add polls

tomvannuenen · tomvannuenen · commit a7536a13cbb1 · 2023-04-14T13:39:41.000-07:00
diff --git a/lessons/1_Functions_and_Conditionals.ipynb b/lessons/1_Functions_and_Conditionals.ipynb
@@ -24,6 +24,7 @@
     "🥊 **Challenge**: Interactive exercise. We'll work through these in the workshop!<br>\n",
     "💡 **Tip**: How to do something a bit more efficiently or effectively.<br>\n",
     "⚠️ **Warning:** Heads-up about tricky stuff or common mistakes.<br>\n",
+    "📝 **Poll:** A Zoom poll to help you learn!<br>\n",
     "🎬 **Demo**: Showing off something more advanced – so you know what Python can be used for!<br>\n",
     "\n",
     "### Sections\n",
@@ -120,7 +121,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "🔔 <span style=\"color:purple\"> **Question**: How many rows are in the data set?</span>"
+    "📝 **Poll PyInt 1-1:**  How many rows are in the data set?"
    ]
   },
   {
@@ -343,7 +344,7 @@
    "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 `year` column?</span>"
+    "📝 **Poll PyInt 1-2:**  What happens when we `apply()` our function to the `year` column?"
    ]
   },
   {
@@ -630,7 +631,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 🥊 <span style=\"color:purple\">Challenge 4: `apply()` a Conditional Function </span>\n",
+    "## 🥊 Challenge 4: `apply()` a Conditional Function\n",
     "\n",
     "Let's put everything we've learned together.\n",
     "\n",
@@ -659,6 +660,13 @@
     "# YOUR CODE HERE\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**📝 Poll PyInt 1-3:** What should our function return if `i == 6000`?"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/lessons/2_Iteration_and_Visualization.ipynb b/lessons/2_Iteration_and_Visualization.ipynb
@@ -26,6 +26,7 @@
     "🥊 **Challenge**: Interactive exercise. We'll work through these in the workshop!<br>\n",
     "💡 **Tip**: How to do something a bit more efficiently or effectively.<br>\n",
     "⚠️ **Warning:** Heads-up about tricky stuff or common mistakes.<br>\n",
+    "📝 **Poll:** A Zoom poll to help you learn!\n",
     "\n",
     "### Sections\n",
     "1. [Iteration: Loops](#iter)\n",
@@ -95,9 +96,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 🥊 <span style=\"color:purple\">Challenge 1: Fixing Loop Syntax</span>\n",
+    "## 🥊 Challenge 1: Fixing Loop Syntax\n",
     "\n",
-    "The following block of code contains **three errors** that are preventing it from running properly. What are the errors? How would you fix them?"
+    "The following block of code contains **three errors** that are preventing it from running properly. \n",
+    "\n",
+    "📝 **Poll PyInt 2-1:** What are the three errors?"
    ]
   },
   {
@@ -295,11 +298,95 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv('../data/gapminder_gni.csv')\n",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><div id=882af88a-033f-431f-a12e-16e795baa656 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('882af88a-033f-431f-a12e-16e795baa656').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "      <th>gniPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>10267083.0</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>31.997</td>\n",
+       "      <td>853.100710</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>11537966.0</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>34.020</td>\n",
+       "      <td>836.197138</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>13079460.0</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>36.088</td>\n",
+       "      <td>739.981106</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>14880372.0</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>38.438</td>\n",
+       "      <td>786.113360</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1982</td>\n",
+       "      <td>12881816.0</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>39.854</td>\n",
+       "      <td>978.011439</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></div>"
+      ],
+      "text/plain": [
+       "       country  year         pop continent  lifeExp   gdpPercap  gniPercap\n",
+       "0  Afghanistan  1962  10267083.0      Asia   31.997  853.100710        NaN\n",
+       "1  Afghanistan  1967  11537966.0      Asia   34.020  836.197138        NaN\n",
+       "2  Afghanistan  1972  13079460.0      Asia   36.088  739.981106        NaN\n",
+       "3  Afghanistan  1977  14880372.0      Asia   38.438  786.113360        NaN\n",
+       "4  Afghanistan  1982  12881816.0      Asia   39.854  978.011439        NaN"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('~/Documents/GitHub/DEV/Python-Intermediate-Pilot/data/gapminder_gni.csv')\n",
     "df.head()"
    ]
   },
@@ -424,7 +511,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "🔔 <span style=\"color:purple\">**Question**: Why are only some of the columns in the `DataFrame` visible in the output?</span>"
+    "📝 **Poll PyInt 2-2:**  Why are only some of the columns in the `DataFrame` visible in the output?"
