📸Tableau Desktop Public: Try the Free Version for Data VisualizationTableau Desktop Public Edition is now free and packed with advanced features previously exclusive to Tableau Desktop Professional—excluding only enterprise database connectivity. Ideal for data enthusiasts, educators, and professionals, this powerful tool supports local file use, drag-and-drop visualizations, and cloud sharing for non-commercial purposes. Learn how to download, install, and get started with creating stunning data visualizations today.
🛷Setting Up Jupyter Notebook on a Windows Server: A Step-by-Step GuideJupyter Notebook is a popular tool among data scientists and programmers for interactive data analysis, visualization, and machine learning. In this guide, we will walk you through the process of setting up Jupyer Notebook on a Windows server to enable remote access.
📯How to read and write JSON files in PythonPython provides built-in support for working with JSON files through the json module. You can read, write, and manipulate JSON data using Python's built- in functions. To read a JSON file in Python, you can follow these steps: Import the json module, open the file, and load the contents as a Python dictionary.
🧧How to Convert Rows to Columns and Columns to Rows in Pandas DataFrame using Python?In Python, you can use the pandas library to work with tabular data. The core data type in pandas is the DataFrame. Sometimes, when working with DataFrame data, you may need to convert rows to columns or columns to rows. Here is a simple example demonstrating how to achieve this.
🚪Python: Updating and Appending pandas DataFrame using DictionaryIn this article, we'll explore efficient methods for updating Pandas DataFrames. We'll look at updating specific columns or rows using dictionary, updating specific values on conditions, and appending new rows. With these techniques, we can streamlined our workflows.
🎶How to Use DAX Studio to Retrieve All Measures in Power BIDAX Studio is an incredibly powerful tool that allows Power BI users to view and analyze their data model in great detail. One of the key features of DAX. Studio is its ability to extract all measures from a Power BI file. In this blog post, we will guide you through the simple steps to achieve this using DAX Studio.
🌝Using the Logger Class in Python for Effective LoggingThe Logger class is a Python package that can be used to create and configure loggers in Python. The class uses two objects, one for general logging and one for error messages. The logger is set up to use the rotating file handler for logging messages.
🌅Saving Dataframes into Oracle Database with PythonYou can use the pandas library in combination with the sqlalchemy and cx_Oracle libraries to save a dataframe to an Oracle database in Python. The table structure should match the structure of the dataframe in terms of column names and data types.
✂️Specify data format when saving Excel via PythonIn this article, we explore how to preserve formatting when saving Excel files using Python, specifically with the xlsxwriter library. Data analysts often face the challenge of converting Excel files into plain text, losing important visual cues. This guide covers essential steps such as importing the xlsxwriter package, creating worksheets for multiple pandas DataFrames, and applying various styles and formats to ensure data types like dates, integers, and decimals are displayed correctly. By setting up header formats, using different cell formats for specific data types, and adjusting column widths, readers will learn how to create well-formatted Excel files effortlessly. Follow these straightforward steps to enhance your data presentation!
📔Calendar Table DAX in Power BIIn Power BI, a date table is necessary because the DAX language does not support built-in date functions. In Tableau, no date tables are needed as it supports built-In date functions and features. To create a dynamic date table for the most recent three years, you can use the following code.
🎣Merge multiple Excel files using PythonThis article provides a comprehensive guide on how to efficiently merge multiple Excel files using Python. It highlights the challenges faced when consolidating similarly formatted Excel files and presents a Python solution utilizing the pandas and os libraries to automate the process. The code snippet allows users to easily combine their Excel files into a single merged_excel.xlsx file. For those without Python installed, alternatives like Deepnote and Google Colab are suggested, complete with step-by-step instructions. The article emphasizes the importance of ensuring consistent file formats and provides pro tips for a smooth merging experience. Perfect for data analysts and anyone needing to handle multiple Excel files regularly.
