edits 2

Author: tomvannuenen

lessons/1_Functions_and_Conditionals.ipynb

@@ -161,7 +161,7 @@
    "outputs": [],
    "source": [
     "# This works\n",
-    "round(3.000, 2)"
+    "round(3.0003, 4)"
    ]
   },
   {
@@ -283,9 +283,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def ...:\n",
-    "    # YOUR CODE HERE\n",
-    "    return ..."
+    "# YOUR CODE HERE\n"
    ]
   },
   {
@@ -336,7 +334,7 @@
     "def add_10(x):\n",
     "    return x + 10\n",
     "\n",
-    "df['pop'].apply(add_10)"
+    "df['year'].apply(add_10)"
    ]
   },
   {
@@ -345,7 +343,7 @@
    "source": [
     "In the code above, we create a function that adds 10 to whatever is passed into it as an argument.\n",
     "\n",
-    "🔔 <span style=\"color:purple\"> **Question**: What happens when we `apply()` our function to the `pop` column?</span>"
+    "🔔 <span style=\"color:purple\"> **Question**: What happens when we `apply()` our function to the `year` column?</span>"
    ]
   },
   {
@@ -416,7 +414,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "number = 110\n",
+    "number = 90\n",
     "\n",
     "if number > 100:\n",
     "    print(number, 'is greater than 100.')\n",
@@ -608,7 +606,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df['gdpPercap_over_800'] = # Your variable here\n",
+    "df['gdpPercap_over_800'] = GDP_higher_than_800\n",
     "df"
    ]
   },

lessons/2_Iteration_and_Visualization.ipynb

@@ -13,7 +13,7 @@
     "### Learning Objectives \n",
     "    \n",
     "* Implement loops to do repeated computations.\n",
-    "* Understand how to use vectorization in Pandas.\n",
+    "* Understand how to implement loops in Pandas with a technique called \"vectorization\".\n",
     "* Apply several Pandas methods to summarize and manipulate data.\n",
     "* Distinguish Pandas methods for `DataFrame` and `Series` objects.\n",
     "* Create simple visualizations using Pandas. \n",
@@ -34,17 +34,6 @@
     "4. [Methods for `Series` Objects](#series_meth)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -55,7 +44,7 @@
     "\n",
     "The strength of using computers is their speed. We can leverage this through repeated computation, also called iteration. In Python, we can do this using **loops**. \n",
     "\n",
-    "A **[for loop](https://www.w3schools.com/python/python_for_loops.asp)** executes some statements once *for* each value in an interable (like a list or a string). It says: \"*for* each thing in this group, *do* these operations\".\n",
+    "A **[for loop](https://www.w3schools.com/python/python_for_loops.asp)** executes some statements once *for* each value in an iterable (like a list or a string). It says: \"*for* each thing in this group, *do* these operations\".\n",
     "\n",
     "Let's take a look at the syntax of a for loop using the above example:"
    ]
@@ -182,7 +171,7 @@
     "In the above example, we are operating on each value in `numbers`. However, instead of simply printing the results, we often will want to save them somehow. We can do this with an **accumulator variable**.\n",
     "\n",
     "A common strategy in programs is to:\n",
-    "1.  Initialize an *accumulator* variable appropriate to the datatype of the output:\n",
+    "1.  Initialize an accumulator variable appropriate to the datatype of the output:\n",
     "    * `int` : `0`\n",
     "    * `str` : `''`\n",
     "    * `list` : `[]`\n",
@@ -286,6 +275,13 @@
     "print(result)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "💡 **Tip**: You might also encounter **[while loops](https://www.w3schools.com/python/python_while_loops.asp)**. A while loop says: \"*while* Condition A is true, *do* these operations\". We don't use these loops frequently in this type of programming so we won't cover them here."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -397,13 +393,6 @@
     "# YOUR CODE HERE\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "💡 **Tip** You might also encounter **[while loops](https://www.w3schools.com/python/python_while_loops.asp)**. A while loop says: \"*while* Condition A is true, *do* these operations\". We don't use these loops frequently in this type of programming so we won't cover them here."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -515,7 +504,9 @@
     "\n",
     "Bar plots show the relationship between a numeric and a categoric variable. Here, we use the `country` (categorical) and `lifeExp` (numeric) columns. Use a bar plot when you want to illustrate differences in frequencies of some category.\n",
     "\n",
-    "In the below cell, we retrieve the 10 data points with the **lowest life expectancy** in our data using the `sort_values()` method, and then plot those data points in a bar plot."
+    "In the below cell, we retrieve the 10 data points with the **lowest life expectancy** in our data using the `sort_values()` method, and then plot those data points in a bar plot.\n",
+    "\n",
+    "💡 **Tip**: Note that `plot.bar()` is a method of its own, and is an alternative to using `plot()` with the `type=bar` argument."
    ]
   },
   {
@@ -527,7 +518,7 @@
     "# Sort values based on low life expectancy, get top 10\n",
     "low_lifeExp = df.sort_values('lifeExp', ascending=True)[:10]\n",
     "\n",
-    "# Visualize with bar plot\n",
+    "# Visualize with bar plot \n",
     "low_lifeExp.plot.bar(x='country', y='lifeExp', figsize=(6,4));"
    ]
   },
@@ -696,12 +687,11 @@
     "\n",
     "## ❗ Key Points\n",
     "\n",
-    "* A `for` loop executes some statements once for each value in an interable.\n",
     "* `for` loops work on lists and other list-like structures, but also on other iterables such as strings.\n",
-    "* We typically use an aggregator variable to store some information we retrieve using a `for` loop.    \n",
-    "* The `.describe()` method in Pandas summarizes numerical data in a dataset.\n",
-    "* We typically do not want to use for-loops in Pandas - instead, we use \"vectorized\" operations.\n",
-    "* The `.plot()` method in Pandas takes a `kind=` argument that determines what kind of plot it is - such as `scatter` or `hist`.\n",
+    "* We typically use an accumulator variable to store some information we retrieve using a `for` loop.    \n",
+    "* We typically do not use for-loops in Pandas - instead, we use \"vectorized\" operations.\n",
+    "* Pandas methods work on either `DataFrame` or `Series` objects--make sure you know which!\n",
+    "* Pandas methods yield as output either `DataFrame` or `Series` objects--make sure you know which!\n",
     "\n",
     "</div>"
    ]

solutions/1_Functions_and_Conditionals.ipynb

@@ -74,7 +74,7 @@
     }
    ],
    "source": [
-    "number = ...\n",
+    "number = 80\n",
     "\n",
     "if number > 100:\n",
     "    print(number, 'is greater than 100.')\n",