{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Import Data and Exploration " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from pandas_profiling import ProfileReport\n", "import os\n", "import sys\n", "import plotly.express as px" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/joao_victor/netflix-recommendation-app\n" ] } ], "source": [ "# Setting the working path\n", "\n", "os.chdir(\"../\") # remove the last directory\n", "path = os.getcwd()\n", "print(path)" ] }, { "cell_type": "code", "execution_count": 3, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
person_ididnamecharacterrole
03748tm84618Robert De NiroTravis BickleACTOR
114658tm84618Jodie FosterIris SteensmaACTOR
27064tm84618Albert BrooksTomACTOR
33739tm84618Harvey KeitelMatthew 'Sport' HigginsACTOR
448933tm84618Cybill ShepherdBetsyACTOR
\n", "
" ], "text/plain": [ " person_id id name character role\n", "0 3748 tm84618 Robert De Niro Travis Bickle ACTOR\n", "1 14658 tm84618 Jodie Foster Iris Steensma ACTOR\n", "2 7064 tm84618 Albert Brooks Tom ACTOR\n", "3 3739 tm84618 Harvey Keitel Matthew 'Sport' Higgins ACTOR\n", "4 48933 tm84618 Cybill Shepherd Betsy ACTOR" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Reading credits table\n", "\n", "df_credits = pd.read_csv(path + \"/data/input/credits.csv\")\n", "df_credits.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 77213 entries, 0 to 77212\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 person_id 77213 non-null int64 \n", " 1 id 77213 non-null object\n", " 2 name 77213 non-null object\n", " 3 character 67586 non-null object\n", " 4 role 77213 non-null object\n", "dtypes: int64(1), object(4)\n", "memory usage: 2.9+ MB\n" ] } ], "source": [ "df_credits.info()" ] }, { "cell_type": "code", "execution_count": 5, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idtitletypedescriptionrelease_yearage_certificationruntimegenresproduction_countriesseasonsimdb_idimdb_scoreimdb_votestmdb_popularitytmdb_score
0ts300399Five Came Back: The Reference FilmsSHOWThis collection includes 12 World War II-era p...1945TV-MA48['documentation']['US']1.0NaNNaNNaN0.600NaN
1tm84618Taxi DriverMOVIEA mentally unstable Vietnam War veteran works ...1976R113['crime', 'drama']['US']NaNtt00753148.3795222.027.6128.2
\n", "
" ], "text/plain": [ " id title type \\\n", "0 ts300399 Five Came Back: The Reference Films SHOW \n", "1 tm84618 Taxi Driver MOVIE \n", "\n", " description release_year \\\n", "0 This collection includes 12 World War II-era p... 1945 \n", "1 A mentally unstable Vietnam War veteran works ... 1976 \n", "\n", " age_certification runtime genres production_countries \\\n", "0 TV-MA 48 ['documentation'] ['US'] \n", "1 R 113 ['crime', 'drama'] ['US'] \n", "\n", " seasons imdb_id imdb_score imdb_votes tmdb_popularity tmdb_score \n", "0 1.0 NaN NaN NaN 0.600 NaN \n", "1 NaN tt0075314 8.3 795222.0 27.612 8.2 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Reading titles table\n", "\n", "df_titles = pd.read_csv(path + \"/data/input/titles.csv\")\n", "df_titles.head(2)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 5806 entries, 0 to 5805\n", "Data columns (total 15 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 id 5806 non-null object \n", " 1 title 5805 non-null object \n", " 2 type 5806 non-null object \n", " 3 description 5788 non-null object \n", " 4 release_year 5806 non-null int64 \n", " 5 age_certification 3196 non-null object \n", " 6 runtime 5806 non-null int64 \n", " 7 genres 5806 non-null object \n", " 8 production_countries 5806 non-null object \n", " 9 seasons 2047 non-null float64\n", " 10 imdb_id 5362 non-null object \n", " 11 imdb_score 5283 non-null float64\n", " 12 imdb_votes 5267 non-null float64\n", " 13 tmdb_popularity 5712 non-null float64\n", " 14 tmdb_score 5488 non-null float64\n", "dtypes: float64(5), int64(2), object(8)\n", "memory usage: 680.5+ KB\n" ] } ], "source": [ "df_titles.info()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# # Aggregate name column by movie id separeting by comma\n", "\n", "# df_credits_agg = df_credits.groupby('id')['name'].agg(','.join).to_frame().reset_index()\n", "# df_credits_agg" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Validate the aggregation\n", "\n", "# df_credits.loc[df_credits['id'] == 'ts9794']" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Merge actors name column from df_titles in df_credits to have a concatenate column with all actors by movie\n", "\n", "# df_merged_titles = pd.merge(df_titles, df_credits_agg, left_on = \"id\", right_on = \"id\")\n", "# df_merged_titles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Nulls Handling" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "seasons 3759\n", "age_certification 2610\n", "imdb_votes 539\n", "imdb_score 523\n", "imdb_id 444\n", "tmdb_score 318\n", "tmdb_popularity 94\n", "description 18\n", "title 1\n", "id 0\n", "type 0\n", "release_year 0\n", "runtime 0\n", "genres 0\n", "production_countries 0\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking for 'null' values\n", "\n", "df_titles.isnull().sum().sort_values(ascending=False)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([nan])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checkin if all rows with 'type' == 'MOVIE' have just null values in seasons column\n", "\n", "df_titles.loc[df_titles[\"type\"] == \"MOVIE\"][\"seasons\"].unique()\n", "\n", "# As seen all null values in season column are from movies, so we'll replace null values for zeros" ] }, { "cell_type": "code", "execution_count": 12, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idtitletypedescriptionrelease_yearage_certificationruntimegenresproduction_countriesseasonsimdb_idimdb_scoreimdb_votestmdb_popularitytmdb_score
0ts300399Five Came Back: The Reference FilmsSHOWThis collection includes 12 World War II-era p...1945TV-MA48['documentation']['US']1.0NaNNaNNaN0.600NaN
1tm84618Taxi DriverMOVIEA mentally unstable Vietnam War veteran works ...1976R113['crime', 'drama']['US']0.0tt00753148.3795222.027.6128.2
2tm127384Monty Python and the Holy GrailMOVIEKing Arthur, accompanied by his squire, recrui...1975PG91['comedy', 'fantasy']['GB']0.0tt00718538.2530877.018.2167.8
3tm70993Life of BrianMOVIEBrian Cohen is an average young Jewish man, bu...1979R94['comedy']['GB']0.0tt00794708.0392419.017.5057.8
4tm190788The ExorcistMOVIE12-year-old Regan MacNeil begins to adapt an e...1973R133['horror']['US']0.0tt00700478.1391942.095.3377.7
................................................
5801tm1014599Fine WineMOVIEA beautiful love story that can happen between...2021no_certification100['romance', 'drama']['NG']0.0tt138574806.939.00.966NaN
5802tm1108171Edis StarlightMOVIERising star Edis's career journey with ups and...2021no_certification74['music', 'documentation'][]0.0NaNNaNNaN1.0368.5
5803tm1045018ClashMOVIEA man from Nigeria returns to his family in Ca...2021no_certification88['family', 'drama']['NG', 'CA']0.0tt146207326.532.00.709NaN
5804tm1098060Shadow PartiesMOVIEA family faces destruction in a long-running c...2021no_certification116['action', 'thriller'][]0.0tt101680946.29.02.186NaN
5805ts271048Mighty Little Bheem: Kite FestivalSHOWWith winter behind them, Bheem and his townspe...2021no_certification0['family', 'comedy', 'animation'][]1.0tt137110948.816.00.97910.0
\n", "

5806 rows × 15 columns

\n", "
" ], "text/plain": [ " id title type \\\n", "0 ts300399 Five Came Back: The Reference Films SHOW \n", "1 tm84618 Taxi Driver MOVIE \n", "2 tm127384 Monty Python and the Holy Grail MOVIE \n", "3 tm70993 Life of Brian MOVIE \n", "4 tm190788 The Exorcist MOVIE \n", "... ... ... ... \n", "5801 tm1014599 Fine Wine MOVIE \n", "5802 tm1108171 Edis Starlight MOVIE \n", "5803 tm1045018 Clash MOVIE \n", "5804 tm1098060 Shadow Parties MOVIE \n", "5805 ts271048 Mighty Little Bheem: Kite Festival SHOW \n", "\n", " description release_year \\\n", "0 This collection includes 12 World War II-era p... 1945 \n", "1 A mentally unstable Vietnam War veteran works ... 1976 \n", "2 King Arthur, accompanied by his squire, recrui... 1975 \n", "3 Brian Cohen is an average young Jewish man, bu... 1979 \n", "4 12-year-old Regan MacNeil begins to adapt an e... 1973 \n", "... ... ... \n", "5801 A beautiful love story that can happen between... 2021 \n", "5802 Rising star Edis's career journey with ups and... 2021 \n", "5803 A man from Nigeria returns to his family in Ca... 2021 \n", "5804 A family faces destruction in a long-running c... 2021 \n", "5805 With winter behind them, Bheem and his townspe... 2021 \n", "\n", " age_certification runtime genres \\\n", "0 TV-MA 48 ['documentation'] \n", "1 R 113 ['crime', 'drama'] \n", "2 PG 91 ['comedy', 'fantasy'] \n", "3 R 94 ['comedy'] \n", "4 R 133 ['horror'] \n", "... ... ... ... \n", "5801 no_certification 100 ['romance', 'drama'] \n", "5802 no_certification 74 ['music', 'documentation'] \n", "5803 no_certification 88 ['family', 'drama'] \n", "5804 no_certification 116 ['action', 'thriller'] \n", "5805 no_certification 0 ['family', 'comedy', 'animation'] \n", "\n", " production_countries seasons imdb_id imdb_score imdb_votes \\\n", "0 ['US'] 1.0 NaN NaN NaN \n", "1 ['US'] 0.0 tt0075314 8.3 795222.0 \n", "2 ['GB'] 0.0 tt0071853 8.2 530877.0 \n", "3 ['GB'] 0.0 tt0079470 8.0 392419.0 \n", "4 ['US'] 0.0 tt0070047 8.1 391942.0 \n", "... ... ... ... ... ... \n", "5801 ['NG'] 0.0 tt13857480 6.9 39.0 \n", "5802 [] 0.0 NaN NaN NaN \n", "5803 ['NG', 'CA'] 0.0 tt14620732 6.5 32.0 \n", "5804 [] 0.0 tt10168094 6.2 9.0 \n", "5805 [] 1.0 tt13711094 8.8 16.0 \n", "\n", " tmdb_popularity tmdb_score \n", "0 0.600 NaN \n", "1 27.612 8.2 \n", "2 18.216 7.8 \n", "3 17.505 7.8 \n", "4 95.337 7.7 \n", "... ... ... \n", "5801 0.966 NaN \n", "5802 1.036 8.5 \n", "5803 0.709 NaN \n", "5804 2.186 NaN \n", "5805 0.979 10.0 \n", "\n", "[5806 rows x 15 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define function to replace null values\n", "\n", "\n", "def replace_nulls(df: pd.DataFrame, columns: list, value: float) -> pd.DataFrame:\n", " \"\"\"\n", " Substitute null values for specified value in a dataframe column(s) inplace.\n", "\n", " Args:\n", " df (pd.DataFrame): Pandas Dataframe\n", " columns (list): Column(s) for transformation\n", " value (float): Value to replace\n", "\n", " Returns:\n", " pd.DataFrame: Pandas Dataframe\n", " \"\"\"\n", " for i in columns:\n", " df[i].fillna(value, inplace=True)\n", " return df\n", "\n", "\n", "replace_nulls(df_titles, [\"seasons\"], 0)\n", "replace_nulls(df_titles, [\"age_certification\"], \"no_certification\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tmdb_score 318\n", "imdb_score 523\n", "dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 'tmdb_score' and 'imdb_score' columns have differents rows with null values\n", "\n", "df_titles[[\"tmdb_score\", \"imdb_score\"]].isnull().sum()\n", "\n", "# So we can substitute