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Created July 11, 2025 17:41
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Star Wars Survey Project Lab Demo Solution
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star_wars_survey_DEMO.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Exploration"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RespondentID</th>\n",
" <th>Have you seen any of the 6 films in the Star Wars franchise?</th>\n",
" <th>Do you consider yourself to be a fan of the Star Wars film franchise?</th>\n",
" <th>Which of the following Star Wars films have you seen? Please select all that apply.</th>\n",
" <th>Unnamed: 4</th>\n",
" <th>Unnamed: 5</th>\n",
" <th>Unnamed: 6</th>\n",
" <th>Unnamed: 7</th>\n",
" <th>Unnamed: 8</th>\n",
" <th>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.</th>\n",
" <th>...</th>\n",
" <th>Unnamed: 28</th>\n",
" <th>Which character shot first?</th>\n",
" <th>Are you familiar with the Expanded Universe?</th>\n",
" <th>Do you consider yourself to be a fan of the Expanded Universe?</th>\n",
" <th>Do you consider yourself to be a fan of the Star Trek franchise?</th>\n",
" <th>Gender</th>\n",
" <th>Age</th>\n",
" <th>Household Income</th>\n",
" <th>Education</th>\n",
" <th>Location (Census Region)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3292879998</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Star Wars: Episode I The Phantom Menace</td>\n",
" <td>Star Wars: Episode II Attack of the Clones</td>\n",
" <td>Star Wars: Episode III Revenge of the Sith</td>\n",
" <td>Star Wars: Episode IV A New Hope</td>\n",
" <td>Star Wars: Episode V The Empire Strikes Back</td>\n",
" <td>Star Wars: Episode VI Return of the Jedi</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>Very favorably</td>\n",
" <td>I don't understand this question</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>18-29</td>\n",
" <td>NaN</td>\n",
" <td>High school degree</td>\n",
" <td>South Atlantic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3292879538</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Yes</td>\n",
" <td>Male</td>\n",
" <td>18-29</td>\n",
" <td>$0 - $24,999</td>\n",
" <td>Bachelor degree</td>\n",
" <td>West South Central</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3292765271</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Star Wars: Episode I The Phantom Menace</td>\n",
" <td>Star Wars: Episode II Attack of the Clones</td>\n",
" <td>Star Wars: Episode III Revenge of the Sith</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>Unfamiliar (N/A)</td>\n",
" <td>I don't understand this question</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>18-29</td>\n",
" <td>$0 - $24,999</td>\n",
" <td>High school degree</td>\n",
" <td>West North Central</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3292763116</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Star Wars: Episode I The Phantom Menace</td>\n",
" <td>Star Wars: Episode II Attack of the Clones</td>\n",
" <td>Star Wars: Episode III Revenge of the Sith</td>\n",
" <td>Star Wars: Episode IV A New Hope</td>\n",
" <td>Star Wars: Episode V The Empire Strikes Back</td>\n",
" <td>Star Wars: Episode VI Return of the Jedi</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>Very favorably</td>\n",
" <td>I don't understand this question</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>Yes</td>\n",
" <td>Male</td>\n",
" <td>18-29</td>\n",
" <td>$100,000 - $149,999</td>\n",
" <td>Some college or Associate degree</td>\n",
" <td>West North Central</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3292731220</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>Star Wars: Episode I The Phantom Menace</td>\n",
" <td>Star Wars: Episode II Attack of the Clones</td>\n",
" <td>Star Wars: Episode III Revenge of the Sith</td>\n",
