About the basic operations of pandas.DataFrame in python

This article mainly introduces you to the method of excluding specific rows in pandas.DataFrame in python. The article gives detailed example codes. I believe it has certain reference value for everyone's understanding and learning. Friends who need it can follow Let’s take a look together.

Preface

When you use Python for data analysis, a data structure that you often use is the pandas DataFrame. About the basic operations of pandas.DataFrame in python , you can check this article.

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([['GD', 'GX', 'FJ'], ['SD', 'SX', 'BJ'], ['HN', 'HB'
 ...: , 'AH'], ['HEN', 'HEN', 'HLJ'], ['SH', 'TJ', 'CQ']], columns=['p1', 'p2
 ...: ', 'p3'])

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

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

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

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('GD')

In [11]: ex_list.remove('HN')

In [12]: ex_list
Out[12]: ['SD', 'HEN', 'SH']

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

Summary

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