Data Cleaning and Deduplication: Removing Noise from Scraped Data

# 1. Overview of Data Cleaning and Deduplication

Data cleaning and deduplication are crucial steps in the data processing workflow, aiming to enhance the quality and credibility of data. Data cleaning involves identifying and correcting errors, inconsistencies, and missing values within data, whereas data deduplication focuses on eliminating duplicate records. These processes are essential for ensuring the accuracy, integrity, and consistency of data, thus providing a reliable foundation for subsequent data analysis and decision-making.

# 2. Theoretical Basis of Data Cleaning and Deduplication

### 2.1 Principles and Methods of Data Cleaning

#### 2.1.1 Necessity of Data Cleaning

Data cleaning is a vital step in data processing, ensuring the accuracy, consistency, and completeness of data. Dirty data, which contains errors, inconsistencies, or missing values, can negatively impact data analysis and decision-making. Data cleaning addresses these issues and lays a solid foundation for subsequent data processing and analysis.

#### 2.1.2 Common Methods of Data Cleaning

There are various methods of data cleaning, ***mon data cleaning methods include:

***Handling Missing Values:** Methods for dealing with missing values include deleting them, filling them with averages or medians, or using machine learning algorithms to predict them.
***Identifying and Handling Outliers:** Outliers are extreme values that significantly deviate from the data distribution. Methods for identifying outliers include statistical methods (like standard deviation or interquartile range) or machine learning algorithms. Handling outliers can involve deleting them or replacing them with medians or averages.
***Data Type Conversion:** Convert data into the correct type, such as converting strings to numbers or dates to timestamps.
***Data Normalization:** Transform data into a standard format, like formatting dates as "YYYY-MM-DD" or currencies as "￥123.45".
***Data Validation:** Check if data conforms to specific rules or constraints, such as verifying the format of email addresses or the length of phone numbers.

### 2.2 Algorithms and Techniques for Data Deduplication

#### 2.2.1 Hash Table-based Deduplication Algorithm

A hash table is a data structure that uses a hash function to map data into key-value pairs. The hash table-based deduplication algorithm achieves deduplication by hashing data items into a hash table. If the data item already exists in the hash table, it indicates that the data item is a duplicate.

**Code Block:**

def hash_table_deduplication(data):
    """
    Hash table-based deduplication algorithm

    Parameters:
        data: list of items to be deduplicated

    Returns:
        List of deduplicated data items
    """
    hash_table = {}
    deduplicated_data = []
    for item in data:
        if item not in hash_table:
            hash_table[item] = True
            deduplicated_data.append(item)
    return deduplicated_data

**Logical Analysis:**

* First, create a hash table `hash_table`.
* Iterate over each element `item` in the `data` list.
* Check if `item` exists in `hash_table`. If not, add `item` to `hash_table` and append it to the `deduplicated_data` list.
* Return the `deduplicated_data` list, which contains deduplicated data items.

#### 2.2.2 Sorting and Merging-based Deduplication Algorithm

Sorting and merging-based deduplication algorithms achieve deduplication by sorting the data and merging adjacent duplicates.

**Code Block:**

def sort_and_merge_deduplication(data):
    """
    Sorting and merging-based deduplication algorithm

    Parameters:
        data: list of items to be deduplicated

    Returns:
        List of deduplicated data items
    """
    data.sort()
    deduplicated_data = []
    for i in range(1, len(data)):
        if data[i] != data[i - 1]:
            deduplicated_data.append(data[i])
    return deduplicated_data

**Logical Analysis:**

* First, sort the `data` list.
* Iterate over each element `item` in the `data` list.
* Check if `item` is equal to the previous element `data[i - 1]`. If not, append `item` to the `deduplicated_data` list.
* Return the `deduplicated_data` list, which contains deduplicated data items.

# 3.1 Practical Operations of Data Cleaning

#### 3.1.1 Handling Missing Values

**Methods for Handling Missing Values**

There are multiple methods for handling missing values, ***mon methods include:

- **Deletion Method:** Directly delete records or features containing missing values.
- **Mean Imputation:** Fill missing values with the mean of the feature.
- **Median Imputation:** Fill missing values with the median of the feature.
- **Mode Imputation:** Fill missing values with the mode of the feature.
- **KNN Imputation:** Use the K-nearest neighbors algorithm to estimate missing values based on the feature values of the K most similar records to the record with missing values.
- **Regression Imputation:** Use a regression model to predict missing values based on other feature values.

**Principles for Choosing a Method for Handling Missing Values**

When choosing a method for handling missing values, consider t