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Tag: NumPy
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Price :10.25– 7.70
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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPythonIn the world of data analysis, having the right tools at your disposal can make all the difference. One of the most powerful and popular tools for data wrangling in Python is the Pandas library. With Pandas, you can easily manipulate and analyze data with ease, making it a must-have tool for any data analyst.
In addition to Pandas, NumPy is another essential library for data manipulation in Python. NumPy provides support for large, multi-dimensional arrays and matrices, making it ideal for handling numerical data. When combined with Pandas, NumPy can help you perform complex mathematical operations on your data quickly and efficiently.
Finally, IPython is a powerful interactive shell that allows you to work with Python in a more dynamic and flexible way. With IPython, you can easily explore your data, experiment with different analysis techniques, and visualize your results in real-time.
In this post, we will explore how to use Pandas, NumPy, and IPython together to wrangle and analyze data effectively. We will cover topics such as data cleaning, data transformation, and data visualization, showcasing the power and flexibility of these essential Python libraries.
Whether you are a seasoned data analyst or just starting out, mastering the art of data wrangling with Pandas, NumPy, and IPython is sure to take your data analysis skills to the next level. Stay tuned for more tips, tricks, and tutorials on how to harness the power of Python for data analysis.
#Python #Data #Analysis #Data #Wrangling #Pandas #NumPy #IPython
Python for Data Analysis : Data Wrangling with Pandas, NumPy, and Jupyter by Wes
Python for Data Analysis : Data Wrangling with Pandas, NumPy, and Jupyter by Wes
Price : 36.87
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Python for Data Analysis : Data Wrangling with Pandas, NumPy, and Jupyter by WesIn today’s data-driven world, being able to effectively wrangle and analyze data is a crucial skill. Python has emerged as one of the most popular programming languages for data analysis, and in his book “Python for Data Analysis,” Wes McKinney provides a comprehensive guide to using Python for data wrangling with Pandas, NumPy, and Jupyter.
Pandas is a powerful data manipulation library that allows you to easily work with structured data, such as tables and time series. NumPy is a fundamental package for scientific computing in Python, providing support for large, multi-dimensional arrays and matrices. Jupyter notebooks provide an interactive computing environment that allows you to create and share code, visualizations, and narrative text.
In “Python for Data Analysis,” Wes McKinney walks you through the basics of data analysis with Python, covering topics such as reading and writing data, cleaning and transforming data, and performing statistical analysis. The book also covers more advanced topics, such as working with time series data, handling missing data, and using visualization tools to explore and communicate your findings.
Whether you’re a data scientist, business analyst, or student looking to improve your data analysis skills, “Python for Data Analysis” is a must-read. With its clear explanations, practical examples, and hands-on exercises, this book will help you master the art of data wrangling with Python and take your data analysis skills to the next level.
#Python #Data #Analysis #Data #Wrangling #Pandas #NumPy #Jupyter #Wes
Python for Natural Language Processing: Programming with Numpy, Scikit-Learn, Ke
Python for Natural Language Processing: Programming with Numpy, Scikit-Learn, Ke
Price :96.22– 76.75
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Natural language processing (NLP) is a fascinating field that involves the interaction between computers and human language. Python has become one of the most popular programming languages for NLP due to its simplicity and powerful libraries.In this post, we will explore how to use Python for NLP by leveraging three essential libraries: Numpy, Scikit-Learn, and Keras. These libraries provide a wide range of tools and functionalities that make it easier to work with text data and build NLP models.
Numpy is a fundamental library for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. Numpy is commonly used in NLP for tasks such as text preprocessing, feature extraction, and data manipulation.
Scikit-Learn is a versatile machine learning library that offers a variety of algorithms and tools for building predictive models. In the context of NLP, Scikit-Learn can be used for tasks such as text classification, sentiment analysis, and topic modeling. Its user-friendly interface makes it easy to experiment with different algorithms and evaluate their performance.
Keras is a high-level neural networks API that simplifies the process of building deep learning models. It provides a user-friendly interface for constructing neural networks and training them on large datasets. In the field of NLP, Keras is commonly used for tasks such as text generation, machine translation, and named entity recognition.
By combining the power of Numpy, Scikit-Learn, and Keras, Python offers a comprehensive toolkit for working with natural language data. Whether you are a beginner or an experienced data scientist, these libraries can help you explore the fascinating world of NLP and build sophisticated models to analyze and generate text.
So if you are interested in diving into the exciting field of natural language processing, consider using Python with Numpy, Scikit-Learn, and Keras to unlock the full potential of your text data. Happy coding!
#Python #Natural #Language #Processing #Programming #Numpy #ScikitLearn
Python Data Cleaning Cookbook: Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI
Price:$49.99– $37.93
(as of Dec 26,2024 21:44:03 UTC – Details)
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Publisher : Packt Publishing; 2nd ed. edition (May 31, 2024)
Language : English
Paperback : 486 pages
ISBN-10 : 1803239875
ISBN-13 : 978-1803239873
Item Weight : 1.85 pounds
Dimensions : 1.15 x 7.5 x 9.25 inches
Data cleaning is an essential step in any data analysis project. In order to effectively analyze and derive insights from your data, it is crucial to first clean and preprocess it. In this post, we will walk through a Python Data Cleaning Cookbook, using popular libraries such as pandas, NumPy, Matplotlib, scikit-learn, and OpenAI.1. Importing the necessary libraries:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
import openai2. Loading the dataset:
df = pd.read_csv(‘data.csv’)3. Handling missing values:
# Check for missing values
print(df.isnull().sum())# Fill missing values with mean
df.fillna(df.mean(), inplace=True)4. Removing duplicates:
# Remove duplicate rows
df.drop_duplicates(inplace=True)5. Handling outliers:
# Detect and remove outliers using Z-score
z_scores = np.abs(stats.zscore(df))
df = df[(z_scores < 3).all(axis=1)] 6. Standardizing the data:
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df)7. Visualizing the data:
# Plot a histogram of a numerical column
plt.hist(df[‘column_name’])
plt.xlabel(‘Column Name’)
plt.ylabel(‘Frequency’)
plt.title(‘Histogram of Column Name’)
plt.show()8. Text data cleaning:
# Clean text data using OpenAI’s GPT-3
clean_text = openai.api.text_completion(
model=”text-davinci-003″,
prompt=”Clean the text data: ” + text_data
)By following these steps and using the powerful capabilities of libraries such as pandas, NumPy, Matplotlib, scikit-learn, and OpenAI, you can effectively prepare your data for analysis and derive meaningful insights. Happy data cleaning!
#Python #Data #Cleaning #Cookbook #Prepare #data #analysis #pandas #NumPy #Matplotlib #scikitlearn #OpenAI
Python for Natural Language Processing: Programming with NumPy, scikit-learn, Keras, and PyTorch (Cognitive Technologies)
Price:$64.99– $45.83
(as of Dec 24,2024 17:10:54 UTC – Details)
Publisher : Springer; Third Edition 2024 (July 10, 2024)
Language : English
Hardcover : 545 pages
ISBN-10 : 3031575482
ISBN-13 : 978-3031575488
Item Weight : 2.07 pounds
Dimensions : 6.14 x 1.19 x 9.21 inches
Python for Natural Language Processing: Programming with NumPy, scikit-learn, Keras, and PyTorch (Cognitive Technologies)Are you interested in delving into the world of Natural Language Processing (NLP) using Python? Look no further than this comprehensive guide that covers the essential tools and libraries for NLP tasks.
In this post, we will explore how to leverage popular Python libraries such as NumPy, scikit-learn, Keras, and PyTorch to perform various NLP tasks. Whether you are a beginner or an experienced programmer, you will find valuable insights and practical examples to enhance your NLP skills.
NumPy provides efficient array manipulation for handling large datasets, while scikit-learn offers a wide range of machine learning algorithms for text classification, clustering, and more. Keras and PyTorch are deep learning frameworks that enable you to build and train neural networks for advanced NLP tasks like sentiment analysis and language modeling.
