import json

处理JSON文件通常涉及读取、解析和写入JSON数据的过程。下面是一些在Python中处理JSON文件的常用方法：
1. 读取JSON文件：
使用Python内置的json模块可以轻松读取JSON文件。首先，需要导入json模块并打开文件，然后使用json.load()方法将JSON数据加载到Python对象中。

import json  
with open('example.json') as file:  
data = json.load(file)

1. 解析JSON数据：

一旦将JSON数据加载到Python对象中，就可以使用Python的语法对其进行处理。例如，可以访问嵌套的字典、列表和键值对等。

# 访问嵌套的字典  
name = data['person']['name']  
age = data['person']['age']  
# 访问列表  
scores = data['scores']  
score_1 = scores[0]  
# 遍历嵌套的字典和列表  
for student in data['students']:  
print(student['name'])  
print(student['age'])  
print(student['scores'])

1. 修改JSON数据：

可以根据需要对JSON数据进行修改。可以修改特定的键值对，添加新的键值对或删除现有的键值对。这里演示如何添加新的键值对。

data['person']['email'] = 'example@example.com'

1. 写入JSON文件：

使用json.dump()方法将修改后的Python对象写入JSON文件。同样需要使用Python内置的open()函数打开文件，指定写入模式，并使用json.dump()方法将数据写入文件。

with open('output.json', 'w') as file:  
json.dump(data, file)

