pandas 熊猫数据透视表重命名列
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pandas pivot table rename columns
提问by muon
How to rename columns with multiple levels after pandas pivot operation?
如何在Pandas数据透视操作后重命名具有多个级别的列？
Here's some code to generate test data:
下面是一些生成测试数据的代码：
import pandas as pd
df = pd.DataFrame({
'c0': ['A','A','B','C'],
'c01': ['A','A1','B','C'],
'c02': ['b','b','d','c'],
'v1': [1, 3,4,5],
'v2': [1, 3,4,5]})
print(df)
gives a test dataframe:
给出一个测试数据框：
c0 c01 c02 v1 v2
0 A A b 1 1
1 A A1 b 3 3
2 B B d 4 4
3 C C c 5 5
applying pivot
应用枢轴
df2 = pd.pivot_table(df, index=["c0"], columns=["c01","c02"], values=["v1","v2"])
df2 = df2.reset_index()
gives
给
how to rename the columns by joining levels?
with format
<c01 value>_<c02 value>_<v1>
如何通过加入级别重命名列？带格式
<c01 value>_<c02 value>_<v1>
for example first column should look like
"A_b_v1"
例如第一列应该看起来像
"A_b_v1"
The order of joining levels isn't really important to me.
加入级别的顺序对我来说并不重要。
回答by Psidom
If you want to coalesce the multi-index into a single string index without caring about the index level order, you can simply mapa joinfunction over the columns, and assign the result list back:
如果您想将多索引合并为单个字符串索引而不关心索引级别顺序，您可以简单地对列执行map一个join函数，并将结果列表分配回：
df2.columns = list(map("_".join, df2.columns))
And for your question, you can loop through the columns where each element is a tuple, unpack the tuple and join them back in the order you want:
对于您的问题，您可以遍历每个元素是元组的列，解压缩元组并按照您想要的顺序将它们连接起来：
df2 = pd.pivot_table(df, index=["c0"], columns=["c01","c02"], values=["v1","v2"])
# Use the list comprehension to make a list of new column names and assign it back
# to the DataFrame columns attribute.
df2.columns = ["_".join((j,k,i)) for i,j,k in df2.columns]
df2.reset_index()
