Detailed explanation of the sample code of pandas.DataFrame method of excluding specific rows in python-Python Tutorial-php.cn

Detailed explanation of the sample code of pandas.DataFrame method of excluding specific rows in python

高洛峰

Release： 2017-03-25 13:39:44

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This article mainly gives you a detailed explanation of the example code for excluding specific rows in pandas.DataFrame in python. The article gives detailed example code. I believe it has certain reference value for everyone's understanding and learning. Friends who need it can come and take a look below.

pandas.DataFrame excludes specific rows

If we want to filter only one or certain rows like Excel, we can use isin( ) method, pass in the values of the required rows in a list, you can also pass in a dictionary and specify columns for filtering.

But if we only want all content that does not contain a specific line, there is no isnotin() method. I encountered such a requirement at work today. After searching frequently, I found that I could only use isin() in another way to achieve this requirement.

The example is as follows:

In [3]: df = pd.DataFrame([[&#39;GD&#39;, &#39;GX&#39;, &#39;FJ&#39;], [&#39;SD&#39;, &#39;SX&#39;, &#39;BJ&#39;], [&#39;HN&#39;, &#39;HB&#39;
 ...: , &#39;AH&#39;], [&#39;HEN&#39;, &#39;HEN&#39;, &#39;HLJ&#39;], [&#39;SH&#39;, &#39;TJ&#39;, &#39;CQ&#39;]], columns=[&#39;p1&#39;, &#39;p2
 ...: &#39;, &#39;p3&#39;])

In [4]: df
Out[4]:
 p1 p2 p3
0 GD GX FJ
1 SD SX BJ
2 HN HB AH
3 HEN HEN HLJ
4 SH TJ CQ

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If you only want the two rows where p1 is GD and HN, you can do this:

In [8]: df[df.p1.isin([&#39;GD&#39;, &#39;HN&#39;])]
Out[8]:
 p1 p2 p3
0 GD GX FJ
2 HN HB AH

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But if we want To get data other than these two rows, you need to take a detour.

The principle is to first take out p1 and convert it into a list, then remove unnecessary rows (values) from the list, and then use isin() in the DataFrame

In [9]: ex_list = list(df.p1)

In [10]: ex_list.remove(&#39;GD&#39;)

In [11]: ex_list.remove(&#39;HN&#39;)

In [12]: ex_list
Out[12]: [&#39;SD&#39;, &#39;HEN&#39;, &#39;SH&#39;]

In [13]: df[df.p1.isin(ex_list)]
Out[13]:
 p1 p2 p3
1 SD SX BJ
3 HEN HEN HLJ
4 SH TJ CQ

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