How to Maximize the Value of Web-Scraped Data: Essential Techniques for Effective Data Cleaning

Data Discovery

Data Discovery is an initial step in data analysis that involves examining and visualizing data to uncover insights, identify patterns, and detect errors within the dataset. During this process, we can discover errors or inconsistencies in the data.

There are several functions and methods in the pandas' library that facilitate data exploration, such as head(), tail(), and describe(). These functions allow us to inspect and visualize the data manually. However, one limitation of these functions is that they can be time-consuming, especially for larger datasets. A Python library called Pandas Profiling comes to the rescue to overcome this limitation. With just a few lines of code, Pandas Profiling generates detailed reports and visualizations of the dataset.

This blog will explore using Pandas Profiling to generate comprehensive data reports. We will also compare the reports generated before and after applying various data-cleaning techniques. By utilizing Pandas Profiling, we can save time and effort in data exploration. The library can be easily installed using pip, and the generated reports will be in HTML format, which can be conveniently viewed in any web browser.

The detailed report comprises three main sections: Overview, Alerts, and Reproduction. In the Overview section, it is highlighted that our dataset contains duplicate rows, which is an error that must be addressed before proceeding with data analysis. Removing duplicate rows from the dataset is known as data deduplication. The subsequent section of the blog provides the code and steps to perform data deduplication to rectify this issue.

After applying the data deduplication technique, let's look at the overview section of the updated report. It shows that no duplicate rows remain in our dataset. By exploring the other sections of the report, we can identify patterns or errors in the data, gain insights, and develop a basic understanding of the dataset. With this initial data-cleaning step completed, we can now learn about additional data-cleaning techniques.

Data Cleaning Methods

Data Deduplication

When working with any dataset, it is crucial to check for duplicate records, as they can significantly impact the accuracy of our analysis. Duplicate records can skew data representation, create pattern confusion, and obscure important information. Moreover, they consume unnecessary storage resources by storing the same data multiple times. Therefore, the initial and crucial step in data cleaning is to perform data deduplication, which involves removing duplicate records from the dataset.

In our specific dataset, the column labeled 'Product Link' contains unique links to each product on the Amazon page. As such, we can leverage this column to identify and eliminate duplicate records. Below is the code snippet that accomplishes this task:

data.drop_duplicates(subset=["Product Link"], inplace=True)

After reading and storing our data in the variable 'data,' we can use the built-in pandas' function drop_duplicates() to perform data deduplication. This function takes a parameter, the column name used, to identify and check for duplicates. It compares the values in the specified column and removes duplicate records from the dataset.

URL Normalization

URL normalization is a data-cleaning process that simplifies and standardizes URLs extracted during web scraping. URLs obtained through scraping often contain unnecessary or redundant components, such as query parameters or trailing slashes. URL normalization aims to remove these extraneous elements while preserving the essential parts that uniquely identify the product or resource. This process results in shorter, more readable URLs, which enhances the accuracy and efficiency of tasks involving URL processing.

In this example, the urllib.parse module from the Python standard library is used to normalize the URL. The urlparse() function breaks down the URL into its components, and the urljoin() function reconstructs the URL by removing the query parameters and trailing the slash. The result is a normalized URL that contains only the essential parts needed to identify the product.

To apply URL normalization to multiple URLs in a dataset, you can put the above code snippet inside a loop and iterate through each URL, normalizing them individually.

This approach creates a pandas dataframe with the extracted URLs as a single row. We can then use pandas string manipulation functions to extract the required URL and perform URL normalization. The simplified URL will be displayed below.

In this code, we use the str.extract() function to extract the central part of the URL, excluding any query parameters or unnecessary components. The result is a new column in the dataframe, Normalized URL, containing simplified URLs.

By running this code, you can see the simplified URL in the output, which will exclude the query parameters and retain only the essential parts that uniquely identify the product.

https://www.amazon.in/Lifelong-LLHFD322-Digital-functions-Technology/dp/B0B242W2WZ/

Whitespace Trimming

During the data scanning process, it is observed that the product names in the dataset contain leading and trailing whitespaces. These whitespaces can introduce inconsistencies and lead to errors in data analysis, particularly with string comparison, matching, and grouping. Removing these unwanted whitespaces is essential to ensure data consistency and accuracy during analysis.

The strip() function removes the leading and trailing whitespaces from the product name in this code. The result is a cleaned product name without any unwanted spaces.

To apply this whitespace removal process to all product names in a dataset, you can put the above code snippet inside a loop and iterate through each product name, removing the leading and trailing whitespaces one by one.

During the analysis of product names, it was discovered that certain products do not have a name, and their remaining data needs to be included. Further investigation revealed that these products were marked as 'Out of Stock' at the time of data scraping, resulting in the unavailability of their data. To obtain the details of these 'Out of Stock products, periodic scraping needs to be performed. When a product transitions to 'In Stock,' its data can be extracted.

To achieve this, you can set up a scraping process that runs at regular intervals to check the availability status of the products. When a product changes from 'Out of Stock' to 'In Stock,' the scraping process can be triggered, allowing you to extract the relevant data.

It's important to note that implementing a periodic scraping process requires automation and scheduling mechanisms to ensure timely data updates. This can be achieved through cron jobs or scheduling libraries in Python, which allow you to automate the scraping process and run it at specified intervals.

