Telegram Group & Telegram Channel
Commonly used Python libraries are:

πŸ‘‰πŸ»NumPy:
This library is used for scientific computing and working with arrays of data. It provides functions for working with arrays of data, including mathematical operations, linear algebra, and random number generation.

πŸ‘‰πŸ»Pandas: This library is used for data manipulation and analysis. It provides tools for importing, cleaning, and transforming data, as well as tools for working with time series data and performing statistical analysis.

πŸ‘‰πŸ»Matplotlib: This library is used for data visualization. It provides functions for creating a wide range of plots, including scatter plots, line plots, bar plots, and histograms.

πŸ‘‰πŸ»Scikit-learn: This library is used for machine learning. It provides a range of algorithms for classification, regression, clustering, and dimensionality reduction, as well as tools for model evaluation and selection.

πŸ‘‰πŸ»TensorFlow: This library is used for deep learning. It provides a range of tools and libraries for building and training neural networks, including support for distributed training and hardware acceleration.

Share and Support
@Python_Codes



tg-me.com/python_codes/260
Create:
Last Update:

Commonly used Python libraries are:

πŸ‘‰πŸ»NumPy:
This library is used for scientific computing and working with arrays of data. It provides functions for working with arrays of data, including mathematical operations, linear algebra, and random number generation.

πŸ‘‰πŸ»Pandas: This library is used for data manipulation and analysis. It provides tools for importing, cleaning, and transforming data, as well as tools for working with time series data and performing statistical analysis.

πŸ‘‰πŸ»Matplotlib: This library is used for data visualization. It provides functions for creating a wide range of plots, including scatter plots, line plots, bar plots, and histograms.

πŸ‘‰πŸ»Scikit-learn: This library is used for machine learning. It provides a range of algorithms for classification, regression, clustering, and dimensionality reduction, as well as tools for model evaluation and selection.

πŸ‘‰πŸ»TensorFlow: This library is used for deep learning. It provides a range of tools and libraries for building and training neural networks, including support for distributed training and hardware acceleration.

Share and Support
@Python_Codes

BY Python Codes


Warning: Undefined variable $i in /var/www/tg-me/post.php on line 283

Share with your friend now:
tg-me.com/python_codes/260

View MORE
Open in Telegram


Python Codes Telegram | DID YOU KNOW?

Date: |

Start with a fresh view of investing strategy. The combination of risks and fads this quarter looks to be topping. That means the future is ready to move in.Likely, there will not be a wholesale shift. Company actions will aim to benefit from economic growth, inflationary pressures and a return of market-determined interest rates. In turn, all of that should drive the stock market and investment returns higher.

Importantly, that investor viewpoint is not new. It cycles in when conditions are right (and vice versa). It also brings the ineffective warnings of an overpriced market with it.Looking toward a good 2022 stock market, there is no apparent reason to expect these issues to change.

Python Codes from cn


Telegram Python Codes
FROM USA