使用另一列中的预定义类别，根据其中存在的文本词对 'string' 列进行分类

Question

我有一个包含电子邮件域的 pandas 列，如下所示：

Sno  Domain_IDs
1   herowire.com
2   xyzenerergy.com
3   financial.com
4   oo-loans.com
5   okwire.com
6   cleaneneregy.com
7   pop-advisors.com

等等....

我在单独的数据框中有以下类别：

Sno category
1   contains wire
2   contains energy
3   contains loans
4   contains advisors

我想创建一个将数据分类如下的数据框：

Sno Domain_IDS         category
1   herowire.com       contains wire
2   xyzenerergy.com    contains energy
3   financial.com      others
4   oo-loans.com       contains loans
5   okwire.com         contains wire
6   cleaneneregy.com   contains energy
7   pop-advisors.com   contains advisors

我尝试使用 lambda 函数和使用 "if else" 语句的标准循环，通过使用

"emailAddress.str.contains('wire')"

包含子句，但出现以下错误：

AttributeError: 'str' object has no attribute 'str'

不知何故，我无法解析数据框中的单行文本。请帮忙。

Answer 1

lst = ["wire", "energy", "loans","advisors"]
def fun(a):
    for i in lst:
        if i in a:
            return i
    return "others"
df["category"] = df.Domain_IDs.apply(lambda x: fun(x))
df

  Sno        Domain_IDs category
0   1      herowire.com     wire
1   2   xyzenenergy.com   energy
2   3     financial.com   others
3   4      oo-loans.com    loans
4   5        okwire.com     wire
5   6   cleanenergy.com   energy
6   7  pop-advisors.com advisors

Answer 2

在域中查找模式，提取并创建类别

pat =  '('+'|'.join(cat['Sno category'].str.split().str[-1])+')'
df['category'] = ('contains ' + df['Domain_IDs'].str.extract(pat)).fillna('other')

   Sno  Domain_IDs          category
0   1   herowire.com        contains wire
1   2   xyzenenergy.com     contains energy
2   3   financial.com       other
3   4   oo-loans.com        contains loans
4   5   okwire.com          contains wire
5   6   cleaneneregy.com    other
6   7   pop-advisors.com    contains advisors

Answer 3

此解决方案允许多个分类：

categories = pd.DataFrame({"category": ["wire", "energy", "loans", "advisors"]})
domains = pd.DataFrame({"Sno": list(range(1, 10)),
                        "Domain_IDs": [
                            "herowire.com",
                            "xyzenergy.com",
                            "financial.com",
                            "oo-loans.com",
                            "okwire.com",
                            "cleanenergy.com",
                            "pop-advisors.com",
                            "energy-advisors.com",
                            "wire-loans.com"]})    
categories["common"] = 0
domains["common"] = 0

possibilities = pd.merge(categories, domains, how="outer")
possibilities["satisfied"] = possibilities.apply(lambda row: row["category"] in row["Domain_IDs"], axis=1)

所以只过滤满足的类别：

possibilities[possibilities["satisfied"]]

给出：

    category  common           Domain_IDs  Sno satisfied
0       wire       0         herowire.com    1      True
4       wire       0           okwire.com    5      True
8       wire       0       wire-loans.com    9      True
10    energy       0        xyzenergy.com    2      True
14    energy       0      cleanenergy.com    6      True
16    energy       0  energy-advisors.com    8      True
21     loans       0         oo-loans.com    4      True
26     loans       0       wire-loans.com    9      True
33  advisors       0     pop-advisors.com    7      True
34  advisors       0  energy-advisors.com    8      True

使用另一列中的预定义类别，根据其中存在的文本词对 'string' 列进行分类

Categorize a 'string' column based on the text word present in it using pre-defined categories in another column

categories

dataframe

python-3.x

pandas

data-science