Python 复制列元素并基于相关列表应用于另一列
这是一个棘手的问题,我很久以来一直在头痛。我有下面的数据框Python 复制列元素并基于相关列表应用于另一列,python,pandas,list,dataframe,pandas-groupby,Python,Pandas,List,Dataframe,Pandas Groupby,这是一个棘手的问题,我很久以来一直在头痛。我有下面的数据框 dct = {'Store': ('A','A','A','A','A','A','B','B','B','C','C','C'), 'code_num':('INC101','INC102','INC103','INC104','INC105','INC106','INC201','INC202','INC203','INC301','INC302','INC303'), 'days':('4','18',
dct = {'Store': ('A','A','A','A','A','A','B','B','B','C','C','C'),
'code_num':('INC101','INC102','INC103','INC104','INC105','INC106','INC201','INC202','INC203','INC301','INC302','INC303'),
'days':('4','18','9','15','3','6','10','5','3','1','8','5'),
'products': ('remote','antenna','remote, antenna','TV','display','TV','display, touchpad','speaker','Cell','display','speaker','antenna')
}
df = pd.DataFrame(dct)
pts = {'Primary': ('TV','TV','TV','Cell','Cell'),
'Related' :('remote','antenna','speaker','display','touchpad')
}
parts = pd.DataFrame(pts)
print(df)
Store code_num days products
0 A INC101 4 remote
1 A INC102 18 antenna
2 A INC103 9 remote, antenna
3 A INC104 15 TV
4 A INC105 3 display
5 A INC106 6 TV
6 B INC201 10 display, touchpad
7 B INC202 5 speaker
8 B INC203 3 Cell
9 C INC301 1 display
10 C INC302 8 speaker
11 C INC303 5 antenna
零件数据框仅供参考,我有另一段代码,它将为每个商店提供相关零件和主要零件的列表
#对于商店A->TV:[“遥控器”、“天线”、“扬声器];存储B->Cell:['display','touchpad']
我期望的数据帧是:
Store code_num days products refer
0 A INC101 4 remote INC106
1 A INC102 18 antenna -> omitted in 1st pass; because >10 days
2 A INC103 9 remote, antenna INC106
3 A INC104 15 TV -> omitted in 1st pass; because >10 days
4 A INC105 3 display
5 A INC106 6 TV INC106
6 B INC201 10 display, touchpad INC203
7 B INC202 5 speaker
8 B INC203 3 Cell INC203
9 C INC301 1 display -> blank because no primary present
10 C INC302 8 speaker -> blank because no primary present
11 C INC303 5 antenna -> blank because no primary present
我有一段代码,可以立即执行整个df。但由于其他业务规则,这将是一部分数据。意味着2和3将被省略,因此,对于某些记录,.iloc值可能不同。因此,如果您在上复制了场景: 你的意见:
dct = {'Store': ('A','A','A','A','A','A','B','B','B','C','C','C'),
'code_num':('INC101','INC102','INC103','INC104','INC105','INC106','INC201','INC202','INC203','INC301','INC302','INC303'),
'days':('4','18','9','15','3','6','10','5','3','1','8','5'),
'products': ('remote','antenna','remote,antenna','TV','display','TV','display,touchpad','speaker','Cell','display','speaker','antenna')
}
df = pd.DataFrame(dct)
pts = {'Primary': ('TV','TV','TV','Cell','Cell'),
'Related' :('remote','antenna','speaker','display','touchpad')
}
parts = pd.DataFrame(pts)
store = {'A':'TV','B':'Cell'}
解决方案:
将零件df转换为字典:
parts_df_dict = dict(zip(parts['Related'],parts['Primary']))
拆分逗号分隔的子产品并使其分隔行:
new_df = pd.DataFrame(df.products.str.split(',').tolist(), index=df.code_num).stack()
new_df = new_df.reset_index([0, 'code_num'])
new_df.columns = ['code_num', 'Prod_seperated']
new_df = new_df.merge(df, on='code_num', how='left')
创建引用列的逻辑:
store_prod = {}
for k,v in store.items():
store_prod[k] = k+'_'+v
new_df['prod_store'] = new_df['Store'].map(store_prod)
new_df['p_store'] = new_df['Store'].map(store)
new_df['main_ind'] = ' '
new_df.loc[(new_df['prod_store']==new_df['Store']+'_'+new_df['Prod_seperated'])&(new_df['days'].astype('int')<10),'main_ind']=new_df['code_num']
refer_dic = new_df.groupby('Store')['main_ind'].max().to_dict()
new_df['prod_subproducts'] = new_df['Prod_seperated'].map(parts_df_dict)
new_df['refer'] = np.where((new_df['p_store']==new_df['prod_subproducts'])&(new_df['days'].astype('int')<=10),new_df['Store'].map(refer_dic),np.nan)
new_df['refer'].fillna(new_df['main_ind'],inplace=True)
new_df.drop(['Prod_seperated','prod_store','p_store','main_ind','prod_subproducts'],axis=1,inplace=True)
new_df.drop_duplicates(inplace=True)
store_prod={}
对于k,v in store.items():
存储产品[k]=k+'+v
new_-df['prod_-store']=new_-df['store'].映射(store_-prod)
new_-df['p_-store']=new_-df['store'].地图(商店)
新的_df['main_ind']='
new_df.loc[(new_df['prod_store']==new_df['store']+''new_df['prod_separated'])和(new_df['days'])。aType('int')您也可以提供零件数据框。@Madan已经在上面的问题中提供了!参考“零件”数据框