Rafa1986/Data-Analytics-Class
0
1{2 "cells": [3 {4 "cell_type": "markdown",5 "metadata": {},6 "source": [7 "TU258 - Lab 2-2 - Data Exploration\n",8 "\n",9 "This lab gives an example of using Pandas dataframes for analysing data\n"10 ]11 },12 {13 "cell_type": "code",14 "execution_count": 1,15 "metadata": {},16 "outputs": [],17 "source": [18 "#import pandas\n",19 "import pandas as pd\n"20 ]21 },22 {23 "cell_type": "code",24 "execution_count": 2,25 "metadata": {},26 "outputs": [],27 "source": [28 "#reading a CSV File into a Panda\n",29 "videoReview = pd.read_csv('/Users/brendan.tierney/Dropbox/4-Datasets/Video_Games_Sales_as_at_22_Dec_2016.csv')"30 ]31 },32 {33 "cell_type": "code",34 "execution_count": 3,35 "metadata": {},36 "outputs": [37 {38 "name": "stdout",39 "output_type": "stream",40 "text": [41 "# print first 3 rows\n",42 " Name Platform Year_of_Release Genre Publisher NA_Sales \\\n",43 "0 Wii Sports Wii 2006.0 Sports Nintendo 41.36 \n",44 "1 Super Mario Bros. NES 1985.0 Platform Nintendo 29.08 \n",45 "2 Mario Kart Wii Wii 2008.0 Racing Nintendo 15.68 \n",46 "\n",47 " EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score Critic_Count \\\n",48 "0 28.96 3.77 8.45 82.53 76.0 51.0 \n",49 "1 3.58 6.81 0.77 40.24 NaN NaN \n",50 "2 12.76 3.79 3.29 35.52 82.0 73.0 \n",51 "\n",52 " User_Score User_Count Developer Rating \n",53 "0 8 322.0 Nintendo E \n",54 "1 NaN NaN NaN NaN \n",55 "2 8.3 709.0 Nintendo E \n"56 ]57 }58 ],59 "source": [60 "print('# print first 3 rows')\n",61 "print(videoReview[:3])\n",62 "\n"63 ]64 },65 {66 "cell_type": "code",67 "execution_count": 4,68 "metadata": {},69 "outputs": [70 {71 "data": {72 "text/html": [73 "<div>\n",74 "<style scoped>\n",75 " .dataframe tbody tr th:only-of-type {\n",76 " vertical-align: middle;\n",77 " }\n",78 "\n",79 " .dataframe tbody tr th {\n",80 " vertical-align: top;\n",81 " }\n",82 "\n",83 " .dataframe thead th {\n",84 " text-align: right;\n",85 " }\n",86 "</style>\n",87 "<table border=\"1\" class=\"dataframe\">\n",88 " <thead>\n",89 " <tr style=\"text-align: right;\">\n",90 " <th></th>\n",91 " <th>Name</th>\n",92 " <th>Platform</th>\n",93 " <th>Year_of_Release</th>\n",94 " <th>Genre</th>\n",95 " <th>Publisher</th>\n",96 " <th>NA_Sales</th>\n",97 " <th>EU_Sales</th>\n",98 " <th>JP_Sales</th>\n",99 " <th>Other_Sales</th>\n",100 " <th>Global_Sales</th>\n",101 " <th>Critic_Score</th>\n",102 " <th>Critic_Count</th>\n",103 " <th>User_Score</th>\n",104 " <th>User_Count</th>\n",105 " <th>Developer</th>\n",106 " <th>Rating</th>\n",107 " </tr>\n",108 " </thead>\n",109 " <tbody>\n",110 " <tr>\n",111 " <th>0</th>\n",112 " <td>Wii Sports</td>\n",113 " <td>Wii</td>\n",114 " <td>2006.0</td>\n",115 " <td>Sports</td>\n",116 " <td>Nintendo</td>\n",117 " <td>41.36</td>\n",118 " <td>28.96</td>\n",119 " <td>3.77</td>\n",120 " <td>8.45</td>\n",121 " <td>82.53</td>\n",122 " <td>76.0</td>\n",123 " <td>51.0</td>\n",124 " <td>8</td>\n",125 " <td>322.0</td>\n",126 " <td>Nintendo</td>\n",127 " <td>E</td>\n",128 " </tr>\n",129 " <tr>\n",130 " <th>1</th>\n",131 " <td>Super Mario Bros.</td>\n",132 " <td>NES</td>\n",133 " <td>1985.0</td>\n",134 " <td>Platform</td>\n",135 " <td>Nintendo</td>\n",136 " <td>29.08</td>\n",137 " <td>3.58</td>\n",138 " <td>6.81</td>\n",139 " <td>0.77</td>\n",140 " <td>40.24</td>\n",141 " <td>NaN</td>\n",142 " <td>NaN</td>\n",143 " <td>NaN</td>\n",144 " <td>NaN</td>\n",145 " <td>NaN</td>\n",146 " <td>NaN</td>\n",147 " </tr>\n",148 " <tr>\n",149 " <th>2</th>\n",150 " <td>Mario Kart Wii</td>\n",151 " <td>Wii</td>\n",152 " <td>2008.0</td>\n",153 " <td>Racing</td>\n",154 " <td>Nintendo</td>\n",155 " <td>15.68</td>\n",156 " <td>12.76</td>\n",157 " <td>3.79</td>\n",158 " <td>3.29</td>\n",159 " <td>35.52</td>\n",160 " <td>82.0</td>\n",161 " <td>73.0</td>\n",162 " <td>8.3</td>\n",163 " <td>709.0</td>\n",164 " <td>Nintendo</td>\n",165 " <td>E</td>\n",166 " </tr>\n",167 " </tbody>\n",168 "</table>\n",169 "</div>"170 ],171 "text/plain": [172 " Name Platform Year_of_Release Genre Publisher NA_Sales \\\n",173 "0 Wii Sports Wii 2006.0 Sports Nintendo 41.36 \n",174 "1 Super Mario Bros. NES 1985.0 Platform Nintendo 29.08 \n",175 "2 Mario Kart Wii Wii 2008.0 Racing Nintendo 15.68 \n",176 "\n",177 " EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score Critic_Count \\\n",178 "0 28.96 3.77 8.45 82.53 76.0 51.0 \n",179 "1 3.58 6.81 0.77 40.24 NaN NaN \n",180 "2 12.76 3.79 3.29 35.52 82.0 73.0 \n",181 "\n",182 " User_Score User_Count Developer Rating \n",183 "0 8 322.0 Nintendo E \n",184 "1 NaN NaN NaN NaN \n",185 "2 8.3 709.0 Nintendo E "186 ]187 },188 "execution_count": 4,189 "metadata": {},190 "output_type": "execute_result"191 }192 ],193 "source": [194 "videoReview.head(3)"195 ]196 },197 {198 "cell_type": "code",199 "execution_count": 5,200 "metadata": {},201 "outputs": [202 {203 "name": "stdout",204 "output_type": "stream",205 "text": [206 "----------\n",207 "# print columns\n",208 "0 Wii Sports\n",209 "1 Super Mario Bros.\n",210 "2 Mario Kart Wii\n",211 "3 Wii Sports Resort\n",212 "4 Pokemon Red/Pokemon Blue\n",213 " ... \n",214 "16714 Samurai Warriors: Sanada Maru\n",215 "16715 LMA Manager 2007\n",216 "16716 Haitaka no Psychedelica\n",217 "16717 Spirits & Spells\n",218 "16718 Winning Post 8 2016\n",219 "Name: Name, Length: 16719, dtype: object\n"220 ]221 }222 ],223 "source": [224 "print('----------')\n",225 "print('# print columns')\n",226 "print(videoReview['Name'])\n",227 "\n"228 ]229 },230 {231 "cell_type": "code",232 "execution_count": 6,233 "metadata": {},234 "outputs": [235 {236 "data": {237 "text/plain": [238 "0 Wii Sports\n",239 "1 Super Mario Bros.\n",240 "2 Mario Kart Wii\n",241 "3 Wii Sports Resort\n",242 "4 Pokemon Red/Pokemon Blue\n",243 " ... \n",244 "16714 Samurai Warriors: Sanada Maru\n",245 "16715 LMA Manager 2007\n",246 "16716 Haitaka no Psychedelica\n",247 "16717 Spirits & Spells\n",248 "16718 Winning Post 8 2016\n",249 "Name: Name, Length: 16719, dtype: object"250 ]251 },252 "execution_count": 6,253 "metadata": {},254 "output_type": "execute_result"255 }256 ],257 "source": [258 "videoReview['Name']"259 ]260 },261 {262 "cell_type": "code",263 "execution_count": 7,264 "metadata": {},265 "outputs": [266 {267 "name": "stdout",268 "output_type": "stream",269 "text": [270 "----------\n",271 "# print columns, first 5 rows\n",272 "0 Wii Sports\n",273 "1 Super Mario Bros.