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

# ## Movie Data Analysis
# 
# #### Objective:
# Conduct exploratory data analysis on movie data
# 
# <a id='toc'></a>
# ### Table of Contents
# * [Import and Explore Data](#importandexplore)
# * [Converting numeric variables to categorical variables](#convert)
# * [Visualization](#visualization)
#     * [Import visualization libraries](#visuallibraries) 
#     * [Jointplots](#jointplots)
#     * [Histograms](#histograms)
#     * [Stacked Histograms](#stacked)
#     * [KDE Plot](#kdeplot)
#     * [Subplots()](#subplots)
#     * [Violinplots vs. Boxplots](#violin_box)
#     * [Facet Grid](#facetgrid)
#     * [Coordinates and Diagonals](#coordinates)
#     * [Building Dashboards](#dashboards)
#     * [Styling Tips](#styling)
#     * [Finishing Touches](#styling2)
#     * [Section Recap](#recap)

# ### Import Libraries

# In[2]:


import pandas as pd
import os


# In[3]:


os.getcwd()


# ---
# <a id='importandexplore'></a>

# ### Import and Explore Data

# In[4]:


movies = pd.read_csv('Movie-Ratings.csv')


# In[4]:


len(movies)


# In[5]:


movies.head()


# In[6]:


movies.columns


# In[7]:


movies.info()


# In[8]:


movies.describe()


# In[5]:


# Rename column names
movies.columns = ['Film', 'Genre', 'CriticRating', 'AudienceRating', \
                 'BudgetMillions', 'Year']


# In[6]:


movies.head()


# In[11]:


movies.info()


# In[12]:


movies.describe()


# ---
# <a id='convert'></a>

# ### Convert numeric variables into categorical variables
# [Table of Contents](#toc)

# In[7]:


# Assign category type to Film, Genre, and Year
movies.Film = movies.Film.astype('category')
movies.Genre = movies.Genre.astype('category')
movies.Year = movies.Year.astype('category')


# In[16]:


movies.info()


# In[17]:


movies.describe()


# In[18]:


movies.Genre.cat.categories


# ---
# <a id='visualization'></a>

# ## Visualization
# [Table of Contents](#toc) | 
# [Matplotlib Website](https://matplotlib.org)

# <a id='visuallibraries'></a>
# ### Import visualization libraries

# In[8]:


from matplotlib import pyplot as plt
import seaborn as sns
get_ipython().run_line_magic('matplotlib', 'inline')
import warnings
warnings.filterwarnings('ignore')


# ---
# <a id='jointplots'></a>

# ### Jointplots
# [Table of Contents](#toc) | [Section](#visualization)

# In[9]:


#<<< Chart 1
j = sns.jointplot(data=movies, x='CriticRating', y='AudienceRating', kind='hex')


# In[10]:


#<<< Chart 2
j = sns.jointplot(data=movies, x='CriticRating', y='BudgetMillions', kind='hex')


# In[28]:


#<<< Chart 2
j = sns.jointplot(data=movies, x='AudienceRating', y='BudgetMillions', kind='hex')


# In[12]:


sns.set_style('darkgrid')


# ---
# <a id='histograms'></a>

# ### Histograms
# [Table of Contents](#toc) | [Section](#visualization)

# In[13]:


m1 = sns.distplot(movies.AudienceRating, bins=15)


# In[30]:


m2 = sns.distplot(movies.CriticRating, bins=15)


# In[32]:


# Pyplot version
sns.set_style('white')
n1 = plt.hist(movies.AudienceRating, bins=15)


# In[33]:


n2 = plt.hist(movies.CriticRating, bins=15)


# #### NOTE: Chart Background Formatting
# To toggle chart background from grid to white background:
# 
# **Display grid**
# ```python
# sns.set_style('darkgrid')
# ```
# 
# **Hide grid**
# ```python
# sns.set_style('white')
# ```

# #### Analysis Notes:
# * Audience ratings adopt a normal distribution.
# * Critics ratings are evenly distributed.
# * Highest ratings (both audience and critics) are for movies with budgets 50 million or less.

# ---
# <a id='stacked'></a>

# ### Stacked Histograms
# [Table of Contents](#toc) | [Section](#visualization)

# In[49]:


# Displaying histograms from multiple data sources in one chart
plt.hist(movies[movies.Genre == 'Action'].BudgetMillions, bins=15)
plt.hist(movies[movies.Genre == 'Drama'].BudgetMillions, bins=15)
plt.hist(movies[movies.Genre == 'Thriller'].BudgetMillions, bins=15)
plt.show()


# In[53]:


# Stack multiple data sources in one histogram chart (MANUAL METHOD)
plt.hist([movies[movies.Genre == 'Action'].BudgetMillions, \
        movies[movies.Genre == 'Drama'].BudgetMillions, \
        movies[movies.Genre == 'Thriller'].BudgetMillions], \
        bins=15, stacked=True)
plt.show()


# In[ ]:





# In[97]:


# Stack multiple data sources in one histogram chart (Better Way Version 1)
genres = movies.Genre.cat.categories
datasource = []
data_label = []
for i in genres:
    datasource.append((movies[movies.Genre==i].BudgetMillions))
    data_label.append(i)
    
plt.hist(datasource, bins=30, stacked=True, label=data_label)
plt.legend()
plt.show()


# In[98]:


# Stack multiple data sources in one histogram chart (Kirill's Method)
list1 = list()
mylabels = list()
for gen in movies.Genre.cat.categories:
    list1.append((movies[movies.Genre==gen].BudgetMillions))
    mylabels.append(gen)

h = plt.hist(list1, bins=30, stacked=True, rwidth=1, label=mylabels)
plt.legend()
plt.show()


# ---
# <a id='kdeplot'></a>

# ### KDE Plot
# [*Kernel Density Estimate*](https://en.wikipedia.org/wiki/Kernel_density_estimation)
# 
# [Table of Contents](#toc) | [Section](#visualization)

# In[101]:


# Scatter Plot Method
vis1 = sns.lmplot(data=movies, x='CriticRating', y='AudienceRating', \
                 fit_reg=False, hue='Genre', \
                 size=7, aspect=1)


# In[108]:


# KDE Plot
k1 = sns.kdeplot(movies.CriticRating, movies.AudienceRating, \
                shade=True, shade_lowest=False, cmap='Reds')


# In[110]:


# KDE Plot (Smoother Chart)
k1 = sns.kdeplot(movies.CriticRating, movies.AudienceRating, \
                shade=True, shade_lowest=False, cmap='Reds')
# TIP:
k1b = sns.kdeplot(movies.CriticRating, movies.AudienceRating, \
                cmap='Reds')


# ---
# <a id='subplots'></a>

# ### Subplots()
# [Table of Contents](#toc) | [Section](#visualization)

# In[121]:


sns.set_style('dark')
k1 = sns.kdeplot(movies.BudgetMillions, movies.AudienceRating)


# In[120]:


k2 = sns.kdeplot(movies.BudgetMillions, movies.CriticRating)


# In[124]:


# Subplot Template
f, axes = plt.subplots(1, 2, figsize=(12,6))


# In[125]:


# Subplot Example 1
f, axes = plt.subplots(1, 2, figsize=(12,6))
k1 = sns.kdeplot(movies.BudgetMillions, movies.AudienceRating, ax=axes[0])
k2 = sns.kdeplot(movies.BudgetMillions, movies.CriticRating, ax=axes[1])


# In[126]:


axes


# In[132]:


# Subplot Example 2 (Multi-dimensional plot)
f, axes = plt.subplots(2, 2, figsize=(12,6)) #More than one dimension
# Need to specify row and column coordinates in the 'ax' parameter
k1 = sns.kdeplot(movies.BudgetMillions, movies.AudienceRating, ax=axes[0,0])
k2 = sns.kdeplot(movies.BudgetMillions, movies.CriticRating, ax=axes[1,1])


# In[136]:


# Subplot Example 3 (Make it prettier)
f, axes = plt.subplots(1, 2, figsize=(12,6), sharex=True, sharey=True)
k1 = sns.kdeplot(movies.BudgetMillions, movies.AudienceRating, ax=axes[0])
k2 = sns.kdeplot(movies.BudgetMillions, movies.CriticRating, ax=axes[1])
# Zoom in on the chart to a specific axis scale
k1.set(xlim=(-20,160))
#k2.set(xlim=(-20,200))


# ---
# <a id='violin_box'></a>

# ### ViolinPlots vs BoxPlots
# [Table of Contents](#toc) | [Section](#visualization)

# In[140]:


#BoxPlots
w = sns.boxplot(data=movies, x='Genre', y='CriticRating')


# In[139]:


#ViolinPlots
z = sns.violinplot(data=movies, x='Genre', y='CriticRating')


# In[142]:


# Side by Side Comparison
f, axes = plt.subplots(1, 2, figsize=(12,6), sharex=True, sharey=True)
w = sns.boxplot(data=movies, x='Genre', y='CriticRating', ax=axes[0])
z = sns.violinplot(data=movies, x='Genre', y='CriticRating', ax=axes[1])


# In[143]:


# Drill down to a specific genre
# BoxPlot
u = sns.boxplot(data=movies[movies.Genre=='Drama'], x='Year', y='CriticRating')


# In[144]:


# ViolinPlot
v = sns.violinplot(data=movies[movies.Genre=='Drama'], x='Year', y='CriticRating')


# In[145]:


# Side by Side Comparison
f, axes = plt.subplots(1, 2, figsize=(12,6), sharex=True, sharey=True)
u = sns.boxplot(data=movies[movies.Genre=='Drama'], x='Year', y='CriticRating', ax=axes[0])
v = sns.violinplot(data=movies[movies.Genre=='Drama'], x='Year', y='CriticRating', ax=axes[1])


# ### Analysis
# * ViolinPlots can sometimes provide a more visually intuitive way to identify concentration of observations
# * BoxPlots are preferred by executives because of the quartile displays
# * **NOTE: In a boxplot the shorter the quartile the denser the concentration of data points in that quartile**

# ---
# <a id='facetgrid'></a>

# ### Facet Grid
# [Table of Contents](#toc) | [Section](#visualization)

# In[15]:


#g = sns.FacetGrid(movies, row='Genre', hue='Genre')
g = sns.FacetGrid(movies, row='Genre', col='Year', hue='Genre')


# In[17]:


g = sns.FacetGrid(movies, row='Genre', col='Year', hue='Genre')
g = g.map(plt.scatter, 'CriticRating', 'AudienceRating')


# In[22]:


#Can populate with any type of chart
g = sns.FacetGrid(movies, row='Genre', col='Year', hue='Genre')
g = g.map(plt.hist, 'BudgetMillions')


# In[19]:


# More scatter plot examples
g = sns.FacetGrid(movies, row='Genre', col='Year', hue='Genre')
kws = dict(s=50, linewidth=0.5, edgecolor='black')
g = g.map(plt.scatter, 'CriticRating', 'AudienceRating', **kws)


# ---
# <a id='coordinates'></a>

# ### Coordinates and Diagonals
# [Table of Contents](#toc) | [Section](#visualization)

# In[26]:


# Controlling Axes and Adding Diagonals
g = sns.FacetGrid(movies, row='Genre', col='Year', hue='Genre')
kws = dict(s=50, linewidth=0.5, edgecolor='black')
g = g.map(plt.scatter, 'CriticRating', 'AudienceRating', **kws)
g.set(xlim=(0,100),ylim=(0,100)) #Not necessary in newer versions of Jupyter notebooks
for ax in g.axes.flat:
    ax.plot((0, 100), (0, 100), c='gray', ls='--')
g.add_legend()


# ---
# <a id='dashboards'></a>

# ### Building Dashboards
# [Table of Contents](#toc) | [Section](#visualization)

# In[27]:


#Import Libraries
from matplotlib import pyplot as plt
import seaborn as sns
get_ipython().run_line_magic('matplotlib', 'inline')
import warnings
warnings.filterwarnings('ignore')


# In[57]:


# Dashboard Example 1
sns.set_style('darkgrid')
f, axes = plt.subplots(2, 2, figsize=(15,15))
k1 = sns.kdeplot(movies.BudgetMillions, movies.AudienceRating, ax=axes[0,0])
k2 = sns.kdeplot(movies.BudgetMillions, movies.CriticRating, ax=axes[0,1])
k3 = sns.violinplot(data=movies, x='Year', y='BudgetMillions', ax=axes[1,0])

#NOTE: a different charting approach for non-Seaborn (pyplot) charts 
#replace 'plt' prefix/object with chart location 'axes[x,y]
"""
Since the subplots() function is built in pyplot regular pyplot charts can be generated
with the 'plt' prefix and chart location is assigned with the 'axes[row,col].' coordinates
"""
#axes[1,1].hist(movies.CriticRating, bins=15) 

k4 = sns.kdeplot(movies.CriticRating, movies.AudienceRating, \
                 shade=True, shade_lowest=False, cmap='Reds', \
                 ax=axes[1,1])
# TIP:
k1b = sns.kdeplot(movies.CriticRating, movies.AudienceRating, \
                  cmap='Reds', ax=axes[1,1])

k1.set(xlim=(-20,160))
k2.set(xlim=(-20,160))
plt.show()


