对pandas处理json数据的方法详解

今天展示一个利用pandas将json数据导入excel例子,主要利用的是pandas里的read_json函数将json数据转化为dataframe。

先拿出我要处理的json字符串：

strtext='[{"ttery":"min","issue":"20130801-3391","code":"8,4,5,2,9","code1":"297734529","code2":null,"time":1013395466000},\
{"ttery":"min","issue":"20130801-3390","code":"7,8,2,1,2","code1":"298058212","code2":null,"time":1013395406000},\
{"ttery":"min","issue":"20130801-3389","code":"5,9,1,2,9","code1":"298329129","code2":null,"time":1013395346000},\
{"ttery":"min","issue":"20130801-3388","code":"3,8,7,3,3","code1":"298588733","code2":null,"time":1013395286000},\
{"ttery":"min","issue":"20130801-3387","code":"0,8,5,2,7","code1":"298818527","code2":null,"time":1013395226000}]'

pandas.read_json的语法如下：

pandas.read_json(path_or_buf=None, orient=None, typ='frame', dtype=True,
convert_axes=True, convert_dates=True, keep_default_dates=True,
numpy=False, precise_float=False, date_unit=None, encoding=None,
lines=False, chunksize=None, compression='infer')

第一参数就是json文件路径或者json格式的字符串。

第二参数orient是表明预期的json字符串格式。orient的设置有以下几个值：

(1).'split' : dict like {index -> [index], columns -> [columns], data -> [values]}

这种就是有索引，有列字段,和数据矩阵构成的json格式。key名称只能是index,columns和data。

'records' : list like [{column -> value}, ... , {column -> value}]

这种就是成员为字典的列表。如我今天要处理的json数据示例所见。构成是列字段为键,值为键值,每一个字典成员就构成了dataframe的一行数据。

'index' : dict like {index -> {column -> value}}

以索引为key,以列字段构成的字典为键值。如：

'columns' : dict like {column -> {index -> value}}

这种处理的就是以列为键，对应一个值字典的对象。这个字典对象以索引为键,以值为键值构成的json字符串。如下图所示:

'values' : just the values array。

values这种我们就很常见了。就是一个嵌套的列表。里面的成员也是列表，2层的。

主要就说下这两个参数吧。下面我们回到示例中来。我们看前面可以发现示例是一个orient为records的json字符串。

这样就好处理了。看代码：

# -*- coding: utf-8 -*-
"""
Created on Sun Aug 5 09:01:38 2018
@author: FanXiaoLei
"""
import pandas as pd
strtext='[{"ttery":"min","issue":"20130801-3391","code":"8,4,5,2,9","code1":"297734529","code2":null,"time":1013395466000},\
{"ttery":"min","issue":"20130801-3390","code":"7,8,2,1,2","code1":"298058212","code2":null,"time":1013395406000},\
{"ttery":"min","issue":"20130801-3389","code":"5,9,1,2,9","code1":"298329129","code2":null,"time":1013395346000},\
{"ttery":"min","issue":"20130801-3388","code":"3,8,7,3,3","code1":"298588733","code2":null,"time":1013395286000},\
{"ttery":"min","issue":"20130801-3387","code":"0,8,5,2,7","code1":"298818527","code2":null,"time":1013395226000}]'

df=pd.read_json(strtext,orient='records')
df.to_excel('pandas处理json.xlsx',index=False,columns=["ttery","issue","code","code1","code2","time"])

最终写入excel如下图：

来源：https://blog.csdn.net/qq_24499417/article/details/81428594