Is this what you want to achieve:

import pandas as pd
df = pd.DataFrame({'A':['row 1','row 2','drop row','row 4','row 5',
                        'drop row','row 6','row 7','drop row','row 9']})

df1 = df[df['A']!='drop row']

print (df)
print (df1)

Original Dataframe:

          A
0     row 1
1     row 2
2  drop row
3     row 4
4     row 5
5  drop row
6     row 6
7     row 7
8  drop row
9     row 9

New DataFrame with rows dropped:

       A
0  row 1
1  row 2
3  row 4
4  row 5
6  row 6
7  row 7
9  row 9

While you cannot skip rows based on content, you can skip rows based on index. Here are some options for you:

skip n number of row:

df = pd.read_csv('xyz.csv', skiprows=2)
#this will skip 2 rows from the top

skip specific rows:

df = pd.read_csv('xyz.csv', skiprows=[0,2,5])
#this will skip rows 1, 3, and 6 from the top
#remember row 0 is the 1st line

skip nth row in the file

#you can also skip by counts. 
#In below example, skip 0th row and every 5th row from there on

def check_row(a):
    if a % 5 == 0:
        return True
    return False

df = pd.read_csv('xyz.txt', skiprows= lambda x:check_row(x))

More details of this can be found in this link about skip rows

Since you cannot do that using skiprows, I could think of this way as efficient :

df = pd.read_csv(filePath)

df = df.loc[df['column1']=="Rows to keep"]

Not a python solution but, this would be absurdly simple to achieve using grep/bash.

printf "Index\tColumn\s1\tColumn\s2\n" > master_file
    for j in *.txt #bunch of text files
      do
        grep -v "Rows to drop" < "$j" >> master_file
    done

Depending on how many files you have and how large, this take a while, but you don't have to read the files into memory first, which presumably is the main reason you want to cherry pick your rows in the first place.

NOTE: You can use subprocess if you want the operation embedded into a python workflow - see here.