时间戳

首页 > 解决方案 > Pandas，使用 read_csv 导入类似 JSON 的文件

pandas - Pandas，使用 read_csv 导入类似 JSON 的文件

问题描述

我想将数据从 .txt 导入数据框。我无法使用经典的 pd.read_csv 导入它，而使用不同类型的 sep 会引发错误。我要导入的数据Cell_Phones_&_Accessories.txt.gz是一种格式。

product/productId: B000JVER7W
product/title: Mobile Action MA730 Handset Manager - Bluetooth Data Suite
product/price: unknown
review/userId: A1RXYH9ROBAKEZ
review/profileName: A. Igoe
review/helpfulness: 0/0
review/score: 1.0
review/time: 1233360000
review/summary: Don't buy!
review/text: First of all, the company took my money and sent me an email telling me the product was shipped. A week and a half later I received another email telling me that they are sorry, but they don't actually have any of these items, and if I received an email telling me it has shipped, it was a mistake.When I finally got my money back, I went through another company to buy the product and it won't work with my phone, even though it depicts that it will. I have sent numerous emails to the company - I can't actually find a phone number on their website - and I still have not gotten any kind of response. What kind of customer service is that? No one will help me with this problem. My advice - don't waste your money!

product/productId: B000JVER7W
product/title: Mobile Action MA730 Handset Manager - Bluetooth Data Suite
product/price: unknown
....

标签： pandas

解决方案

您可以使用jenfor 分隔符，然后通过 first:和分割pivot：

df = pd.read_csv('Cell_Phones_&_Accessories.txt', sep='¥', names=['data'], engine='python')

df1 = df.pop('data').str.split(':', n=1, expand=True)
df1.columns = ['a','b']

df1 = df1.assign(c=(df1['a'] == 'product/productId').cumsum())
df1 = df1.pivot('c','a','b')

用于提高性能的Python 解决方案defaultdict和构造函数：DataFrame

from collections import defaultdict

data = defaultdict(list)
with open("Cell_Phones_&_Accessories.txt") as f:
  for line in f.readlines():
      if len(line) > 1:
          key, value = line.strip().split(':', 1)
          data[key].append(value)

df = pd.DataFrame(data)

推荐阅读