Python性能调优：JSON到CSV，大文件

Python Performance Tuning: JSON to CSV, big file

一位同事要求我将"yelp数据集挑战"中的6个大文件从"扁平"的常规JSON转换为CSV(他认为这些文件看起来像有趣的教学数据)。

我想我可以用：

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# With thanks to http://www.diveintopython3.net/files.html and https://www.reddit.com/r/MachineLearning/comments/33eglq/python_help_jsoncsv_pandas/cqkwyu8/

import os
import pandas

jsondir = 'c:\\example\\bigfiles\'
csvdir = 'c:\\example\\bigcsvfiles\'
if not os.path.exists(csvdir): os.makedirs(csvdir)

for file in os.listdir(jsondir):
with open(jsondir+file, 'r', encoding='utf-8') as f: data = f.readlines()
df = pandas.read_json('[' + ','.join(map(lambda x: x.rstrip(), data)) + ']')
df.to_csv(csvdir+os.path.splitext(file)[0]+'.csv',index=0,quoting=1)

不幸的是，我的计算机内存达不到这个文件大小的任务。(即使我摆脱了循环，虽然它在不到一分钟的时间内发出50MB的文件，但它仍在努力避免冻结我的计算机或崩溃在100MB以上的文件上，最大的文件是3.25GB。)

是否还有其他简单但性能良好的东西可以替代？

在一个循环中是很好的，但是如果文件名对内存有影响的话(只有6个文件)，我也可以用单独的文件名运行6次。

这里是一个".json"文件内容的例子——注意每个文件实际上有很多json对象，每行1个。

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{"business_id":"xyzzy","name":"Business A","neighborhood":"","address":"XX YY ZZ","city":"Tempe","state":"AZ","postal_code":"85283","latitude":33.32823894longitude":-111.28948,"stars":3,"review_count":3,"is_open":0,"attributes":["BikeParking: True","BusinessAcceptsBitcoin: False","BusinessAcceptsCreditCards: True","BusinessParking: {'garage': False, 'street': False, 'validated': False, 'lot': True, 'valet': False}","DogsAllowed: False","RestaurantsPriceRange2: 2","WheelchairAccessible: True"],"categories":["Tobacco Shops","Nightlife","Vape Shops","Shopping"],"hours":["Monday 11:0-21:0","Tuesday 11:0-21:0","Wednesday 11:0-21:0","Thursday 11:0-21:0","Friday 11:0-22:0","Saturday 10:0-22:0","Sunday 11:0-18:0"],"type":"business"}
{"business_id":"dsfiuweio2f","name":"Some Place","neighborhood":"","address":"Strip or something","city":"Las Vegas","state":"NV","postal_code":"89106","latitude":36.189134,"longitude":-115.92094,"stars":1.5,"review_count":2,"is_open":1,"attributes":["BusinessAcceptsBitcoin: False","BusinessAcceptsCreditCards: True"],"categories":["Caterers","Grocery","Food","Event Planning & Services","Party & Event Planning","Specialty Food"],"hours":["Monday 0:0-0:0","Tuesday 0:0-0:0","Wednesday 0:0-0:0","Thursday 0:0-0:0","Friday 0:0-0:0","Saturday 0:0-0:0","Sunday 0:0-0:0"],"type":"business"}
{"business_id":"abccb","name":"La la la","neighborhood":"Blah blah","address":"Yay that","city":"Toronto","state":"ON","postal_code":"M6H 1L5","latitude":43.283984,"longitude":-79.28284,"stars":2,"review_count":6,"is_open":1,"attributes":["Alcohol: none","Ambience: {'romantic': False, 'intimate': False, 'classy': False, 'hipster': False, 'touristy': False, 'trendy': False, 'upscale': False, 'casual': False}","BikeParking: True","BusinessAcceptsCreditCards: True","BusinessParking: {'garage': False, 'street': False, 'validated': False, 'lot': False, 'valet': False}","Caters: True","GoodForKids: True","GoodForMeal: {'dessert': False, 'latenight': False, 'lunch': False, 'dinner': False, 'breakfast': False, 'brunch': False}","HasTV: True","NoiseLevel: quiet","OutdoorSeating: False","RestaurantsAttire: casual","RestaurantsDelivery: True","RestaurantsGoodForGroups: True","RestaurantsPriceRange2: 1","RestaurantsReservations: False","RestaurantsTableService: False","RestaurantsTakeOut: True","WiFi: free"],"categories":["Restaurants","Pizza","Chicken Wings","Italian"],"hours":["Monday 11:0-2:0","Tuesday 11:0-2:0","Wednesday 11:0-2:0","Thursday 11:0-3:0","Friday 11:0-3:0","Saturday 11:0-3:0","Sunday 11:0-2:0"],"type":"business"}

嵌套的JSON数据可以简单地保留为表示它的字符串文本——我只想将顶级键转换为CSV文件标题。