熊猫按列值分组并提取一列文本答案

【问题标题】：pandas group by on column values and extract one column text熊猫按列值分组并提取一列文本
【发布时间】：2021-11-18 12:35:37
【问题描述】：

我有结果、学生、版本和状态列。在这个我想通过使用 Student ， Version 和 result = pass count 和 result = fail count 来分组

类似于 df.groupby(["student", "version", "result=pass"]).size().reset_index(name="new_result")

下面是我的数据框

result	student	version	status	Failed Subject
pass	Student-A	L-1.0	Active
fail	Student-A	L-1.0	Active	Mathematics
fail	Student-A	L-1.0	Active	Physics
pass	Student-A	M-1.0	Active
fail	Student-A	M-1.0	Active	Science
pass	Student-B	N-1.0	Active
pass	Student-B	N-1.0	Active
pass	Student-B	N-1.0	Active
pass	Student-C	O-1.0	Active
pass	Student-C	O-1.0	Active
fail	Student-C	O-1.0	Active	English
fail	Student-C	P-1.0	Active	Computers
fail	Student-C	P-1.0	Active	Mathematics

我希望我的输出数据框如下：

student	version	pass_count	fail_count	status	total_count (pass+fail)	Failed Subject
Student-A	L-1.0	1	2	Active	3	Mathematics,Physics
Student-A	M-1.0	1	1	Active	2	Science
Student-B	N-1.0	3	0	Active	3
Student-C	O-1.0	1	1	Active	2	English
Student-C	P-1.0	0	2	Active	2	Computers,Mathematics

我可以使用以下但不是总计数来获得通过和失败计数，请任何人帮助

pd.pivot_table(master_df, index=['status', 'student', 'version'], columns=['result'], aggfunc=len, fill_value=0)

【问题讨论】：

标签： python pandas group-by

【解决方案1】：

你可以使用.groupby() + agg()，如下：

df_out = (df.groupby(['student', 'version', 'status'], as_index=False)
            .agg(**{'pass_count': ('result', lambda x: x[x == 'pass'].size),
                    'fail_count': ('result', lambda x: x[x == 'fail'].size),                    
                    'total_count': ('result', 'size'),
                    'Failed Subject': ('Failed Subject', lambda x: ','.join(x.dropna()))
                    })
          )

结果：

print(df_out)

     student version  status  pass_count  fail_count  total_count         Failed Subject
0  Student-A   L-1.0  Active           1           2            3    Mathematics,Physics
1  Student-A   M-1.0  Active           1           1            2                Science
2  Student-B   N-1.0  Active           3           0            3                       
3  Student-C   O-1.0  Active           2           1            3                English
4  Student-C   P-1.0  Active           0           2            2  Computers,Mathematics

【讨论】：