如何用 NaN 替换 pandas 中的值？

Question

我是 pandas 的新手，我正在尝试在 Dataframe 中加载 csv。我的数据缺失值表示为？ ，我正在尝试用标准缺失值替换它 - NaN

请帮我解决这个问题。 我已尝试通读 Pandas 文档，但我无法理解。

def readData(filename):
    DataLabels =["age", "workclass", "fnlwgt", "education", "education-num", "marital-status",
               "occupation", "relationship", "race", "sex", "capital-gain",
               "capital-loss", "hours-per-week", "native-country", "class"] 

    # ==== trying to replace ? with Nan using na_values
    rawfile = pd.read_csv(filename, header=None, names=DataLabels, na_values=["?"])
    age = rawfile["age"]
    print(age)
    print(rawfile[25:40])

    #========trying to replace ?
    rawfile.replace("?", "NaN")
    print(rawfile[25:40])
    return rawfile

    age   workclass  fnlwgt      education  education-num       marital-status        occupation    relationship                 race    sex  capital-gain  capital-loss  hours-per-week  native-country   class
25   56   Local-gov  216851      Bachelors             13   Married-civ-spouse      Tech-support         Husband                White   Male             0             0              40   United-States    >50K
26   19     Private  168294        HS-grad              9        Never-married      Craft-repair       Own-child                White   Male             0             0              40   United-States   <=50K
27   54           ?  180211   Some-college             10   Married-civ-spouse                 ?         Husband   Asian-Pac-Islander   Male             0             0              60           South    >50K
28   39     Private  367260        HS-grad              9             Divorced   Exec-managerial   Not-in-family                White   Male             0             0              80   United-States   <=50K
29   49     Private  193366        HS-grad              9   Married-civ-spouse      Craft-repair         Husband                White   Male             0             0              40   United-States   <=50K

