您可以定义一个自定义函数，如果value大于0，该函数将向列sample添加连字符：

def add_hypen(df, df_name):
    df['sample'] = df['sample'].where(df['value'].le(0), lambda x: x + f'_{df_name}')
    return df 

df1 = add_hypen(df1, 'A')
df2 = add_hypen(df2, 'B')
df3 = add_hypen(df3, 'C')

output = pd.concat([df1, df2, df3])

如果在每个 Dataframe 中样本id已经彼此不同，则drop_duplicates将足以去除value为零的重复：

output = output.drop_duplicates()

否则，您可能需要另一个concat来删除重复项：

output = (pd.concat([output[output['value'].ne(0)], output[output['value'].eq(0)].drop_duplicates()],
                    ignore_index=True).sort_values(by='sample'))

一个命题使用pandas.concat与keys参数：

out = (
        pd.concat([df1, df2, df3], keys=["A", "B", "C"])
            .reset_index(names=["suffix", "index"])
            .assign(sample= lambda x: np.where(x["value"].gt(0),
                                               x["sample"].add("_" + x["suffix"]), x["sample"]))
            .loc[lambda s: ~s["sample"].duplicated(keep="first"), ["sample", "value"]]
            .sort_values(by="sample")
​
      )

#输出：

print(out)

       sample  value
0     Sample1    0.0
1     Sample2    0.0
5   Sample2_B    0.2
9   Sample2_C    0.3
2   Sample3_A    0.1
6   Sample3_B    0.5
10  Sample3_C    0.6
3     Sample4    0.0

编码：

alpha = ['', 'A', 'B', 'C']
df = pd.concat([df1, df2, df3]).drop_duplicates().reset_index(drop=True)
df['sample'] = df['sample']+df.groupby('sample').cumcount().apply(lambda x: '_'+alpha[x] if alpha[x]!='' else alpha[x])
df

输出量：

sample         value
0   Sample1     0.0
1   Sample2     0.0
2   Sample3     0.1
3   Sample4     0.0
4   Sample2_A   0.2
5   Sample3_A   0.5
6   Sample2_B   0.3
7   Sample3_B   0.6

另一种可能的解决方案，基于pandas.DataFrame.merge和pandas.melt：

(df1[~df1.value.eq(0)].merge(df2[~df2.value.eq(0)], on='sample', how='outer')
 .merge(df3[~df3.value.eq(0)], on='sample', how='outer')
 .set_axis(['sample'] + list('ABC'), axis='columns')
 .melt(id_vars='sample').dropna()
 .assign(sample = lambda x: x['sample'] + '_' + x['variable'])
 .merge(df1[df1.value.eq(0)], on='sample', how='outer')
 .assign(value = lambda x: x.sum(numeric_only=True, axis=1))
 .sort_values('sample').loc[:, ['sample', 'value']])

输出量：

sample  value
5    Sample1    0.0
6    Sample2    0.0
2  Sample2_B    0.2
4  Sample2_C    0.3
0  Sample3_A    0.1
1  Sample3_B    0.5
3  Sample3_C    0.6
7    Sample4    0.0