First of all, 1GB is not huge - pretty much any modern device can keep that in its working memory. Second, pandas doesn't let you poke around the CSV file, you can only tell it how much data to 'load' - I'd suggest using the built-in csv module if you want to do more advanced CSV processing.
Unfortunately, the csv module's reader() will produce an exhaustible iterator for your file so you cannot just build it as a simple loop and wait for the next lines to become available - you'll have to collect the new lines manually and then feed them to it to achieve the effect you want, something like:
import csv
import time
filename = "path/to/your/file.csv"
with open(filename, "rb") as f: # on Python 3.x use: open(filename, "r", newline="")
reader = csv.reader(f) # create a CSV reader
header = next(reader) # grab the first line and keep it as a header reference
print("CSV header: {}".format(header))
for row in reader: # iterate over the available rows
print("Processing row: {}".format(row)) # process each row however you want
# file exhausted, entering a 'waiting for new data' state where we manually read new lines
while True: # process ad infinitum...
reader = csv.reader(f.readlines()) # create a CSV reader for the new lines
for row in reader: # iterate over the new rows, if any
print("Processing new row: {}".format(row)) # process each row however you want
time.sleep(10) # wait 10 seconds before attempting again
Beware of the edge cases that may break this process - for example, if you attempt to read new lines as they are being added some data might get lost/split (in dependence of the flushing mechanism used for addition), if you delete previous lines the reader might get corrupted etc. If possible at all, I'd suggest controlling the CSV writing process in such a way that it informs explicitly your processing routines.
UPDATE: The above is processing the CSV file line by line, it never gets loaded whole into the working memory. The only part that actually loads more than one line in memory is when an update to the file occurs where it picks up all the new lines because it's faster to process them that way and, unless you're expecting millions of rows of updates between two checks, the memory impact would be negligible. However, if you want to have that part processed line-by-line as well, here's how to do it:
import csv
import time
filename = "path/to/your/file.csv"
with open(filename, "rb") as f: # on Python 3.x use: open(filename, "r", newline="")
reader = csv.reader(f) # create a CSV reader
header = next(reader) # grab the first line and keep it as a header reference
print("CSV header: {}".format(header))
for row in reader: # iterate over the available rows
print("Processing row: {}".format(row)) # process each row however you want
# file exhausted, entering a 'waiting for new data' state where we manually read new lines
while True: # process ad infinitum...
line = f.readline() # collect the next line, if any available
if line.strip(): # new line found, we'll ignore empty lines too
row = next(csv.reader([line])) # load a line into a reader, parse it immediately
print("Processing new row: {}".format(row)) # process the row however you want
continue # avoid waiting before grabbing the next line
time.sleep(10) # wait 10 seconds before attempting again