数据

成人资料

39, State-gov, 77516, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Male, 2174, 0, 40, United-States, <=50K
50, Self-emp-not-inc, 83311, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 13, United-States, <=50K
38, Private, 215646, HS-grad, 9, Divorced, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
53, Private, 234721, 11th, 7, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, <=50K
28, Private, 338409, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, Black, Female, 0, 0, 40, Cuba, <=50K
37, Private, 284582, Masters, 14, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, <=50K
49, Private, 160187, 9th, 5, Married-spouse-absent, Other-service, Not-in-family, Black, Female, 0, 0, 16, Jamaica, <=50K
52, Self-emp-not-inc, 209642, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, >50K
31, Private, 45781, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 14084, 0, 50, United-States, >50K
42, Private, 159449, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 5178, 0, 40, United-States, >50K
37, Private, 280464, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, Black, Male, 0, 0, 80, United-States, >50K
30, State-gov, 141297, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, Asian-Pac-Islander, Male, 0, 0, 40, India, >50K
23, Private, 122272, Bachelors, 13, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 30, United-States, <=50K
32, Private, 205019, Assoc-acdm, 12, Never-married, Sales, Not-in-family, Black, Male, 0, 0, 50, United-States, <=50K
40, Private, 121772, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, Asian-Pac-Islander, Male, 0, 0, 40, ?, >50K
34, Private, 245487, 7th-8th, 4, Married-civ-spouse, Transport-moving, Husband, Amer-Indian-Eskimo, Male, 0, 0, 45, Mexico, <=50K
25, Self-emp-not-inc, 176756, HS-grad, 9, Never-married, Farming-fishing, Own-child, White, Male, 0, 0, 35, United-States, <=50K
32, Private, 186824, HS-grad, 9, Never-married, Machine-op-inspct, Unmarried, White, Male, 0, 0, 40, United-States, <=50K
38, Private, 28887, 11th, 7, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, <=50K
43, Self-emp-not-inc, 292175, Masters, 14, Divorced, Exec-managerial, Unmarried, White, Female, 0, 0, 45, United-States, >50K
40, Private, 193524, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 60, United-States, >50K
54, Private, 302146, HS-grad, 9, Separated, Other-service, Unmarried, Black, Female, 0, 0, 20, United-States, <=50K
35, Federal-gov, 76845, 9th, 5, Married-civ-spouse, Farming-fishing, Husband, Black, Male, 0, 0, 40, United-States, <=50K
43, Private, 117037, 11th, 7, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 2042, 40, United-States, <=50K
59, Private, 109015, HS-grad, 9, Divorced, Tech-support, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
56, Local-gov, 216851, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, >50K
19, Private, 168294, HS-grad, 9, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 40, United-States, <=50K
54, ?, 180211, Some-college, 10, Married-civ-spouse, ?, Husband, Asian-Pac-Islander, Male, 0, 0, 60, South, >50K
39, Private, 367260, HS-grad, 9, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 80, United-States, <=50K
49, Private, 193366, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
23, Local-gov, 190709, Assoc-acdm, 12, Never-married, Protective-serv, Not-in-family, White, Male, 0, 0, 52, United-States, <=50K
20, Private, 266015, Some-college, 10, Never-married, Sales, Own-child, Black, Male, 0, 0, 44, United-States, <=50K
45, Private, 386940, Bachelors, 13, Divorced, Exec-managerial, Own-child, White, Male, 0, 1408, 40, United-States, <=50K
30, Federal-gov, 59951, Some-college, 10, Married-civ-spouse, Adm-clerical, Own-child, White, Male, 0, 0, 40, United-States, <=50K
22, State-gov, 311512, Some-college, 10, Married-civ-spouse, Other-service, Husband, Black, Male, 0, 0, 15, United-States, <=50K
48, Private, 242406, 11th, 7, Never-married, Machine-op-inspct, Unmarried, White, Male, 0, 0, 40, Puerto-Rico, <=50K
21, Private, 197200, Some-college, 10, Never-married, Machine-op-inspct, Own-child, White, Male, 0, 0, 40, United-States, <=50K
19, Private, 544091, HS-grad, 9, Married-AF-spouse, Adm-clerical, Wife, White, Female, 0, 0, 25, United-States, <=50K
31, Private, 84154, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 38, ?, >50K
48, Self-emp-not-inc, 265477, Assoc-acdm, 12, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, <=50K
31, Private, 507875, 9th, 5, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 43, United-States, <=50K
53, Self-emp-not-inc, 88506, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, <=50K
24, Private, 172987, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 50, United-States, <=50K
49, Private, 94638, HS-grad, 9, Separated, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
25, Private, 289980, HS-grad, 9, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 35, United-States, <=50K
57, Federal-gov, 337895, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, Black, Male, 0, 0, 40, United-States, >50K
53, Private, 144361, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 38, United-States, <=50K
44, Private, 128354, Masters, 14, Divorced, Exec-managerial, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
41, State-gov, 101603, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
29, Private, 271466, Assoc-voc, 11, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 43, United-States, <=50K
25, Private, 32275, Some-college, 10, Married-civ-spouse, Exec-managerial, Wife, Other, Female, 0, 0, 40, United-States, <=50K
18, Private, 226956, HS-grad, 9, Never-married, Other-service, Own-child, White, Female, 0, 0, 30, ?, <=50K
47, Private, 51835, Prof-school, 15, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 1902, 60, Honduras, >50K
50, Federal-gov, 251585, Bachelors, 13, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 55, United-States, >50K
47, Self-emp-inc, 109832, HS-grad, 9, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 60, United-States, <=50K
43, Private, 237993, Some-college, 10, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, >50K
46, Private, 216666, 5th-6th, 3, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, Mexico, <=50K
35, Private, 56352, Assoc-voc, 11, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, Puerto-Rico, <=50K
41, Private, 147372, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 48, United-States, <=50K
30, Private, 188146, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 5013, 0, 40, United-States, <=50K
30, Private, 59496, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 2407, 0, 40, United-States, <=50K
32, ?, 293936, 7th-8th, 4, Married-spouse-absent, ?, Not-in-family, White, Male, 0, 0, 40, ?, <=50K
48, Private, 149640, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, <=50K
42, Private, 116632, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 45, United-States, >50K
29, Private, 105598, Some-college, 10, Divorced, Tech-support, Not-in-family, White, Male, 0, 0, 58, United-States, <=50K
36, Private, 155537, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
28, Private, 183175, Some-college, 10, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
53, Private, 169846, HS-grad, 9, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 40, United-States, >50K
49, Self-emp-inc, 191681, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, >50K
25, ?, 200681, Some-college, 10, Never-married, ?, Own-child, White, Male, 0, 0, 40, United-States, <=50K
19, Private, 101509, Some-college, 10, Never-married, Prof-specialty, Own-child, White, Male, 0, 0, 32, United-States, <=50K
31, Private, 309974, Bachelors, 13, Separated, Sales, Own-child, Black, Female, 0, 0, 40, United-States, <=50K
29, Self-emp-not-inc, 162298, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 70, United-States, >50K
23, Private, 211678, Some-college, 10, Never-married, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
79, Private, 124744, Some-college, 10, Married-civ-spouse, Prof-specialty, Other-relative, White, Male, 0, 0, 20, United-States, <=50K

