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Виталий Тодоров
Asked:2021-11-30 17:07:28 +0000 UTC2021-11-30 17:07:28 +0000 UTC

获取DataFrame每一列的唯一值

  • 772

为每一列获取唯一值的好方法DataFrame。

数据框： https ://yadi.sk/i/CiNCLc2juUxaXw

我的解决方案：

adult_train_drop = adult_train.drop(['Age', 'fnlwgt', 'Education_Num', 'Capital_Gain', 'Capital_Loss', 'Hours_per_week'], axis=1)
for col in adult_train_drop:
    print(col, ': ', adult_train[col].unique())
    print()

结果：

Workclass :  [' State-gov' ' Self-emp-not-inc' ' Private' ' Federal-gov' ' Local-gov'
 nan ' Self-emp-inc' ' Without-pay' ' Never-worked']

Education :  [' Bachelors' ' HS-grad' ' 11th' ' Masters' ' 9th' ' Some-college'
 ' Assoc-acdm' ' Assoc-voc' ' 7th-8th' ' Doctorate' ' Prof-school'
 ' 5th-6th' ' 10th' ' 1st-4th' ' Preschool' ' 12th']

Martial_Status :  [' Never-married' ' Married-civ-spouse' ' Divorced'
 ' Married-spouse-absent' ' Separated' ' Married-AF-spouse' ' Widowed']

Occupation :  [' Adm-clerical' ' Exec-managerial' ' Handlers-cleaners' ' Prof-specialty'
 ' Other-service' ' Sales' ' Craft-repair' ' Transport-moving'
 ' Farming-fishing' ' Machine-op-inspct' ' Tech-support' nan
 ' Protective-serv' ' Armed-Forces' ' Priv-house-serv']

Relationship :  [' Not-in-family' ' Husband' ' Wife' ' Own-child' ' Unmarried'
 ' Other-relative']

Race :  [' White' ' Black' ' Asian-Pac-Islander' ' Amer-Indian-Eskimo' ' Other']

Sex :  [' Male' ' Female']

Country :  [' United-States' ' Cuba' ' Jamaica' ' India' nan ' Mexico' ' South'
 ' Puerto-Rico' ' Honduras' ' England' ' Canada' ' Germany' ' Iran'
 ' Philippines' ' Italy' ' Poland' ' Columbia' ' Cambodia' ' Thailand'
 ' Ecuador' ' Laos' ' Taiwan' ' Haiti' ' Portugal' ' Dominican-Republic'
 ' El-Salvador' ' France' ' Guatemala' ' China' ' Japan' ' Yugoslavia'
 ' Peru' ' Outlying-US(Guam-USVI-etc)' ' Scotland' ' Trinadad&Tobago'
 ' Greece' ' Nicaragua' ' Vietnam' ' Hong' ' Ireland' ' Hungary'
 ' Holand-Netherlands']

Target :  [' <=50K' ' >50K']

有可能得到类似的结果，但是

  1. 使代码本身更紧凑（一行）；
  2. 答案也会更好，也DataFrame类似。
python

1 个回答

  1. Best Answer

    我找到了一个非常简短且合乎逻辑的选择：

    pd.DataFrame(adult_train_drop.apply(pd.unique)).rename(columns={0:'Unique values'})

    结论：

                    Unique values
Workclass       [ State-gov, Self-emp-not-inc, Private, Fed...
Education       [ Bachelors, HS-grad, 11th, Masters, 9th, ...
Martial_Status  [ Never-married, Married-civ-spouse, Divorce...
Occupation      [ Adm-clerical, Exec-managerial, Handlers-cl...
Relationship    [ Not-in-family, Husband, Wife, Own-child, ...
Race            [ White, Black, Asian-Pac-Islander, Amer-In...
Sex             [ Male, Female]
Country         [ United-States, Cuba, Jamaica, India, nan,...
Target          [ <=50K, >50K]
    • 2

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