Skip to content

Instantly share code, notes, and snippets.

@acstrahl

acstrahl/fandango.ipynb

Created March 10, 2025 22:08
Show Gist options
  • Save acstrahl/3c77e097a569ce8ae52e9bceea3548a1 to your computer and use it in GitHub Desktop.
Save acstrahl/3c77e097a569ce8ae52e9bceea3548a1 to your computer and use it in GitHub Desktop.
Download ZIP
Fandango Project Lab Walkthrough Solution Notebook
Display the source blob
Display the rendered blob
Raw
fandango.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fandango Movie Rating Inflation Investigation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"prior = pd.read_csv(\"fandango_score_comparison.csv\")\n",
"after = pd.read_csv(\"movie_ratings_16_17.csv\")"
]
},
{
"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>FILM</th>\n",
" <th>RottenTomatoes</th>\n",
" <th>RottenTomatoes_User</th>\n",
" <th>Metacritic</th>\n",
" <th>Metacritic_User</th>\n",
" <th>IMDB</th>\n",
" <th>Fandango_Stars</th>\n",
" <th>Fandango_Ratingvalue</th>\n",
" <th>RT_norm</th>\n",
" <th>RT_user_norm</th>\n",
" <th>...</th>\n",
" <th>IMDB_norm</th>\n",
" <th>RT_norm_round</th>\n",
" <th>RT_user_norm_round</th>\n",
" <th>Metacritic_norm_round</th>\n",
" <th>Metacritic_user_norm_round</th>\n",
" <th>IMDB_norm_round</th>\n",
" <th>Metacritic_user_vote_count</th>\n",
" <th>IMDB_user_vote_count</th>\n",
" <th>Fandango_votes</th>\n",
" <th>Fandango_Difference</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Avengers: Age of Ultron (2015)</td>\n",
" <td>74</td>\n",
" <td>86</td>\n",
" <td>66</td>\n",
" <td>7.1</td>\n",
" <td>7.8</td>\n",
" <td>5.0</td>\n",
" <td>4.5</td>\n",
" <td>3.70</td>\n",
" <td>4.3</td>\n",
" <td>...</td>\n",
" <td>3.90</td>\n",
" <td>3.5</td>\n",
" <td>4.5</td>\n",
" <td>3.5</td>\n",
" <td>3.5</td>\n",
" <td>4.0</td>\n",
" <td>1330</td>\n",
" <td>271107</td>\n",
" <td>14846</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Cinderella (2015)</td>\n",
" <td>85</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>7.5</td>\n",
" <td>7.1</td>\n",
" <td>5.0</td>\n",
" <td>4.5</td>\n",
" <td>4.25</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>3.55</td>\n",
" <td>4.5</td>\n",
" <td>4.0</td>\n",
" <td>3.5</td>\n",
" <td>4.0</td>\n",
" <td>3.5</td>\n",
" <td>249</td>\n",
" <td>65709</td>\n",
" <td>12640</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ant-Man (2015)</td>\n",
" <td>80</td>\n",
" <td>90</td>\n",
" <td>64</td>\n",
" <td>8.1</td>\n",
" <td>7.8</td>\n",
" <td>5.0</td>\n",
" <td>4.5</td>\n",
" <td>4.00</td>\n",
" <td>4.5</td>\n",
" <td>...</td>\n",
" <td>3.90</td>\n",
" <td>4.0</td>\n",
" <td>4.5</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>627</td>\n",
" <td>103660</td>\n",
" <td>12055</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Do You Believe? (2015)</td>\n",
" <td>18</td>\n",
" <td>84</td>\n",
" <td>22</td>\n",
" <td>4.7</td>\n",
" <td>5.4</td>\n",
" <td>5.0</td>\n",
" <td>4.5</td>\n",
" <td>0.90</td>\n",
" <td>4.2</td>\n",
" <td>...</td>\n",
" <td>2.70</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>2.5</td>\n",
" <td>2.5</td>\n",
" <td>31</td>\n",
" <td>3136</td>\n",
" <td>1793</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Hot Tub Time Machine 2 (2015)</td>\n",
" <td>14</td>\n",
" <td>28</td>\n",
" <td>29</td>\n",
" <td>3.4</td>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>3.0</td>\n",
" <td>0.70</td>\n",
" <td>1.4</td>\n",
" <td>...</td>\n",
" <td>2.55</td>\n",
" <td>0.5</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>2.5</td>\n",
" <td>88</td>\n",
" <td>19560</td>\n",
" <td>1021</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" FILM RottenTomatoes RottenTomatoes_User \\\n",
"0 Avengers: Age of Ultron (2015) 74 86 \n",
"1 Cinderella (2015) 85 80 \n",
"2 Ant-Man (2015) 80 90 \n",
"3 Do You Believe? (2015) 18 84 \n",
"4 Hot Tub Time Machine 2 (2015) 14 28 \n",
"\n",
" Metacritic Metacritic_User IMDB Fandango_Stars Fandango_Ratingvalue \\\n",
"0 66 7.1 7.8 5.0 4.5 \n",
"1 67 7.5 7.1 5.0 4.5 \n",
"2 64 8.1 7.8 5.0 4.5 \n",
"3 22 4.7 5.4 5.0 4.5 \n",
"4 29 3.4 5.1 3.5 3.0 \n",
"\n",
" RT_norm RT_user_norm ... IMDB_norm RT_norm_round RT_user_norm_round \\\n",
"0 3.70 4.3 ... 3.90 3.5 4.5 \n",
"1 4.25 4.0 ... 3.55 4.5 4.0 \n",
"2 4.00 4.5 ... 3.90 4.0 4.5 \n",
"3 0.90 4.2 ... 2.70 1.0 4.0 \n",
"4 0.70 1.4 ... 2.55 0.5 1.5 \n",
"\n",
" Metacritic_norm_round Metacritic_user_norm_round IMDB_norm_round \\\n",
"0 3.5 3.5 4.0 \n",
"1 3.5 4.0 3.5 \n",
"2 3.0 4.0 4.0 \n",
"3 1.0 2.5 2.5 \n",
"4 1.5 1.5 2.5 \n",
"\n",
" Metacritic_user_vote_count IMDB_user_vote_count Fandango_votes \\\n",
"0 1330 271107 14846 \n",
"1 249 65709 12640 \n",
"2 627 103660 12055 \n",
"3 31 3136 1793 \n",
"4 88 19560 1021 \n",
"\n",
" Fandango_Difference \n",
"0 0.5 \n",
"1 0.5 \n",
"2 0.5 \n",
"3 0.5 \n",
"4 0.5 \n",
"\n",
"[5 rows x 22 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prior.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 146 entries, 0 to 145\n",
"Data columns (total 22 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 FILM 146 non-null object \n",
" 1 RottenTomatoes 146 non-null int64 \n",
" 2 RottenTomatoes_User 146 non-null int64 \n",
" 3 Metacritic 146 non-null int64 \n",
" 4 Metacritic_User 146 non-null float64\n",
" 5 IMDB 146 non-null float64\n",
" 6 Fandango_Stars 146 non-null float64\n",
" 7 Fandango_Ratingvalue 146 non-null float64\n",
" 8 RT_norm 146 non-null float64\n",
" 9 RT_user_norm 146 non-null float64\n",
" 10 Metacritic_norm 146 non-null float64\n",
" 11 Metacritic_user_nom 146 non-null float64\n",
" 12 IMDB_norm 146 non-null float64\n",
" 13 RT_norm_round 146 non-null float64\n",
" 14 RT_user_norm_round 146 non-null float64\n",
" 15 Metacritic_norm_round 146 non-null float64\n",
" 16 Metacritic_user_norm_round 146 non-null float64\n",
" 17 IMDB_norm_round 146 non-null float64\n",
" 18 Metacritic_user_vote_count 146 non-null int64 \n",
" 19 IMDB_user_vote_count 146 non-null int64 \n",
" 20 Fandango_votes 146 non-null int64 \n",
" 21 Fandango_Difference 146 non-null float64\n",
"dtypes: float64(15), int64(6), object(1)\n",
"memory usage: 25.2+ KB\n"
]
}
],
"source": [
"prior.info()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>movie</th>\n",
" <th>year</th>\n",
" <th>metascore</th>\n",
" <th>imdb</th>\n",
" <th>tmeter</th>\n",
" <th>audience</th>\n",
" <th>fandango</th>\n",
" <th>n_metascore</th>\n",
" <th>n_imdb</th>\n",
" <th>n_tmeter</th>\n",
" <th>n_audience</th>\n",
" <th>nr_metascore</th>\n",
" <th>nr_imdb</th>\n",
" <th>nr_tmeter</th>\n",
" <th>nr_audience</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10 Cloverfield Lane</td>\n",
" <td>2016</td>\n",
" <td>76</td>\n",
" <td>7.2</td>\n",
" <td>90</td>\n",
" <td>79</td>\n",
" <td>3.5</td>\n",
" <td>3.80</td>\n",
" <td>3.60</td>\n",
" <td>4.50</td>\n",
" <td>3.95</td>\n",
" <td>4.0</td>\n",
" <td>3.5</td>\n",
" <td>4.5</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>13 Hours</td>\n",
" <td>2016</td>\n",
" <td>48</td>\n",
" <td>7.3</td>\n",
" <td>50</td>\n",
" <td>83</td>\n",
" <td>4.5</td>\n",
" <td>2.40</td>\n",
" <td>3.65</td>\n",
" <td>2.50</td>\n",
" <td>4.15</td>\n",
" <td>2.5</td>\n",
" <td>3.5</td>\n",
" <td>2.5</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A Cure for Wellness</td>\n",
" <td>2016</td>\n",
" <td>47</td>\n",
" <td>6.6</td>\n",
" <td>40</td>\n",
" <td>47</td>\n",
" <td>3.0</td>\n",
" <td>2.35</td>\n",
" <td>3.30</td>\n",
" <td>2.00</td>\n",
" <td>2.35</td>\n",
" <td>2.5</td>\n",
" <td>3.5</td>\n",
" <td>2.0</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A Dog's Purpose</td>\n",
" <td>2017</td>\n",
" <td>43</td>\n",
" <td>5.2</td>\n",
" <td>33</td>\n",
" <td>76</td>\n",
" <td>4.5</td>\n",
" <td>2.15</td>\n",
" <td>2.60</td>\n",
" <td>1.65</td>\n",
" <td>3.80</td>\n",
" <td>2.0</td>\n",
" <td>2.5</td>\n",
" <td>1.5</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A Hologram for the King</td>\n",
" <td>2016</td>\n",
" <td>58</td>\n",
" <td>6.1</td>\n",
" <td>70</td>\n",
" <td>57</td>\n",
" <td>3.0</td>\n",
" <td>2.90</td>\n",
" <td>3.05</td>\n",
" <td>3.50</td>\n",
" <td>2.85</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.5</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" movie year metascore imdb tmeter audience fandango \\\n",
"0 10 Cloverfield Lane 2016 76 7.2 90 79 3.5 \n",
"1 13 Hours 2016 48 7.3 50 83 4.5 \n",
"2 A Cure for Wellness 2016 47 6.6 40 47 3.0 \n",
"3 A Dog's Purpose 2017 43 5.2 33 76 4.5 \n",
"4 A Hologram for the King 2016 58 6.1 70 57 3.0 \n",
"\n",
" n_metascore n_imdb n_tmeter n_audience nr_metascore nr_imdb \\\n",
"0 3.80 3.60 4.50 3.95 4.0 3.5 \n",
"1 2.40 3.65 2.50 4.15 2.5 3.5 \n",
"2 2.35 3.30 2.00 2.35 2.5 3.5 \n",
"3 2.15 2.60 1.65 3.80 2.0 2.5 \n",
"4 2.90 3.05 3.50 2.85 3.0 3.0 \n",
"\n",
" nr_tmeter nr_audience \n",
"0 4.5 4.0 \n",
"1 2.5 4.0 \n",
"2 2.0 2.5 \n",
"3 1.5 4.0 \n",
"4 3.5 3.0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"after.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 214 entries, 0 to 213\n",
"Data columns (total 15 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 movie 214 non-null object \n",
" 1 year 214 non-null int64 \n",
" 2 metascore 214 non-null int64 \n",
" 3 imdb 214 non-null float64\n",
" 4 tmeter 214 non-null int64 \n",
" 5 audience 214 non-null int64 \n",
" 6 fandango 214 non-null float64\n",
" 7 n_metascore 214 non-null float64\n",
" 8 n_imdb 214 non-null float64\n",
" 9 n_tmeter 214 non-null float64\n",
" 10 n_audience 214 non-null float64\n",
" 11 nr_metascore 214 non-null float64\n",
" 12 nr_imdb 214 non-null float64\n",
" 13 nr_tmeter 214 non-null float64\n",
" 14 nr_audience 214 non-null float64\n",
"dtypes: float64(10), int64(4), object(1)\n",
"memory usage: 25.2+ KB\n"
]
}
],
"source": [
"after.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"prior = prior[['FILM', 'Fandango_Stars', 'Fandango_Ratingvalue', 'Fandango_votes', 'Fandango_Difference']].copy()\n",
"after = after[['movie', 'year', 'fandango']].copy()"
]
},
{
"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>FILM</th>\n",
" <th>Fandango_Stars</th>\n",
" <th>Fandango_Ratingvalue</th>\n",
" <th>Fandango_votes</th>\n",
" <th>Fandango_Difference</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Avengers: Age of Ultron (2015)</td>\n",
" <td>5.0</td>\n",
" <td>4.5</td>\n",
" <td>14846</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Cinderella (2015)</td>\n",
" <td>5.0</td>\n",
" <td>4.5</td>\n",
" <td>12640</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ant-Man (2015)</td>\n",
" <td>5.0</td>\n",
" <td>4.5</td>\n",
" <td>12055</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Do You Believe? (2015)</td>\n",
" <td>5.0</td>\n",
" <td>4.5</td>\n",
" <td>1793</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Hot Tub Time Machine 2 (2015)</td>\n",
" <td>3.5</td>\n",
" <td>3.0</td>\n",
" <td>1021</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" FILM Fandango_Stars Fandango_Ratingvalue \\\n",
"0 Avengers: Age of Ultron (2015) 5.0 4.5 \n",
"1 Cinderella (2015) 5.0 4.5 \n",
"2 Ant-Man (2015) 5.0 4.5 \n",
"3 Do You Believe? (2015) 5.0 4.5 \n",
"4 Hot Tub Time Machine 2 (2015) 3.5 3.0 \n",
"\n",
" Fandango_votes Fandango_Difference \n",
"0 14846 0.5 \n",
"1 12640 0.5 \n",
"2 12055 0.5 \n",
"3 1793 0.5 \n",
"4 1021 0.5 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prior.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>movie</th>\n",
" <th>year</th>\n",
" <th>fandango</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10 Cloverfield Lane</td>\n",
" <td>2016</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>13 Hours</td>\n",
" <td>2016</td>\n",
" <td>4.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A Cure for Wellness</td>\n",
" <td>2016</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A Dog's Purpose</td>\n",
" <td>2017</td>\n",
" <td>4.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A Hologram for the King</td>\n",
" <td>2016</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" movie year fandango\n",
"0 10 Cloverfield Lane 2016 3.5\n",
"1 13 Hours 2016 4.5\n",
"2 A Cure for Wellness 2016 3.0\n",
"3 A Dog's Purpose 2017 4.5\n",
"4 A Hologram for the King 2016 3.0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"after.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>FILM</th>\n",
" <th>Fandango_Stars</th>\n",
" <th>Fandango_Ratingvalue</th>\n",
" <th>Fandango_votes</th>\n",
" <th>Fandango_Difference</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Avengers: Age of Ultron (2015)</td>\n",
" <td>5.0</td>\n",
" <td>4.5</td>\n",
" <td>14846</td>\n",
" <td>0.5</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Cinderella (2015)</td>\n",
" <td>5.0</td>\n",
" <td>4.5</td>\n",
" <td>12640</td>\n",
" <td>0.5</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ant-Man (2015)</td>\n",
" <td>5.0</td>\n",
" <td>4.5</td>\n",
" <td>12055</td>\n",
" <td>0.5</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Do You Believe? (2015)</td>\n",
" <td>5.0</td>\n",
" <td>4.5</td>\n",
" <td>1793</td>\n",
" <td>0.5</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Hot Tub Time Machine 2 (2015)</td>\n",
" <td>3.5</td>\n",
" <td>3.0</td>\n",
" <td>1021</td>\n",
" <td>0.5</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" FILM Fandango_Stars Fandango_Ratingvalue \\\n",
"0 Avengers: Age of Ultron (2015) 5.0 4.5 \n",
"1 Cinderella (2015) 5.0 4.5 \n",
"2 Ant-Man (2015) 5.0 4.5 \n",
"3 Do You Believe? (2015) 5.0 4.5 \n",
"4 Hot Tub Time Machine 2 (2015) 3.5 3.0 \n",
"\n",
" Fandango_votes Fandango_Difference Year \n",
"0 14846 0.5 2015 \n",
"1 12640 0.5 2015 \n",
"2 12055 0.5 2015 \n",
"3 1793 0.5 2015 \n",
"4 1021 0.5 2015 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Adding 'year' column\n",
"prior['Year'] = prior['FILM'].str[-5:-1]\n",
"prior.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Year\n",
"2015 129\n",
"2014 17\n",
"Name: count, dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prior['Year'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"FILM object\n",
"Fandango_Stars float64\n",
"Fandango_Ratingvalue float64\n",
"Fandango_votes int64\n",
"Fandango_Difference float64\n",
"Year object\n",
"dtype: object"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prior.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Year\n",
"2015 129\n",
"Name: count, dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fandango_2015 = prior[prior['Year'] == '2015'].copy()\n",
"\n",
"fandango_2015['Year'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"year\n",
"2016 191\n",
"2017 23\n",
"Name: count, dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"after['year'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"movie object\n",
"year int64\n",
"fandango float64\n",
"dtype: object"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"after.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"year\n",
"2016 191\n",
"Name: count, dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fandango_2016 = after[after['year'] == 2016].copy()\n",
"fandango_2016['year'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from numpy import arange\n",
"\n",
"plt.style.use('tableau-colorblind10')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Solarize_Light2',\n",
" '_classic_test_patch',\n",
" '_mpl-gallery',\n",
" '_mpl-gallery-nogrid',\n",
" 'bmh',\n",
" 'classic',\n",
" 'dark_background',\n",
" 'fast',\n",
" 'fivethirtyeight',\n",
" 'ggplot',\n",
" 'grayscale',\n",
" 'seaborn-v0_8',\n",
" 'seaborn-v0_8-bright',\n",
" 'seaborn-v0_8-colorblind',\n",
" 'seaborn-v0_8-dark',\n",
" 'seaborn-v0_8-dark-palette',\n",
" 'seaborn-v0_8-darkgrid',\n",
" 'seaborn-v0_8-deep',\n",
" 'seaborn-v0_8-muted',\n",
" 'seaborn-v0_8-notebook',\n",
" 'seaborn-v0_8-paper',\n",
" 'seaborn-v0_8-pastel',\n",
" 'seaborn-v0_8-poster',\n",
" 'seaborn-v0_8-talk',\n",
" 'seaborn-v0_8-ticks',\n",
" 'seaborn-v0_8-white',\n",
" 'seaborn-v0_8-whitegrid',\n",
" 'tableau-colorblind10']"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.style.available"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 800x550 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fandango_2015['Fandango_Stars'].plot.kde(label='2015', legend=True, figsize=(8,5.5))\n",
"fandango_2016['fandango'].plot.kde(label='2016', legend=True)\n",
"\n",
"plt.title(\"Comparing distribution shapes for Fandango's ratings\\n(2015 vs 2016)\",\n",
" y=1.07) # the `y` parameter pads the title upward\n",
"plt.xlabel('Stars')\n",
"plt.xlim(0,5) # because ratings start at 0 and end at 5\n",
"plt.xticks(arange(0,5.1,.5))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2015\n",
"----------------\n",
"Fandango_Stars\n",
"3.0 8.527132\n",
"3.5 17.829457\n",
"4.0 28.682171\n",
"4.5 37.984496\n",
"5.0 6.976744\n",
"Name: proportion, dtype: float64\n",
"2016\n",
"----------------\n",
"fandango\n",
"2.5 3.141361\n",
"3.0 7.329843\n",
"3.5 24.083770\n",
"4.0 40.314136\n",
"4.5 24.607330\n",
"5.0 0.523560\n",
"Name: proportion, dtype: float64\n"
]
}
],
"source": [
"print('2015' + '\\n' + '-' * 16)\n",
"counts_2015 = fandango_2015['Fandango_Stars'].value_counts(normalize=True).sort_index() * 100\n",
"print(counts_2015)\n",
"\n",
"print('2016' + '\\n' + '-' * 16)\n",
"counts_2016 = fandango_2016['fandango'].value_counts(normalize=True).sort_index() * 100\n",
"print(counts_2016)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create a DataFrame for alignment\n",
"data = pd.DataFrame({'2015': counts_2015, '2016': counts_2016})\n",
"\n",
"# Plot side-by-side bars\n",
"data.plot(kind='bar', width=0.8) # Automatically aligns and separates bars\n",
"\n",
"# Add labels, title, and legend\n",
"plt.title(\"Comparing Fandango Ratings (2015 vs 2016)\")\n",
"plt.xlabel('Fandango Stars')\n",
"plt.ylabel('Proportion (%)')\n",
"plt.xticks(arange(0,5.1,.5), rotation=0) # Keep x-ticks horizontal\n",
"plt.legend(title='Year')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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>2015</th>\n",
" <th>2016</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4.085271</td>\n",
" <td>3.887435</td>\n",
" </tr>\n",
" <tr>\n",
" <th>median</th>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mode</th>\n",
" <td>4.500000</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2015 2016\n",
"mean 4.085271 3.887435\n",
"median 4.000000 4.000000\n",
"mode 4.500000 4.000000"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_2015 = fandango_2015['Fandango_Stars'].mean()\n",
"mean_2016 = fandango_2016['fandango'].mean()\n",
"\n",
"median_2015 = fandango_2015['Fandango_Stars'].median()\n",
"median_2016 = fandango_2016['fandango'].median()\n",
"\n",
"mode_2015 = fandango_2015['Fandango_Stars'].mode()[0] # the output of Series.mode() is a bit uncommon\n",
"mode_2016 = fandango_2016['fandango'].mode()[0]\n",
"\n",
"summary = pd.DataFrame()\n",
"summary['2015'] = [mean_2015, median_2015, mode_2015]\n",
"summary['2016'] = [mean_2016, median_2016, mode_2016]\n",
"summary.index = ['mean', 'median', 'mode']\n",
"summary"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.style.use('fivethirtyeight')\n",
"summary['2015'].plot.bar(color = '#0066FF', align = 'center', label = '2015', width = .25)\n",
"summary['2016'].plot.bar(color = '#CC0000', align = 'edge', label = '2016', width = .25,\n",
" rot = 0, figsize = (8,5))\n",
"\n",
"plt.title('Comparing summary statistics: 2015 vs 2016', y = 1.07)\n",
"plt.ylim(0,5.5)\n",
"plt.yticks(arange(0,5.1,.5))\n",
"plt.ylabel('Stars')\n",
"plt.legend(framealpha = 0, loc = 'upper center')\n",
"plt.show()"
]
},
{
"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
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment