SlideShare a Scribd company logo
1 of 44
Pokemon Data Analysis
By: Chris, David, Marco, Dalton, Jeffrey
Introduction
What is Pokemon?
What are our goals?
● Answer who is the best Starter Pokemon
● Analyze Generations I-VII to see how Pokemon has changed over the years.
● Pick the best 6 Pokemon lineup across ALL generations.
● Briefly analyze Legendary Pokemon
Check out my mixtape...or my Kaggle
https://www.kaggle.com/jeffsuth/pokemon-data-analysis
Feel free to open it up and take a look and fork your own copy to play around with!
Key Terms
Pokemon- Pocket Monsters. You catch, train, and compete with them!
Generation- New Pokemon Game or new Pokemon region.
Base Stats- Sum of all stats combined.
Type (Weakness)- Pokemon can be up to 2 out of 18 different types each with different
strengths and weaknesses to other types. (Like 18 way rock-paper-scissors)
Evolution (Mega Evolution)- When a Pokemon reaches a certain threshold, it transforms into
a more powerful form. Mega-evolutions goes even further beyond using a special item.
Variable Declaration
Define different stats to be used based
on the given dataset
Define Pokemon Generation based on
Pokedex Number
Legendaries as well
Starter Pokemon Analysis
Which Starter Pokemon should I choose?
Code!
Starter Weaknesses Code
Forgive the mismatching colors...
Overall Pokemon Analysis
Height and Weight
Base stats
Primary and Secondary Type
Cosmeom
0.01m tall and weighs over
999kg.
Cosmeom
Base Stats
Pokemon Primary Types
yy = pd.value_counts(my_data['type1'])
fig, ax = plt.subplots()
fig.set_size_inches(11.7, 8.27)
sns.set_style("whitegrid")
ax = sns.barplot(x=yy.index, y=yy, data=my_data)
ax.set_xticklabels(ax.get_xticklabels(), rotation = 75, fontsize = 12)
ax.set(xlabel='Primary types', ylabel='Count')
ax.set_title('Distribution of Primary Pokemon type')
g = sns.jointplot("attack", "hp", data=my_data, kind="kdeg
Pokemon Generation Analysis
“Do Pokemon generally get stronger after every generation?”
Generation Analysis
● Base Stats mean of all pokemon in each
generation.
● Mod Stats mean of all Pokemon in each
generation
Yes, Pokemon generally get
stronger after each generation.
Best of Each Generation I - II
Best of Each Generation III - V
Best of Each Generation VI - VII
Top 6 w/ Legendary
The search for the top 6 non legendary Pokemon
● Sort pokemon by generation
● Filter out legendary pokemon
● Implement a weakness rating
system
● Calculate base stats and mod
stats
● Remove null entries
● Find the top 6 Pokemon
based on mod stats and
domain expertise
Looking at stats
Base stats: attack + defence + hp + special attack + special defence
Weakness rating (1 - 10): 1 = stronger - 10 = weaker
(weakness count - min weakness) * (10 - 1) / (max weakness - min weakness) + 1
Weakness count: All weaknesses added together
Mod base stats:
(attack * 1.5) + defence + hp + (special attack * 1.5) + special defence + (speed * 2)
True base stats: All of the above combined
Base Stats vs Mod Stats Example
Base stats Mod stats True base stats
Generations 1 and 2: Who’s that Pokemon?!
Generation 1 Generation 2
Generations 3 and 4: Who’s that Pokemon?!
Generation 3 Generation 4
Generations 5 and 6: Who’s that Pokemon?!
Generation5 Generation 6
Generation 7: Who’s that Pokemon?!
Generation 7: Who’s that Pokemon?!
Best Pokemon Team
Top 6 Stats
Legendary Analysis
What are some key characteristics of a Legendary Pokemon? Their type? Their
height or weight? What are the qualities that will usually determine if a Pokemon is
a Legendary or not?
Conclusions!
Who is the strongest Starter Pokemon?
What is the best 6 Pokemon lineup?(w/ Legendaries and w/o Legendaries)
What is the trend of Pokemon by each Generation?
What can this information be used for?

More Related Content

What's hot

オンプレでPrivate Registry使ったDockerイメージの運用について
オンプレでPrivate Registry使ったDockerイメージの運用についてオンプレでPrivate Registry使ったDockerイメージの運用について
オンプレでPrivate Registry使ったDockerイメージの運用についてYASUKAZU NAGATOMI
 
いまさら聞けないUnity小技
いまさら聞けないUnity小技いまさら聞けないUnity小技
いまさら聞けないUnity小技Yuichi Ishii
 
ゲームAI入門(後半)
ゲームAI入門(後半)ゲームAI入門(後半)
ゲームAI入門(後半)Youichiro Miyake
 
.NET 6 時代のデスクトップ アプリケーション開発
.NET 6 時代のデスクトップ アプリケーション開発.NET 6 時代のデスクトップ アプリケーション開発
.NET 6 時代のデスクトップ アプリケーション開発Fujio Kojima
 
Aiwolf seminar20180630
Aiwolf seminar20180630Aiwolf seminar20180630
Aiwolf seminar20180630Atom Sonoda
 
Popcntによるハミング距離計算
Popcntによるハミング距離計算Popcntによるハミング距離計算
Popcntによるハミング距離計算Norishige Fukushima
 
「原神」におけるコンソールプラットフォーム開発
「原神」におけるコンソールプラットフォーム開発「原神」におけるコンソールプラットフォーム開発
「原神」におけるコンソールプラットフォーム開発Unity Technologies Japan K.K.
 
Why your Spark Job is Failing
Why your Spark Job is FailingWhy your Spark Job is Failing
Why your Spark Job is FailingDataWorks Summit
 
【Unite Tokyo 2019】Unityプログレッシブライトマッパー2019
【Unite Tokyo 2019】Unityプログレッシブライトマッパー2019【Unite Tokyo 2019】Unityプログレッシブライトマッパー2019
【Unite Tokyo 2019】Unityプログレッシブライトマッパー2019UnityTechnologiesJapan002
 
A Deep Dive into Stateful Stream Processing in Structured Streaming with Tath...
A Deep Dive into Stateful Stream Processing in Structured Streaming with Tath...A Deep Dive into Stateful Stream Processing in Structured Streaming with Tath...
A Deep Dive into Stateful Stream Processing in Structured Streaming with Tath...Databricks
 
ROP 輕鬆談
ROP 輕鬆談ROP 輕鬆談
ROP 輕鬆談hackstuff
 
Pythonが動く仕組み(の概要)
Pythonが動く仕組み(の概要)Pythonが動く仕組み(の概要)
Pythonが動く仕組み(の概要)Yoshiaki Shibutani
 
深さ優先探索による塗りつぶし
深さ優先探索による塗りつぶし深さ優先探索による塗りつぶし
深さ優先探索による塗りつぶしAtCoder Inc.
 
文字列カーネルによる辞書なしツイート分類 〜文字列カーネル入門〜
文字列カーネルによる辞書なしツイート分類 〜文字列カーネル入門〜文字列カーネルによる辞書なしツイート分類 〜文字列カーネル入門〜
文字列カーネルによる辞書なしツイート分類 〜文字列カーネル入門〜Takeshi Arabiki
 
Ibis: Seamless Transition Between Pandas and Apache Spark
Ibis: Seamless Transition Between Pandas and Apache SparkIbis: Seamless Transition Between Pandas and Apache Spark
Ibis: Seamless Transition Between Pandas and Apache SparkDatabricks
 
InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...
InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...
InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...InfluxData
 
TDOH x 台科 pwn課程
TDOH x 台科 pwn課程TDOH x 台科 pwn課程
TDOH x 台科 pwn課程Weber Tsai
 
CPU / GPU高速化セミナー!性能モデルの理論と実践:実践編
CPU / GPU高速化セミナー!性能モデルの理論と実践:実践編CPU / GPU高速化セミナー!性能モデルの理論と実践:実践編
CPU / GPU高速化セミナー!性能モデルの理論と実践:実践編Fixstars Corporation
 
【Unity道場 2月】シェーダを書けるプログラマになろう
【Unity道場 2月】シェーダを書けるプログラマになろう【Unity道場 2月】シェーダを書けるプログラマになろう
【Unity道場 2月】シェーダを書けるプログラマになろうUnity Technologies Japan K.K.
 

What's hot (20)

オンプレでPrivate Registry使ったDockerイメージの運用について
オンプレでPrivate Registry使ったDockerイメージの運用についてオンプレでPrivate Registry使ったDockerイメージの運用について
オンプレでPrivate Registry使ったDockerイメージの運用について
 
いまさら聞けないUnity小技
いまさら聞けないUnity小技いまさら聞けないUnity小技
いまさら聞けないUnity小技
 
PFI Seminar 2010/02/18
PFI Seminar 2010/02/18PFI Seminar 2010/02/18
PFI Seminar 2010/02/18
 
ゲームAI入門(後半)
ゲームAI入門(後半)ゲームAI入門(後半)
ゲームAI入門(後半)
 
.NET 6 時代のデスクトップ アプリケーション開発
.NET 6 時代のデスクトップ アプリケーション開発.NET 6 時代のデスクトップ アプリケーション開発
.NET 6 時代のデスクトップ アプリケーション開発
 
Aiwolf seminar20180630
Aiwolf seminar20180630Aiwolf seminar20180630
Aiwolf seminar20180630
 
Popcntによるハミング距離計算
Popcntによるハミング距離計算Popcntによるハミング距離計算
Popcntによるハミング距離計算
 
「原神」におけるコンソールプラットフォーム開発
「原神」におけるコンソールプラットフォーム開発「原神」におけるコンソールプラットフォーム開発
「原神」におけるコンソールプラットフォーム開発
 
Why your Spark Job is Failing
Why your Spark Job is FailingWhy your Spark Job is Failing
Why your Spark Job is Failing
 
【Unite Tokyo 2019】Unityプログレッシブライトマッパー2019
【Unite Tokyo 2019】Unityプログレッシブライトマッパー2019【Unite Tokyo 2019】Unityプログレッシブライトマッパー2019
【Unite Tokyo 2019】Unityプログレッシブライトマッパー2019
 
A Deep Dive into Stateful Stream Processing in Structured Streaming with Tath...
A Deep Dive into Stateful Stream Processing in Structured Streaming with Tath...A Deep Dive into Stateful Stream Processing in Structured Streaming with Tath...
A Deep Dive into Stateful Stream Processing in Structured Streaming with Tath...
 
ROP 輕鬆談
ROP 輕鬆談ROP 輕鬆談
ROP 輕鬆談
 
Pythonが動く仕組み(の概要)
Pythonが動く仕組み(の概要)Pythonが動く仕組み(の概要)
Pythonが動く仕組み(の概要)
 
深さ優先探索による塗りつぶし
深さ優先探索による塗りつぶし深さ優先探索による塗りつぶし
深さ優先探索による塗りつぶし
 
文字列カーネルによる辞書なしツイート分類 〜文字列カーネル入門〜
文字列カーネルによる辞書なしツイート分類 〜文字列カーネル入門〜文字列カーネルによる辞書なしツイート分類 〜文字列カーネル入門〜
文字列カーネルによる辞書なしツイート分類 〜文字列カーネル入門〜
 
Ibis: Seamless Transition Between Pandas and Apache Spark
Ibis: Seamless Transition Between Pandas and Apache SparkIbis: Seamless Transition Between Pandas and Apache Spark
Ibis: Seamless Transition Between Pandas and Apache Spark
 
InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...
InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...
InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...
 