" <td>Star Wars: Episode IV A New Hope</td>\n",
" <td>Star Wars: Episode V The Empire Strikes Back</td>\n",
" <td>Star Wars: Episode VI Return of the Jedi</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Greedo</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Male</td>\n",
" <td>18-29</td>\n",
" <td>$100,000 - $149,999</td>\n",
" <td>Some college or Associate degree</td>\n",
" <td>West North Central</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" RespondentID Have you seen any of the 6 films in the Star Wars franchise? \\\n",
"0 3292879998 Yes \n",
"1 3292879538 No \n",
"2 3292765271 Yes \n",
"3 3292763116 Yes \n",
"4 3292731220 Yes \n",
"\n",
" Do you consider yourself to be a fan of the Star Wars film franchise? \\\n",
"0 Yes \n",
"1 NaN \n",
"2 No \n",
"3 Yes \n",
"4 Yes \n",
"\n",
" Which of the following Star Wars films have you seen? Please select all that apply. \\\n",
"0 Star Wars: Episode I The Phantom Menace \n",
"1 NaN \n",
"2 Star Wars: Episode I The Phantom Menace \n",
"3 Star Wars: Episode I The Phantom Menace \n",
"4 Star Wars: Episode I The Phantom Menace \n",
"\n",
" Unnamed: 4 \\\n",
"0 Star Wars: Episode II Attack of the Clones \n",
"1 NaN \n",
"2 Star Wars: Episode II Attack of the Clones \n",
"3 Star Wars: Episode II Attack of the Clones \n",
"4 Star Wars: Episode II Attack of the Clones \n",
"\n",
" Unnamed: 5 \\\n",
"0 Star Wars: Episode III Revenge of the Sith \n",
"1 NaN \n",
"2 Star Wars: Episode III Revenge of the Sith \n",
"3 Star Wars: Episode III Revenge of the Sith \n",
"4 Star Wars: Episode III Revenge of the Sith \n",
"\n",
" Unnamed: 6 \\\n",
"0 Star Wars: Episode IV A New Hope \n",
"1 NaN \n",
"2 NaN \n",
"3 Star Wars: Episode IV A New Hope \n",
"4 Star Wars: Episode IV A New Hope \n",
"\n",
" Unnamed: 7 \\\n",
"0 Star Wars: Episode V The Empire Strikes Back \n",
"1 NaN \n",
"2 NaN \n",
"3 Star Wars: Episode V The Empire Strikes Back \n",
"4 Star Wars: Episode V The Empire Strikes Back \n",
"\n",
" Unnamed: 8 \\\n",
"0 Star Wars: Episode VI Return of the Jedi \n",
"1 NaN \n",
"2 NaN \n",
"3 Star Wars: Episode VI Return of the Jedi \n",
"4 Star Wars: Episode VI Return of the Jedi \n",
"\n",
" 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. \\\n",
"0 3.0 \n",
"1 NaN \n",
"2 1.0 \n",
"3 5.0 \n",
"4 5.0 \n",
"\n",
" ... Unnamed: 28 Which character shot first? \\\n",
"0 ... Very favorably I don't understand this question \n",
"1 ... NaN NaN \n",
"2 ... Unfamiliar (N/A) I don't understand this question \n",
"3 ... Very favorably I don't understand this question \n",
"4 ... Somewhat favorably Greedo \n",
"\n",
" Are you familiar with the Expanded Universe? \\\n",
"0 Yes \n",
"1 NaN \n",
"2 No \n",
"3 No \n",
"4 Yes \n",
"\n",
" Do you consider yourself to be a fan of the Expanded Universe? \\\n",
"0 No \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 No \n",
"\n",
" Do you consider yourself to be a fan of the Star Trek franchise? Gender \\\n",
"0 No Male \n",
"1 Yes Male \n",
"2 No Male \n",
"3 Yes Male \n",
"4 No Male \n",
"\n",
" Age Household Income Education \\\n",
"0 18-29 NaN High school degree \n",
"1 18-29 $0 - $24,999 Bachelor degree \n",
"2 18-29 $0 - $24,999 High school degree \n",
"3 18-29 $100,000 - $149,999 Some college or Associate degree \n",
"4 18-29 $100,000 - $149,999 Some college or Associate degree \n",
"\n",
" Location (Census Region) \n",
"0 South Atlantic \n",
"1 West South Central \n",
"2 West North Central \n",
"3 West North Central \n",
"4 West North Central \n",
"\n",
"[5 rows x 38 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"star_wars = pd.read_csv(\"star_wars.csv\", encoding=\"ISO-8859-1\")\n",
"star_wars.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1186 entries, 0 to 1185\n",
