"""
# Predicting The Sales of Video Games [WIP]
![Arcade](https://images.unsplash.com/photo-1513528473392-f3fffb1b31a9?ixlib=rb-0.3.5&ixid=eyJhcHBfaWQiOjEyMDd9&s=44845c69fe4febae451d4133ccb7518b&auto=format&fit=crop&w=750&q=80)

"""
"""
The purpose of this experiment is to practice Univariate and Multivariate EDA. 
"""
#Libraries/Dependices

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns

%reload_ext autoreload
%autoreload 2
%matplotlib inline

from fastai.structured import *
from fastai.column_data import *
np.set_printoptions(threshold=50, edgeitems=20)

import os
print(os.listdir("../input"))


PATH = "../input/Video_Games_Sales_as_at_22_Dec_2016.csv"
df_Games = pd.read_csv("../input/Video_Games_Sales_as_at_22_Dec_2016.csv", index_col=0)
df_Games.head(30)
df_Games.info()
df_Games.describe()
"""
## EDA Time!
"""
"""
Global Sales
"""
sns.distplot(df_Games['Global_Sales'], kde=False)
"""
Year of Release
"""
sns.distplot(df_Games['Year_of_Release'].dropna())
"""
Critic Score
"""
sns.distplot(df_Games['Critic_Score'].dropna())
"""
Platforms
"""
plt.figure(figsize=(12,8))
sns.countplot(df_Games['Platform'].sort_index())
"""
Genres
"""
plt.figure(figsize=(14,8))
sns.countplot(df_Games['Genre'].sort_index())
"""
Publishers
"""
"""
Developers
"""
plt.figure(figsize=(12,8))
sns.countplot(df_Games['Developer'].dropna(), order = df_Games['Developer'].value_counts().iloc[:40].index)
plt.xticks(rotation=90);
"""
Lets try and get an understanding of what variables have good correlations each other.
"""
"""
Lets try and find the genre with the most sales [A BIT LATER AS ITS Multivariate analysis]
"""
sales_genres = df_Games[['Genre', 'Global_Sales']]
sales_genres.head()