Pandas In 7 Days

Make data analysis fast, reliable, and clean with Python, Pandas and Matplotlib. KEY FEATURES ● A detailed walk-through of the Pandas library's features with multiple examples. ● Numerous graphical representations and reporting capabilities using popular Matplotlib. ● A high-level overview of extracting data from including files, databases, and the web. DESCRIPTION No matter how large or small your dataset is, the author 'Fabio Nelli' simply used this book to teach all the finest technical coaching on applying Pandas to conduct data analysis with zero worries. Both newcomers and seasoned professionals will benefit from this book. It teaches you how to use the pandas library in just one week. Every day of the week, you'll learn and practise the features and data analysis exercises listed below: Day 01: Get familiar with the fundamental data structures of pandas, including Declaration, data upload, indexing, and so on. Day 02: Execute commands and operations related to data selection and extraction, including slicing, sorting, masking, iteration, and query execution. Day 03: Advanced commands and operations such as grouping, multi-indexing, reshaping, cross-tabulations, and aggregations. Day 04: Working with several data frames, including comparison, joins, concatenation, and merges. Day 05: Cleaning, pre-processing, and numerous strategies for data extraction from external files, the web, databases, and other data sources. Day 06: Working with missing data, interpolation, duplicate labels, boolean data types, text data, and time-series datasets. Day 07: Introduction to Jupyter Notebooks, interactive data analysis, and analytical reporting with Matplotlib's stunning graphics. WHAT YOU WILL LEARN ●Extract, cleanse, and process data from databases, text files, HTML pages, and JSON data. ●Work with DataFrames and Series, and apply functions to scale data manipulations. ●Graph your findings using charts typically used in modern business analytics. ●Learn to use all of the pandas basic and advanced features independently. ● Storing and manipulating labeled/columnar data efficiently. WHO THIS BOOK IS FOR If you're looking to expedite a data science or sophisticated data analysis project, you've come to the perfect place. Each data analysis topic is covered step-by-step with real-world examples. Python knowledge isn't required however, knowing a little bit helps. TABLE OF CONTENTS 1. Pandas, the Python library 2. Setting up a Data Analysis Environment 3. Day 1 - Data Structures in Pandas library 4. Day 2 - Working within a DataFrame, Basic Functionalities 5. Day 3 - Working within a DataFrame, Advanced Functionalities 6. Day 4 - Working with two or more DataFrames 7. Day 5 - Working with data sources and real-word datasets 8. Day 6 - Troubleshooting Challenges wit Real Datasets 9. Day 7 - Data Visualization and Reporting 10. Conclusion – Moving Beyond

Manage and Automate Data Analysis with Pandas in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple data sets. Pandas for Everyone, 2nd Edition, brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set. New features to the second edition include: Extended coverage of plotting and the seaborn data visualization library Expanded examples and resources Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries Online bonus material on geopandas, Dask, and creating interactive graphics with Altair Chen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine data sets and handle missing data Reshape, tidy, and clean data sets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large data sets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” one Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning

Learn the fundamentals of data science with Python by analyzing real datasets and solving problems using pandas Key Features • Learn how to apply data retrieval, transformation, visualization, and modeling techniques using pandas • Become highly efficient in unlocking deeper insights from your data, including databases, web data, and more • Build your experience and confidence with hands-on exercises and activities Book Description The Pandas Workshop will teach you how to be more productive with data and generate real business insights to inform your decision-making. You will be guided through real-world data science problems and shown how to apply key techniques in the context of realistic examples and exercises. Engaging activities will then challenge you to apply your new skills in a way that prepares you for real data science projects. You'll see how experienced data scientists tackle a wide range of problems using data analysis with pandas. Unlike other Python books, which focus on theory and spend too long on dry, technical explanations, this workshop is designed to quickly get you to write clean code and build your understanding through hands-on practice. As you work through this Python pandas book, you'll tackle various real-world scenarios, such as using an air quality dataset to understand the pattern of nitrogen dioxide emissions in a city, as well as analyzing transportation data to improve bus transportation services. By the end of this data analytics book, you'll have the knowledge, skills, and confidence you need to solve your own challenging data science problems with pandas. What you will learn • Access and load data from different sources using pandas • Work with a range of data types and structures to understand your data • Perform data transformation to prepare it for analysis • Use Matplotlib for data visualization to create a variety of plots • Create data models to find relationships and test hypotheses • Manipulate time-series data to perform date-time calculations • Optimize your code to ensure more efficient business data analysis Who this book is for This data analysis book is for anyone with prior experience working with the Python programming language who wants to learn the fundamentals of data analysis with pandas. Previous knowledge of pandas is not necessary.

