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# Visualizing Proportions
In this lesson, you will use a different nature-focused dataset to visualize proportions, such as how many different types of fungi populate a given dataset about mushrooms. Let's explore these fascinating fungi using a dataset sourced from Audubon listing details about 23 species of gilled mushrooms in the Agaricus and Lepiota families. You will experiment with tasty visualizations such as:
- Pie charts 🥧
- Waffle charts 🧇
- Donut charts 🍩
as well as
- Stacked bar charts
## Pre-Lecture Quiz
[Pre-lecture quiz]()
## Get to know your mushrooms 🍄
Mushrooms are very interesting. Let's import a dataset to study them.
```python
import pandas as pd
import matplotlib.pyplot as plt
mushrooms = pd.read_csv('../../data/mushrooms.csv')
mushrooms.head()
```
A table is printed out with some great data for analysis:
| class | cap-shape | cap-surface | cap-color | bruises | odor | gill-attachment | gill-spacing | gill-size | gill-color | stalk-shape | stalk-root | stalk-surface-above-ring | stalk-surface-below-ring | stalk-color-above-ring | stalk-color-below-ring | veil-type | veil-color | ring-number | ring-type | spore-print-color | population | habitat |
| --------- | --------- | ----------- | --------- | ------- | ------- | --------------- | ------------ | --------- | ---------- | ----------- | ---------- | ------------------------ | ------------------------ | ---------------------- | ---------------------- | --------- | ---------- | ----------- | --------- | ----------------- | ---------- | ------- |
| Poisonous | Convex | Smooth | Brown | Bruises | Pungent | Free | Close | Narrow | Black | Enlarging | Equal | Smooth | Smooth | White | White | Partial | White | One | Pendant | Black | Scattered | Urban |
| Edible | Convex | Smooth | Yellow | Bruises | Almond | Free | Close | Broad | Black | Enlarging | Club | Smooth | Smooth | White | White | Partial | White | One | Pendant | Brown | Numerous | Grasses |
| Edible | Bell | Smooth | White | Bruises | Anise | Free | Close | Broad | Brown | Enlarging | Club | Smooth | Smooth | White | White | Partial | White | One | Pendant | Brown | Numerous | Meadows |
| Poisonous | Convex | Scaly | White | Bruises | Pungent | Free | Close | Narrow | Brown | Enlarging | Equal | Smooth | Smooth | White | White | Partial | White | One | Pendant | Black | Scattered | Urban |
Right away, you notice that all the data is textual. You will have to edit this data to be able to use it in a chart. Most of the data, in fact, is represented as an object:
```python
print(mushrooms.select_dtypes(["object"]).columns)
```
The output is:
```output
Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',
'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',
'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',
'stalk-surface-below-ring', 'stalk-color-above-ring',
'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',
'ring-type', 'spore-print-color', 'population', 'habitat'],
dtype='object')
```
Take this data and convert the 'class' column to a category:
```python
cols = mushrooms.select_dtypes(["object"]).columns
mushrooms[cols] = mushrooms[cols].astype('category')
```
Now, if you print out the mushrooms data, you can see that it has been grouped into categories according to the poisonous/edible class:
| | cap-shape | cap-surface | cap-color | bruises | odor | gill-attachment | gill-spacing | gill-size | gill-color | stalk-shape | ... | stalk-surface-below-ring | stalk-color-above-ring | stalk-color-below-ring | veil-type | veil-color | ring-number | ring-type | spore-print-color | population | habitat |
| --------- | --------- | ----------- | --------- | ------- | ---- | --------------- | ------------ | --------- | ---------- | ----------- | --- | ------------------------ | ---------------------- | ---------------------- | --------- | ---------- | ----------- | --------- | ----------------- | ---------- | ------- |
| class | | | | | | | | | | | | | | | | | | | | | |
| Edible | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | ... | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 | 4208 |
| Poisonous | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | ... | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 | 3916 |
If you follow the order presented in this table to create your class category labels, you can build a pie chart:
```python
labels=['Edible','Poisonous']
plt.pie(edibleclass['population'],labels=labels,autopct='%.1f %%')
plt.title('Edible?')
plt.show()
```
Voila, a pie chart showing the proportions of this data according to these two classes of mushroom. It's quite important to get the order of labels correct, especially here, so be sure to verify the order the label array is built!