Answer 1

您可以使用replace仅为该列替换它：

df['workclass'].replace('?', np.NaN)

或整个df：

df.replace('?', np.NaN)

更新

好的，我发现了您的问题，默认情况下，如果您不传递分隔符，则read_csv将使用逗号','作为分隔符。

您的数据，尤其是您遇到问题的一个示例：

54, ?, 180211, Some-college, 10, Married-civ-spouse, ?, Husband, Asian-Pac-Islander, Male, 0, 0, 60, South, >50K

实际上有一个逗号和一个空格作为分隔符，所以当你传递na_value=['?']时，这不匹配，因为你所有的值前面都有一个空格字符，你无法观察到。

如果你把你的行改成这样：

rawfile = pd.read_csv(filename, header=None, names=DataLabels, sep=',\s', na_values=["?"])

那么您应该会发现一切正常：

27      54               NaN  180211  Some-college             10 

Answer 2

使用 numpy.nan

Numpy - 用 NaN 替换数字

import numpy as np
df.applymap(lambda x: np.nan if x == '?' else x)

Answer 3

df=df.replace({'?':np.NaN})

使用字典将任何值替换为 NaN

Answer 4

好的，我得到了它：

 #========trying to replace ?
    newraw= rawfile.replace('[?]', np.nan, regex=True)
    print newraw[25:40]

Answer 5

伙计们，有很多方法，这是最好的，如果你认为你的 CSV 文件有任何 NAN 对象，比如“丢失”，只需使用

    rawfile = pd.read_csv("Property_train.csv", na_values=["missing"])

Answer 6

有时会有空格？ 在由 informatica 或 HANA 等系统生成的文件中

首先你需要去除 DataFrame 中的空白

temp_df_trimmed = temp_df.apply(lambda x: x.str.strip() if x.dtype == "object" else x)

然后应用函数替换数据

temp_df_trimmed['RC'] = temp_df_trimmed['RC'].map(lambda x: np.nan if x=="?"  else x)

Answer 7

另一种方法是使用mask ，在满足条件的情况下将这些值替换为NaN ：

In [7]: df.mask(df == '?')
Out[7]: 
    age  workclass  fnlwgt     education  education-num      marital-status       occupation
25   56  Local-gov  216851     Bachelors             13  Married-civ-spouse     Tech-support
26   19    Private  168294       HS-grad              9       Never-married     Craft-repair
27   54        NaN  180211  Some-college             10  Married-civ-spouse              NaN
28   39    Private  367260       HS-grad              9            Divorced  Exec-managerial
29   49    Private  193366       HS-grad              9  Married-civ-spouse     Craft-repair

Answer 8

所以有几种方法可以做到这一点，非常相似但有一点不同你必须在两者中导入 numpy

import numpy as np
df.replace("?", np.nan, inplace=True)

inplace=True 表示立即对 dataframe 进行更改，并将更新

第二种方法

import numpy as np
df = df.replace("?", np.nan)

第二个，您必须将更改更新到 dataframe