Data, Databases and the Web数据、数据库与网络

Raw data (numbers, no meaning without context).原始数据（数字，脱离上下文没有含义）。

22.4, 65, 1013.2
22.6, 64, 1013.0
22.9, 63, 1012.7

The three columns are temperature (°C), humidity (%), and pressure (hPa) — but without column headers these numbers carry no meaning.三列分别是温度（°C）、湿度（%）、气压（hPa）——但如果没有列标题，这些数字毫无意义。

Processing step: label, aggregate, interpret.处理步骤：标注、聚合、解释。

# Python: compute average temperature from a list of readings
readings = [22.4, 22.6, 22.9]
avg_temp = sum(readings) / len(readings)
print(f"Average temperature: {avg_temp:.1f} °C")
# Output: Average temperature: 22.6 °C

sum(readings) adds all values; len(readings) returns the list length; f"..." is a formatted string literal (f-string).sum(readings) 求和，len(readings) 取列表长度，f"..." 是格式化字符串。

Information.信息。 "Average temperature over the last 3 minutes: 22.6 °C. Trend: rising. Humidity dropping. Pressure falling slowly — possible rain within 6 hours." The same raw numbers now carry actionable meaning. CSTA 3A-DA-12: "Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.""过去 3 分钟平均温度：22.6 °C。趋势：上升。湿度下降。气压缓慢下降——6 小时内可能有雨。"同样的原始数字现在具有了可行动的含义。CSTA 3A-DA-12："创建计算模型，表示从现象或过程中收集的数据的不同元素之间的关系。"

scores.txt (sample content).scores.txt（示例内容）。

87
92
74
95
88

Python: read, process, write.Python：读取、处理、写入。

# Read all scores from file
with open("scores.txt", "r") as f:
    scores = [int(line.strip()) for line in f]

# Process: compute average
average = sum(scores) / len(scores)

# Write summary to a new file
with open("summary.txt", "w") as f:
    f.write(f"Count: {len(scores)}\n")
    f.write(f"Average: {average:.1f}\n")

print("Done. summary.txt written.")

open("scores.txt", "r") opens the file in read-only mode; line.strip() removes the trailing newline; int(...) converts the string to an integer; open("summary.txt", "w") creates or overwrites the file in write mode; f.write(...) writes content; \n is the newline character.open("scores.txt", "r") 以只读模式（"r"，read）打开文件；line.strip() 去除换行符；int(...) 将字符串转为整数；open("summary.txt", "w") 以写入模式（"w"，write）创建/覆盖文件；f.write(...) 写入内容；\n 是换行符。

Output in summary.txt.summary.txt 中的输出。

Count: 5
Average: 87.2

students.csv content.students.csv 内容。

name,grade,score
Alice,11,92
Bob,11,78
Carol,12,95
David,12,82
Eve,11,88

Python: filter rows where score > 85.Python：过滤成绩 > 85 的行。

import csv

with open("students.csv", "r") as f:
    reader = csv.DictReader(f)
    for row in reader:
        if int(row["score"]) > 85:
            print(row["name"], row["score"])

# Output:
# Alice 92
# Carol 95
# Eve 88

import csv loads the built-in csv module; csv.DictReader(f) reads each row as a dictionary (e.g. {"name": "Alice", "grade": "11", "score": "92"}); int(row["score"]) converts the string to an integer before comparing; row["name"] accesses a field by column name.import csv 导入内置 csv 模块；csv.DictReader(f) 将每行读为字典（{"name": "Alice", "grade": "11", "score": "92"}）；int(row["score"]) 将字符串转为整数后比较；row["name"] 通过列名访问字段。

Table 1: Students (primary key = student_id).表 1：Students（主键 = student_id）。

Table 2: Scores (primary key = score_id; foreign key = student_id references Students).表 2：Scores（主键 = score_id；外键 = student_id 引用 Students）。