![pie chart](images/pie1.png)
## 🚀 Challenge

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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# 🍄 Mushroom Proportions"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Import the mushroom dataset"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 27,
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"mushrooms = pd.read_csv('../../data/mushrooms.csv')\n",
"mushrooms.head()"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" class cap-shape cap-surface cap-color bruises odor \\\n",
"0 Poisonous Convex Smooth Brown Bruises Pungent \n",
"1 Edible Convex Smooth Yellow Bruises Almond \n",
"2 Edible Bell Smooth White Bruises Anise \n",
"3 Poisonous Convex Scaly White Bruises Pungent \n",
"4 Edible Convex Smooth Green No Bruises None \n",
"\n",
" gill-attachment gill-spacing gill-size gill-color ... \\\n",
"0 Free Close Narrow Black ... \n",
"1 Free Close Broad Black ... \n",
"2 Free Close Broad Brown ... \n",
"3 Free Close Narrow Brown ... \n",
"4 Free Crowded Broad Black ... \n",
"\n",
" stalk-surface-below-ring stalk-color-above-ring stalk-color-below-ring \\\n",
"0 Smooth White White \n",
"1 Smooth White White \n",
"2 Smooth White White \n",
"3 Smooth White White \n",
"4 Smooth White White \n",
"\n",
" veil-type veil-color ring-number ring-type spore-print-color population \\\n",
"0 Partial White One Pendant Black Scattered \n",
"1 Partial White One Pendant Brown Numerous \n",
"2 Partial White One Pendant Brown Numerous \n",
"3 Partial White One Pendant Black Scattered \n",
"4 Partial White One Evanescent Brown Abundant \n",
"\n",
" habitat \n",
"0 Urban \n",
"1 Grasses \n",
"2 Meadows \n",
"3 Urban \n",
"4 Grasses \n",
"\n",
"[5 rows x 23 columns]"
],
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>class</th>\n",
" <th>cap-shape</th>\n",
" <th>cap-surface</th>\n",
" <th>cap-color</th>\n",
" <th>bruises</th>\n",
" <th>odor</th>\n",
" <th>gill-attachment</th>\n",
" <th>gill-spacing</th>\n",
" <th>gill-size</th>\n",
" <th>gill-color</th>\n",
" <th>...</th>\n",
" <th>stalk-surface-below-ring</th>\n",
" <th>stalk-color-above-ring</th>\n",
" <th>stalk-color-below-ring</th>\n",
" <th>veil-type</th>\n",
" <th>veil-color</th>\n",
" <th>ring-number</th>\n",
" <th>ring-type</th>\n",
" <th>spore-print-color</th>\n",
" <th>population</th>\n",
" <th>habitat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Poisonous</td>\n",
" <td>Convex</td>\n",
" <td>Smooth</td>\n",
" <td>Brown</td>\n",
" <td>Bruises</td>\n",
" <td>Pungent</td>\n",
" <td>Free</td>\n",
" <td>Close</td>\n",
" <td>Narrow</td>\n",
" <td>Black</td>\n",
" <td>...</td>\n",
" <td>Smooth</td>\n",
" <td>White</td>\n",
" <td>White</td>\n",
" <td>Partial</td>\n",
" <td>White</td>\n",
" <td>One</td>\n",
" <td>Pendant</td>\n",
" <td>Black</td>\n",
" <td>Scattered</td>\n",
" <td>Urban</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Edible</td>\n",
" <td>Convex</td>\n",
" <td>Smooth</td>\n",
" <td>Yellow</td>\n",
" <td>Bruises</td>\n",
" <td>Almond</td>\n",
" <td>Free</td>\n",
" <td>Close</td>\n",
" <td>Broad</td>\n",
" <td>Black</td>\n",
" <td>...</td>\n",
" <td>Smooth</td>\n",
" <td>White</td>\n",
" <td>White</td>\n",
" <td>Partial</td>\n",
" <td>White</td>\n",
" <td>One</td>\n",
" <td>Pendant</td>\n",
" <td>Brown</td>\n",
" <td>Numerous</td>\n",
" <td>Grasses</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Edible</td>\n",
" <td>Bell</td>\n",
" <td>Smooth</td>\n",
" <td>White</td>\n",
" <td>Bruises</td>\n",
" <td>Anise</td>\n",
" <td>Free</td>\n",
" <td>Close</td>\n",
" <td>Broad</td>\n",
" <td>Brown</td>\n",
" <td>...</td>\n",
" <td>Smooth</td>\n",
" <td>White</td>\n",
" <td>White</td>\n",
" <td>Partial</td>\n",
" <td>White</td>\n",
" <td>One</td>\n",
" <td>Pendant</td>\n",
" <td>Brown</td>\n",
" <td>Numerous</td>\n",
" <td>Meadows</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Poisonous</td>\n",
