Fixed various typos throughout the Quick Start-Data Transformation & Workflow: Style notebooks

data-transform.ipynb

@@ -15,7 +15,7 @@
     "\n",
     "The goal of this chapter is to give you an overview of all the key tools for transforming a data frame, a special kind of object that holds tabular data.\n",
     "\n",
-    "We'll come back these functions in more detail in later chapters, as we start to dig into specific types of data (e.g. numbers, strings, dates).\n",
+    "We'll come back to these functions in more detail in later chapters, as we start to dig into specific types of data (e.g. numbers, strings, dates).\n",
     "\n",
     "### Prerequisites\n",
     "\n",
@@ -211,7 +211,7 @@
     "\n",
     "1. we will use `query()` to find only the rows where the destination `\"dest\"` column has the value `\"IAH\"`. This doesn't change the index, it only removes irrelevant rows. In effect, this step removes rows we're not interested in.\n",
     "2. we will use `groupby()` to group rows by the year, month, and day (we pass a list of columns to the `groupby()` function). This step changes the index; the new index will have three columns in that track the year, month, and day. In effect, this step changes the index.\n",
-    "3. we will choose which columns we wish to keep after the `groupby()` operation by passing a list of them to a set of square brackets (the double brackets are because it's a list within a data frame). Here we just want one column, `\"arr_delay\"`. This doesn't affect the index. In effect, this step removes columns we're not interested in.\n",
+    "3. we will choose which columns we wish to keep after the `groupby()` operation by passing a list of them to a set of square brackets (the double brackets are there because it's a list within a data frame). Here we just want one column, `\"arr_delay\"`. This doesn't affect the index. In effect, this step removes columns we're not interested in.\n",
     "4. finally, we must specify what `groupby()` operation we wish to apply; when aggregating the information in multiple rows down to one row, we need to say how that information should be aggregated. In this case, we'll use the `mean()`. In effect, this step applies a statistic to the variable(s) we selected earlier, across the groups we created earlier."
    ]
   },
@@ -501,7 +501,7 @@
    "source": [
     "## Manipulating Columns\n",
     "\n",
-    "This section will show you how to apply various operations you may need to columns in your data frame.\n",
+    "This section will show you how to apply various operations you may need to apply to columns in your data frame.\n",
     "\n",
     "::: {.callout-note}\n",
     "Some **pandas** operations can apply either to columns or rows, depending on the syntax used. For example, accessing values by position can be achieved in the same way for rows and columns via `.iloc` where to access the ith row you would use `df.iloc[i]` and to access the jth column you would use `df.iloc[:, j]` where `:` stands in for 'any row'.\n",
@@ -1001,7 +1001,7 @@
    "id": "b003ea0d",
    "metadata": {},
    "source": [
-    "This now represents the mean departure delay by month. Notice that our index has changed! We now have month where we original had an index that was just the row number. The index plays an important role in grouping operations because it keeps track of the groups you have in the rest of your data frame.\n",
+    "This now represents the mean departure delay by month. Notice that our index has changed! We now have month where we originally had an index that was just the row number. The index plays an important role in grouping operations because it keeps track of the groups you have in the rest of your data frame.\n",
     "\n",
     "Often, you might want to do multiple summary operations in one go. The most comprehensive syntax for this is via `.agg()`. We can reproduce what we did above using `.agg()`:"
    ]

workflow-style.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Workflow: Style {#sec-workflow-style}\n",
     "\n",
-    "Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread. Even as a very new programmer it's a good idea to work on your code style. Use a consistent style makes it easier for others (including future-you!) to read your work, and is particularly important if you need to get help from someone else.\n",
+    "Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread. Even as a very new programmer it's a good idea to work on your code style. Using a consistent style makes it easier for others (including future-you!) to read your work, and is particularly important if you need to get help from someone else.\n",
     "\n",
     "This chapter will introduce you to some important style points drawn from [Clean Code in Python](https://testdriven.io/blog/clean-code-python/), [Tips for Better Coding](https://aeturrell.github.io/coding-for-economists/code-best-practice.html) from *Coding for Economists*, the UK Government Statistical Service’s [Quality Assurance of Code for Analysis and Research](https://best-practice-and-impact.github.io/qa-of-code-guidance/intro.html) guidance, and the bible of Python style guides, [PEP 8 — Style Guide for Python Code](https://peps.python.org/pep-0008/).\n",
     "\n",
@@ -193,7 +193,7 @@
    "source": [
     "## Principles of Clean Code\n",
     "\n",
-    "While automation can help apply style, it can't help you write *clean code*. Clean code is a set of rules and principles that helps to keep your code readable, maintainable, and extendable. Writing code is easy; writing clean code is hard! However, if you follow these principles, you won't go far wong.\n",
+    "While automation can help apply style, it can't help you write *clean code*. Clean code is a set of rules and principles that helps to keep your code readable, maintainable, and extendable. Writing code is easy; writing clean code is hard! However, if you follow these principles, you won't go far wrong.\n",
     "\n",
     "### Do not repeat yourself (DRY)\n",
     "\n",
@@ -215,7 +215,7 @@
     "\n",
     "Do not have a single file that does everything. If you split your code into separate, independent modules it will be easier to read, debug, test, and use. You can check the basics of coding chapter to see how to create and import functions from other scripts. But even within a script, you can still make your code modular by defining functions that have clear inputs and outputs.\n",
     "\n",
-    "A good rule of thumb is that if a code that achieves one end goes longer than about 30 lines, it should probably go into a function. Scripts longer than about 500 lines are ripe for splitting up too.\n",
+    "A good rule of thumb is that if a piece of code that achieves one end goes longer than about 30 lines, it should probably go into a function. Scripts longer than about 500 lines are ripe for splitting up too.\n",
     "\n",
     "Relatedly, do not have a single function that tries to do everything. Functions should have limits too; they should do approximately one thing. If you're naming a function and you have to use 'and' in the name then it's probably worth splitting it into two functions.\n",
     "\n",