these null values for the column mean" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Check for normal distribution of the imdb_score column\n", "\n", "sns.distplot(df_titles[\"imdb_score\"])\n", "plt.xlabel(\"Imdb Score\")\n", "plt.ylabel(\"Frequencia\")\n", "\n", "plt.axvline(x=df_titles[\"imdb_score\"].mean(), color=\"red\", label=\"Média\") # média\n", "plt.axvline(\n", " x=df_titles[\"imdb_score\"].median(), color=\"blue\", label=\"Mediana\"\n", ") # mediana\n", "\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", " warnings.warn(msg, FutureWarning)\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Check for normal distribution of the tmdb_score column\n", "\n", "sns.distplot(df_titles[\"tmdb_score\"])\n", "plt.xlabel(\"Tmdb Score\")\n", "plt.ylabel(\"Frequencia\")\n", "\n", "plt.axvline(x=df_titles[\"tmdb_score\"].mean(), color=\"red\", label=\"Média\") # média\n", "plt.axvline(\n", " x=df_titles[\"tmdb_score\"].median(), color=\"blue\", label=\"Mediana\"\n", ") # mediana\n", "\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 5283.000000\n", "mean 6.533447\n", "std 1.160932\n", "min 1.500000\n", "25% 5.800000\n", "50% 6.600000\n", "75% 7.400000\n", "max 9.600000\n", "Name: imdb_score, dtype: float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can algo use describe function to see the mean and median values\n", "\n", "df_titles[\"imdb_score\"].describe()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# These columns are on the same number scale (same min and max values), thus we can create a column for universal score, using tmdb score whenever there is no imdb score\n", "\n", "# df_titles[['tmdb_score', 'imdb_score']].describe()" ] }, { "cell_type": "code", "execution_count": 18, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
column_namepercent_missing
idid0.000000
titletitle0.017224
typetype0.000000
descriptiondescription0.310024
release_yearrelease_year0.000000
age_certificationage_certification0.000000
runtimeruntime0.000000
genresgenres0.000000
production_countriesproduction_countries0.000000
seasonsseasons0.000000
imdb_idimdb_id7.647261
imdb_scoreimdb_score9.007923
imdb_votesimdb_votes9.283500
tmdb_popularitytmdb_popularity1.619015
tmdb_scoretmdb_score5.477093
\n", "
" ], "text/plain": [ " column_name percent_missing\n", "id id 0.000000\n", "title title 0.017224\n", "type type 0.000000\n", "description description 0.310024\n", "release_year release_year 0.000000\n", "age_certification age_certification 0.000000\n", "runtime runtime 0.000000\n", "genres genres 0.000000\n", "production_countries production_countries 0.000000\n", "seasons seasons 0.000000\n", "imdb_id imdb_id 7.647261\n", "imdb_score imdb_score 9.007923\n", "imdb_votes imdb_votes 9.283500\n", "tmdb_popularity tmdb_popularity 1.619015\n", "tmdb_score tmdb_score 5.477093" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check the % of null values in the dataset.\n", "\n", "df_missing = df_titles[[\"tmdb_score\", \"imdb_score\"]]\n", "percent_missing = df_titles.isnull().sum() * 100 / len(df_titles)\n", "missing_value_df = pd.DataFrame(\n", " {\"column_name\": df_titles.columns, \"percent_missing\": percent_missing}\n", ")\n", "missing_value_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use \"substitute with the mean\" strategy " ] }, { "cell_type": "code", "execution_count": 19, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idtitletypedescriptionrelease_yearage_certificationruntimegenresproduction_countriesseasonsimdb_idimdb_scoreimdb_votestmdb_popularitytmdb_score
0ts300399Five Came Back: The Reference FilmsSHOWThis collection includes 12 World War II-era p...1945TV-MA48['documentation']['US']1.0NaN6.533447NaN0.6006.818039
1tm84618Taxi DriverMOVIEA mentally unstable Vietnam War veteran works ...1976R113['crime', 'drama']['US']0.0tt00753148.300000795222.027.6128.200000
2tm127384Monty Python and the Holy GrailMOVIEKing Arthur, accompanied by his squire, recrui...1975PG91['comedy', 'fantasy']['GB']0.0tt00718538.200000530877.018.2167.800000
3tm70993Life of BrianMOVIEBrian Cohen is an average young Jewish man, bu...1979R94['comedy']['GB']0.0tt00794708.000000392419.017.5057.800000
4tm190788The ExorcistMOVIE12-year-old Regan MacNeil begins to adapt an e...1973R133['horror']['US']0.0tt00700478.100000391942.095.3377.700000
................................................