" <td>Convex</td>\n",
" <td>Scaly</td>\n",
" <td>White</td>\n",
" <td>Bruises</td>\n",
" <td>Pungent</td>\n",
" <td>Free</td>\n",
" <td>Close</td>\n",
" <td>Narrow</td>\n",
" <td>Brown</td>\n",
" <td>...</td>\n",
" <td>Smooth</td>\n",
" <td>White</td>\n",
" <td>White</td>\n",
" <td>Partial</td>\n",
" <td>White</td>\n",
" <td>One</td>\n",
" <td>Pendant</td>\n",
" <td>Black</td>\n",
" <td>Scattered</td>\n",
" <td>Urban</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Edible</td>\n",
" <td>Convex</td>\n",
" <td>Smooth</td>\n",
" <td>Green</td>\n",
" <td>No Bruises</td>\n",
" <td>None</td>\n",
" <td>Free</td>\n",
" <td>Crowded</td>\n",
" <td>Broad</td>\n",
" <td>Black</td>\n",
" <td>...</td>\n",
" <td>Smooth</td>\n",
" <td>White</td>\n",
" <td>White</td>\n",
" <td>Partial</td>\n",
" <td>White</td>\n",
" <td>One</td>\n",
" <td>Evanescent</td>\n",
" <td>Brown</td>\n",
" <td>Abundant</td>\n",
" <td>Grasses</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 23 columns</p>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 27
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Create a pie chart displaying the proportion of Poisonous vs. Edible mushrooms"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 28,
"source": [
"print(mushrooms.select_dtypes([\"object\"]).columns)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',\n",
" 'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',\n",
" 'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',\n",
" 'stalk-surface-below-ring', 'stalk-color-above-ring',\n",
" 'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',\n",
" 'ring-type', 'spore-print-color', 'population', 'habitat'],\n",
" dtype='object')\n"
]
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 29,
"source": [
"cols = mushrooms.select_dtypes([\"object\"]).columns\n",
"mushrooms[cols] = mushrooms[cols].astype('category')"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 30,
"source": [
"edibleclass=mushrooms.groupby(['class']).count()\n",
"edibleclass"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" cap-shape cap-surface cap-color bruises odor gill-attachment \\\n",
"class \n",
"Edible 4208 4208 4208 4208 4208 4208 \n",
"Poisonous 3916 3916 3916 3916 3916 3916 \n",
"\n",
" gill-spacing gill-size gill-color stalk-shape ... \\\n",
"class ... \n",
"Edible 4208 4208 4208 4208 ... \n",
"Poisonous 3916 3916 3916 3916 ... \n",
"\n",
" stalk-surface-below-ring stalk-color-above-ring \\\n",
"class \n",
"Edible 4208 4208 \n",
"Poisonous 3916 3916 \n",
"\n",
" stalk-color-below-ring veil-type veil-color ring-number \\\n",
"class \n",
"Edible 4208 4208 4208 4208 \n",
"Poisonous 3916 3916 3916 3916 \n",
"\n",
" ring-type spore-print-color population habitat \n",
"class \n",
"Edible 4208 4208 4208 4208 \n",
"Poisonous 3916 3916 3916 3916 \n",
"\n",
"[2 rows x 22 columns]"
],
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cap-shape</th>\n",
" <th>cap-surface</th>\n",
" <th>cap-color</th>\n",
" <th>bruises</th>\n",
" <th>odor</th>\n",
" <th>gill-attachment</th>\n",
" <th>gill-spacing</th>\n",
" <th>gill-size</th>\n",
" <th>gill-color</th>\n",
" <th>stalk-shape</th>\n",
" <th>...</th>\n",
" <th>stalk-surface-below-ring</th>\n",
" <th>stalk-color-above-ring</th>\n",
" <th>stalk-color-below-ring</th>\n",
" <th>veil-type</th>\n",
" <th>veil-color</th>\n",
" <th>ring-number</th>\n",
" <th>ring-type</th>\n",
" <th>spore-print-color</th>\n",
" <th>population</th>\n",
" <th>habitat</th>\n",
" </tr>\n",
" <tr>\n",
" <th>class</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Edible</th>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>...</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" <td>4208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Poisonous</th>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>...</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" <td>3916</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 22 columns</p>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 30
