我如何避免使用Pandas？

Question

如果可能的话，我想知道如何在不使用for循环的情况下优化此代码。我想做的是对df系列中的所有值进行分类，['СССССССССааиа]]逐个查看列表代表和列表dem中的关键字。谢谢大家!

conditions = ['a','b']
list_rep = ['a1','a2']
list_dem = ['b1','b2']

for i in list_rep:
    df['Состояние'] = [conditions[0] if i in str(x).lower() else x for x in df['Состояние']]

for i in list_exp:
    df['Состояние'] = [conditions[1] if i in str(x).lower() else x for x in df['Состояние']]    

for i in conditions:
    df['Состояние'] = [i if i in str(x).lower() else x for x in df['Состояние']]

df['Состояние'] = [x if x in conditions else '-' for x in df['Состояние']]

Answer 1

首先使用^{}FIRST，然后使用^{}和join对regexOR使用^{cd4>}并在^{}中设置新值，然后使用^{}替换缺少的值：

df = pd.DataFrame({'Состояние':['abc','def','opa1','ujb2','a1sb1d','B21op']})
print (df)
  Состояние
0       abc
1       def
2      opa1
3      ujb2
4    a1sb1d
5     B21op

---

conditions = ['a','b']
list_rep = ['a1','a2']
list_dem = ['b1','b2']


s = df['Состояние'].str.lower()
m1 = s.str.contains('|'.join(list_rep))
m2 = s.str.contains('|'.join(list_dem))

df['Состояние'] = np.select([m1, m2], [conditions[0], conditions[1]], s)

df['Состояние'] = df['Состояние'].str.extract(f'({"|".join(conditions)})').fillna('-')
print (df)
  Состояние
0         a
1         -
2         a
3         b
4         a
5         b

另一个想法是创建映射字典，首先使用^{}和^{}，然后使用^{}，最后替换缺少的值：

conditions = ['a','b']
list_rep = ['a1','a2']
list_dem = ['b1','b2']

d = {**dict.fromkeys(list_rep,conditions[0]),
     **dict.fromkeys(list_dem,conditions[1]),
     **dict(zip(conditions,conditions))}
print (d)
{'a1': 'a', 'a2': 'a', 'b1': 'b', 'b2': 'b', 'a': 'a', 'b': 'b'}

pat = rf'({"|".join(d.keys())})'
df['Состояние'] = (df['Состояние'].str.lower()
                                  .str.extract(pat, expand=False)
                                  .map(d)
                                  .fillna('-'))
print (df)
  Состояние
0         a
1         -
2         a
3         b
4         a
5         b