Featured Post

Step-by-Step Guide to Reading Different Files in Python

-
Image
 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...
Post a Comment

vi Editor Top Commands for Ubuntu OS

-
I have given VI editor commands to use in Ubuntu operating system. You can practice using them for your benefit and you can complete your work quickly.

Ubuntu vi editor commands

To begin changing or adding to text with vi, you can enter Insert or Replace modes, as shown in the following list. When you enter Insert or Replace mode, the characters you type will appear in the text document (as opposed to being interpreted as commands). 

vi filename

This is the first command to enter into editor mode. Once you enter into editor mode, you need to enter other commands to complete your editing work.

Press the Esc key to exit to Normal mode after you are done inserting or replacing text.

List of VI Editor Commands..

i—Typed text appears before current character.
a—Typed text appears after current character.
o—Open a new line below current line to begin typing.
s—Erase current character and replace with new text.
c?—Replace ? with l, w, $, or c to change the current letter, word, end of line, or line.
r—Replace current character with the next one you type.
Shift+i—Typed text appears at the beginning of current line.
Shift+a—Typed text appears at the end of current line.
Shift+o—Open a new line above current line to begin typing.
Shift+s—Erase current line and enter new text.
Shift+c—Erase from cursor to end of line and enter new text.
Shift+r—Overwrite as you type from current character going forward.

Shortcut keys to delete or paste


x—Delete text under cursor.
d?—Replace ? with l, w, $, or d to cut the current letter, word, or end of line from cursor or entire line.
y?—Replace ? with l, w, or $ to copy (yank) the current letter, word, or end of line from cursor.
p—Pastes cut or yanked text after cursor.
Shift+x—Delete text to left of cursor.
Shift+d—Cut from cursor to end of line.
Shift+y—Yank current line .
Shift+p—Pastes cut or yanked text before cursor.

How to Save VI editor changes...

Press - Esc key
:x
Press Enter
Your work will be saved.


Summary

The above commands useful to work with VI editor. You can complete your task quickly and saves lot of time.

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

Step-by-Step Guide to Reading Different Files in Python

-
Image
 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...
Read more