5801tm1014599Fine WineMOVIEA beautiful love story that can happen between...2021no_certification100['romance', 'drama']['NG']0.0tt138574806.90000039.00.9666.818039
5802tm1108171Edis StarlightMOVIERising star Edis's career journey with ups and...2021no_certification74['music', 'documentation'][]0.0NaN6.533447NaN1.0368.500000
5803tm1045018ClashMOVIEA man from Nigeria returns to his family in Ca...2021no_certification88['family', 'drama']['NG', 'CA']0.0tt146207326.50000032.00.7096.818039
5804tm1098060Shadow PartiesMOVIEA family faces destruction in a long-running c...2021no_certification116['action', 'thriller'][]0.0tt101680946.2000009.02.1866.818039
5805ts271048Mighty Little Bheem: Kite FestivalSHOWWith winter behind them, Bheem and his townspe...2021no_certification0['family', 'comedy', 'animation'][]1.0tt137110948.80000016.00.97910.000000
\n", "

5806 rows × 15 columns

\n", "
" ], "text/plain": [ " id title type \\\n", "0 ts300399 Five Came Back: The Reference Films SHOW \n", "1 tm84618 Taxi Driver MOVIE \n", "2 tm127384 Monty Python and the Holy Grail MOVIE \n", "3 tm70993 Life of Brian MOVIE \n", "4 tm190788 The Exorcist MOVIE \n", "... ... ... ... \n", "5801 tm1014599 Fine Wine MOVIE \n", "5802 tm1108171 Edis Starlight MOVIE \n", "5803 tm1045018 Clash MOVIE \n", "5804 tm1098060 Shadow Parties MOVIE \n", "5805 ts271048 Mighty Little Bheem: Kite Festival SHOW \n", "\n", " description release_year \\\n", "0 This collection includes 12 World War II-era p... 1945 \n", "1 A mentally unstable Vietnam War veteran works ... 1976 \n", "2 King Arthur, accompanied by his squire, recrui... 1975 \n", "3 Brian Cohen is an average young Jewish man, bu... 1979 \n", "4 12-year-old Regan MacNeil begins to adapt an e... 1973 \n", "... ... ... \n", "5801 A beautiful love story that can happen between... 2021 \n", "5802 Rising star Edis's career journey with ups and... 2021 \n", "5803 A man from Nigeria returns to his family in Ca... 2021 \n", "5804 A family faces destruction in a long-running c... 2021 \n", "5805 With winter behind them, Bheem and his townspe... 2021 \n", "\n", " age_certification runtime genres \\\n", "0 TV-MA 48 ['documentation'] \n", "1 R 113 ['crime', 'drama'] \n", "2 PG 91 ['comedy', 'fantasy'] \n", "3 R 94 ['comedy'] \n", "4 R 133 ['horror'] \n", "... ... ... ... \n", "5801 no_certification 100 ['romance', 'drama'] \n", "5802 no_certification 74 ['music', 'documentation'] \n", "5803 no_certification 88 ['family', 'drama'] \n", "5804 no_certification 116 ['action', 'thriller'] \n", "5805 no_certification 0 ['family', 'comedy', 'animation'] \n", "\n", " production_countries seasons imdb_id imdb_score imdb_votes \\\n", "0 ['US'] 1.0 NaN 6.533447 NaN \n", "1 ['US'] 0.0 tt0075314 8.300000 795222.0 \n", "2 ['GB'] 0.0 tt0071853 8.200000 530877.0 \n", "3 ['GB'] 0.0 tt0079470 8.000000 392419.0 \n", "4 ['US'] 0.0 tt0070047 8.100000 391942.0 \n", "... ... ... ... ... ... \n", "5801 ['NG'] 0.0 tt13857480 6.900000 39.0 \n", "5802 [] 0.0 NaN 6.533447 NaN \n", "5803 ['NG', 'CA'] 0.0 tt14620732 6.500000 32.0 \n", "5804 [] 0.0 tt10168094 6.200000 9.0 \n", "5805 [] 1.0 tt13711094 8.800000 16.0 \n", "\n", " tmdb_popularity tmdb_score \n", "0 0.600 6.818039 \n", "1 27.612 8.200000 \n", "2 18.216 7.800000 \n", "3 17.505 7.800000 \n", "4 95.337 7.700000 \n", "... ... ... \n", "5801 0.966 6.818039 \n", "5802 1.036 8.500000 \n", "5803 0.709 6.818039 \n", "5804 2.186 6.818039 \n", "5805 0.979 10.000000 \n", "\n", "[5806 rows x 15 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define function to replace null values for column mean\n", "\n", "\n", "def null_for_mean(df: pd.DataFrame, columns: list) -> pd.DataFrame:\n", " \"\"\"\n", " Substitute null values for the specified column mean.\n", "\n", " Args:\n", " df (pd.DataFrame): Pandas Dataframe\n", " columns (list): column(s) for transformation\n", "\n", " Returns:\n", " pd.DataFrame: Pandas Dataframe\n", " \"\"\"\n", " for i in columns:\n", " df[i].fillna(df[i].mean(), inplace=True)\n", " return df\n", "\n", "\n", "null_for_mean(\n", " df_titles, [\"imdb_score\", \"tmdb_score\"]\n", ") # replace null for mean in 'imdb_score', 'tmdb_score' columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tmdb_score 0\n", "imdb_score 0\n", "dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Validate change of null replace to mean\n", "\n", "df_titles[[\"tmdb_score\", \"imdb_score\"]].isnull().sum()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "imdb_votes 539\n", "imdb_id 444\n", "tmdb_popularity 94\n", "description 18\n", "title 1\n", "id 0\n", "type 0\n", "release_year 0\n", "age_certification 0\n", "runtime 0\n", "genres 0\n", "production_countries 0\n", "seasons 0\n", "imdb_score 0\n", "tmdb_score 0\n", "dtype: int64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checkin columns with null values still\n", "\n", "df_titles.isnull().sum().sort_values(ascending=False)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_8096/1949421916.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.dropna will be keyword-only.\n", " df_titles.dropna(0, subset=[\"description\", \"title\", \"tmdb_popularity\"], inplace=True)\n" ] } ], "source": [ "# Remove rows with null values in description, title and tmdb_popularity columns\n", "\n", "df_titles.dropna(0, subset=[\"description\", \"title\", \"tmdb_popularity\"], inplace=True)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "imdb_votes 516\n", "imdb_id 431\n", "id 0\n", "title 0\n", "type 0\n", "description 0\n", "release_year 0\n", "age_certification 0\n", "runtime 0\n", "genres 0\n", "production_countries 0\n", "seasons 0\n", "imdb_score 0\n", "tmdb_popularity 0\n", "tmdb_score 0\n", "dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_titles.isnull().sum().sort_values(ascending=False)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# Extracting the first value from genres array\n", "\n", "genres = {}\n", "\n", "\n", "def transform_genres(row):\n", " parsed = (str(row)[1:-1]).split(\",\")\n", "\n", " for i in range(len(parsed)):\n", " parsed[i] = parsed[i].strip()[1:-1]\n", "\n", " for i in parsed:\n", " if i not in genres.keys():\n", " genres[i] = 0\n", " continue\n", " genres[i] += 1\n", "\n", " return parsed[0] if parsed[0] != \"\" else \"none\"\n", "\n", "\n", "df_titles[\"genres_transformed\"] = df_titles[\"genres\"].map(transform_genres)\n", "df_titles[\"production_countries_transformed\"] = df_titles[\"production_countries\"].map(\n", " transform_genres\n", ")\n", "\n", "# another way\n", "# import ast\n", "# df_titles['new_col'] = df_titles['genres'].apply(ast.literal_eval).str[0]\n", "# df_titles.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exploratory Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we will create a recommendation app after the exploration, so we have to analyze and look for the most promissing faetures" ] }, { "cell_type": "code", "execution_count": 25, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idtitletypedescriptionrelease_yearage_certificationruntimegenresproduction_countriesseasonsimdb_idimdb_scoreimdb_votestmdb_popularitytmdb_scoregenres_transformedproduction_countries_transformed
0ts300399Five Came Back: The Reference FilmsSHOWThis collection includes 12 World War II-era p...1945TV-MA48['documentation']['US']1.0NaN6.533447NaN0.6006.818039documentationUS
1tm84618Taxi DriverMOVIEA mentally unstable Vietnam War veteran works ...1976R113['crime', 'drama']['US']0.0tt00753148.300000795222.027.6128.200000crimeUS
2tm127384Monty Python and the Holy GrailMOVIEKing Arthur, accompanied by his squire, recrui...1975PG91['comedy', 'fantasy']['GB']0.0tt00718538.200000530877.018.2167.800000comedyGB
\n", "
" ], "text/plain": [ " id title type \\\n", "0 ts300399 Five Came Back: The Reference Films SHOW \n", "1 tm84618 Taxi Driver MOVIE \n", "2 tm127384 Monty Python and the Holy Grail MOVIE \n", "\n", " description release_year \\\n", "0 This collection includes 12 World War II-era p... 1945 \n", "1 A mentally unstable Vietnam War veteran works ... 1976 \n", "2 King Arthur, accompanied by his squire, recrui... 1975 \n", "\n", " age_certification runtime genres production_countries \\\n", "0 TV-MA 48 ['documentation'] ['US'] \n", "1 R 113 ['crime', 'drama'] ['US'] \n", "2 PG 91 ['comedy', 'fantasy'] ['GB'] \n", "\n", " seasons imdb_id imdb_score imdb_votes tmdb_popularity tmdb_score \\\n", "0 1.0 NaN 6.533447 NaN 0.600 6.818039 \n", "1 0.0 tt0075314 8.300000 795222.0 27.612 8.200000 \n", "2 0.0 tt0071853 8.200000 530877.0 18.216 7.800000 \n", "\n", " genres_transformed production_countries_transformed \n", "0 documentation US \n", "1 crime US \n", "2 comedy GB " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_titles.head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Check correlation\n", "\n", "sns.heatmap(df_titles.corr(), cmap=\"Blues\", annot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analyzing Age Certification\n", "\n", "- **G:** General audiences – All ages admitted.\n", "- **PG:** Parental Guidance Suggested.\n", "- **PG-13:** Parents Strongly Cautioned.\n", "- **TV-MA:** Adults Only.\n", "- **TV-G:** Suitable for All Ages.\n", "- **TV-Y:** Appropriate for All Children.\n", "- **TV-Y7:** Designed for Children Age 7 and Above.\n", "- **TV-14** Parental Guidance Suggested for Children Under 14 Year of Age.\n", "- **TV-PG:** Parental Guidance Suggested.\n", "- **NC-17** Adults Only.\n", "- **R** Restricted. " ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_8096/3813717038.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n", " df_titles.groupby([\"age_certification\"])[\"imdb_score\", \"tmdb_score\"]\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data = (\n", " df_titles.groupby([\"age_certification\"])[\"imdb_score\", \"tmdb_score\"]\n", " .mean()\n", " .reset_index()\n", ")\n", "plt.figure(figsize=(9, 6))\n", "\n", "sns.lineplot(data=data, x=\"age_certification\", y=\"tmdb_score\")\n", "sns.lineplot(data=data, x=\"age_certification\", y=\"imdb_score\")\n", "\n", "# plt.grid()\n", "plt.legend(labels=[\"Imdb Score\", \"Tmdb Score\"])\n", "plt.xticks(rotation=45)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax1 = plt.subplots(figsize=(10, 5))\n", "\n", "data_score = df_titles.groupby([\"age_certification\"])[\"tmdb_score\"].mean().reset_index()\n", "data_popularity = (\n", " df_titles.groupby([\"age_certification\"])[\"tmdb_popularity\"].mean().reset_index()\n", ")\n", "\n", "ax1 = sns.lineplot(\n", " data=data_score,\n", " x=\"age_certification\",\n", " y=\"tmdb_score\",\n", " color=\"red\",\n", " ax=ax1,\n", " label=\"Tmdb Score\",\n", " marker=\"o\",\n", ")\n", "\n", "ax2 = ax1.twinx()\n", "ax2 = sns.lineplot(\n", " data=data_popularity,\n", " x=\"age_certification\",\n", " y=\"tmdb_popularity\",\n", " ax=ax2,\n", " label=\"Tmdb Popularity\",\n", " marker=\"o\",\n", ")\n", "\n", "# Show two lines legends\n", "\n", "lines_1, labels_1 = ax1.get_legend_handles_labels()\n", "lines_2, labels_2 = ax2.get_legend_handles_labels()\n", "\n", "lines = lines_1 + lines_2\n", "labels = labels_1 + labels_2\n", "\n", "ax1.legend(lines, labels, loc=0)\n", "ax2.get_legend().remove()\n", "\n", "plt.xticks(rotation=90)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['US', 'IN', 'GB', 'JP', 'KR']" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_top_country = (\n", " df_titles.groupby([\"production_countries_transformed\"])[\"id\"]\n", " .count()\n", " .reset_index()\n", " .sort_values(by=[\"id\"], ascending=False)\n", " .head(5)\n", ")\n", "top_countries = list(data_top_country[\"production_countries_transformed\"])\n", "top_countries" ] }, { "cell_type": "code", "execution_count": 30, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
indexproduction_countries_transformedgenres_transformedid
0493UScomedy569
1496USdrama319
2495USdocumentation316
3239INdrama239
4506USthriller138
...............
78283JPnone1
79279JPfamily1
80249INsport1
81245INnone1
82242INhistory1
\n", "

83 rows × 4 columns

\n", "
" ], "text/plain": [ " index production_countries_transformed genres_transformed id\n", "0 493 US comedy 569\n", "1 496 US drama 319\n", "2 495 US documentation 316\n", "3 239 IN drama 239\n", "4 506 US thriller 138\n", ".. ... ... ... ...\n", "78 283 JP none 1\n", "79 279 JP family 1\n", "80 249 IN sport 1\n", "81 245 IN none 1\n", "82 242 IN history 1\n", "\n", "[83 rows x 4 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_country = (\n", " df_titles.groupby([\"production_countries_transformed\", \"genres_transformed\"])[\"id\"]\n", " .count()\n", " .reset_index()\n", " .sort_values(by=[\"id\"], ascending=False)\n", ")\n", "data_top_country_df = data_country[\n", " data_country[\"production_countries_transformed\"].isin(top_countries)\n", "].reset_index()\n", "data_top_country_df" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n", "/home/joao_victor/anaconda3/envs/netflix-app/lib/python3.10/site-packages/plotly/express/_core.py:271: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", " trace_data = trace_data.append(trace_data.iloc[0])\n" ] }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "genres_transformed=comedy
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "comedy", "line": { "color": "#636efa", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "comedy", "r": [ 569, 105, 67, 35, 19, 569 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "IN", "GB", "KR", "JP", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=drama
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "drama", "line": { "color": "#EF553B", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "drama", "r": [ 319, 239, 80, 59, 53, 319 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "IN", "KR", "GB", "JP", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=documentation
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "documentation", "line": { "color": "#00cc96", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "documentation", "r": [ 316, 50, 9, 6, 5, 316 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "GB", "IN", "JP", "KR", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=thriller
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "thriller", "line": { "color": "#ab63fa", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "thriller", "r": [ 138, 96, 20, 16, 4, 138 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "IN", "GB", "KR", "JP", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=scifi
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "scifi", "line": { "color": "#FFA15A", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "scifi", "r": [ 103, 68, 17, 11, 3, 103 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "JP", "KR", "GB", "IN", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=reality
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "reality", "line": { "color": "#19d3f3", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "reality", "r": [ 94, 12, 10, 5, 3, 94 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "GB", "JP", "KR", "IN", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=action
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "action", "line": { "color": "#FF6692", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "action", "r": [ 92, 49, 26, 20, 11, 92 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "JP", "IN", "KR", "GB", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=animation
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "animation", "line": { "color": "#B6E880", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "animation", "r": [ 87, 27, 13, 9, 5, 87 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "JP", "GB", "IN", "KR", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=crime
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "crime", "line": { "color": "#FF97FF", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "crime", "r": [ 87, 27, 23, 12, 4, 87 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "GB", "IN", "KR", "JP", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=romance
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "romance", "line": { "color": "#FECB52", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "romance", "r": [ 65, 57, 9, 4, 3, 65 ], "showlegend": true, "subplot": "polar", "theta": [ "IN", "US", "GB", "JP", "KR", "IN" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=fantasy
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "fantasy", "line": { "color": "#636efa", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "fantasy", "r": [ 54, 23, 12, 5, 3, 54 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "JP", "IN", "GB", "KR", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=horror
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "horror", "line": { "color": "#EF553B", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "horror", "r": [ 51, 10, 5, 2, 2, 51 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "IN", "GB", "KR", "JP", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=family
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "family", "line": { "color": "#00cc96", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "family", "r": [ 32, 3, 3, 2, 1, 32 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "KR", "GB", "IN", "JP", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=music
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "music", "line": { "color": "#ab63fa", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "music", "r": [ 26, 3, 2, 2, 26 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "GB", "IN", "JP", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=western
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "western", "line": { "color": "#FFA15A", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "western", "r": [ 19, 1, 19 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "JP", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=none
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "none", "line": { "color": "#19d3f3", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "none", "r": [ 17, 3, 2, 1, 1, 17 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "KR", "GB", "JP", "IN", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=war
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "war", "line": { "color": "#FF6692", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "war", "r": [ 16, 6, 3, 2, 16 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "GB", "KR", "IN", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=history
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "history", "line": { "color": "#B6E880", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "history", "r": [ 2, 1, 2 ], "showlegend": true, "subplot": "polar", "theta": [ "US", "IN", "US" ], "type": "scatterpolar" }, { "hovertemplate": "genres_transformed=sport
id=%{r}
production_countries_transformed=%{theta}", "legendgroup": "sport", "line": { "color": "#FF97FF", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "sport", "r": [ 1, 1 ], "showlegend": true, "subplot": "polar", "theta": [ "IN", "IN" ], "type": "scatterpolar" } ], "layout": { "legend": { "title": { "text": "genres_transformed" }, "tracegroupgap": 0 }, "margin": { "t": 60 }, "polar": { "angularaxis": { "direction": "clockwise", "rotation": 90 }, "domain": { "x": [ 0, 1 ], "y": [ 0, 1 ] } }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.line_polar(\n", " data_top_country_df,\n", " r=\"id\",\n", " theta=\"production_countries_transformed\",\n", " line_close=True,\n", " color=\"genres_transformed\",\n", ")\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data_runtime = df_titles[[\"id\", \"runtime\", \"genres_transformed\"]]\n", "plt.figure(figsize=(16, 6))\n", "plt.suptitle(\n", " \"Data Distribution Across Genres\", fontsize=18, weight=600, color=\"#333d29\"\n", ")\n", "ax = sns.stripplot(\n", " x=data_runtime[\"genres_transformed\"], y=data_runtime[\"runtime\"], jitter=0.05, size=5\n", ")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 33, "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", " \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", "
idtitletypedescriptionrelease_yearage_certificationruntimegenresproduction_countriesseasonsimdb_idimdb_scoreimdb_votestmdb_popularitytmdb_scoregenres_transformedproduction_countries_transformed
0ts300399Five Came Back: The Reference FilmsSHOWThis collection includes 12 World War II-era p...1945TV-MA48['documentation']['US']1.0NaN6.533447NaN0.6006.818039documentationUS
1tm84618Taxi DriverMOVIEA mentally unstable Vietnam War veteran works ...1976R113['crime', 'drama']['US']0.0tt00753148.300000795222.027.6128.200000crimeUS
\n", "
" ], "text/plain": [ " id title type \\\n", "0 ts300399 Five Came Back: The Reference Films SHOW \n", "1 tm84618 Taxi Driver MOVIE \n", "\n", " description release_year \\\n", "0 This collection includes 12 World War II-era p... 1945 \n", "1 A mentally unstable Vietnam War veteran works ... 1976 \n", "\n", " age_certification runtime genres production_countries \\\n", "0 TV-MA 48 ['documentation'] ['US'] \n", "1 R 113 ['crime', 'drama'] ['US'] \n", "\n", " seasons imdb_id imdb_score imdb_votes tmdb_popularity tmdb_score \\\n", "0 1.0 NaN 6.533447 NaN 0.600 6.818039 \n", "1 0.0 tt0075314 8.300000 795222.0 27.612 8.200000 \n", "\n", " genres_transformed production_countries_transformed \n", "0 documentation US \n", "1 crime US " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_titles.head(2)" ] }, { "cell_type": "code", "execution_count": 34, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idtitletypedescriptionrelease_yearage_certificationruntimegenresproduction_countriesseasonsimdb_idimdb_scoreimdb_votestmdb_popularitytmdb_scoregenres_transformedproduction_countries_transformed
0ts300399Five Came Back: The Reference FilmsSHOWThis collection includes 12 World War II-era p...1945TV-MA48['documentation']['US']1.0NaN6.533447NaN0.6006.818039documentationUS
1tm84618Taxi DriverMOVIEA mentally unstable Vietnam War veteran works ...1976R113['crime', 'drama']['US']0.0tt00753148.300000795222.027.6128.200000crimeUS
2tm127384Monty Python and the Holy GrailMOVIEKing Arthur, accompanied by his squire, recrui...1975PG91['comedy', 'fantasy']['GB']0.0tt00718538.200000530877.018.2167.800000comedyGB
3tm70993Life of BrianMOVIEBrian Cohen is an average young Jewish man, bu...1979R94['comedy']['GB']0.0tt00794708.000000392419.017.5057.800000comedyGB
4tm190788The ExorcistMOVIE12-year-old Regan MacNeil begins to adapt an e...1973R133['horror']['US']0.0tt00700478.100000391942.095.3377.700000horrorUS
......................................................
