diff --git a/2-Working-With-Data/08-data-preparation/README.md b/2-Working-With-Data/08-data-preparation/README.md
index eb0f91a..fd599af 100644
--- a/2-Working-With-Data/08-data-preparation/README.md
+++ b/2-Working-With-Data/08-data-preparation/README.md
@@ -1,4 +1,4 @@
-# Working with Data: Cleaning and Transformations
+# Working with Data: Data Preparation
## Pre-Lecture Quiz
diff --git a/3-Data-Visualization/09-visualization-quantities/README.md b/3-Data-Visualization/09-visualization-quantities/README.md
new file mode 100644
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@@ -0,0 +1,204 @@
+# Visualizing Quantities
+
+In this lesson, you will use three different libraries to learn how to create interesting visualizations all around the concept of quantity. Using a cleaned dataset about the birds of Minnesota, you can learn many interesting facts about local wildlife.
+## Pre-Lecture Quiz
+
+[Pre-lecture quiz]()
+
+## Observe wingspan with Matplotlib
+
+An excellent library to create both simple and sophisticated plots and charts of various kinds is [Matplotlib](https://matplotlib.org/stable/index.html). In general terms, the process of plotting data using these libraries includes identifying the parts of your dataframe that you want to target, performing any transforms on that data necessary, assigning its x and y axis values, deciding what kind of plot to show, and then showing the plot. Matplotlib offers a large variety of visualizations, but for this lesson, let's focus on the ones most appropriate for visualizing quantity: line charts, scatterplots, and bar plots.
+
+> ✅ Use the best chart to suit your data's structure and the story you want to tell.
+> - To analyze trends over time: line
+> - To compare values: bar, column, pie, scatterplot
+> - To show how parts relate to a whole: pie
+> - To show distribution of data: scatterplot, bar
+> - To show trends: line, column
+> - To show relationships between values: line, scatterplot, bubble
+
+If you have a dataset and need to discover how much of a given item is included, one of the first tasks you have at hand will be to inspect its values.
+
+✅ There are very good 'cheat sheets' available for Matplotlib [here](https://github.com/matplotlib/cheatsheets/blob/master/cheatsheets-1.png) and [here](https://github.com/matplotlib/cheatsheets/blob/master/cheatsheets-2.png).
+
+## Build a line plot about bird wingspan values
+
+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
+import matplotlib.pyplot as plt
+birds = pd.read_csv('../../data/birds.csv')
+birds.head()
+```
+This data is a mix of text and numbers:
+
+
+| | Name | ScientificName | Category | Order | Family | Genus | ConservationStatus | MinLength | MaxLength | MinBodyMass | MaxBodyMass | MinWingspan | MaxWingspan |
+| ---: | :--------------------------- | :--------------------- | :-------------------- | :----------- | :------- | :---------- | :----------------- | --------: | --------: | ----------: | ----------: | ----------: | ----------: |
+| 0 | Black-bellied whistling-duck | Dendrocygna autumnalis | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Dendrocygna | LC | 47 | 56 | 652 | 1020 | 76 | 94 |
+| 1 | Fulvous whistling-duck | Dendrocygna bicolor | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Dendrocygna | LC | 45 | 53 | 712 | 1050 | 85 | 93 |
+| 2 | Snow goose | Anser caerulescens | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Anser | LC | 64 | 79 | 2050 | 4050 | 135 | 165 |
+| 3 | Ross's goose | Anser rossii | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Anser | LC | 57.3 | 64 | 1066 | 1567 | 113 | 116 |
+| 4 | Greater white-fronted goose | Anser albifrons | Ducks/Geese/Waterfowl | Anseriformes | Anatidae | Anser | LC | 64 | 81 | 1930 | 3310 | 130 | 165 |
+
+Let's start by plotting some of the numeric data using a basic line plot. Suppose you wanted a view of the maximum wingspan for these interesting birds.
+
+```python
+wingspan = birds['MaxWingspan']
+wingspan.plot()
+```
+![Max Wingspan](images/max-wingspan.png)
+
+What do you notice immediately? There seems to be at least one outlier - that's quite a wingspan! A 2300 centimeter wingspan equals 23 meters - are there Pterodactyls roaming Minnesota? Let's investigate.
+
+While you could do a quick sort in Excel to find those outliers, which are probably typos, continue the visualization process by working from within the plot.
+
+Add labels to the y-axis to show what kind of birds are in question:
+
+```
+plt.title('Max Wingspan in Centimeters')
+plt.ylabel('Wingspan (CM)')
+plt.xlabel('Birds')
+plt.xticks(rotation=45)
+x = birds['Name']
+y = birds['MaxWingspan']
+
+plt.plot(x, y)
+
+plt.show()
+```
+![wingspan with labels](images/max-wingspan-labels.png)
+
+Even with the rotation of the labels set to 45 degrees, there are too many to read. Let's try a different strategy: label only those outliers and set the labels within the chart. You can use a scatter chart to make more room for the labeling:
+
+```python
+plt.title('Max Wingspan in Centimeters')
+plt.ylabel('Wingspan (CM)')
+plt.tick_params(axis='both',which='both',labelbottom=False,bottom=False)
+
+for i in range(len(birds)):
+ x = birds['Name'][i]
+ y = birds['MaxWingspan'][i]
+ plt.plot(x, y, 'bo')
+ if birds['MaxWingspan'][i] > 500:
+ plt.text(x, y * (1 - 0.05), birds['Name'][i], fontsize=12)
+
+plt.show()
+```
+What's going on here? You used `tick_params` to hide the bottom labels and then created a loop over your birds dataset. Plotting the chart with small round blue dots by using `bo`, you checked for any bird with a maximum wingspan over 500 and displayed their label next to the dot if so. You offset the labels a little on the y axis (`y * (1 - 0.05)`) and used the bird name as a label.
+
+What did you discover?
+
+![outliers](images/labeled-wingspan.png)
+## Filter your data
+
+Both the Bald Eagle and the Prairie Falcon, while probably very large birds, appear to be mislabeled, with an extra `0` added to their maximum wingspan. It's unlikely that you'll meet a Bald Eagle with a 25 meter wingspan, but if so, please let us know! Let's create a new dataframe without those two outliers:
+
+```python
+plt.title('Max Wingspan in Centimeters')
+plt.ylabel('Wingspan (CM)')
+plt.xlabel('Birds')
+plt.tick_params(axis='both',which='both',labelbottom=False,bottom=False)
+for i in range(len(birds)):
+ x = birds['Name'][i]
+ y = birds['MaxWingspan'][i]
+ if birds['Name'][i] not in ['Bald eagle', 'Prairie falcon']:
+ plt.plot(x, y, 'bo')
+plt.show()
+```
+
+By filtering out outliers, your data is now more cohesive and understandable.
+
+![scatterplot of wingspans](images/scatterplot-wingspan.png)
+
+Now that we have a cleaner dataset at least in terms of wingspan, let's discover more about these birds.
+
+While line and scatter plots can display information about data values and their distributions, we want to think about the values inherent in this dataset. You could create visualizations to answer this following questions about quantity:
+
+> How many categories of birds are there, and what are their numbers?
+> How many birds are extinct, endangered, rare, or common?
+> How many are there of the various genus and orders in Linnaeus's terminology?
+## Explore bar charts
+
+Bar charts are very useful when you need to show groupings of data. Let's explore the categories of birds that exist in this dataset to see which is the most common by number.
+
+In the notebook file, create a basic bar chart
+
+✅ Note, you can either filter out the two outlier birds we identified in the previous section, edit the typo in their wingspan, or leave them in for these exercises which do not depend on wingspan values.
+
+If you want to create a bar chart, you can select the data you want to focus on. Bar charts can be created from raw data:
+
+```python
+birds.plot(x='Category',
+ kind='bar',
+ stacked=True,
+ title='Birds of Minnesota')
+
+```
+![full data as a bar chart](images/full-data-bar.png)
+
+This bar chart, however, is unreadable because there is too much non-grouped data. You need to select only the data that you want to chart, so let's look at the length of birds based on their category.
+
+Filter your data to include only the bird's category.
+
+✅ Notice that you use Pandas to manage the data, and then let Matplotlib do the charting.
+
+Since there are many categories, you can display this chart vertically and tweak its height to account for all the data:
+
+```python
+category_count = birds.value_counts(birds['Category'].values, sort=True)
+plt.rcParams['figure.figsize'] = [6, 12]
+category_count.plot.barh()
+```
+![category and length](images/category-counts.png)
+
+This bar chart shows a good view of the amount of birds in each category. In a blink of an eye, you see that the largest number of birds in this region are in the Ducks/Geese/Waterfowl category. Minnesota is the 'land of 10,000 lakes' so this isn't surprising!
+
+✅ Try some other counts on this dataset. Does anything surprise you?
+
+## Comparing data
+
+You can try different comparisons of grouped data by creating new axes. Try a comparison of the MaxLength of a bird, based on its category:
+
+```python
+maxlength = birds['MaxLength']
+plt.barh(y=birds['Category'], width=maxlength)
+plt.rcParams['figure.figsize'] = [6, 12]
+plt.show()
+```
+![comparing data](images/category-length.png)
+
+Nothing is surprising here: hummingbirds have the least MaxLength compared to Pelicans or Geese. It's good when data makes logical sense!
