Regular Expression to Remove Rows

Question

It's a very interesting question and I am seeking help of experts to understand more about it and how to do it. I have a DataFrame (which I made while parsing data from Big Iron... still exists). Now I want to remove some rows by using regular expression but don't know how does it work in Pandas.

24 | DRFT.146.856 |    Dollar-  |  (60.00) | DEBITS-  |  0.00 |  CREDITSDRA- | 0.00   
25 |   0616-21.01 |      2407   |  WAYZAT  |   TMCD   | JUNE  |      16,DRA  |2013   
26 |          AND | CORRECTION  |JOURNAL00 |    <DB>  |KLRETY | CATEGORYDRA- |    *   
27 | DRFT.146.867 |    Dollar-  | (200.00) | DEBITS-  |  0.00 |  CREDITSDRA- | 0.00   
28 | DRFT.146.922 |   Dollar-   | (25.00)  |DEBITS-   | 0.00  | CREDITSDRA-  |0.00   
29 | DRFT.146.963 |    Dollar-  | (100.00) | DEBITS-  |  0.00 |  CREDITSDRA- | 0.00   
30 | DRFT.146.964 |    Dollar-  | (100.00) | DEBITS-  |  0.00 |  CREDITSDRA- | 0.00  

The row of concern is 25 & 26 where the data is not following any pattern. Any clue.

Answer 1

A couple of possible contenders:

In [11]: df[2].str.contains('Dollar')
Out[11]:
0     True
1    False
2    False
3     True
4     True
5     True
6     True
Name: 2, dtype: bool

In [12]: df[3].str.startswith('(')
Out[12]:
0     True
1    False
2    False
3     True
4     True
5     True
6     True
Name: 3, dtype: bool

Doing this kind of thing is always a bit of a dark art (as there is usually a lot of data and some could look very similar to the good data)...

In [13]: df[df[3].str.startswith('(')]
Out[13]:
    0             1        2         3        4       5            6   7
0  24  DRFT.146.856  Dollar-    (60.00)  DEBITS-   0.00  CREDITSDRA-   0
3  27  DRFT.146.867  Dollar-   (200.00)  DEBITS-   0.00  CREDITSDRA-   0
4  28  DRFT.146.922  Dollar-    (25.00)  DEBITS-   0.00  CREDITSDRA-   0
5  29  DRFT.146.963  Dollar-   (100.00)  DEBITS-   0.00  CREDITSDRA-   0
6  30  DRFT.146.964  Dollar-   (100.00)  DEBITS-   0.00  CREDITSDRA-   0