Please tell a story with the help of the following visuals: - Line Chart - Pie Chart - Bar Chart - Histogram - Scatterplot Please create your visuals individually and then arrange them in a 2D grid of...

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Please tell a story with the help of the following visuals:


- Line Chart


- Pie Chart


- Bar Chart


- Histogram


- Scatterplot


Please create your visuals individually and then arrange them in a 2D grid of columns and rows.


Please upload a pdf of your JupyterNotebook. Your pdfshould start with a markdown cell that includes your name, course and assignment name (1 pt) (Alternatively, upload it to GitHub and provide a url). Please include the url and the description of your data - you may use multiple data sources(1pt), a 3min story (1pt), all of the visuals both individually and in a grid. Each one of the visuals should be clearly readable, titled and labeled. (2pt).






I would like to use the video games data attached.


Line Chart - genres bought on the most popular system


Pie Chart - popularity of systems


Bar Chart - Showing popularity of genres


Histogram - frequency of Nintendo as Publisher


Scatter Plot - comparing NA sales to Japan sales

vgsales-sfrb4yet.csv
Answered Same DayOct 04, 2021

Answer To: Please tell a story with the help of the following visuals: - Line Chart - Pie Chart - Bar Chart -...

Sandeep Kumar answered on Oct 04 2021
121 Votes
{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Line Chart - genres bought on the most popular system\n",
"\n",
"Pie Chart - popularity of systems\n",
"\n",
"Bar Chart - Showing popularity of genres\n",
"\n",
"Histogram - frequency of Nintendo as Publisher\n",
"\n",
"Scatter Plot - comparing NA sales to Japan sales"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"
NamePlatformYearGenrePublisherNorthAmerica_SalesEurope_SalesJapan_SalesOther_SalesGlobal_Sales
Rank
1Wii SportsWii2006.0SportsNintendo41.4929.023.778.4682.74
2Super Mario Bros.NES1985.0PlatformNintendo29.083.586.810.7740.24
3Mario Kart WiiWii2008.0RacingNintendo15.8512.883.793.3135.82
4Wii Sports ResortWii2009.0SportsNintendo15.7511.013.282.9633.00
5Pokemon Red/Pokemon BlueGB1996.0Role-PlayingNintendo11.278.8910.221.0031.37
.................................
16596Woody Woodpecker in Crazy Castle 5GBA2002.0PlatformKemco0.010.000.000.000.01
16597Men in Black II: Alien EscapeGC2003.0ShooterInfogrames0.010.000.000.000.01
16598SCORE International Baja 1000: The Official GamePS22008.0RacingActivision0.000.000.000.000.01
16599Know How 2DS2010.0Puzzle7G//AMES0.000.010.000.000.01
16600Spirits & SpellsGBA2003.0PlatformWanadoo0.010.000.000.000.01
\n",
"

16598 rows × 10 columns

\n",
"
"
],
"text/plain": [
" Name Platform Year \\\n",
"Rank \n",
"1 Wii Sports Wii 2006.0 \n",
"2 Super Mario Bros. NES 1985.0 \n",
"3 Mario Kart Wii Wii 2008.0 \n",
"4 Wii Sports Resort Wii 2009.0 \n",
"5 Pokemon Red/Pokemon Blue GB 1996.0 \n",
"... ... ... ... \n",
"16596 Woody Woodpecker in Crazy Castle 5 GBA 2002.0 \n",
"16597 Men in Black II: Alien Escape GC 2003.0 \n",
"16598 SCORE International Baja 1000: The Official Game PS2 2008.0 \n",
"16599 Know How 2 DS 2010.0 \n",
"16600 Spirits & Spells GBA 2003.0 \n",
"\n",
" Genre Publisher NorthAmerica_Sales Europe_Sales \\\n",
"Rank \n",
"1 Sports Nintendo 41.49 29.02 \n",
"2 Platform Nintendo 29.08 3.58 \n",
"3 Racing Nintendo 15.85 12.88 \n",
"4 Sports Nintendo 15.75 11.01 \n",
"5 Role-Playing Nintendo 11.27 8.89 \n",
"... ... ... ... ... \n",
"16596 Platform Kemco 0.01 0.00 \n",
"16597 Shooter Infogrames 0.01 0.00 \n",
"16598 Racing Activision 0.00 0.00 \n",
"16599 Puzzle 7G//AMES 0.00 0.01 \n",
"16600 Platform Wanadoo 0.01 0.00 \n",
"\n",
" Japan_Sales Other_Sales Global_Sales \n",
"Rank \n",
"1 3.77 8.46 82.74 \n",
"2 6.81 0.77 40.24 \n",
"3 3.79 3.31 35.82 \n",
"4 3.28 2.96 33.00 \n",
"5 10.22 1.00 31.37 \n",
"... ... ... ... \n",
"16596 0.00 0.00 0.01 \n",
"16597 0.00 0.00 0.01 \n",
"16598 0.00 0.00 0.01 \n",
"16599 0.00 0.00 0.01 \n",
"16600 0.00 0.00 0.01 \n",
"\n",
"[16598 rows x 10 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.read_csv('vgsales-sfrb4yet.csv', index_col=[0])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png":...
SOLUTION.PDF
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