# ---
# <a id='styling'></a>

# ### Styling Tips
# [Table of Contents](#toc) | [Section](#visualization)

# In[43]:


#Import Libraries
from matplotlib import pyplot as plt
import seaborn as sns
get_ipython().run_line_magic('matplotlib', 'inline')
import warnings
warnings.filterwarnings('ignore')


# In[61]:


# Styling a dashboard
#Set background to black without a grid
sns.set_style('dark', {'axes.facecolor':'black'}) #white, whitegrid, dark, darkgrid, ticks
f, axes = plt.subplots(2, 2, figsize=(15,15))
#Plot [0,0]
k1 = sns.kdeplot(movies.BudgetMillions, movies.AudienceRating, \
                 shade=True, shade_lowest=True, cmap='inferno', \
                 ax=axes[0,0])
k1b = sns.kdeplot(movies.BudgetMillions, movies.AudienceRating, \
                  cmap='cool', ax=axes[0,0])
#Plot [0,1]
k2 = sns.kdeplot(movies.BudgetMillions, movies.CriticRating, \
                 shade=True, shade_lowest=True, cmap='inferno', \
                 ax=axes[0,1])
k2 = sns.kdeplot(movies.BudgetMillions, movies.CriticRating, \
                 cmap='cool', ax=axes[0,1])

#Plot [1,0]
k3 = sns.violinplot(data=movies, x='Year', y='BudgetMillions', ax=axes[1,0], \
                    palette='YlOrRd')
#Plot [1,1]
k4 = sns.kdeplot(movies.CriticRating, movies.AudienceRating, \
                 shade=True, shade_lowest=False, cmap='Blues_r', \
                 ax=axes[1,1])
k4b = sns.kdeplot(movies.CriticRating, movies.AudienceRating, \
                  cmap='gist_gray_r', ax=axes[1,1])

k1.set(xlim=(-20,160))
k2.set(xlim=(-20,160))
plt.show()


# ---
# <a id='styling2'></a>

# ### More Styling Tips
# [Table of Contents](#toc) | [Section](#visualization)

# #### Thematic Edits
# * Change background to 'whitegrid'
# * Add a chart into a plt.subplot() frame to change chart size
# * Include a chart title, and specify font size, color, and style
# * Add labels to x and y axes
# * Change the size of the chart ticks
# * Improve the legend appearance: change fontsize, backgroundcolor 
# * OPTION: can specify actual color of the data
# 

# In[85]:


#Thematic Edits
list1 = list()
mylabels = list()
for gen in movies.Genre.cat.categories:
    list1.append((movies[movies.Genre==gen].BudgetMillions))
    mylabels.append(gen)

sns.set_style('whitegrid')
fig, ax = plt.subplots()
fig.set_size_inches(12, 8)
h = plt.hist(list1, bins=30, stacked=True, rwidth=1, label=mylabels, )
plt.title('Movie Budget Distribution', fontsize=35, color='DarkBlue', fontname='Console')
plt.ylabel('Number of Movies', fontsize=25, color='Red')
plt.xlabel('Budget', fontsize=25, color='Green')
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.legend(frameon=True, fancybox=True, shadow=True, \
           framealpha=1, prop={'size':20})
plt.show()


# ---
# <a id='recap'></a>

# ### Section Recap
# [Table of Contents](#toc) | [Section](#visualization)
# 
# **In this section we learned:**
# 1. [Category data type in Python](#convert)
# 2. [Jointplots](#jointplots)
# 3. [Histograms](#histograms)
# 4. [Stacked Histograms](#stacked)
# 5. [KDE Plot](#kdeplot)
# 6. [Using the Subplots() function](#subplots)
# 7. [Violinplots vs. Boxplots](#violin_box)
# 8. [Creating a Facet Grid](#facetgrid)
# 9. [Coordinates and Diagonals](#coordinates)
# 10. [Building Dashboards](#dashboards)
# 11. [Styling Tips](#styling)
# 12. [Finishing Touches](#styling2)

# In[ ]: