如何从熊猫数据框中的两列中提取匹配的关键字？答案

【问题标题】：How to extract matching keywords from two columns in a pandas dataframe?如何从熊猫数据框中的两列中提取匹配的关键字？
【发布时间】：2019-10-12 23:12:49
【问题描述】：

我在数据框中有两列都是字符串，其中 column1 在 column2 中有一些匹配的关键字。我想在新列中从 column1 和 column2 中提取那些匹配的关键字。

df['column3']=df.column1.apply(lambda x : df.column2[df.column2.str.contains(x)]

我期待这样的输出

column1                     column2                 column3
A girl is going to market   girl market school      girl market
A girl is going to school   girl market school      girl school
The sky is blue in color    sky blue orange color   sky blue color

【问题讨论】：

标签： python-3.x pandas dataframe text-extraction

【解决方案1】：

使用apply

例如：

df["column3"] = df.apply(lambda x: " ".join(i for i in x["column2"].split() if i in x["column1"]),axis=1) 
print(df)

输出：

                     column1                column2         column3
0  A girl is going to market     girl market school     girl market
1  A girl is going to school     girl market school     girl school
2   The sky is blue in color  sky blue orange color  sky blue color

【讨论】：

【解决方案2】：

使用np.intersect1d

df['column3'] = df.apply(lambda x: ' '.join(np.intersect1d(x['column1'].split(),x['column2'].split())), axis=1)

输出

                     column1                    column2         column3
0  A girl is going to market         girl market school     girl market
1  A girl is going to school         girl market school     girl school
2   The sky is blue in color      sky blue orange color  blue color sky

如果顺序很重要

 df['column3'] = df.apply(lambda x: ' '.join(np.array(x['column1'].split())[np.in1d(x['column1'].split(),x['column2'].split())]), axis=1)

输出

                     column1                    column2         column3
0  A girl is going to market         girl market school     girl market
1  A girl is going to school         girl market school     girl school
2   The sky is blue in color      sky blue orange color  sky blue color

【讨论】：

【解决方案3】：

使用sets的交集（&）的另一种解决方案：

df['column3'] = df.apply(lambda x: ' '.join(set(x['column1'].split()) &
                                            set(x['column2'].split())), axis=1)

[出]

                     column1                column2         column3
0  A girl is going to market     girl market school     market girl
1  A girl is going to school     girl market school     girl school
2   The sky is blue in color  sky blue orange color  sky color blue

【讨论】：