如何为 Pandas Dataframe 非规范化 YAML? [英] How to denormalize YAML for Pandas Dataframe?

问题描述

我正在尝试将 YAML 文件中的数据导入 Pandas DataFrame.以下面的例子data.yml:

I am trying to get data from a YAML file into a Pandas DataFrame. Take the following example data.yml:

---
 - doc: "Book1"
   reviews:
     - reviewer: "Paul"
       stars: "5"
     - reviewer: "Sam"
       stars: "2"
 - doc: "Book2"
   reviews:
     - reviewer: "John"
       stars: "4"
     - reviewer: "Sam"
       stars: "3"
     - reviewer: "Pete"
       stars: "2"
...

所需的 DataFrame 如下所示:

The desired DataFrame would look like this:

     doc reviews.reviewer reviews.stars
0  Book1             Paul             5
1  Book1              Sam             2
2  Book2             John             4
3  Book2              Sam             3
4  Book2             Pete             2

我尝试以不同的方式将 YAML 数据提供给 Pandas(例如 with open('data.yml') as f: data = pd.DataFrame(yaml.load(f)))，但单元格总是包含嵌套的字典.这个解决方案适用于一般的 JSON 数据，但它的代码相当多，似乎可能存在更简单的 YAML 解决方案.

I've tried feeding the YAML data to Pandas different ways (like with open('data.yml') as f: data = pd.DataFrame(yaml.load(f))), but the cells always contain the nested dicts. This solution works for general JSON data, but it's quite a bit of code and it seems like a simpler solution for YAML might exist.

是否有一种内置的或 Pythonic 的方式来对 YAML 进行非规范化以这种方式转换为 Pandas 数据帧?

Is there a built-in or Pythonic way to denormalize YAML for conversion to a Pandas Dataframe in this way?

推荐答案

你应该使用 json_normalize 在 YAML 加载后扁平化字典:

You should use json_normalize to flatten the dictionary after YAML loads:

pd.io.json.json_normalize(yaml.load(f), 'reviews', 'doc')

  reviewer stars    doc
0     Paul     5  Book1
1      Sam     2  Book1
2     John     4  Book2
3      Sam     3  Book2
4     Pete     2  Book2

这篇关于如何为 Pandas Dataframe 非规范化 YAML?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！