熊猫地图创建NaNs

Question

我的意图是更换标签。我发现了使用字典并将其映射到数据帧。为此，我首先提取了必要的字段并创建了一个字典，然后我将其输入到地图功能中。

我的计划如下：

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)

我的labels.csv如下：

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

我的dat.csv如下：

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

预期结果如下：

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

映射失败。结果总是会导致出现我不想要的NaN。谁能告诉我为什么？

Answer 1

试试这个：

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

您也可以考虑使用merge：

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