This is an assignment for one of my classes that I need help on. I attached the fileMicrosoft Word -...

Question

This is an assignment for one of my classes that I need help on. I attached the file

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 library. You are free to use any dataset and develop any type of visualization you find most interesting, as long as it uses the Seaborn library. 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 resources: 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.

ds2500lab03seaborn-sl2wpaie.pdf

Nithin · Accepted Answer

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0158404",
   "metadata": {},
   "outputs": [],
   "source": [
    "Name :
",
    "
",
    "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
",
    "plt.style.use('classic')
",
    "%matplotlib inline
",
    "
",
    "import seaborn as sns
",
    "sns.set()
",
    "
",
    "import numpy as np
",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c935df81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
",
       "
",
       "    .dataframe tbody tr th:only-of-type {
",
       "        vertical-align: middle;
",
       "    }
",
       "
",
       "    .dataframe tbody tr th {
",
       "        vertical-align: top;
",
       "    }
",
       "
",
       "    .dataframe thead th {
",
       "        text-align: right;
",
       "    }
",
       "
",
       "
",
       "  
",
       "    
",
       "      
",
       "      sepal_length
",
       "      sepal_width
",
       "      petal_length
",
       "      petal_width
",
       "      species
",
       "    
",
       "  
",
       "  
",
       "    
",
       "      0
",
       "      5.1
",
       "      3.5
",
       "      1.4
",
       "      0.2
",
       "      setosa
",
       "    
",
       "    
",
       "      1
",
       "      4.9
",
       "      3.0
",
       "      1.4
",
       "      0.2
",
       "      setosa
",
       "    
",
       "    
",
       "      2
",
       "      4.7
",
       "      3.2
",
       "      1.3
",
       "      0.2
",
       "      setosa
",
       "    
",
       "    
",
       "      3
",
       "      4.6
",
       "      3.1
",
       "      1.5
",
       "      0.2
",
       "      setosa
",
       "    
",
       "    
",
       "      4
",
       "      5.0
",
       "      3.6
",
       "      1.4
",
       "      0.2
",
       "      setosa
",
       "    
",
       "  
",
       "
",
       ""
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species
",
       "0           5.1          3.5           1.4          0.2  setosa
",
       "1           4.9          3.0           1.4          0.2  setosa
",
       "2           4.7          3.2           1.3          0.2  setosa
",
       "3           4.6          3.1           1.5          0.2  setosa
",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Iis Dataset
",
    "
",
    "iris = sns.load_dataset("iris")
",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bb916975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png":

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...

Answer To: Microsoft Word - ds2500_lab03_seaborn.docx DS 2501: Intermediate Programming with Data / Lab...

Answer To This Question Is Available To Download

Related Questions & Answers

Submit New Assignment

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa