通过搜索另一个数据框映射新列的值

Question

我有两个数据帧：df_geo和df_event。我想在df_event中创建两个新列。数据帧类似于以下内容，尽管为简单起见已删除了其他列：

data_geo =  [['010','00','000','00000','00000','00000','United States'], 
             ['040','01','000','00000','00000','00000','Alabama'],
             ['050','01','001','00000','00000','00000','Autauga County'],
             ['040','02','000','00000','00000','00000','Alaska'],
             ['050','02','090','00000','00000','00000','Fairbanks North Star Borough'],
             ['162','02','000','00000','24230','00000','Fairbanks city'],
             ['040','09','000','00000','00000','00000','Connecticut']
             ['050','09','001','00000','00000','00000','Fairfield County']
             ['061','09','001','04720','00000','00000','Bethel town'],
             ['040','17','000','00000','00000','00000','Illinois']
             ['061','17','109','05638','00000','00000','Bethel township']] 

dfgeo = pd.DataFrame(data_geo, columns = ['summary_level', 'state_fips','county_fips','subdivision_code_fips','place_code_fips','city_code_fips','area_name']) 

df_geo.info()

RangeIndex: 43847 entries, 0 to 43846
Data columns (total 7 columns):
summary_level            43847 non-null object
state_fips               43847 non-null object
county_fips              43847 non-null object
subdivision_code_fips    43847 non-null object
place_code_fips          43847 non-null object
city_code_fips           43847 non-null object
area_name                43847 non-null object

data_event = [['Event Id','_','Alabama'], 
              ['Event Id','_','Connecticut'],
              ['Event Id','Autauga County','Alabama'],
              ['Event Id','Fairfield County','Connecticut'],
              ['Event Id','Fairbanks North Star Borough','Alaska']] 

df_event = pd.DataFrame(data_event, columns = ['unique_str','county','state']) 

df_event.info()

RangeIndex: 1261 entries, 0 to 1260
Data columns (total 3 columns):
unique_str              1261 non-null object
county                   999 non-null object
state                   1261 non-null object
dtypes: object(3) 

在df_event中，只要事件在状态级别发生，“ _”将替换NaN值。

GOAL创建一个函数，该函数可以从county中获取state和df_event输入，并在同一数据帧中创建两个新列。新列基于state_fips中county_fips和df_geo的值。一个示例如下所示：

inputA map_new_col('df_geo','Connecticut','Fairfield County'):   

resultA = ['Event Id','Connecticut','Fairfield County','09','001']
                                                       ^New columns

inputB map_new_col('df_geo','Alaska','Fairbanks North Star Borough'):   

resultB = ['Event Id','Alaska','Fairbanks North Star Borough','02','090']
                                                               ^New columns

因为我还需要在1200个（且正在增长的）事件列表上使用此函数，所以该函数必须在lamba函数或可以在整个数据帧中映射的其他对象中起作用。

最终，我的目标是能够一直执行相同的搜索，直到city_code_fips，但我什至无法在最初的搜索过程中全神贯注！如果我做到了这一点，我知道在搜索“ Bethel镇”时，所有搜索词都必须完全相同，以免出现“ Bethel乡镇”。

我知道这是一个多步骤的过程，但是感谢您的帮助。谢谢。

Answer 1

用途：

df = dfgeo.melt(id_vars=['state_fips','county_fips'], value_vars='area_name')
print (df)
   state_fips county_fips   variable                         value
0          00         000  area_name                 United States
1          01         000  area_name                       Alabama
2          01         001  area_name                Autauga County
3          02         000  area_name                        Alaska
4          02         090  area_name  Fairbanks North Star Borough
5          02         000  area_name                Fairbanks city
6          09         000  area_name                   Connecticut
7          09         001  area_name              Fairfield County
8          09         001  area_name                   Bethel town
9          17         000  area_name                      Illinois
10         17         109  area_name               Bethel township

df_event['state_fips'] = df_event['state'].map(df.set_index('value')['state_fips'])
df_event['county_fips'] = df_event['county'].map(df.set_index('value')['county_fips'])
print (df_event)
  unique_str                        county        state state_fips county_fips
0   Event Id                             _      Alabama         01         NaN
1   Event Id                             _  Connecticut         09         NaN
2   Event Id                Autauga County      Alabama         01         001
3   Event Id              Fairfield County  Connecticut         09         001
4   Event Id  Fairbanks North Star Borough       Alaska         02         090