make和3d数据

Question

我有四个外勤部

dfB = pd.DataFrame([[cheapest_brandB[0],wertBereichB]], columns=['brand', 'price'], index= 
       ['cheap'])
dfC = pd.DataFrame([[cheapest_brandC[0],wertBereichC]], columns=['brand', 'price'], index= 
      ['cheap'])
dfG = pd.DataFrame([[cheapest_brandG[0],wertBereichG]], columns=['brand', 'price'], index= 
      ['cheap'])
dfO = pd.DataFrame([[cheapest_brandO[0],wertBereichO]], columns=['brand', 'price'], index= 
      ['cheap'])

结果：

        brand                             price
cheap  ASUS                {'gte': 821.84, 'lte': 1200.91}

        brand                            price
cheap    HP                {'gte': 187.82, 'lte': 993.73}

        brand                            price
cheap  Google              {'gte': 1047.3, 'lte': 2093.59}

        brand                            price
cheap   MSI                {'gte': 1047.3, 'lte': 2093.59}

我想要制作3d df，以便它们每个都属于一个特定的索引，就像这样。

                 Gaming                                         Casual                      ....

        brand             price                     brand           price
cheap   ASUS    {'gte': 821.84, 'lte': 1200.91}     HP      {'gte': 187.82, 'lte': 993.73}    ....
light   ..                 ..                       ..                 ..

Answer 1

在您提供的数据文件中：

import pandas as pd

dfs = [
    pd.DataFrame(
        {"brand": ["ASUS"], "price": [{"gte": 821.84, "lte": 1200.91}]}, index=["cheap"]
    ),
    pd.DataFrame(
        {"brand": ["HP"], "price": [{"gte": 187.82, "lte": 993.73}]}, index=["cheap"]
    ),
    pd.DataFrame(
        {"brand": ["Google"], "price": [{"gte": 1047.3, "lte": 2093.59}]},
        index=["cheap"],
    ),
    pd.DataFrame(
        {"brand": ["MSI"], "price": [{"gte": 1047.3, "lte": 2093.59}]}, index=["cheap"]
    ),
]

有一种方法可以做到：

for df, name in zip(dfs, ["Gaming", "Casual", "Pro", "Other"]):
    df.columns = pd.MultiIndex.from_product([[name], ["brand", "price"]])

df = pd.concat(dfs, axis=1)

因此：

print(df)
# Output
      Gaming                                  Casual  \
       brand                            price  brand   
cheap   ASUS  {'gte': 821.84, 'lte': 1200.91}     HP   

                                          Pro  \
                                price   brand   
cheap  {'gte': 187.82, 'lte': 993.73}  Google   

                                       Other
                                 price brand                            price  
cheap  {'gte': 1047.3, 'lte': 2093.59}   MSI  {'gte': 1047.3, 'lte': 2093.59}