7. Array-Oriented Programming with NumPy

Objectives

In this chapter, you’ll:

• Learn what arrays are and how they differ from lists.
• Use the numpy module’s highperformance ndarrays.
• Compare list and ndarray performance with the IPython %timeit magic.
• Use ndarrays to store and retrieve data efficiently.
• Create and initialize ndarrays.

Objectives (cont.)

• Refer to individual ndarray elements.
• Iterate through ndarrays.
• Create and manipulate multidimensional ndarrays.
• Perform common ndarray manipulations.
• Create and manipulate pandas one-dimensional Series and two-dimensional DataFrames.

Objectives (cont.)

• Customize Series and DataFrame indices.
• Calculate basic descriptive statistics for data in a Series and a DataFrame.
• Customize floating-point number precision in pandas output formatting.

Outline

• 7.1 Introduction
• 7.2 Creating arrays from Existing Data
• 7.3 array Attributes
• 7.4 Filling arrays with Specific Values
• 7.5 Creating arrays from Ranges

Outline (cont.)

• 7.6 List vs. array Performance: Introducing %timeit
• 7.7 array Operators
• 7.8 NumPy Calculation Methods
• 7.9 Universal Functions
• 7.10 Indexing and Slicing
• 7.11 Views: Shallow Copies

Outline (cont.)

• 7.12 Deep Copies
• 7.13 Reshaping and Transposing
• 7.14 Intro to Data Science: pandas Series and DataFrames
  • 7.14.1 pandas Series
  • 7.14.2 DataFrames
• 7.15 Wrap-Up
• Exercises

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