5801tm1014599Fine WineMOVIEA beautiful love story that can happen between...2021no_certification100['romance', 'drama']['NG']0.0tt138574806.90000039.00.9666.818039romanceNG
5802tm1108171Edis StarlightMOVIERising star Edis's career journey with ups and...2021no_certification74['music', 'documentation'][]0.0NaN6.533447NaN1.0368.500000musicnone
5803tm1045018ClashMOVIEA man from Nigeria returns to his family in Ca...2021no_certification88['family', 'drama']['NG', 'CA']0.0tt146207326.50000032.00.7096.818039familyNG
5804tm1098060Shadow PartiesMOVIEA family faces destruction in a long-running c...2021no_certification116['action', 'thriller'][]0.0tt101680946.2000009.02.1866.818039actionnone
5805ts271048Mighty Little Bheem: Kite FestivalSHOWWith winter behind them, Bheem and his townspe...2021no_certification0['family', 'comedy', 'animation'][]1.0tt137110948.80000016.00.97910.000000familynone
\n", "

5699 rows × 17 columns

\n", "
" ], "text/plain": [ " id title type \\\n", "0 ts300399 Five Came Back: The Reference Films SHOW \n", "1 tm84618 Taxi Driver MOVIE \n", "2 tm127384 Monty Python and the Holy Grail MOVIE \n", "3 tm70993 Life of Brian MOVIE \n", "4 tm190788 The Exorcist MOVIE \n", "... ... ... ... \n", "5801 tm1014599 Fine Wine MOVIE \n", "5802 tm1108171 Edis Starlight MOVIE \n", "5803 tm1045018 Clash MOVIE \n", "5804 tm1098060 Shadow Parties MOVIE \n", "5805 ts271048 Mighty Little Bheem: Kite Festival SHOW \n", "\n", " description release_year \\\n", "0 This collection includes 12 World War II-era p... 1945 \n", "1 A mentally unstable Vietnam War veteran works ... 1976 \n", "2 King Arthur, accompanied by his squire, recrui... 1975 \n", "3 Brian Cohen is an average young Jewish man, bu... 1979 \n", "4 12-year-old Regan MacNeil begins to adapt an e... 1973 \n", "... ... ... \n", "5801 A beautiful love story that can happen between... 2021 \n", "5802 Rising star Edis's career journey with ups and... 2021 \n", "5803 A man from Nigeria returns to his family in Ca... 2021 \n", "5804 A family faces destruction in a long-running c... 2021 \n", "5805 With winter behind them, Bheem and his townspe... 2021 \n", "\n", " age_certification runtime genres \\\n", "0 TV-MA 48 ['documentation'] \n", "1 R 113 ['crime', 'drama'] \n", "2 PG 91 ['comedy', 'fantasy'] \n", "3 R 94 ['comedy'] \n", "4 R 133 ['horror'] \n", "... ... ... ... \n", "5801 no_certification 100 ['romance', 'drama'] \n", "5802 no_certification 74 ['music', 'documentation'] \n", "5803 no_certification 88 ['family', 'drama'] \n", "5804 no_certification 116 ['action', 'thriller'] \n", "5805 no_certification 0 ['family', 'comedy', 'animation'] \n", "\n", " production_countries seasons imdb_id imdb_score imdb_votes \\\n", "0 ['US'] 1.0 NaN 6.533447 NaN \n", "1 ['US'] 0.0 tt0075314 8.300000 795222.0 \n", "2 ['GB'] 0.0 tt0071853 8.200000 530877.0 \n", "3 ['GB'] 0.0 tt0079470 8.000000 392419.0 \n", "4 ['US'] 0.0 tt0070047 8.100000 391942.0 \n", "... ... ... ... ... ... \n", "5801 ['NG'] 0.0 tt13857480 6.900000 39.0 \n", "5802 [] 0.0 NaN 6.533447 NaN \n", "5803 ['NG', 'CA'] 0.0 tt14620732 6.500000 32.0 \n", "5804 [] 0.0 tt10168094 6.200000 9.0 \n", "5805 [] 1.0 tt13711094 8.800000 16.0 \n", "\n", " tmdb_popularity tmdb_score genres_transformed \\\n", "0 0.600 6.818039 documentation \n", "1 27.612 8.200000 crime \n", "2 18.216 7.800000 comedy \n", "3 17.505 7.800000 comedy \n", "4 95.337 7.700000 horror \n", "... ... ... ... \n", "5801 0.966 6.818039 romance \n", "5802 1.036 8.500000 music \n", "5803 0.709 6.818039 family \n", "5804 2.186 6.818039 action \n", "5805 0.979 10.000000 family \n", "\n", " production_countries_transformed \n", "0 US \n", "1 US \n", "2 GB \n", "3 GB \n", "4 US \n", "... ... \n", "5801 NG \n", "5802 none \n", "5803 NG \n", "5804 none \n", "5805 none \n", "\n", "[5699 rows x 17 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_titles" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/joao_victor/netflix-recommendation-app/data/output/df_titles.csv\n" ] } ], "source": [ "out_path = os.getcwd() + \"/data/output/df_titles.csv\"\n", "print(out_path)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "df_titles.to_csv(out_path)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.10.4 ('netflix-app')", "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": "3.10.4" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "43130da1fbae14895eb338e4f0e60d3310ef5c08adc6957cf17fde952d5329db" } } }, "nbformat": 4, "nbformat_minor": 2 }