\n",274 "2 Mario Kart Wii\n",275 "3 Wii Sports Resort\n",276 "4 Pokemon Red/Pokemon Blue\n",277 "Name: Name, dtype: object\n"278 ]279 }280 ],281 "source": [282 "print('----------')\n",283 "print('# print columns, first 5 rows')\n",284 "print(videoReview['Name'][:5])\n",285 "\n",286 "#videoReview['Name'].head(5)\n",287 "#videoReview['Name'].tail(5)\n"288 ]289 },290 {291 "cell_type": "code",292 "execution_count": 8,293 "metadata": {},294 "outputs": [295 {296 "name": "stdout",297 "output_type": "stream",298 "text": [299 "----------\n",300 "# Platform #\n",301 "Platform\n",302 "PS2 2161\n",303 "DS 2152\n",304 "PS3 1331\n",305 "Wii 1320\n",306 "X360 1262\n",307 "PSP 1209\n",308 "PS 1197\n",309 "PC 974\n",310 "XB 824\n",311 "GBA 822\n",312 "GC 556\n",313 "3DS 520\n",314 "PSV 432\n",315 "PS4 393\n",316 "N64 319\n",317 "XOne 247\n",318 "SNES 239\n",319 "SAT 173\n",320 "WiiU 147\n",321 "2600 133\n",322 "NES 98\n",323 "GB 98\n",324 "DC 52\n",325 "GEN 29\n",326 "NG 12\n",327 "SCD 6\n",328 "WS 6\n",329 "3DO 3\n",330 "TG16 2\n",331 "GG 1\n",332 "PCFX 1\n",333 "Name: count, dtype: int64\n"334 ]335 }336 ],337 "source": [338 "print('----------')\n",339 "print('# Platform #')\n",340 "print(videoReview['Platform'].value_counts())\n",341 "\n"342 ]343 },344 {345 "cell_type": "code",346 "execution_count": 9,347 "metadata": {},348 "outputs": [349 {350 "name": "stdout",351 "output_type": "stream",352 "text": [353 "----------\n",354 "#shape\n",355 "Number of rows = 16719\n",356 "Number of columns = 16\n",357 "Shape = (16719, 16)\n"358 ]359 }360 ],361 "source": [362 "print('----------')\n",363 "print('#shape')\n",364 "print('Number of rows = ', videoReview.shape[0])\n",365 "print('Number of columns = ', videoReview.shape[1])\n",366 "print('Shape = ', videoReview.shape)\n",367 "\n"368 ]369 },370 {371 "cell_type": "code",372 "execution_count": 10,373 "metadata": {},374 "outputs": [375 {376 "name": "stdout",377 "output_type": "stream",378 "text": [379 "----------\n",380 "# Name #\n",381 "Name\n",382 "Need for Speed: Most Wanted 12\n",383 "FIFA 14 9\n",384 "Ratatouille 9\n",385 "LEGO Marvel Super Heroes 9\n",386 "Madden NFL 07 9\n",387 " ..\n",388 "Jewels of the Tropical Lost Island 1\n",389 "Sherlock Holmes and the Mystery of Osborne House 1\n",390 "The King of Fighters '95 (CD) 1\n",391 "Megamind: Mega Team Unite 1\n",392 "Haitaka no Psychedelica 1\n",393 "Name: count, Length: 11562, dtype: int64\n"394 ]395 }396 ],397 "source": [398 "print('----------')\n",399 "print('# Name #')\n",400 "print(videoReview['Name'].value_counts())\n",401 "\n"402 ]403 },404 {405 "cell_type": "code",406 "execution_count": 11,407 "metadata": {},408 "outputs": [409 {410 "name": "stdout",411 "output_type": "stream",412 "text": [413 "----------\n",414 "#head\n",415 " Name Platform Year_of_Release Genre Publisher \\\n",416 "0 Wii Sports Wii 2006.0 Sports Nintendo \n",417 "1 Super Mario Bros. NES 1985.0 Platform Nintendo \n",418 "2 Mario Kart Wii Wii 2008.0 Racing Nintendo \n",419 "3 Wii Sports Resort Wii 2009.0 Sports Nintendo \n",420 "4 Pokemon Red/Pokemon Blue GB 1996.0 Role-Playing Nintendo \n",421 "\n",422 " NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score \\\n",423 "0 41.36 28.96 3.77 8.45 82.53 76.0 \n",424 "1 29.08 3.58 6.81 0.77 40.24 NaN \n",425 "2 15.68 12.76 3.79 3.29 35.52 82.0 \n",426 "3 15.61 10.93 3.28 2.95 32.77 80.0 \n",427 "4 11.27 8.89 10.22 1.00 31.37 NaN \n",428 "\n",429 " Critic_Count User_Score User_Count Developer Rating \n",430 "0 51.0 8 322.0 Nintendo E \n",431 "1 NaN NaN NaN NaN NaN \n",432 "2 73.0 8.3 709.0 Nintendo E \n",433 "3 73.0 8 192.0 Nintendo E \n",434 "4 NaN NaN NaN NaN NaN \n",435 " Name Platform Year_of_Release Genre Publisher \\\n",436 "0 Wii Sports Wii 2006.0 Sports Nintendo \n",437 "1 Super Mario Bros. NES 1985.0 Platform Nintendo \n",438 "2 Mario Kart Wii Wii 2008.0 Racing Nintendo \n",439 "3 Wii Sports Resort Wii 2009.0 Sports Nintendo \n",440 "4 Pokemon Red/Pokemon Blue GB 1996.0 Role-Playing Nintendo \n",441 "5 Tetris GB 1989.0 Puzzle Nintendo \n",442 "6 New Super Mario Bros. DS 2006.0 Platform Nintendo \n",443 "7 Wii Play Wii 2006.0 Misc Nintendo \n",444 "\n",445 " NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score \\\n",446 "0 41.36 28.96 3.77 8.45 82.53 76.0 \n",447 "1 29.08 3.58 6.81 0.77 40.24 NaN \n",448 "2 15.68 12.76 3.79 3.29 35.52 82.0 \n",449 "3 15.61 10.93 3.28 2.95 32.77 80.0 \n",450 "4 11.27 8.89 10.22 1.00 31.37 NaN \n",451 "5 23.20 2.26 4.22 0.58 30.26 NaN \n",452 "6 11.28 9.14 6.50 2.88 29.80 89.0 \n",453 "7 13.96 9.18 2.93 2.84 28.92 58.0 \n",454 "\n",455 " Critic_Count User_Score User_Count Developer Rating \n",456 "0 51.0 8 322.0 Nintendo E \n",457 "1 NaN NaN NaN NaN NaN \n",458 "2 73.0 8.3 709.0 Nintendo E \n",459 "3 73.0 8 192.0 Nintendo E \n",460 "4 NaN NaN NaN NaN NaN \n",461 "5 NaN NaN NaN NaN NaN \n",462 "6 65.0 8.5 431.0 Nintendo E \n",463 "7 41.0 6.6 129.0 Nintendo E \n"464 ]465 }466 ],467 "source": [468 "print('----------')\n",469 "print('#head')\n",470 "print(videoReview.head())\n",471 "print(videoReview.head(8))\n",472 "\n"473 ]474 },475 {476 "cell_type": "code",477 "execution_count": 12,478 "metadata": {},479 "outputs": [480 {481 "name": "stdout",482 "output_type": "stream",483 "text": [484 "----------\n",485 "#tail\n",486 " Name Platform Year_of_Release Genre \\\n",487 "16714 Samurai Warriors: Sanada Maru PS3 2016.0 Action \n",488 "16715 LMA Manager 2007 X360 2006.0 Sports \n",489 "16716 Haitaka no Psychedelica PSV 2016.0 Adventure \n",490 "16717 Spirits & Spells GBA 2003.0 Platform \n",491 "16718 Winning Post 8 2016 PSV 2016.0 Simulation \n",492 "\n",493 " Publisher NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales \\\n",494 "16714 Tecmo Koei 0.00 0.00 0.01 0.0 0.01 \n",495 "16715 Codemasters 0.00 0.01 0.00 0.0 0.01 \n",496 "16716 Idea Factory 0.00 0.00 0.01 0.0 0.01 \n",497 "16717 Wanadoo 0.01 0.00 0.00 0.0 0.01 \n",498 "16718 Tecmo Koei 0.00 0.00 0.01 0.0 0.01 \n",499 "\n",500 " Critic_Score Critic_Count User_Score User_Count Developer Rating \n",501 "16714 NaN NaN NaN NaN NaN NaN \n",502 "16715 NaN NaN NaN NaN NaN NaN \n",503 "16716 NaN NaN NaN NaN NaN NaN \n",504 "16717 NaN NaN NaN NaN NaN NaN \n",505 "16718 NaN NaN NaN NaN NaN NaN \n",506 " Name Platform \\\n",507 "16711 Aiyoku no Eustia PSV \n",508 "16712 Woody Woodpecker in Crazy Castle 5 GBA \n",509 "16713 SCORE International Baja 1000: The Official Game PS2 \n",510 "16714 Samurai Warriors: Sanada Maru PS3 \n",511 "16715 LMA Manager 2007 X360 \n",512 "16716 Haitaka no Psychedelica PSV \n",513 "16717 Spirits & Spells GBA \n",514 "16718 Winning Post 8 2016 PSV \n",515 "\n",516 " Year_of_Release Genre Publisher NA_Sales EU_Sales \\\n",517 "16711 2014.0 Misc dramatic create 0.00 0.00 \n",518 "16712 2002.0 Platform Kemco 0.01 0.00 \n",519 "16713 2008.0 Racing Activision 0.00 0.00 \n",520 "16714 2016.0 Action Tecmo Koei 0.00 0.00 \n",521 "16715 2006.0 Sports Codemasters 0.00 0.01 \n",522 "16716 2016.0 Adventure Idea Factory 0.00 0.00 \n",523 "16717 2003.0 Platform Wanadoo 0.01 0.00 \n",524 "16718 2016.0 Simulation Tecmo Koei 0.00 0.00 \n",525 "\n",526 " JP_Sales Other_Sales Global_Sales Critic_Score Critic_Count \\\n",527 "16711 0.01 0.0 0.01 NaN NaN \n",528 "16712 0.00 0.0 0.01 NaN NaN \n",529 "16713 0.00 0.0 0.01 NaN NaN \n",530 "16714 0.01 0.0 0.01 NaN NaN \n",531 "16715 0.00 0.0 0.01 NaN NaN \n",532 "16716 0.01 0.0 0.01 NaN NaN \n",533 "16717 0.00 0.0 0.01 NaN NaN \n",534 "16718 0.01 0.0 0.01 NaN NaN \n",535 "\n",536 " User_Score User_Count Developer Rating \n",537 "16711 NaN NaN NaN NaN \n",538 "16712 NaN NaN NaN NaN \n",539 "16713 NaN NaN NaN NaN \n",540 "16714 NaN NaN NaN NaN \n",541 "16715 NaN NaN NaN NaN \n",542 "16716 NaN NaN NaN NaN \n",543 "16717 NaN NaN NaN NaN \n",544 "16718 NaN NaN NaN NaN \n"545 ]546 }547 ],548 "source": [549 "print('----------')\n",550 "print('#tail')\n",551 "print(videoReview.tail())\n",552 "print(videoReview.tail(8))\n",553 "\n"554 ]555 },556 {557 "cell_type": "code",558 "execution_count": 13,559 "metadata": {},560 "outputs": [561 {562 "name": "stdout",563 "output_type": "stream",564 "text": [565 "----------\n",566 "#Describe\n",567 " Year_of_Release NA_Sales EU_Sales JP_Sales \\\n",568 "count 16450.000000 16719.000000 16719.000000 16719.000000 \n",569 "mean 2006.487356 0.263330 0.145025 0.077602 \n",570 "std 5.878995 0.813514 0.503283 0.308818 \n",571 "min 1980.000000 0.000000 0.000000 0.000000 \n",572 "25% 2003.000000 0.000000 0.000000 0.000000 \n",573 "50% 2007.000000 0.080000 0.020000 0.000000 \n",574 "75% 2010.000000 0.240000 0.110000 0.040000 \n",575 "max 2020.000000 41.360000 28.960000 10.220000 \n",576 "\n",577 " Other_Sales Global_Sales Critic_Score Critic_Count User_Count \n",578 "count 16719.000000 16719.000000 8137.000000 8137.000000 7590.000000 \n",579 "mean 0.047332 0.533543 68.967679 26.360821 162.229908 \n",580 "std 0.186710 1.547935 13.938165 18.980495 561.282326 \n",581 "min 0.000000 0.010000 13.000000 3.000000 4.000000 \n",582 "25% 0.000000 0.060000 60.000000 12.000000 10.000000 \n",583 "50% 0.010000 0.170000 71.000000 21.000000 24.000000 \n",584 "75% 0.030000 0.470000 79.000000 36.000000 81.000000 \n",585 "max 10.570000 82.530000 98.000000 113.000000 10665.000000 \n",586 "count 16719\n",587 "unique 31\n",588 "top PS2\n",589 "freq 2161\n",590 "Name: Platform, dtype: object\n",591 "count 16450.000000\n",592 "mean 2006.487356\n",593 "std 5.878995\n",594 "min 1980.000000\n",595 "25% 2003.000000\n",596 "50% 2007.000000\n",597 "75% 2010.000000\n",598 "max 2020.000000\n",599 "Name: Year_of_Release, dtype: float64\n"600 ]601 }602 ],603 "source": [604 "print('----------')\n",605 "print('#Describe')\n",606 "print(videoReview.describe()) # calculates measures of central tendency\n",607 "print(videoReview['Platform'].describe())\n",608 "print(videoReview['Year_of_Release'].describe())\n",609 "\n"610 ]611 },612 {613 "cell_type": "code",614 "execution_count": 14,615 "metadata": {},616 "outputs": [617 {618 "name": "stdout",619 "output_type": "stream",620 "text": [621 "----------\n",622 "#info\n",623 "<class 'pandas.core.frame.DataFrame'>\n",624 "RangeIndex: 16719 entries, 0 to 16718\n",625 "Data columns (total 16 columns):\n",626 " # Column Non-Null Count Dtype \n",627 "--- ------ -------------- ----- \n",628 " 0 Name 16717 non-null object \n",629 " 1 Platform 16719 non-null object \n",630 " 2 Year_of_Release 16450 