Python Data Analysis: An end-to-end guide covering data processing, data manipulation and data visualization. 4 Ed

Welcome to Packt Early Access....10 Python Data Analysis, Fourth Edition: An end-to-end guide covering data processing, data manipulation and data visualization....10 Chapter 1: Getting Started with Python Libraries....12 Join our book community on Discord....13 Navigating the landscape of data analysis....14 Exploring libraries for data analysis....15 Data analysis process methodologies....16 Knowledge discovery from data (KDD)....16 SEMMA....18 CRISP-DM....19 Standard process of data analysis....21 Compare Data Analysis, Data Science and Data Engineering....23 Data Science Domain Job Roles and Skillsets....24 Roles of Data Analyst, Data Scientist, and Data Engineer....24 Skillsets for Data Analyst and Data Scientist....25 Roles of ML Engineer and NLP Engineer....28 Skill set for Data Engineer and ML Engineer....29 A quick look at MLOps....31 Installing Python 3....32 Python installation and setup on Windows....32 Python installation and setup on Linux....33 Python installation and setup on Mac OS X with a GUI installer....33 Python installation and setup on Mac OS X with brew....34 Software tools used in this book....34 Using IPython as a shell....35 Hands on with Ipython....36 Reading manual pages....39 Where to find help?....40 Using JupyterLab....41 Using Jupyter Notebooks....42 Advanced features of Jupyter Notebooks....44 Using PyCharm and VS Code....53 Pycharm....53 Visual Studio Code....54 Using Databricks for PySpark....56 Summary....57 Chapter 2: NumPy and pandas....59 Join our book community on Discord....60 Technical requirements....61 Grasping the essence of NumPy arrays....62 Array properties and attributes....66 Selecting array elements....67 NumPy array numerical data types....69 Data type objects....72 Data type character codes....73 Data type constructors....74 Data type attributes....75 Converting arrays....75 Manipulating array shapes....76 Stacking arrays....79 Splitting arrays....83 Creating views and copies....85 Slicing NumPy Array....88 Broadcasting arrays....92 More on NumPy Methods....94 Creating Pandas DataFrames and Series....98 Describing pandas DataFrames....100 Understanding pandas Series....101 Pandas Series Features....103 Reading and querying the Quandl and Nasdaq Data Link data....105 Grouping and joining pandas DataFrames....109 Concatenating DataFrames....113 Working with missing values....115 Creating pivot tables....116 Dealing with dates....118 Date Features....120 Date Methods....123 Summary....127 References....127 Chapter 3: Statistics for Data Insights....129 Join our book community on Discord....130 Technical requirements....131 Understanding attributes of data and their types....131 Nominal attributes....132 Ordinal attributes....132 Numeric attributes....132 Discrete and continuous attributes....133 Measuring central tendency....134 Mean....134 Mode....135 Median....135 Measuring dispersion....136 Range....136 Inter Quartile Range (IQR)....136 Variance....137 Standard deviation....138 Skewness and kurtosis....139 Understanding relationships using covariance and correlation coefficients....141 Covariance....141 Correlation....141 Pearson's correlation coefficient....142 Spearman's rank correlation coefficient....142 Kendall's rank correlation coefficient....143 Collecting samples....144 Probability Sampling....144 Non-probability sampling....145 Performing parametric tests....146 Understanding t-tests....146 One Sample t-test....147 Two Sample t-test....148 Paired Sample t-test....149 ANOVA....150 One-way ANOVA....151 Two-way ANOVA....152 Performing non-parametric tests....152 Chi-Square Test....153 Mann-Whitney U Test....155 Wilcoxon Signed-Rank Test....156 Kruskal-Walis Test....157 AB testing....159 Performing Sampling and Split the Data into Groups....163 Formulating a Hypothesis and Performing Sampling....164 Bayes theorem....165 Summary....167 Chapter 4: Linear Algebra....169 Join our book community on Discord....170 What is linear algebra?....171 Introduction to scalar, vector, matrix, and tensor....172 Scalar and vectors....172 Matrices and tensors....173 Working with linear algebra in python....174 Fitting polynomials with NumPy....175 Exploring matrix operations....179 The determinant operation....179 Finding the rank of a matrix....180 Matrix inverse using NumPy....180 Solving linear equations using NumPy....182 Eigenvalues, eigenvectors, and matrix decomposition....183 Eigenvectors and Eigenvalues....184 Decomposing a matrix using SVD....185 LU Decomposition....186 QR Decomposition....188 Probability distributions and random number generation....189 Probability Functions for Random Variables....190 Probability Mass Functions....190 Density Functions....190 Types of data distributions....191 Discrete Probability Distributions....191 Continuous Probability Distributions....198 Generating random numbers....206 Test normality of data using SciPy....207 Histogram....208 Anderson-Darling Test....212 D'Agostino-Pearson test....213 Creating a masked array using numpy.ma subpackage....214 Summary....216