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

# In[1]:


import pandas as pd
star_wars = pd.read_csv("star_wars.csv", encoding="ISO-8859-1")


# In[2]:


star_wars = star_wars[pd.notnull(star_wars['RespondentID'])]


# In[3]:


star_wars.head(10)


# In[4]:


star_wars.columns


# In[5]:


yes_no = {
    "Yes": True,
    "No": False
}

for col in ['Have you seen any of the 6 films in the Star Wars franchise?', 
            'Do you consider yourself to be a fan of the Star Wars film franchise?']:
    star_wars[col] = star_wars[col].map(yes_no)

star_wars.head()


# In[6]:


import numpy as np

star_map = {
    "Star Wars: Episode I  The Phantom Menace": True,
    np.nan: False,
    "Star Wars: Episode II  Attack of the Clones": True,
    "Star Wars: Episode III  Revenge of the Sith": True,
    "Star Wars: Episode IV  A New Hope": True,
    "Star Wars: Episode V The Empire Strikes Back": True,
    "Star Wars: Episode VI Return of the Jedi": True
}
for col in star_wars.columns[3:9]:
    star_wars[col] = star_wars[col].map(star_map)


# In[7]:


star_wars = star_wars.rename(columns={
    "Which of the following Star Wars films have you seen? Please select all that apply.": "seen_1",
    'Unnamed: 4':'seen_2',
    'Unnamed: 5':'seen_3',
    'Unnamed: 6':'seen_4',
    'Unnamed: 7':'seen_5',
    'Unnamed: 8':'seen_6'})
star_wars.head()


# In[8]:


star_wars[star_wars.columns[9:15]] = star_wars[star_wars.columns[9:15]].astype(float)

star_wars = star_wars.rename(columns={
    "Please rank the Star Wars films in order of preference with 1 being your favorite film in the franchise and 6 being your least favorite film.": "ranking_1",
    'Unnamed: 10':'ranking_2',
    'Unnamed: 11':'ranking_3',
    'Unnamed: 12':'ranking_4',
    'Unnamed: 13':'ranking_5',
    'Unnamed: 14':'ranking_6'})
star_wars.head()


# In[9]:


star_wars[star_wars.columns[9:15]].mean()


# In[10]:


get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt

plt.bar(range(6), star_wars[star_wars.columns[9:15]].mean())
plt.title('Average Overall Ranking')


# # Overall Rankings
# 
# To this point, the data has been imported, cleaned up, and formatted for analysis.  The average ranking for each movie was calculated and graphed above.  Lower rankings are better, because the scale is 1 = Best to 6 = Worst.  As expected, "Episode V: The Empire Strikes Back" has the lowest score, meaning it is regarded as the best.

# In[11]:


star_wars[star_wars.columns[3:9]].sum()


# In[12]:


plt.bar(range(6), star_wars[star_wars.columns[3:9]].sum())
plt.title('Overall View Count')


# # Overall Viewings
# 
# The overall view count for the original trilogy (Episodes 4-6) is much higher.  Obviously, part of this is due to the amount of time those films have been released in comparison to the sequel trilogy (Episodes 1-3).  This also helps to explain why the popularity of the original trilogy is much higher in the ranking chart.

# In[13]:


males = star_wars[star_wars["Gender"] == "Male"]
females = star_wars[star_wars["Gender"] == "Female"]


# In[14]:


plt.bar(range(6), males[males.columns[9:15]].mean())
plt.title('Male Rankings')
plt.show()

plt.bar(range(6), females[females.columns[9:15]].mean())
plt.title('Female Rankings')
plt.show()


# In[15]:


plt.bar(range(6), males[males.columns[3:9]].sum())
plt.title('Male Viewings')
plt.show()

plt.bar(range(6), females[females.columns[3:9]].sum())
plt.title('Female Viewings')
plt.show()


# # Outcomes by Gender
# 
# When viewed through the lens of gender, the rankings follow the same trends as the overall ranking, meaning there is no film more heavily favored by one gender or the other.  View counts are higher by males, which is to be expected given the film subject matter.  The interesting point to make is that with this higher view count, and thus an opportunity to rate the prequels more often, males rate the prequels lower than females.