当前有一个DataFrame [1],我想要一个满足我的要求的输出[2]。到目前为止,我所做的是搜索特定的关键字并输出新的DataFrame。但是现在我还有一个附加的搜索条件“ avgSalience”。
像下面这样执行查询,结果仅在包含特定关键字的行中按预期进行,但是我想做的是搜索特定关键字,看看它们是否具有一定的avgSalience,如果是,我想输出这些单词和每个单词链接到相同的URL。
df=df[df['name'].str.lower().str.contains('google|facebook')]
df=df[df['avgSalience']>0.01]
[1]
+----------------------+------------+-------------------------+-----------+-------------------+
| url | date | word | mentioned | avgSalience |
|----------------------+------------+-------------------------+-----------+-------------------+
| newspaperarticle.com | 2018-12-22 | canada | 1 | 1.2 |
| articleUSA.com | 2018-12-23 | facebook | 2 | 0.7 |
| articleUSA.com | 2018-12-23 | bad | 3 | 1.1 |
| articleUSA.com | 2018-12-23 | strong | 1 | 0.5 |
+----------------------+------------+-------------------------+-----------+-------------------
[[2]期望的输出,如果我希望facebook关键字的平均显着性大于> 0.5
+----------------------+------------+-------------------------+-----------+-------------------+
| url | date | word | mentioned | avgSalience |
|----------------------+------------+-------------------------+-----------+-------------------+
|
| articleUSA.com | 2018-12-23 | facebook | 2 | 0.7 |
| articleUSA.com | 2018-12-23 | bad | 3 | 1.1 |
| articleUSA.com | 2018-12-23 | strong | 1 | 0.5 |
+----------------------+------------+-------------------------+-----------+-------------------
将它们组合在一起,这样应该可以工作
df=df[(df['name'].str.lower().str.contains('google|facebook')) & (df['avgSalience']>0.01)]