Mike Miller’s Post

ArcPy in ArcGIS Pro and PyScripter make it easy to automate repetitive tasks through programming using python scripts to enhance my GIS workflow and data interoperability. Found it useful to understand the fundamentals of Python such as knowing the types of data and the elements or values which they can store or represent. For instance: 1. Strings store values in double or single quotes and the values can either be numbers or characters and sometimes these elements can be included together as a sting. Indexing helps to locate the values from index number 0 and upwards which is good for slicing of elements or values. e.g. on this written code: odwaroad = "Streets.shp". If I wanted to callot the first three characters I would write odwaroad = [0:2],. 2. Variables assign values to them, for instance x = 8 , so to attain the value we can say print x, then Boom. 3. Python lists are based on values that are indexed and in a sequence. They can br manipulated, changed, etc. 4. Numbers data types store numerical elements and they do not need to be surrounded by quotes. The values range from simple to complex mathematical expressions or equations. 5. Dictionaries store an unordered collection of data. To be able to retrieve the value dictionaries use or store data using the #key:valuepair combination which uses curled brackets ; like dataDict = { "×":127483, "b":3637,7}. To access both x and b pairs, one must just print it. 6. Turples are just like Python lists, they store data in sequential elements but data cannot be changed or modified as is the case with lists. Have you written your first code 😉 yet ?

DAY 01/100 Project Link: https://lnkd.in/d4Nj3QjR Are you ready for a challenge? #100DaysOfProjects is the perfect opportunity to push yourself and complete a project every day for the next 100 days. Join the movement and share your progress! #personaldevelopment #motivation #selfimprovement #Python #expensetracker Project Name: Advanced Expense Tracker Application Objective: To create a robust, user-friendly, and feature-rich Expense Tracker application in Python that allows users to record daily expenses, manage expense categories, export data to Excel, and perform basic data analysis. Key Features: Expense Recording: Users can input daily expenses with details such as amount, description, date, and category. Options are provided to add, edit, and delete expense entries. Expense Categories: Predefined expense categories (e.g., groceries, transportation, entertainment) are available. Users can add new categories as needed. Data Management: Expense data is stored using appropriate data structures (lists). Error handling is implemented to validate user input. Expense Export to Excel: Functionality to export expenses to an Excel spreadsheet (.xlsx file). Utilizes the openpyxl library to create and populate the Excel file. Expense Summary and Analysis: Provides a summary of total spending, spending by category, and average daily/weekly/monthly expenses. Basic data analysis features, such as finding the highest and lowest expenses. User Interface: Interactive command-line interface with clear instructions and menu options. Allows users to perform various functionalities seamlessly. Data Validation and Error Handling: Implements validation mechanisms to ensure entered data is valid. Gracefully handles errors, providing helpful error messages to guide users. Code Structure and Modularity: Organizes code into functions and classes to promote modularity and maintainability. Uses meaningful variable names and includes comments for clarity. Advanced Features (Optional): Graphical visualization of expense data using libraries like Matplotlib or Seaborn. Budget-setting option for categories with notifications on exceeding limits. Search functionality for specific expenses based on keywords or dates. Submission: Submit Python source code files (.py) and any necessary files. Include a README file with instructions on using the Expense Tracker application. Provide a brief explanation of the data structures and algorithms used. Testing: Thoroughly test the Expense Tracker to ensure it functions correctly and handles various user scenarios. This summary outlines the key components and functionalities of the Expense Tracker project as per the given requirements. Keep in mind that this is a basic implementation, and you can expand and enhance the application based on your specific needs and preferences.

It's a must-know Python Module. I'm talking about the Os Module. It's a Python Module used to interact with the Operating System allowing you to perform operations like: - File and directory creation - File and directory removal - Get the content for the specific path - Get file sizes - Much more After getting in touch with Command Line, having my hands dirty with Os has been much easier. This is because some keywords are similar in spelling and perform the same operation. To mention some --> mkdir (make directory), ls (list), rm (remove), etc. In the image, you can see some lines of code of a little project. Did you ever used it? Tell one application you used it for. _________________________________________________________________________________ ⚡Join 120+ Data Analytics passionate and follow my personal hashtag #KarimNiangTips or profile Abdoul Karim N.. 🚀200+ tips to improve your skills on: 📌 Excel 📌 PowerQuery   📌 PowerBI 📌 SQL 📌 #Python 

My take on Snowflake table row-level data comparison by making use of a Snowpark python integration to deliver fast comparison results on a index level. Method is explained in a quick 2 minute read :