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 33,
"source": [
"labels=['Edible','Poisonous']\n",
"plt.pie(edibleclass['population'],labels=labels,autopct='%.1f %%')\n",
"plt.title('Edible?')\n",
"plt.show()"
],
"outputs": [
{
"output_type": "error",
"ename": "KeyError",
"evalue": "'class'",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2890\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2891\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2892\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'class'",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-33-04c1f5ff0aea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0medibleclass\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'class'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0medibleclass\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'population'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mautopct\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%.1f %%'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Edible?'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2900\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2901\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2902\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2903\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2904\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2891\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2892\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2893\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2895\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'class'"
]
}
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5
3-Data-Visualization/README.md
Unescape View File

@ -6,8 +6,7 @@
Visualizing data is one of the most important tasks of a data scientist. Images are worth 1000 words, and a visualization can help you identify all kinds of interesting parts of your data such as spikes, outliers, groupings, tendencies, and more, that can help you understand the story your data is trying to tell.
In these five lessons, you will explore data sourced from nature and create interesting and beautiful visualizations using various techniques.
In these five lessons, you will explore data sourced from nature and create interesting and beautiful visualizations using various techniques.
### Topics
1. [Quantities](10-visualization-quantities/README.md)
@ -22,7 +21,7 @@ These visualization lessons were written with 🌸 by [Jen Looper](https://twitt
🍯 Data for US Honey Production is sourced from Jessica Li's project on [Kaggle](https://www.kaggle.com/jessicali9530/honey-production). The [data](https://usda.library.cornell.edu/concern/publications/rn301137d) is derived from the [United States Department of Agriculture](https://www.nass.usda.gov/About_NASS/index.php).
🍄 Data for mushrooms is also sourced from [Kaggle](https://www.kaggle.com/hatterasdunton/mushroom-classification-updated-dataset) revised by Hatteras Dunton. This dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family Mushroom drawn from The Audubon Society Field Guide to North American Mushrooms (1981). This dataset was donated to UCI ML 27 in 1987.
🍄 Data for mushrooms is also sourced from [Kaggle](https://www.kaggle.com/hatterasdunton/mushroom-classification-updated-dataset) revised by Hatteras Dunton. This dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family. Mushroom drawn from The Audubon Society Field Guide to North American Mushrooms (1981). This dataset was donated to UCI ML 27 in 1987.
🦆 Data for Minnesota Birds is from [Kaggle](https://www.kaggle.com/hannahcollins/minnesota-birds) scraped from [Wikipedia](https://en.wikipedia.org/wiki/List_of_birds_of_Minnesota) by Hannah Collins.

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