我有一个这种形式的数据框： value1 value2 value3 value4 random string column groupindex1 10 2 3 4 stuff group 2index2 5 4 3 2 other stuff group 1index3 6 7 8 9 other stuff group 1index4 1 2 2 4 yet other stuff group 2index5 6 1 8 11 other stuff group 1可以使用以下代码生成此测试示例：df = pd.DataFrame([[10, 2, 3, 4, 'stuff', 'group 2'], [5, 4, 3, 2, 'other stuff', 'group 1'], [6, 7, 8, 9, 'other stuff', 'group 1'], [1, 2, 2, 4, 'yet other stuff', 'group 2'], [6, 1, 8, 11, 'other stuff', 'group 1']], columns = ['value1', 'value2', 'value3', 'value4', 'random string column', 'group'], index=['index1', 'index2', 'index3', 'index4', 'index5'])我想根据此规范创建一个名为“组识别列”的新列：按组列分组取每个值列的总和获取每组总和最大的值列的列名在此示例中，预期输出为： value1 value2 value3 value4 random string column group Group Identifying Columnindex1 10 2 3 4 stuff group 2 value1index2 5 4 3 2 other stuff group 1 value4index3 6 7 8 9 other stuff group 1 value4index4 1 2 2 4 yet other stuff group 2 value1index5 6 1 8 11 other stuff group 1 value4我已经尝试了几次 groupby / apply / transform 等，但我不能完全让它正确。
2 回答
繁星点点滴滴
TA贡献1803条经验 获得超3个赞
让我们按组提取列然后映射：
max_cols = (df.filter(like='value') # choose the value columns, also df.iloc[:, :4]
.groupby(df['group']).sum() # calculate sum per group
.idxmax(axis=1) # find col with max value
)
df['Column'] = df['group'].map(max_cols)
还groupby().transform()：
df['Column'] = (df.filter(like='value')
.groupby(df['group']).transform('sum')
.idxmax(axis=1)
)
输出：
value1 value2 value3 value4 random string column group Column
index1 10 2 3 4 stuff group 2 value1
index2 5 4 3 2 other stuff group 1 value4
index3 6 7 8 9 other stuff group 1 value4
index4 1 2 2 4 yet other stuff group 2 value1
index5 6 1 8 11 other stuff group 1 value4
江户川乱折腾
TA贡献1851条经验 获得超5个赞
这是map和的一种潜在解决方案dictionary：
#creates a dictionary with the maximum sum per group
d = df.groupby('group').sum().idxmax(axis=1).to_dict()
#mapping the dictionary to 'group' column to generated a new column
df['Group Identifying Column'] = df['group'].map(d)
或者，您可以切断该dictionary部分并简单地执行以下操作：
df['Group Identifying Column'] = df.group.map(df.groupby('group').sum().idxmax(axis=1))
输出：
value1 value2 ... group Group Identifying Column
index1 10 2 ... group 2 value1
index2 5 4 ... group 1 value4
index3 6 7 ... group 1 value4
index4 1 2 ... group 2 value1
index5 6 1 ... group 1 value4
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