python: pandas to_json官方文档及实例

时间格式参数：（timestamp）

# 单位：秒，格式： ISO8601（大写）
df = df.to_json(orient="records", date_format = 'ISO8601', date_unit = 's')

官方文档：

date_format{None, ‘epoch’, ‘iso’}

Type of date conversion. ‘epoch’ = epoch milliseconds, ‘iso’ = ISO8601. The default depends on the orient. For orient='table', the default is ‘iso’. For all other orients, the default is ‘epoch’.

date_unitstr, default ‘ms’ (milliseconds)

The time unit to encode to, governs timestamp and ISO8601 precision. One of ‘s’, ‘ms’, ‘us’, ‘ns’ for second, millisecond, microsecond, and nanosecond respectively.

DataFrame转换为json的格式类型参数：orient

orient : str
Indication of expected JSON string format.

Series:

default is ‘index’

allowed values are: {‘split’,’records’,’index’,’table’}.

DataFrame:

default is ‘columns’

allowed values are: {‘split’, ‘records’, ‘index’, ‘columns’, ‘values’, ‘table’}.

The format of the JSON string

‘split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}

‘records’ : list like [{column -> value}, … , {column -> value}]

‘index’ : dict like {index -> {column -> value}}

‘columns’ : dict like {column -> {index -> value}}

‘values’ : just the values array

‘table’ : dict like {‘schema’: {schema}, ‘data’: {data}}

Describing the data, where data component is like orient='records'.

Changed in version 0.20.0.

For example:

import pandas as pd
df = pd.DataFrame({'name':['tian','ning'], 'age':[19,20]})
"""python
   name  age
0  tian   19
1  ning   20
"""

columns

df.to_json(orient='columns')

# {"name":{"0":"tian","1":"ning"},"age":{"0":19,"1":20}}
"""
{
    "name":{
        "0":"tian",
        "1":"ning"
    },
    "age":{
        "0":19,
        "1":20
    }
}
"""

table

df.to_json(orient='table')

# {"schema":{"fields":[{"name":"index","type":"integer"},{"name":"name","type":"string"},{"name":"age","type":"integer"}],"primaryKey":["index"],"pandas_version":"0.20.0"},"data":[{"index":0,"name":"tian","age":19},{"index":1,"name":"ning","age":20}]}

"""
{
    "schema":{
        "fields":[
            {
                "name":"index",
                "type":"integer"
            },
            {
                "name":"name",
                "type":"string"
            },
            {
                "name":"age",
                "type":"integer"
            }
        ],
        "primaryKey":[
            "index"
        ],
        "pandas_version":"0.20.0"
    },
    "data":[
        {
            "index":0,
            "name":"tian",
            "age":19
        },
        {
            "index":1,
            "name":"ning",
            "age":20
        }
    ]
}
"""

index

df.to_json(orient='index')

# {"0":{"name":"tian","age":19},"1":{"name":"ning","age":20}}
"""
{
    "0":{
        "name":"tian",
        "age":19
    },
    "1":{
        "name":"ning",
        "age":20
    }
}
"""

values

df.to_json(orient='values')

# [["tian",19],["ning",20]]
"""
[
    [
        "tian",
        19
    ],
    [
        "ning",
        20
    ]
]
"""

split

df.to_json(orient='split')

# {"columns":["name","age"],"index":[0,1],"data":[["tian",19],["ning",20]]}
"""
{
    "columns":[
        "name",
        "age"
    ],
    "index":[
        0,
        1
    ],
    "data":[
        [
            "tian",
            19
        ],
        [
            "ning",
            20
        ]
    ]
}
"""

records

df.to_json(orient='records')

# [{"name":"tian","age":19},{"name":"ning","age":20}]
"""
[
    {
        "name":"tian",
        "age":19
    },
    {
        "name":"ning",
        "age":20
    }
]
"""

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