By combining these powerful tools, you can create sophisticated NLP models that can extract insights from text data, automate text processing tasks, and improve the performance of your NLP applications.
Stay tuned for more tips, tutorials, and resources on Python for Natural Language Processing with NumPy, scikit-learn, Keras, and PyTorch. Start your NLP journey today and unlock the full potential of cognitive technologies in your projects.
#Python #Natural #Language #Processing #Programming #NumPy #scikitlearn #Keras #PyTorch #Cognitive #Technologies
Ultimate Python Libraries for Data Analysis and Visualization: Leverage Pandas, NumPy, Matplotlib, Seaborn, Julius AI and No-Code Tools for Data … and Statistical Analysis (English Edition)
Price: $37.95
(as of Dec 18,2024 08:55:32 UTC – Details)
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Publisher : Orange Education Pvt Ltd (April 4, 2024)
Language : English
Paperback : 265 pages
ISBN-10 : 8197081913
ISBN-13 : 978-8197081910
Item Weight : 1.02 pounds
Dimensions : 9.25 x 7.5 x 0.59 inches
Are you looking to take your data analysis and visualization skills to the next level? Look no further! In our latest post, we explore the ultimate Python libraries that will help you leverage the power of data analysis and visualization.From the versatile Pandas and NumPy libraries for data manipulation and calculations, to the powerful Matplotlib and Seaborn libraries for creating stunning visualizations, we’ve got you covered. Plus, we’ll introduce you to cutting-edge AI tools like Julius AI and no-code platforms for seamless data analysis and statistical analysis.
Whether you’re a beginner or an experienced data analyst, these libraries will help you unlock the full potential of your data. Don’t miss out on this essential guide to mastering data analysis and visualization with Python!
#Ultimate #Python #Libraries #Data #Analysis #Visualization #Leverage #Pandas #NumPy #Matplotlib #Seaborn #Julius #NoCode #Tools #Data #Statistical #Analysis #English #Edition
Data Analysis and Visualization with Python: From Pandas, NumPy and Scikit-Learn to Advanced Modeling Techniques
Price: $19.99
(as of Dec 16,2024 09:35:06 UTC – Details)
ASIN : B0DNY1114Z
Publisher : Independently published (November 23, 2024)
Language : English
Paperback : 371 pages
ISBN-13 : 979-8301064364
Item Weight : 2.34 pounds
Dimensions : 8.5 x 0.84 x 11 inches
Data analysis and visualization are essential skills in today’s data-driven world. Python has become one of the most popular languages for performing these tasks, thanks to libraries like Pandas, NumPy, and Scikit-Learn that make working with data easy and efficient.In this post, we will explore how to use these libraries to analyze and visualize data, from basic operations like cleaning and manipulating datasets to more advanced modeling techniques.
First, we will start by importing the necessary libraries and loading a dataset using Pandas. We will then cover basic data manipulation operations like filtering, sorting, and grouping data using Pandas and NumPy.
Next, we will move on to data visualization using libraries like Matplotlib and Seaborn. We will explore different types of plots like scatter plots, histograms, and bar charts to visualize the data and gain insights from it.
Finally, we will delve into more advanced modeling techniques using Scikit-Learn, such as linear regression, decision trees, and neural networks. We will cover topics like model evaluation, hyperparameter tuning, and cross-validation to build accurate and reliable models.
By the end of this post, you will have a solid understanding of how to perform data analysis and visualization using Python and be equipped with the knowledge to apply advanced modeling techniques to real-world datasets. So, grab your favorite dataset and let’s dive into the world of data analysis and visualization with Python!
