Как преобразовать серии в dataframe в Pandas

Question

У меня есть два CSV, которые мне нужно сравнить с ними по одному столбцу. И мне нужно поставить согласованные строки в один csv и непревзойденные строки в другом. Итак, я создал индекс в этом столбце во втором csv и зациклился первым.

df1 = pd.read_csv(file1,nrows=100) 
df2 = pd.read_csv(file2,nrows=100) 
df2.set_index('crc', inplace = True) 
matched_list = [] 
non_matched_list = [] 
    for _, row in df1.iterrows(): 
     try: 
      x = df2.loc[row['crc']]  
      matched_list.append(x) 
     except KeyError: 
      non_matched_list.append(row) 

В х здесь есть ряд в следующем формате

policyID     448094 
statecode      FL 
county    CLAY COUNTY 
eq_site_limit   1322376.3 
hu_site_limit   1322376.3 
fl_site_limit   1322376.3 
fr_site_limit   1322376.3 
tiv_2011    1322376.3 
tiv_2012    1438163.57 
eq_site_deductible    0 
hu_site_deductible   0.0 
fl_site_deductible    0 
fr_site_deductible    0 
point_latitude   30.063936 
point_longitude  -81.707664 
line     Residential 
construction    Masonry 
point_granularity    3 
Name: 448094,FL,CLAY COUNTY,1322376.3,1322376.3,1322376.3,1322376.3,1322376.3,0,0.0, dtype: object 

Мой выход CSV должен быть в следующем формате

policyID,statecode,county,eq_site_limit,hu_site_limit,fl_site_limit,fr_site_limit,tiv_2011,tiv_2012,eq_site_deductible,hu_site_deductible,fl_site_deductible,fr_site_deductible,point_latitude,point_longitude,line,construction,point_granularity 
114455,FL,CLAY COUNTY,498960,498960,498960,498960,498960,792148.9,0,9979.2,0,0,30.102261,-81.711777,Residential,Masonry,1 

Для всех серий в согласованный и не имеет аналогов. Как мне это сделать? Я не могу избавиться от индекса во втором csv, поскольку производительность важна.

Ниже приведены два файла csv. File1:

policyID,statecode,county,crc,hu_site_limit,fl_site_limit,fr_site_limit,tiv_2011,tiv_2012,eq_site_deductible,hu_site_deductible,fl_site_deductible,fr_site_deductible,point_latitude,point_longitude,line,construction,point_granularity 
114455,FL,CLAY COUNTY,589658,498960,498960,498960,498960,792148.9,0,9979.2,0,0,30.102261,-81.711777,Residential,Masonry,1 
448094,FL,CLAY COUNTY,1322376.3,1322376.3,1322376.3,1322376.3,1322376.3,1438163.57,0,0,0,0,30.063936,-81.707664,Residential,Masonry,3 
206893,FL,CLAY COUNTY,745689.4,190724.4,190724.4,190724.4,190724.4,192476.78,0,0,0,0,30.089579,-81.700455,Residential,Wood,1 
333743,FL,CLAY COUNTY,0,12563.76,0,0,79520.76,86854.48,0,0,0,0,30.063236,-81.707703,Residential,Wood,3 
172534,FL,CLAY COUNTY,0,254281.5,0,254281.5,254281.5,246144.49,0,0,0,0,30.060614,-81.702675,Residential,Wood,1 
785275,FL,CLAY COUNTY,0,515035.62,0,0,515035.62,884419.17,0,0,0,0,30.063236,-81.707703,Residential,Masonry,3 
995932,FL,CLAY COUNTY,0,19260000,0,0,19260000,20610000,0,0,0,0,30.102226,-81.713882,Commercial,Reinforced Concrete,1 
223488,FL,CLAY COUNTY,328500,328500,328500,328500,328500,348374.25,0,16425,0,0,30.102217,-81.707146,Residential,Wood,1 
433512,FL,CLAY COUNTY,315000,315000,315000,315000,315000,265821.57,0,15750,0,0,30.118774,-81.704613,Residential,Wood,1 
142071,FL,CLAY COUNTY,705600,705600,705600,705600,705600,1010842.56,14112,35280,0,0,30.100628,-81.703751,Residential,Masonry,1 

File2:

policyID,statecode,county,crc,hu_site_limit,fl_site_limit,fr_site_limit,tiv_2011,tiv_2012,eq_site_deductible,hu_site_deductible,fl_site_deductible,fr_site_deductible,point_latitude,point_longitude,line,construction,point_granularity 
119736,FL,CLAY COUNTY,498960,498960,498960,498960,498960,792148.9,0,9979.2,0,0,30.102261,-81.711777,Residential,Masonry,1 
448094,FL,CLAY COUNTY,1322376.3,1322376.3,1322376.3,1322376.3,1322376.3,1438163.57,0,0,0,0,30.063936,-81.707664,Residential,Masonry,3 
206893,FL,CLAY COUNTY,190724.4,190724.4,190724.4,190724.4,190724.4,192476.78,0,0,0,0,30.089579,-81.700455,Residential,Wood,1 
333743,FL,CLAY COUNTY,0,79520.76,0,0,79520.76,86854.48,0,0,0,0,30.063236,-81.707703,Residential,Wood,3 
172534,FL,CLAY COUNTY,0,254281.5,0,254281.5,254281.5,246144.49,0,0,0,0,30.060614,-81.702675,Residential,Wood,1 
785275,FL,CLAY COUNTY,0,51564.9,0,0,515035.62,884419.17,0,0,0,0,30.063236,-81.707703,Residential,Masonry,3 
995932,FL,CLAY COUNTY,0,457962,0,0,19260000,20610000,0,0,0,0,30.102226,-81.713882,Commercial,Reinforced Concrete,1 
223488,FL,CLAY COUNTY,328500,328500,328500,328500,328500,348374.25,0,16425,0,0,30.102217,-81.707146,Residential,Wood,1 
433512,FL,CLAY COUNTY,315000,315000,315000,315000,315000,265821.57,0,15750,0,0,30.118774,-81.704613,Residential,Wood,1 
142071,FL,CLAY COUNTY,705600,705600,705600,705600,705600,1010842.56,14112,35280,0,0,30.100628,-81.703751,Residential,Masonry,1 
253816,FL,CLAY COUNTY,831498.3,831498.3,831498.3,831498.3,831498.3,1117791.48,0,0,0,0,30.10216,-81.719444,Residential,Masonry,1 
894922,FL,CLAY COUNTY,0,24059.09,0,0,24059.09,33952.19,0,0,0,0,30.095957,-81.695099,Residential,Wood,1 

Edit: Добавлен образец CSV

Answer 1

Я думаю, что вы можете сделать это следующим образом:

df1.loc[df1.crc.isin(df2.index)].to_csv('/path/to/matched.csv', index=False) 
df1.loc[~df1.crc.isin(df2.index)].to_csv('/path/to/unmatched.csv', index=False) 

вместо зацикливания .. .

Demo:

In [62]: df1.loc[df1.crc.isin(df2.index)].to_csv(r'c:/temp/matched.csv', index=False) 

In [63]: df1.loc[~df1.crc.isin(df2.index)].to_csv(r'c:/temp/unmatched.csv', index=False) 

Результаты:

matched.csv:

policyID,statecode,county,crc,hu_site_limit,fl_site_limit,fr_site_limit,tiv_2011,tiv_2012,eq_site_deductible,hu_site_deductible,fl_site_deductible,fr_site_deductible,point_latitude,point_longitude,line,construction,point_granularity 
448094,FL,CLAY COUNTY,1322376.3,1322376.3,1322376.3,1322376.3,1322376.3,1438163.57,0,0.0,0,0,30.063935999999998,-81.70766400000001,Residential,Masonry,3 
333743,FL,CLAY COUNTY,0.0,12563.76,0.0,0.0,79520.76,86854.48,0,0.0,0,0,30.063236,-81.70770300000001,Residential,Wood,3 
172534,FL,CLAY COUNTY,0.0,254281.5,0.0,254281.5,254281.5,246144.49,0,0.0,0,0,30.060614,-81.702675,Residential,Wood,1 
785275,FL,CLAY COUNTY,0.0,515035.62,0.0,0.0,515035.62,884419.17,0,0.0,0,0,30.063236,-81.70770300000001,Residential,Masonry,3 
995932,FL,CLAY COUNTY,0.0,19260000.0,0.0,0.0,19260000.0,20610000.0,0,0.0,0,0,30.102226,-81.713882,Commercial,Reinforced Concrete,1 
223488,FL,CLAY COUNTY,328500.0,328500.0,328500.0,328500.0,328500.0,348374.25,0,16425.0,0,0,30.102217,-81.707146,Residential,Wood,1 
433512,FL,CLAY COUNTY,315000.0,315000.0,315000.0,315000.0,315000.0,265821.57,0,15750.0,0,0,30.118774,-81.704613,Residential,Wood,1 
142071,FL,CLAY COUNTY,705600.0,705600.0,705600.0,705600.0,705600.0,1010842.56,14112,35280.0,0,0,30.100628000000004,-81.703751,Residential,Masonry,1 

unmatched.csv:

policyID,statecode,county,crc,hu_site_limit,fl_site_limit,fr_site_limit,tiv_2011,tiv_2012,eq_site_deductible,hu_site_deductible,fl_site_deductible,fr_site_deductible,point_latitude,point_longitude,line,construction,point_granularity 
114455,FL,CLAY COUNTY,589658.0,498960.0,498960.0,498960.0,498960.0,792148.9,0,9979.2,0,0,30.102261,-81.711777,Residential,Masonry,1 
206893,FL,CLAY COUNTY,745689.4,190724.4,190724.4,190724.4,190724.4,192476.78,0,0.0,0,0,30.089578999999997,-81.700455,Residential,Wood,1