将 pandas dataframe 转换为 json-ag捕鱼王app官网

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。