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id | name | city | |
---|---|---|---|
1 | John | London | |
2 | Mary | London | |
3 | Anna | Reading | |
4 | Kevin | Reading | |
5 | Liam | Manchester |
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library(tidyverse) | |
library(rvest) | |
url <- "https://specphone.com/Samsung-Galaxy-S22.html" | |
web <- read_html(url) | |
topics <- web %>% | |
html_nodes("div.topic") %>% | |
html_text() |
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library(caret) | |
model <- train(mpg ~ ., data = mtcars, method = "lm") | |
print(model) |
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.open pizzas.db | |
-- drop table pizza; | |
CREATE TABLE pizza ( | |
id int, | |
name text, | |
calorie int, | |
price real | |
); |
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id | customer_names | customer_ages | customer_country | |
---|---|---|---|---|
1 | Lisa | 24 | Thailand | |
2 | Jisoo | 25 | Korea | |
3 | Jenny | 24 | Korea | |
4 | Rose | 26 | Korea | |
5 | Hana | 28 | China |
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import streamlit as st | |
import pandas as pd | |
st.header("Hello World 👏") | |
st.write("This is my first app") | |
url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv " | |
df = pd.read_csv(url) | |
df2 = df.groupby('species')['body_mass_g'].mean() |
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import streamlit as st | |
import pandas as pd | |
st.title('🎈 App Name') | |
st.sidebar.header("Input") | |
st.subheader("URL") | |
url = 'https://raw.githubusercontent.com/dataprofessor/data/master/iris.csv ' | |
url_input = st.sidebar.text_input("URL:", url) |
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<!DOCTYPE html> | |
<html> | |
<head> | |
<title>DataRockie School</title> | |
</head> | |
<body> | |
<h1>Welcome to our intro to web scraping class</h1> | |
<p>Awesome work</p> | |
<div> |
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Var1 | Var2 | value | |
---|---|---|---|
1 | 1 | 100 | |
2 | 1 | 101 | |
3 | 1 | 102 | |
4 | 1 | 103 | |
5 | 1 | 104 | |
6 | 1 | 105 | |
7 | 1 | 105 | |
8 | 1 | 106 | |
9 | 1 | 107 |
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Year | Region | Sales | |
---|---|---|---|
1951 | N.Amer | 45939 | |
1951 | Europe | 21574 | |
1951 | Asia | 2876 | |
1951 | S.Amer | 1815 | |
1951 | Oceania | 1646 | |
1951 | Africa | 89 | |
1951 | Mid.Amer | 555 | |
1956 | N.Amer | 60423 | |
1956 | Europe | 29990 |
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