在 Pandas 数据框中将值（例如性别）从字符串映射到 int

Question

我有一个名为的数据框df_base，看起来像这样。正如您所看到的，有一个名为Sexthat's maleor的列female。我想将这些值分别映射为 0 和 1。

+---+-------------+----------+--------+---------------------------------------------------+--------+-----+-------+-------+------------------+---------+-------+----------+
|   | PassengerId | Survived | Pclass |                       Name                        |  Sex   | Age | SibSp | Parch |      Ticket      |  Fare   | Cabin | Embarked |
+---+-------------+----------+--------+---------------------------------------------------+--------+-----+-------+-------+------------------+---------+-------+----------+
| 0 |           1 |        0 |      3 | Braund, Mr. Owen Harris                           | male   |  22 |     1 |     0 | A/5 21171        |    7.25 | NaN   | S        |
| 1 |           2 |        1 |      1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female |  38 |     1 |     0 | PC 17599         | 71.2833 | C85   | C        |
| 2 |           3 |        1 |      3 | Heikkinen, Miss. Laina                            | female |  26 |     0 |     0 | STON/O2. 3101282 |   7.925 | NaN   | S        |
| 3 |           4 |        1 |      1 | Futrelle, Mrs. Jacques Heath (Lily May Peel)      | female |  35 |     1 |     0 | 113803           |    53.1 | C123  | S        |
| 4 |           5 |        0 |      3 | Allen, Mr. William Henry                          | male   |  35 |     0 |     0 | 373450           |    8.05 | NaN   | S        |
+---+-------------+----------+--------+---------------------------------------------------+--------+-----+-------+-------+------------------+---------+-------+----------+

我在 StackOverflow 上看到了一些方法，但我想知道执行以下映射最有效的方法是什么：

+---------+---------+
| Old Sex | New Sex |
+---------+---------+
| male    |       0 |
| female  |       1 |
| female  |       1 |
| female  |       1 |
| male    |       0 |
+---------+---------+

我正在使用这个：

df_base['Sex'].replace(['male','female'],[0,1],inplace=True)

……但我不禁觉得这有点粗制滥造。有更好的方法吗？还有 using.loc但它会围绕 Dataframe 的行循环，因此效率较低，对吗？

Answer 1

我认为如果只有并且存在于列中，字典的使用会更好/map更快：malefemaleSex

df_base['Sex'] = df_base['Sex'].map(dict(zip(['male','female'],[0,1]))

什么是相同的：

df_base['Sex'] = df_base['Sex'].map({'male': 0,'female': 1})

解决方案如果仅存在female并且male值将布尔掩码转换为True/False整数1,0：

df_base['Sex'] = (df_base['Sex'] == 'female').astype(int)

表现：

np.random.seed(2019)

import perfplot    

def ma(df):
    df = df.copy()
    df['Sex_new'] = df['Sex'].map({'male': 0,'female': 1})
    return df

def rep1(df):
    df = df.copy()
    df['Sex'] = df['Sex'].replace(['male','female'],[0,1])
    return df

def nwhere(df):
    df = df.copy()
    df['Sex_new'] = np.where(df['Sex'] == 'male', 0, 1)
    return df

def mask1(df):
    df = df.copy()
    df['Sex_new'] = (df['Sex'] == 'female').astype(int)
    return df

def mask2(df):
    df = df.copy()
    df['Sex_new'] = (df['Sex'].values == 'female').astype(int)
    return df


def make_df(n):
    df = pd.DataFrame({'Sex': np.random.choice(['male','female'], size=n)})

    return df

perfplot.show(
    setup=make_df,
    kernels=[ma,  rep1, nwhere, mask1, mask2],
    n_range=[2**k for k in range(2, 18)],
    logx=True,
    logy=True,
    equality_check=False,  # rows may appear in different order
    xlabel='len(df)')

结论：

如果仅替换 2 个值是最慢的replace，numpy.where, map and mask则类似。为了提高性能，将 numpy 数组与.values.
此外，一切都取决于数据，因此最好用真实数据进行测试。