这些是处理JSON文件的基本步骤。根据你的具体需求，可能需要更复杂的逻辑来处理更复杂的JSON结构。

实战：
假设我有下面这份fio的json结果文档,叫做fio.json好了，我对其进行解析，抓我需要的write的IOPS和BW的结果

{"fio version" : "fio-3.1","timestamp" : 1696925985,"timestamp_ms" : 1696925985515,"time" : "Tue Oct 10 16:19:45 2023","jobs" : [{"jobname" : "128k_write_mix0_num1_iodepth64","groupid" : 0,"error" : 0,"eta" : 0,"elapsed" : 301,"job options" : {"name" : "128k_write_mix0_num1_iodepth64","filename" : "/dev/nvme0n1","ioengine" : "libaio","direct" : "1","rw" : "write","rwmixread" : "0","bs" : "128k","numjobs" : "1","iodepth" : "64","runtime" : "300","time_based" : "1","write_iops_log" : "./1010/128k_write_mix0_num1_iodepth64","write_bw_log" : "./1010/128k_write_mix0_num1_iodepth64","log_avg_msec" : "1000"},"read" : {"io_bytes" : 0,"io_kbytes" : 0,"bw" : 0,"iops" : 0.000000,"runtime" : 0,"total_ios" : 0,"short_ios" : 0,"drop_ios" : 0,"slat_ns" : {"min" : 0,"max" : 0,"mean" : 0.000000,"stddev" : 0.000000},"clat_ns" : {"min" : 0,"max" : 0,"mean" : 0.000000,"stddev" : 0.000000,"percentile" : {"1.000000" : 0,"5.000000" : 0,"10.000000" : 0,"20.000000" : 0,"30.000000" : 0,"40.000000" : 0,"50.000000" : 0,"60.000000" : 0,"70.000000" : 0,"80.000000" : 0,"90.000000" : 0,"95.000000" : 0,"99.000000" : 0,"99.500000" : 0,"99.900000" : 0,"99.950000" : 0,"99.990000" : 0,"0.00" : 0,"0.00" : 0,"0.00" : 0}},"lat_ns" : {"min" : 0,"max" : 0,"mean" : 0.000000,"stddev" : 0.000000},"bw_min" : 0,"bw_max" : 0,"bw_agg" : 0.000000,"bw_mean" : 0.000000,"bw_dev" : 0.000000,"bw_samples" : 0,"iops_min" : 0,"iops_max" : 0,"iops_mean" : 0.000000,"iops_stddev" : 0.000000,"iops_samples" : 0},"write" : {"io_bytes" : 995677306880,"io_kbytes" : 972341120,"bw" : 3241104,"iops" : 25321.130122,"runtime" : 300003,"total_ios" : 7596415,"short_ios" : 0,"drop_ios" : 0,"slat_ns" : {"min" : 3449,"max" : 120791,"mean" : 9509.510472,"stddev" : 4055.313047},"clat_ns" : {"min" : 275173,"max" : 26670061,"mean" : 2517367.006916,"stddev" : 1031195.720264,"percentile" : {"1.000000" : 1056768,"5.000000" : 1253376,"10.000000" : 1368064,"20.000000" : 1630208,"30.000000" : 1892352,"40.000000" : 2146304,"50.000000" : 2408448,"60.000000" : 2670592,"70.000000" : 2867200,"80.000000" : 3129344,"90.000000" : 3817472,"95.000000" : 4358144,"99.000000" : 5734400,"99.500000" : 6455296,"99.900000" : 8454144,"99.950000" : 9371648,"99.990000" : 15269888,"0.00" : 0,"0.00" : 0,"0.00" : 0}},"lat_ns" : {"min" : 279136,"max" : 26675989,"mean" : 2526944.836070,"stddev" : 1031563.153543},"bw_min" : 1596160,"bw_max" : 3427584,"bw_agg" : 99.997880,"bw_mean" : 3241035.300000,"bw_dev" : 309378.872013,"bw_samples" : 300,"iops_min" : 12470,"iops_max" : 26778,"iops_mean" : 25320.580000,"iops_stddev" : 2417.018326,"iops_samples" : 300},"trim" : {"io_bytes" : 0,"io_kbytes" : 0,"bw" : 0,"iops" : 0.000000,"runtime" : 0,"total_ios" : 0,"short_ios" : 0,"drop_ios" : 0,"slat_ns" : {"min" : 0,"max" : 0,"mean" : 0.000000,"stddev" : 0.000000},"clat_ns" : {"min" : 0,"max" : 0,"mean" : 0.000000,"stddev" : 0.000000,"percentile" : {"1.000000" : 0,"5.000000" : 0,"10.000000" : 0,"20.000000" : 0,"30.000000" : 0,"40.000000" : 0,"50.000000" : 0,"60.000000" : 0,"70.000000" : 0,"80.000000" : 0,"90.000000" : 0,"95.000000" : 0,"99.000000" : 0,"99.500000" : 0,"99.900000" : 0,"99.950000" : 0,"99.990000" : 0,"0.00" : 0,"0.00" : 0,"0.00" : 0}},"lat_ns" : {"min" : 0,"max" : 0,"mean" : 0.000000,"stddev" : 0.000000},"bw_min" : 0,"bw_max" : 0,"bw_agg" : 0.000000,"bw_mean" : 0.000000,"bw_dev" : 0.000000,"bw_samples" : 0,"iops_min" : 0,"iops_max" : 0,"iops_mean" : 0.000000,"iops_stddev" : 0.000000,"iops_samples" : 0},"usr_cpu" : 16.971887,"sys_cpu" : 16.700555,"ctx" : 3797071,"majf" : 0,"minf" : 10,"iodepth_level" : {"1" : 0.100000,"2" : 0.100000,"4" : 0.100000,"8" : 0.100000,"16" : 0.100000,"32" : 0.100000,">=64" : 99.999171},"latency_ns" : {"2" : 0.000000,"4" : 0.000000,"10" : 0.000000,"20" : 0.000000,"50" : 0.000000,"100" : 0.000000,"250" : 0.000000,"500" : 0.000000,"750" : 0.000000,"1000" : 0.000000},"latency_us" : {"2" : 0.000000,"4" : 0.000000,"10" : 0.000000,"20" : 0.000000,"50" : 0.000000,"100" : 0.000000,"250" : 0.000000,"500" : 0.010000,"750" : 0.032186,"1000" : 0.438352},"latency_ms" : {"2" : 34.327943,"4" : 57.133003,"10" : 8.031209,"20" : 0.030922,"50" : 0.010000,"100" : 0.000000,"250" : 0.000000,"500" : 0.000000,"750" : 0.000000,"1000" : 0.000000,"2000" : 0.000000,">=2000" : 0.000000},"latency_depth" : 64,"latency_target" : 0,"latency_percentile" : 100.000000,"latency_window" : 0}],"disk_util" : [{"name" : "nvme0n1","read_ios" : 44,"write_ios" : 7593512,"read_merges" : 0,"write_merges" : 0,"read_ticks" : 4,"write_ticks" : 19039100,"in_queue" : 18805644,"util" : 100.000000}]
}

with open('fio.json') as file:  data = json.load(file)
#这里的data['jobs'][0]代表取字典jobs的值后，是一个[],看作为List，需要取list里面的值，我们这里只有一个，取[0],取到以后再取key ['write'],在这个字典下取key['iops'],则可以拿到对应的iops的值
iops = data['jobs'][0]['write']['iops']
bw = data['jobs'][0]['write']['bw']

这里我们最后 iops和bw的值分别为：25321.130122 和 3241104
感兴趣可以一个一个试，看输出的是什么：
即打印

print(data['jobs'])
print(data['jobs'][0])

以此类推，观察每一步有啥输出，有助理解