Multivariate Crop Data Analysis in Python is a comprehensive and accessible guide designed to simplify the complexities of multivariate statistical analysis for a diverse audience, including students, researchers and agricultural professionals. With a strong emphasis on clarity and usability, this book presents advanced statistical concepts in a straightforward manner, progressively building readers' understanding.

Drawing on real-world agricultural datasets, this book bridges theory and practice through a structured exploration of essential multivariate techniques, including multivariate distributions, multivariate hypothesis testing (Hotteling T2, MANOVA), principal component analysis (PCA), factor analysis, cluster analysis and discriminant analysis.

It begins with foundational topics such as matrix algebra and Python installation, providing the necessary groundwork for readers to effectively engage with multivariate analysis. All datasets used are derived from actual agricultural experiments, ensuring authenticity and relevance. Each chapter is accompanied with Python code that supports hands-on learning and demonstrates direct application to crop experimental data. These code snippets are fully customizable, enabling readers to adapt the methods to their own research needs. Unlike many texts that are either theoretical or practical instruction, Multivariate Crop Data Analysis in Python strikes a balanced approach between two.

This book equips readers with the tools needed to perform effective multivariate analysis and extract meaningful insights from complex crop data.

Prity Kumari, alumnus of Banaras Hindu University in Varanasi, Uttar Pradesh, India, has been serving as an Associate Professor in College of Agriculture at Anand Agricultural University, Jabugam, Gujarat. Her research expertise spans a diverse range of areas, focusing on time series forecasting through statistical models and cutting-edge Deep Learning AI techniques. Dr. Kumari adeptly applies methodologies such as ARIMA, ARCH/GARCH, ANN, ML, Memory based deep learning models and CNN to advance her research. Her scholarly achievements extend to the publication of research papers in reputable journals, as well as the authorship of several books and book chapters that reflect her academic credit. Notably, she actively mentors and guides numerous master's and Ph.D. students in the domain of agriculture. Beyond her academic responsibilities, Dr. Kumari has served as a visiting fellow at Western Sydney University, further enriching her research insights.

Radhika Ghetiya, currently pursuing a Ph.D. in Agricultural Statistics. She completed her M.Sc. in Agricultural Statistics with the highest Overall Grade Point Average (OGPA), earning the Best Thesis Award and a Gold Medal. She also holds a B.Sc. (Hons.) in Agriculture, where she graduated with the highest OGPA, receiving the Vice-Chancellor’s Gold Medal, Best Student’s Award and several subject-oriented accolades. Her academic excellence is further demonstrated by the publication of research papers, popular articles, authorship of two books and contributions to book chapters. Proficient in R and Python, her areas of interest include data analysis, regression analysis and remote sensing, showcasing her expertise in both theoretical and applied aspects of statistics.

Praful Sondarava, completed his B.Sc. (Hons.) in Agriculture from Junagadh Agricultural University, where he also interned at ICBA, Dubai, UAE. He pursued a Master’s in Genetics and Plant Breeding at Anand Agricultural University and is currently enrolled in a Ph.D. program in the same field. Proficient in R, Python and SPSS, Praful has contributed to academic literature through research papers, popular articles and a book chapter. His research interests encompass quantitative genetics, data analysis and bioinformatics.

Chapter 1. Introduction
        1.1 Why multivariate analysis in agriculture?
        1.2 Prerequisites
        1.3 Objectives
        1.4 Technique selection based on data type
        1.5 Steps to Install Python on your PC
Chapter 2. Python Installation
        2.1 Overview
        2.2 Steps for installation of Python on Windows
        2.3 What is Jupyter Notebook?
Chapter 3. Matrix
        3.1 Introduction
        3.2 Definitions and Types of Matrices
        3.3 Mathematical Operations of Matrices
        3.4 Determinants
        3.5 Inverse
        3.6 Trace
        3.7 Eigen Value and Eigen Vector
        3.8 Singular Value Decomposition
        3.9 Python Code Illustration
Chapter 4. Multivariate Normal Distribution
        4.1 Introduction
        4.2 Properties of Multivariate Normal Distribution
        4.3 Maximum Likelihood Estimation
        4.4 Wishart distribution
        4.5 Python Code Illustration
Chapter 5. Multivariate Tests
        5.1 Introduction
        5.2 Significance Test: One-Sample Problem
        5.3 Significance Test: Two Independent Sample Problem
        5.4 Significance Test: Paired-Sample Problem
        5.5 Python Code Illustration
Chapter 6. Multivariate Analysis of Variance (MANOVA)
        6.1 Introduction
        6.3 Two-way MANOVA
        6.4 Python Code Illustration
Chapter 7. Principal Component Analysis (PCA)
        7.1 Introduction
        7.2 Terminologies
        7.3 PCA procedure
        7.4 Python Code Illustration
Chapter 8. Factor Analysis (FA)
        8.1 Introduction
        8.2 Terminologies
        8.3 Need for Factor Analysis
        8.4 Types of Factor Analysis
        8.5 Types of Factor Extraction Methods
        8.6 Assumptions of Factor Analysis
        8.7 Similarities between PCA & EFA
        8.8 Differences between PCA & EFA
        8.9 Factor Analysis Procedure
        8.10 Python Code Illustration
Chapter 9. Discriminant Function Analysis (DFA)
        9.1. Introduction
        9.2. Procedure of DFA
        9.3. Python Code Illustration
Chapter 10. Cluster Analysis
        10.1 Introduction
        10.2 Clustering Procedures
        10.3 Python Code Illustration