英文:
Python // Pandas: Column Won't Rename
问题
# 更改“Day”列名可能与“Day”列作为索引而不是列有关吗？
# 这是我的代码和未处理数据示例：
# 导入pandas和numpy库
import pandas as pd
import numpy as np
# 将excel文件加载到DataFrame中
df = pd.read_excel("Marginal CPA data - NOV.xlsx")
# 删除底部行
df = df[:-1]
# 过滤名为“Campaign Type (Search ACQ) - ONC”的列，只保留值为“NonBrand”的行
df = df[df["Campaign Type (Search ACQ) - ONC"] == "NonBrand"]
# 制作一个数据透视表
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)
# 将列索引重置为单一级别
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",
"Day": "Date"})
英文:
The "Day" column won't rename. Could it have something to do with the "Day" column being an index and not a column? Here's my code and a sample of the unprocessed data:
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]
# Rename columns
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"})
| Campaign Type (Search ACQ) - ONC | Product (ACQ Search) - ONC | CDJ | Day | Media Cost | CAFE Approvals |
|---|---|---|---|---|---|
| NonBrand | Consumer | CPA | 11 Jan 2023 | 29019.77415 | 94 |
| NonBrand | Consumer | Non CPA (CDJ) | 17 Jan 2023 | 24640.36448 | 86 |
| NonBrand | Consumer | Non CPA (CDJ) | 12 Jan 2023 | 23627.78256 | 78 |
| NonBrand | Student | CPA | 17 Jan 2023 | 29863.95447 | 152 |
| NonBrand | Miles | CPA | 23 Jan 2023 | 380.94 | 1 |
| NonBrand | Miles | CPA | 07 Jan 2023 | 1786.51 | 5 |
| NonBrand | Consumer | CPA | 19 Jan 2023 | 26745.81705 | 64 |
| NonBrand | Secured | CPA | 20 Jan 2023 | 1551.35 | 19 |
| NonBrand | Consumer | Non CPA (CDJ) | 02 Feb 2023 | 41185.11225 | 66 |
答案1
得分: 1
cols = {
"Media Cost_CPA": "CPA花费",
"Media Cost_Non CPA (CDJ)": "CDJ花费",
"CAFE Approvals_CPA": "CPA批准",
"CAFE Approvals_Non CPA (CDJ)": "CDJ批准"
}
indx = {
"Day": "日期"
}
df_pivot = df_pivot.rename(columns=cols).rename_axis(index=indx)
英文:
rename_axis might work better for a MultiIndex:
cols = {
"Media Cost_CPA": "CPA Spend",
"Media Cost_Non CPA (CDJ)": "CDJ Spend",
"CAFE Approvals_CPA": "CPA Approvals",
"CAFE Approvals_Non CPA (CDJ)": "CDJ Approvals"
}
indx = {
"Day": "Date"
}
df_pivot = df_pivot.rename(columns=cols).rename_axis(index=indx)
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