英文:
Why reaching nested meta gives NaN when normalizing a json with pandas?
问题
以下是您要翻译的内容：
我的输入是一个Python字典（类似JSON）：
d = {
"type": "type1",
"details": {
"name": "foo",
"date": {
"timestamp": "01/02/2023 21:42:44",
"components": {
"day": 2,
"month": 1,
"year": 2023,
"time": "21:42:44"
}
}
},
"infos": {
"records": [
{
"field1": "qux",
"field2": "baz",
}
],
"class": "P"
}
}
我使用以下代码：
df = pd.json_normalize(
d,
record_path=["infos", "records"],
meta=[
"type",
["details", "date", "timestamp"],
["details", "date", "components", "year"],
["infos", "class"]
],
errors="ignore"
)
这给了我以下输出：
field1 field2 type details.date.timestamp details.date.components.year infos.class
0 qux baz type1 NaN NaN P
但我期望得到这个输出：
field1 field2 type details.date.timestamp details.date.components.year infos.class
0 qux baz type1 01/02/2023 21:42:44 2023 P
老实说，我对`meta`参数感到非常困惑！我不知道我做错了什么...
您能解释一下它的逻辑吗？
英文:
My input is a Python dictionnary (json-like) :
d = {
"type": "type1",
"details": {
"name": "foo",
"date": {
"timestamp": "01/02/2023 21:42:44",
"components": {
"day": 2,
"month": 1,
"year": 2023,
"time": "21:42:44"
}
}
},
"infos": {
"records": [
{
"field1": "qux",
"field2": "baz",
}
],
"class": "P"
}
}
I'm using the code below :
df = pd.json_normalize(
d,
record_path=["infos", "records"],
meta=[
"type",
["details", "date", "timestamp"],
["details", "date", "components", "year"],
["infos", "class"]
],
errors="ignore"
)
Which gives me this output :
field1 field2 type details.date.timestamp details.date.components.year infos.class
0 qux baz type1 NaN NaN P
But I'm expecting this one :
field1 field2 type details.date.timestamp details.date.components.year infos.class
0 qux baz type1 01/02/2023 21:42:44 2023 P
To be honest, I'm going crazy with the meta parameter! I ignore what I'm doing wrong..
Can you explain its logic, please ?
答案1
得分: 2
我认为你应该在`record_path=`中额外添加`[]`：
```py
df = pd.json_normalize(
d,
record_path=[["infos", "records"]], # <-- 在这里加上 []
meta=[
"type",
["details", "date", "timestamp"],
["details", "date", "components", "year"],
["infos", "class"],
],
errors="ignore",
)
print(df)
打印：
field1 field2 type details.date.timestamp details.date.components.year infos.class
0 qux baz type1 01/02/2023 21:42:44 2023 P
<details>
<summary>英文:</summary>
I think you should put extra `[]` in `record_path=`:
```py
df = pd.json_normalize(
d,
record_path=[["infos", "records"]], # <-- put [] here
meta=[
"type",
["details", "date", "timestamp"],
["details", "date", "components", "year"],
["infos", "class"],
],
errors="ignore",
)
print(df)
Prints:
field1 field2 type details.date.timestamp details.date.components.year infos.class
0 qux baz type1 01/02/2023 21:42:44 2023 P
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