{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IMDB | IST 652 FINAL PROJECT | ALI HO & KENDRA OSBURN" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# =======================================================\n", "# PART 2: A - SCRAPING & SCRIPTING \n", "# =======================================================" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## THE LIBRARIES" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "from bs4 import BeautifulSoup\n", "import json\n", "import csv\n", "import pandas as pd\n", "from urllib.parse import quote\n", "import requests\n", "import time\n", "import re" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## THE DATA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Kaggle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Data from kaggle.com_" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6820" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kaggle = pd.read_csv(\"movies.csv\", encoding = \"ISO-8859-1\")\n", "len(kaggle)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3726" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kaggle_cleaned = pd.read_csv(\"working_movies_usa.csv\", encoding = \"ISO-8859-1\" )\n", "len(kaggle_cleaned)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### IMDB" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Data from scraping imdb.com (see below for details)_" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7299" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "imdb_707 = pd.read_csv(\"movies_IST707.csv\", encoding = \"ISO-8859-1\" )\n", "len(imdb_707)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9678" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "imdb_ids = pd.read_csv(\"ids_from_imdb.csv\", encoding = \"ISO-8859-1\" )\n", "len(imdb_ids)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9684" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "imdb_scores = pd.read_csv(\"V2_IMDB_score_data.csv\", encoding = \"ISO-8859-1\")\n", "len(imdb_scores)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### TMDB" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Data from hitting the TMDB api (see below for details)_" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "79920" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# First attempt\n", "tmdb_movies = pd.read_csv(\"tmdb_movies_csv.csv\", encoding = \"ISO-8859-1\")\n", "len(tmdb_movies)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20000" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Taking every actor in our kaggle dataset and getting their movie credits using TMDB's api\n", "tmdb_actors = pd.read_csv(\"tmdb_20k.csv\", encoding = \"ISO-8859-1\")\n", "len(tmdb_actors)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3532" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Removing things without budget, revenue, production studio or genre\n", "tmdb_actors_cleaned = pd.read_csv(\"tmdb_20k_cleaned.csv\", encoding = \"ISO-8859-1\")\n", "len(tmdb_actors_cleaned)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9678" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmdb_v2 = pd.read_csv(\"tmdb_from_imdb_v2.csv\", encoding = \"ISO-8859-1\")\n", "len(tmdb_v2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The-Numbers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Data from scraping the-numbers.com (see below for details)_" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5825" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tn_1 = pd.read_csv(\"V2_TN_budget_data_and_url.csv\", encoding = \"ISO-8859-1\")\n", "len(tn_1)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1987" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tn_2 = pd.read_csv(\"V2_TN_reports_dates.csv\", encoding = \"ISO-8859-1\")\n", "len(tn_2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## THE SCRAPING & API CALLING\n", "### IMDB" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# ==============================================================\n", "# GETTING THE IMDB ID & SCORE\n", "# ==============================================================\n", "\n", "def get_info_from_movies(movies):\n", " ids_for_movies_in_year = []\n", " for i, movie in enumerate(movies):\n", " link_with_id = movie.find('a', {'href': re.compile('/title/tt')})\n", " imdb_id = link_with_id.attrs['href'].split('/')[2]\n", " clean = \"\".join(line.strip() for line in movie.text.split(\"\\n\"))\n", "# valiant regex attempt \n", "# rating = re.compile('\\)(.*)')\n", "# name = re.compile('\\..*\\ ')\n", "# date = re.compile('(\\d{3}).')\n", " title_rating_string = clean.split('0Rate')[0]\n", " rating = title_rating_string.split(')')[1]\n", " name = title_rating_string.split('.')[1].split('(')[0]\n", " date = title_rating_string.split('(')[1].split(')')[0]\n", " movie_dict = {\n", " 'imdb_id': imdb_id,\n", " 'name': name,\n", " 'imdb_rating': rating,\n", " 'date': date\n", " }\n", " ids_for_movies_in_year.append(movie_dict)\n", " return(ids_for_movies_in_year)\n", " \n", "def get_imdb_html(year, urlending):\n", " url = ('https://www.imdb.com/search/title/?title_type=feature&boxoffice_gross_us=1,&release_date='+str(year)+'-01-01,'+str(year)+'-12-31&countries=us&view=simple&count=250'+urlending)\n", " headers = {'Accept-Language': 'en-US'}\n", " movies_html = requests.get(url.format(), headers=headers).content\n", " soup = BeautifulSoup(movies_html, 'html.parser')\n", " soup_main = soup.find(\"div\", {\"id\": \"main\"})\n", " movies_list = soup_main.find('div', class_=\"lister list detail sub-list\")\n", " movies = soup_main.find_all('div', class_=\"lister-item mode-simple\")\n", " return movies\n", "\n", "\n", "def get_imdb_scores(year, urlending):\n", " movies = get_imdb_html(year, urlending)\n", " return get_info_from_movies(movies)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def run_imdb_scores_script():\n", " all_the_ids = []\n", " for year in range(1970, 2020):\n", " all_the_ids += get_imdb_scores(year, '')\n", " all_the_ids += get_imdb_scores(year, '&start=251')\n", " all_the_ids_df = pd.DataFrame(all_the_ids)\n", " all_the_ids_df.to_csv('imdb_ids.csv')\n", " \n", "# save a small (2018) subset \n", "# all_the_2018_ids_df = pd.DataFrame(all_the_ids[48])\n", "# all_the_2018_ids_df.to_csv\n", "\n", "# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n", "# # UNCOMMENT TO RUN <3 \n", "# run_imdb_scores_script()\n", "# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### TMDB" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# ==============================================================\n", "# GETTING THE SUPPLIMENTAL INFO FROM TMBD API\n", "# ==============================================================\n", "\n", "# NOTE: This is a fun time capsule of how we wrote these files\n", "# at the start of this project!! We'd like to think we've \n", "# cleaned things up a bit!!\n", "\n", "def write_csv(data):\n", " df = pd.DataFrame(data)\n", " df.to_csv('2018_movies.csv', index=False)\n", "\n", "headers = {'Accept-Language': 'en-US'}\n", "payload = \"{}\"\n", "\n", "api_key = open(\"tmdb_api_key.txt\")\n", "api_key = api_key.read()\n", "\n", "\n", "def get_tmdb_info(imdb_id_file):\n", " all_movie_data = []\n", " with open(imdb_id_file, encoding='utf-8') as csvfile:\n", " movies = csv.reader(csvfile)\n", " for movie in movies:\n", " try:\n", " url = \"https://api.themoviedb.org/3/movie/\"\n", " thing_looking_for = movie[1]\n", " my_api_key = \"?api_key=\" + api_key\n", " full_url = url + thing_looking_for + my_api_key\n", " res = requests.get(full_url, payload, headers=headers)\n", " data = res.content.decode('UTF-8')\n", " jdata = json.loads(data)\n", " try:\n", " title = jdata['title']\n", " budget = jdata['budget']\n", " genres = jdata['genres']\n", " production_companies = jdata['production_companies']\n", " release_date = jdata['release_date']\n", " revenue = jdata['revenue']\n", " profit = revenue - budget\n", " popularity = jdata['popularity']\n", " vote_average = jdata['vote_average']\n", " vote_count = jdata['vote_count']\n", " except KeyError:\n", " title = 'NA'\n", " budget = 'NA'\n", " genres = 'NA'\n", " production_companies = 'NA'\n", " release_date = 'NA'\n", " revenue = 'NA'\n", " profit = 'NA'\n", " popularity = 'NA'\n", " vote_average = 'NA'\n", " vote_count = 'NA'\n", "\n", " movie_data = {\n", " 'release_date': release_date,\n", " 'title': title,\n", " 'budget': budget,\n", " 'genres': genres,\n", " 'production_companies': production_companies,\n", " 'revenue': revenue,\n", " 'profit': profit,\n", " 'popularity': popularity,\n", " 'vote_average': vote_average,\n", " 'vote_count': vote_count\n", " }\n", " all_movie_data.append(movie_data)\n", " except UnicodeDecodeError:\n", " director_data = {}\n", " all_movie_data_df = pd.DataFrame(all_movie_data)\n", " print(all_movie_data_df)\n", " write_csv(all_movie_data)\n", " \n", "# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n", "# # UNCOMMENT TO RUN <3 \n", "# get_tmdb_info('imdb_ids_2018.csv')\n", "# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The-Numbers\n", "##### BUDGET CSV" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "# 1. get the page (get_tn_data)\n", "# 2. get the soup (get_soup)\n", "# 3. get the data from the soup (get_data_from_soup)\n", "\n", "def get_data_from_soup(soup_data):\n", " all_movies = []\n", " for data in soup_data[1:]:\n", " data_array = data.text.split('\\n')\n", " movie_data = {\n", " 'num': data_array[0],\n", " 'release_date': data_array[1],\n", " 'name': data_array[2],\n", " 'production_budget': data_array[3],\n", " 'domestic_gross': data_array[4],\n", " 'worldwide_gross': data_array[5]\n", " }\n", " all_movies.append(movie_data)\n", " return all_movies\n", "\n", "def get_soup(num):\n", " url = ('https://www.the-numbers.com/movie/budgets/all'+num)\n", " headers = {'Accept-Language': 'en-US'}\n", " movies_html = requests.get(url.format(), headers=headers).content\n", " soup = BeautifulSoup(movies_html, 'html.parser')\n", " soup_data = soup.find_all(\"tr\")\n", " return soup_data\n", "\n", "def get_tn_data(num):\n", " tn_soup = get_soup(num)\n", " return get_data_from_soup(tn_soup)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 1. create array to house all data\n", "# 2. quick workaround for our first page\n", "# 3. iterate through the the-numbers url\n", "# 4. save to df, save to csv\n", "\n", "def run_TN_script():\n", " all_pages = []\n", " all_pages += get_tn_data('')\n", " for i in range(1,59):\n", " results = get_tn_data('/'+ str(i) + '01')\n", " all_pages += results\n", " all_pages_df = pd.DataFrame(all_pages)\n", " all_pages_df.to_csv('TN_budget_data.csv')\n", "\n", "# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n", "# # UNCOMMENT