TDOH x 台科 pwn課程
TDOH x 台科 pwn課程TDOH x 台科 pwn課程
TDOH x 台科 pwn課程
 
CPU / GPU高速化セミナー!性能モデルの理論と実践:実践編
CPU / GPU高速化セミナー!性能モデルの理論と実践:実践編CPU / GPU高速化セミナー!性能モデルの理論と実践:実践編
CPU / GPU高速化セミナー!性能モデルの理論と実践:実践編
 
【Unity道場 2月】シェーダを書けるプログラマになろう
【Unity道場 2月】シェーダを書けるプログラマになろう【Unity道場 2月】シェーダを書けるプログラマになろう
【Unity道場 2月】シェーダを書けるプログラマになろう
 

Recently uploaded

Log Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptxLog Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptxJohnnyPlasten
 
Accredited-Transport-Cooperatives-Jan-2021-Web.pdf
Accredited-Transport-Cooperatives-Jan-2021-Web.pdfAccredited-Transport-Cooperatives-Jan-2021-Web.pdf
Accredited-Transport-Cooperatives-Jan-2021-Web.pdfadriantubila
 
Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night
Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 nightCheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night
Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 nightDelhi Call girls
 
Best VIP Call Girls Noida Sector 22 Call Me: 8448380779
Best VIP Call Girls Noida Sector 22 Call Me: 8448380779Best VIP Call Girls Noida Sector 22 Call Me: 8448380779
Best VIP Call Girls Noida Sector 22 Call Me: 8448380779Delhi Call girls
 
Call Girls in Sarai Kale Khan Delhi 💯 Call Us 🔝9205541914 🔝( Delhi) Escorts S...
Call Girls in Sarai Kale Khan Delhi 💯 Call Us 🔝9205541914 🔝( Delhi) Escorts S...Call Girls in Sarai Kale Khan Delhi 💯 Call Us 🔝9205541914 🔝( Delhi) Escorts S...
Call Girls in Sarai Kale Khan Delhi 💯 Call Us 🔝9205541914 🔝( Delhi) Escorts S...Delhi Call girls
 
VidaXL dropshipping via API with DroFx.pptx
VidaXL dropshipping via API with DroFx.pptxVidaXL dropshipping via API with DroFx.pptx
VidaXL dropshipping via API with DroFx.pptxolyaivanovalion
 
Schema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdfSchema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdfLars Albertsson
 
Halmar dropshipping via API with DroFx
Halmar  dropshipping  via API with DroFxHalmar  dropshipping  via API with DroFx
Halmar dropshipping via API with DroFxolyaivanovalion
 
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al BarshaAl Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al BarshaAroojKhan71
 
Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data  2023Data-Analysis for Chicago Crime Data  2023
Data-Analysis for Chicago Crime Data 2023ymrp368
 
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...SUHANI PANDEY
 
BigBuy dropshipping via API with DroFx.pptx
BigBuy dropshipping via API with DroFx.pptxBigBuy dropshipping via API with DroFx.pptx
BigBuy dropshipping via API with DroFx.pptxolyaivanovalion
 
Smarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxSmarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxolyaivanovalion
 
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort ServiceBDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort ServiceDelhi Call girls
 
Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...amitlee9823
 
Midocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFxMidocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFxolyaivanovalion
 
Best VIP Call Girls Noida Sector 39 Call Me: 8448380779
Best VIP Call Girls Noida Sector 39 Call Me: 8448380779Best VIP Call Girls Noida Sector 39 Call Me: 8448380779
Best VIP Call Girls Noida Sector 39 Call Me: 8448380779Delhi Call girls
 
Determinants of health, dimensions of health, positive health and spectrum of...
Determinants of health, dimensions of health, positive health and spectrum of...Determinants of health, dimensions of health, positive health and spectrum of...
Determinants of health, dimensions of health, positive health and spectrum of...shambhavirathore45
 
