Appendix D — Code topics

This appendix gathers together links to parts of the book that cover various code topics in Python:

• Comments: Note 2.2 and Note 7.2.
• Variables: Section 4.7.
• Types of values in Python: Section 7.3.
  • Lists in Python: Section 7.3.2.
  • Arrays: Section 7.3.1.
• array length: Section 6.5.
• Indexing into arrays with integers: Section 6.6.
• Selecting multiple elements from arrays with slicing: Section 10.2.
• Strings — values containing text: Section 7.4.
• Randomness from your computer: Section 2.2.
• Types of brackets in Python: Note 4.2.
• Squaring values and arrays: Section 16.7.1.
• Introducing functions: Section 5.7.
• Named arguments to functions: Section 5.8.
• Functions and methods: Note 5.4.
• Ranges (continuous sequences) of integers: Section 5.9.
• range in Python (compared to np.arange): Section 5.10.
• Advanced ranges (for example, using floating point values), using np.arange: Note 21.7.
• Repeating elements of an array with np.repeat: Section 7.6}
• The absolute function (np.abs) to convert all values to positive: Note 16.3.
• for loops: Section 6.6.2.
• if statements: Section 8.10.
• Python modules and submodules: Note 12.7.
• Finding number of repeats using np.bincount: Section 11.6
• Combining Boolean arrays with &: Section 10.6
• range in Python for loops: Section 6.6.3
• Testing whether all elements of an array are the same using np.all: Note 13.5
• Plotting and histograms (and histogram bins and bin edges): Section 12.15.2.
• Shuffling arrays with rnd.permuted: Section 8.14.
• Choosing values at random with np.choice: Section 5.11.
• Counting True values with sum: Section 5.15.
• Making arrays by random resampling with np.choice: Section 5.12.
• Underscores in Python integers: Note 21.5.
• Building strings with format-strings: Section 21.1.1.
• What is an operator?: Note 23.2.
• Logical operators — or, and: Section 23.1.
• Concatenating arrays: Section 12.15.1.
• The Comma-Separated-Values (CSV) file format for data tables: Section 16.1.1.
• Introducing the Pandas Python data science library: Section 16.1.2.