diff --git a/index.ipynb b/index.ipynb
index cd283629c8d..ad06e8f2ac9 100644
--- a/index.ipynb
+++ b/index.ipynb
@@ -1 +1,3153 @@
-{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Python For loops Lab"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Learning Objectives"]}, {"cell_type": "markdown", "metadata": {}, "source": ["* Understand how for loops can help us reduce repetition\n", "* Understand the syntax of for loops "]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Picking up where we last left off"]}, {"cell_type": "markdown", "metadata": {}, "source": ["In the last lesson, we worked with some of our travel data.  Let's retrieve a list with our travel information again from excel.  First, we read the information from excel as a list of dictionaries, with each dictionary representing a location.  And we assign this list to the variable `cities`."]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["import pandas\n", "file_name = './cities.xlsx'\n", "travel_df = pandas.read_excel(file_name)\n", "cities = travel_df.to_dict('records')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Next, we retrieve the first three city names, stored as the `'City'` attribute of each dictionary, and `'Population'` of each of the cities.  Then we plot the names as our `x_values` and the populations as our `y_values`."]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["import plotly\n", "\n", "plotly.offline.init_notebook_mode(connected=True)\n", "\n", "x_values = [cities[0]['City'], cities[1]['City'], cities[2]['City']]\n", "y_values = [cities[0]['Population'], cities[1]['Population'], cities[2]['Population']]\n", "trace_first_three_pops = {'x': x_values, 'y': y_values, 'type': 'bar'}\n", "plotly.offline.iplot([trace_first_three_pops])"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Of course, as you may have spotted, there is a good amount of repetition in displaying this data.  Just take a look at how we retrieved the data for our `x_values` and `y_values`.  "]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["x_values = [cities[0]['City'], cities[1]['City'], cities[2]['City']]\n", "y_values = [cities[0]['Population'], cities[1]['Population'], cities[2]['Population']]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["So in this lesson, we will use our `for` loop to display information about our travel locations with less repetition."]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Working with the For Loop"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Our `cities` list contains information about the top 12 cities by population.  For our upcoming iteration tasks, it will be useful to have a list of the numbers 0 through 11.  Use what we know about `len` and `range`to generate a list of numbers 0 through 11.  Assign this to a variable called `city_indices`."]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["city_indices = None\n", "city_indices # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Now we want to create labels for each of the cities. We'll provide a list of the `city_names` for you. "]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["city_names = ['Buenos Aires',\n", " 'Toronto',\n", " 'Marakesh',\n", " 'Albuquerque',\n", " 'Los Cabos',\n", " 'Greenville',\n", " 'Archipelago Sea',\n", " 'Pyeongchang',\n", " 'Walla Walla Valley',\n", " 'Salina Island',\n", " 'Solta',\n", " 'Iguazu Falls']"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Your task is to assign the variable `names_and_ranks` to a list, with each element equal to the city name and it's corresponding rank.  For example, the first element would be, `\"1. Buenos Aires\"` and the second would be `\"2. Toronto\"`.  Use a `for` loop and the lists `city_indices` and `city_names` to accomplish this.  We'll need to perform some nifty string interpolation to format our strings properly.  Check out [f-string interpolation](https://www.programiz.com/python-programming/string-interpolation#f) to see how we can pass values into a string.  Remember that list indices start at zero, but we want our `names_and_ranks` list to start at one!"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["names_and_ranks = [] \n", "# write a for loop that adds the properly formatted string to the names_and_ranks list"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["names_and_ranks[0] # '1. Buenos Aires'\n", "names_and_ranks[1] # '2. Toronto'\n", "names_and_ranks[-1] # '12. Iguazu Falls'"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Ok, now let's create a new variable called `city_populations`.  Use a `for` loop to iterate through `cities` and have `city_populations` equal to each of the populations."]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["city_populations = []"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["city_populations[0] # 2891\n", "city_populations[1] # 2732\n", "city_populations[-1] # 0"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Great! Now we can begin to plot this data.  First, let's create a trace of our populations and set it to the variable `trace_populations`."]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["trace_populations = {'x': names_and_ranks, \n", "                     'y': city_populations, \n", "                     'text': names_and_ranks, \n", "                     'type': 'bar', \n", "                     'name': 'populations'}"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["import plotly\n", "plotly.offline.init_notebook_mode(connected=True)\n", "plotly.offline.iplot([trace_populations])"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Now we want declare a variable called `city_areas` that points to a list of all of the areas of the cities.  Let's use a `for` loop to iterate through our `cities` and have `city_areas` equal to each area of the city.  "]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["city_areas = []"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["trace_areas = {'x': names_and_ranks, 'y': city_areas, 'text': names_and_ranks, 'type': 'bar', 'name': 'areas'}"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["import plotly\n", "plotly.offline.init_notebook_mode(connected=True)\n", "plotly.offline.iplot([trace_populations, trace_areas])"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Summary"]}, {"cell_type": "markdown", "metadata": {}, "source": ["In this section we saw how we can use `for` loops to go through elements of a list and perform the same operation on each.  By using `for` loops we were able to reduce the amount of code that we wrote and while also writing more expressive code."]}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6"}}, "nbformat": 4, "nbformat_minor": 2}
\ No newline at end of file
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+    "# Python For loops Lab"
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+   "source": [
+    "### Learning Objectives"
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+    "* Understand how for loops can help us reduce repetition\n",
+    "* Understand the syntax of for loops "
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+    "### Picking up where we last left off"
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+    "In the last lesson, we worked with some of our travel data.  Let's retrieve a list with our travel information again from excel.  First, we read the information from excel as a list of dictionaries, with each dictionary representing a location.  And we assign this list to the variable `cities`."
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+   "execution_count": 1,
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+   "source": [
+    "import pandas\n",
+    "file_name = './cities.xlsx'\n",
+    "travel_df = pandas.read_excel(file_name)\n",
+    "cities = travel_df.to_dict('records')"
+   ]
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+       "        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly\n",
+    "\n",
+    "plotly.offline.init_notebook_mode(connected=True)\n",
+    "\n",
+    "x_values = [cities[0]['City'], cities[1]['City'], cities[2]['City']]\n",
+    "y_values = [cities[0]['Population'], cities[1]['Population'], cities[2]['Population']]\n",
+    "trace_first_three_pops = {'x': x_values, 'y': y_values, 'type': 'bar'}\n",
+    "plotly.offline.iplot([trace_first_three_pops])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, as you may have spotted, there is a good amount of repetition in displaying this data.  Just take a look at how we retrieved the data for our `x_values` and `y_values`.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_values = [cities[0]['City'], cities[1]['City'], cities[2]['City']]\n",
+    "y_values = [cities[0]['Population'], cities[1]['Population'], cities[2]['Population']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So in this lesson, we will use our `for` loop to display information about our travel locations with less repetition."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Working with the For Loop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our `cities` list contains information about the top 12 cities by population.  For our upcoming iteration tasks, it will be useful to have a list of the numbers 0 through 11.  Use what we know about `len` and `range`to generate a list of numbers 0 through 11.  Assign this to a variable called `city_indices`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_indices = list(range(0, len(cities)))\n",
+    "city_indices # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want to create labels for each of the cities. We'll provide a list of the `city_names` for you. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city_names = ['Buenos Aires',\n",
+    " 'Toronto',\n",
+    " 'Marakesh',\n",
+    " 'Albuquerque',\n",
+    " 'Los Cabos',\n",
+    " 'Greenville',\n",
+    " 'Archipelago Sea',\n",
+    " 'Pyeongchang',\n",
+    " 'Walla Walla Valley',\n",
+    " 'Salina Island',\n",
+    " 'Solta',\n",
+    " 'Iguazu Falls']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your task is to assign the variable `names_and_ranks` to a list, with each element equal to the city name and it's corresponding rank.  For example, the first element would be, `\"1. Buenos Aires\"` and the second would be `\"2. Toronto\"`.  Use a `for` loop and the lists `city_indices` and `city_names` to accomplish this.  We'll need to perform some nifty string interpolation to format our strings properly.  Check out [f-string interpolation](https://www.programiz.com/python-programming/string-interpolation#f) to see how we can pass values into a string.  Remember that list indices start at zero, but we want our `names_and_ranks` list to start at one!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['1. Buenos Aires', '2. Toronto', '3. Marakesh', '4. Albuquerque', '5. Los Cabos', '6. Greenville', '7. Archipelago Sea', '8. Pyeongchang', '9. Walla Walla Valley', '10. Salina Island', '11. Solta', '12. Iguazu Falls']\n"
+     ]
+    }
+   ],
+   "source": [
+    "names_and_ranks = [] \n",
+    "for city_index in city_indices:\n",
+    "    rank = city_index + 1\n",
+    "    names_and_ranks.append(f'{rank}. {city_names[city_index]}')\n",
+    "# write a for loop that adds the properly formatted string to the names_and_ranks list\n",
+    "print(names_and_ranks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'12. Iguazu Falls'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names_and_ranks[0] # '1. Buenos Aires'\n",
+    "names_and_ranks[1] # '2. Toronto'\n",
+    "names_and_ranks[-1] # '12. Iguazu Falls'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ok, now let's create a new variable called `city_populations`.  Use a `for` loop to iterate through `cities` and have `city_populations` equal to each of the populations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[2891, 2732, 929, 559, 288, 93, 60, 44, 33, 3, 2, 0]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_populations = []\n",
+    "for city_index in city_indices:\n",
+    "    city_populations.append(cities[city_index]['Population'])\n",
+    "city_populations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_populations[0] # 2891\n",
+    "city_populations[1] # 2732\n",
+    "city_populations[-1] # 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great! Now we can begin to plot this data.  First, let's create a trace of our populations and set it to the variable `trace_populations`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trace_populations = {'x': names_and_ranks, \n",
+    "                     'y': city_populations, \n",
+    "                     'text': names_and_ranks, \n",
+    "                     'type': 'bar', \n",
+    "                     'name': 'populations'}"
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+       "                        [{\"name\": \"populations\", \"text\": [\"1. Buenos Aires\", \"2. Toronto\", \"3. Marakesh\", \"4. Albuquerque\", \"5. Los Cabos\", \"6. Greenville\", \"7. Archipelago Sea\", \"8. Pyeongchang\", \"9. Walla Walla Valley\", \"10. Salina Island\", \"11. Solta\", \"12. Iguazu Falls\"], \"type\": \"bar\", \"x\": [\"1. Buenos Aires\", \"2. Toronto\", \"3. Marakesh\", \"4. Albuquerque\", \"5. Los Cabos\", \"6. Greenville\", \"7. Archipelago Sea\", \"8. Pyeongchang\", \"9. Walla Walla Valley\", \"10. Salina Island\", \"11. Solta\", \"12. Iguazu Falls\"], \"y\": [2891, 2732, 929, 559, 288, 93, 60, 44, 33, 3, 2, 0]}],\n",
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+       "                        {\"responsive\": true}\n",
+       "                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('58d0a984-71a9-41c3-aec2-f40d5fee0cb4');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })\n",
+       "                };\n",
+       "                });\n",
+       "            </script>\n",
+       "        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly\n",
+    "plotly.offline.init_notebook_mode(connected=True)\n",
+    "plotly.offline.iplot([trace_populations])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we want declare a variable called `city_areas` that points to a list of all of the areas of the cities.  Let's use a `for` loop to iterate through our `cities` and have `city_areas` equal to each area of the city.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[203, 630, 230, 491, 3751, 68, 2000, 1464, 35, 26, 59, 2396]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_areas = []\n",
+    "for city_index in city_indices:\n",
+    "    city_areas.append(cities[city_index]['Area'])\n",
+    "city_areas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trace_areas = {'x': names_and_ranks, 'y': city_areas, 'text': names_and_ranks, 'type': 'bar', 'name': 'areas'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "        <script type=\"text/javascript\">\n",
+       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
+       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (typeof require !== 'undefined') {\n",
+       "        require.undef(\"plotly\");\n",
+       "        requirejs.config({\n",
+       "            paths: {\n",
+       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
+       "            }\n",
+       "        });\n",
+       "        require(['plotly'], function(Plotly) {\n",
+       "            window._Plotly = Plotly;\n",
+       "        });\n",
+       "        }\n",
+       "        </script>\n",
+       "        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "linkText": "Export to plot.ly",
+        "plotlyServerURL": "https://plot.ly",
+        "showLink": false
+       },
+       "data": [
+        {
+         "name": "populations",
+         "text": [
+          "1. Buenos Aires",
+          "2. Toronto",
+          "3. Marakesh",
+          "4. Albuquerque",
+          "5. Los Cabos",
+          "6. Greenville",
+          "7. Archipelago Sea",
+          "8. Pyeongchang",
+          "9. Walla Walla Valley",
+          "10. Salina Island",
+          "11. Solta",
+          "12. Iguazu Falls"
+         ],
+         "type": "bar",
+         "x": [
+          "1. Buenos Aires",
+          "2. Toronto",
+          "3. Marakesh",
+          "4. Albuquerque",
+          "5. Los Cabos",
+          "6. Greenville",
+          "7. Archipelago Sea",
+          "8. Pyeongchang",
+          "9. Walla Walla Valley",
+          "10. Salina Island",
+          "11. Solta",
+          "12. Iguazu Falls"
+         ],
+         "y": [
+          2891,
+          2732,
+          929,
+          559,
+          288,
+          93,
+          60,
+          44,
+          33,
+          3,
+          2,
+          0
+         ]
+        },
+        {
+         "name": "areas",
+         "text": [
+          "1. Buenos Aires",
+          "2. Toronto",
+          "3. Marakesh",
+          "4. Albuquerque",
+          "5. Los Cabos",
+          "6. Greenville",
+          "7. Archipelago Sea",
+          "8. Pyeongchang",
+          "9. Walla Walla Valley",
+          "10. Salina Island",
+          "11. Solta",
+          "12. Iguazu Falls"
+         ],
+         "type": "bar",
+         "x": [
+          "1. Buenos Aires",
+          "2. Toronto",
+          "3. Marakesh",
+          "4. Albuquerque",
+          "5. Los Cabos",
+          "6. Greenville",
+          "7. Archipelago Sea",
+          "8. Pyeongchang",
+          "9. Walla Walla Valley",
+          "10. Salina Island",
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+          "12. Iguazu Falls"
+         ],
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+          630,
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\"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},\n",
+       "                        {\"responsive\": true}\n",
+       "                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('fc910ef1-b269-4615-b723-b59e8374750c');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })\n",
+       "                };\n",
+       "                });\n",
+       "            </script>\n",
+       "        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly\n",
+    "plotly.offline.init_notebook_mode(connected=True)\n",
+    "plotly.offline.iplot([trace_populations, trace_areas])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section we saw how we can use `for` loops to go through elements of a list and perform the same operation on each.  By using `for` loops we were able to reduce the amount of code that we wrote and while also writing more expressive code."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}