Costco Dataset: Warehouse Sales Data Analysis

Why does Costco use proxy ip for sales data collection?

Recently a lot of retail analytics friends are studying Costco's warehouse data, but directly climb the official website data 80% will hit the nail. Like last week, the old king wanted to crawl the merchandise inventory data, just ran the script for two days, the IP address was pulled black - this is typical of theAnti-crawl mechanism in actionThe

At this time the proxy ip will come in handy, equivalent to the crawler program to wear a "cloak of invisibility". For example, with ipipgo's residential agent, each request is changed to a real user's network environment, the server simply can not distinguish between machines or real people. Tested with their dynamic IP pool, continuous collection for a week did not trigger the wind control.

import requests
proxies = {
    'http': 'http://用户名:密码@proxy.ipipgo.com:31052',
    'https': 'http://用户名:密码@proxy.ipipgo.com:31052'
}
response = requests.get('https://www.costco.com/api/sales', proxies=proxies)

Three Steps to Multi-Regional Comparison Analysis

The difference in Costco's pricing strategy in different states is pretty interesting. Trying to compare electronics prices in Los Angeles and New York, you can only see a single region's data using local IPs alone. That's when it's needed:

1. In the background of ipipgo to choose the United States West IP room → Grabbing California data
2. Switching U.S. East Residential Proxy IP → Get New York Quotes
3. Setting up automatic IP rotation rules → cut different nodes every hour

Practical tips for avoiding backcrawl

Don't think that just because you hang up an agent that everything is fine, you have to be strategic with your combinations:

- The requesting head camouflage technique: randomly switch browser fingerprints, don't always use the python default header
- Decentralized flow controlDon't pile on data at 10 a.m., learn from real users who work in the middle of the night.
- Failure to retry mechanism: When you get a 403 error, sleep for 30 seconds, cut ipipgo's new IP and try again.

Data Cleaning Visualization Examples

Getting the raw data has to be processed first, cleansing the promotion date field like this:

import pandas as pd
df['promotion_date'] = pd.to_datetime(df['event_date'].str[:10])
monthly_sales = df.groupby(pd.Grouper(key='promotion_date', freq='M'))['sales'].sum()
monthly_sales.plot(kind='line', title='Monthly Sales Trends 2023')

Frequently Asked Questions

Q: Can't I use a free proxy? Do I have to buy ipipgo?
A: Nine out of ten free proxies are unstable and disconnect in the middle of the collection is equal to doing nothing. ipipgo's commercial-grade proxies have exclusive channels, and the last time I drove 20 threads at the same time, I didn't fall off the line!

Q: Does the data analysis need to be refreshed in real time?
A: Depends on the specific needs. Inventory data is recommended to be picked once an hour, and price data is enough to be picked twice a day. In ipipgo background can be set up timed tasks, remember to openIntelligent Rate Adjustmentfunctionality

Q: How do I break the CAPTCHA when I encounter it?
A: Don't fight hard, switch ipipgo's immediatelyHigh Stash Agents+ Modify browser fingerprints. If you can't go on the manual coding service, they have an integrated solution at home

the right tool saves effort and leads better results

Using ipipgo proxy to pick Costco data for more than a year, the biggest feeling is just three points:
1. Dynamic residential proxies are really solid against anti-climbing, especially theirIP Survival CycleBipartite Parenting
2. The nodes are widely distributed enough to hold cross-country comparisons.
3. Technical customer service response fast, the last time I encountered cookie validation problems, ten minutes to give the solution

Engaging in data analysis is like fighting a war, and proxy IP is your scout. If you choose a reliable partner, you will be able to get over half of the hurdle of data collection. Especially like ipipgo such a veteran service provider, with a solid heart, at least do not have to worry about tomorrow's IP pool suddenly failed, do not you think so?