non-null float64\n",631 " 3 Genre 16717 non-null object \n",632 " 4 Publisher 16665 non-null object \n",633 " 5 NA_Sales 16719 non-null float64\n",634 " 6 EU_Sales 16719 non-null float64\n",635 " 7 JP_Sales 16719 non-null float64\n",636 " 8 Other_Sales 16719 non-null float64\n",637 " 9 Global_Sales 16719 non-null float64\n",638 " 10 Critic_Score 8137 non-null float64\n",639 " 11 Critic_Count 8137 non-null float64\n",640 " 12 User_Score 10015 non-null object \n",641 " 13 User_Count 7590 non-null float64\n",642 " 14 Developer 10096 non-null object \n",643 " 15 Rating 9950 non-null object \n",644 "dtypes: float64(9), object(7)\n",645 "memory usage: 2.0+ MB\n",646 "None\n"647 ]648 }649 ],650 "source": [651 "print('----------')\n",652 "print('#info')\n",653 "print(videoReview.info()) # memory footprint and datatypes"654 ]655 },656 {657 "cell_type": "code",658 "execution_count": 15,659 "metadata": {},660 "outputs": [661 {662 "name": "stdout",663 "output_type": "stream",664 "text": [665 "----------\n",666 "Transpose - Describe\n",667 " count mean std min 25% 50% \\\n",668 "Year_of_Release 16450.0 2006.487356 5.878995 1980.00 2003.00 2007.00 \n",669 "NA_Sales 16719.0 0.263330 0.813514 0.00 0.00 0.08 \n",670 "EU_Sales 16719.0 0.145025 0.503283 0.00 0.00 0.02 \n",671 "JP_Sales 16719.0 0.077602 0.308818 0.00 0.00 0.00 \n",672 "Other_Sales 16719.0 0.047332 0.186710 0.00 0.00 0.01 \n",673 "Global_Sales 16719.0 0.533543 1.547935 0.01 0.06 0.17 \n",674 "Critic_Score 8137.0 68.967679 13.938165 13.00 60.00 71.00 \n",675 "Critic_Count 8137.0 26.360821 18.980495 3.00 12.00 21.00 \n",676 "User_Count 7590.0 162.229908 561.282326 4.00 10.00 24.00 \n",677 "\n",678 " 75% max \n",679 "Year_of_Release 2010.00 2020.00 \n",680 "NA_Sales 0.24 41.36 \n",681 "EU_Sales 0.11 28.96 \n",682 "JP_Sales 0.04 10.22 \n",683 "Other_Sales 0.03 10.57 \n",684 "Global_Sales 0.47 82.53 \n",685 "Critic_Score 79.00 98.00 \n",686 "Critic_Count 36.00 113.00 \n",687 "User_Count 81.00 10665.00 \n"688 ]689 }690 ],691 "source": [692 "print('----------')\n",693 "print('Transpose - Describe')\n",694 "print(videoReview.describe().transpose())\n",695 "\n"696 ]697 },698 {699 "cell_type": "code",700 "execution_count": 16,701 "metadata": {},702 "outputs": [703 {704 "name": "stdout",705 "output_type": "stream",706 "text": [707 "----------\n",708 "Iterate some rows from DF\n",709 "#### Printing row ####\n",710 "Wii Sports\n",711 "#### Printing row ####\n",712 "Super Mario Bros.\n",713 "#### Printing row ####\n",714 "Mario Kart Wii\n",715 "#### Printing row ####\n",716 "Wii Sports Resort\n",717 "#### Printing row ####\n",718 "Pokemon Red/Pokemon Blue\n",719 "#### Printing row ####\n",720 "Tetris\n",721 "#### Printing row ####\n",722 "New Super Mario Bros.\n",723 "#### Printing row ####\n",724 "Wii Play\n",725 "#### Printing row ####\n",726 "New Super Mario Bros. Wii\n"727 ]728 }729 ],730 "source": [731 "print('----------')\n",732 "print('Iterate some rows from DF')\n",733 "for i, row in videoReview[:9].iterrows():\n",734 " print('#### Printing row ####')\n",735 " print(row['Name'])\n",736 "\n"737 ]738 },739 {740 "cell_type": "code",741 "execution_count": 17,742 "metadata": {},743 "outputs": [744 {745 "name": "stdout",746 "output_type": "stream",747 "text": [748 "----------\n",749 "Group by Year, Platform by Count : \n",750 " Name Genre