot the dataset first\n",
" cm = pl.cm.RdBu\n",
" cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
" ax = pl.subplot(len(datasets), len(classifiers) + 1, i)\n",
" # Plot the training points\n",
" ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n",
" # and testing points\n",
" ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)\n",
" ax.set_xlim(xx.min(), xx.max())\n",
" ax.set_ylim(yy.min(), yy.max())\n",
" ax.set_xticks(())\n",
" ax.set_yticks(())\n",
" i += 1\n",
"\n",
" # iterate over classifiers\n",
" for name, clf in zip(names, classifiers):\n",
" ax = pl.subplot(len(datasets), len(classifiers) + 1, i)\n",
" clf.fit(X_train, y_train)\n",
" score = clf.score(X_test, y_test)\n",
"\n",
" # Plot the decision boundary. For that, we will asign a color to each\n",
" # point in the mesh [x_min, m_max]x[y_min, y_max].\n",
" if hasattr(clf, \"decision_function\"):\n",
" Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
" else:\n",
" Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
"\n",
" # Put the result into a color plot\n",
" Z = Z.reshape(xx.shape)\n",
" ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
"\n",
" # Plot also the training points\n",
" ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n",
" # and testing points\n",
" ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n",
" alpha=0.6)\n",
"\n",
" ax.set_xlim(xx.min(), xx.max())\n",
" ax.set_ylim(yy.min(), yy.max())\n",
" ax.set_xticks(())\n",
" ax.set_yticks(())\n",
" ax.set_title(name)\n",
" ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),\n",
" size=15, horizontalalignment='right')\n",
" i += 1\n",
"\n",
"figure.subplots_adjust(left=.02, right=.98)\n",
"pl.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<p style=\"text-align: center;\"><i class=\"fa-space-shuttle fa-5x fa\"> Gracias!</i></p>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<p style=\"text-align: center;\"><i class=\"fa-github fa-2x fa\"> [damianavila](https://github.com/damianavila)</i></p>\n",
"\n",
"<p style=\"text-align: center;\"><i class=\"fa-twitter fa-2x fa\"> [@damian_avila](http://twitter.com/damian_avila)</i></p>\n",
"\n",
"<p style=\"text-align: center;\"><i class=\"fa-envelope fa-2x fa\"> <a href=\"mailto:[email protected]\">[email protected]</a></i></p>\n",
"\n",
"<p style=\"text-align: center;\"><i class=\"fa-user fa-2x fa\"> [www.damian.oquanta.info](http://www.damian.oquanta.info)</i></p>"
]
}
],
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