#!/usr/bin/env python
# coding: utf-8

# # Pokemon Comparisons
# 
# This project is used to compare pokemon to see which generations have the strongest, whether there is a correlation between attack power and defense power in pokemon from all generations, and to break each pokemon down by type to see which type is the strongest.

# In[1]:


import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
get_ipython().run_line_magic('matplotlib', 'inline')
df = pd.read_csv('Pokemon.csv')
print(df)


# ## Comparing Attack and Defense Power Across Generations

# In[2]:


gen1 = df[df['Generation'] == 1]
gen2 = df[df['Generation'] == 2]
gen3 = df[df['Generation'] == 3]
gen4 = df[df['Generation'] == 4]
gen5 = df[df['Generation'] == 5]
gen6 = df[df['Generation'] == 6]

plt.figure(figsize=(20, 10))

plt.subplot(1, 2, 1)
plt.hist(gen1['Attack'], normed=True, color='blue', alpha=.5)
plt.hist(gen2['Attack'], normed=True, color='red', alpha=.5) 
plt.hist(gen3['Attack'], normed=True, color='yellow', alpha=.5)
plt.hist(gen4['Attack'], normed=True, color='green', alpha=.5)
plt.hist(gen5['Attack'], normed=True, color='orange', alpha=.5)
plt.hist(gen6['Attack'], normed=True, color='pink', alpha=.5)
plt.title('Attack Power Between Generations')
plt.xlabel('Attack Power')

plt.subplot(1, 2, 2)
plt.hist(gen1['Defense'], normed=True, color='blue', alpha=.5)
plt.hist(gen2['Defense'], normed=True, color='red', alpha=.5) 
plt.hist(gen3['Defense'], normed=True, color='yellow', alpha=.5)
plt.hist(gen4['Defense'], normed=True, color='green', alpha=.5)
plt.hist(gen5['Defense'], normed=True, color='orange', alpha=.5)
plt.hist(gen6['Defense'], normed=True, color='pink', alpha=.5)
plt.title('Defense Power Between Generations')
plt.xlabel('Defense Power')

plt.show()


# ## Comparing Pokemon Attack Power and Defense Power

# In[3]:


plt.scatter(x = df['Attack'], y = df['Defense'])
plt.xlabel('Attack Power')
plt.ylabel('Defense Power')
plt.title('Pokemon Attack and Defense Power Comparison')
plt.show()


# ## Strongest Pokemon Type

# In[4]:


groupByType1 = df.groupby('Type 1')

typesOfPokemon = groupByType1.groups.keys()
numOfPokemonInType = groupByType1.size()
y_pos = np.arange(len(numOfPokemonInType))

plt.figure(figsize=(20,10))
plt.bar(y_pos, numOfPokemonInType, align='center', alpha=0.5)
plt.xticks(y_pos, typesOfPokemon)
plt.ylabel('# of Pokemon in Type')
plt.title('Type of Pokemon')
 
plt.show()


# In[5]:


groupByType1.mean().loc[:, ['Attack', 'Defense', 'HP']].sort_values('Attack')