Featured Post

Top Questions People Ask About Pandas, NumPy, Matplotlib & Scikit-learn — Answered!

-
Image
 Whether you're a beginner or brushing up on your skills, these are the real-world questions Python learners ask most about key libraries in data science. Let’s dive in! 🐍 🐼 Pandas: Data Manipulation Made Easy 1. How do I handle missing data in a DataFrame? df.fillna( 0 ) # Replace NaNs with 0 df.dropna() # Remove rows with NaNs df.isna(). sum () # Count missing values per column 2. How can I merge or join two DataFrames? pd.merge(df1, df2, on= 'id' , how= 'inner' ) # inner, left, right, outer 3. What is the difference between loc[] and iloc[] ? loc[] uses labels (e.g., column names) iloc[] uses integer positions df.loc[ 0 , 'name' ] # label-based df.iloc[ 0 , 1 ] # index-based 4. How do I group data and perform aggregation? df.groupby( 'category' )[ 'sales' ]. sum () 5. How can I convert a column to datetime format? df[ 'date' ] = pd.to_datetime(df[ 'date' ]) ...
Post a Comment

10 Excusive Steps You need for Web Scrapping

-
Here're ten Python technics to clean the scraped data. The scraped Text has unwanted hidden data. So, as part of cleaning it try to remove these ten in your data.

10 Steps for Web scrapping

Data is prime input for text analytics projects. After cleaning, you can feed to Machine/Deep Learning systems.
  1. Removing HTML tags
  2. Tokenization
  3. Removing unnecessary tokens and stop-words
  4. Handling contractions
  5. Correcting spelling errors
  6. Stemming
  7. Lemmatization
  8. Tagging
  9. Chunking
  10. Parsing

10 Technics to Clean Text in Python
10 Technics to Clean Text in Python


1. Removing HTML tags

The unstructured text contains a lot of noise ( data from web pages, blogs, and online repositories.)when you use web/screen scraping. 

The HTML tags, JavaScript, and Iframe tags typically don't add much value to understanding and analyzing text. Our purpose is to remove HTML tags, and other noise.


2. Tokenization

  • Tokens are independent and minimal textual components. And have a definite syntax and semantics. A paragraph of text or a text document has several elements. Those you can further break down into clauses, phrases, and words. 
  • The popular tokenization techniques include sentence and word tokenization. These, you can use to break down a text document (or corpus) into sentences. And each sentence into words. 
  • Thus, tokenization is the process of breaking down or splitting textual data into smaller and more meaningful components called tokens.


Python is popular in Text analytics. Here, you will find various cleaning techniques used in text analytics.
Text Analytics in Python 


3. Removing Unnecessary Stop Words

Stopwords have little or no significance and are usually removed from a text when processing it. These usually occur most frequently if you aggregate a corpus of text based on the particular tokens and their frequencies. Words like "a," "the," "and," and so on are stopwords.


4. Handling contractions

The best examples of contractions are you'll, it's, etc.

5. Correcting spelling errors

Auto correcting spelling errors. While doing a Google search, you will find it corrects your spelling automatically.

6. Stemming

Here you can reduce words to the root level. The best example is Snowball, this you stem it to root level as Snow and Ball.


7. Lemmatization

Based on the context, bring the words to the root level, and make them meaningful.


8. Tagging

This is the concept of group particular words under a Tag.


9. Chunking

It is of constructing from various words of Verbs, Nouns, Adjectives, etc. Check out here on Data Chunking.


10. Parsing

The data will pass through some syntax rules. The output will then feed to Machine Learning systems. The syntax rules vary from project to project.

Comments

Post a Comment

Thanks for your message. We will get back you.

Popular posts from this blog

SQL Query: 3 Methods for Calculating Cumulative SUM

-
Image
SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT t1.id, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON t1.id >= t2.id GROUP BY t1.id, t1.value ORDER BY t1.id; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than...
Read more

5 SQL Queries That Popularly Used in Data Analysis

-
Image
 Here are five popular SQL queries frequently used in data analysis. 1. SELECT with Aggregations Summarize data by calculating aggregates like counts, sums, averages, etc. SELECT department, COUNT(*) as employee_count, AVG(salary) as average_salary FROM employees GROUP BY department; 2. JOIN Operations  Combine data from multiple tables based on a related column. SELECT e.employee_id, e.name, d.department_name FROM employees e JOIN departments d ON e.department_id = d.department_id; 3. WHERE Clause for Filtering Filter records based on specified conditions. SELECT * FROM sales WHERE sale_date BETWEEN '2024-01-01' AND '2024-12-31'   AND amount > 1000; 4. ORDER BY Clause for Sorting Sort results in ascending or descending order based on one or more columns. SELECT product_name, price FROM products ORDER BY price DESC; 5. GROUP BY with HAVING Clause Group records and apply conditions to the aggregated results. SELECT department, SUM(salary) as total_salaries FROM employ...
Read more

Big Data: Top Cloud Computing Interview Questions (1 of 4)

-
Image
The below are frequently asked interview questions on Cloud computing: 1) What is the difference between Cloud and Grid? Grid: -Information service -Security Service -Data management -Execution Manageement Cloud: - Maintains up-to-date information of resources -Create VMs according to user requirement -Application deploment -User management 2) What are the different cloud standards? -Interoperability standards -Security standards -Portability Standards -Governance and Risk standards 3) What are the two different sub-systems in Cloud computing ? -Management sub system -Resource sub system 4)What is Cloud compouting? The promise of cloud computing is ubiquitous access to a broad set of applications and services, which are delivered over the network to multiple customer. 5) Why we need specialized network for Cloud services? The public Internet is the simplest choice for delivering cloud-based services. In this model, the cloud provider simply purchases Inter...
Read more