使用Pandas转置和重索引DataFrame时遇格式问题求助

原始DataFrame #

Sample ID  'Deinococcus soli' Cha et al. 2014  16SrX (Apple proliferation group)  16SrXII (Stolbur group) 
0   C1day1_barcode01                                  21                                  1                        0
1  C1day21_barcode19                                  22                                  0                        0
2   C3day1_barcode03                                  13                                  0                        0
3  C3day14_barcode15                                  14                                  2                        2
4  T1day21_barcode22                                  19                                  1                        1

期望输出 #

Sample ID  C1day1_barcode01  C1day21_barcode19  C3day1_barcode03  C3day14_barcode15  T1day21_barcode22
0    'Deinococcus soli' Cha et al. 2014                21                 22                13                 14                 19
1     16SrX (Apple proliferation group)                 1                  0                 0                  2                  1
2               16SrXII (Stolbur group)                 0                  0                 0                  2                  1

解决方案 #

用「索引设置+转置+列名重定义」的组合操作即可解决，步骤如下：

1. 将Sample ID列设为DataFrame的索引，避免转置后它被当成普通数据行：

df.set_index('Sample ID', inplace=True)

2. 执行转置操作，并将转置后的原索引（物种名称）转为普通列：

df_transposed = df.T.reset_index()

3. 重新定义列名：把第一个列命名为Sample ID，后续列名使用原DataFrame的索引值（即各个样本ID）：

df_transposed.columns = ['Sample ID'] + df.index.tolist()

4. 重置行索引，移除多余的默认索引列：

df_transposed.reset_index(drop=True, inplace=True)

完整代码示例 #

import pandas as pd

# 构造原始DataFrame
df = pd.DataFrame({
    "Sample ID": ["C1day1_barcode01", "C1day21_barcode19", "C3day1_barcode03", "C3day14_barcode15", "T1day21_barcode22"],
    "'Deinococcus soli' Cha et al. 2014": [21,22,13,14,19],
    "16SrX (Apple proliferation group)": [1,0,0,2,1],
    "16SrXII (Stolbur group)": [0,0,0,2,1]
})

# 核心处理流程
df.set_index('Sample ID', inplace=True)
df_transposed = df.T.reset_index()
df_transposed.columns = ['Sample ID'] + df.index.tolist()
df_transposed.reset_index(drop=True, inplace=True)

# 输出结果
print(df_transposed)

内容的提问来源于stack exchange，提问作者chan-98