Publisher NA_Sales EU_Sales \\\n",751 "Year_of_Release Platform \n",752 "1980.0 2600 9 9 9 9 9 \n",753 "1981.0 2600 46 46 46 46 46 \n",754 "1982.0 2600 36 36 36 36 36 \n",755 "1983.0 2600 11 11 11 11 11 \n",756 " NES 6 6 6 6 6 \n",757 "... ... ... ... ... ... \n",758 "2016.0 X360 13 13 13 13 13 \n",759 " XOne 87 87 87 87 87 \n",760 "2017.0 PS4 1 1 1 1 1 \n",761 " PSV 2 2 2 2 2 \n",762 "2020.0 DS 1 1 1 1 1 \n",763 "\n",764 " JP_Sales Other_Sales Global_Sales Critic_Score \\\n",765 "Year_of_Release Platform \n",766 "1980.0 2600 9 9 9 0 \n",767 "1981.0 2600 46 46 46 0 \n",768 "1982.0 2600 36 36 36 0 \n",769 "1983.0 2600 11 11 11 0 \n",770 " NES 6 6 6 0 \n",771 "... ... ... ... ... \n",772 "2016.0 X360 13 13 13 0 \n",773 " XOne 87 87 87 60 \n",774 "2017.0 PS4 1 1 1 0 \n",775 " PSV 2 2 2 0 \n",776 "2020.0 DS 1 1 1 0 \n",777 "\n",778 " Critic_Count User_Score User_Count Developer \\\n",779 "Year_of_Release Platform \n",780 "1980.0 2600 0 0 0 0 \n",781 "1981.0 2600 0 0 0 0 \n",782 "1982.0 2600 0 0 0 0 \n",783 "1983.0 2600 0 0 0 0 \n",784 " NES 0 0 0 0 \n",785 "... ... ... ... ... \n",786 "2016.0 X360 0 12 7 12 \n",787 " XOne 60 74 66 74 \n",788 "2017.0 PS4 0 0 0 0 \n",789 " PSV 0 0 0 0 \n",790 "2020.0 DS 0 1 0 1 \n",791 "\n",792 " Rating \n",793 "Year_of_Release Platform \n",794 "1980.0 2600 0 \n",795 "1981.0 2600 0 \n",796 "1982.0 2600 0 \n",797 "1983.0 2600 0 \n",798 " NES 0 \n",799 "... ... \n",800 "2016.0 X360 12 \n",801 " XOne 71 \n",802 "2017.0 PS4 0 \n",803 " PSV 0 \n",804 "2020.0 DS 1 \n",805 "\n",806 "[241 rows x 14 columns]\n"807 ]808 }809 ],810 "source": [811 "#Subsetting and ordering Pandas\n",812 "print('----------')\n",813 "print('Group by Year, Platform by Count : ')\n",814 "print(videoReview.groupby(['Year_of_Release','Platform']).count())\n",815 "\n"816 ]817 },818 {819 "cell_type": "code",820 "execution_count": 18,821 "metadata": {},822 "outputs": [823 {824 "name": "stdout",825 "output_type": "stream",826 "text": [827 "Group by : for Year=2016 group by Platform and count Global Sales\n",828 "Platform\n",829 "3DS 15.14\n",830 "PC 5.27\n",831 "PS3 3.58\n",832 "PS4 69.29\n",833 "PSV 4.27\n",834 "Wii 0.18\n",835 "WiiU 4.58\n",836 "X360 1.52\n",837 "XOne 26.27\n",838 "Name: Global_Sales, dtype: float64\n"839 ]840 }841 ],842 "source": [843 "print('Group by : for Year=2016 group by Platform and count Global Sales')\n",844 "print(videoReview[videoReview.Year_of_Release==2016.0].groupby('Platform')['Global_Sales'].sum())"845 ]846 },847 {848 "cell_type": "code",849 "execution_count": 19,850 "metadata": {},851 "outputs": [852 {853 "name": "stdout",854 "output_type": "stream",855 "text": [856 "----------\n",857 "Sorting and Ordering\n",858 " Name Platform Year_of_Release Genre Publisher \\\n",859 "15 Wii Fit Plus Wii 2009.0 Sports Nintendo \n",860 "8 New Super Mario Bros. Wii Wii 2009.0 Platform Nintendo \n",861 "7 Wii Play Wii 2006.0 Misc Nintendo \n",862 "3 Wii Sports Resort Wii 2009.0 Sports Nintendo \n",863 "2 Mario Kart Wii Wii 2008.0 Racing Nintendo \n",864 "0 Wii Sports Wii 2006.0 Sports Nintendo \n",865 "\n",866 " NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score \\\n",867 "15 9.01 8.49 2.53 1.77 21.79 80.0 \n",868 "8 14.44 6.94 4.70 2.24 28.32 87.0 \n",869 "7 13.96 9.18 2.93 2.84 28.92 58.0 \n",870 "3 15.61 10.93 3.28 2.95 32.77 80.0 \n",871 "2 15.68 12.76 3.79 3.29 