将 pandas df 映射到 JSON Schema

Question

这是我的 df：

	text	date	channel	sentiment	product	segment
0	I like the new layout	2021-08-30T18:15:22Z	Snowflake	predict	Skills	EMEA

我需要将其转换为与以下内容匹配的 JSON 输出：

[
  {
    "text": "I like the new layout",
    "date": "2021-08-30T18:15:22Z",
    "channel": "Snowflake",
    "sentiment": "predict",
    "fields": [
      {
        "field": "product",
        "value": "Skills"
      },
      {
        "field": "segment",
        "value": "EMEA"
      }
    ]
  }
]

我无法将列的键映射到第一个字典中的值，并将列和行映射到最终字典中的新键。我已经尝试了使用 df.groupby 和 .apply() 的各种选项，但我做不到。

我尝试过的示例：

df.groupby(['text', 'date','channel','sentiment','product','segment']).apply(
     lambda r: r[['27cf2f]].to_dict(orient='records')).unstack('text').apply(lambda s: [
{s.index.name: idx, 'fields': value}
for idx, value in s.items()]
).to_json(orient='records')

感谢所有帮助！

Answer 1

一种选择是使用嵌套列表推导：

# Start with your example data
d = {'text': ['I like the new layout'],
     'date': ['2021-08-30T18:15:22Z'],
     'channel': ['Snowflake'],
     'sentiment': ['predict'],
     'product': ['Skills'],
     'segment': ['EMEA']}

df = pd.DataFrame(d)

# Specify field column names
fieldcols = ['product', 'segment']

# Build a dict for each group as a Series named `fields`
res = (df.groupby(['text', 'date','channel','sentiment'])
 .apply(lambda s: [{'field': field, 
                    'value': value}
                   for field in fieldcols
                   for value in s[field].values])
).rename('fields')

# Convert Series to DataFrame and then to_json
res = res.reset_index().to_json(orient='records')

# Print result
import json
print(json.dumps(json.loads(res), indent=2))

[
  {
    "text": "I like the new layout",
    "date": "2021-08-30T18:15:22Z",
    "channel": "Snowflake",
    "sentiment": "predict",
    "fields": [
      {
        "field": "product",
        "value": "Skills"
      },
      {
        "field": "segment",
        "value": "EMEA"
      }
    ]
  }
]