+
+You can create more interesting visualizations of bar charts by superimposing data. Let's superimpose Minimum and Maximum Length on a given bird category:
+
+```python
+minLength = birds['MinLength']
+maxLength = birds['MaxLength']
+category = birds['Category']
+
+plt.barh(category, maxLength)
+plt.barh(category, minLength)
+
+plt.show()
+```
+In this plot you can see the range, per category, of the Minimum Length and Maximum length of a given bird category. You can safely say that, given this data, the bigger the bird, the larger its length range. Fascinating!
+
+![superimposed values](images/superimposed.png)
+
+## 🚀 Challenge
+
+This bird dataset offers a wealth of information about different types of birds within a particular ecosystem. Search around the internet and see if you can find other bird-oriented datasets. Practice building charts and graphs around these birds to discover facts you didn't realize.
+## Post-Lecture Quiz
+
+[Post-lecture quiz]()
+
+## Review & Self Study
+
+This first lesson has given you some information about how to use Matplotlib to visualize quantities. Do some research around other ways to work with datasets for visualization. [Plotly](https://github.com/plotly/plotly.py) is one that we won't cover in these lessons, so take a look at what it can offer.
+## Assignment
+
+[Lines, Scatters, and Bars](assignment.md)
diff --git a/3-Data-Visualization/09-visualization-quantities/assignment.md b/3-Data-Visualization/09-visualization-quantities/assignment.md
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+# Lines, Scatters and Bars
+
+## Instructions
+
+In this lesson, you worked with line charts, scatterplots, and bar charts to show interesting facts about this dataset. In this assignment, dig deeper into the dataset to discover a fact about a given type of bird. For example, create a notebook visualizing all the interesting data you can uncover about Snow Geese. Use the three plots mentioned above to tell a story in your notebook.
+
+## Rubric
+
+Exemplary | Adequate | Needs Improvement
+--- | --- | -- |
+A notebook is presented with good annotations, solid storytelling, and attractive graphs | The notebook is missing one of these elements | The notebook is missing two of these elements
\ No newline at end of file
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diff --git a/3-Data-Visualization/09-visualization-quantities/notebook.ipynb b/3-Data-Visualization/09-visualization-quantities/notebook.ipynb
new file mode 100644
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--- /dev/null
+++ b/3-Data-Visualization/09-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
+}
\ No newline at end of file
diff --git a/3-Data-Visualization/09-visualization-quantities/solution/notebook.ipynb b/3-Data-Visualization/09-visualization-quantities/solution/notebook.ipynb
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@@ -0,0 +1,560 @@
+{
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.0"
+ },
+ "orig_nbformat": 4,
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3.7.0 64-bit"
+ },
+ "interpreter": {
+ "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Let's learn about birds"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Import pandas and matplotlib along with the data"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "birds = pd.read_csv('../../../data/birds.csv')\n",
+ "birds.head()\n"
+ ],
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " Name ScientificName \\\n",
+ "0 Black-bellied whistling-duck Dendrocygna autumnalis \n",
+ "1 Fulvous whistling-duck Dendrocygna bicolor \n",
+ "2 Snow goose Anser caerulescens \n",
+ "3 Ross's goose Anser rossii \n",
+ "4 Greater white-fronted goose Anser albifrons \n",
+ "\n",
+ " Category Order Family Genus \\\n",
+ "0 Ducks/Geese/Waterfowl Anseriformes Anatidae Dendrocygna \n",
+ "1 Ducks/Geese/Waterfowl Anseriformes Anatidae Dendrocygna \n",
+ "2 Ducks/Geese/Waterfowl Anseriformes Anatidae Anser \n",
+ "3 Ducks/Geese/Waterfowl Anseriformes Anatidae Anser \n",
+ "4 Ducks/Geese/Waterfowl Anseriformes Anatidae Anser \n",
+ "\n",
+ " ConservationStatus MinLength MaxLength MinBodyMass MaxBodyMass \\\n",
+ "0 LC 47.0 56.0 652.0 1020.0 \n",
