Matplotlib Collection

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🔮Psychic

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“The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw”

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📦Normal

★★★★

“Python Data Science Handbook: full text in Jupyter Notebooks”

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🔮Psychic

★★★★

“Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.”

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🔮Psychic

★★★★

“matplotlib: plotting with Python”

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🔮Psychic

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“阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构”

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🔮Psychic

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“PyGWalker: Turn your dataframe into an interactive UI for visual analysis”

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📦Normal

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“Essential Cheat Sheets for deep learning and machine learning researchers https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5”

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🔮Psychic

★★★

“Statistical data visualization in Python”

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📦Normal

★★★

“人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域”

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📦Normal

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“Draw pretty maps from OpenStreetMap data! Built with osmnx +matplotlib + shapely”

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🔮Psychic

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“An open access book on scientific visualization using python and matplotlib”

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🔮Psychic

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“Open Machine Learning Course”

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📦Normal

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“《利用Python进行数据分析·第2版》”

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🔮Psychic

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“Official Matplotlib cheat sheets”

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📦Normal

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“中文 Python 笔记”

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🔮Psychic

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“Panel: The powerful data exploration & web app framework for Python”

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📦Normal

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“A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.”

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🔮Psychic

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“Visual analysis and diagnostic tools to facilitate machine learning model selection.”