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...
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Beginner's Tutorial on SaS Visual Analytics
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SAS visual analytics is a completely new architecture from SAS. It has the capability to manage large amounts of data and bring it into memory to analyze it, explore it and publish reports.
Although the data amounts are massive — up to 1.1 billion rows of data, the SAS LASR Analytic Server, to use its full name, was designed to be intuitive to users without an advanced degree in computer science.
A report from Simply hired.
All about SAS analytics Server - The SAS Analytic Server begins with an eight-blade server with 96 processor cores, 768 gigabytes memory and 4.8 terabytes (TB) of disk storage.
The upper end of the reference configurations is 96 blades with 1,152 cores, 9.2 TB memory and 57.6 TB of disk storage, enough disk space to store the entire Library of Congress six times.
Where to Learn SAS Visual Analytics
The speed of in-memory architecture offers tremendous benefit. Organisations can explore huge data volumes and get answers to critical questions in near-real time. SAS Visual Analytics offers a double bonus: the speed of in-memory analytics plus self-service eliminates the traditional wait for IT-generated reports.
Businesses today must base decisions on insight gleaned from data, and that process needs to be close to instantaneous.
Despite being user-friendly, the server has been developed to make it easy for IT to manage the data and secure it without sacrificing usability, Guard said. It includes a visual analytics explorer for ad hoc analysis and discovery, he added.
SAS Visual Analytics helps business users to visually explore data on their own. But it goes well beyond traditional query and reporting.
Running on low-cost, industry-standard blade servers, its high-performance in-memory architecture delivers answers in seconds or minutes instead of hours or days.
Where SAS differs
SAS analytics differ from many business intelligence (BI) solutions which simply move data from a SQL database into memory. That does not support regressions or logistics models becase those capabilities are not built into databases.
In banking, analysts may develop hundreds of models a year; with SAS they will be able to do it 10 to 20 times faster. The importance of changing models rapidly is incredibly important in the banking industry.
A demo on SAS visual analytics:
The computerWorld says-SAS also plans to broaden its user base by making its software more appealing beyond computer statisticians and data scientists.
To this end, the company has paired its data exploration software, called SAS Visual Analytics, with its software for developing predictive models, called SAS Visual Statistics.
The pairing can allow non-data scientists, such as line of business analysts and risk managers, to predict future trends based on current data.
How companies will benefit
With SAS Analytic Server companies can solve problems they had never dealt with before because they it offers speed of analysis at a large scale. Users don’t have to analyze samples; they can look at everything.
AS Visual Analytics will let us quickly dig into our big data to uncover opportunities, and in time, to fully exploit them.”The SAS LASR Analytic Server, uses Hadoop (embedded Hadoop Distributed File System) as local storage at the server for fault tolerance.
SAS LASR Analytic Server has been tested on billions of rows of data and is extremely scalable, bypassing the known column limitations of many relational database management systems (RDBMS).
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...
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...
Pandas is a powerful data manipulation and analysis library in Python that provides a wide range of functions and tools to work with structured data. Whether you are a data scientist, analyst, or just a curious learner, Pandas can help you efficiently handle and analyze data. In this blog post, we will walk through a step-by-step guide on how to start a Pandas project from scratch. By following these steps, you will be able to import data, explore and manipulate it, perform calculations and transformations, and save the results for further analysis. So let's dive into the world of Pandas and get started with your own project! Simple Pandas project Import the necessary libraries: import pandas as pd import numpy as np Read data from a file into a Pandas DataFrame: df = pd.read_csv('/path/to/file.csv') Explore and manipulate the data: View the first few rows of the DataFrame: print(df.head()) Access specific columns or rows in the DataFrame: print(df['column_name']) ...
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