nomnomAhora realice las siguientes consultas
Empieza por sentirte cómodo explorando los datos de nomnom:
SELECT * FROM nomnom;
¿Cuáles son los distintos barrios? Escriba un query para saberlo
| drinks = ["capucchino", "latte", "chai", "americano", "mocha", "expresso"] | |
| sales1 = [91, 76, 56, 66, 52, 27] | |
| sales2 = [65, 82, 36, 68, 28, 40] | |
| # Datos de sales1 (barras azules) | |
| n = 1 # el primer dataset | |
| t = 2 # numero de datasets | |
| d = 6 # numero de sets de barras | |
| w = 0.8 # ancho de cada barra |
Hemos decidido perseguir el sueño de ser dueños de una pequeña empresa y abrir una tienda de muebles llamada Lovely Loveseats for Neat Suites en Fleet Street. Con nuestro nuevo conocimiento de la programación de Python, vamos a construir un sistema para ayudar a acelerar el proceso de creación de recibos para sus clientes.
En este proyecto, almacenaremos los nombres y precios del catálogo de una tienda de muebles en variables. A continuación, procesará el precio total y la lista de artículos de los clientes, imprimiéndolos en el terminal de salida.
1- Añadamos en nuestro primer artículo, el Lovely Loveseat que es el nombre de la tienda. Cree una variable llamada lovely_loveseat_description y asígnele la siguiente cadena: "Lovely Loveseat. Tufted polyester blend on wood. 32 inches high x 40 inches wide x 30 inches deep. Red or white."
1- Imprima su nombre usando el comando print().
2- Si su impresion uso comillas dobles " cambieles a comillas simples. Si usted uso comillas ' cambielas por comillas dobles.
3- Escriba la variable meal y asignele el valor de breakfast e imprimalo. Luego cambie el valor asignado por lunch e imprimalo.
4- Donde se encuentra el error de la siguiente linea de codigo? meal = "lunch, breakfast and "tea". Guarde en un string su respuesta y asignela a una variable llamada error_encontrado. Ahora en una nueva variabla llamada tipo_de_error asignele un string con el tipo de error: "sintax error" o "name error".
| lines = [] | |
| class Perceptron: | |
| def __init__(self, num_inputs=3, weights=[1,1,1]): | |
| self.num_inputs = num_inputs | |
| self.weights = weights | |
| def weighted_sum(self, inputs): | |
| weighted_sum = 0 | |
| for i in range(self.num_inputs): |
| age, workclass, fnlwgt, education, education-num, marital-status, occupation, relationship, race, sex, capital-gain, capital-loss, hours-per-week, native-country, income | |
| 39, State-gov, 77516, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Male, 2174, 0, 40, United-States, <=50K | |
| 50, Self-emp-not-inc, 83311, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 13, United-States, <=50K | |
| 38, Private, 215646, HS-grad, 9, Divorced, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K | |
| 53, Private, 234721, 11th, 7, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, <=50K | |
| 28, Private, 338409, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, Black, Female, 0, 0, 40, Cuba, <=50K | |
| 37, Private, 284582, Masters, 14, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, <=50K | |
| 49, Private, 160187, 9th, 5, Married-spouse-absent, Other-service, Not-in-family, Black, Female, 0, 0, 16, Jama |
| Name | Landmass | Zone | Area | Population | Language | Religion | Bars | Stripes | Colors | Red | Green | Blue | Gold | White | Black | Orange | Mainhue | Circles | Crosses | Saltires | Quarters | Sunstars | Crescent | Triangle | Icon | Animate | Text | Topleft | Botright | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Afghanistan | 5 | 1 | 648 | 16 | 10 | 2 | 0 | 3 | 5 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | green | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | black | green | |
| Albania | 3 | 1 | 29 | 3 | 6 | 6 | 0 | 0 | 3 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | red | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | red | red | |
| Algeria | 4 | 1 | 2388 | 20 | 8 | 2 | 2 | 0 | 3 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | green | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | green | white | |
| American-Samoa | 6 | 3 | 0 | 0 | 1 | 1 | 0 | 0 | 5 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | blue | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | blue | red | |
| Andorra | 3 | 1 | 0 | 0 | 6 | 0 | 3 | 0 | 3 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | gold | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | blue | red | |
| Angola | 4 | 2 | 1247 | 7 | 10 | 5 | 0 | 2 | 3 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | red | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | red | black | |
| Anguilla | 1 | 4 | 0 | 0 | 1 | 1 | 0 | 1 | 3 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | white | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | white | blue | |
| Antigua-Barbuda | 1 | 4 | 0 | 0 | 1 | 1 | 0 | 1 | 5 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | red | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | black | red | |
| Argentina | 2 | 3 | 2777 | 28 | 2 | 0 | 0 | 3 | 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | blue | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | blue | blue |
| vhigh,vhigh,2,2,small,low,unacc | |
| vhigh,vhigh,2,2,small,med,unacc | |
| vhigh,vhigh,2,2,small,high,unacc | |
| vhigh,vhigh,2,2,med,low,unacc | |
| vhigh,vhigh,2,2,med,med,unacc | |
| vhigh,vhigh,2,2,med,high,unacc | |
| vhigh,vhigh,2,2,big,low,unacc | |
| vhigh,vhigh,2,2,big,med,unacc | |
| vhigh,vhigh,2,2,big,high,unacc | |
| vhigh,vhigh,2,4,small,low,unacc |
| from collections import Counter | |
| def split(dataset, labels, column): | |
| data_subsets = [] | |
| label_subsets = [] | |
| counts = list(set([data[column] for data in dataset])) | |
| counts.sort() | |
| for k in counts: | |
| new_data_subset = [] | |
| new_label_subset = [] |
| vhigh,vhigh,2,2,small,low,unacc | |
| vhigh,vhigh,2,2,small,med,unacc | |
| vhigh,vhigh,2,2,small,high,unacc | |
| vhigh,vhigh,2,2,med,low,unacc | |
| vhigh,vhigh,2,2,med,med,unacc | |
| vhigh,vhigh,2,2,med,high,unacc | |
| vhigh,vhigh,2,2,big,low,unacc | |
| vhigh,vhigh,2,2,big,med,unacc | |
| vhigh,vhigh,2,2,big,high,unacc | |
| vhigh,vhigh,2,4,small,low,unacc |