JSON 简介

使用 pandas 高效导入数据

Amany Mahfouz

Instructor

JavaScript 对象表示法（JSON）

常见的 Web 数据格式
非表格型
- 记录不必具有相同属性集
数据组织为对象集合
对象是属性-值对的集合
嵌套 JSON：对象内含对象

读取 JSON 数据

read_json()
- 接受 JSON 的路径字符串，或 JSON 字符串本身
- 用 dtype 关键字参数指定数据类型
- 用 orient 标注非常见 JSON 布局
  - 取值见 pandas 文档

数据取向

JSON 不是表格数据
- pandas 会推断表格化方式
pandas 可自动处理常见取向

记录取向

最常见的 JSON 布局

[
  {
      "age_adjusted_death_rate": "7.6",
      "death_rate": "6.2",
      "deaths": "32",
      "leading_cause": "Accidents Except Drug Posioning (V01-X39, X43, X45-X59, Y85-Y86)",
      "race_ethnicity": "Asian and Pacific Islander",
      "sex": "F",
      "year": "2007"
  },
  {
      "age_adjusted_death_rate": "8.1",
      "death_rate": "8.3",
      "deaths": "87",
      ...

列取向

比记录取向更省空间

{
  "age_adjusted_death_rate": {
      "0": "7.6",
      "1": "8.1",
      "2": "7.1",
      "3": ".",
      "4": ".",
      "5": "7.3",
      "6": "13",
      "7": "20.6",
      "8": "17.4",
      "9": ".",
      "10": ".",
      "11": "19.8",
      ...

指定取向

拆分取向数据 - nyc_death_causes.json

{
  "columns": [
      "age_adjusted_death_rate",
      "death_rate",
      "deaths",
      "leading_cause",
      "race_ethnicity",
      "sex",
      "year"
  ],
  "index": [...],
  "data": [
      [
          "7.6",

指定取向

import pandas as pd

death_causes = pd.read_json("nyc_death_causes.json",
                            orient="split")

print(death_causes.head())

  age_adjusted_death_rate death_rate deaths             leading_cause              race_ethnicity sex  year
0                     7.6        6.2     32  Accidents Except Drug...  Asian and Pacific Islander   F  2007
1                     8.1        8.3     87  Accidents Except Drug...          Black Non-Hispanic   F  2007
2                     7.1        6.1     71  Accidents Except Drug...                    Hispanic   F  2007
3                       .          .      .  Accidents Except Drug...          Not Stated/Unknown   F  2007
4                       .          .      .  Accidents Except Drug...       Other Race/ Ethnicity   F  2007

[5 rows x 7 columns]

Vamos praticar!

使用 pandas 高效导入数据