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#library
#Python_tricks

🆔 @Python4all_pro

This Python tool looks super useful to crawl websites and convert data into LLM-ready markdown or structured data.

I find myself doing this a lot and most of the time it is a tedious effort. Great to see a service that does data extraction catered for LLM-based pipelines.

https://github.com/mendableai/firecrawl


#framework
#python
#Python_tricks
#DataScience

🆔 @Python4all_pro

Excel Basics for Data Analysis

‼️What you'll learn

Display working knowledge of Excel for Data Analysis.

Perform basic spreadsheet tasks including navigation, data entry, and using formulas.

Employ data quality techniques to import and clean data in Excel.

Analyze data in spreadsheets by using filter, sort, look-up functions, as well as pivot tables

📥https://imp.i384100.net/Qy9rYo

#علم_داده #اکسل
#DataScience
#Python #Free_course
🆔 @Python4all_pro

#NumPy cheat sheet for #datascience :

*Array Creation*

1. numpy.array() - Create an array from a list or other iterable.
2. numpy.zeros() - Create an array filled with zeros.
3. numpy.ones() - Create an array filled with ones.
4. numpy.empty() - Create an empty array.
5. numpy.arange() - Create an array with evenly spaced values.
6. numpy.linspace() - Create an array with evenly spaced values.

*Array Operations*

1. + - Element-wise addition.
2. - - Element-wise subtraction.
3. * - Element-wise multiplication.
4. / - Element-wise division.
5. ** - Element-wise exponentiation.
6. numpy.sum() - Sum of all elements.
7. numpy.mean() - Mean of all elements.
8. numpy.median() - Median of all elements.
9. numpy.std() - Standard deviation.
10. numpy.var() - Variance.

*Array Indexing*

ادامه در پست بعد👇

#cheat_sheet #Python
🆔 @Python4all_pro