Abraham Zamudio Chauca robintux
robintux
/ load2k19DataOsinerg_Ver2.R
Created
June 13, 2020 16:09
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| # ============================================================================= | |
| # Abraham Zamudio [GMMNS] | |
| # 2020/06/13 | |
| # Lima-Peru | |
| # ============================================================================= | |
| # | |
| # La data proviene de : | |
| # https://www.osinergmin.gob.pe/seccion/institucional/regulacion-tarifaria/publicaciones/regulacion-tarifaria | |
| # Explorar : |
robintux
/ CharlaCTIC_NoSupervisado.ipynb
Created
October 16, 2020 03:42
Charla : Aprendizaje no Supervisado (15/10/2020)
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robintux
/ script01.R
Last active
October 20, 2020 15:29
Charla : "Visualizacion de datos usando R" . Charla preparada para el Centro de Estudiantes de Economía [UNIVERSIDAD NACIONAL DE SAN CRISTÓBAL DE HUAMANGA]
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| #### Primeros pasos #### | |
| library(quantmod) | |
| library(help= quantmod) | |
| #### carguemos datos financieros #### | |
| help(getSymbols) | |
| # descarguemos los precios de la empresa IBM |
robintux
/ script02.R
Created
October 20, 2020 15:30
Charla : "Visualizacion de datos usando R" . Charla preparada para el Centro de Estudiantes de Economía [UNIVERSIDAD NACIONAL DE SAN CRISTÓBAL DE HUAMANGA]
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| sessionInfo() | |
| library(help= datasets) | |
| #### ggplot2 #### | |
| library(ggplot2) | |
| library(car) | |
| #### cargamos datos #### | |
| data(Salaries) | |
| class(Salaries) |
robintux
/ welcome-to-machine-learning-with-colaboratory.ipynb
Created
May 6, 2021 14:08
— forked from craftoid/welcome-to-machine-learning-with-colaboratory.ipynb
Welcome to Machine Learning with Colaboratory
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robintux
/ get_etf_holdings.py
Last active
September 21, 2021 13:51
Funciones para descargar los componentes de un etf usando la web : https://www.barchart.com
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| # Implementamos un par de funciones que nos permitan obtener la composicion de ETFs | |
| from bs4 import BeautifulSoup | |
| import matplotlib | |
| import matplotlib.pyplot as plt | |
| import re | |
| import requests | |
| import numpy as np | |
| import pandas as pd |
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| data = [ | |
| 446.6565, | |
| 454.4733, | |
| 455.663, | |
| 423.6322, | |
| 456.2713, | |
| 440.5881, | |
| 425.3325, | |
| 485.1494, |
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| 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()]) |
robintux
/ MostrarValores.py
Created
July 21, 2022 17:47
Funcion para mostrar la altura de las barras en un sns.barplot
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| 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: |