"Data columns (total 38 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 RespondentID 1186 non-null int64 \n",
" 1 Have you seen any of the 6 films in the Star Wars franchise? 1186 non-null object \n",
" 2 Do you consider yourself to be a fan of the Star Wars film franchise? 836 non-null object \n",
" 3 Which of the following Star Wars films have you seen? Please select all that apply. 673 non-null object \n",
" 4 Unnamed: 4 571 non-null object \n",
" 5 Unnamed: 5 550 non-null object \n",
" 6 Unnamed: 6 607 non-null object \n",
" 7 Unnamed: 7 758 non-null object \n",
" 8 Unnamed: 8 738 non-null object \n",
" 9 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. 835 non-null float64\n",
" 10 Unnamed: 10 836 non-null float64\n",
" 11 Unnamed: 11 835 non-null float64\n",
" 12 Unnamed: 12 836 non-null float64\n",
" 13 Unnamed: 13 836 non-null float64\n",
" 14 Unnamed: 14 836 non-null float64\n",
" 15 Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her. 829 non-null object \n",
" 16 Unnamed: 16 831 non-null object \n",
" 17 Unnamed: 17 831 non-null object \n",
" 18 Unnamed: 18 823 non-null object \n",
" 19 Unnamed: 19 825 non-null object \n",
" 20 Unnamed: 20 814 non-null object \n",
" 21 Unnamed: 21 826 non-null object \n",
" 22 Unnamed: 22 820 non-null object \n",
" 23 Unnamed: 23 812 non-null object \n",
" 24 Unnamed: 24 827 non-null object \n",
" 25 Unnamed: 25 830 non-null object \n",
" 26 Unnamed: 26 821 non-null object \n",
" 27 Unnamed: 27 814 non-null object \n",
" 28 Unnamed: 28 826 non-null object \n",
" 29 Which character shot first? 828 non-null object \n",
" 30 Are you familiar with the Expanded Universe? 828 non-null object \n",
" 31 Do you consider yourself to be a fan of the Expanded Universe? 213 non-null object \n",
" 32 Do you consider yourself to be a fan of the Star Trek franchise? 1068 non-null object \n",
" 33 Gender 1046 non-null object \n",
" 34 Age 1046 non-null object \n",
" 35 Household Income 858 non-null object \n",
" 36 Education 1036 non-null object \n",
" 37 Location (Census Region) 1043 non-null object \n",
"dtypes: float64(6), int64(1), object(31)\n",
"memory usage: 352.2+ KB\n"
]
}
],
"source": [
"star_wars.info()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Have you seen any of the 6 films in the Star Wars franchise?\n",
"Yes 936\n",
"No 250\n",
"Name: count, dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"star_wars[\"Have you seen any of the 6 films in the Star Wars franchise?\"].value_counts(dropna=False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Do you consider yourself to be a fan of the Star Wars film franchise?\n",
"Yes 552\n",
"NaN 350\n",
"No 284\n",
"Name: count, dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"star_wars[\"Do you consider yourself to be a fan of the Star Wars film franchise?\"].value_counts(dropna=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Cleaning"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Do you consider yourself to be a fan of the Star Wars film franchise?\n",
"True 552\n",
"NaN 350\n",
"False 284\n",
"Name: count, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yes_no = {\"Yes\": True, \"No\": False, True: True, False: False} # True and False keys ensure correct values aren't overwritten if cell is run more than once\n",
"\n",
"for col in [\n",
" \"Have you seen any of the 6 films in the Star Wars franchise?\",\n",
" \"Do you consider yourself to be a fan of the Star Wars film franchise?\",\n",
" \"Are you familiar with the Expanded Universe?\",\n",
" \"Do you consider yourself to be a fan of the Star Trek franchise?\"\n",
" \n",
" ]:\n",
" star_wars[col] = star_wars[col].map(yes_no, na_action='ignore')\n",
"\n",
"star_wars[\"Do you consider yourself to be a fan of the Star Wars film franchise?\"].value_counts(dropna=False)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Which of the following Star Wars films have you seen? Please select all that apply.</th>\n",
