我发布了＆＃34;第1部分＆＃34;这个问题让我得到了我需要的函数的答案here，但我认为这保证了它自己的问题。如果没有，我会删除。
我想将函数应用于将完整状态名称替换为缩写（New York -> NY）的数据框。但是我在我的数据集中注意到，如果一个州是大写的，它显然不符合dicitonary。我试图解决它，但似乎无法破解代码：
import pandas as pd
import numpy as np
dfp = pd.DataFrame({'A' : [np.NaN,np.NaN,3,4,5,5,3,1,5,np.NaN],
'B' : [1,0,3,5,0,0,np.NaN,9,0,0],
'C' : ['Pharmacy of IDAHO','NY Pharma','NJ Pharmacy','Idaho Rx','CA Herbals','Florida Pharma','AK RX','Ohio Drugs','PA Rx','USA Pharma'],
'D' : [123456,123456,1234567,12345678,12345,12345,12345678,123456789,1234567,np.NaN],
'E' : ['Assign','Unassign','Assign','Ugly','Appreciate','Undo','Assign','Unicycle','Assign','Unicorn',]})
import us
statez = us.states.mapping('abbr', 'name')
inv_map = {v: k for k, v in statez.items()}
def replace_states(company):
# find all states that exist in the string
state_found = filter(lambda state: state.lower() in company.lower(), statez.values())
# replace each state with its abbreviation
for state in state_found:
print(state, inv_map[state])
company = company.replace(state, inv_map[state])
print("---" , company)
# return the modified string (or original if no states were found)
return company
dfp['C'] = dfp['C'].map(replace_states)
输出：注意＆＃34; IDAHO的药房＆＃34;
Idaho ID
--- Pharmacy of IDAHO
Idaho ID
--- ID Rx
Florida FL
--- FL Pharma
Ohio OH
--- OH Drug
有没有办法让这个函数不区分大小写？
答案 0 :(得分：0)
我会找到它的索引，然后使用它来替换它，无论如何：
# replace each state with its abbreviation
for state in state_found:
print(state, inv_map[state])
index = company.lower().find(state.lower())
company = company.replace(company[index:index + len(state)], inv_map[state])
print("---" , company)
这保留了字符串的所有其他部分的大小写。
答案 1 :(得分：0)
用缩写替换状态名称（不区分大小写的矢量化解决方案）：
t1 = dfp.C.str.split(expand=True)
t2 = t1.stack().str.title().map(inv_map).unstack()
t1[t2.notnull()] = t2
dfp['new'] = t1.stack().groupby(level=0).agg(' '.join)
结果：
In [152]: x
Out[152]:
A B C D E new
0 NaN 1.0 Pharmacy of IDAHO 123456.0 Assign Pharmacy of ID
1 NaN 0.0 NY Pharma 123456.0 Unassign NY Pharma
2 3.0 3.0 NJ Pharmacy 1234567.0 Assign NJ Pharmacy
3 4.0 5.0 Idaho Rx 12345678.0 Ugly ID Rx
4 5.0 0.0 CA Herbals 12345.0 Appreciate CA Herbals
5 5.0 0.0 Florida Pharma 12345.0 Undo FL Pharma
6 3.0 NaN AK RX 12345678.0 Assign AK RX
7 1.0 9.0 Ohio Drugs 123456789.0 Unicycle OH Drugs
8 5.0 0.0 PA Rx 1234567.0 Assign PA Rx
9 NaN 0.0 USA Pharma NaN Unicorn USA Pharma
说明：
In [155]: t1 = dfp.C.str.split(expand=True)
In [156]: t1
Out[156]:
0 1 2
0 Pharmacy of IDAHO
1 NY Pharma None
2 NJ Pharmacy None
3 Idaho Rx None
4 CA Herbals None
5 Florida Pharma None
6 AK RX None
7 Ohio Drugs None
8 PA Rx None
9 USA Pharma None
In [157]: t2 = t1.stack().str.title().map(inv_map).unstack()
In [158]: t2
Out[158]:
0 1 2
0 NaN NaN ID
1 NaN NaN None
2 NaN NaN None
3 ID NaN None
4 NaN NaN None
5 FL NaN None
6 NaN NaN None
7 OH NaN None
8 NaN NaN None
9 NaN NaN None
In [159]: t1[t2.notnull()] = t2
In [160]: t1
Out[160]:
0 1 2
0 Pharmacy of ID
1 NY Pharma None
2 NJ Pharmacy None
3 ID Rx None
4 CA Herbals None
5 FL Pharma None
6 AK RX None
7 OH Drugs None
8 PA Rx None
9 USA Pharma None
用状态替换状态缩写（不区分大小写的矢量化解决方案）：
In [88]: dfp['state'] = dfp.C.str.extract(r'\b([A-Z]{2})\b', expand=False)
In [89]: dfp
Out[89]:
A B C D E state
0 NaN 1.0 Pharmacy of IDAHO 123456.0 Assign NaN
1 NaN 0.0 NY Pharma 123456.0 Unassign NY
2 3.0 3.0 NJ Pharmacy 1234567.0 Assign NJ
3 4.0 5.0 Idaho Rx 12345678.0 Ugly NaN
4 5.0 0.0 CA Herbals 12345.0 Appreciate CA
5 5.0 0.0 Florida Pharma 12345.0 Undo NaN
6 3.0 NaN AK RX 12345678.0 Assign AK
7 1.0 9.0 Ohio Drugs 123456789.0 Unicycle NaN
8 5.0 0.0 PA Rx 1234567.0 Assign PA
9 NaN 0.0 USA Pharma NaN Unicorn NaN
In [90]: dfp.C = dfp.C.replace(dfp.state.tolist(),
dfp.state.map(statez).tolist(),
regex=True)
In [91]: dfp
Out[91]:
A B C D E state
0 NaN 1.0 Pharmacy of IDAHO 123456.0 Assign NaN
1 NaN 0.0 New York Pharma 123456.0 Unassign NY
2 3.0 3.0 New Jersey Pharmacy 1234567.0 Assign NJ
3 4.0 5.0 Idaho Rx 12345678.0 Ugly NaN
4 5.0 0.0 California Herbals 12345.0 Appreciate CA
5 5.0 0.0 Florida Pharma 12345.0 Undo NaN
6 3.0 NaN Alaska RX 12345678.0 Assign AK
7 1.0 9.0 Ohio Drugs 123456789.0 Unicycle NaN
8 5.0 0.0 Pennsylvania Rx 1234567.0 Assign PA
9 NaN 0.0 USA Pharma NaN Unicorn NaN