Filter dataframe rows if value in column is in a set list of values [duplicate]

I have a 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']

<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']. That means I want a syntax like this:

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?

Use the isin method:

rpt[rpt['STK_ID'].isin(stk_list)]

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.

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:

>>> 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, e.g.

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

We can join these strings with the regex 'or' character | and pass the string to str.contains to filter the 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.

For example,

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

would match PANDAS, PanDAs, paNdAs123, and so on.

you can also use ranges by using:

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