IntroductionNumPy ArraysNumPy Arrays and FeaturesExercise 3.01: Creating a NumPy Array (from a List)Exercise 3.02: Adding Two NumPy ArraysExercise 3.03: Mathematical Operations on NumPy ArraysAdvanced Mathematical Operations Exercise 3.04: Advanced Mathematical Operations on NumPy ArraysExercise 3.05: Generating Arrays Using arange and linspace MethodsExercise 3.06: Creating Multi-Dimensional ArraysExercise 3.07: The Dimension, Shape, Size, and Data Type of Two-dimensional ArraysExercise 3.08: Zeros, Ones, Random, Identity Matrices, and VectorsExercise 3.09: Reshaping, Ravel, Min, Max, and SortingExercise 3.10: Indexing and SlicingConditional SubSettingExercise 3.11: Array Operations Stacking ArraysPandas DataFramesExercise 3.12: Creating a Pandas SeriesExercise 3.13: Pandas Series and Data HandlingExercise 3.14: Creating Pandas DataFramesExercise 3.15: Viewing a DataFrame PartiallyIndexing and Slicing ColumnsIndexing and Slicing RowsExercise 3.16: Creating and Deleting a New Column or RowStatistics and Visualization with NumPy and PandasRefresher on Basic Descriptive StatisticsExercise 3.17: Introduction to Matplotlib through a Scatter PlotThe Definition of Statistical Measures – Central Tendency and SpreadRandom Variables and Probability DistributionWhat is a Probability Distribution?Discrete DistributionsContinuous DistributionsData Wrangling in Statistics and VisualizationUsing NumPy and Pandas to Calculate Basic Descriptive StatisticsRandom Number Generation Using NumPyExercise 3.18: Generating Random Numbers from a Uniform DistributionExercise 3.19: Generating Random Numbers from a Binomial Distribution and Bar PlotExercise 3.20: Generating Random Numbers from a Normal Distribution and HistogramsExercise 3.21: Calculating Descriptive Statistics from a DataFrameExercise 3.22: Built-in Plotting UtilitiesActivity 3.01: Generating Statistics from a CSV FileSummary