{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Is Fandango Still Inflating Ratings?\n", "\n", "In October 2015, Walt Hickey from FiveThirtyEight published [a popular article](https://fivethirtyeight.com/features/fandango-movies-ratings/) where he presented strong evidence that suggests that Fandango's movie rating system was biased and dishonest. In this project, we'll analyze more recent movie ratings data to determine whether there has been any change in Fandango's rating system following Hickey's analysis.\n", "\n", "# Understanding the Data\n", "\n", "We'll work with two samples of movie ratings: the data in one sample was collected _prior_ to Hickey's analysis, while the other sample was collected _after_. Let's start by reading in the two samples (which are stored as CSV files) and exploring their structure." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FILMRottenTomatoesRottenTomatoes_UserMetacriticMetacritic_UserIMDBFandango_StarsFandango_RatingvalueRT_normRT_user_normMetacritic_normMetacritic_user_nomIMDB_normRT_norm_roundRT_user_norm_roundMetacritic_norm_roundMetacritic_user_norm_roundIMDB_norm_roundMetacritic_user_vote_countIMDB_user_vote_countFandango_votesFandango_Difference
0Avengers: Age of Ultron (2015)7486667.17.85.04.53.704.33.303.553.903.54.53.53.54.01330271107148460.5
1Cinderella (2015)8580677.57.15.04.54.254.03.353.753.554.54.03.54.03.524965709126400.5
2Ant-Man (2015)8090648.17.85.04.54.004.53.204.053.904.04.53.04.04.0627103660120550.5
\n", "
" ], "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", "\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", "\n", " RT_norm RT_user_norm Metacritic_norm Metacritic_user_nom IMDB_norm \\\n", "0 3.70 4.3 3.30 3.55 3.90 \n", "1 4.25 4.0 3.35 3.75 3.55 \n", "2 4.00 4.5 3.20 4.05 3.90 \n", "\n", " RT_norm_round RT_user_norm_round Metacritic_norm_round \\\n", "0 3.5 4.5 3.5 \n", "1 4.5 4.0 3.5 \n", "2 4.0 4.5 3.0 \n", "\n", " Metacritic_user_norm_round IMDB_norm_round Metacritic_user_vote_count \\\n", "0 3.5 4.0 1330 \n", "1 4.0 3.5 249 \n", "2 4.0 4.0 627 \n", "\n", " IMDB_user_vote_count Fandango_votes Fandango_Difference \n", "0 271107 14846 0.5 \n", "1 65709 12640 0.5 \n", "2 103660 12055 0.5 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "pd.options.display.max_columns = 100 # Avoid having displayed truncated output\n", "\n", "previous = pd.read_csv('fandango_score_comparison.csv')\n", "after = pd.read_csv('movie_ratings_16_17.csv')\n", "\n", "previous.head(3)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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movieyearmetascoreimdbtmeteraudiencefandangon_metascoren_imdbn_tmetern_audiencenr_metascorenr_imdbnr_tmeternr_audience
010 Cloverfield Lane2016767.290793.53.803.604.53.954.03.54.54.0
113 Hours2016487.350834.52.403.652.54.152.53.52.54.0
2A Cure for Wellness2016476.640473.02.353.302.02.352.53.52.02.5
\n", "
" ], "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", "\n", " n_metascore n_imdb n_tmeter n_audience nr_metascore nr_imdb \\\n", "0 3.80 3.60 4.5 3.95 4.0 3.5 \n", "1 2.40 3.65 2.5 4.15 2.5 3.5 \n", "2 2.35 3.30 2.0 2.35 2.5 3.5 \n", "\n", " nr_tmeter nr_audience \n", "0 4.5 4.0 \n", "1 2.5 4.0 \n", "2 2.0 2.5 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "after.head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we isolate only the columns that provide information about Fandango to make the relevant data more readily available for later use. We'll make copies [to avoid any `SettingWithCopyWarning`](https://www.dataquest.io/blog/settingwithcopywarning/) later on. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FILMFandango_StarsFandango_RatingvalueFandango_votesFandango_Difference
0Avengers: Age of Ultron (2015)5.04.5148460.5
1Cinderella (2015)5.04.5126400.5
2Ant-Man (2015)5.04.5120550.5
\n", "
" ], "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", "\n", " Fandango_votes Fandango_Difference \n", "0 14846 0.5 \n", "1 12640 0.5 \n", "2 12055 0.5 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fandango_previous = previous[['FILM', 'Fandango_Stars', 'Fandango_Ratingvalue', 'Fandango_votes',\n", " 'Fandango_Difference']].copy()\n", "fandango_after = after[['movie', 'year', 'fandango']].copy()\n", "\n", "fandango_previous.head(3)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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movieyearfandango
010 Cloverfield Lane20163.5
113 Hours20164.5
2A Cure for Wellness20163.0
\n", "