#Data #Analysis #Visualization #Python #Pandas #NumPy #ScikitLearn #Advanced #Modeling #Techniques
Python Machine Learning for Beginners: Learning from scratch NumPy, Pandas, Matplotlib, Seaborn, Scikitlearn, and TensorFlow for Machine Learning and … Learning & Data Science for Beginners)
Price: $24.99
(as of Dec 16,2024 01:04:44 UTC – Details)
Publisher : AI Publishing LLC (October 23, 2020)
Language : English
Paperback : 304 pages
ISBN-10 : 1734790156
ISBN-13 : 978-1734790153
Item Weight : 14.6 ounces
Dimensions : 6 x 0.69 x 9 inches
Welcome to our beginner’s guide to Python Machine Learning! In this post, we will cover the basics of machine learning from scratch, focusing on essential libraries such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and TensorFlow.Machine learning is a powerful tool that allows computers to learn from data and make predictions or decisions without being explicitly programmed. Python is one of the most popular programming languages for machine learning due to its simplicity and extensive libraries for data manipulation and visualization.
To start your journey in Python Machine Learning, it is essential to understand the basics of NumPy and Pandas. NumPy is a library for numerical computing in Python, allowing you to perform complex mathematical operations efficiently. Pandas, on the other hand, is a powerful data manipulation tool that makes working with structured data easy and intuitive.
Once you have a solid foundation in NumPy and Pandas, you can move on to data visualization with libraries like Matplotlib and Seaborn. These libraries allow you to create stunning visualizations of your data, making it easier to understand patterns and relationships.
Next, you can explore machine learning algorithms with Scikit-learn, a comprehensive library for machine learning in Python. Scikit-learn provides a wide range of algorithms for classification, regression, clustering, and more, making it easy to train and evaluate machine learning models.
Finally, you can dive into deep learning with TensorFlow, a powerful library for building and training neural networks. TensorFlow is widely used in cutting-edge research and industry applications, making it an essential tool for anyone interested in advanced machine learning.
By mastering these essential libraries and tools, you will be well on your way to becoming a proficient Python Machine Learning practitioner. Stay tuned for more tutorials and resources on Learning & Data Science for Beginners!
#Python #Machine #Learning #Beginners #Learning #scratch #NumPy #Pandas #Matplotlib #Seaborn #Scikitlearn #TensorFlow #Machine #Learning #Learning #Data #Science #Beginners
Mastering OpenCV with Python: Use NumPy, Scikit, TensorFlow, and Matplotlib to learn Advanced algorithms for Machine Learning through a set of Practical Projects (English Edition)
Price: $37.95
(as of Dec 13,2024 13:40:09 UTC – Details)
From the Publisher
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Unlocking Visual Insights: OpenCV Made Simple and Powerful.
Unlock the captivating world of computer vision with this comprehensive guide that takes you on an enriching journey from novice to expert. Packed with step-by-step tutorials, easy-to-understand explanations, and detailed code examples, this book ensures that you grasp even the most intricate concepts effortlessly. You’ll find yourself immersed in the world of computer vision as we demystify complex algorithms and techniques with hands-on, real-world projects that bring your learning to life.
Whether you’re a seasoned developer or just starting your coding adventure, our easy-to-follow language and engaging approach make this book your ideal companion. Explore the power of OpenCV, delve into image manipulation, unravel the secrets of feature detection, and seamlessly integrate machine learning into your projects. With this book in your hands, you’ll gain the skills and confidence to conquer the dynamic field of computer vision and embark on exciting journeys of your own.
WHAT WILL YOU LEARN
● Master Image Processing and Machine Learning with OpenCV using advanced Tools and Libraries.
● Create Real-World Projects with Hands-On Experience.
● Explore Machine Learning for Computer Vision.
● Develop Confidence in Practical Computer Vision Projects.
● Conquer Real-World Image Processing Challenges.
● Apply Computer Vision Across Diverse Industries.
● Boost Your Career in Computer Vision.
WHO IS THIS BOOK FOR?
This beginner-friendly book in computer vision requires no prior experience, making it accessible to newcomers. While a basic programming understanding is helpful, it’s designed to guide individuals from diverse backgrounds into the captivating realms of AI, computer vision, and image processing.
KEY FEATURES OpenCV Mastery: Harness the full potential of OpenCV. Comprehensive Coverage: From fundamentals to advanced techniques. Practical Exercises: Apply knowledge through hands-on tasks.
