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Add code from part 1 and part 2 of the linalg tutorials
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scipy-linalg/README.md

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# Linear Algebra In Python: Matrix Inverses and Least Squares
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# Working With Linear Systems in Python
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This repository contains the code and data files used in the Real Python tutorial [Linear Algebra in Python: Matrix Inverses and Least Squares](https://realpython.com/python-linear-algebra/).
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This directory contains code associated with the Real Python tutorials:
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- [Working With Linear Systems in Python With `scipy.linalg`](https://realpython.com/python-scipy-linalg/)
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- [Linear Algebra in Python: Matrix Inverses and Least Squares](https://realpython.com/python-linear-algebra/)
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See the tutorials for details.
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## Installation
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Use [Conda](https://docs.conda.io/en/latest/miniconda.html) to create and activate a virtual environment:
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```console
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$ conda create --name linalg
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$ conda activate linalg
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```
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Install packages into your environment:
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```console
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(linalg) $ conda install numpy scipy matplotlib pandas jupyter
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```
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## Code
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The code from the tutorials is available as notebooks that you can open with [Jupyter](https://realpython.com/jupyter-notebook-introduction/):
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- Working With Linear Systems in Python With `scipy.linalg` [notebook](linear-systems.ipynb)
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- Linear Algebra in Python: Matrix Inverses and Least Squares [notebook](matrix-inverses-least-squares.ipynb)
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## Data
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You can find [`vehicles_cleaned.csv`](vehicles_cleaned.csv) in this directory. The dataset is adapted from https://www.kaggle.com/austinreese/craigslist-carstrucks-data and used in Linear Algebra in Python: Matrix Inverses and Least Squares. The [vehicles-data-preparation](vehicles-data-preparation.ipynb) notebook shows how the dataset was cleaned and prepared.
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## About the Author
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Renato Candido - <https://realpython.com/team/rcandido/>
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## License
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Distributed under the MIT license. See [`LICENSE`](../LICENSE) for more information.

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