Pandas中Day列无法重命名求助：是否因该列为索引所致？

你的猜测完全正确："Day"是DataFrame的多级索引成员，而非普通列，所以df.rename(columns=...)无法修改它的名称——这个方法只作用于列，不作用于索引。

解决方法 #

方法1：将索引转为列后重命名（适合需要把Day作为普通列的场景） #

先把索引转换为普通列，重命名后可选择是否重新设置为索引：

import pandas as pd
import numpy as np

# Load the excel file into a dataframe
df = pd.read_excel("Marginal CPA data - NOV.xlsx")

# Delete the bottom row
df = df[:-1]

# Filter the column labeled "Campaign Type (Search ACQ) - ONC" to keep only rows with value "NonBrand"
df = df[df["Campaign Type (Search ACQ) - ONC"] == "NonBrand"]

# Make a pivot table
pivot_table = pd.pivot_table(df, values=["Media Cost", "CAFE Approvals"],
                              index=["Campaign Type (Search ACQ) - ONC", "Product (ACQ Search) - ONC", "Day"],
                              columns=["CDJ"], aggfunc="sum")

df_pivot = pivot_table.fillna(value=0)

# Reset the column index to a single level
df_pivot.columns = ["_".join(col) for col in df_pivot.columns]

# 重置索引，将多级索引转为普通列
df_pivot = df_pivot.reset_index()

# 现在可以正常重命名Day列为Date
df_pivot = df_pivot.rename(columns={"Media Cost_CPA": "CPA Spend", "Media Cost_Non CPA (CDJ)": "CDJ Spend",
                                     "CAFE Approvals_CPA": "CPA Approvals", "CAFE Approvals_Non CPA (CDJ)": "CDJ Approvals",
                                    "Day": "Date"})

# （可选）如果需要将Date重新设为索引，执行以下代码
# df_pivot = df_pivot.set_index(["Campaign Type (Search ACQ) - ONC", "Product (ACQ Search) - ONC", "Date"])

方法2：直接重命名索引级别（适合保持Day为索引的场景） #

无需转换列，直接修改索引的级别名称，更高效：

import pandas as pd
import numpy as np

# Load the excel file into a dataframe
df = pd.read_excel("Marginal CPA data - NOV.xlsx")

# Delete the bottom row
df = df[:-1]

# Filter the column labeled "Campaign Type (Search ACQ) - ONC" to keep only rows with value "NonBrand"
df = df[df["Campaign Type (Search ACQ) - ONC"] == "NonBrand"]

# Make a pivot table
pivot_table = pd.pivot_table(df, values=["Media Cost", "CAFE Approvals"],
                              index=["Campaign Type (Search ACQ) - ONC", "Product (ACQ Search) - ONC", "Day"],
                              columns=["CDJ"], aggfunc="sum")

df_pivot = pivot_table.fillna(value=0)

# Reset the column index to a single level
df_pivot.columns = ["_".join(col) for col in df_pivot.columns]

# 重命名列
df_pivot = df_pivot.rename(columns={"Media Cost_CPA": "CPA Spend", "Media Cost_Non CPA (CDJ)": "CDJ Spend",
                                     "CAFE Approvals_CPA": "CPA Approvals", "CAFE Approvals_Non CPA (CDJ)": "CDJ Approvals"})

# 直接修改索引的第三个级别名称（索引从0开始，Day是第3个级别）
df_pivot.index.names = ["Campaign Type (Search ACQ) - ONC", "Product (ACQ Search) - ONC", "Date"]

未处理数据样本 #

内容的提问来源于stack exchange，提问作者jimlearnscoding