Pandas json：修订间差异
（创建页面，内容为“===pandas json=== 通常情况下，我们使用的都是 pandas 中的 to_json() 函数，可以通过设置 orient 参数来转换成为我们想要的 json 格式，orient 函数有以下几个参数： *Series可选参数为："index"（默认）, "split", "records" *DataFrame可选参数："columns"(默认)，split"，"records"， "index"，"values" ---- =====dataframe <--> json===== ====data==== data: area_proc.csv {| class="wikitable" !area_id!!area_na…”） |
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===pandas json=== | === pandas json === | ||
通常情况下，我们使用的都是 pandas 中的 to_json() 函数，可以通过设置 orient 参数来转换成为我们想要的 json 格式，orient 函数有以下几个参数： | 通常情况下，我们使用的都是 pandas 中的 to_json() 函数，可以通过设置 orient 参数来转换成为我们想要的 json 格式，orient 函数有以下几个参数： | ||
*Series可选参数为："index"（默认）, "split", "records" | *Series可选参数为："index"（默认）, "split", "records" | ||
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---- | ---- | ||
=== | === data -> dataframe === | ||
====data==== | ==== data ==== | ||
area_proc.csv | |||
{| class="wikitable" | {| class="wikitable" | ||
!area_id!!area_name!!prod_type!!prod_name!!num!!unit!!total | !area_id!!area_name!!prod_type!!prod_name!!num!!unit!!total | ||
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|} | |} | ||
====dataframe==== | ==== dataframe ==== | ||
... | ... | ||
df1 = pd.read_csv(u'area_proc.csv') | df1 = pd.read_csv(u'area_proc.csv') | ||
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3 11 SHA P2 Pear 15 2.5 37.5 | 3 11 SHA P2 Pear 15 2.5 37.5 | ||
=== dataframe -> json === | |||
dataframe.to_json(orient="?", force_ascii=False) | dataframe.to_json(orient="?", force_ascii=False) | ||
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---- | ---- | ||
=== json -> dataframe === | |||
[[分类:Develop]] | [[分类:Develop]] | ||
[[分类:Python]] | [[分类:Python]] | ||
[[分类:Pandas]] | [[分类:Pandas]] | ||
2022年12月28日 (三) 13:47的版本
pandas json
通常情况下，我们使用的都是 pandas 中的 to_json() 函数，可以通过设置 orient 参数来转换成为我们想要的 json 格式，orient 函数有以下几个参数：
- Series可选参数为："index"（默认）, "split", "records"
- DataFrame可选参数："columns"(默认)，split"，"records"， "index"，"values"
data -> dataframe
data
area_proc.csv
| area_id | area_name | prod_type | prod_name | num | unit | total |
|---|---|---|---|---|---|---|
| 10 | PEK | P1 | Apple | 12 | 5.2 | 62.4 |
| 10 | PEK | P2 | Pear | 20 | 3.3 | 66 |
| 11 | SHA | P1 | Apple | 8 | 4.5 | 36 |
| 11 | SHA | P2 | Pear | 15 | 2.5 | 37.5 |
dataframe
... df1 = pd.read_csv(u'area_proc.csv') area_id area_name prod_type prod_name num unit total 0 10 PEK P1 Apple 12 5.2 62.4 1 10 PEK P2 Pear 20 3.3 66.0 2 11 SHA P1 Apple 8 4.5 36.0 3 11 SHA P2 Pear 15 2.5 37.5
dataframe -> json
dataframe.to_json(orient="?", force_ascii=False)
?--> columns, split, records, index, values
dataframe -> json : columns
df1.to_json(orient="columns",force_ascii=False)
'{"area_id":{"0":10,"1":10,"2":11,"3":11},"area_name":{"0":"PEK","1":"PEK","2":"SHA","3":"SHA"},"prod_type":{"0":"P1","1":"P2","2":"P1","3":"P2"},"prod_name":{"0":"Apple","1":"Pear","2":"Apple","3":"Pear"},"num":{"0":12,"1":20,"2":8,"3":15},"unit":{"0":5.2,"1":3.3,"2":4.5,"3":2.5},"total":{"0":62.4,"1":66.0,"2":36.0,"3":37.5}}'
dataframe -> json : split =
df1.to_json(orient="split",force_ascii=False)
'{"columns":["area_id","area_name","prod_type","prod_name","num","unit","total"],"index":[0,1,2,3],"data":[[10,"PEK","P1","Apple",12,5.2,62.4],[10,"PEK","P2","Pear",20,3.3,66.0],[11,"SHA","P1","Apple",8,4.5,36.0],[11,"SHA","P2","Pear",15,2.5,37.5]]}'
dataframe -> json : records =
df1.to_json(orient="records",force_ascii=False)
'[{"area_id":10,"area_name":"PEK","prod_type":"P1","prod_name":"Apple","num":12,"unit":5.2,"total":62.4},{"area_id":10,"area_name":"PEK","prod_type":"P2","prod_name":"Pear","num":20,"unit":3.3,"total":66.0},{"area_id":11,"area_name":"SHA","prod_type":"P1","prod_name":"Apple","num":8,"unit":4.5,"total":36.0},{"area_id":11,"area_name":"SHA","prod_type":"P2","prod_name":"Pear","num":15,"unit":2.5,"total":37.5}]'
dataframe -> json : index =
df1.to_json(orient="index",force_ascii=False)
'{"0":{"area_id":10,"area_name":"PEK","prod_type":"P1","prod_name":"Apple","num":12,"unit":5.2,"total":62.4},"1":{"area_id":10,"area_name":"PEK","prod_type":"P2","prod_name":"Pear","num":20,"unit":3.3,"total":66.0},"2":{"area_id":11,"area_name":"SHA","prod_type":"P1","prod_name":"Apple","num":8,"unit":4.5,"total":36.0},"3":{"area_id":11,"area_name":"SHA","prod_type":"P2","prod_name":"Pear","num":15,"unit":2.5,"total":37.5}}'
dataframe -> json : values =
df1.to_json(orient="values",force_ascii=False)
'[[10,"PEK","P1","Apple",12,5.2,62.4],[10,"PEK","P2","Pear",20,3.3,66.0],[11,"SHA","P1","Apple",8,4.5,36.0],[11,"SHA","P2","Pear",15,2.5,37.5]]'