Streamline previous content

02_functions.ipynb

@@ -57,7 +57,7 @@
     "\n",
     "A function may come with an *explicit* **[return value](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)** (i.e., \"result\" or \"output\") specified with the `return` statement: Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects** (e.g., the [print()](https://docs.python.org/3/library/functions.html#print) built-in). Strictly speaking, they also have an *implicit* return value of `None` that is different from the `False` we saw in Chapter 1.\n",
     "\n",
-    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in Chapter 6. Widely adopted standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb). Widely adopted standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
    ]
   },
   {

05_numbers.ipynb

@@ -21,7 +21,7 @@
    "source": [
     "After learning about the basic building blocks of expressing and structuring the business logic in programs, we focus our attention on the **data types** Python offers us, both built-in and available via the [standard library](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
-    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in Chapter 6. An important fact that holds for all objects of these types is that they are **immutable**. To re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
+    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
     "\n",
     "Chapters 7, 8, and 9 then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
     "\n",
@@ -4519,7 +4519,7 @@
     }
    },
    "source": [
-    "To preserve the precision for more advanced mathematical functions, `Decimal` objects come with many methods bound on them. For example, [ln()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. In general, the functions in the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) should only be used with `float` objects as they do *not* preserve precision."
+    "To preserve the precision for more advanced mathematical functions, `Decimal` objects come with many **methods bound** on them. For example, [ln()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. In general, the functions in the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) should only be used with `float` objects as they do *not* preserve precision."
    ]
   },
   {