About the Author
Ayush brings a wealth of experience spanning 5 years across AI, ML, and computer vision. His journey is marked by a strong commitment to technology deployment, guiding projects from initial design to final product. In his current role, Ayush is deeply engaged in computer vision projects showcasing his proficiency in creating advanced models for image processing. His career underscores his commitment to learning and practical application in the tech industry.
Meet the Technical Reviewer
Kaushal Singh, an accomplished Data Scientist and AI researcher with a rich and diverse career spanning 5 years. He is currently working as an Assistant Professor and Training and Placement Officer at the Department of Computer Science and Information Technology, School of Engineering, P P Savani University, Surat, Gujarat, with a passion for harnessing the power of data and artificial intelligence, he made significant contributions to the field through research and technical expertise. Throughout his career, he delved deep into the realms of data science and artificial intelligence, honing his skills in machine learning, deep learning, and data analysis. His proficiency extends to developing state-of-the-art AI models and leveraging data-driven insights to solve complex real-world challenges. He is not only a practitioner but also a dedicated researcher.
Copyright Disclaimer
Copyright at 2023, Orange Education Pvt Ltd, AVA
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without convincing, either express or implied.
Neither the author nor Orange Education Pvt Ltd. or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Orange Education Pvt Ltd. has endeavored to provide brand information about all of the companies and products mentioned in this book by the appropriate use of capital. However, Orange Education Pvt Ltd. cannot surety the accuracy of this information. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
First published: November 2023
Published by: Orange Education Pvt Ltd, AVA
Publisher : Orange Education Pvt Ltd (November 17, 2023)
Language : English
Paperback : 423 pages
ISBN-10 : 9390475791
ISBN-13 : 978-9390475797
Item Weight : 1.6 pounds
Dimensions : 7.5 x 0.96 x 9.25 inches
If you’re looking to take your machine learning skills to the next level, then Mastering OpenCV with Python is the book for you. In this comprehensive guide, you’ll learn how to use NumPy, Scikit, TensorFlow, and Matplotlib to implement advanced algorithms for machine learning through a series of practical projects.Whether you’re a seasoned Python programmer or just starting out, this book will provide you with the tools and techniques you need to master OpenCV and take your machine learning projects to the next level. From image processing and object detection to face recognition and deep learning, you’ll learn how to apply cutting-edge algorithms to real-world problems.
With step-by-step instructions and code examples, Mastering OpenCV with Python will help you build a solid foundation in machine learning and computer vision. So why wait? Pick up your copy today and start mastering OpenCV with Python!
#Mastering #OpenCV #Python #NumPy #Scikit #TensorFlow #Matplotlib #learn #Advanced #algorithms #Machine #Learning #set #Practical #Projects #English #Edition
Scientific Computing with Python – Second Edition: High-performance scientific computing with NumPy, SciPy, and pandas
Price:$43.99– $39.99
(as of Nov 23,2024 08:11:23 UTC – Details)
Publisher : Packt Publishing; 2nd ed. edition (July 23, 2021)
Language : English
Paperback : 392 pages
ISBN-10 : 1838822321
ISBN-13 : 978-1838822323
Item Weight : 1.5 pounds
Dimensions : 9.25 x 7.52 x 0.81 inches
Are you interested in diving deeper into the world of scientific computing with Python? Look no further than the second edition of “Scientific Computing with Python.” This comprehensive guide will take you through high-performance scientific computing using essential libraries like NumPy, SciPy, and pandas.Whether you’re a beginner or an experienced Python programmer, this book will provide you with the tools and techniques needed to tackle complex scientific computing tasks. From data manipulation and analysis to numerical computing and visualization, this book covers it all.
With detailed explanations, practical examples, and hands-on exercises, you’ll learn how to leverage the power of Python for scientific computing. Whether you’re working in academia, research, or industry, this book will help you take your Python skills to the next level and become a more efficient and effective scientific programmer.
Don’t miss out on the opportunity to enhance your scientific computing skills with Python. Pick up a copy of “Scientific Computing with Python – Second Edition” today and start unleashing the full potential of Python for your scientific projects.
#Scientific #Computing #Python #Edition #Highperformance #scientific #computing #NumPy #SciPy #pandas