Zuja dropshipping via API with DroFx.pptx
Zuja dropshipping via API with DroFx.pptxZuja dropshipping via API with DroFx.pptx
Zuja dropshipping via API with DroFx.pptxolyaivanovalion
 

Recently uploaded (20)

Log Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptxLog Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptx
 
Accredited-Transport-Cooperatives-Jan-2021-Web.pdf
Accredited-Transport-Cooperatives-Jan-2021-Web.pdfAccredited-Transport-Cooperatives-Jan-2021-Web.pdf
Accredited-Transport-Cooperatives-Jan-2021-Web.pdf
 
Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night
Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 nightCheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night
Cheap Rate Call girls Sarita Vihar Delhi 9205541914 shot 1500 night
 
Best VIP Call Girls Noida Sector 22 Call Me: 8448380779
Best VIP Call Girls Noida Sector 22 Call Me: 8448380779Best VIP Call Girls Noida Sector 22 Call Me: 8448380779
Best VIP Call Girls Noida Sector 22 Call Me: 8448380779
 
Call Girls in Sarai Kale Khan Delhi 💯 Call Us 🔝9205541914 🔝( Delhi) Escorts S...
Call Girls in Sarai Kale Khan Delhi 💯 Call Us 🔝9205541914 🔝( Delhi) Escorts S...Call Girls in Sarai Kale Khan Delhi 💯 Call Us 🔝9205541914 🔝( Delhi) Escorts S...
Call Girls in Sarai Kale Khan Delhi 💯 Call Us 🔝9205541914 🔝( Delhi) Escorts S...
 
VidaXL dropshipping via API with DroFx.pptx
VidaXL dropshipping via API with DroFx.pptxVidaXL dropshipping via API with DroFx.pptx
VidaXL dropshipping via API with DroFx.pptx
 
Sampling (random) method and Non random.ppt
Sampling (random) method and Non random.pptSampling (random) method and Non random.ppt
Sampling (random) method and Non random.ppt
 
Schema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdfSchema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdf
 
Halmar dropshipping via API with DroFx
Halmar  dropshipping  via API with DroFxHalmar  dropshipping  via API with DroFx
Halmar dropshipping via API with DroFx
 
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al BarshaAl Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
 
Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data  2023Data-Analysis for Chicago Crime Data  2023
Data-Analysis for Chicago Crime Data 2023
 
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...
VIP Model Call Girls Hinjewadi ( Pune ) Call ON 8005736733 Starting From 5K t...
 
BigBuy dropshipping via API with DroFx.pptx
BigBuy dropshipping via API with DroFx.pptxBigBuy dropshipping via API with DroFx.pptx
BigBuy dropshipping via API with DroFx.pptx
 
Smarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxSmarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptx
 
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort ServiceBDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service
BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service
 
Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
 
Midocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFxMidocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFx
 
Best VIP Call Girls Noida Sector 39 Call Me: 8448380779
Best VIP Call Girls Noida Sector 39 Call Me: 8448380779Best VIP Call Girls Noida Sector 39 Call Me: 8448380779
Best VIP Call Girls Noida Sector 39 Call Me: 8448380779
 
Determinants of health, dimensions of health, positive health and spectrum of...
Determinants of health, dimensions of health, positive health and spectrum of...Determinants of health, dimensions of health, positive health and spectrum of...
Determinants of health, dimensions of health, positive health and spectrum of...
 
