根据Pandas中的字符串列表过滤出行 [英] Filter out rows based on list of strings in Pandas

问题描述

我有一个很大的时间序列数据帧(称为 df )，前5条记录如下:

I have a large time series data frame (called df), and the first 5 records look like this:

df

         stn     years_of_data  total_minutes avg_daily TOA_daily   K_daily
date                        
1900-01-14  AlberniElementary      4    5745    34.100  114.600 0.298
1900-01-14  AlberniWeather         6    7129    29.500  114.600 0.257
1900-01-14  Arbutus                8    11174   30.500  114.600 0.266
1900-01-14  Arrowview              7    10080   27.600  114.600 0.241
1900-01-14  Bayside                7    9745    33.800  114.600 0.295

目标:

我正在尝试删除列表中字符串中 任何 的行 出现在 'stn' 列中.因此，我基本上是在尝试过滤此数据集，以使其不包含以下列表中包含任何字符串的行.

I am trying to remove rows where any of the strings in a list are present in the 'stn' column. So,I am basically trying to filter this dataset to not include rows containing any of the strings in following list.

尝试:

remove_list = ['Arbutus','Bayside']

cleaned = df[df['stn'].str.contains('remove_list')]

返回:

出[78]:

stn years_of_data   total_minutes   avg_daily   TOA_daily   K_daily
date    

一无所有！

我尝试了一些引号，方括号甚至是lambda函数的组合；尽管我还很新，所以可能没有正确使用语法..

I have tried a few combinations of quotes, brackets, and even a lambda function; though I am fairly new, so probably not using syntax properly..

推荐答案

使用 isin :

cleaned = df[~df['stn'].isin(remove_list)]

In [7]:

remove_list = ['Arbutus','Bayside']
df[~df['stn'].isin(remove_list)]
Out[7]:
                          stn  years_of_data  total_minutes  avg_daily  \
date                                                                     
1900-01-14  AlberniElementary              4           5745       34.1   
1900-01-14     AlberniWeather              6           7129       29.5   
1900-01-14          Arrowview              7          10080       27.6   

            TOA_daily  K_daily  
date                            
1900-01-14      114.6    0.298  
1900-01-14      114.6    0.257  
1900-01-14      114.6    0.241  

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