moving data to home folder

3-Data-Visualization/10-visualization-quantities/README.md

@@ -23,7 +23,9 @@ If you have a dataset and need to discover how much of a given item is included,
 
 ## Build a line plot about bird wingspan values
 
-Open the `notebook.ipynb` file at the root of this lesson folder and add a cell:
+Open the `notebook.ipynb` file at the root of this lesson folder and add a cell.
+
+> Note: the data is stored in the root of this repo in the `/data` folder.
 
 ```python
 import pandas as pd

3-Data-Visualization/10-visualization-quantities/notebook.ipynb

@@ -0,0 +1,35 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Let's learn about birds\n"
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python",
+   "version": "3.7.0",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

3-Data-Visualization/11-visualization-distributions/README.md

@@ -13,7 +13,7 @@ Let's discover some facts about the distributions of data in this dataset. In th
 ```python
 import pandas as pd
 import matplotlib.pyplot as plt
-birds = pd.read_csv('../data/birds.csv')
+birds = pd.read_csv('../../data/birds.csv')
 birds.head()
 ```
 

3-Data-Visualization/11-visualization-distributions/notebook.ipynb

@@ -0,0 +1,19 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Bird distributions"
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

3-Data-Visualization/11-visualization-distributions/solution/notebook.ipynb

@@ -15,7 +15,7 @@
   "orig_nbformat": 4,
   "kernelspec": {
    "name": "python3",
-   "display_name": "Python 3.7.0 64-bit ('3.7')"
+   "display_name": "Python 3.7.0 64-bit"
   },
   "interpreter": {
    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
@@ -44,7 +44,7 @@
    "source": [
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "birds = pd.read_csv('../../data/birds.csv')\n",
+    "birds = pd.read_csv('../../../data/birds.csv')\n",
     "birds.head()"
    ],
    "outputs": [

3-Data-Visualization/12-visualization-proportions/notebook.ipynb

@@ -0,0 +1,19 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 🍄 Mushroom Proportions"
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

3-Data-Visualization/12-visualization-proportions/solution/notebook.ipynb

@@ -20,7 +20,7 @@
    "source": [
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "mushrooms = pd.read_csv('../../data/mushrooms.csv')\n",
+    "mushrooms = pd.read_csv('../../../data/mushrooms.csv')\n",
     "mushrooms.head()"
    ],
    "outputs": [

3-Data-Visualization/13-visualization-relationships/README.md

@@ -1,4 +1,10 @@
-# Visualizing Relationships
+# Visualizing Relationships: All About Honey 🍯
+
+Continuing with the nature focus of our research, let's discover interesting visualizations to show the relationships between various types of honey, according to a dataset derived from the [United States Department of Agriculture](https://www.nass.usda.gov/About_NASS/index.php). 
+
+This dataset of about 600 items displays honey production in many U.S. states. So, for example, you can look at the number of colonies, yield per colony, total production, stocks, price per pound, and value of the honey produced in a given state from 1998-2012, with one row per year for each state. 
+
+It will be interesting to visualize the relationship between a given state's production per year and, for example, the price of honey in that state. Alternately, you could visualize the relationship between states' honey yield per colony. This year span covers the devastating 'CCD' or 'Colony Collapse Disorder' first seen in 2006 (http://npic.orst.edu/envir/ccd.html), so it is a poignant dataset to study. 🐝
 
 ## Pre-Lecture Quiz
 

3-Data-Visualization/13-visualization-relationships/notebook.ipynb

@@ -0,0 +1,19 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Visualizing Honey Production 🍯 🐝"
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

3-Data-Visualization/13-visualization-relationships/solution/notebook.ipynb

@@ -0,0 +1,19 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Visualizing Honey Production 🍯 🐝"
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}