使用 Python 拆分 JSON 文件的最简单方法

Question

我正在研究 2015 年到 2020 年世界幸福报告的交互式可视化。数据被分成 6 个 csv 文件。使用 pandas，我已成功清理数据并将它们连接成一个大的 JSON 文件，格式如下：

[
  {
    "Country": "Switzerland",
    "Year": 2015,
    "Happiness Rank": 1,
    "Happiness Score": 7.587000000000001,
  },
  {
    "Country": "Iceland",
    "Year": 2015,
    "Happiness Rank": 2,
    "Happiness Score": 7.561,
  },
  {
    "Country": "Switzerland",
    "Year": 2016,
    "Happiness Rank": 2,
    "Happiness Score": 7.5089999999999995,
  },
  {
    "Country": "Iceland",
    "Year": 2016,
    "Happiness Rank": 3,
    "Happiness Score": 7.501,
  },
  {
    "Country": "Switzerland",
    "Year": 2017,
    "Happiness Rank": 3,
    "Happiness Score": 7.49399995803833,
  },
  {
    "Country": "Iceland",
    "Year": 2017,
    "Happiness Rank": 1,
    "Happiness Score": 7.801,
  }
]

现在，我想以编程方式格式化 JSON 文件，使其具有以下格式：

{
    "2015": {
        "Switzerland": {
            "Happiness Rank": 1,
            "Happiness Score": 7.587000000000001
        },
        "Iceland": {
            "Happiness Rank": 2,
            "Happiness Score": 7.561
        }
    },
    "2016": {
        "Switzerland": {
            "Happiness Rank": 2,
            "Happiness Score": 7.5089999999999995
        },
        "Iceland": {
            "Happiness Rank": 3,
            "Happiness Score": 7.501
        }
    },
    "2017": {
        "Switzerland": {
            "Happiness Rank": 3,
            "Happiness Score": 7.49399995803833
        },
        "Iceland": {
            "Happiness Rank": 1,
            "Happiness Score": 7.801
        }
    }
}

它必须以编程方式完成，因为有 900 多个不同的（国家、年份）对。我想要这种格式的 JSON，因为它使 JSON 文件更具可读性，并且更容易 select 适当的数据。如果我想要 2015 年冰岛的排名，我可以做 data[2015]["Iceland"]["Happiness Rank"]

有谁知道在 Python 中最简单/最方便的方法吗？

Answer 1

我假设您拥有创建此 JSON 的原始 pandas 数据框。使用pandas，你可以做到df = df.groupby(['Year', 'Country'])。然后您可以按照 pandas groupby to nested json 中的过程将其转换为 JSON.

Answer 2

您可能会发现 itertools 模块中的 groupby 很有用。我能够用

做到这一点

import itertools
groups = itertools.groupby(data, lambda x: x["Year"])
newdict = {str(year): {entry["Country"]:entry for entry in group} for year, group in groups}

其中data是你给出的例子形式的数据

它会保留dict中原来的字段，但是这样可以方便的删除

for countries in newdict.values():
    for c in countries.values():
        del c["Year"]
        del c["Country"]

Answer 3

如果 data 是您的原始词典列表：

def by_year(data):
    from itertools import groupby
    from operator import itemgetter

    retain_keys = ("Happiness Rank", "Happiness Score")

    for year, group in groupby(data, key=itemgetter("Year")):
        as_tpl = tuple(group)
        yield str(year), dict(zip(map(itemgetter("Country"), as_tpl), [{k: d[k] for k in retain_keys} for d in as_tpl]))


print(dict(by_year(data)))

输出：

{'2015': {'Switzerland': {'Happiness Rank': 1, 'Happiness Score': 7.587000000000001}, 'Iceland': {'Happiness Rank': 2, 'Happiness Score': 7.561}}, '2016': {'Switzerland': {'Happiness Rank': 2, 'Happiness Score': 7.5089999999999995}, 'Iceland': {'Happiness Rank': 3, 'Happiness Score': 7.501}}, '2017': {'Switzerland': {'Happiness Rank': 3, 'Happiness Score': 7.49399995803833}, 'Iceland': {'Happiness Rank': 1, 'Happiness Score': 7.801}}}
>>>

这假设 data 中的词典已经按年份分组。

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