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Take the next steps in your data science career! This friendly and hands-on guide shows you how to start mastering Pandas with skills you already know from spreadsheet software. In Pandas in Action you will learn how to: Import datasets, identify issues with their data structures, and optimize them for efficiency Sort, filter, pivot, and draw conclusions from a dataset and its subsets Identify trends from text-based and time-based data Organize, group, merge, and join separate datasets Use a GroupBy object to store multiple DataFrames Pandas has rapidly become one of Python's most popular data analysis libraries. In Pandas in Action, a friendly and example-rich introduction, author Boris Paskhaver shows you how to master this versatile tool and take the next steps in your data science career. You’ll learn how easy Pandas makes it to efficiently sort, analyze, filter and munge almost any type of data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Data analysis with Python doesn’t have to be hard. If you can use a spreadsheet, you can learn pandas! While its grid-style layouts may remind you of Excel, pandas is far more flexible and powerful. This Python library quickly performs operations on millions of rows, and it interfaces easily with other tools in the Python data ecosystem. It’s a perfect way to up your data game. About the book Pandas in Action introduces Python-based data analysis using the amazing pandas library. You’ll learn to automate repetitive operations and gain deeper insights into your data that would be impractical—or impossible—in Excel. Each chapter is a self-contained tutorial. Realistic downloadable datasets help you learn from the kind of messy data you’ll find in the real world. What's inside Organize, group, merge, split, and join datasets Find trends in text-based and time-based data Sort, filter, pivot, optimize, and draw conclusions Apply aggregate operations About the reader For readers experienced with spreadsheets and basic Python programming. About the author Boris Paskhaver is a software engineer, Agile consultant, and online educator. His programming courses have been taken by 300,000 students across 190 countries. Table of Contents PART 1 CORE PANDAS 1 Introducing pandas 2 The Series object 3 Series methods 4 The DataFrame object 5 Filtering a DataFrame PART 2 APPLIED PANDAS 6 Working with text data 7 MultiIndex DataFrames 8 Reshaping and pivoting 9 The GroupBy object 10 Merging, joining, and concatenating 11 Working with dates and times 12 Imports and exports 13 Configuring pandas 14 Visualization

Red Panda: Biology and Conservation of the First Panda, Second Edition, provides the most up-to-date research, data, and conservation solutions for the red pandas, Ailurus species. Since the publication of the previous edition in 2010, the International Union for Conservation of Nature (IUCN) updated the threat level of red pandas, and they are now considered to be endangered. This latest edition is updated to provide an in-depth look at the scientific and conservation-based issues urgently facing the red panda today. Led by one of the world’s leading authorities and advocates for red panda conservation, this new edition includes data from the Population and Habitat Viability (PHVA) workshops conducted in three of the species’ range states, Nepal, China, and India; these workshops utilized firsthand information on the decrease of red panda populations due to factors including deforestation, illegal pet trade, human population growth, and climate change. This book also includes updated information from the first edition on reproduction, anatomy, veterinary care, zoo management, and fossil history. Discusses the evidence for two species of red panda and how this might impact conservation efforts Reports on status in the wild, looks at conservation issues and considers the future of this unique species Written by long-standing red panda experts as well as those specializing in fields involving cutting-edge red panda research Includes new chapters on topic including the impact of climate change, how bamboo influences distribution, and conservation in Bhutan and Myanmar

The book comprises high-quality refereed research papers presented at the Second International Conference on Artificial Intelligence and Logistics Engineering (ICAILE2022), held in Kyiv, Ukraine, on February 20–22, 2022, organized jointly by the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute," Wuhan University of Technology, Nanning University, National Aviation University, and the International Research Association of Modern Education and Computer Science. The topics discussed in the book include state-of-the-art papers in artificial intelligence and logistics engineering. It is an excellent source of references for researchers, graduate students, engineers, management practitioners, and undergraduate students interested in artificial intelligence and its applications in logistics engineering.

Combines the latest findings from the field and the laboratory with panel and workshop summaries from a recent international conference.