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Microsoft Word - ds2500_lab03_seaborn.docx DS 2501: Intermediate Programming with Data / Lab Practicum Prof. Rachlin and Prof. Park Northeastern University Seaborn Hack-a-thon In this lab exercise...

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Microsoft Word - ds2500_lab03_seaborn.docx
DS 2501: Intermediate Programming with Data / Lab Practicum
Prof. Rachlin and Prof. Park
Northeastern University

Seaborn Hack-a-thon

In this lab exercise you’ll explore the capabilities of the Seaborn visualization li
ary. You are
free to use any dataset and develop any type of visualization you find most interesting, as long
as it uses the Seaborn li
ary.

1. Review the Seaborn examples we did in class.

2. Now put Seaborn to work! First, find some interesting data to investigate. You can use
the datasets built into Seaborn:

import seaborn as sns
sns.get_dataset_names()
iris = sns.load_dataset(‘iris’)

or explore an online data repository such as:
• https:
www.data.gov
• https:
data.boston.gov

3. You are encouraged (but not required) to use Pandas to filter and refine your data.

4. Create an interesting visualization of your data. Include a sentence or two explaining
your visualization and what it reveals about your data. (This sentence may be embedded
in your code as a triple-quoted string.)

5. Feel free to explore plotting features not covered in class. Here are some useful
esources:

https:
seaborn.pydata.org/examples/index.html
https:
seaborn.pydata.org/tutorial.html
https:
seaborn.pydata.org/api.html

6. Submit your work to GradeScope by the end of the day on Thursday.
Answered Same Day Sep 29, 2021

Solution

Nithin answered on Sep 30 2021
143 Votes
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "b0158404",
"metadata": {},
"outputs": [],
"source": [
"Name :\n",
"\n",
"Assignment : Seaborn Vizualistion "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "021042d8",
"metadata": {},
"outputs": [],
"source": [
"-----------------------------------------------------------------------------------------------------"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "12ce1618",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.style.use('classic')\n",
"%matplotlib inline\n",
"\n",
"import seaborn as sns\n",
"sns.set()\n",
"\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "c935df81",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"