Zuja dropshipping via API with DroFx.pptx
Zuja dropshipping via API with DroFx.pptxZuja dropshipping via API with DroFx.pptx
Zuja dropshipping via API with DroFx.pptx
 

Pokemon Kaggle Data Analysis - Data Science Final 2018

  • 1. Pokemon Data Analysis By: Chris, David, Marco, Dalton, Jeffrey
  • 2. Introduction What is Pokemon? What are our goals? ● Answer who is the best Starter Pokemon ● Analyze Generations I-VII to see how Pokemon has changed over the years. ● Pick the best 6 Pokemon lineup across ALL generations. ● Briefly analyze Legendary Pokemon
  • 3. Check out my mixtape...or my Kaggle https://www.kaggle.com/jeffsuth/pokemon-data-analysis Feel free to open it up and take a look and fork your own copy to play around with!
  • 4. Key Terms Pokemon- Pocket Monsters. You catch, train, and compete with them! Generation- New Pokemon Game or new Pokemon region. Base Stats- Sum of all stats combined. Type (Weakness)- Pokemon can be up to 2 out of 18 different types each with different strengths and weaknesses to other types. (Like 18 way rock-paper-scissors) Evolution (Mega Evolution)- When a Pokemon reaches a certain threshold, it transforms into a more powerful form. Mega-evolutions goes even further beyond using a special item.
  • 5. Variable Declaration Define different stats to be used based on the given dataset Define Pokemon Generation based on Pokedex Number Legendaries as well
  • 6. Starter Pokemon Analysis Which Starter Pokemon should I choose?
  • 8.
  • 9.
  • 12. Overall Pokemon Analysis Height and Weight Base stats Primary and Secondary Type
  • 13.
  • 14. Cosmeom 0.01m tall and weighs over 999kg. Cosmeom
  • 16. Pokemon Primary Types yy = pd.value_counts(my_data['type1']) fig, ax = plt.subplots() fig.set_size_inches(11.7, 8.27) sns.set_style("whitegrid") ax = sns.barplot(x=yy.index, y=yy, data=my_data) ax.set_xticklabels(ax.get_xticklabels(), rotation = 75, fontsize = 12) ax.set(xlabel='Primary types', ylabel='Count') ax.set_title('Distribution of Primary Pokemon type')
  • 17.
  • 18. g = sns.jointplot("attack", "hp", data=my_data, kind="kdeg
  • 19. Pokemon Generation Analysis “Do Pokemon generally get stronger after every generation?”
  • 20. Generation Analysis ● Base Stats mean of all pokemon in each generation. ● Mod Stats mean of all Pokemon in each generation Yes, Pokemon generally get stronger after each generation.
  • 21. Best of Each Generation I - II
  • 22. Best of Each Generation III - V
  • 23. Best of Each Generation VI - VII
  • 24. Top 6 w/ Legendary
  • 25. The search for the top 6 non legendary Pokemon ● Sort pokemon by generation ● Filter out legendary pokemon ● Implement a weakness rating system ● Calculate base stats and mod stats ● Remove null entries ● Find the top 6 Pokemon based on mod stats and domain expertise
  • 26. Looking at stats Base stats: attack + defence + hp + special attack + special defence Weakness rating (1 - 10): 1 = stronger - 10 = weaker (weakness count - min weakness) * (10 - 1) / (max weakness - min weakness) + 1 Weakness count: All weaknesses added together Mod base stats: (attack * 1.5) + defence + hp + (special attack * 1.5) + special defence + (speed * 2) True base stats: All of the above combined
  • 27. Base Stats vs Mod Stats Example Base stats Mod stats True base stats
  • 28. Generations 1 and 2: Who’s that Pokemon?!
  • 30. Generations 3 and 4: Who’s that Pokemon?!
  • 32. Generations 5 and 6: Who’s that Pokemon?!
  • 34. Generation 7: Who’s that Pokemon?!
  • 35. Generation 7: Who’s that Pokemon?!
  • 37.
  • 39. Legendary Analysis What are some key characteristics of a Legendary Pokemon? Their type? Their height or weight? What are the qualities that will usually determine if a Pokemon is a Legendary or not?
  • 40.
  • 41.
  • 42.
  • 43.
  • 44. Conclusions! Who is the strongest Starter Pokemon? What is the best 6 Pokemon lineup?(w/ Legendaries and w/o Legendaries) What is the trend of Pokemon by each Generation? What can this information be used for?

Editor's Notes

  1. Jeff Describe dataset from kaggle! 801 entries
  2. Jeff Pokemon,is a popular japanese role-playing game developed by Gamefreak and published by Nintendo. The game consists of catching creatures called “Pokemon” and having them battle each other in a turn-based combat system. Each pokemon has unique stats, weaknesses and characteristics that distinguish them from other pokemon in combat. These stats, weaknesses and characteristics also dictate how a player forms their team of Pokemon. With the vast number of Pokemon available in the game, the curiosity of the best Pokemon team combination arises. Players that develop a powerful Pokemon team can gain a competitive edge and possibly compete in tournaments. Preparing and choosing the right Pokemon team is important. Our project will help inform players about which Pokemon would be best for combat, helping them form the best possible team. We aim to separate the wheat from chaff, and discover which pokemon are the most effective across various strategies and playstyles. With our huge dataset of 801 pokemon acquired from Kaggle. We will first analyze all the starter Pokemon from generation I to generation VII, to see who has the highest base stats in each region and is the strongest starter Pokemon overall. Then we will move on to analyzing the base stats of the entire pool of Pokemon to see what 6 Pokemon will make the strongest competitive team based on our modified stats and evaluation of weaknesses determined from our domain expertise. Lastly, we will cover generational analysis to see how Pokemon has changed over the years and a brief analysis of Legendary Pokemon. Pull intro stuff off proposal Basic background Each section brief overview Talk about why in each section Show code, graphs and significant observations from that.
  3. Jeff
  4. Dalton Before we go into our project we need to go over some important key terms for you guys to understand.
  5. Jeff
  6. Jeff The age old question...Some pick based on what is the cutest or coolest, but we will tell you which to pick if you really want to do well in the game and beat your friends.
  7. Jeff This is the format for all the graphs focusing on analyzing the starter Pokemon. It will be all the same format, but each analyzes something different starting with base stats, then modded stats. Then offensive and defensive stats. Also, I’m sure there is an easier way to color the bars in a bar graph but I never figured it out so I made a little for loop to color all the grass Pokemon green, all fire red...We honestly thought of just photoshopping and just choosing a new fill for the different bars which may have been just as easy.
  8. Jeff
  9. Jeff
  10. Jeff We read in all the against_...all the different weaknesses a Pokemon can have and append it to a list. We do the same thing but for getting the different numerical values of weaknesses a Pokemon can have. We then look at which weaknesses have the highest mean weaknesses against the pokemon we are looking at (in this case the 21 starters) and it tells us that flying is the strongest move set against all the starters. We then randomly assign colors to all the different weaknesses and print them out for all starter pokemon.
  11. Jeff We see that flying has a 4x damage multiplier and no 0x. So flying moves will always hit any of the starters and will super effective to almost half of the starter Pokemon.
  12. Chris So after we analyzed all the starter Pokemon, we turned into analyzing every Pokemon in the game. So we decided to look into Pokemon heights and weights just for fun. And then we look at their base stats as jeffrey explained And we also looked at the pokemon’s primary and secondary types. and so as we can see on the chart on the right there is pretty much and even percentage ag of dual and single types of pokemon but there is slightly more dual types
  13. Chris These are just some graphs where we analyzed pokemon height and weight. We see that the average height of all Pokemon is around 1.13m and the average weight of all pokemon is about 59kg. However, there are of course some extremes that throw these numbers off like Onix who is 8.8m tall and weighs 210.kg.
  14. Chris Here is a better representation using just a simple scatter plot. This Pokemon is cosmoem and completely throws off the data by being 0.01m tall and weighing over 999kg(cosmeom weight is actually imeasurable but 999kg is the highest the weight scale goes.
