パンダの地図作成NaN

Question

私の意図はラベルを置き換えることです。辞書を使ってデータフレームにマップする方法について知りました。そのために、まず必要なフィールドを抽出して辞書を作成し、マップ関数に渡しました。

次のように私のプログラムは次のように

factor_name = 'Help in household' 
df = pd.read_csv('dat.csv') 
labels = pd.read_csv('labels.csv') 
fact_df = labels.loc[labels['Column'] == factor_name] 
fact_dict = dict(zip(fact_df['Level'], fact_df['Rename'])) 
print df.index.to_series().map(fact_dict) 

私labels.csvは次のとおりです。

Column,Name,Level,Rename 
Help in household,Every day,4,Every day 
Help in household,Never,1,Never 
Help in household,Once a month,2,Once a month 
Help in household,Once a week,3,Once a week 
State,AN,AN,Andaman & Nicobar 
State,AP,AP,Andhra Pradesh 
State,AR,AR,Arunachal Pradesh 
State,BR,BR,Bihar 
State,CG,CG,Chattisgarh 
State,CH,CH,Chandigarh 
State,DD,DD,Daman & Diu 
State,DL,DL,Delhi 
State,DN,DN,Dadra & Nagar Haveli 
State,GA,GA,Goa 
State,GJ,GJ,Gujarat 
State,HP,HP,Himachal Pradesh 
State,HR,HR,Haryana 
State,JH,JH,Jharkhand 
State,JK,JK,Jammu & Kashmir 
State,KA,KA,Karnataka 
State,KL,KL,Kerala 
State,MG,MG,Meghalaya 
State,MH,MH,Maharashtra 
State,MN,MN,Manipur 
State,MP,MP,Madhya Pradesh 
State,MZ,MZ,Mizoram 
State,NG,NG,Nagaland 
State,OR,OR,Orissa 
State,PB,PB,Punjab 
State,PY,PY,Pondicherry 
State,RJ,RJ,Rajasthan 
State,SK,SK,Sikkim 
State,TN,TN,Tamil Nadu 
State,TR,TR,Tripura 
State,UK,UK,Uttarakhand 
State,UP,UP,Uttar Pradesh 
State,WB,WB,West Bengal 

次のように私のdat.csvがある：

Id,Help in household,Maths,Reading,Science,Social 
11011001001,4,20.37,,27.78, 
11011001002,3,12.96,,38.18, 
11011001003,4,27.78,70,, 
11011001004,4,,56.67,,36 
11011001005,1,,,14.55,8.33 
11011001006,4,,23.33,,30 
11011001007,4,40.74,70,, 
11011001008,3,,26.67,,22.92 

意図した結果であります次のようになります。

4 Every day 
1 Never 
2 Once a month 
3 Once a week 

マッピングが失敗します。その結果、常にNaNが表示され、これは私が望ましくないものです。なぜ誰かが私に言うことができますか？

In [140]: df['Help in household'] \ 
      .astype(str) \ 
      .map(labels.loc[labels['Column']=='Help in household',['Level','Rename']] 
         .set_index('Level')['Rename']) 
Out[140]: 
0  Every day 
1 Once a week 
2  Every day 
3  Every day 
4   Never 
5  Every day 
6  Every day 
7 Once a week 
Name: Help in household, dtype: object 

あなたはまた、mergeを使用して検討してください：

Answer 1

これを試してみてください

In [147]: df.assign(Level=df['Help in household'].astype(str)) \ 
      .merge(labels.loc[labels['Column']=='Help in household',['Level','Rename']], 
        on='Level') 
Out[147]: 
      Id Help in household Maths Reading Science Social Level  Rename 
0 11011001001     4 20.37  NaN 27.78  NaN  4 Every day 
1 11011001003     4 27.78 70.00  NaN  NaN  4 Every day 
2 11011001004     4 NaN 56.67  NaN 36.00  4 Every day 
3 11011001006     4 NaN 23.33  NaN 30.00  4 Every day 
4 11011001007     4 40.74 70.00  NaN  NaN  4 Every day 
5 11011001002     3 12.96  NaN 38.18  NaN  3 Once a week 
6 11011001008     3 NaN 26.67  NaN 22.92  3 Once a week 
7 11011001005     1 NaN  NaN 14.55 8.33  1  Never