Abraham Zamudio Chauca robintux

## funciones_auxiliares.py

# Gráfico de correlograma
def plot_correlogram(x, lags=None, title=None):
    lags = min(10, int(len(x)/5)) if lags is None else lags
    fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(14, 8))
    x.plot(ax=axes[0][0], title='Time Series')
    x.rolling(21).mean().plot(ax=axes[0][0], c='k', lw=1)
    q_p = np.max(q_stat(acf(x, nlags=lags), len(x))[1])
    stats = f'Q-Stat: {np.max(q_p):>8.2f}\nADF: {adfuller(x)[1]:>11.2f}'
    axes[0][0].text(x=.02, y=.85, s=stats, transform=axes[0][0].transAxes)

## tagged_reg_Expression
patrones = [
    (r"[Aa]m$", "BEM"),  # irregular forms of 'to be'
    (r"[Aa]re$", "BER"),  #
    (r"[Ii]s$", "BEZ"),  #
    (r"[Ww]as$", "BEDZ"),  #
    (r"[Ww]ere$", "BED"),  #
    (r"[Bb]een$", "BEN"),  #
    (r"[Hh]ave$", "HV"),  # irregular forms of 'to have'
    (r"[Hh]as$", "HVZ"),  #
    (r"[Hh]ad$", "HVD"),  #

## Python2_UNI_file.py
try:
  f = open('fichero.txt')  # El fichero no existe
except ... :
  print('¡El fichero no existe!')
else:
  print(f.read())

## python2_UNI_classPerro.py
class Perro:
    # El método __init__ es llamado al crear el objeto
    def __init__(self, nombre, raza):
        print(f"Creando perro {nombre}, {raza}")

        # Atributos de instancia
        self.nombre = nombre
        self.raza = raza

mi_perro = Perro("Toby", "Bulldog")

## Spark_on_colab.sh
# Instalamos el jdk (java)
!apt-get install openjdk-8-jdk-headless -qq > /dev/null

# Descargamos spark
!wget https://dlcdn.apache.org/spark/spark-3.4.0/spark-3.4.0-bin-hadoop3.tgz

# Descomprimimos el binario de spark
!tar xvzf spark-3.4.0-bin-hadoop3.tgz

# Cargamos el modulo os

## ejemplo_html_code.html
<!DOCTYPE html>
<html>
<body>

<h1>My First Heading</h1>
<h3>My Third Heading</h3>
<div id ="names">
    <ul>
        <li>Liam James</li>
        <li>William Jones</li>

## MostrarValores.py
def show_values(axs, orient="v", space=.01):
    def _single(ax):
        if orient == "v":
            for p in ax.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height() + (p.get_height()*0.01)
                value = '{:.1f}'.format(p.get_height())
                ax.text(_x, _y, value, ha="center")
        elif orient == "h":
            for p in ax.patches:

## visualizar_clasificador.py
def visualizar_clasificador(clasificador, X, y):
    #definimos los máximos valores de X e y para la malla
    min_x, max_x = X[:, 0].min() - 1.0, X[:, 0].max() + 1.0
    min_y, max_y = X[:, 1].min() - 1.0, X[:, 1].max() + 1.0
    #definimos el paso de la malla
    mesh_step_size = 0.01
    #definimos la malla para x e y
    x_vals, y_vals = np.mgrid[min_x:max_x:mesh_step_size, min_y:max_y:mesh_step_size]
    #corremos el clasificador sobre la malla
    resultados = clasificador.predict(np.c_[x_vals.ravel(), y_vals.ravel()])

## repasopython.ipynb

      
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              November 26, 2021 22:02
            
              
                RepasoPython.ipynb
              
          
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## Data4SeS.py

data = [
    446.6565,
    454.4733,
    455.663,
    423.6322,
    456.2713,
    440.5881,
    425.3325,
    485.1494,

	# Gráfico de correlograma
	def plot_correlogram(x, lags=None, title=None):
	lags = min(10, int(len(x)/5)) if lags is None else lags
	fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(14, 8))
	x.plot(ax=axes[0][0], title='Time Series')
	x.rolling(21).mean().plot(ax=axes[0][0], c='k', lw=1)
	q_p = np.max(q_stat(acf(x, nlags=lags), len(x))[1])
	stats = f'Q-Stat: {np.max(q_p):>8.2f}\nADF: {adfuller(x)[1]:>11.2f}'
	axes[0][0].text(x=.02, y=.85, s=stats, transform=axes[0][0].transAxes)
	patrones = [
	(r"[Aa]m$", "BEM"), # irregular forms of 'to be'
	(r"[Aa]re$", "BER"), #
	(r"[Ii]s$", "BEZ"), #
	(r"[Ww]as$", "BEDZ"), #
	(r"[Ww]ere$", "BED"), #
	(r"[Bb]een$", "BEN"), #
	(r"[Hh]ave$", "HV"), # irregular forms of 'to have'
	(r"[Hh]as$", "HVZ"), #
	(r"[Hh]ad$", "HVD"), #
	try:
	f = open('fichero.txt') # El fichero no existe
	except ... :
	print('¡El fichero no existe!')
	else:
	print(f.read())
	class Perro:
	# El método __init__ es llamado al crear el objeto
	def __init__(self, nombre, raza):
	print(f"Creando perro {nombre}, {raza}")

	# Atributos de instancia
	self.nombre = nombre
	self.raza = raza

	mi_perro = Perro("Toby", "Bulldog")
	# Instalamos el jdk (java)
	!apt-get install openjdk-8-jdk-headless -qq > /dev/null

	# Descargamos spark
	!wget https://dlcdn.apache.org/spark/spark-3.4.0/spark-3.4.0-bin-hadoop3.tgz

	# Descomprimimos el binario de spark
	!tar xvzf spark-3.4.0-bin-hadoop3.tgz

	# Cargamos el modulo os
	<!DOCTYPE html>
	<html>
	<body>

	<h1>My First Heading</h1>
	<h3>My Third Heading</h3>
	<div id ="names">
	<ul>
	<li>Liam James</li>
	<li>William Jones</li>
	def show_values(axs, orient="v", space=.01):
	def _single(ax):
	if orient == "v":
	for p in ax.patches:
	_x = p.get_x() + p.get_width() / 2
	_y = p.get_y() + p.get_height() + (p.get_height()*0.01)
	value = '{:.1f}'.format(p.get_height())
	ax.text(_x, _y, value, ha="center")
	elif orient == "h":
	for p in ax.patches:
	def visualizar_clasificador(clasificador, X, y):
	#definimos los máximos valores de X e y para la malla
	min_x, max_x = X[:, 0].min() - 1.0, X[:, 0].max() + 1.0
	min_y, max_y = X[:, 1].min() - 1.0, X[:, 1].max() + 1.0
	#definimos el paso de la malla
	mesh_step_size = 0.01
	#definimos la malla para x e y
	x_vals, y_vals = np.mgrid[min_x:max_x:mesh_step_size, min_y:max_y:mesh_step_size]
	#corremos el clasificador sobre la malla
	resultados = clasificador.predict(np.c_[x_vals.ravel(), y_vals.ravel()])

	data = [
	446.6565,
	454.4733,
	455.663,
	423.6322,
	456.2713,
	440.5881,
	425.3325,
	485.1494,