  15. Dalton My personal favorite graph that covers all 801 Pokemon and the different base stats total there can be. Ranges from 180 to 780. Spikes around 400 and 600.
  16. Dalton
  17. Dalton
  18. Jeff
  19. We analyzed and compared between each Pokemon generation to see if Pokemon get stronger after every generation Chris First we wanted to see the Pokemon sample size of each generation shown in these charts . So we see on the right that generation 5 contains the most pokemon followed by gen 1 and 3. And we have generation 6 with the least amount of pokemon That is possible conclusion for the first 3 generations . We wanted to explore the question of whether or not people would have to keep buying new pokemon games to stay competitive.
  20. When we look at the average of base stats on the left we can see a gradual rise from gen 1 to gen 4 but when we look at mod stats mean where we compensate for more powerful stats such as speed and special attack to better reflect combat power we can see that there is a huge increase of pokemon power from generation 1 to 3 But then there was a significant drop in overall pokemon power in generation 5. And this is interesting because generation 5 was when the Pokemon series swaped to a new console so it was pretty much a start to a whole new game series so there were alot of weaker pokemon that came out for all the players that just bought the game. And after gen 5 we see another big rise in overall pokemon power. When we examine THE mod stats mean across all generation we could say that yes Pokemon do generally get stronger in the context that gen 4 and 5 was the beginning of a new pokemon series. So basically if you want to stay competitive with your friends buying all the new pokemon generations is recommended, and thats basically what the developers want so they can take of all of your money.
  21. Fewer Legendaries
  22. Wow so many Dragons!
  23. Reduction in dragon hegemony
  24. David
  25. Marco BEGIN
  26. Marco
  27. Marco
  28. Marco
  29. Dalton
  30. All pokemon (left) then Legendary (right)
  31. All pokemon (left) then Legendary (right)
  32. Dalton (Left is all Pokemon, right is only Legendaries) In general, Legendary Pokemon will have higher stats than all the other Pokemon. We see the average for their offensive and defensive stats are higher than those of all pokemon.
  33. Dalton There is 70 legendary Pokemon across all generations.
  34. Conclusion For our first time coding a project in Python, overall we did a great job! We collaborated between 4 people for all this code (David's section is in R and was not able to be moved to Python in time but will be attached) and were able to coordinate the code so that it fit perfectly into one project. There is a lot of meaningful conclusions we pulled from examining all this data so let's dive into those going one section at a time. Starter Pokemon We see that Greninja is by far the strongest starter Pokemon with 640 total base stats, beating out even mega-evolved Pokemon! Flying type moves are most effective overall against any of the starter Pokemon. The most common secondary type for a starter Pokemon is fighting and only 18 out of the 21 Pokemon are Dual types (having both Primary and Secondary types) All 801 Pokemon There are more Dual type than Single type Pokemon. The average height of a Pokemon is 1.13m and the average weight is 59kg. Ice and Rock moves are the most effective against most Pokemon. Most Pokemon are Water Primary Type and Flying Secondary Type. Best 6 Pokemon Team With Legendaries is (in power order) Mewtwo, Rayquaza, Kyogre, Groudon, Arceus, and Zygarde. Without Legendaries is (in power order) Greninja, Slaking, Garchomp, Wishiwashi, Metagross, and Arcanine. Generational Analysis Most Pokemon come from generations I and V. Generation III has the best competitive Pokemon (based on mod_stats) and Generation IV has the highest base stats Pokemon. Legendary Analysis Generation VII has the most Legendary Pokemon out of all the generations. Most Legendary Pokemon have a base happiness of 0! Most Legendary Pokemon are Psychic Primary type and Flying Secondary Type. In conclusion, we learned a lot from doing EDA on this data and aquired an even deeper appreciation of Pokemon and desire to keep working with big data sets and explore what challenges Kaggle has to offer. With a limited background in Python, we achieved many goals that we set out to do and picked up tons of coding skills along the way.