Alice's name is stored once in Students. Her two score rows in Scores reference her by student_id = 1. If her name changes, we update it in one place only — that is the benefit of normalisation. Without a relational database, every score row would repeat her full name, wasting space and risking inconsistency.Alice 的姓名在 Students 中只存储一次。她在 Scores 中的两条成绩行通过 student_id = 1 引用她。如果她的姓名变了，只需在一个地方更新——这就是规范化的好处。没有关系数据库，每条成绩行都要重复她的全名，浪费空间且容易出现不一致。

Query 1: Get all students.查询 1：获取所有学生。

SELECT * FROM Students;

SELECT * selects all columns; FROM Students specifies the Students table. All three rows are returned.SELECT * 选择所有列，FROM Students 从 Students 表查询。返回所有三行。

Query 2: Get names and scores where score > 85.查询 2：获取成绩 > 85 的姓名和成绩。

SELECT student_id, subject, score
FROM Scores
WHERE score > 85
ORDER BY score DESC;

WHERE score > 85 filters rows to those with a score above 85; ORDER BY score DESC sorts the results from highest to lowest (DESC = descending).WHERE score > 85 过滤条件；ORDER BY score DESC 按成绩从高到低排序（DESC = descending，降序）。

Expected result (from the §4 data):预期结果（来自 §4 数据）：

Query 3: Count how many CS scores exist.查询 3：统计 CS 成绩的条数。

SELECT COUNT(*) FROM Scores WHERE subject = 'CS';

COUNT(*) counts the number of matching rows. Result: 2 (Carol and Alice each have one CS record).COUNT(*) 计算匹配行的数量。结果：2（Carol 和 Alice 各有一条 CS 记录）。

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <title>Student Records</title>
</head>
<body>
  <h1>Students</h1>
  <table border="1">
    <tr>
      <th>ID</th><th>Name</th><th>Grade</th>
    </tr>
    <tr><td>1</td><td>Alice</td><td>11</td></tr>
    <tr><td>2</td><td>Bob</td><td>11</td></tr>
    <tr><td>3</td><td>Carol</td><td>12</td></tr>
  </table>
</body>
</html>

<!DOCTYPE html> declares the document type as HTML5; <head> holds metadata (<meta charset="UTF-8"> sets the character encoding; <title> sets the browser-tab title); <body> contains the visible content; <table> creates a table; <tr> is a table row; <th> is a header cell; <td> is a data cell.<!DOCTYPE html> 声明文档类型为 HTML5；<head> 含元数据（<meta charset="UTF-8"> 设置字符编码，<title> 设置浏览器标签标题）；<body> 含可见内容；<table> 创建表格；<tr> 是表格行（table row）；<th> 是表头单元格（table header）；<td> 是普通数据单元格（table data）。

Sample JSON response (the text the API sends back).示例 JSON 响应（接口返回的文本）。

{
  "city": "Toronto",
  "country": "CA",
  "temperature": 22.4,
  "humidity": 65,
  "conditions": ["cloudy", "mild"],
  "forecast": {
    "tomorrow": "rain",
    "temp_high": 18.0
  }
}

Python: parse and extract fields.Python：解析并提取字段。

import json

# Simulated API response text
response_text = '''
{
  "city": "Toronto",
  "country": "CA",
  "temperature": 22.4,
  "humidity": 65,
  "conditions": ["cloudy", "mild"],
  "forecast": {"tomorrow": "rain", "temp_high": 18.0}
}
'''

# Parse JSON string into a Python dictionary
data = json.loads(response_text)

# Access fields
city = data["city"]
temp = data["temperature"]
tomorrow = data["forecast"]["tomorrow"]

print(f"{city}: {temp}°C, tomorrow: {tomorrow}")
# Output: Toronto: 22.4°C, tomorrow: rain

import json loads the json module; json.loads(...) parses a JSON string into a Python dictionary (loads = load string); data["city"] accesses a value by key; data["forecast"]["tomorrow"] accesses a nested object by first retrieving the "forecast" dictionary, then its "tomorrow" key.import json 导入 json 模块；json.loads(...) 将 JSON 字符串（loads = load string）转为 Python 字典；data["city"] 通过键访问字典值；data["forecast"]["tomorrow"] 访问嵌套对象——先取 "forecast" 字典，再取其 "tomorrow" 键。