" <th>Unnamed: 4</th>\n",
" <th>Unnamed: 5</th>\n",
" <th>Unnamed: 6</th>\n",
" <th>Unnamed: 7</th>\n",
" <th>Unnamed: 8</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Which of the following Star Wars films have you seen? Please select all that apply. \\\n",
"0 True \n",
"1 False \n",
"2 True \n",
"3 True \n",
"4 True \n",
"\n",
" Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 \n",
"0 True True True True True \n",
"1 False False False False False \n",
"2 True True False False False \n",
"3 True True True True True \n",
"4 True True True True True "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"movie_mapping = {\n",
" \"Star Wars: Episode I The Phantom Menace\": True,\n",
" np.nan: False,\n",
" \"Star Wars: Episode II Attack of the Clones\": True,\n",
" \"Star Wars: Episode III Revenge of the Sith\": True,\n",
" \"Star Wars: Episode IV A New Hope\": True,\n",
" \"Star Wars: Episode V The Empire Strikes Back\": True,\n",
" \"Star Wars: Episode VI Return of the Jedi\": True,\n",
" True: True,\n",
" False: False\n",
"}\n",
"\n",
"for col in star_wars.columns[3:9]:\n",
" star_wars[col] = star_wars[col].map(movie_mapping)\n",
" \n",
"star_wars.iloc[:,3:9].head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>seen_1</th>\n",
" <th>seen_2</th>\n",
" <th>seen_3</th>\n",
" <th>seen_4</th>\n",
" <th>seen_5</th>\n",
" <th>seen_6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" seen_1 seen_2 seen_3 seen_4 seen_5 seen_6\n",
"0 True True True True True True\n",
"1 False False False False False False\n",
"2 True True True False False False\n",
"3 True True True True True True\n",
"4 True True True True True True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"star_wars = star_wars.rename(columns={\n",
" \"Which of the following Star Wars films have you seen? Please select all that apply.\": \"seen_1\",\n",
" \"Unnamed: 4\": \"seen_2\",\n",
" \"Unnamed: 5\": \"seen_3\",\n",
" \"Unnamed: 6\": \"seen_4\",\n",
" \"Unnamed: 7\": \"seen_5\",\n",
" \"Unnamed: 8\": \"seen_6\"\n",
" })\n",
"\n",
"star_wars.iloc[:,3:9].head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ranking_ep1</th>\n",
" <th>ranking_ep2</th>\n",
" <th>ranking_ep3</th>\n",
" <th>ranking_ep4</th>\n",
" <th>ranking_ep5</th>\n",
" <th>ranking_ep6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ranking_ep1 ranking_ep2 ranking_ep3 ranking_ep4 ranking_ep5 \\\n",
"0 3.0 2.0 1.0 4.0 5.0 \n",
"1 NaN NaN NaN NaN NaN \n",
"2 1.0 2.0 3.0 4.0 5.0 \n",
"3 5.0 6.0 1.0 2.0 4.0 \n",
"4 5.0 4.0 6.0 2.0 1.0 \n",
"\n",
" ranking_ep6 \n",
"0 6.0 \n",
"1 NaN \n",
"2 6.0 \n",
"3 3.0 \n",
"4 3.0 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"star_wars = star_wars.rename(columns={\n",
" \"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_ep1\",\n",
" \"Unnamed: 10\": \"ranking_ep2\",\n",
" \"Unnamed: 11\": \"ranking_ep3\",\n",
" \"Unnamed: 12\": \"ranking_ep4\",\n",
" \"Unnamed: 13\": \"ranking_ep5\",\n",
" \"Unnamed: 14\": \"ranking_ep6\"\n",
" })\n",
"\n",
"star_wars.iloc[:,9:15].head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RespondentID int64\n",
"Have you seen any of the 6 films in the Star Wars franchise? bool\n",
"Do you consider yourself to be a fan of the Star Wars film franchise? object\n",
"seen_1 bool\n",
"seen_2 bool\n",
"seen_3 bool\n",
"seen_4 bool\n",
"seen_5 bool\n",
"seen_6 bool\n",
"ranking_ep1 float64\n",
"ranking_ep2 float64\n",
"ranking_ep3 float64\n",
"ranking_ep4 float64\n",
"ranking_ep5 float64\n",
"ranking_ep6 float64\n",
"Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her. object\n",
"Unnamed: 16 object\n",
"Unnamed: 17 object\n",
"Unnamed: 18 object\n",
"Unnamed: 19 object\n",
"Unnamed: 20 object\n",
"Unnamed: 21 object\n",
"Unnamed: 22 object\n",
"Unnamed: 23 object\n",
"Unnamed: 24 object\n",
"Unnamed: 25 object\n",
"Unnamed: 26 object\n",
"Unnamed: 27 object\n",
"Unnamed: 28 object\n",
"Which character shot first? object\n",
"Are you familiar with the Expanded Universe? object\n",
"Do you consider yourself to be a fan of the Expanded Universe? object\n",
"Do you consider yourself to be a fan of the Star Trek franchise? object\n",
"Gender object\n",
"Age object\n",
"Household Income object\n",
"Education object\n",
"Location (Census Region) object\n",
"dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"star_wars.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Han Solo</th>\n",
" <th>Luke Skywalker</th>\n",
" <th>Princess Leia Organa</th>\n",
" <th>Anakin Skywalker</th>\n",
" <th>Obi Wan Kenobi</th>\n",
" <th>Emperor Palpatine</th>\n",
" <th>Darth Vader</th>\n",
" <th>Lando Calrissian</th>\n",
" <th>Boba Fett</th>\n",
" <th>C-3P0</th>\n",
" <th>R2 D2</th>\n",
" <th>Jar Jar Binks</th>\n",
" <th>Padme Amidala</th>\n",
" <th>Yoda</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Unfamiliar (N/A)</td>\n",
" <td>Unfamiliar (N/A)</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Somewhat favorably</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Unfamiliar (N/A)</td>\n",
" <td>Unfamiliar (N/A)</td>\n",
" <td>Unfamiliar (N/A)</td>\n",
" <td>Unfamiliar (N/A)</td>\n",
" <td>Unfamiliar (N/A)</td>\n",
" <td>Unfamiliar (N/A)</td>\n",
" <td>Unfamiliar (N/A)</td>\n",
" <td>Unfamiliar (N/A)</td>\n",
" <td>Unfamiliar (N/A)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Somewhat unfavorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very favorably</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Very favorably</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Somewhat unfavorably</td>\n",
" <td>Very favorably</td>\n",
" <td>Very unfavorably</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Neither favorably nor unfavorably (neutral)</td>\n",
" <td>Very favorably</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Very unfavorably</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Somewhat favorably</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Han Solo Luke Skywalker Princess Leia Organa \\\n",
"0 Very favorably Very favorably Very favorably \n",
"1 NaN NaN NaN \n",
"2 Somewhat favorably Somewhat favorably Somewhat favorably \n",
"3 Very favorably Very favorably Very favorably \n",
"4 Very favorably Somewhat favorably Somewhat favorably \n",
"\n",
" Anakin Skywalker Obi Wan Kenobi Emperor Palpatine \\\n",
"0 Very favorably Very favorably Very favorably \n",
"1 NaN NaN NaN \n",
"2 Somewhat favorably Somewhat favorably Unfamiliar (N/A) \n",
"3 Very favorably Very favorably Somewhat favorably \n",
"4 Somewhat unfavorably Very favorably Very unfavorably \n",
"\n",
" Darth Vader Lando Calrissian \\\n",
"0 Very favorably Unfamiliar (N/A) \n",
"1 NaN NaN \n",
"2 Unfamiliar (N/A) Unfamiliar (N/A) \n",
"3 Very favorably Somewhat favorably \n",
"4 Somewhat favorably Neither favorably nor unfavorably (neutral) \n",
"\n",
" Boba Fett C-3P0 R2 D2 \\\n",
"0 Unfamiliar (N/A) Very favorably Very favorably \n",
"1 NaN NaN NaN \n",
"2 Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) \n",
"3 Somewhat unfavorably Very favorably Very favorably \n",
"4 Very favorably Somewhat favorably Somewhat favorably \n",
"\n",
" Jar Jar Binks Padme Amidala Yoda \n",
"0 Very favorably Very favorably Very favorably \n",
"1 NaN NaN NaN \n",
"2 Unfamiliar (N/A) Unfamiliar (N/A) Unfamiliar (N/A) \n",
"3 Very favorably Very favorably Very favorably \n",
"4 Very unfavorably Somewhat favorably Somewhat favorably "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Renaming the rest of the columns for completeness's sake...\n",
"star_wars = star_wars.rename(columns={\n",
" 'Please state whether you view the following characters favorably, unfavorably, or are unfamiliar with him/her.': 'Han Solo',\n",
" 'Unnamed: 16': 'Luke Skywalker',\n",
" 'Unnamed: 17': 'Princess Leia Organa',\n",
" 'Unnamed: 18': 'Anakin Skywalker',\n",
" 'Unnamed: 19': 'Obi Wan Kenobi',\n",
" 'Unnamed: 20': 'Emperor Palpatine',\n",
" 'Unnamed: 21': 'Darth Vader',\n",
" 'Unnamed: 22': 'Lando Calrissian',\n",
" 'Unnamed: 23': 'Boba Fett',\n",
" 'Unnamed: 24': 'C-3P0',\n",
" 'Unnamed: 25': 'R2 D2',\n",
" 'Unnamed: 26': 'Jar Jar Binks',\n",
" 'Unnamed: 27': 'Padme Amidala',\n",
" 'Unnamed: 28': 'Yoda'\n",
"})\n",
"\n",
"star_wars.iloc[:,15:29].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Average Ranking for Each Movie"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ranking_ep5 2.513158\n",
"ranking_ep6 3.047847\n",
"ranking_ep4 3.272727\n",
"ranking_ep1 3.732934\n",
"ranking_ep2 4.087321\n",
"ranking_ep3 4.341317\n",
"dtype: float64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_ranking = star_wars[star_wars.columns[9:15]].mean().sort_values()\n",
"mean_ranking"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 600x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax = plt.subplots(figsize=(6,3))\n",
"rankings = ax.barh(mean_ranking.index, mean_ranking, color='#fe9b00')\n",
"\n",
"ax.set_facecolor('#fff4d6')\n",
"ax.set_title('Average Ranking of Each Movie')\n",
"for spine in ['top', 'right', 'bottom', 'left']:\n",
" ax.spines[spine].set_visible(False)\n",
"ax.invert_yaxis()\n",
"ax.text(2.6,0.35,'*Lowest rank is the most\\n liked', fontstyle='italic')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Episode 5 (Empire Strikes Back) is the highest rated movie by far. The \"prequels\" (Episodes 1, 2, and 3) were generally less well-received than the originals, so it makes sense that their ratings were lower overall than the original trilogy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Total Number of Survey Respondents Who Have Seen Each Movie"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"seen_1 673\n",
"seen_2 571\n",
"seen_3 550\n",
"seen_4 607\n",
"seen_5 758\n",
"seen_6 738\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_seen = star_wars[star_wars.columns[3:9]].sum()\n",
"total_seen"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 600x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Version 1: simple and quick\n",
"plt.bar(range(6), star_wars[star_wars.columns[3:9]].sum())\n",
"\n",
"# Version 2: optimized for stakeholders\n",
"fig,ax = plt.subplots(figsize=(6,3))\n",
"rankings = ax.barh(total_seen.index, total_seen, color='#ff1947')\n",
"ax.set_facecolor('#fff4d6')\n",
"ax.set_title('Total Respondents Who Have Seen Each Movie')\n",
"ax.set_xlabel(\"Number of Respondents\")\n",
"for spine in ['top', 'right', 'bottom', 'left']:\n",
" ax.spines[spine].set_visible(False)\n",
"ax.invert_yaxis()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exploring by Gender"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RespondentID</th>\n",
" <th>Have you seen any of the 6 films in the Star Wars franchise?</th>\n",
" <th>Do you consider yourself to be a fan of the Star Wars film franchise?</th>\n",
" <th>seen_1</th>\n",
" <th>seen_2</th>\n",
" <th>seen_3</th>\n",
" <th>seen_4</th>\n",
" <th>seen_5</th>\n",
" <th>seen_6</th>\n",
" <th>ranking_ep1</th>\n",
" <th>...</th>\n",
" <th>Yoda</th>\n",
" <th>Which character shot first?</th>\n",
" <th>Are you familiar with the Expanded Universe?</th>\n",
" <th>Do you consider yourself to be a fan of the Expanded Universe?</th>\n",
" <th>Do you consider yourself to be a fan of the Star Trek franchise?</th>\n",
" <th>Gender</th>\n",
" <th>Age</th>\n",
" <th>Household Income</th>\n",
" <th>Education</th>\n",
" <th>Location (Census Region)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3292879998</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>Very favorably</td>\n",
" <td>I don't understand this question</td>\n",
" <td>True</td>\n",
" <td>No</td>\n",
" <td>False</td>\n",
" <td>Male</td>\n",
" <td>18-29</td>\n",
" <td>NaN</td>\n",
" <td>High school degree</td>\n",
" <td>South Atlantic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3292879538</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" <td>Male</td>\n",
" <td>18-29</td>\n",
" <td>$0 - $24,999</td>\n",
" <td>Bachelor degree</td>\n",
" <td>West South Central</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" RespondentID Have you seen any of the 6 films in the Star Wars franchise? \\\n",
"0 3292879998 True \n",
"1 3292879538 False \n",
"\n",
" Do you consider yourself to be a fan of the Star Wars film franchise? \\\n",
"0 True \n",
"1 NaN \n",
"\n",
" seen_1 seen_2 seen_3 seen_4 seen_5 seen_6 ranking_ep1 ... \\\n",
"0 True True True True True True 3.0 ... \n",
"1 False False False False False False NaN ... \n",
"\n",
" Yoda Which character shot first? \\\n",
"0 Very favorably I don't understand this question \n",
"1 NaN NaN \n",
"\n",
" Are you familiar with the Expanded Universe? \\\n",
"0 True \n",
"1 NaN \n",
"\n",
" Do you consider yourself to be a fan of the Expanded Universe? \\\n",
"0 No \n",
"1 NaN \n",
"\n",
" Do you consider yourself to be a fan of the Star Trek franchise? Gender \\\n",
"0 False Male \n",
"1 True Male \n",
"\n",
" Age Household Income Education Location (Census Region) \n",
"0 18-29 NaN High school degree South Atlantic \n",
"1 18-29 $0 - $24,999 Bachelor degree West South Central \n",
"\n",
"[2 rows x 38 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RespondentID</th>\n",
" <th>Have you seen any of the 6 films in the Star Wars franchise?</th>\n",
" <th>Do you consider yourself to be a fan of the Star Wars film franchise?</th>\n",
" <th>seen_1</th>\n",
" <th>seen_2</th>\n",
" <th>seen_3</th>\n",
" <th>seen_4</th>\n",
" <th>seen_5</th>\n",
" <th>seen_6</th>\n",
" <th>ranking_ep1</th>\n",
" <th>...</th>\n",
" <th>Yoda</th>\n",
" <th>Which character shot first?</th>\n",
" <th>Are you familiar with the Expanded Universe?</th>\n",
" <th>Do you consider yourself to be a fan of the Expanded Universe?</th>\n",
" <th>Do you consider yourself to be a fan of the Star Trek franchise?</th>\n",
" <th>Gender</th>\n",
" <th>Age</th>\n",
" <th>Household Income</th>\n",
" <th>Education</th>\n",
" <th>Location (Census Region)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>3291439708</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>Greedo</td>\n",
" <td>True</td>\n",
" <td>No</td>\n",
" <td>True</td>\n",
" <td>Female</td>\n",
" <td>&gt; 60</td>\n",
" <td>$50,000 - $99,999</td>\n",
" <td>Bachelor degree</td>\n",
" <td>South Atlantic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>3291439404</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>Somewhat favorably</td>\n",
" <td>I don't understand this question</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>Female</td>\n",
" <td>&gt; 60</td>\n",
" <td>$25,000 - $49,999</td>\n",
" <td>Graduate degree</td>\n",
" <td>Pacific</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" RespondentID \\\n",
"111 3291439708 \n",
"112 3291439404 \n",
"\n",
" Have you seen any of the 6 films in the Star Wars franchise? \\\n",
"111 True \n",
"112 True \n",
"\n",
" Do you consider yourself to be a fan of the Star Wars film franchise? \\\n",
"111 True \n",
"112 False \n",
"\n",
" seen_1 seen_2 seen_3 seen_4 seen_5 seen_6 ranking_ep1 ... \\\n",
"111 True True False False True True 1.0 ... \n",
"112 True False False False False False 1.0 ... \n",
"\n",
" Yoda Which character shot first? \\\n",
"111 Somewhat favorably Greedo \n",
"112 Somewhat favorably I don't understand this question \n",
"\n",
" Are you familiar with the Expanded Universe? \\\n",
"111 True \n",
"112 False \n",
"\n",
" Do you consider yourself to be a fan of the Expanded Universe? \\\n",
"111 No \n",
"112 NaN \n",
"\n",
" Do you consider yourself to be a fan of the Star Trek franchise? Gender \\\n",
"111 True Female \n",
"112 False Female \n",
"\n",
" Age Household Income Education Location (Census Region) \n",
"111 > 60 $50,000 - $99,999 Bachelor degree South Atlantic \n",
"112 > 60 $25,000 - $49,999 Graduate degree Pacific \n",
"\n",
"[2 rows x 38 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"males = star_wars[star_wars[\"Gender\"] == \"Male\"]\n",
"females = star_wars[star_wars[\"Gender\"] == \"Female\"]\n",
"\n",
"display(males.head(2))\n",
"display(females.head(2))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ranking_ep1 4.037825\n",
"ranking_ep2 4.224586\n",
"ranking_ep3 4.274882\n",
"ranking_ep4 2.997636\n",
"ranking_ep5 2.458629\n",
"ranking_ep6 3.002364\n",
"dtype: float64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"ranking_ep1 3.429293\n",
"ranking_ep2 3.954660\n",
"ranking_ep3 4.418136\n",
"ranking_ep4 3.544081\n",
"ranking_ep5 2.569270\n",
"ranking_ep6 3.078086\n",
"dtype: float64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"seen_1 361\n",
"seen_2 323\n",
"seen_3 317\n",
"seen_4 342\n",
"seen_5 392\n",
"seen_6 387\n",
"dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"seen_1 298\n",
"seen_2 237\n",
"seen_3 222\n",
"seen_4 255\n",
"seen_5 353\n",
"seen_6 338\n",
"dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"male_ranking_avgs=males[males.columns[9:15]].mean()\n",
"female_ranking_avgs = females[females.columns[9:15]].mean()\n",
"\n",
"male_tot_seen = males[males.columns[3:9]].sum()\n",
"female_tot_seen = females[females.columns[3:9]].sum()\n",
"\n",
"for mean_stats in [male_ranking_avgs, female_ranking_avgs, male_tot_seen, female_tot_seen]:\n",
" display(mean_stats)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ind = np.arange(6)\n",
"height = 0.35\n",
"offset = ind + height\n",
"\n",
"fig,ax = plt.subplots(1, 2, figsize=(8,4))\n",
"malebar = ax[0].barh(ind, male_ranking_avgs, color='#fe9b00', height=height)\n",
"femalebar = ax[0].barh(offset, female_ranking_avgs, color='#c94402', height=height)\n",
"ax[0].set_facecolor('#fff4d6')\n",
"ax[0].set_title('Movie Rankings by Gender')\n",
"ax[0].set_yticks(ind + height / 2)\n",
"ax[0].set_yticklabels(('Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5',\n",
" 'Episode 6'))\n",
"ax[0].set_xlabel('Average Ranking')\n",
"ax[0].legend((malebar[0], femalebar[0]), ('Men', 'Women'),bbox_to_anchor=[-0.3,1])\n",
"ax[0].invert_yaxis()\n",
"\n",
"male2bar = ax[1].barh(ind, male_tot_seen, color='#ff1947', height=height)\n",
"female2bar = ax[1].barh(offset, female_tot_seen, color='#9b052d', height=height)\n",
"ax[1].set_facecolor('#fff4d6')\n",
"ax[1].set_title('# of Respondents by Gender')\n",
"ax[1].set_yticks([])\n",
"ax[1].set_xlabel('Number of Respondents')\n",
"\n",
"ax[1].legend((male2bar[0], female2bar[0]), ('Men', 'Women'),bbox_to_anchor=[1,1])\n",
"ax[1].invert_yaxis()\n",
"\n",
"for a in ax:\n",
" for spine in ['top', 'right', 'bottom', 'left']:\n",
" a.spines[spine].set_visible(False)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Episode 5 was highest ranked among both men and women, but the ranking differences between the genders was negligible with a slight exception for Episode 4. Men tended to rank Episode 4 slightly higher than women. Overall more men have seen each of the 6 films than women survey respondents, with the two most popular movies among each gender being Episode 5 and 6."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.0rc1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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