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JSON 是 JavaScript Object Notation 的缩写，它是基于 JavaScript 中对象的格式，是一种表示结构化数据的编码技术。它是基于 JavaScript 中对象的格式，是一种表示结构化数据的编码技术。现在它被广泛使用，特别是在服务器和 Web 应用程序之间共享数据。

我们将在本文中介绍如何将 DataFrame 转换为 JSON 字符串。

我们将使用以下 DataFrame。

import pandas as pd

df = pd.DataFrame(
    [["Jay", 16, "BBA"], ["Jack", 19, "BTech"], ["Mark", 18, "BSc"]],
    columns=["Name", "Age", "Course"],
)

print(df)

输出：

   Name  Age Course
0   Jay   16    BBA
1  Jack   19  BTech
2  Mark   18    BSc

Pandas DataFrame 有一个方法 dataframe.to_json()，它可以将 DataFrame 转换为 JSON 字符串或存储为外部 JSON 文件。最终的 JSON 格式取决于 orient 参数的值，默认情况下是'columns'，但也可以指定为'records'、'index'、'split'、'table'和'values'。

所有的格式将在下面介绍。

orient = 'columns'

import pandas as pd

df = pd.DataFrame(
    [["Jay", 16, "BBA"], ["Jack", 19, "BTech"], ["Mark", 18, "BSc"]],
    columns=["Name", "Age", "Course"],
)

js = df.to_json(orient="columns")

print(js)

输出：

{"Name":{"0":"Jay","1":"Jack","2":"Mark"},
 "Age":{"0":16,"1":19,"2":18},
 "Course":{"0":"BBA","1":"BTech","2":"BSc"}}

orient = 'records'

import pandas as pd

df = pd.DataFrame(
    [["Jay", 16, "BBA"], ["Jack", 19, "BTech"], ["Mark", 18, "BSc"]],
    columns=["Name", "Age", "Course"],
)

js = df.to_json(orient="records")

print(js)

输出：

[{"Name":"Jay","Age":16,"Course":"BBA"},{"Name":"Jack","Age":19,"Course":"BTech"},{"Name":"Mark","Age":18,"Course":"BSc"}]

orient = 'index'

import pandas as pd

df = pd.DataFrame(
    [["Jay", 16, "BBA"], ["Jack", 19, "BTech"], ["Mark", 18, "BSc"]],
    columns=["Name", "Age", "Course"],
)

js = df.to_json(orient="index")

print(js)

输出：

{"0":{"Name":"Jay","Age":16,"Course":"BBA"},
 "1":{"Name":"Jack","Age":19,"Course":"BTech"},
 "2":{"Name":"Mark","Age":18,"Course":"BSc"}}

orient = 'split'

import pandas as pd

df = pd.DataFrame(
    [["Jay", 16, "BBA"], ["Jack", 19, "BTech"], ["Mark", 18, "BSc"]],
    columns=["Name", "Age", "Course"],
)

js = df.to_json(orient="split")

print(js)

输出：

{"columns":["Name","Age","Course"],
 "index":[0,1,2],
 "data":[["Jay",16,"BBA"],["Jack",19,"BTech"],["Mark",18,"BSc"]]}

orient = 'table'

import pandas as pd

df = pd.DataFrame(
    [["Jay", 16, "BBA"], ["Jack", 19, "BTech"], ["Mark", 18, "BSc"]],
    columns=["Name", "Age", "Course"],
)

js = df.to_json(orient="table")

print(js)

输出：

{"schema": {"fields":[{"name":"index","type":"integer"},{"name":"Name","type":"string"},{"name":"Age","type":"integer"},{"name":"Course","type":"string"}],"primaryKey":["index"],"pandas_version":"0.20.0"}, "data": [{"index":0,"Name":"Jay","Age":16,"Course":"BBA"},{"index":1,"Name":"Jack","Age":19,"Course":"BTech"},{"index":2,"Name":"Mark","Age":18,"Course":"BSc"}]}

如前所述，我们还可以直接将 JSON 输出到外部文件。它可以通过在 dataframe.to_json() 函数中提供文件的路径来实现，如下所示。

import pandas as pd

df = pd.DataFrame(
    [["Jay", 16, "BBA"], ["Jack", 19, "BTech"], ["Mark", 18, "BSc"]],
    columns=["Name", "Age", "Course"],
)

df.to_json("path\example.json", orient="table")

上面的代码将 JSON 文件导出到指定的路径。