如果列中的值在一组值的列表中，则过滤数据帧行[重复]

本文翻译自：Filter dataframe rows if value in column is in a set list of values [duplicate]

This question already has an answer here: 这个问题已经在这里有了答案：

• How to filter Pandas dataframe using 'in' and 'not in' like in SQL 7 answers 如何像SQL 7 一样使用“ in”和“ not in”来过滤熊猫数据框

I have a Python pandas DataFrame rpt : 我有一个Python pandas DataFrame rpt ：

rpt
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 47518 entries, ('000002', '20120331') to ('603366', '20091231')
Data columns:
STK_ID                    47518  non-null values
STK_Name                  47518  non-null values
RPT_Date                  47518  non-null values
sales                     47518  non-null values

I can filter the rows whose stock id is '600809' like this: rpt[rpt['STK_ID'] == '600809'] 我可以像这样过滤库存ID为'600809'的行： rpt[rpt['STK_ID'] == '600809']

<class 'pandas.core.frame.DataFrame'>
MultiIndex: 25 entries, ('600809', '20120331') to ('600809', '20060331')
Data columns:
STK_ID                    25  non-null values
STK_Name                  25  non-null values
RPT_Date                  25  non-null values
sales                     25  non-null values

and I want to get all the rows of some stocks together, such as ['600809','600141','600329'] . 我想将所有股票的所有行放在一起，例如['600809','600141','600329'] 。 That means I want a syntax like this: 这意味着我想要这样的语法：

stk_list = ['600809','600141','600329']

rst = rpt[rpt['STK_ID'] in stk_list] # this does not works in pandas 

Since pandas not accept above command, how to achieve the target? 由于大熊猫不接受上述命令，如何实现目标？

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#1楼

参考：https://stackoom.com/question/oct3/如果列中的值在一组值的列表中-则过滤数据帧行-重复

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#2楼

Use the isin method. 使用isin方法。 rpt[rpt['STK_ID'].isin(stk_list)] . rpt[rpt['STK_ID'].isin(stk_list)] 。

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#3楼

您还可以通过以下方式使用范围：

b = df[(df['a'] > 1) & (df['a'] < 5)]

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#4楼

isin() is ideal if you have a list of exact matches, but if you have a list of partial matches or substrings to look for, you can filter using the str.contains method and regular expressions. 如果您有一个完全匹配的列表，则isin()是理想的选择，但是如果要查找部分匹配或子字符串的列表，则可以使用str.contains方法和正则表达式进行过滤。

For example, if we want to return a DataFrame where all of the stock IDs which begin with '600' and then are followed by any three digits: 例如，如果我们要返回一个DataFrame，其中所有以'600'开头，然后再跟任意三位数字的股票ID：

>>> rpt[rpt['STK_ID'].str.contains(r'^600[0-9]{3}$')] # ^ means start of string
...   STK_ID   ...                                    # [0-9]{3} means any three digits
...  '600809'  ...                                    # $ means end of string
...  '600141'  ...
...  '600329'  ...
...      ...   ...

Suppose now we have a list of strings which we want the values in 'STK_ID' to end with, eg 假设现在有一个字符串列表，我们希望以'STK_ID'的值结尾，例如

endstrings = ['01$', '02$', '05$']

We can join these strings with the regex 'or' character | 我们可以将这些字符串与正则表达式“或”字符连接起来| and pass the string to str.contains to filter the DataFrame: 并将字符串传递给str.contains以过滤DataFrame：

>>> rpt[rpt['STK_ID'].str.contains('|'.join(endstrings)]
...   STK_ID   ...
...  '155905'  ...
...  '633101'  ...
...  '210302'  ...
...      ...   ...

Finally, contains can ignore case (by setting case=False ), allowing you to be more general when specifying the strings you want to match. 最后， contains可以忽略大小写（通过设置case=False ），使您在指定要匹配的字符串时更加通用。

For example, 例如，

str.contains('pandas', case=False)

would match PANDAS , PanDAs , paNdAs123 , and so on. 会匹配PANDAS ， PanDAs ， paNdAs123等。

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#5楼

You can also directly query your DataFrame for this information. 您也可以直接在DataFrame中查询此信息。

rpt.query('STK_ID in (600809,600141,600329)')

Or similarly search for ranges: 或类似地搜索范围：

rpt.query('60000 < STK_ID < 70000')

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#6楼

您可以使用query ，即：

b = df.query('a > 1 & a < 5')