    ]
   },
   {
@@ -522,13 +609,6 @@
     "low_lifeExp.plot.bar(x='country', y='lifeExp', figsize=(6,4));"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "🔔 <span style=\"color:purple\">**Question**: Do you notice any pattern in the data?</span>"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -619,7 +699,7 @@
     "\n",
     "A histogram shows the distribution of a variable using binned values. We can call this using the syntax: `df[column].plot(kind='hist')`. Use a histogram if you want to show distributions of continuous variables.\n",
     "\n",
-    "🔔 <span style=\"color:purple\">**Question**: Try changing the value for the `bins` parameter. What is happening?</span>"
+    "📝 **Poll PyInt 2-3:** Try changing the value for the `bins` parameter. What does the `bins` parameter seem to be determining?</span>"
    ]
   },
   {
@@ -666,7 +746,7 @@
     "    plt.legend()\n",
     "    plt.show()\n",
     "\n",
-    "plot_life_expectancy(df, countries)"
+    "plot_life_expectancy(df, country_list)"
    ]
   },
   {
diff --git a/lessons/3_Project.ipynb b/lessons/3_Project.ipynb
@@ -25,7 +25,9 @@
     "🔔 **Question**: A quick question to help you understand what's going on.<br>\n",
     "🥊 **Challenge**: Interactive exercise. We'll work through these in the workshop!<br>\n",
     "💡 **Tip**: How to do something a bit more efficiently or effectively.<br>\n",
-    "⚠️ **Warning:** Heads-up about tricky stuff or common mistakes.\n",
+    "⚠️ **Warning:** Heads-up about tricky stuff or common mistakes.<br>\n",
+    "📝 **Poll:** A Zoom poll to help you learn!\n",
+    "\n",
     "\n",
     "### Sections\n",
     "1. [🚀 Project](#project)\n",
@@ -54,7 +56,7 @@
     "- `household_tenure`: Self-Reported household tenure\n",
     "- `interview_language`: Language of interview\n",
     "\n",
-    "For this 🚀 Project, the goal we want to accomplish is **visualizing the relationship between poverty level and general health**. We will bring together basic programming and data science techniques you have learned to do this.\n",
+    "For this project, the goal we want to accomplish is **visualizing the relationship between poverty level and general health**. We will bring together basic programming and data science techniques you have learned to do this.\n",
     "\n",
     "🔔 **Question**: Are there other research questions you could imagine asking with this dataset?"
    ]
@@ -178,7 +180,7 @@
    "source": [
     "🔔 **Question**: Let's take a look at the final data frame.\n",
     "\n",
-    "1. <span style=\"color:purple\"> How many rows and columns are there in the concatenated DataFrame?</span>\n",
+    "1. How many rows and columns are there in the concatenated DataFrame?\n",
     "2. How many numeric columns are there in the dataset?\n",
     "3. What data type are the values in the `poverty_level` column?\n"
    ]
@@ -246,9 +248,9 @@
     "Now that we have preprocessed data, we want to analyze it. Recall that our goal is to visualize a relationship between poverty level and general health. Before we do this, we should get a better grasp of what is in our data.\n",
     "\n",
     "## Counting Values\n",
-    "The first thing we will want to do is count values of poverty levels: we want to see how many levels there are, and how the data are distributed. \n",
-    "1. Run `value_counts()` on the `poverty_level` column. \n",
-    "2. <span style=\"color:purple\"> Look through the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html) and **normalize** the output of `value_counts()`.</span>"
+    "The first thing we will want to do is count values of poverty levels: we want to see how many levels there are, and how the data are distributed. First, run `value_counts()` on the `poverty_level` column. \n",
+    "\n",
+    "📝 **Poll - PyInt 3-1**: Look through the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html) for `value_counts()`. What parameter can you use to normalize the output?\n"
    ]
   },
   {
@@ -348,7 +350,7 @@
     "Fill in the blanks below, following the steps. \n",
     "\n",
     "1. Run a **normalized** `value_counts()` on the `general_health` column of `df_above` and `df_below`.\n",
-    "2. Run `plot()` on the output of the resulting DataFrame. Enter the values for two arguments: `kind` must be set to `bar`, and `alpha` must be set to `.5`.\n"
+    "2. We are running `plot()` on the output of the resulting DataFrame. Enter the values for two arguments: `kind` must be set to `bar`, and `alpha` must be set to `.5`.\n"
    ]
   },
   {
@@ -368,7 +370,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "🔔 <span style=\"color:purple\"> **Question**: What is the `alpha` parameter doing? Read through the [documentation](https://pandas.pydata.org/docs/user_guide/visualization.html) to find out.</span>"
+    "📝 **Poll - PyInt 3-2**:What is the `alpha` parameter doing? Read through the [documentation](https://pandas.pydata.org/docs/user_guide/visualization.html) to find out.</span>"
    ]
   },
   {
