Python 3.x 通过从列表中获取列名,将数据帧的列相乘
我有一个数据框架,其中有分类列和数字列Python 3.x 通过从列表中获取列名,将数据帧的列相乘,python-3.x,pandas,dataframe,Python 3.x,Pandas,Dataframe,我有一个数据框架,其中有分类列和数字列 data = [['A',"India",10,20,30,15,"Cochin"],['B',"India",10,20,30,40,"Chennai"],['C',"India",10,20,30,15,"Chennai"]] df = pd.DataFrame(data,columns=['Product','Country',"2016 Total","2017 Total","2018 Total","2019 Total","Region"])
data = [['A',"India",10,20,30,15,"Cochin"],['B',"India",10,20,30,40,"Chennai"],['C',"India",10,20,30,15,"Chennai"]]
df = pd.DataFrame(data,columns=['Product','Country',"2016 Total","2017 Total","2018 Total","2019 Total","Region"])
Product Country 2016 Total 2017 Total 2018 Total 2019 Total Region
0 A India 10 20 30 15 Cochin
1 B India 10 20 30 40 Chennai
2 C India 10 20 30 15 Chennai
我知道数值变量列的名称(需要动态捕获):
您可以使用筛选包含总计的列(与列列表的结果类似),然后使用overaxis=1
,然后使用s.map()
:
使用条件by和for尝试以下操作:
df['Negative'] = df[cols_list].T.product().apply(lambda x: x < 0)
Product Country 2016 Total 2017 Total 2018 Total 2019 Total Region Negative
0 A India 10 20 30 15 Cochin No
1 B India 10 20 30 40 Chennai No
2 C India 10 20 30 15 Chennai No
df['Negative']=df.filter(like='Total').prod(axis=1).lt(0).map({True:'Yes',False:'No'})
print(df)
Product Country 2016 Total 2017 Total 2018 Total 2019 Total Region \
0 A India 10 20 30 15 Cochin
1 B India 10 20 30 40 Chennai
2 C India 10 20 30 15 Chennai
Negative
0 No
1 No
2 No
#solution with f-strings for get cols_list by year arange
cols_list = [f'{x} Total' for x in np.arange(start_year, current_year+1)]
print (cols_list)
['2016 Total', '2017 Total', '2018 Total', '2019 Total']
df['Negative'] = np.where(df[cols_list].prod(axis=1).lt(0), 'Yes', 'No')
print (df)
Product Country 2016 Total 2017 Total 2018 Total 2019 Total Region \
0 A India 10 20 30 15 Cochin
1 B India 10 20 30 40 Chennai
2 C India 10 20 30 15 Chennai
Negative
0 No
1 No
2 No
df['Negative'] = df[cols_list].T.product().apply(lambda x: x < 0)
>>> t = df[cols_list].T
>>> t
0 1 2
2016 10 10 10
2017 20 20 20
2018 30 30 30
>>> p = t.product()
>>> p
0 6000
1 6000
2 6000
dtype: int64
>>> neg = p.apply(lambda x: x < 0)
>>> neg
0 False
1 False
2 False
dtype: bool