pandas 熊猫将文本特征转换为数值
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pandas convert text feature to numeric value
提问by Don Smythe
I can convert all text features in a pandas dataframe by casting to 'category' using the df.astype() method as below. However I find category hard to work with (eg for plotting data) and would prefer to create a new column of integers
我可以通过使用 df.astype() 方法转换为“类别”来转换Pandas数据框中的所有文本特征，如下所示。但是我发现 category 很难处理（例如用于绘制数据），并且更愿意创建一个新的整数列
#convert all objects to categories
object_types = dataset.select_dtypes(include=['O'])
for col in object_types:
dataset['{0}_category'.format(col)] = dataset[col].astype('category')
I can convert the text to integers using this hack:
我可以使用这个 hack 将文本转换为整数：
#convert all objects to int values
object_types = dataset.select_dtypes(include=['O'])
new_cols = {}
for col in object_types:
data_set = set(dataset[col].tolist())
data_indexed = {}
for i, item in enumerate(data_set):
data_indexed[item] = i
new_list = []
for item in dataset[col].tolist():
new_list.append(data_indexed[item])
new_cols[col]=new_list
for key, val in new_cols.items():
dataset['{0}_int_value'.format(key)] = val
But is there a better (or existing) way to do the same?
但是有没有更好的（或现有的）方法来做同样的事情？
回答by MaxU
I would use factorizemethod, which is designed for this particular task:
我会使用factorize方法，它是为这个特定任务设计的：
In [90]: x
Out[90]:
A B
9 c z
10 c z
4 b x
5 b y
1 a w
7 b z
In [91]: x.apply(lambda col: pd.factorize(col, sort=True)[0])
Out[91]:
A B
9 2 3
10 2 3
4 1 1
5 1 2
1 0 0
7 1 3
or:
或者：
In [92]: x.apply(lambda col: pd.factorize(col)[0])
Out[92]:
A B
9 0 0
10 0 0
4 1 1
5 1 2
1 2 3
7 1 0
回答by piRSquared
consider df
考虑 df
df = pd.DataFrame(dict(A=list('aaaabbbbcccc'),
B=list('wwxxxyyzzzzz')))
df
you can convert to integers like this
你可以像这样转换成整数
def intify(s):
u = np.unique(s)
i = np.arange(len(u))
return s.map(dict(zip(u, i)))
or shorter version
或更短的版本
def intify(s):
u = np.unique(s)
return s.map({k: i for i, k in enumerate(u)})
df.apply(intify)
Or in a single line
或者在一行中
df.apply(lambda s: s.map({k:i for i,k in enumerate(s.unique())}))
