Question

我具有功能=> city，它是分类数据，即字符串，但是不是使用replace()进行硬编码吗？有什么聪明的方法吗？

train['city'].unique()
Output: ['city_149', 'city_83', 'city_16', 'city_64', 'city_100', 'city_21',
       'city_114', 'city_103', 'city_97', 'city_160', 'city_65',
       'city_90', 'city_75', 'city_136', 'city_159', 'city_67', 'city_28',
       'city_10', 'city_73', 'city_76', 'city_104', 'city_27', 'city_30',
       'city_61', 'city_99', 'city_41', 'city_142', 'city_9', 'city_116',
       'city_128', 'city_74', 'city_69', 'city_1', 'city_176', 'city_40',
       'city_123', 'city_152', 'city_165', 'city_89', 'city_36', .......]

我正在尝试什么：

train.replace(['city_149', 'city_83', 'city_16', 'city_64', 'city_100', 'city_21',
           'city_114', 'city_103', 'city_97', 'city_160', 'city_65',
           'city_90', 'city_75', 'city_136', 'city_159', 'city_67', 'city_28',
           'city_10', 'city_73', 'city_76', 'city_104', 'city_27', 'city_30',
           'city_61', 'city_99', 'city_41', 'city_142', 'city_9', 'city_116',
           'city_128', 'city_74', 'city_69', 'city_1', 'city_176', 'city_40',
           'city_123', 'city_152', 'city_165', 'city_89', 'city_36', .......], [1,2,3,4,5,6,7,8,9....], inplace=True)

有没有更好的方法可以将数据转换为数字？因为唯一值的数量为123。所以我需要对1,2,3,4，... 123中的数字进行硬编码以进行转换。提出一些更好的方法将其转换为数值。

Answer 1

尝试pd.factorize()：

train['city'] = pd.factorize(train.city)[0]

或categorical dtypes：

train['city'] = train['city'].astype('category').cat.codes

例如：

>>> train
       city
0  city_151
1  city_149
2  city_151
3  city_149
4  city_149
5  city_149
6  city_151
7  city_151
8  city_150
9  city_151

factorize：

train['city'] = pd.factorize(train.city)[0]

>>> train
   city
0     0
1     1
2     0
3     1
4     1
5     1
6     0
7     0
8     2
9     0

或astype('category')：

train['city'] = train['city'].astype('category').cat.codes

>>> train
   city
0     2
1     0
2     2
3     0
4     0
5     0
6     2
7     2
8     1
9     2

Answer 2

您可以通过mapping完成此操作：

   value_mapper = dict(zip(train['city'].unique(), np.arange(1, 124)))
    train['city'].map(value_mapper)

或更惯用的categorical data：

pd.Categorical(train['city']).codes

Answer 3

如果您的值始终在整数前带有下划线，则列表理解可能对您有用：

data = [int(x.split('_')[-1]) for x in train['city']]

理解会遍历x中的每个train['city']，将x拆分为下划线分隔的部分，并将最后一部分转换为整数。如果您有多个下划线，例如foo_bar_5，则此方法有效。

如何将分类数据转换为数值数据？

3 个答案: