diff --git a/3-Data-Visualization/13-visualization-relationships/README.md b/3-Data-Visualization/13-visualization-relationships/README.md
index f9fadc9..8a57b4a 100644
--- a/3-Data-Visualization/13-visualization-relationships/README.md
+++ b/3-Data-Visualization/13-visualization-relationships/README.md
@@ -10,6 +10,88 @@ It will be interesting to visualize the relationship between a given state's pro
[Pre-lecture quiz]()
+In this lesson, you can use Seaborn, which you use before, as a good library to visualize relationships between variables. Particularly interesting is the use of Seaborn's `relplot` function that allows scatter plots and line plots to quickly visualize '[statistical relationships](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships)', which allow the data scientist to better understand how variables relate to each other.
+## Scatterplots
+
+Use a scatterplot to show how the price of honey has evolved, year over year, per state. Seaborn, using `relplot`, conveniently groups the state data and displays data points for both categorical and numeric data.
+
+Let's start by importing the data and Seaborn:
+
+```python
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+honey = pd.read_csv('../../data/honey.csv')
+honey.head()
+```
+You notice that the honey data has several interesting columns, including year and price per pound. Let's explore this data, grouped by U.S. state:
+
+| state | numcol | yieldpercol | totalprod | stocks | priceperlb | prodvalue | year |
+| ----- | ------ | ----------- | --------- | -------- | ---------- | --------- | ---- |
+| AL | 16000 | 71 | 1136000 | 159000 | 0.72 | 818000 | 1998 |
+| AZ | 55000 | 60 | 3300000 | 1485000 | 0.64 | 2112000 | 1998 |
+| AR | 53000 | 65 | 3445000 | 1688000 | 0.59 | 2033000 | 1998 |
+| CA | 450000 | 83 | 37350000 | 12326000 | 0.62 | 23157000 | 1998 |
+| CO | 27000 | 72 | 1944000 | 1594000 | 0.7 | 1361000 | 1998 |
+
+
+Create a basic scatterplot to show the relationship between the price per pound of honey and its U.S. state of origin. Make the `y` axis tall enough to display all the states:
+
+```python
+sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5);
+```
+
+
+Now, show the same data with a honey color scheme to show how the price evolves over the years. You can do this by adding a 'hue' parameter to show the change, year over year:
+
+> ✅ Learn more about the [color palettes you can use in Seaborn](https://seaborn.pydata.org/tutorial/color_palettes.html) - try a beautiful rainbow color scheme!
+
+```python
+sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5);
+```
+
+
+With this color scheme change, you can see that there's obviously a strong progression over the years in terms of honey price per pound. Indeed, if you look at a sample set in the data to verify (pick a given state, Arizona for example) you can see a pattern of price increases year over year, with few exceptions:
+
+| state | numcol | yieldpercol | totalprod | stocks | priceperlb | prodvalue | year |
+| ----- | ------ | ----------- | --------- | ------- | ---------- | --------- | ---- |
+| AZ | 55000 | 60 | 3300000 | 1485000 | 0.64 | 2112000 | 1998 |
+| AZ | 52000 | 62 | 3224000 | 1548000 | 0.62 | 1999000 | 1999 |
+| AZ | 40000 | 59 | 2360000 | 1322000 | 0.73 | 1723000 | 2000 |
+| AZ | 43000 | 59 | 2537000 | 1142000 | 0.72 | 1827000 | 2001 |
+| AZ | 38000 | 63 | 2394000 | 1197000 | 1.08 | 2586000 | 2002 |
+| AZ | 35000 | 72 | 2520000 | 983000 | 1.34 | 3377000 | 2003 |
+| AZ | 32000 | 55 | 1760000 | 774000 | 1.11 | 1954000 | 2004 |
+| AZ | 36000 | 50 | 1800000 | 720000 | 1.04 | 1872000 | 2005 |
+| AZ | 30000 | 65 | 1950000 | 839000 | 0.91 | 1775000 | 2006 |
+| AZ | 30000 | 64 | 1920000 | 902000 | 1.26 | 2419000 | 2007 |
+| AZ | 25000 | 64 | 1600000 | 336000 | 1.26 | 2016000 | 2008 |
+| AZ | 20000 | 52 | 1040000 | 562000 | 1.45 | 1508000 | 2009 |
+| AZ | 24000 | 77 | 1848000 | 665000 | 1.52 | 2809000 | 2010 |
+| AZ | 23000 | 53 | 1219000 | 427000 | 1.55 | 1889000 | 2011 |
+| AZ | 22000 | 46 | 1012000 | 253000 | 1.79 | 1811000 | 2012 |
+
+
+Another way to visualize this progression is to use size, rather than color. For colorblind users, this might be a better option. Edit your visualization to show an increase of price by an increase in dot circumference:
+
+```python
+sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspect=.5);
+```
+You can see the size of the dots gradually increasing.
+
+
+
+Is this a simple case of supply and demand? Is there less honey available for purchase year over year, and thus the price increases?
+
+To discover a correlation between price, number of colonies, and yield per colony, let's explore some line charts.
+
+
+
+
+## Multi-line Plots
+
+
+
## 🚀 Challenge
## Post-Lecture Quiz
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diff --git a/3-Data-Visualization/13-visualization-relationships/solution/notebook.ipynb b/3-Data-Visualization/13-visualization-relationships/solution/notebook.ipynb
index ca53da0..06221d4 100644
--- a/3-Data-Visualization/13-visualization-relationships/solution/notebook.ipynb
+++ b/3-Data-Visualization/13-visualization-relationships/solution/notebook.ipynb
@@ -6,12 +6,237 @@
"# Visualizing Honey Production 🍯 🐝"
],
"metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "honey = pd.read_csv('../../../data/honey.csv')\n",
+ "honey.head()"
+ ],
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " state numcol yieldpercol totalprod stocks priceperlb \\\n",
+ "0 AL 16000.0 71 1136000.0 159000.0 0.72 \n",
+ "1 AZ 55000.0 60 3300000.0 1485000.0 0.64 \n",
+ "2 AR 53000.0 65 3445000.0 1688000.0 0.59 \n",
+ "3 CA 450000.0 83 37350000.0 12326000.0 0.62 \n",
+ "4 CO 27000.0 72 1944000.0 1594000.0 0.70 \n",
+ "\n",
+ " prodvalue year \n",
+ "0 818000.0 1998 \n",
+ "1 2112000.0 1998 \n",
+ "2 2033000.0 1998 \n",
+ "3 23157000.0 1998 \n",
+ "4 1361000.0 1998 "
+ ],
+ "text/html": [
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+ "metadata": {},
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+ "metadata": {}
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+ {
+ "cell_type": "markdown",
+ "source": [
+ "Use a scatterplot to show the relationship between a state and its price per pound for local honey"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "source": [
+ "sns.relplot(x=\"priceperlb\", y=\"state\", data=honey, height=15, aspect=.5);\n"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/svg+xml": "\n\n\n