you can also use ranges by using:

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

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 directly query your DataFrame for this information.

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

Or similarly search for ranges:

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

You can use query, i.e.:

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

Slicing data with pandas

Given a dataframe like this:

    RPT_Date  STK_ID STK_Name  sales
0 1980-01-01       0   Arthur      0
1 1980-01-02       1    Beate      4
2 1980-01-03       2    Cecil      2
3 1980-01-04       3     Dana      8
4 1980-01-05       4     Eric      4
5 1980-01-06       5    Fidel      5
6 1980-01-07       6   George      4
7 1980-01-08       7     Hans      7
8 1980-01-09       8   Ingrid      7
9 1980-01-10       9    Jones      4

There are multiple ways of selecting or slicing the data.

Using .isin

The most obvious is the .isin feature. You can create a mask that gives you a series of True/False statements, which can be applied to a dataframe like this:

mask = df['STK_ID'].isin([4, 2, 6])

mask
0    False
1    False
2     True
3    False
4     True
5    False
6     True
7    False
8    False
9    False
Name: STK_ID, dtype: bool

df[mask]
    RPT_Date  STK_ID STK_Name  sales
2 1980-01-03       2    Cecil      2
4 1980-01-05       4     Eric      4
6 1980-01-07       6   George      4

Masking is the ad-hoc solution to the problem, but does not always perform well in terms of speed and memory.

With indexing

By setting the index to the STK_ID column, we can use the pandas builtin slicing object .loc

df.set_index('STK_ID', inplace=True)
         RPT_Date STK_Name  sales
STK_ID                           
0      1980-01-01   Arthur      0
1      1980-01-02    Beate      4
2      1980-01-03    Cecil      2
3      1980-01-04     Dana      8
4      1980-01-05     Eric      4
5      1980-01-06    Fidel      5
6      1980-01-07   George      4
7      1980-01-08     Hans      7
8      1980-01-09   Ingrid      7
9      1980-01-10    Jones      4

df.loc[[4, 2, 6]]
         RPT_Date STK_Name  sales
STK_ID                           
4      1980-01-05     Eric      4
2      1980-01-03    Cecil      2
6      1980-01-07   George      4

This is the fast way of doing it, even if the indexing can take a little while, it saves time if you want to do multiple queries like this.

Merging dataframes

This can also be done by merging dataframes. This would fit more for a scenario where you have a lot more data than in these examples.

stkid_df = pd.DataFrame({"STK_ID": [4,2,6]})
df.merge(stkid_df, on='STK_ID')
   STK_ID   RPT_Date STK_Name  sales
0       2 1980-01-03    Cecil      2
1       4 1980-01-05     Eric      4
2       6 1980-01-07   George      4

Note

All the above methods work even if there are multiple rows with the same 'STK_ID'

You can also achieve similar results by using ‘query’ and @:

eg:

df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']})
df = pd.DataFrame({'A' : [5,6,3,4], 'B' : [1,2,3, 5]})
list_of_values = [3,6]
result= df.query("A in @list_of_values")
result
   A  B
1  6  2
2  3  3