Python 中的平铺数据映射功能是指将数据从嵌套格式转换为单层格式,并将其中的每个键映射到原始嵌套层次结构中的路径。这样可以使数据更易于处理和分析。
平铺数据映射的使用方法如下:
- 导入需要使用的库
import pandas as pd
from pandas.io.json import json_normalize
- 将数据加载到 Pandas DataFrame 中
data = pd.read_json('data.json')
- 使用
json_normalize
函数将嵌套数据进行平铺
flatdata = json_normalize(data)
- 映射数据的键到原始嵌套层次结构中的路径
flatdata.columns = flatdata.columns.map(lambda x: '.'.join(x.split('.')[1:]))
- 完整示例代码:
import pandas as pd
data = pd.read_json('data.json')
flatdata = pd.json_normalize(data)
flatdata.columns = flatdata.columns.map(lambda x: '.'.join(x.split('.')[1:]))
- 示例说明1:
假设有以下示例嵌套 JSON 数据:
{
"name": "John",
"age": 30,
"address": {
"street": "123 Main St",
"city": "Anytown",
"state": "CA",
"zip": 12345
},
"phone": {
"home": "555-555-1234",
"work": "555-555-5678"
}
}
使用 Python 进行平铺映射后,得到的结果如下:
name | age | address.street | address.city | address.state | address.zip | phone.home | phone.work
---- | --- | -------------- | ------------ | ------------- | ----------- | ---------- | -----------
John | 30 | 123 Main St | Anytown | CA | 12345 | 555-555-1234 | 555-555-5678
其中,每个键都映射到原始嵌套层次结构中的路径。
- 示例说明2:
假设有以下示例嵌套 JSON 数组数据:
[
{
"name": "John",
"age": 30,
"address": {
"street": "123 Main St",
"city": "Anytown",
"state": "CA",
"zip": 12345
},
"phone": {
"home": "555-555-1234",
"work": "555-555-5678"
}
},
{
"name": "Mary",
"age": 25,
"address": {
"street": "456 Elm St",
"city": "Anytown",
"state": "CA",
"zip": 12345
},
"phone": {
"home": "555-555-9876",
"work": "555-555-4321"
}
}
]
使用 Python 进行平铺映射后,得到的结果如下:
name | age | address.street | address.city | address.state | address.zip | phone.home | phone.work
---- | --- | -------------- | ------------ | ------------- | ----------- | ---------- | -----------
John | 30 | 123 Main St | Anytown | CA | 12345 | 555-555-1234 | 555-555-5678
Mary | 25 | 456 Elm St | Anytown | CA | 12345 | 555-555-9876 | 555-555-4321
其中每个键都映射到原始嵌套层次结构中的路径,并且将每个对象作为单独的行映射到 DataFrame 中。