How to batch process a dataset: Pandas chunks to load large files

Don't let large files jam your computer

Python data processing old iron should have encountered this situation: just got a dozen G CSV files, the urge to use pandas.read_csv () load, the results of the memory directly soared to 90% +, the program is stuck in the pro-mother do not recognize. This time do not rush to smash the keyboard.chunk-loading blitzIt'll save your dog's life!

To cite a real case: last month, an e-commerce friend wanted to analyze user behavior data, 20G log files with ordinary methods to load directly to the 16G memory of the computer strike. Later, he switched to chunked processing withProxy IP Pool for ipipgoDoing distributed collection not only doubles the processing speed, but also avoids IP blocking by the platform due to frequent requests.

Hands-on with chunked loading

Pandas comes with the chunksize parameter is a godsend for handling large files, the specific operation is simpler than making instant noodles:

import pandas as pd
chunk_size = 50000 Adjust according to the memory situation
for chunk in pd.read_csv('oversized file.csv', chunksize=chunk_size):
     Write your processing logic here
    process(chunk)

Note the three main points:
1. memory is like a girlfriend's patience, to besave. It is recommended to first estimate the number of chunks by dividing the total number of lines in the file by 10
2. Remember to delink unused variables after each chunk is processed.
3. When you need to calculate across chunks (e.g., tallying totals), you need to do a good job of accumulating as if you were saving private money.

When chunk loading meets proxy IP

If the data needs to be crawled and updated in real time, here's a tawdry operation: integrate proxy IP services into the data processing flow. For example, useAPI for ipipgoDynamic acquisition of proxies with multi-threading to handle different chunks of data.

The actual test in crawling an e-commerce platform data, after using ipipgo's rotating IP function, the collection success rate from 48% directly pull to 92%, the key is that their API response speed is fast enough, will not become a bottleneck in the processing process.

Guidelines on demining of common problems

Q: How to merge the data after chunking?
A：建议先用to_csv追加模式保存中间结果，最后统一合并。内存不够的话可以分批次合并，记得加代理IP防护，别让辛苦处理的数据在传输时丢了

Q: How to configure proxy IP into pandas?
A: If you are getting data via web request, you can set it up like this in the requests library:

proxies = {"http": "http://user:pass@ipipgo-proxy:port"}
response = requests.get(url, proxies=proxies)

Q: What if the processing time is too long?
A: three directions of optimization: ① on multi-threading / multi-processing ② upgrade ipipgo business package to get faster IP ③ move the data pre-processing step forward to the collection stage

Why ipipgo?

A bloody lesson learned from using seven or eight proxy service providers:
1. Some proxy IPs are said to be high-speed, but in reality they are slower than bicycles.
2. Frequent disappearance of overseas IPs
3. Customer service response is comparable to a sloth
And ipipgo'sMilitary-grade encrypted circuits+7×24 hours technical support, which is as steady as an old dog when processing millions of data. Especially their smart routing feature that automatically selects the fastest nodes is so important for scenarios that require real-time data processing.

Finally, a piece of advice: processing big data is like stir-fry, fire (chunk size) and seasoning (proxy IP) with a good, in order to make good food. The next time you encounter a large file do not just hard, try the combination of chunk loading + ipipgo, guaranteed to make your data processing process silky smooth to fly up to ~!