TO RUN <3 \n", "# run_TN_script()\n", "# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### REPORTS CSV" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def format_data(data_array):\n", " movie_data = {\n", " 'Released': data_array[2],\n", " 'ReleasedWorldwide': data_array[3],\n", " 'Released_2': data_array[4],\n", " 'ReleasedWorldwide_2': data_array[5],\n", " 'Title': data_array[6],\n", " 'TheatricalDistributor': data_array[7],\n", " 'Genre': data_array[8],\n", " 'Source': data_array[9],\n", " 'ProductionMethod': data_array[10],\n", " 'CreativeType': data_array[11],\n", " 'ProductionBudget': data_array[12],\n", " 'OpeningWeekendTheaters': data_array[13],\n", " 'MaximumTheaters': data_array[14],\n", " 'TheatricalEngagements': data_array[15],\n", " 'OpeningWeekendRevenue': data_array[16],\n", " 'DomesticBoxOffice': data_array[17],\n", " 'Infl.Adj.Dom.BoxOffice': data_array[18],\n", " 'InternationalBoxOffice': data_array[19],\n", " 'WorldwideBoxOffice': data_array[20]\n", " }\n", " return movie_data\n", "\n", "def get_report(year):\n", " url = ('https://www.the-numbers.com/movies/report/All/All/All/All/All/All/All/All/All/.1/None/'+str(year)+'/'+ str(year + 1)+'/None/None/None/None/None/None?show-release-date=On&view-order-by=domestic-box-office&show-release-year=On&view-order-direction=desc&show-production-budget=On&show-opening-weekend-theaters=On&show-domestic-box-office=On&show-maximum-theaters=On&show-inflation-adjusted-domestic-box-office=On&show-theatrical-engagements=On&show-international-box-office=On&show-opening-weekend-revenue=On&show-worldwide-box-office=On&show-worldwide-release-date=On&show-worldwide-release-year=On&show-theatrical-distributor=On&show-genre=On&show-source=On&show-production-method=On&show-creative-type=On')\n", " headers = {'Accept-Language': 'en-US'}\n", " movies_html = requests.get(url.format(), headers=headers).content\n", " soup = BeautifulSoup(movies_html, 'html.parser')\n", " soup_data = soup.find_all(\"tr\")\n", " all_movies = []\n", " for data in soup_data[1:]:\n", " data_array = data.text.split('\\n')\n", " try:\n", " url = data.find_all('a')[0]\n", " cast_data = get_cast(url)\n", "# Saving summary data for V2\n", "# summary_data = get_summary(url)\n", " data_object = format_data(data_array)\n", " data_object.update(cast_data)\n", " all_movies.append(data_object)\n", " except:\n", " print('no report')\n", " return all_movies\n", " \n", "def get_summary(url):\n", " url = 'https://www.the-numbers.com' + url.attrs['href']\n", " headers = {'Accept-Language': 'en-US'}\n", " movies_html = requests.get(url.format(), headers=headers).content\n", " soup = BeautifulSoup(movies_html, 'html.parser')\n", " soup_main = soup.find(\"div\", {\"id\": \"summary\"})\n", " return \"coming soon\"\n", "\n", "def get_cast(url):\n", " url = 'https://www.the-numbers.com' + url.attrs['href'].split(\"#\")[0]+\"#tab=cast-and-crew\"\n", " headers = {'Accept-Language': 'en-US'}\n", " movies_html = requests.get(url.format(), headers=headers).content\n", " soup = BeautifulSoup(movies_html, 'html.parser')\n", " soup_main = soup.find(\"div\", {\"id\": \"cast-and-crew\"})\n", " soup_data = soup_main.find_all(\"div\", class_=\"cast_new\")\n", " cast_data = {}\n", " leads = []\n", " supporting = []\n", " production = []\n", " for data in soup_data:\n", " if 'Lead' in data.h1.text:\n", " cast = data.find_all(\"tr\")\n", " for castmember in cast:\n", " leads.append(castmember.text.strip().split('\\n')[0])\n", " if 'Supporting' in data.h1.text:\n", " cast = data.find_all(\"tr\")\n", " for castmember in cast:\n", " supporting.append(castmember.text.strip().split('\\n')[0])\n", " if 'Production' in data.h1.text:\n", " cast = data.find_all(\"tr\")\n", " for castmember in cast:\n", " production.append({castmember.text.strip().split('\\n')[2]: castmember.text.strip().split('\\n')[0]})\n", " cast_data.update({'star': leads[0]})\n", " cast_data.update({'leads': leads})\n", " cast_data.update({'supporting': supporting})\n", " cast_data.update({'production': production})\n", " return cast_data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def run_TN_reports_script():\n", " all_pages = []\n", " for year in range(2000,2020):\n", " results = get_report(year)\n", " all_pages += results\n", " pd.DataFrame(all_pages).to_csv('TN_reports_data.csv')\n", "\n", "# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n", "# # UNCOMMENT TO RUN <3 \n", "# run_TN_reports_script()\n", "# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## THE CLEANING & PREP\n", "##### Baby's First Lambda" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def clean_merged_file(merged_file):\n", " big_movies = pd.read_csv(merged_file, encoding = \"ISO-8859-1\")\n", " big_movies_clean = pd.DataFrame({ \n", " \"id\": big_movies['id'],\n", " \"imdb_id\": big_movies['imdb_id'],\n", " \"name\": big_movies['name'], \n", " \"budget\": big_movies['budget'],\n", " \"revenue\": big_movies['revenue'],\n", " \"runtime\": big_movies['runtime'],\n", " \"score\": big_movies['score'],\n", " \"vote_count\": big_movies['vote_count'],\n", " \"released\": big_movies['released'],\n", " \"tagline\": big_movies['tagline'],\n", " \"production_companies\": big_movies['production_companies'],\n", " \"genres\": big_movies['genres']\n", " })\n", " return big_movies_clean\n", "\n", "def get_all_from_list(list_of_things, num, key_to_get):\n", " if list_of_things == '[]':\n", " return 'na'\n", " else:\n", " try:\n", " return eval(list_of_things)[num][key_to_get]\n", " except:\n", " return eval(list_of_things)[0][key_to_get]\n", "\n", "# NOTE: This section only worked for some iterations of our data\n", "# A clear sign we must refactor!\n", "\n", "def widen_df(big_movies_clean):\n", " # the slash at the end of the line is so we can split it into two lines\n", " # PRODUCTION COMPANIES\n", "\n", " big_movies_clean['production_company_1'] = big_movies.apply \\\n", " (lambda x: get_all_from_list(x['production_companies'], 0, 'name'),axis=1)\n", " big_movies_clean['production_company_2'] = big_movies.apply \\\n", " (lambda x: get_all_from_list(x['production_companies'], 1, 'name'),axis=1)\n", " big_movies_clean['production_company_3'] = big_movies.apply \\\n", " (lambda x: get_all_from_list(x['production_companies'], 2, 'name'),axis=1)\n", "\n", " # # GENRES\n", " big_movies_clean['genre_1'] = big_movies.apply \\\n", " (lambda x: get_all_from_list(x['genres'], 0, 'name'),axis=1)\n", " big_movies_clean['genre_2'] = big_movies.apply \\\n", " (lambda x: get_all_from_list(x['genres'], 1, 'name'),axis=1)\n", " big_movies_clean['genre_3'] = big_movies.apply \\\n", " (lambda x: get_all_from_list(x['genres'], 2, 'name'),axis=1)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def run_clean_file_script():\n", " big_movies_clean = clean_merged_file(\"testing_first_merge.csv\")\n", " big_movies_clean_v2 = widen_df(big_movies_clean)\n", " big_movies_clean_v2.to_csv('big_movies_clean_v2.csv')\n", " \n", "# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n", "# # UNCOMMENT TO RUN <3 \n", "# run_clean_file_script()\n", "# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# =======================================================\n", "# PART 2: B - ANALYSIS \n", "# =======================================================" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## THE LIBRARIES" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatchesphew" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## THE INITIAL EDA" ] }, { "cell_type": "code", "execution_count": 158, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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budgetrevenueprofitpopularityvote_averagevote_countmonthdayyear
count1.360000e+021.360000e+021.360000e+02136.000000136.000000136.000000136.000000136.000000136.000000
mean5.053109e+071.867990e+081.362679e+0818.4074636.5073532017.9926476.69117615.3161762017.933824
std5.611861e+073.136210e+082.714360e+089.1423070.8303172434.2810473.4630648.5839660.862402
min2.581570e+054.537000e+03-6.047735e+073.5380004.00000012.0000001.0000001.0000002008.000000
25%1.300000e+072.145820e+071.378588e+0612.4420006.000000440.7500003.7500007.7500002018.000000
50%3.000000e+076.295402e+072.931490e+0715.7405006.5000001151.5000007.00000015.0000002018.000000
75%6.219255e+071.879769e+081.407841e+0821.2302507.0000002522.50000010.00000022.0000002018.000000
max3.000000e+082.046240e+091.746240e+0971.5370008.40000014913.00000012.00000031.0000002019.000000
\n", "
" ], "text/plain": [ " budget revenue profit popularity vote_average \\\n", "count 1.360000e+02 1.360000e+02 1.360000e+02 136.000000 136.000000 \n", "mean 5.053109e+07 1.867990e+08 1.362679e+08 18.407463 6.507353 \n", "std 5.611861e+07 3.136210e+08 2.714360e+08 9.142307 0.830317 \n", "min 2.581570e+05 4.537000e+03 -6.047735e+07 3.538000 4.000000 \n", "25% 1.300000e+07 2.145820e+07 1.378588e+06 12.442000 6.000000 \n", "50% 3.000000e+07 6.295402e+07 2.931490e+07 15.740500 6.500000 \n", "75% 6.219255e+07 1.879769e+08 1.407841e+08 21.230250 7.000000 \n", "max 3.000000e+08 2.046240e+09 1.746240e+09 71.537000 8.400000 \n", "\n", " vote_count month day year \n", "count 136.000000 136.000000 136.000000 136.000000 \n", "mean 2017.992647 6.691176 15.316176 2017.933824 \n", "std 2434.281047 3.463064 8.583966 0.862402 \n", "min 12.000000 1.000000 1.000000 2008.000000 \n", "25% 440.750000 3.750000 7.750000 2018.000000 \n", "50% 1151.500000 7.000000 15.000000 2018.000000 \n", "75% 2522.500000 10.000000 22.000000 2018.000000 \n", "max 14913.000000 12.000000 31.000000 2019.000000 " ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ======================================================================\n", "# DOING EXPLORATORY DATA ANALYSIS ON A SMALL SUBSET (2018) OF THE DATA\n", "# ======================================================================\n", "\n", "# NOTE: The comments in this section are a nice melding of Ali practicality \n", "# and Kendra color. Please do not fault Ali for the bad puns and word play\n", "\n", "# -------------------------------------\n", "# BUT FIRST, WE PREP!!\n", "# -------------------------------------\n", "# STEP 1: readin' and cleanin'\n", "movies = pd.read_csv('2018_movies.csv')\n", "movies.head()\n", "movies.shape\n", "# just say nah to na \n", "# droping the first row of NaNs\n", "movies = movies.drop([0,])\n", "movies.shape\n", "# dropping na and NaN in place\n", "movies.dropna(inplace = True)\n", "\n", "# STEP 2: droppin' and removin'\n", "# say bye bye to those without budget\n", "# (removing movies without the information we need)\n", "index_names = movies[movies[\"budget\"] == 0].index\n", "# We can see that 325 movies in our df have a budget of 0 dollars... \n", "# We have to drop these movies \n", "index_names\n", "# Repeating above... but with revenue\n", "movies.drop(index_names, inplace = True)\n", "index_names = movies[movies[\"revenue\"] == 0].index\n", "movies.drop(index_names, inplace = True)\n", "\n", "# STEP 3: formattin' and finessin'\n", "# Type-casting isn't just for Hollywood \n", "# Checking to see the data type for the release_date column \n", "movies.release_date.dtype\n", "#It shows that it is saved as an object, we want to convert this to date format\n", "#Changing the data type to date by using the pd_to_datetime function, this will allow us to extract each element of the date\n", "movies[\"release_date\"] = pd.to_datetime(movies[\"release_date\"])\n", "#now we want to extract the month, day, and year and create new columns named month, day and year \n", "movies[\"month\"], movies[\"day\"], movies[\"year\"] = movies[\"release_date\"].dt.month, movies[\"release_date\"].dt.day, movies[\"release_date\"].dt.year\n", "\n", "# STEP 4: surmisin' and summarisin'\n", "# Getting summary statistics for our df \n", "movies.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's interesting to note that the lowest budget for a movie in our df is only 258,157 and the maximum budget is 300,000,000. The lowest revenue is 4,537. The maximum revenue is 2,046,240,000. This is a massive revenue. We aggregated a profit column and the minimum profit is - 60,477,350 and maximum profit is 1,746,240,000. This is a massive profit. Their is a large range in popularity scores. The maximum popularity score is 71.54 and the minimum is 3.54. The mean popularity is 18.4 and 75% of the movies have a popularity score less than 21.2. This makes us question if the maximum popularity score might be an error, or it might correspond to the movie with the highest profit. We should investigate what movie this score references. The vote_average column has a range of 4 - 8.4, with an average of 6.5. The vote_count has a maximum of 14,913. This might reference the same movie that had the largest popularity. The max value in this column, also appears to be an outlier, as 75% of the movies have less than 2151 votes and the average vote count is 1701" ] }, { "cell_type": "code", "execution_count": 159, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 1.360000e+02\n", "mean 5.053109e+07\n", "std 5.611861e+07\n", "min 2.581570e+05\n", "25% 1.300000e+07\n", "50% 3.000000e+07\n", "75% 6.219255e+07\n", "max 3.000000e+08\n", "Name: budget, dtype: float64" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# STEP 5: column creatin'\n", "#We have decided that we want to aggregate a percent profit column as well, in an attempt to normalize the data \n", "#To do this we are diving the profit column by the budget column and multiply the result by 100 and saving it in a \n", "#new column named percent_profit\n", "movies[\"percent_profit\"] = movies[\"profit\"]/movies[\"budget\"]*100\n", "# Saving a column as-is for future use\n", "movies_original_df = movies\n", "movies.budget.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we are going to discretize the budget column. \n", "We are discretizing the budget column into four groups: extremely_low, low, high and extremely_high. To do this we first \n", "need to create a list of the categories\n" ] }, { "cell_type": "code", "execution_count": 153, "metadata": {}, "outputs": [], "source": [ "categories = [\"extremely_low\", \"low\", \"high\", \"extremely_high\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to show where we want to insert the breaks. We have decided that extremely low budgets are budgets less \n", "than 13,000,000, low have budgets between 13,000,000 and 30,000,000, high have budgets between 30,000,000 and \n", "62,192,550, and extremely_high have budgets between 62,192,550 and 300,000,000. We chose the values based on the \n", "quartiles." ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [], "source": [ "#Saving the movies df as movies_discretized_df \n", "movies_discretized_df = movies\n", "#Discretizing the budget columns using the cut function from pandas\n", "movies_discretized_df[\"budget\"] = pd.cut(movies_discretized_df[\"budget\"], [0, 13000000, 30000000, 62192550, 300000001], labels = categories)" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 1.360000e+02\n", "mean 1.867990e+08\n", "std 3.136210e+08\n", "min 4.537000e+03\n", "25% 2.145820e+07\n", "50% 6.295402e+07\n", "75% 1.879769e+08\n", "max 2.046240e+09\n", "Name: revenue, dtype: float64" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Now we are going to repeat the steps to discretize the revenue column \n", "movies_discretized_df.revenue.describe()\n", "#We are using the same categories as above " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`extremely_low` revenue are revenues less than 21,458,200, `low` are revenues between 21,458,200 and \n", "62,954,020, `high` revenues are revenues between 62,954,020 and 187,976,900, and `extremely_high` revenues between \n", "187,976,900 and 2,046,240,000. \n" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 1.360000e+02\n", "mean 1.362679e+08\n", "std 2.714360e+08\n", "min -6.047735e+07\n", "25% 1.378588e+06\n", "50% 2.931490e+07\n", "75% 1.407841e+08\n", "max 1.746240e+09\n", "Name: profit, dtype: float64" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_discretized_df[\"revenue\"] = pd.cut(movies_discretized_df[\"revenue\"], [0, 21458200, 62954020, 187976900, 2046240001], labels = categories)\n", "#Now we are going to repeat the steps to discretized the profit column\n", "movies_discretized_df.profit.describe()" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 1.360000e+02\n", "mean 1.362679e+08\n", "std 2.714360e+08\n", "min -6.047735e+07\n", "25% 1.378588e+06\n", "50% 2.931490e+07\n", "75% 1.407841e+08\n", "max 1.746240e+09\n", "Name: profit, dtype: float64" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Now we are going to repeat the steps to discretized the profit column\n", "movies_discretized_df.profit.describe()" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CategoricalDtype(categories=['negative', 'low', 'high', 'extremely_high'], ordered=True)" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''negative profit are profits less than $0, low profits are profits between $0 and \n", "$29,314,900, high profits are profits between $29,314,900 and $140,784,100, and extremely_high profits between \n", "$140,784,100 and $1,746,240,001. \n", "'''\n", "categories = [\"negative\", \"low\", \"high\", \"extremely_high\"]\n", "movies_discretized_df[\"profit\"] = pd.cut(movies_discretized_df[\"profit\"], [-60477351, 0, 29314900, 140784100, 1746240001], labels = categories)\n", "movies_discretized_df.profit.dtype" ] }, { "cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 extremely_high\n", "3 extremely_high\n", "4 extremely_high\n", "6 extremely_high\n", "8 extremely_high\n", "Name: popularity, dtype: category\n", "Categories (4, object): [extremely_low < low < high < extremely_high]" ] }, "execution_count": 120, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Now we are going to repeat the steps to discretize the popularity column \n", "movies_discretized_df.popularity.describe()\n", "categories = [\"extremely_low\", \"low\", \"high\", \"extremely_high\"]\n", "'''extremely_low popularity are popularities less than 12.442, low popularities are popularities between 12.442 and \n", "15.7405, high popularity are popularities between 15.7405 and 21.23025 and extremely_high popularity between 21.23025\n", "and 71.538'''\n", "movies_discretized_df[\"popularity\"] = pd.cut(movies_discretized_df[\"popularity\"], [0, 12.442, 15.7405, 21.23025, 71.538], labels = categories)\n", "movies_discretized_df[\"popularity\"].head()" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 high\n", "3 extremely_high\n", "4 extremely_high\n", "6 extremely_high\n", "8 high\n", "Name: vote_average, dtype: category\n", "Categories (4, object): [extremely_low < low < high < extremely_high]" ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Now we are going to repeat the steps to discretize the vote avg \n", "movies_discretized_df.vote_average.describe()\n", "#We are using the same categories as above \n", "'''extremely_low vote_average are vote averages less than 6, low are between 6 to 6.5, high between 6.5 and 7 and \n", "extremely_high 7 and 8.5'''\n", "movies_discretized_df[\"vote_average\"] = pd.cut(movies_discretized_df[\"vote_average\"], [0, 6, 6.5, 7, 8.5], labels = categories)\n", "movies_discretized_df[\"vote_average\"].head()" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 extremely_high\n", "3 extremely_high\n", "4 extremely_high\n", "6 extremely_high\n", "8 extremely_high\n", "Name: vote_count, dtype: category\n", "Categories (4, object): [extremely_low < low < high < extremely_high]" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We are using the same categories as above \n", "'''extremely_low vote counts are vote counts less than 440, low are between 440 and 1151, high are between 1151 and 2522 \n", "and extremely_high are between 2522 and 14913'''\n", "movies_discretized_df[\"vote_count\"] = pd.cut(movies_discretized_df[\"vote_count\"], [0, 440, 1151, 2522, 14914], labels = categories)\n", "movies_discretized_df[\"vote_count\"].head()" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 136.000000\n", "mean 15.316176\n", "std 8.583966\n", "min 1.000000\n", "25% 7.750000\n", "50% 15.000000\n", "75% 22.000000\n", "max 31.000000\n", "Name: day, dtype: float64" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_discretized_df.percent_profit.describe()\n", "'''extremely_low are percent profits between -100 and 0, low between 6.5 and 108, high between 108 and 436 and \n", "extremely_high between 436 and 6527'''\n", "categories = [\"negative\", \"low\", \"high\", \"extremely_high\"]\n", "movies_discretized_df[\"percent_profit\"] = pd.cut(movies_discretized_df[\"percent_profit\"], [-100, 0, 108, 436, 6528], labels = categories )\n", "movies_discretized_df[\"percent_profit\"]\n", "movies_discretized_df.day.describe()" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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titlebudgetgenresproduction_companiesrevenueprofitpopularityvote_averagevote_countmonthyearpercent_profitweek
1Aquamanextremely_high[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...[{'id': 429, 'logo_path': '/2Tc1P3Ac8M479naPp1...extremely_highextremely_highextremely_highhighextremely_high122018extremely_highweek_1
3Spider-Man: Into the Spider-Verseextremely_high[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...[{'id': 5, 'logo_path': '/71BqEFAF4V3qjjMPCpLu...extremely_highextremely_highextremely_highextremely_highextremely_high122018highweek_1
4Bohemian Rhapsodyhigh[{'id': 18, 'name': 'Drama'}, {'id': 10402, 'n...[{'id': 3281, 'logo_path': '/8tMybAieh64uzvm8k...extremely_highextremely_highextremely_highextremely_highextremely_high102018extremely_highweek_4
6Avengers: Infinity Warextremely_high[{'id': 12, 'name': 'Adventure'}, {'id': 28, '...[{'id': 420, 'logo_path': '/hUzeosd33nzE5MCNsZ...extremely_highextremely_highextremely_highextremely_highextremely_high42018extremely_highweek_4
8Hereditaryextremely_low[{'id': 27, 'name': 'Horror'}, {'id': 9648, 'n...[{'id': 24277, 'logo_path': '/mRSBVNNL2lZvJKVG...highhighextremely_highhighextremely_high62018extremely_highweek_1
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" ], "text/plain": [ " title budget \\\n", "1 Aquaman extremely_high \n", "3 Spider-Man: Into the Spider-Verse extremely_high \n", "4 Bohemian Rhapsody high \n", "6 Avengers: Infinity War extremely_high \n", "8 Hereditary extremely_low \n", "\n", " genres \\\n", "1 [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam... \n", "3 [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam... \n", "4 [{'id': 18, 'name': 'Drama'}, {'id': 10402, 'n... \n", "6 [{'id': 12, 'name': 'Adventure'}, {'id': 28, '... \n", "8 [{'id': 27, 'name': 'Horror'}, {'id': 9648, 'n... \n", "\n", " production_companies revenue \\\n", "1 [{'id': 429, 'logo_path': '/2Tc1P3Ac8M479naPp1... extremely_high \n", "3 [{'id': 5, 'logo_path': '/71BqEFAF4V3qjjMPCpLu... extremely_high \n", "4 [{'id': 3281, 'logo_path': '/8tMybAieh64uzvm8k... extremely_high \n", "6 [{'id': 420, 'logo_path': '/hUzeosd33nzE5MCNsZ... extremely_high \n", "8 [{'id': 24277, 'logo_path': '/mRSBVNNL2lZvJKVG... high \n", "\n", " profit popularity vote_average vote_count month \\\n", "1 extremely_high extremely_high high extremely_high 12 \n", "3 extremely_high extremely_high extremely_high extremely_high 12 \n", "4 extremely_high extremely_high extremely_high extremely_high 10 \n", "6 extremely_high extremely_high extremely_high extremely_high 4 \n", "8 high extremely_high high extremely_high 6 \n", "\n", " year percent_profit week \n", "1 2018 extremely_high week_1 \n", "3 2018 high week_1 \n", "4 2018 extremely_high week_4 \n", "6 2018 extremely_high week_4 \n", "8 2018 extremely_high week_1 " ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We are setting new categories for the day column by creating a new column for week \n", "'''week_1 is the first 7 days of the month, week_2 is days 8 - 14, week_3 is days 15 - 21, and week_4 are the \n", "rest of the days'''\n", "categories = [\"week_1\", \"week_2\", \"week_3\", \"week_4\"]\n", "\n", "movies_discretized_df[\"week\"] = pd.cut(movies_discretized_df[\"day\"], [0, 8, 15, 22, 32], labels = categories)\n", "movies_discretized_df.head()\n", "#We have successfully discretized the df, now we can remove the day and release_date column \n", "movies_discretized_df.drop(columns=['day', 'release_date'], inplace = True)\n", "#Checking to make sure that it worked\n", "movies_discretized_df.head()\n" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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counts
budgetpercent_profit
extremely_lownegative7
low8
high5
extremely_high15
lownegative9
low12
high8
extremely_high6
highnegative11
low10
high6
extremely_high5
extremely_highnegative2
low9
high15
extremely_high8
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" ], "text/plain": [ " counts\n", "budget percent_profit \n", "extremely_low negative 7\n", " low 8\n", " high 5\n", " extremely_high 15\n", "low negative 9\n", " low 12\n", " high 8\n", " extremely_high 6\n", "high negative 11\n", " low 10\n", " high 6\n", " extremely_high 5\n", "extremely_high negative 2\n", " low 9\n", " high 15\n", " extremely_high 8" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Question 1: \n", " #How are the amounts of percent_profits distributed across budget levels? \n", "\n", "'''We want to compare the budget category percentage make up for each percent_profit level. To do this we need to \n", "get the count for each budget level, the count for each percent_profit level by budget level and then divide \n", "the count of the percent_profit/count of budget level and multiply by 100. We have to do this for each \n", "budget level and level of percent_profits. We think that we could potentially answer this question by group bys.'''\n", "movies_discretized_count = movies_discretized_df.groupby([\"budget\", \"percent_profit\"])[\"budget\"].count()\n", "'''Taking the output from the line above and converting it to a data frame. We are using pandas, which we important as pd. \n", "First, we call the package we are using then the function from that package and then what we want to run the function on.\n", "pd.function(item to use). We are using the DataFrame function from the pandas package on the series created by our group by'''\n", "movies_discretized_count_df = pd.DataFrame(movies_discretized_count)\n", "#Checking to see what our df looks like. \n", "movies_discretized_count_df\n", "#Changing the column name from budget to counts\n", "movies_discretized_count_df.columns = [\"counts\"]\n", "#Checking to see what our df looks like. \n", "movies_discretized_count_df" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countsbudget_categorypercent_profit_category
budgetpercent_profit
extremely_lownegative7extremely_lownegative
low8extremely_lowlow
high5extremely_lowhigh
extremely_high15extremely_lowextremely_high
lownegative9lownegative
low12lowlow
high8lowhigh
extremely_high6lowextremely_high
highnegative11highnegative
low10highlow
high6highhigh
extremely_high5highextremely_high
extremely_highnegative2extremely_highnegative
low9extremely_highlow
high15extremely_highhigh
extremely_high8extremely_highextremely_high
\n", "
" ], "text/plain": [ " counts budget_category percent_profit_category\n", "budget percent_profit \n", "extremely_low negative 7 extremely_low negative\n", " low 8 extremely_low low\n", " high 5 extremely_low high\n", " extremely_high 15 extremely_low extremely_high\n", "low negative 9 low negative\n", " low 12 low low\n", " high 8 low high\n", " extremely_high 6 low extremely_high\n", "high negative 11 high negative\n", " low 10 high low\n", " high 6 high high\n", " extremely_high 5 high extremely_high\n", "extremely_high negative 2 extremely_high negative\n", " low 9 extremely_high low\n", " high 15 extremely_high high\n", " extremely_high 8 extremely_high extremely_high" ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We want to get a total count for the number of percent_profit counts for each budget level. We will experiment to see how this is possible \n", "'''This shows that we have 2 indexes budget and percent_profit... We want to create columns from each index\n", "We are creating a new column named budget by extracting the values from the first index (0) which is the budget\n", "index'''\n", "movies_discretized_count_df[\"budget_category\"]=movies_discretized_count_df.index.get_level_values(0)\n", "#We are creating a new column named total_donations by extracting the values from the second index (1) which is total_donations\n", "movies_discretized_count_df[\"percent_profit_category\"] = movies_discretized_count_df.index.get_level_values(1)\n", "#Checking to make sure it worked... \n", "movies_discretized_count_df\n", "#It did! " ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countsbudget_categorypercent_profit_category
07extremely_lownegative
18extremely_lowlow
25extremely_lowhigh
315extremely_lowextremely_high
49lownegative
512lowlow
68lowhigh
76lowextremely_high
811highnegative
910highlow
106highhigh
115highextremely_high
122extremely_highnegative
139extremely_highlow
1415extremely_highhigh
158extremely_highextremely_high
\n", "
" ], "text/plain": [ " counts budget_category percent_profit_category\n", "0 7 extremely_low negative\n", "1 8 extremely_low low\n", "2 5 extremely_low high\n", "3 15 extremely_low extremely_high\n", "4 9 low negative\n", "5 12 low low\n", "6 8 low high\n", "7 6 low extremely_high\n", "8 11 high negative\n", "9 10 high low\n", "10 6 high high\n", "11 5 high extremely_high\n", "12 2 extremely_high negative\n", "13 9 extremely_high low\n", "14 15 extremely_high high\n", "15 8 extremely_high extremely_high" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Now we want to remove the indexes so, we can create a new group by to get the sum of the counts for each group \n", "#To do this we are using the reset_index(drop = True) This will drop our group by indexes and allow us to create a new one. \n", "movies_discretized_count_df = movies_discretized_count_df.reset_index(drop = True)\n", "movies_discretized_count_df" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "budget_category\n", "extremely_low 35\n", "low 35\n", "high 32\n", "extremely_high 34\n", "Name: counts, dtype: int64" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Now we are getting the sum of each budget category. \n", "budget_discretized_count_df = movies_discretized_count_df.groupby([\"budget_category\"])[\"counts\"].sum()\n", "#Checking the results\n", "budget_discretized_count_df" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countsbudget_categorypercent_profit_categorybudget_category_count
07extremely_lownegative35
18extremely_lowlow35
25extremely_lowhigh35
315extremely_lowextremely_high35
49lownegative35
512lowlow35
68lowhigh35
76lowextremely_high35
811highnegative32
910highlow32
106highhigh32
115highextremely_high32
122extremely_highnegative34
139extremely_highlow34
1415extremely_highhigh34
158extremely_highextremely_high34
\n", "
" ], "text/plain": [ " counts budget_category percent_profit_category budget_category_count\n", "0 7 extremely_low negative 35\n", "1 8 extremely_low low 35\n", "2 5 extremely_low high 35\n", "3 15 extremely_low extremely_high 35\n", "4 9 low negative 35\n", "5 12 low low 35\n", "6 8 low high 35\n", "7 6 low extremely_high 35\n", "8 11 high negative 32\n", "9 10 high low 32\n", "10 6 high high 32\n", "11 5 high extremely_high 32\n", "12 2 extremely_high negative 34\n", "13 9 extremely_high low 34\n", "14 15 extremely_high high 34\n", "15 8 extremely_high extremely_high 34" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''We ultimately want a column that contains the total counts for each budget group. We are probably doing this in \n", "a roundabout way, but as I am extremely new to python this is the best way I can think of doing it. We are going to create \n", "a new column that replicates the income_level called income_level_count and then we will use the replace function to \n", "replace the 1s with their total count, the 2s with their total count... '''\n", "#First, replicating the income level column in a column named budget_category_count\n", "movies_discretized_count_df[\"budget_category_count\"] = movies_discretized_count_df[\"budget_category\"] \n", "#Now replacing the income level with the total count for each income level \n", "movies_discretized_count_df[\"budget_category_count\"] = movies_discretized_count_df[\"budget_category_count\"].replace([\"extremely_low\"], 35)\n", "movies_discretized_count_df[\"budget_category_count\"] = movies_discretized_count_df[\"budget_category_count\"].replace([\"low\"], 35)\n", "movies_discretized_count_df[\"budget_category_count\"] = movies_discretized_count_df[\"budget_category_count\"].replace([\"high\"], 32)\n", "movies_discretized_count_df[\"budget_category_count\"] = movies_discretized_count_df[\"budget_category_count\"].replace([\"extremely_high\"], 34)\n", "#Checking to see if that worked: \n", "movies_discretized_count_df" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countsbudget_categorypercent_profit_categorybudget_category_countpercent
07extremely_lownegative3520.000000
18extremely_lowlow3522.857143
25extremely_lowhigh3514.285714
315extremely_lowextremely_high3542.857143
49lownegative3525.714286
512lowlow3534.285714
68lowhigh3522.857143
76lowextremely_high3517.142857
811highnegative3234.375000
910highlow3231.250000
106highhigh3218.750000
115highextremely_high3215.625000
122extremely_highnegative345.882353
139extremely_highlow3426.470588
1415extremely_highhigh3444.117647
158extremely_highextremely_high3423.529412
\n", "
" ], "text/plain": [ " counts budget_category percent_profit_category budget_category_count \\\n", "0 7 extremely_low negative 35 \n", "1 8 extremely_low low 35 \n", "2 5 extremely_low high 35 \n", "3 15 extremely_low extremely_high 35 \n", "4 9 low negative 35 \n", "5 12 low low 35 \n", "6 8 low high 35 \n", "7 6 low extremely_high 35 \n", "8 11 high negative 32 \n", "9 10 high low 32 \n", "10 6 high high 32 \n", "11 5 high extremely_high 32 \n", "12 2 extremely_high negative 34 \n", "13 9 extremely_high low 34 \n", "14 15 extremely_high high 34 \n", "15 8 extremely_high extremely_high 34 \n", "\n", " percent \n", "0 20.000000 \n", "1 22.857143 \n", "2 14.285714 \n", "3 42.857143 \n", "4 25.714286 \n", "5 34.285714 \n", "6 22.857143 \n", "7 17.142857 \n", "8 34.375000 \n", "9 31.250000 \n", "10 18.750000 \n", "11 15.625000 \n", "12 5.882353 \n", "13 26.470588 \n", "14 44.117647 \n", "15 23.529412 " ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Okay, we are one step closer... Now, we need to create a column that takes the counts/budget_category_counts * 100 \n", "movies_discretized_count_df[\"percent\"] = movies_discretized_count_df[\"counts\"]/movies_discretized_count_df[\"budget_category_count\"] *100\n", "#Looking at our data frame... It worked!!! \n", "movies_discretized_count_df" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#We no longer need the count columns\n", "movies_discretized_count_df.drop([\"counts\", \"budget_category_count\"], axis = 1, inplace = True ) \n", "'''Attempting to graph this data using a grouped bar chart: \n", "formula: df.pivot(columns, group, values).plot(kind = \"type of graph\", color = [\"color to use, can be a list of colors\"], \n", "title = \"you can set the title of your graph here\")'''\n", "graph = movies_discretized_count_df.pivot(\"budget_category\", \"percent_profit_category\", \n", " \"percent\").plot(kind=\"bar\", color = [\"crimson\", \"salmon\", \"palegreen\", \"darkgreen\"], \n", " title = \"Percent of Percent Profit to Budget Category\")\n", "#Changing the y label of our graph to Percent\n", "plt.ylabel(\"Percent\")\n", "#Changing the x axis label of our graph to Budget Category \n", "plt.xlabel(\"Budget Category\")\n", "#Making it so the tick labels are not angled \n", "plt.xticks(rotation = 0)\n", "#How to change the tick labels (we ended up not needing this, but want to keep for future reference)\n", "#plt.Axes.set_xticklabels(graph, labels = ['extremely low', 'low', 'high', 'extremely high'])\n", "#moving the legend position to underneath the graph, also setting it to have 4 columns so the legend is in a \n", "#straight single line and adding a legend title\n", "plt.legend( loc = \"lower center\", bbox_to_anchor = (.5, -.4), ncol = 4, title = \"Percent Profit Category\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This graph proved very interesting. Movies with an extremely low budget have the highest percentage make-up of making an extremely \n", "high percent profit. Movies with an extremely high budget are the most likely to be profitable overall, being that they \n", "are the least likely to have a negative profit, with only 5.9% of the movies classified as having an extremely high \n", "budget in our dataset made a negative profit. Movies with an low or high budget only make an extremely high \n", "percent profit less than 17.1% and 15.6% of the time respectively. They also have the highest chance of making a low or \n", "negative profit out of all of the budget categories. Based, on this analysis, percent profits are not uniformally \n", "distributed across budget levels. Movies with an extremely high budget are the least likely to have a negative percent \n", "profit. Movies with an extremely low budget are the most likely to have an extremely high percent profit. Our \n", "recommendation to studios, would be to either have a extremely low or extremely high budget and to veer away from \n", "productions with an extremely low or high budget. Further analysis for tighter recommendatios is needed.\n" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [], "source": [ "#Question: Do big name production companies impact the percent profit? \n", "\n", "#To answer this question we are first going to create a for loop that will loop through the production_companies column \n", "#in order movies_discretized_df and store the production company in a list called production_company. The only issue \n", "#with this method, is that if a movie has more than one production company this will not be shown and only the last \n", "#company included in the for loop will be given credit. \n", "\n", "production_company = []\n", "for movie in movies_discretized_df['production_companies']:\n", " if \"Universal\" in movie:\n", " production_company.append(\"Universal\")\n", " elif \"Sony\" in movie: \n", " production_company.append(\"Sony\")\n", " elif \"Fox\" in movie: \n", " production_company.append(\"Fox\")\n", " elif \"DreamWorks\" in movie: \n", " production_company.append(\"DW\")\n", " elif \"MGM\" in movie: \n", " production_company.append(\"MGM\")\n", " elif \"Paramount\" in movie: \n", " production_company.append(\"Paramount\")\n", " elif \"Disney\" in movie: \n", " production_company.append(\"Disney\")\n", " elif \"Warner Bros\" in movie:\n", " production_company.append(\"WB\")\n", " else:\n", " production_company.append(\"None\")" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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titlebudgetgenresproduction_companiesrevenueprofitpopularityvote_averagevote_countmonthyearpercent_profitweekmain_production_co
1Aquamanextremely_high[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...[{'id': 429, 'logo_path': '/2Tc1P3Ac8M479naPp1...extremely_highextremely_highextremely_highhighextremely_high122018extremely_highweek_1WB
3Spider-Man: Into the Spider-Verseextremely_high[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...[{'id': 5, 'logo_path': '/71BqEFAF4V3qjjMPCpLu...extremely_highextremely_highextremely_highextremely_highextremely_high122018highweek_1Sony
4Bohemian Rhapsodyhigh[{'id': 18, 'name': 'Drama'}, {'id': 10402, 'n...[{'id': 3281, 'logo_path': '/8tMybAieh64uzvm8k...extremely_highextremely_highextremely_highextremely_highextremely_high102018extremely_highweek_4Fox
6Avengers: Infinity Warextremely_high[{'id': 12, 'name': 'Adventure'}, {'id': 28, '...[{'id': 420, 'logo_path': '/hUzeosd33nzE5MCNsZ...extremely_highextremely_highextremely_highextremely_highextremely_high42018extremely_highweek_4None
8Hereditaryextremely_low[{'id': 27, 'name': 'Horror'}, {'id': 9648, 'n...[{'id': 24277, 'logo_path': '/mRSBVNNL2lZvJKVG...highhighextremely_highhighextremely_high62018extremely_highweek_1None
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" ], "text/plain": [ " title budget \\\n", "1 Aquaman extremely_high \n", "3 Spider-Man: Into the Spider-Verse extremely_high \n", "4 Bohemian Rhapsody high \n", "6 Avengers: Infinity War extremely_high \n", "8 Hereditary extremely_low \n", "\n", " genres \\\n", "1 [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam... \n", "3 [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam... \n", "4 [{'id': 18, 'name': 'Drama'}, {'id': 10402, 'n... \n", "6 [{'id': 12, 'name': 'Adventure'}, {'id': 28, '... \n", "8 [{'id': 27, 'name': 'Horror'}, {'id': 9648, 'n... \n", "\n", " production_companies revenue \\\n", "1 [{'id': 429, 'logo_path': '/2Tc1P3Ac8M479naPp1... extremely_high \n", "3 [{'id': 5, 'logo_path': '/71BqEFAF4V3qjjMPCpLu... extremely_high \n", "4 [{'id': 3281, 'logo_path': '/8tMybAieh64uzvm8k... extremely_high \n", "6 [{'id': 420, 'logo_path': '/hUzeosd33nzE5MCNsZ... extremely_high \n", "8 [{'id': 24277, 'logo_path': '/mRSBVNNL2lZvJKVG... high \n", "\n", " profit popularity vote_average vote_count month \\\n", "1 extremely_high extremely_high high extremely_high 12 \n", "3 extremely_high extremely_high extremely_high extremely_high 12 \n", "4 extremely_high extremely_high extremely_high extremely_high 10 \n", "6 extremely_high extremely_high extremely_high extremely_high 4 \n", "8 high extremely_high high extremely_high 6 \n", "\n", " year percent_profit week main_production_co \n", "1 2018 extremely_high week_1 WB \n", "3 2018 high week_1 Sony \n", "4 2018 extremely_high week_4 Fox \n", "6 2018 extremely_high week_4 None \n", "8 2018 extremely_high week_1 None " ] }, "execution_count": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_discretized_df[\"main_production_co\"] = production_company\n", "movies_discretized_df.head()" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\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", "
counts
main_production_copercent_profit
DWnegative1
Disneynegative1
low3
high2
extremely_high2
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" ], "text/plain": [ " counts\n", "main_production_co percent_profit \n", "DW negative 1\n", "Disney negative 1\n", " low 3\n", " high 2\n", " extremely_high 2" ] }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Now we want to group by production company and percent profit \n", "'''We want to compare the production company percentage make up for each percent_profit level. To do this we need to \n", "get the count for each production company, the count for each percent_profit level by production company and then divide \n", "the count of the percent_profit/count of production company and multiply by 100. We have to do this for each \n", "production company and level of percent_profits. We think that we could potentially answer this question by group bys.'''\n", "movies_discretized_count_q2 = movies_discretized_df.groupby([\"main_production_co\", \"percent_profit\"])[\"main_production_co\"].count()\n", "'''Taking the output from the line above and converting it to a data frame. We are using pandas, which we important as pd. \n", "First, we call the package we are using then the function from that package and then what we want to run the function on.\n", "pd.function(item to use). We are using the DataFrame function from the pandas package on the series created by our group by'''\n", "movies_discretized_count_df_q2 = pd.DataFrame(movies_discretized_count_q2)\n", "#Checking to see what our df looks like. \n", "movies_discretized_count_df_q2\n", "#Changing the column name from budget to counts\n", "movies_discretized_count_df_q2.columns = [\"counts\"]\n", "#Checking to see what our df looks like. \n", "movies_discretized_count_df_q2.head()" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countsproduction_companypercent_profit_category
main_production_copercent_profit
DWnegative1DWnegative
Disneynegative1Disneynegative
low3Disneylow
high2Disneyhigh
extremely_high2Disneyextremely_high
Foxnegative1Foxnegative
low4Foxlow
high3Foxhigh
extremely_high3Foxextremely_high
Nonenegative22Nonenegative
low17Nonelow
high13Nonehigh
extremely_high14Noneextremely_high
Paramountnegative2Paramountnegative
low4Paramountlow
high2Paramounthigh
extremely_high1Paramountextremely_high
Sonynegative1Sonynegative
low3Sonylow
high2Sonyhigh
extremely_high2Sonyextremely_high
Universalnegative1Universalnegative
low6Universallow
high4Universalhigh
extremely_high9Universalextremely_high
WBlow2WBlow
high8WBhigh
extremely_high3WBextremely_high
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" ], "text/plain": [ " counts production_company \\\n", "main_production_co percent_profit \n", "DW negative 1 DW \n", "Disney negative 1 Disney \n", " low 3 Disney \n", " high 2 Disney \n", " extremely_high 2 Disney \n", "Fox negative 1 Fox \n", " low 4 Fox \n", " high 3 Fox \n", " extremely_high 3 Fox \n", "None negative 22 None \n", " low 17 None \n", " high 13 None \n", " extremely_high 14 None \n", "Paramount negative 2 Paramount \n", " low 4 Paramount \n", " high 2 Paramount \n", " extremely_high 1 Paramount \n", "Sony negative 1 Sony \n", " low 3 Sony \n", " high 2 Sony \n", " extremely_high 2 Sony \n", "Universal negative 1 Universal \n", " low 6 Universal \n", " high 4 Universal \n", " extremely_high 9 Universal \n", "WB low 2 WB \n", " high 8 WB \n", " extremely_high 3 WB \n", "\n", " percent_profit_category \n", "main_production_co percent_profit \n", "DW negative negative \n", "Disney negative negative \n", " low low \n", " high high \n", " extremely_high extremely_high \n", "Fox negative negative \n", " low low \n", " high high \n", " extremely_high extremely_high \n", "None negative negative \n", " low low \n", " high high \n", " extremely_high extremely_high \n", "Paramount negative negative \n", " low low \n", " high high \n", " extremely_high extremely_high \n", "Sony negative negative \n", " low low \n", " high high \n", " extremely_high extremely_high \n", "Universal negative negative \n", " low low \n", " high high \n", " extremely_high extremely_high \n", "WB low low \n", " high high \n", " extremely_high extremely_high " ] }, "execution_count": 135, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We want to get a total count for the number of percent_profit counts for each production company.\n", "'''This shows that we have 2 indexes budget and percent_profit... We want to create columns from each index\n", "We are creating a new column named budget by extracting the values from the first index (0) which is the budget\n", "index'''\n", "movies_discretized_count_df_q2[\"production_company\"]=movies_discretized_count_df_q2.index.get_level_values(0)\n", "#We are creating a new column named total_donations by extracting the values from the second index (1) which is total_donations\n", "movies_discretized_count_df_q2[\"percent_profit_category\"] = movies_discretized_count_df_q2.index.get_level_values(1)\n", "#Checking to make sure it worked... \n", "movies_discretized_count_df_q2\n", "#It did! " ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "production_company\n", "DW 1\n", "Disney 8\n", "Fox 11\n", "None 66\n", "Paramount 9\n", "Sony 8\n", "Universal 20\n", "WB 13\n", "Name: counts, dtype: int64" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Now we want to remove the indexes so, we can create a new group by to get the sum of the counts for each group \n", "#To do this we are using the reset_index(drop = True) This will drop our group by indexes and allow us to create a new one. \n", "movies_discretized_count_df_q2 = movies_discretized_count_df_q2.reset_index(drop = True)\n", "#Now we are getting the sum of each production company category. \n", "production_company_discretized_count_df_q2 = movies_discretized_count_df_q2.groupby([\"production_company\"])[\"counts\"].sum()\n", "#Checking the results\n", "production_company_discretized_count_df_q2" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countsproduction_companypercent_profit_categoryproduction_company_count
01DWnegative1
11Disneynegative8
23Disneylow8
32Disneyhigh8
42Disneyextremely_high8
51Foxnegative11
64Foxlow11
73Foxhigh11
83Foxextremely_high11
922Nonenegative66
1017Nonelow66
1113Nonehigh66
1214Noneextremely_high66
132Paramountnegative9
144Paramountlow9
152Paramounthigh9
161Paramountextremely_high9
171Sonynegative8
183Sonylow8
192Sonyhigh8
202Sonyextremely_high8
211Universalnegative20
226Universallow20
234Universalhigh20
249Universalextremely_high20
252WBlow13
268WBhigh13
273WBextremely_high13
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" ], "text/plain": [ " counts production_company percent_profit_category \\\n", "0 1 DW negative \n", "1 1 Disney negative \n", "2 3 Disney low \n", "3 2 Disney high \n", "4 2 Disney extremely_high \n", "5 1 Fox negative \n", "6 4 Fox low \n", "7 3 Fox high \n", "8 3 Fox extremely_high \n", "9 22 None negative \n", "10 17 None low \n", "11 13 None high \n", "12 14 None extremely_high \n", "13 2 Paramount negative \n", "14 4 Paramount low \n", "15 2 Paramount high \n", "16 1 Paramount extremely_high \n", "17 1 Sony negative \n", "18 3 Sony low \n", "19 2 Sony high \n", "20 2 Sony extremely_high \n", "21 1 Universal negative \n", "22 6 Universal low \n", "23 4 Universal high \n", "24 9 Universal extremely_high \n", "25 2 WB low \n", "26 8 WB high \n", "27 3 WB extremely_high \n", "\n", " production_company_count \n", "0 1 \n", "1 8 \n", "2 8 \n", "3 8 \n", "4 8 \n", "5 11 \n", "6 11 \n", "7 11 \n", "8 11 \n", "9 66 \n", "10 66 \n", "11 66 \n", "12 66 \n", "13 9 \n", "14 9 \n", "15 9 \n", "16 9 \n", "17 8 \n", "18 8 \n", "19 8 \n", "20 8 \n", "21 20 \n", "22 20 \n", "23 20 \n", "24 20 \n", "25 13 \n", "26 13 \n", "27 13 " ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''We ultimately want a column that contains the total counts for each production company. We are going to create \n", "a new column that replicates the production company called production_company_count and then we will use the replace function to \n", "replace the 1s with their total count, the 2s with their total count... '''\n", "#First, replicating the income level column in a column named budget_category_count\n", "movies_discretized_count_df_q2[\"production_company_count\"] = movies_discretized_count_df_q2[\"production_company\"] \n", "#Now replacing the income level with the total count for each income level \n", "movies_discretized_count_df_q2[\"production_company_count\"] = movies_discretized_count_df_q2[\"production_company_count\"].replace([\"DW\"], 1)\n", "movies_discretized_count_df_q2[\"production_company_count\"] = movies_discretized_count_df_q2[\"production_company_count\"].replace([\"Disney\"], 8)\n", "movies_discretized_count_df_q2[\"production_company_count\"] = movies_discretized_count_df_q2[\"production_company_count\"].replace([\"Fox\"], 11)\n", "movies_discretized_count_df_q2[\"production_company_count\"] = movies_discretized_count_df_q2[\"production_company_count\"].replace([\"None\"], 66)\n", "movies_discretized_count_df_q2[\"production_company_count\"] = movies_discretized_count_df_q2[\"production_company_count\"].replace([\"Paramount\"], 9)\n", "movies_discretized_count_df_q2[\"production_company_count\"] = movies_discretized_count_df_q2[\"production_company_count\"].replace([\"Sony\"], 8)\n", "movies_discretized_count_df_q2[\"production_company_count\"] = movies_discretized_count_df_q2[\"production_company_count\"].replace([\"Universal\"], 20)\n", "movies_discretized_count_df_q2[\"production_company_count\"] = movies_discretized_count_df_q2[\"production_company_count\"].replace([\"WB\"], 13)\n", "movies_discretized_count_df_q2" ] }, { "cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countsproduction_companypercent_profit_categoryproduction_company_countpercent
01DWnegative1100.0
11Disneynegative812.5
23Disneylow837.5
32Disneyhigh825.0
42Disneyextremely_high825.0
\n", "
" ], "text/plain": [ " counts production_company percent_profit_category \\\n", "0 1 DW negative \n", "1 1 Disney negative \n", "2 3 Disney low \n", "3 2 Disney high \n", "4 2 Disney extremely_high \n", "\n", " production_company_count percent \n", "0 1 100.0 \n", "1 8 12.5 \n", "2 8 37.5 \n", "3 8 25.0 \n", "4 8 25.0 " ] }, "execution_count": 138, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Okay, we are one step closer... Now, we need to create a column that takes the counts/budget_category_counts * 100 \n", "movies_discretized_count_df_q2[\"percent\"] = movies_discretized_count_df_q2[\"counts\"]/movies_discretized_count_df_q2[\"production_company_count\"] *100\n", "#Looking at our data frame... It worked!!! \n", "movies_discretized_count_df_q2.head()" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 139, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#We no longer need the count columns\n", "movies_discretized_count_df_q2.drop([\"counts\", \"production_company_count\"], axis = 1, inplace = True ) \n", "'''Attempting to graph this data using a grouped bar chart: \n", "formula: df.pivot(columns, group, values).plot(kind = \"type of graph\", color = [\"color to use, can be a list of colors\"], \n", "title = \"you can set the title of your graph here\")'''\n", "graph = movies_discretized_count_df_q2.pivot(\"production_company\", \"percent_profit_category\", \n", " \"percent\").plot(kind=\"bar\", color = [\"crimson\", \"salmon\", \"palegreen\", \"darkgreen\"], \n", " title = \"Percent of Percent Profit to Production Company\")\n", "#Changing the y label of our graph to Percent\n", "plt.ylabel(\"Percent\")\n", "#Changing the x axis label of our graph to Budget Category \n", "plt.xlabel(\"Production Company\")\n", "#Making it so the tick labels are not angled \n", "plt.xticks(rotation = 0)\n", "#How to change the tick labels (we ended up not needing this, but want to keep for future reference)\n", "#plt.Axes.set_xticklabels(graph, labels = ['extremely low', 'low', 'high', 'extremely high'])\n", "#moving the legend position to underneath the graph, also setting it to have 4 columns so the legend is in a \n", "#straight single line and adding a legend title\n", "plt.legend( loc = \"lower center\", bbox_to_anchor = (.5, -.4), ncol = 4, title = \"Percent Profit Category\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This graph provides some insights, however, most of our movies have more than one main production company and only \n", "one production company is being shown. For example, DreamWorks and Universal had a movie named First Man and it was \n", "profitable. However, based on the way that we assigned a main production company, only Universal was given credit for \n", "that movie." ] }, { "cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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week_2negative5
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" ], "text/plain": [ " counts\n", "week percent_profit \n", "week_1 negative 5\n", " low 13\n", " high 5\n", " extremely_high 14\n", "week_2 negative 5" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Question: \n", "#Does time of the month the movie is released affect percent profit? \n", "'''We want to compare the percent_profit level percentage make up for each time of month. To do this we need to \n", "get the count for each time of month, the count for each percent_profit level by time of month company and then divide \n", "the count of the percent_profit/count of time of month and multiply by 100. We have to do this for each \n", "time of month and level of percent_profits.'''\n", "movies_discretized_count_q3 = movies_discretized_df.groupby([\"week\", \"percent_profit\"])[\"week\"].count()\n", "'''Taking the output from the line above and converting it to a data frame. We are using pandas, which we important as pd. \n", "First, we call the package we are using then the function from that package and then what we want to run the function on.\n", "pd.function(item to use). We are using the DataFrame function from the pandas package on the series created by our group by'''\n", "movies_discretized_count_df_q3 = pd.DataFrame(movies_discretized_count_q3)\n", "#Checking to see what our df looks like. \n", "movies_discretized_count_df_q3\n", "#Changing the column name from week to counts\n", "movies_discretized_count_df_q3.columns = [\"counts\"]\n", "#Checking to see what our df looks like. \n", "movies_discretized_count_df_q3.head()" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countsweekpercent_profit_category
weekpercent_profit
week_1negative5week_1negative
low13week_1low
high5week_1high
extremely_high14week_1extremely_high
week_2negative5week_2negative
low9week_2low
high13week_2high
extremely_high7week_2extremely_high
week_3negative9week_3negative
low10week_3low
high6week_3high
extremely_high8week_3extremely_high
week_4negative10week_4negative
low7week_4low
high10week_4high
extremely_high5week_4extremely_high
\n", "
" ], "text/plain": [ " counts week percent_profit_category\n", "week percent_profit \n", "week_1 negative 5 week_1 negative\n", " low 13 week_1 low\n", " high 5 week_1 high\n", " extremely_high 14 week_1 extremely_high\n", "week_2 negative 5 week_2 negative\n", " low 9 week_2 low\n", " high 13 week_2 high\n", " extremely_high 7 week_2 extremely_high\n", "week_3 negative 9 week_3 negative\n", " low 10 week_3 low\n", " high 6 week_3 high\n", " extremely_high 8 week_3 extremely_high\n", "week_4 negative 10 week_4 negative\n", " low 7 week_4 low\n", " high 10 week_4 high\n", " extremely_high 5 week_4 extremely_high" ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We want to get a total count for the number of percent_profit counts for each week.\n", "'''This shows that we have 2 indexes week and percent_profit... We want to create columns from each index\n", "We are creating a new column named week by extracting the values from the first index (0) which is the week\n", "index'''\n", "movies_discretized_count_df_q3[\"week\"]=movies_discretized_count_df_q3.index.get_level_values(0)\n", "#We are creating a new column named total_donations by extracting the values from the second index (1) which is percent_profit\n", "movies_discretized_count_df_q3[\"percent_profit_category\"] = movies_discretized_count_df_q3.index.get_level_values(1)\n", "#Checking to make sure it worked... \n", "movies_discretized_count_df_q3\n", "#It did! " ] }, { "cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "week\n", "week_1 37\n", "week_2 34\n", "week_3 33\n", "week_4 32\n", "Name: counts, dtype: int64" ] }, "execution_count": 142, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Now we want to remove the indexes so, we can create a new group by to get the sum of the counts for each group \n", "#To do this we are using the reset_index(drop = True) This will drop our group by indexes and allow us to create a new one. \n", "movies_discretized_count_df_q3 = movies_discretized_count_df_q3.reset_index(drop = True)\n", "# Now we are getting the sum of each production company category. \n", "week_discretized_count_df_q3 = movies_discretized_count_df_q3.groupby([\"week\"])[\"counts\"].sum()\n", "#Checking the results\n", "week_discretized_count_df_q3" ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countsweekpercent_profit_categoryweek_count
05week_1negative37
113week_1low37
25week_1high37
314week_1extremely_high37
45week_2negative34
\n", "
" ], "text/plain": [ " counts week percent_profit_category week_count\n", "0 5 week_1 negative 37\n", "1 13 week_1 low 37\n", "2 5 week_1 high 37\n", "3 14 week_1 extremely_high 37\n", "4 5 week_2 negative 34" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''We ultimately want a column that contains the total counts for each week. We are going to create \n", "a new column that replicates the week called week_count and then we will use the replace function to \n", "replace the 1s with their total count, the 2s with their total count... '''\n", "#First, replicating the income level column in a column named budget_category_count\n", "movies_discretized_count_df_q3[\"week_count\"] = movies_discretized_count_df_q3[\"week\"] \n", "#Now replacing the income level with the total count for each income level \n", "movies_discretized_count_df_q3[\"week_count\"] = movies_discretized_count_df_q3[\"week_count\"].replace([\"week_1\"], 37)\n", "movies_discretized_count_df_q3[\"week_count\"] = movies_discretized_count_df_q3[\"week_count\"].replace([\"week_2\"], 34)\n", "movies_discretized_count_df_q3[\"week_count\"] = movies_discretized_count_df_q3[\"week_count\"].replace([\"week_3\"], 33)\n", "movies_discretized_count_df_q3[\"week_count\"] = movies_discretized_count_df_q3[\"week_count\"].replace([\"week_4\"], 32)\n", "movies_discretized_count_df_q3.head()" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countsweekpercent_profit_categoryweek_countpercent
05week_1negative3713.513514
113week_1low3735.135135
25week_1high3713.513514
314week_1extremely_high3737.837838
45week_2negative3414.705882
\n", "
" ], "text/plain": [ " counts week percent_profit_category week_count percent\n", "0 5 week_1 negative 37 13.513514\n", "1 13 week_1 low 37 35.135135\n", "2 5 week_1 high 37 13.513514\n", "3 14 week_1 extremely_high 37 37.837838\n", "4 5 week_2 negative 34 14.705882" ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Okay, we are one step closer... Now, we need to create a column that takes the counts/week_count * 100 \n", "movies_discretized_count_df_q3[\"percent\"] = movies_discretized_count_df_q3[\"counts\"]/movies_discretized_count_df_q3[\"week_count\"] *100\n", "#Looking at our data frame... It worked!!! \n", "movies_discretized_count_df_q3.head()" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#We no longer need the count columns\n", "movies_discretized_count_df_q3.drop([\"counts\", \"week_count\"], axis = 1, inplace = True ) \n", "'''Attempting to graph this data using a grouped bar chart: \n", "formula: df.pivot(columns, group, values).plot(kind = \"type of graph\", color = [\"color to use, can be a list of colors\"], \n", "title = \"you can set the title of your graph here\")'''\n", "graph = movies_discretized_count_df_q3.pivot(\"week\", \"percent_profit_category\", \n", " \"percent\").plot(kind=\"bar\", color = [\"crimson\", \"salmon\", \"palegreen\", \"darkgreen\"], \n", " title = \"Percent of Percent Profit to Week\")\n", "#Changing the y label of our graph to Percent\n", "plt.ylabel(\"Percent\")\n", "#Changing the x axis label of our graph to Budget Category \n", "plt.xlabel(\"Week\")\n", "#Making it so the tick labels are not angled \n", "plt.xticks(rotation = 0)\n", "#How to change the tick labels (we ended up not needing this, but want to keep for future reference)\n", "#plt.Axes.set_xticklabels(graph, labels = ['extremely low', 'low', 'high', 'extremely high'])\n", "#moving the legend position to underneath the graph, also setting it to have 4 columns so the legend is in a \n", "#straight single line and adding a legend title\n", "plt.legend( loc = \"lower center\", bbox_to_anchor = (.5, -.4), ncol = 4, title = \"Percent Profit Category\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is interesting in that it shows that movies released within the first two weeks of the month tend to be more \n", "profitable. We would like to look at a breakdown of month to percent profit for further analysis" ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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titlebudgetgenresproduction_companiesrevenueprofitpopularityvote_averagevote_countmonthyearpercent_profitweekmain_production_co
