將 Pandas DataFrame 轉換為 JSON
Manav Narula
2023年1月30日
2020年12月31日
JSON 是 JavaScript Object Notation 的縮寫,它是基於 JavaScript 中物件的格式,是一種表示結構化資料的編碼技術。它是基於 JavaScript 中物件的格式,是一種表示結構化資料的編碼技術。現在它被廣泛使用,特別是在伺服器和 Web 應用程式之間共享資料。
我們將在本文中介紹如何將 DataFrame 轉換為 JSON 字串。
我們將使用以下 DataFrame。
import pandas as pd
df = pd.DataFrame([['Jay',16,'BBA'],
['Jack',19,'BTech'],
['Mark',18,'BSc']],
columns = ['Name','Age','Course'])
print(df)
輸出:
Name Age Course
0 Jay 16 BBA
1 Jack 19 BTech
2 Mark 18 BSc
Pandas DataFrame 有一個方法 dataframe.to_json()
,它可以將 DataFrame 轉換為 JSON 字串或儲存為外部 JSON 檔案。最終的 JSON 格式取決於 orient
引數的值,預設情況下是'columns'
,但也可以指定為'records'
、'index'
、'split'
、'table'
和'values'
。
所有的格式將在下面介紹。
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)
輸出:
{"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)
輸出:
[{"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)
輸出:
{"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)
輸出:
{"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)
輸出:
{"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,我們還可以直接將 JSON 輸出到外部檔案。它可以通過在 dataframe.to_json()
函式中提供檔案的路徑來實現,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')
上面的程式碼將 JSON 檔案匯出到指定的路徑。
Author: Manav Narula
Manav is a IT Professional who has a lot of experience as a core developer in many live projects. He is an avid learner who enjoys learning new things and sharing his findings whenever possible.
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