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SELECT | |
artist, | |
week, | |
streams_millions, | |
streams_millions - LAG(streams_millions, 1, streams_millions) OVER ( | |
PARTITION BY artist | |
ORDER BY week | |
) AS 'streams_millions_change', | |
chart_position, | |
LAG(chart_position, 1, chart_position) OVER ( |
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SELECT purchase_id, DATE(purchase_date, '7 days') | |
FROM purchases; | |
SELECT STRFTIME('%H', purchase_date) | |
FROM purchases; | |
SELECT date, (CAST(high AS 'REAL') + | |
CAST(low as 'REAL')) / 2.0 AS 'average' | |
FROM weather | |
; |
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CREATE TABLE book ( | |
title varchar(100), | |
isbn varchar(50) PRIMARY KEY, | |
pages integer, | |
price money, | |
description varchar(256), | |
publisher varchar(100) | |
); | |
CREATE TABLE chapter ( |
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"""analyzing_hotel_dbs_wpy.py""" | |
# %% [markdown] | |
# # Analyzing Hotel Databases with Python | |
# | |
# Sleep Away Inc., a hotel corporation, has just hired you as a data analyst. | |
# | |
# 1. They would like you to analyze their most recent data about: | |
# - customers who cancelled their booking and | |
# - customers who did not cancel. |
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<!DOCTYPE html> | |
<html> | |
<head> | |
<title element="Everyday with Isa" /> | |
</head> | |
<body> | |
<a href="#contact"><img | |
src="https://content.codecademy.com/courses/learn-html/elements-and-structure/profile.jpg" | |
/></a> | |
<h3>by Isabelle Rodriguez | 1 day ago</h3> |
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<!DOCTYPE html> | |
<html> | |
<head> | |
<meta charset="utf-8" /> | |
<title>Aguillar Family Wine Festival</title> | |
<style> | |
body { | |
background-color: #242f44; | |
color: white; | |
font-family: 'Oswald', sans-serif; |
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0d 0a 0d 0a 3c 73 74 79 6c 65 3e 68 74 6d 6c 2c 20 62 6f 64 79 2c 20 64 69 76 2c 20 73 70 61 6e 2c 20 61 70 70 6c 65 74 2c 20 6f 62 6a 65 63 74 2c 20 68 31 2c 20 68 32 2c 20 68 33 2c 20 68 34 2c 20 68 35 2c 20 68 36 2c 20 70 2c 20 62 6c 6f 63 6b 71 75 6f 74 65 2c 20 70 72 65 2c 20 61 2c 20 61 62 62 72 2c 20 61 63 72 6f 6e 79 6d 2c 20 61 64 64 72 65 73 73 2c 20 62 69 67 2c 20 63 69 74 65 2c 20 63 6f 64 65 2c 20 64 65 6c 2c 20 64 66 6e 2c 20 65 6d 2c 20 69 6d 67 2c 20 69 6e 73 2c 20 6b 62 64 2c 20 71 2c 20 73 2c 20 73 61 6d 70 2c 20 73 6d 61 6c 6c 2c 20 73 74 72 69 6b 65 2c 20 73 74 72 6f 6e 67 2c 20 73 75 62 2c 20 73 75 70 2c 20 74 74 2c 20 76 61 72 2c 20 62 2c 20 75 2c 20 69 2c 20 63 65 6e 74 65 72 2c 20 64 6c 2c 20 64 74 2c 20 64 64 2c 20 6f 6c 2c 20 75 6c 2c 20 6c 69 2c 20 66 69 65 6c 64 73 65 74 2c 20 66 6f 72 6d 2c 20 6c 61 62 65 6c 2c 20 6c 65 67 65 6e 64 2c 20 74 61 62 6c 65 2c 20 63 61 70 74 69 6f 6e 2c 20 74 62 6f 64 79 2c 20 74 66 6f 6f 74 2c 20 74 68 65 61 64 2c 20 74 72 2c 20 74 68 2c 20 74 64 2c 2 |
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123adana123456 | |
123adana12345 | |
123adana123456789 | |
123adanapassword | |
123adanailoveyou | |
123adanaprincess | |
123adana1234567 | |
123adanarockyou | |
123adana12345678 | |
123adanaabc123 |
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# 1. Import the necessary libraries and add five sentences from the data that will be used for training data. | |
# Use `Tokenizer` class from `keras.preprocessing.text` for tokenizing the sentences. | |
import string | |
import spacy | |
import gensim | |
import nltk | |
from nltk import ngrams | |
from nltk.corpus import stopwords | |
from nltk.tokenize import PunktSentenceTokenizer, word_tokenize |
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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 |
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