+ "1 LC 45.0 53.0 712.0 1050.0 \n",
+ "2 LC 64.0 79.0 2050.0 4050.0 \n",
+ "3 LC 57.3 64.0 1066.0 1567.0 \n",
+ "4 LC 64.0 81.0 1930.0 3310.0 \n",
+ "\n",
+ " MinWingspan MaxWingspan \n",
+ "0 76.0 94.0 \n",
+ "1 85.0 93.0 \n",
+ "2 135.0 165.0 \n",
+ "3 113.0 116.0 \n",
+ "4 130.0 165.0 "
+ ],
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Name \n",
+ " ScientificName \n",
+ " Category \n",
+ " Order \n",
+ " Family \n",
+ " Genus \n",
+ " ConservationStatus \n",
+ " MinLength \n",
+ " MaxLength \n",
+ " MinBodyMass \n",
+ " MaxBodyMass \n",
+ " MinWingspan \n",
+ " MaxWingspan \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " Black-bellied whistling-duck \n",
+ " Dendrocygna autumnalis \n",
+ " Ducks/Geese/Waterfowl \n",
+ " Anseriformes \n",
+ " Anatidae \n",
+ " Dendrocygna \n",
+ " LC \n",
+ " 47.0 \n",
+ " 56.0 \n",
+ " 652.0 \n",
+ " 1020.0 \n",
+ " 76.0 \n",
+ " 94.0 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " Fulvous whistling-duck \n",
+ " Dendrocygna bicolor \n",
+ " Ducks/Geese/Waterfowl \n",
+ " Anseriformes \n",
+ " Anatidae \n",
+ " Dendrocygna \n",
+ " LC \n",
+ " 45.0 \n",
+ " 53.0 \n",
+ " 712.0 \n",
+ " 1050.0 \n",
+ " 85.0 \n",
+ " 93.0 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " Snow goose \n",
+ " Anser caerulescens \n",
+ " Ducks/Geese/Waterfowl \n",
+ " Anseriformes \n",
+ " Anatidae \n",
+ " Anser \n",
+ " LC \n",
+ " 64.0 \n",
+ " 79.0 \n",
+ " 2050.0 \n",
+ " 4050.0 \n",
+ " 135.0 \n",
+ " 165.0 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " Ross's goose \n",
+ " Anser rossii \n",
+ " Ducks/Geese/Waterfowl \n",
+ " Anseriformes \n",
+ " Anatidae \n",
+ " Anser \n",
+ " LC \n",
+ " 57.3 \n",
+ " 64.0 \n",
+ " 1066.0 \n",
+ " 1567.0 \n",
+ " 113.0 \n",
+ " 116.0 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " Greater white-fronted goose \n",
+ " Anser albifrons \n",
+ " Ducks/Geese/Waterfowl \n",
+ " Anseriformes \n",
+ " Anatidae \n",
+ " Anser \n",
+ " LC \n",
+ " 64.0 \n",
+ " 81.0 \n",
+ " 1930.0 \n",
+ " 3310.0 \n",
+ " 130.0 \n",
+ " 165.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 2
+ }
+ ],
+ "metadata": {
+ "tags": []
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Let's visualize these birds' wingspan by showing a very basic line plot"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "source": [
+ "\n",
+ "plt.title('Max Wingspan in Centimeters')\n",
+ "plt.ylabel('Wingspan (CM)')\n",
+ "plt.xlabel('Birds')\n",
+ "wingspan = birds.MaxWingspan \n",
+ "wingspan.plot()\n"
+ ],
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "execution_count": 19
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
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"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Show the birds' names correlated to their wingspan"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "source": [
+ "\n",
+ "plt.title('Max Wingspan in Centimeters')\n",
+ "plt.ylabel('Wingspan (CM)')\n",
+ "plt.xlabel('Birds')\n",
+ "plt.xticks(rotation=45)\n",
+ "x = birds['Name'] \n",
+ "y = birds['MaxWingspan']\n",
+ "\n",
+ "plt.plot(x, y)\n",
+ "\n",
+ "plt.show()"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
+ " x[:, None]\n",
+ "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
+ " x = x[:, np.newaxis]\n",
+ "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n",
+ " y = y[:, np.newaxis]\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n",
+ "image/png": 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"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Label the birds whose wingspan is particularly large and are probably outliers"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "source": [
+ "plt.title('Max Wingspan in Centimeters')\n",
+ "plt.ylabel('Wingspan (CM)')\n",
+ "plt.tick_params(axis='both',which='both',labelbottom=False,bottom=False)\n",
+ "\n",
+ "for i in range(len(birds)):\n",
+ " x = birds['Name'][i]\n",
+ " y = birds['MaxWingspan'][i]\n",
+ " plt.plot(x, y, 'bo')\n",
+ " if birds['MaxWingspan'][i] > 500:\n",
+ " plt.text(x, y * (1 - 0.05), birds['Name'][i], fontsize=12)\n",
+ " \n",
+ "plt.show()\n",
+ " \n",
+ "\n",
+ " \n"
+ ],
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n