Question

我的数据框的列非常不一致。例如：

index

我正在尝试拆分CM列。我尝试了一下，这让我非常接近：

df = pd.DataFrame(columns=["CID", "CM"], data=[['xxx-1','skill_start=skill1,skill2,||skill_complete=skill1,'],['xxx-2','survey=1||skill_start=skill1,skill3||skill_complete=skill3'],['xxx-3','skill_start=skill2,skill3||skill_complete=skill2,skill3||abandon_custom=0']])

但是因为数据不一致，所以列不能整齐地排列。如何以一种分类的方式将其拆分？

所需输出示例：

df = df.join(metrics['CM'].str.split('\|\|', expand=True).add_prefix('CM'))

Answer 1

您使用多个定界符尝试过此操作，不确定是否是您要找的内容：

df1 = df['CM'].str.split('\|\||,|=', expand=True).add_prefix('CM_')
df = pd.concat([df['CID'], df1], axis=1)
print(df)

     CID         CM_0    CM_1         CM_2            CM_3            CM_4            CM_5            CM_6  CM_7
0  xxx-1  skill_start  skill1       skill2                  skill_complete          skill1                  None
1  xxx-2       survey       1  skill_start          skill1          skill3  skill_complete          skill3  None
2  xxx-3  skill_start  skill2       skill3  skill_complete          skill2          skill3  abandon_custom     0

Answer 2

我解决了！

解决方案是使用正则表达式提取器创建一个仅具有我要查找的值的新数据框，在需要的地方使用get_dummies，然后将其重新连接到主数据框。

skill_start = df['CM'].str.extract(r'skill_start=(?P<skill_start>.*?)\|\|')
surveys = df['CM'].str.extract(r'survey_response=(?P<survey_response>[1|2|3|4|5])')
skill_complete = df['CM'].str.extract(r'skill_complete=(?P<skill_complete>.*?)\|\|')
escalated_custom = df['CM'].str.extract(r'escalated_custom=(?P<escalated_custom>[0|1])')
abandoned_custom = df['CM'].str.extract(r'abandoned_custom=(?P<abandoned_custom>[0|1])')

skill_start = pd.concat([skill_start,skill_start.skill_start.str.get_dummies(sep=',')],1)
skill_start = skill_start.add_prefix('skill_start:')
skill_complete = pd.concat([skill_complete,skill_complete.skill_complete.str.get_dummies(sep=',')],1)
skill_complete = skill_complete.add_prefix('skill_complete:')

new_df = df.join(surveys).join(skill_start).join(skill_complete).join(escalated_custom).join(abandoned_custom)

熊猫：通过多个定界符对列进行排序和拆分

2 个答案: