Data Cleaning Pipeline: Pandas Missing Value Processing in Action

When a crawler encounters data mutilation, is your cleaning process hardcore enough?

Brothers engaged in data collection understand that the hard work of climbing down the data is often missing arms and legs. Like we go to the supermarket to buy special goods, there are always a few empty spaces on the shelves especially eye-catching. At this time, if you will not deal with missing values, subsequent analysis can definitely make you doubt your life. Today we will chatter how to use Pandas to patch the data, by the way, said the proxy IP in this matter in the beginning of the wonderful use.

The Hidden Killer of Data Cleaning

First, a word of caution to the guys:Don't just come up and delete data when dealing with missing values.! Especially when collecting with proxy IP, a lot of missing is actually a website anti-climbing mechanism at work. Last week a buddy feedback, he climbed an e-commerce platform, the price field 30% are empty, and later found out that it was triggered by the frequency limit. At this time, if you directly delete the data, it is equivalent to white work.

A common pitfall scenario in real life:

Top 3 Tips for Patching Your Data

It is recommended to use ipipgo's proxy pool with the processing here, theirCity-level IP rotationIt is particularly suitable for filling in missing data. This is done in three steps:

1. Flagging suspicious data: circle missing areas with df.loc, record timestamps and capture IPs
2. Intelligent backfill strategy: numerical type is filled with the average value of 5% before and after, and the category type is directly labeled "to be reclaimed".
3. Secondary collection verification: change ip ipgo different geographical IP re-request, avoid being ban

 As a real example
import pandas as pd
from ipipgo import ProxyPool Here we access the ipipgo SDK.

proxy = ProxyPool(key='your key')
problem_data = df[df['price'].isna()]

for index, row in problem_data.iterrows():
    new_proxy = proxy.get(city='Shanghai') Automatically switch city node
     Code to re-initiate the request...

Proxy IP's Anti-Rollover Guide

Anyone who has used ipipgo knows that they have aAbnormal Traffic Fusing Mechanism. It is especially useful in data cleansing, when an IP continuously triggers missing alerts, the system will automatically cut to the alternate line. Here to teach you a small trick: the missing records of geographic location information, and proxy IP belongs to do correlation analysis, can quickly locate the target site's geographic blocking strategy.

For example, when helping customers deal with travel platform data recently, it was found that using Shenzhen IP to collect hotel prices, the missing rate was as high as 40%. After switching to ipipgo's Kunming node, the missing rate was directly reduced to 5% or less. This kind of practical experience, just read the document can not learn.

Frequently Asked Questions QA

Q: Why is the data more messy after filling with fillna() instead?
A: 80% of the data type is not distinguished, the text field do not fill with mean value! It is recommended to use df.dtypes to check the type first, and then with the proxy IP to re-capture the key fields.

Q: What is a reasonable setting for concurrent requests for ipipgo?
A: According to the actual test, 5-10 threads for ordinary websites are just enough to work with their smart router. If you are collecting Amazon and other strictly regulated websites, it is recommended to control within 3 threads and use their smart router.Residential AgentsThe line is more stable.

Q: How do you verify the reliability of the processed data?
A: It is recommended to use the comparison verification method: collect the same batch of data with proxy IPs in different regions, and do cross-verification of the three sets of results. ipipgo supports simultaneous acquisition of IP resources in the north and south of the country, which is especially suitable for this kind of verification scenario.

The Last Rule of Survival

Remember, data cleansing is not a one-time deal. Especially if you are using a crawler for continuous collection, it is recommended to use ipipgo's daily24-hour dynamic IP packagesDo incremental cleaning. When you encounter stubborn type of missing data, don't fight to the death, change the IP segment and fight again. After all, in the data battlefield, living long is the real skill.