用二进制数字填充NaN值

Question

大家好，我有一些数据，在“性别”列中，它被列为男性或女性，当这些数据被翻译到google colab上时，它将所有数据作为“性别”行中的NaN进行传输，我想知道是否有一种方法可以让这些数据表示男性为0，女性为1。我尝试过使用replace函数，但是我一直得到与图中所示相同的错误，有帮助吗？Code/ErrorData

Answer 1

如果试图用整数替换字符串值，则应将

df.sex.replace(to_replace=["Male","Female"], value=["0", "1"])

如果你想走这条路的话，用类似的方法把它转换成整数

df['sex'] = df['sex'].astype(int)

Answer 2

enter code here
# import pandas library
import pandas as pd
data = pd.read_csv(file)

# creating a dict file
gender = {'male': 1,'female': 2}

# traversing through dataframe
# Gender column and writing
# values where key matches
data.Gender = [gender[item] for item in data.Gender]
print(data)

Answer 3

只是为了将示例数据复制为您的数据，并在以后的过程中对其进行解释，以解析它以获得所需的结果，希望它能有所帮助

#!/home/Karn_python3/bin/python
from __future__ import (absolute_import, division, print_function)
import pandas as pd
import numpy as np
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('max_colwidth', None)
pd.set_option('expand_frame_repr', False)


# Read CSV and create dataframe.
df = pd.read_csv('adult_test.csv')

# It appears as your column name might have spaces around it, so let's trim them first.
# first to avoid any mapping/processing issues of data
df = df.apply(lambda x: x.str.strip() if x.dtype == "object" else x)

# Create a dictionary and map that to the desired column, which is easy and
# faster than replace.
m = {'Male': 0, 'Female': 1}

# As there may be Nan values so, better to fill them with int values
# whatever you like as used fillna & used 0 and convert the dtype to int
# otherwise you will get it float.
df['Sex'] = df['Sex'].map(m).fillna(0).astype(int)
print(df.head(20))

结果输出：

                     Age         Workclass    fnlwgt     Education  Education_Num      Martial_Status         Occupation   Relationship   Race  Sex  Capital_Gain  Capital_Loss  Hours_per_week        Country  Target
0   |1x3 Cross validator               NaN       NaN           NaN            NaN                 NaN                NaN            NaN    NaN    0           NaN           NaN             NaN            NaN     NaN
1                     25           Private  226802.0          11th            7.0       Never-married  Machine-op-inspct      Own-child  Black    0           0.0           0.0            40.0  United-States  <=50K.
2                     38           Private   89814.0       HS-grad            9.0  Married-civ-spouse    Farming-fishing        Husband  White    0           0.0           0.0            50.0  United-States  <=50K.
3                     28         Local-gov  336951.0    Assoc-acdm           12.0  Married-civ-spouse    Protective-serv        Husband  White    0           0.0           0.0            40.0  United-States   >50K.
4                     44           Private  160323.0  Some-college           10.0  Married-civ-spouse  Machine-op-inspct        Husband  Black    0        7688.0           0.0            40.0  United-States   >50K.
5                     18               NaN  103497.0  Some-college           10.0       Never-married                NaN      Own-child  White    1           0.0           0.0            30.0  United-States  <=50K.
6                     34           Private  198693.0          10th            6.0       Never-married      Other-service  Not-in-family  White    0           0.0           0.0            30.0  United-States  <=50K.
7                     29               NaN  227026.0       HS-grad            9.0       Never-married                NaN      Unmarried  Black    0           0.0           0.0            40.0  United-States  <=50K.
8                     63  Self-emp-not-inc  104626.0   Prof-school           15.0  Married-civ-spouse     Prof-specialty        Husband  White    0        3103.0           0.0            32.0  United-States   >50K.
9                     24           Private  369667.0  Some-college           10.0       Never-married      Other-service      Unmarried  White    1           0.0           0.0            40.0  United-States  <=50K.
10                    55           Private  104996.0       7th-8th            4.0  Married-civ-spouse       Craft-repair        Husband  White    0           0.0           0.0            10.0  United-States  <=50K.
11                    65           Private  184454.0       HS-grad            9.0  Married-civ-spouse  Machine-op-inspct        Husband  White    0        6418.0           0.0            40.0  United-States   >50K.
12                    36       Federal-gov  212465.0     Bachelors           13.0  Married-civ-spouse       Adm-clerical        Husband  White    0           0.0           0.0            40.0  United-States  <=50K.
13                    26           Private   82091.0       HS-grad            9.0       Never-married       Adm-clerical  Not-in-family  White    1           0.0           0.0            39.0  United-States  <=50K.
14                    58               NaN  299831.0       HS-grad            9.0  Married-civ-spouse                NaN        Husband  White    0           0.0           0.0            35.0  United-States  <=50K.
15                    48           Private  279724.0       HS-grad            9.0  Married-civ-spouse  Machine-op-inspct        Husband  White    0        3103.0           0.0            48.0  United-States   >50K.
16                    43           Private  346189.0       Masters           14.0  Married-civ-spouse    Exec-managerial        Husband  White    0           0.0           0.0            50.0  United-States   >50K.
17                    20         State-gov  444554.0  Some-college           10.0       Never-married      Other-service      Own-child  White    0           0.0           0.0            25.0  United-States  <=50K.
18                    43           Private  128354.0       HS-grad            9.0  Married-civ-spouse       Adm-clerical           Wife  White    1           0.0           0.0            30.0  United-States  <=50K.
19                    37           Private   60548.0       HS-grad            9.0             Widowed  Machine-op-inspct      Unmarried  White    1           0.0           0.0            20.0  United-States  <=50K.

为了更好地组织数据：

因为我们也有Nan值，所以我们最好将它们合并到类似dict的m = {'Male': 0, 'Female': 1, np.nan: 0}中，这样我们就可以将它们全部映射，而不是稍后使用fillna

df = pd.read_csv('adult_test.csv')
df = df.apply(lambda x: x.str.strip() if x.dtype == "object" else x)
m = {'Male': 0, 'Female': 1, np.nan: 0}
df['Sex'] = df['Sex'].map(m)
print(df.head(20))

另一个带有replace的解决方案：

只是在再次使用dict的同时使用replace

df = pd.read_csv('adult_test.csv')
df = df.apply(lambda x: x.str.strip() if x.dtype == "object" else x)
m = {'Male': 0, 'Female': 1, np.nan: 0}
df = df.replace({'Sex': m})
print(df.head(20))

请参阅@jpp的答案Replace values in a pandas series via dictionary efficiently