By periodically scraping the website and capturing the data when the products are 'In Stock,' you can ensure that the details of these products are obtained and available for analysis.

Numeric Formatting

When scraping numeric values, they are often obtained in string format and may include commas and decimal points. However, for numerical calculations and statistical analysis, it is crucial to have consistent data in integer or float format. Therefore, removing commas and decimal points is crucial to ensure data consistency and convert it into a suitable format for numerical analysis.

The replace() function removes commas and decimal points from the numeric value in this code. The result is a cleaned numeric value without any commas or decimal points. Depending on your specific requirements, you can then choose to convert the cleaned value to either an integer or a float.

To apply this removal process to all numeric values in a column, you can put the above code snippet inside a loop and iterate through each value, removing commas and decimal points individually.

Upon examining the dataset, it is evident that numeric formatting should be applied to the columns 'Number of Ratings,' 'Original Price,' and 'Offer Price.' However, it is crucial to note that the 'Star Rating' column should not undergo numeric formatting, as the decimal points in this column play a significant role in the analysis.

Standardizing Units of Measurement

When working with datasets, it is common to encounter data represented in different measurement units. This variation can lead to inconsistencies and make data analysis challenging. To overcome this issue, it is crucial to standardize the units of measurement within the dataset.

In our specific dataset, we have identified three columns that require a unit of measurement standardization: "min_temperature," "item_weight," and "capacity." These columns likely contain values expressed in different units. We can convert all measurements within these columns to a standard unit by applying unit standardization techniques, ensuring consistency and facilitating easier data comparison and analysis.

After standardizing the units of measurement in the 'min_temperature' column, it is essential to apply numeric formatting to facilitate ease of analysis. Since the temperatures are provided in two different units and string format, converting them to a numeric format will ensure consistency and enable numerical calculations.

To standardize the units of measurement in the 'capacity' column and convert all values to liters, the same logic can be applied as in the previous example. The only difference is the conversion formula used.

After standardizing the units of measurement in the 'item_weight' column and converting all values to kilograms, we can further enhance the dataset by removing the unit from each data entry and updating the column names to reflect the standardized units.

In this updated code, after extracting the numeric values from the 'item_weight' column using regular expressions and converting them to float, we apply the conversion formula to convert the values to kilograms. Next, we remove the unit from each data entry by updating the column names to reflect the standardized units.

By executing this code, the 'item_weight' column will be standardized, all values will be converted to kilograms, and the column names will reflect the standardized units: 'min_temperature (in Celsius),' 'capacity (in liters),' and 'item_weight (in kg).' The resulting dataframe will have the updated column names and units ready for further analysis.

Column Merging

To improve data quality and reduce complexity, it is essential to identify and remove redundant or duplicate columns that represent the same information before performing data analysis. This process, called column merging, helps streamline the dataset and enhance its integrity.

In our specific dataset, we identified two columns named 'wattage' and 'output_wattage,' representing the same information. However, upon inspection, it is observed that the 'wattage' column does not contain any values. Consequently, it can be safely removed from the dataset.

In this code, the drop() function removes the 'wattage' column by specifying axis=1 to indicate that we want to drop a column. After executing this code, the resulting dataframe will no longer contain the 'wattage' column, removing the redundant column from the dataset.

You can apply the same code snippet or methodology to remove any other unwanted columns from your dataset, ensuring data cleanliness and improved data analysis.

data = data.drop('wattage',axis=1)

Column Extraction

Column splitting is a data-cleaning technique that separates information within a single column into multiple columns. It is beneficial when a column contains multiple pieces of information, allowing for more accessible data analysis and visualization.

Our dataset has a column called 'Best Sellers Rank' that includes two ranks for each product: one for the Home and Kitchen category and another for the Air Fryer category. To improve data analysis, we need to split this column into two separate columns: 'Home and Kitchen Rank' and 'Air Fryer Rank.'

We can utilize the str.split() function, which splits the 'Best Sellers Rank' column based on a comma delimiter. By setting the expand=True parameter, the split values will be assigned to separate columns. We will remove the leading '#' symbol and any extra whitespace using the str.replace() and str.strip() functions to ensure clean and consistent data.

Executing this code will result in a dataframe with two new columns: 'Home and Kitchen Rank' and 'Air Fryer Rank,' containing the respective ranks for each product in their appropriate columns. This column-splitting process greatly facilitates data analysis and improves our understanding of the data.

To apply the column-splitting code to all the products in our dataset, we can utilize a loop.

Conclusion

This blog post emphasizes the importance of addressing errors in scraped data and provides techniques to handle them effectively. It highlights the significant impact that data errors can have on our analysis and emphasizes the need for accurate data to facilitate accurate insights and learning.

Data cleaning is highlighted as a critical step in data analysis, ensuring the data is error-free and ready for analysis. By ensuring data cleanliness, we can maximize the potential of our data and enhance our analytical efforts.

The blog also promotes Actowiz Solutions' web scraping services as a reliable solution for obtaining clean and ready-to-use data. Actowiz Solutions specializes in providing high-quality data that empowers analytical endeavors. Readers are encouraged to take the next step towards maximizing their data's potential by engaging Actowiz Solutions' web scraping services and are prompted to contact them for further information.

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