35.52 82.0 \n",872 "0 41.36 28.96 3.77 8.45 82.53 76.0 \n",873 "\n",874 " Critic_Count User_Score User_Count Developer Rating \n",875 "15 33.0 7.4 52.0 Nintendo E \n",876 "8 80.0 8.4 594.0 Nintendo E \n",877 "7 41.0 6.6 129.0 Nintendo E \n",878 "3 73.0 8 192.0 Nintendo E \n",879 "2 73.0 8.3 709.0 Nintendo E \n",880 "0 51.0 8 322.0 Nintendo E \n"881 ]882 }883 ],884 "source": [885 "#More Panda functions - Sorting & Ordering\n",886 "print('----------')\n",887 "print('Sorting and Ordering')\n",888 "df = videoReview[(videoReview.Platform=='Wii') & (videoReview.NA_Sales>9)]\n",889 "print(df.sort_values('Global_Sales', ascending=True))"890 ]891 },892 {893 "cell_type": "code",894 "execution_count": 20,895 "metadata": {},896 "outputs": [],897 "source": [898 "#Writing a Panda to a CSV file\n",899 "df.to_csv('/Users/brendan.tierney/video_games_wii.csv', sep=',')"900 ]901 },902 {903 "cell_type": "code",904 "execution_count": 21,905 "metadata": {},906 "outputs": [],907 "source": [908 "#Go inspect the CSV file created.\n",909 "#Is it what you expected?\n",910 "#Could the output be formatted differently?\n",911 "#If so, look up the Pandas 'to_csv' function to see what you can change\n"912 ]913 },914 {915 "cell_type": "code",916 "execution_count": 22,917 "metadata": {},918 "outputs": [919 {920 "name": "stdout",921 "output_type": "stream",922 "text": [923 "----------\n",924 "Plotting - Histogram\n"925 ]926 },927 {928 "data": {929 "text/plain": [930 "<Axes: ylabel='Frequency'>"931 ]932 },933 "execution_count": 22,934 "metadata": {},935 "output_type": "execute_result"936 },937 {938 "data": {939 "image/png": "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",940 "text/plain": [941 "<Figure size 640x480 with 1 Axes>"942 ]943 },944 "metadata": {},945 "output_type": "display_data"946 }947 ],948 "source": [949 "#Creating Graphs for a Panda\n",950 "#plotting\n",951 "print('----------')\n",952 "print('Plotting - Histogram')\n",953 "videoReview['Year_of_Release'].plot(kind='hist')"954 ]955 },956 {957 "cell_type": "code",958 "execution_count": 23,959 "metadata": {},960 "outputs": [],961 "source": [962 "#If the chart does not appear in the above cell, just go back to that cell and rerun. It should appear now."963 ]964 },965 {966 "cell_type": "code",967 "execution_count": 24,968 "metadata": {},969 "outputs": [],970 "source": [971 "#Can you create any other plots?"972 ]973 },974 {975 "cell_type": "code",976 "execution_count": null,977 "metadata": {},978 "outputs": [],979 "source": []980 },981 {982 "cell_type": "code",983 "execution_count": null,984 "metadata": {},985 "outputs": [],986 "source": []987 },988 {989 "cell_type": "code",990 "execution_count": null,991 "metadata": {},992 "outputs": [],993 "source": []994 },995 {996 "cell_type": "code",997 "execution_count": null,998 "metadata": {},999 "outputs": [],1000 "source": []1001 }1002 ],1003 "metadata": {1004 "kernelspec": {1005 "display_name": "Python 3 (ipykernel)",1006 "language": "python",1007 "name": "python3"1008 },1009 "language_info": {1010 "codemirror_mode": {1011 "name": "ipython",1012 "version": 31013 },1014 "file_extension": ".py",1015 "mimetype": "text/x-python",1016 "name": "python",1017 "nbconvert_exporter": "python",1018 "pygments_lexer": "ipython3",1019 "version": "3.12.4"1020 }1021 },1022 "nbformat": 4,1023 "nbformat_minor": 41024}1025