1Aquamanextremely_high[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...[{'id': 429, 'logo_path': '/2Tc1P3Ac8M479naPp1...extremely_highextremely_highextremely_highhighextremely_high122018extremely_highweek_1WB
3Spider-Man: Into the Spider-Verseextremely_high[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...[{'id': 5, 'logo_path': '/71BqEFAF4V3qjjMPCpLu...extremely_highextremely_highextremely_highextremely_highextremely_high122018highweek_1Sony
4Bohemian Rhapsodyhigh[{'id': 18, 'name': 'Drama'}, {'id': 10402, 'n...[{'id': 3281, 'logo_path': '/8tMybAieh64uzvm8k...extremely_highextremely_highextremely_highextremely_highextremely_high102018extremely_highweek_4Fox
6Avengers: Infinity Warextremely_high[{'id': 12, 'name': 'Adventure'}, {'id': 28, '...[{'id': 420, 'logo_path': '/hUzeosd33nzE5MCNsZ...extremely_highextremely_highextremely_highextremely_highextremely_high42018extremely_highweek_4None
8Hereditaryextremely_low[{'id': 27, 'name': 'Horror'}, {'id': 9648, 'n...[{'id': 24277, 'logo_path': '/mRSBVNNL2lZvJKVG...highhighextremely_highhighextremely_high62018extremely_highweek_1None
\n", "
" ], "text/plain": [ " title budget \\\n", "1 Aquaman extremely_high \n", "3 Spider-Man: Into the Spider-Verse extremely_high \n", "4 Bohemian Rhapsody high \n", "6 Avengers: Infinity War extremely_high \n", "8 Hereditary extremely_low \n", "\n", " genres \\\n", "1 [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam... \n", "3 [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam... \n", "4 [{'id': 18, 'name': 'Drama'}, {'id': 10402, 'n... \n", "6 [{'id': 12, 'name': 'Adventure'}, {'id': 28, '... \n", "8 [{'id': 27, 'name': 'Horror'}, {'id': 9648, 'n... \n", "\n", " production_companies revenue \\\n", "1 [{'id': 429, 'logo_path': '/2Tc1P3Ac8M479naPp1... extremely_high \n", "3 [{'id': 5, 'logo_path': '/71BqEFAF4V3qjjMPCpLu... extremely_high \n", "4 [{'id': 3281, 'logo_path': '/8tMybAieh64uzvm8k... extremely_high \n", "6 [{'id': 420, 'logo_path': '/hUzeosd33nzE5MCNsZ... extremely_high \n", "8 [{'id': 24277, 'logo_path': '/mRSBVNNL2lZvJKVG... high \n", "\n", " profit popularity vote_average vote_count month \\\n", "1 extremely_high extremely_high high extremely_high 12 \n", "3 extremely_high extremely_high extremely_high extremely_high 12 \n", "4 extremely_high extremely_high extremely_high extremely_high 10 \n", "6 extremely_high extremely_high extremely_high extremely_high 4 \n", "8 high extremely_high high extremely_high 6 \n", "\n", " year percent_profit week main_production_co \n", "1 2018 extremely_high week_1 WB \n", "3 2018 high week_1 Sony \n", "4 2018 extremely_high week_4 Fox \n", "6 2018 extremely_high week_4 None \n", "8 2018 extremely_high week_1 None " ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Taking a brief detour back to our non-discretized df\n", "movies_original_df.head()" ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# QUESTION: Do \"Good\" Movies Make Money? -- We're defining \"Good\" as vote average\n", "plt.plot(movies_original_df.profit, movies_original_df.vote_average, 'o')\n", "plt.title('Do \"Good\" Movies Make Money?')\n", "plt.xlabel('Profit')\n", "plt.ylabel('Vote Average')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 156, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# QUESTION: Does Popularity = Profit?\n", "plt.plot(movies_original_df.profit, movies_original_df.popularity, 'o')\n", "plt.title('Does Popularity = Profits?')\n", "plt.xlabel('Profit')\n", "plt.ylabel('Popularity')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 160, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# QUESTION: How does budget impact vote average?\n", "plt.plot(movies_original_df.budget, movies_original_df.vote_average, 'o')\n", "plt.title('How does Budget Impact Vote Average?')\n", "plt.xlabel('Budget')\n", "plt.ylabel('Vote Average')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 161, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# QUESTION: How does budget impact popularity?\n", "plt.plot(movies_original_df.budget, movies_original_df.popularity, 'o')\n", "plt.title('How does Budget Impact Popularity?')\n", "plt.xlabel('Budget')\n", "plt.ylabel('Popularity')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 162, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# QUESTION: Is there a relationship between \"Above Average Movies\" and Budget/Price?\n", "below_avg = movies_original_df[movies_original_df.vote_average < 6.5]\n", "above_avg = movies_original_df[movies_original_df.vote_average >= 6.5]\n", "plt.plot(below_avg.budget, below_avg.profit, 'o', label=\"below average\")\n", "plt.plot(above_avg.budget, above_avg.profit, 'o', label=\"above average\")\n", "\n", "plt.title('BUDGET vs PROFIT by AVERAGE VOTE!')\n", "plt.xlabel('BUDGET')\n", "plt.ylabel('PROFIT')\n", "plt.legend()\n", "plt.show() " ] }, { "cell_type": "code", "execution_count": 163, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# QUESTION: Is there a relationship between \"Above Average Movies\" and Budget/Price?\n", "below_avg = movies_original_df[movies_original_df.vote_average < 6.5]\n", "above_avg = movies_original_df[movies_original_df.vote_average >= 6.5]\n", "plt.plot(below_avg.budget, below_avg.percent_profit, 'o', label=\"below average\")\n", "plt.plot(above_avg.budget, above_avg.percent_profit, 'o', label=\"above average\")\n", "\n", "plt.title('BUDGET vs PERCENT PROFIT by AVERAGE VOTE!')\n", "plt.xlabel('BUDGET')\n", "plt.ylabel('PROFIT')\n", "plt.legend()\n", "plt.show() " ] }, { "cell_type": "code", "execution_count": 164, "metadata": {}, "outputs": [], "source": [ "# BIG QUESTION: What role do production companies play in the entertainment industry? \n", "# Is there a relationship between production studio and average vote? \n", "# Production studio and budget?\n", "# Production studio and percent profit?" ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [], "source": [ "# Adding the BIG EIGHT Production Studios to the DF\n", "\n", "# WARNER BROS\n", "wb = []\n", "for movie in movies_original_df['production_companies']:\n", " if \"Warner Bros\" in movie:\n", " wb.append(True)\n", " else:\n", " wb.append(False)\n", "movies_original_df['wb'] = wb\n", "\n", "# MGM\n", "mgm = []\n", "for movie in movies_original_df['production_companies']:\n", " if \"MGM\" in movie:\n", " mgm.append(True)\n", " else:\n", " mgm.append(False)\n", "movies_original_df['mgm'] = mgm\n", "\n", "# DREAMWORKS\n", "dw = []\n", "for movie in movies_original_df['production_companies']:\n", " if \"DreamWorks\" in movie:\n", " dw.append(True)\n", " else:\n", " dw.append(False)\n", "movies_original_df['dw'] = dw\n", "\n", "# SONY\n", "sony = []\n", "for movie in movies_original_df['production_companies']:\n", " if \"Sony\" in movie:\n", " sony.append(True)\n", " else:\n", " sony.append(False)\n", "movies_original_df['sony'] = sony\n", "\n", "# DISNEY\n", "disney = []\n", "for movie in movies_original_df['production_companies']:\n", " if \"Disney\" in movie:\n", " disney.append(True)\n", " else:\n", " disney.append(False)\n", "movies_original_df['disney'] = disney\n", "\n", "#FOX\n", "fox = []\n", "for movie in movies_original_df['production_companies']:\n", " if \"Century Fox\" in movie:\n", " fox.append(True)\n", " else:\n", " fox.append(False)\n", "movies_original_df['fox'] = fox\n", "\n", "# PARAMOUNT\n", "paramount = []\n", "for movie in movies_original_df['production_companies']:\n", " if \"Paramount\" in movie:\n", " paramount.append(True)\n", " else:\n", " paramount.append(False)\n", "movies_original_df['paramount'] = paramount\n", "\n", "#UNIVERSAL\n", "universal = []\n", "for movie in movies_original_df['production_companies']:\n", " if \"Universal\" in movie:\n", " universal.append(True)\n", " else:\n", " universal.append(False)\n", "movies_original_df['universal'] = universal" ] }, { "cell_type": "code", "execution_count": 166, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sony = movies_original_df[movies_original_df.sony == True]\n", "wb = movies_original_df[movies_original_df.wb == True]\n", "disney = movies_original_df[movies_original_df.disney == True]\n", "fox = movies_original_df[movies_original_df.fox == True]\n", "universal = movies_original_df[movies_original_df.universal == True]\n", "paramount = movies_original_df[movies_original_df.paramount == True]\n", "dw = movies_original_df[movies_original_df.dw == True]\n", "mgm = movies_original_df[movies_original_df.mgm == True]\n", "\n", "plt.plot(sony.budget, sony.revenue, 'o', label=\"Sony\")\n", "plt.plot(wb.budget, wb.revenue, 'o', label=\"Warner Bros.\")\n", "plt.plot(disney.budget, disney.revenue, 'o', label=\"Disney\")\n", "plt.plot(fox.budget, fox.revenue, 'o', label=\"Fox\")\n", "plt.plot(universal.budget, universal.revenue, 'o', label=\"Universal\")\n", "plt.plot(paramount.budget, paramount.revenue, 'o', label=\"Paramount\")\n", "plt.plot(dw.budget, dw.revenue, 'o', label=\"DreamWorks\")\n", "plt.plot(mgm.budget, mgm.revenue, 'o', label=\"MGM\")\n", "\n", "plt.title('BUDGET vs REVENUE by PRODUCTION COMPANY')\n", "plt.xlabel('BUDGET')\n", "plt.ylabel('REVENUE')\n", "plt.legend( loc = \"lower center\", bbox_to_anchor = (.5, -.4), ncol = 4, title = \"Production Company\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 167, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sony = movies_original_df[movies_original_df.sony == True]\n", "wb = movies_original_df[movies_original_df.wb == True]\n", "disney = movies_original_df[movies_original_df.disney == True]\n", "fox = movies_original_df[movies_original_df.fox == True]\n", "universal = movies_original_df[movies_original_df.universal == True]\n", "paramount = movies_original_df[movies_original_df.paramount == True]\n", "dw = movies_original_df[movies_original_df.dw == True]\n", "mgm = movies_original_df[movies_original_df.mgm == True]\n", "\n", "plt.plot(sony.budget, sony.percent_profit, 'o', label=\"Sony\")\n", "plt.plot(wb.budget, wb.percent_profit, 'o', label=\"Warner Bros.\")\n", "plt.plot(disney.budget, disney.percent_profit, 'o', label=\"Disney\")\n", "plt.plot(fox.budget, fox.percent_profit, 'o', label=\"Fox\")\n", "plt.plot(universal.budget, universal.percent_profit, 'o', label=\"Universal\")\n", "plt.plot(paramount.budget, paramount.percent_profit, 'o', label=\"Paramount\")\n", "plt.plot(dw.budget, dw.percent_profit, 'o', label=\"DreamWorks\")\n", "plt.plot(mgm.budget, mgm.percent_profit, 'o', label=\"MGM\")\n", "\n", "plt.title('BUDGET vs PERCENT PROFIT by PRODUCTION COMPANY')\n", "plt.xlabel('BUDGET')\n", "plt.ylabel('PERCENT PROFIT')\n", "plt.legend( loc = \"lower center\", bbox_to_anchor = (.5, -.4), ncol = 4, title = \"Production Company\")\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }