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Titlebook: A Python Data Analyst’s Toolkit; Learn Python and Pyt Gayathri Rajagopalan Book 2021 Gayathri Rajagopalan 2021 Python.Data Analytics.Pandas

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Prepping Your Data with Pandas,the products obtained, energetic aspects, and the determination of kinetic parameters. Therefore, the references used in preparing this chapter predominantly emphasize the aforementioned aspects, particularly for lignocellulosic materials and their derivatives.
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Statistics and Probability with Python,rystallites and contain very little organic matrix. These properties make dental enamel an excellent material for cutting and masticating food (i.e., processes that involve friction and wear). In contrast, dentin is not as hard as enamel, but it is tougher. Dentin is a heterogeneous material and can
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Back Matterd on the mathematical and computational t- ory behind a technique for deformable image registration termed Hyperelastic Warping, and demonstration of the technique via applications in image regist- tion and str978-1-4899-9649-7978-0-306-48608-1
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ries like Matplotlib and Seaborn. Case studies will be included as examples to help readers understand some real-world applications of data analysis. .The final chapters of book focus on statistics, elucidating978-1-4842-6398-3978-1-4842-6399-0
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Front Matterh as polycyclic aromatic hydrocarbons, toxic dyes, pesticides, and heavy metals, which are now of critical environmental importance due to their harmful and mutagenic effects on humans, plants, and aquatic organisms. Biodegradation is naturally performed by microorganisms, implying the decomposition
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Getting Familiar with Python,, households, cars, medical devices, and phones, to name a few. The sustained consumption of these materials will cause a tremendous accumulation of plastic waste in land and water bodies based on synthetic counterparts that are not biodegradable. Due to the necessity of plastics, numerous efforts w
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