How Can I Efficiently Read Large CSV Files in Python 2.7?

Reading Large CSV Files with Python 2.7

Navigating the challenge of reading colossal CSV files with Python 2.7 can evoke memory woes, especially with files exceeding 300,000 rows. To surmount this hurdle, it's crucial to avoid reading the entire file into memory.

Memory Management Techniques

Employing generators allows for memory-efficient processing. Instead of accumulating all rows in a list, yield each row individually. This approach, exemplified by the getstuff function's generator, reduces memory consumption significantly.

Additionally, consider optimizations like the dropwhile and takewhile functions from the itertools module. These facilitate efficient filtering by skipping irrelevant rows, further conserving memory.

Performance Optimization

Beyond memory management, boosting performance involves minimizing unnecessary operations. The getdata function should iterate directly over the getstuff generator, eliminating needless intermediate lists.

Example Usage

Reworking the code using generators yields a much more efficient solution:

def getstuff(filename, criterion):
    ...  # Same generator code as above

def getdata(filename, criteria):
    ...  # Same generator code as above

# Process rows directly
for row in getdata(somefilename, sequence_of_criteria):
    ...  # Process the current row

This code effectively processes one row at a time, vastly reducing memory usage and improving performance, even for immense CSV files.

The above is the detailed content of How Can I Efficiently Read Large CSV Files in Python 2.7?. For more information, please follow other related articles on the PHP Chinese website!