" ], "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" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fandango_after.head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our goal is to determine whether there has been any change in Fandango's rating system following Hickey's analysis. The population of interest for our analysis comprises all the movie ratings stored on Fandango's website, regardless of the releasing year.\n", "\n", "Because we want to determine if the parameters of this population changed after Hickey's analysis, we're interested in sampling the population at two different periods in time — before and after Hickey's analysis — so we can compare the two states.\n", "\n", "The data we're working with was sampled at the moments we want: one sample was taken prior to the analysis, and the other was taken after the analysis. We want to describe the population, so we need to make sure that the samples are representative; otherwise, we should expect a large sampling error and, ultimately, inaccurate conclusions.\n", "\n", "From Hickey's article and from the `README.md` of [the data set's repository](https://github.com/fivethirtyeight/data/tree/master/fandango), we can see that he used the following sampling criteria:\n", "\n", "* The movie must have had at least 30 fan ratings on Fandango's website at the time of sampling (Aug. 24, 2015).\n", "* The movie must have had tickets on sale in 2015.\n", "\n", "The sampling was clearly not random because not every movie had the same chance to be included in the sample — some movies didn't have a chance at all (like those having under 30 fan ratings or those without tickets on sale in 2015). It's questionable whether this sample is representative of the entire population we're interested in describing. It seems more likely that it isn't, mostly because this sample is subject to *temporal trends* (e.g., movies in 2015 might have been outstandingly good or bad compared to other years).\n", "\n", "The sampling conditions for our other sample were the following (as it can be read in the `README.md` of [the data set's repository](https://github.com/mircealex/Movie_ratings_2016_17)):\n", "\n", "* The movie must have been released in 2016 or later.\n", "* The movie must have had a considerable number of votes and reviews (it's unclear how many from the `README.md` or from the data).\n", "\n", "This second sample is also subject to temporal trends, and it's unlikely to be representative of our population of interest.\n", "\n", "Both these authors had certain research questions in mind when they sampled the data, and they used a set of criteria to get a sample that would fit their questions. Their sampling method is called [**purposive sampling**](https://youtu.be/CdK7N_kTzHI) (or judgmental/selective/subjective sampling). While these samples were good enough for their research, they don't seem too useful for us.\n", "\n", "# Changing the Goal of our Analysis\n", "\n", "At this point, we can either collect new data or change our the goal of our analysis. We choose the latter and place some limitations on our initial goal.\n", "\n", "Instead of trying to determine whether there has been any change in Fandango's rating system following Hickey's analysis, our new goal is to determine whether there's any difference between Fandango's ratings for popular movies in 2015 and Fandango's ratings for popular movies in 2016. This new goal should also be a fairly good proxy for our initial goal.\n", "\n", "# Isolating the Samples We Need\n", "\n", "With this new research goal, we have two populations of interest:\n", "\n", "1. All Fandango's ratings for popular movies released in 2015.\n", "2. All Fandango's ratings for popular movies released in 2016.\n", "\n", "We need to be clear about what counts as popular movies. We'll use Hickey's benchmark of 30 fan ratings and count a movie as popular only if it has 30 fan ratings or more on Fandango's website.\n", "\n", "Although one of the sampling criteria in our second sample is movie popularity, the sample doesn't provide information about the number of fan ratings. We should be skeptical once more and ask whether this sample is truly representative and contains popular movies (movies with over 30 fan ratings).\n", "\n", "One quick way to check the representativity of this sample is to randomly sample 10 movies from it and then check the number of fan ratings ourselves on Fandango's website. Ideally, at least 8 out of the 10 movies have 30 fan ratings or more." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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movieyearfandango
108Mechanic: Resurrection20164.0
206Warcraft20164.0
106Max Steel20163.5
107Me Before You20164.5
51Fantastic Beasts and Where to Find Them20164.5
33Cell20163.0
59Genius20163.5
152Sully20164.5
4A Hologram for the King20163.0
31Captain America: Civil War20164.5
\n", "
" ], "text/plain": [ " movie year fandango\n", "108 Mechanic: Resurrection 2016 4.0\n", "206 Warcraft 2016 4.0\n", "106 Max Steel 2016 3.5\n", "107 Me Before You 2016 4.5\n", "51 Fantastic Beasts and Where to Find Them 2016 4.5\n", "33 Cell 2016 3.0\n", "59 Genius 2016 3.5\n", "152 Sully 2016 4.5\n", "4 A Hologram for the King 2016 3.0\n", "31 Captain America: Civil War 2016 4.5" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fandango_after.sample(10, random_state = 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above we used a value of 1 as the random seed. This is good practice because it suggests that we weren't trying out various random seeds just to get a favorable sample.\n", "\n", "As of April 2018, these are the fan ratings we found:\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MovieFan ratings
Mechanic: Resurrection2247
Warcraft7271
Max Steel493
Me Before You5263
Fantastic Beasts and Where to Find Them13400
Cell17
Genius127
Sully11877
A Hologram for the King\t500
Captain America: Civil War35057
\n", "\n", "\n", "90% of the movies in our sample are popular. This is enough for us to move forward with a bit more confidence.\n", "\n", "Let's also double-check the other dataset for popular movies. The documentation states clearly that there are only movies with at least 30 fan ratings, but it should take only a couple of seconds to double-check here." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(fandango_previous['Fandango_votes'] < 30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you explore the two datasets, you'll notice that there are movies with a release year different than 2015 or 2016. For our purposes, we'll need to isolate only the movies released in 2015 and 2016.\n", "\n", "Let's start with Hickey's dataset and isolate only the movies released in 2015. There's no special column for the releasing year, but we should be able to extract it from the strings in the `FILM` column." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FILMFandango_StarsFandango_RatingvalueFandango_votesFandango_Difference
0Avengers: Age of Ultron (2015)5.04.5148460.5
1Cinderella (2015)5.04.5126400.5
\n", "
" ], "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", "\n", " Fandango_votes Fandango_Difference \n", "0 14846 0.5 \n", "1 12640 0.5 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fandango_previous.head(2)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FILMFandango_StarsFandango_RatingvalueFandango_votesFandango_DifferenceYear
0Avengers: Age of Ultron (2015)5.04.5148460.52015
1Cinderella (2015)5.04.5126400.52015
\n", "
" ], "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", "\n", " Fandango_votes Fandango_Difference Year \n", "0 14846 0.5 2015 \n", "1 12640 0.5 2015 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fandango_previous['Year'] = fandango_previous['FILM'].str[-5:-1]\n", "fandango_previous.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's examine the frequency distribution for the `Year` column and then isolate the movies released in 2015." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2015 129\n", "2014 17\n", "Name: Year, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fandango_previous['Year'].value_counts()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2015 129\n", "Name: Year, dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fandango_2015 = fandango_previous[fandango_previous['Year'] == '2015'].copy()\n", "fandango_2015['Year'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! Now, let's isolate the movies in the other dataset." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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movieyearfandango
010 Cloverfield Lane20163.5
113 Hours20164.5
\n", "
" ], "text/plain": [ " movie year fandango\n", "0 10 Cloverfield Lane 2016 3.5\n", "1 13 Hours 2016 4.5" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fandango_after.head(2)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2016 191\n", "2017 23\n", "Name: year, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fandango_after['year'].value_counts()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2016 191\n", "Name: year, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fandango_2016 = fandango_after[fandango_after['year'] == 2016].copy()\n", "fandango_2016['year'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing Distribution Shapes for 2015 and 2016\n", "\n", "Our goal is to determine whether or not there is any difference between Fandango's ratings for popular movies in 2015 and Fandango's ratings for popular movies in 2016. One way to do this is to analyze and compare the distributions of movie ratings for the two samples.\n", "\n", "We'll start with comparing the shape of the two distributions using kernel density plots. We'll use [the FiveThirtyEight style](https://www.dataquest.io/blog/making-538-plots/) for the plots." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from numpy import arange\n", "%matplotlib inline\n", "plt.style.use('fivethirtyeight')\n", "\n", "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": "markdown", "metadata": {}, "source": [ "Two aspects are interesting in the figure above:\n", "* Both distributions are strongly left-skewed.\n", "* The 2016 distribution is slightly shifted to the left relative to the 2015 distribution.\n", "\n", "The left skew suggests that movies on Fandango are given mostly high and very high fan ratings. Coupled with the fact that Fandango sells tickets, the high ratings are a bit dubious. It'd be really interesting to investigate this further — ideally in a separate project, since this is irrelevant for the current goal of our analysis.\n", "\n", "The slight left shift of the 2016 distribution is very interesting for our analysis. It shows that ratings were slightly lower in 2016 compared to 2015. This suggests that there was a difference indeed between Fandango's ratings for popular movies in 2015 and Fandango's ratings for popular movies in 2016. We can also see the direction of the difference: the ratings in 2016 were slightly lower compared to 2015.\n", "\n", "\n", "# Comparing Relative Frequencies\n", "\n", "It seems we're following a good thread so far, but we need to analyze more granular information. Let's examine the frequency tables of the two distributions to analyze some numbers. Because the datasets have different numbers of movies, we normalize the tables and show percentages instead." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2015\n", "----------------\n" ] }, { "data": { "text/plain": [ "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: Fandango_Stars, dtype: float64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print('2015' + '\\n' + '-' * 16) # To help us distinguish between the two tables immediately and\n", " # avoid silly mistakes as we read to and fro\n", "fandango_2015['Fandango_Stars'].value_counts(normalize = True).sort_index() * 100" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2016\n", "----------------\n" ] }, { "data": { "text/plain": [ "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: fandango, dtype: float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print('2016' + '\\n' + '-' * 16)\n", "fandango_2016['fandango'].value_counts(normalize = True).sort_index() * 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In 2016, very high ratings (4.5 and 5 stars) had significantly lower percentages compared to 2015. In 2016, under 1% of the movies had a perfect rating of 5 stars, compared to 2015 when the percentage was close to 7%. Ratings of 4.5 were also more popular in 2015 — there were approximately 13% more movies rated with a 4.5 in 2015 compared to 2016.\n", "\n", "The minimum rating is also lower in 2016 — 2.5 instead of 3 stars, the minimum of 2015. There clearly is a difference between the two frequency distributions.\n", "\n", "For some other ratings, the percentage went up in 2016. There was a greater percentage of movies in 2016 that received 3.5 and 4 stars, compared to 2015. 3.5 and 4.0 are high ratings, and this challenges the direction of the change we saw on the kernel density plots.\n", "\n", "# Determining the Direction of the Change\n", "\n", "Let's take a couple of summary metrics for more precise information about the direction of the change. In what follows, we'll compute the mean, the median, and the mode for both distributions, and then we'll use a bar graph to plot the values." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mode4.5000004.000000
\n", "
" ], "text/plain": [ " 2015 2016\n", "mean 4.085271 3.887435\n", "median 4.000000 4.000000\n", "mode 4.500000 4.000000" ] }, "execution_count": 17, "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": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "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": "markdown", "metadata": {}, "source": [ "The mean rating was lower in 2016 with approximately 0.2. This means a drop of almost 5% relative to the mean rating in 2015." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.048426835689519929" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(summary.loc['mean'][0] - summary.loc['mean'][1]) / summary.loc['mean'][0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While the median is the same for both distributions, the mode is lower in 2016 by 0.5. Coupled with what we saw for the mean, the direction of the change we saw on the kernel density plot is confirmed: on average, popular movies released in 2016 were rated slightly lower than popular movies released in 2015.\n", "\n", "# Conclusion\n", "\n", "Our analysis showed that there is indeed a slight difference between Fandango's ratings for popular movies in 2015 and Fandango's ratings for popular movies in 2016. We also determined that, on average, popular movies released in 2016 were rated lower on Fandango than popular movies released in 2015.\n", "\n", "We cannot be completely sure what caused the change, but the chances are very high that it was caused by Fandango fixing the biased rating system following Hickey's analysis." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }