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add setup script
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{
"cells": [
{
"cell_type": "markdown",
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"id": "academic-calvin",
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"metadata": {},
"source": [
"### Skrypt do ściagnięcia zbiory danych"
]
},
{
"cell_type": "code",
"execution_count": null,
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"id": "compound-politics",
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"metadata": {},
"outputs": [],
"source": [
"!pip install --user kaggle \n",
"!pip install --user pandas\n",
"!pip install --user numpy\n",
"!pip install --user seaborn\n",
"!pip install -U scikit-learn"
]
},
{
"cell_type": "code",
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"execution_count": null,
"id": "hundred-limitation",
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"metadata": {},
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"outputs": [],
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"source": [
"!echo \"Downloading dataset from Kaggle...\"\n",
"!kaggle datasets download -d harshitshankhdhar/imdb-dataset-of-top-1000-movies-and-tv-shows\n",
"!echo \"Done.\""
]
},
{
"cell_type": "code",
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"execution_count": null,
"id": "provincial-circuit",
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"metadata": {},
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"outputs": [],
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"source": [
"!echo \"Unzipping archive\"\n",
"!files=$(unzip imdb-dataset-of-top-1000-movies-and-tv-shows.zip | tail -n +2 | cut -d ' ' -f 4)\n",
"!echo \"Done.\""
]
},
{
"cell_type": "code",
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"execution_count": 81,
"id": "armed-brisbane",
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"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"data=pd.read_csv('imdb_top_1000.csv')\n",
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"# data"
]
},
{
"cell_type": "code",
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"execution_count": 82,
"id": "nominated-grenada",
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1001 imdb_top_1000.csv\n"
]
}
],
"source": [
"#Wielkosc zbioru\n",
"!wc -l imdb_top_1000.csv"
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]
},
{
"cell_type": "markdown",
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"id": "generic-success",
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"metadata": {},
"source": [
"## Usuwanie kolumn\n",
"- Poster_Link: kolumna zawierająca linki do plakatów promujących film\n",
"- Overview: kolumna zawierająca recenzje poszczególnych filmów"
]
},
{
"cell_type": "code",
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"execution_count": 83,
"id": "compliant-synthesis",
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"metadata": {},
"outputs": [],
"source": [
"data.drop(columns=[\"Poster_Link\"], inplace=True)\n",
"data.drop(columns=[\"Overview\"], inplace=True)\n",
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"\n",
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"# data"
]
},
{
"cell_type": "code",
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"execution_count": 84,
"id": "reserved-whole",
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"metadata": {},
"outputs": [],
"source": [
"# Lowercase na polach tekstowych\n",
"data[\"Series_Title\"] = data[\"Series_Title\"].str.lower()\n",
"data[\"Genre\"] = data[\"Genre\"].str.lower()\n",
"data[\"Director\"] = data[\"Director\"].str.lower()\n",
"data[\"Star1\"] = data[\"Star1\"].str.lower()\n",
"data[\"Star2\"] = data[\"Star2\"].str.lower()\n",
"data[\"Star3\"] = data[\"Star3\"].str.lower()\n",
"data[\"Star4\"] = data[\"Star4\"].str.lower()\n",
"\n",
"# Usunięcie Nan i string to int \n",
"data = data.replace(np.nan, '', regex=True)\n",
"data[\"Gross\"] = data[\"Gross\"].str.replace(',', '')\n",
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"data[\"Gross\"] = pd.to_numeric(data[\"Gross\"], errors='coerce')\n",
"\n",
"data = data.dropna()"
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]
},
{
"cell_type": "code",
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"execution_count": 86,
"id": "given-sodium",
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"metadata": {},
"outputs": [
{
"data": {
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"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Series_Title</th>\n",
" <th>Released_Year</th>\n",
" <th>Certificate</th>\n",
" <th>Runtime</th>\n",
" <th>Genre</th>\n",
" <th>IMDB_Rating</th>\n",
" <th>Meta_score</th>\n",
" <th>Director</th>\n",
" <th>Star1</th>\n",
" <th>Star2</th>\n",
" <th>Star3</th>\n",
" <th>Star4</th>\n",
" <th>No_of_Votes</th>\n",
" <th>Gross</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>831</td>\n",
" <td>831</td>\n",
" <td>831</td>\n",
" <td>831</td>\n",
" <td>831</td>\n",
" <td>831.000000</td>\n",
" <td>831</td>\n",
" <td>831</td>\n",
" <td>831</td>\n",
" <td>831</td>\n",
" <td>831</td>\n",
" <td>831</td>\n",
" <td>8.310000e+02</td>\n",
" <td>8.310000e+02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>831</td>\n",
" <td>95</td>\n",
" <td>14</td>\n",
" <td>133</td>\n",
" <td>182</td>\n",
" <td>NaN</td>\n",
" <td>64</td>\n",
" <td>472</td>\n",
" <td>556</td>\n",
" <td>704</td>\n",
" <td>737</td>\n",
" <td>782</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>a streetcar named desire</td>\n",
" <td>2014</td>\n",
" <td>U</td>\n",
" <td>101 min</td>\n",
" <td>drama</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>steven spielberg</td>\n",
" <td>tom hanks</td>\n",
" <td>emma watson</td>\n",
" <td>rupert grint</td>\n",
" <td>michael caine</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>1</td>\n",
" <td>31</td>\n",
" <td>200</td>\n",
" <td>21</td>\n",
" <td>75</td>\n",
" <td>NaN</td>\n",
" <td>81</td>\n",
" <td>13</td>\n",
" <td>12</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.946931</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.152499e+05</td>\n",
" <td>6.803475e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.283204</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.436443e+05</td>\n",
" <td>1.097500e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.600000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.508800e+04</td>\n",
" <td>1.305000e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.700000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.143000e+04</td>\n",
" <td>3.253559e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.900000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.867340e+05</td>\n",
" <td>2.353089e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.100000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.457210e+05</td>\n",
" <td>8.075089e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9.300000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.343110e+06</td>\n",
" <td>9.366622e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text/plain": [
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" Series_Title Released_Year Certificate Runtime Genre \\\n",
"count 831 831 831 831 831 \n",
"unique 831 95 14 133 182 \n",
"top a streetcar named desire 2014 U 101 min drama \n",
"freq 1 31 200 21 75 \n",
"mean NaN NaN NaN NaN NaN \n",
"std NaN NaN NaN NaN NaN \n",
"min NaN NaN NaN NaN NaN \n",
"25% NaN NaN NaN NaN NaN \n",
"50% NaN NaN NaN NaN NaN \n",
"75% NaN NaN NaN NaN NaN \n",
"max NaN NaN NaN NaN NaN \n",
"\n",
" IMDB_Rating Meta_score Director Star1 Star2 \\\n",
"count 831.000000 831 831 831 831 \n",
"unique NaN 64 472 556 704 \n",
"top NaN steven spielberg tom hanks emma watson \n",
"freq NaN 81 13 12 7 \n",
"mean 7.946931 NaN NaN NaN NaN \n",
"std 0.283204 NaN NaN NaN NaN \n",
"min 7.600000 NaN NaN NaN NaN \n",
"25% 7.700000 NaN NaN NaN NaN \n",
"50% 7.900000 NaN NaN NaN NaN \n",
"75% 8.100000 NaN NaN NaN NaN \n",
"max 9.300000 NaN NaN NaN NaN \n",
"\n",
" Star3 Star4 No_of_Votes Gross \n",
"count 831 831 8.310000e+02 8.310000e+02 \n",
"unique 737 782 NaN NaN \n",
"top rupert grint michael caine NaN NaN \n",
"freq 5 4 NaN NaN \n",
"mean NaN NaN 3.152499e+05 6.803475e+07 \n",
"std NaN NaN 3.436443e+05 1.097500e+08 \n",
"min NaN NaN 2.508800e+04 1.305000e+03 \n",
"25% NaN NaN 7.143000e+04 3.253559e+06 \n",
"50% NaN NaN 1.867340e+05 2.353089e+07 \n",
"75% NaN NaN 4.457210e+05 8.075089e+07 \n",
"max NaN NaN 2.343110e+06 9.366622e+08 "
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]
},
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"execution_count": 86,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"data.describe(include='all')"
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]
},
{
"cell_type": "code",
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"execution_count": 74,
"id": "effective-treasury",
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"IMDB_Rating 7.9\n",
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"No_of_Votes 186734.0\n",
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"Gross 23530892.0\n",
"dtype: float64"
]
},
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"execution_count": 74,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.median()"
]
},
{
"cell_type": "code",
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"execution_count": 87,
"id": "egyptian-sacramento",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(831, 14)"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 88,
"id": "intended-christmas",
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"(601, 14)\n",
"(115, 14)\n",
"(115, 14)\n"
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]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"import sklearn\n",
"\n",
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"data_train, data_test = train_test_split(data, test_size=230, random_state=1)\n",
"data_test, data_dev = train_test_split(data_test, test_size=115, random_state=1)\n",
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"print(data_train.shape)\n",
"print(data_test.shape)\n",
"print(data_dev.shape)"
]
},
{
"cell_type": "code",
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"execution_count": 76,
"id": "little-gravity",
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"0.1913477537437604"
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]
},
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"execution_count": 76,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_test.size/data_train.size"
]
},
{
"cell_type": "code",
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"execution_count": 89,
"id": "executive-canada",
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Series_Title</th>\n",
" <th>Released_Year</th>\n",
" <th>Certificate</th>\n",
" <th>Runtime</th>\n",
" <th>Genre</th>\n",
" <th>IMDB_Rating</th>\n",
" <th>Meta_score</th>\n",
" <th>Director</th>\n",
" <th>Star1</th>\n",
" <th>Star2</th>\n",
" <th>Star3</th>\n",
" <th>Star4</th>\n",
" <th>No_of_Votes</th>\n",
" <th>Gross</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
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" <td>601</td>\n",
" <td>601</td>\n",
" <td>601</td>\n",
" <td>601</td>\n",
" <td>601</td>\n",
" <td>601.000000</td>\n",
" <td>601</td>\n",
" <td>601</td>\n",
" <td>601</td>\n",
" <td>601</td>\n",
" <td>601</td>\n",
" <td>601</td>\n",
" <td>6.010000e+02</td>\n",
" <td>6.010000e+02</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
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" <td>601</td>\n",
" <td>90</td>\n",
" <td>13</td>\n",
" <td>121</td>\n",
" <td>162</td>\n",
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" <td>NaN</td>\n",
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" <td>59</td>\n",
" <td>378</td>\n",
" <td>438</td>\n",
" <td>530</td>\n",
" <td>556</td>\n",
" <td>577</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
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" <td>what ever happened to baby jane?</td>\n",
" <td>2014</td>\n",
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" <td>U</td>\n",
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" <td>101 min</td>\n",
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" <td>drama</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
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" <td>martin scorsese</td>\n",
" <td>clint eastwood</td>\n",
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" <td>emma watson</td>\n",
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" <td>joe pesci</td>\n",
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" <td>michael caine</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>0.280238</td>\n",
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" <td>3.407094e+05</td>\n",
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" <td>6.846300e+04</td>\n",
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" <td>1.897160e+05</td>\n",
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" <td>4.622520e+05</td>\n",
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" <td>2.303232e+06</td>\n",
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" </tr>\n",
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"text/plain": [
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" Series_Title Released_Year Certificate Runtime \\\n",
"count 601 601 601 601 \n",
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"\n",
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" Genre IMDB_Rating Meta_score Director Star1 \\\n",
"count 601 601.000000 601 601 601 \n",
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"\n",
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" Star2 Star3 Star4 No_of_Votes Gross \n",
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]
},
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"execution_count": 89,
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"metadata": {},
"output_type": "execute_result"
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],
"source": [
"data_train.describe(include=\"all\")"
]
},
{
"cell_type": "code",
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"id": "alert-campus",
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"outputs": [
{
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" <td>frank darabont</td>\n",
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" <td>2.343110e+06</td>\n",
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"text/plain": [
" Series_Title Released_Year Certificate Runtime Genre IMDB_Rating \\\n",
update
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"\n",
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" Meta_score Director Star1 Star2 Star3 \\\n",
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"\n",
update
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" Star4 No_of_Votes Gross \n",
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]
},
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"execution_count": 78,
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"metadata": {},
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],
"source": [
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]
},
{
"cell_type": "code",
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"execution_count": 79,
"id": "little-mathematics",
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"metadata": {},
"outputs": [
{
"data": {
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" <td>115</td>\n",
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" <td>115</td>\n",
" <td>115</td>\n",
" <td>115</td>\n",
" <td>115</td>\n",
" <td>115</td>\n",
" <td>115</td>\n",
" <td>1.150000e+02</td>\n",
" <td>1.150000e+02</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
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" <td>115</td>\n",
" <td>56</td>\n",
" <td>8</td>\n",
" <td>72</td>\n",
" <td>71</td>\n",
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" <td>NaN</td>\n",
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" <td>42</td>\n",
" <td>101</td>\n",
" <td>104</td>\n",
" <td>115</td>\n",
" <td>115</td>\n",
" <td>112</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
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" <td>mr. smith goes to washington</td>\n",
" <td>2004</td>\n",
" <td>UA</td>\n",
" <td>120 min</td>\n",
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" <td>drama</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
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" <td>billy wilder</td>\n",
" <td>johnny depp</td>\n",
" <td>charlize theron</td>\n",
" <td>joel edgerton</td>\n",
" <td>kevin bacon</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>1</td>\n",
" <td>6</td>\n",
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" <td>28</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
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" <td>NaN</td>\n",
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" <td>12</td>\n",
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" <td>3</td>\n",
" <td>3</td>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
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" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>7.940870</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>3.048547e+05</td>\n",
" <td>7.129188e+07</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>0.269143</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>3.400764e+05</td>\n",
" <td>1.275242e+08</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.600000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>2.522900e+04</td>\n",
" <td>3.600000e+03</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.700000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>8.123350e+04</td>\n",
" <td>3.425538e+06</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.900000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>1.820090e+05</td>\n",
" <td>2.018666e+07</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.100000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>4.148195e+05</td>\n",
" <td>8.406197e+07</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>8.800000</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>2.067042e+06</td>\n",
" <td>9.366622e+08</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" Series_Title Released_Year Certificate Runtime \\\n",
"count 115 115 115 115 \n",
"unique 115 56 8 72 \n",
"top mr. smith goes to washington 2004 UA 120 min \n",
"freq 1 6 28 5 \n",
"mean NaN NaN NaN NaN \n",
"std NaN NaN NaN NaN \n",
"min NaN NaN NaN NaN \n",
"25% NaN NaN NaN NaN \n",
"50% NaN NaN NaN NaN \n",
"75% NaN NaN NaN NaN \n",
"max NaN NaN NaN NaN \n",
"\n",
" Genre IMDB_Rating Meta_score Director Star1 \\\n",
"count 115 115.000000 115 115 115 \n",
"unique 71 NaN 42 101 104 \n",
"top drama NaN billy wilder johnny depp \n",
"freq 8 NaN 12 3 3 \n",
"mean NaN 7.940870 NaN NaN NaN \n",
"std NaN 0.269143 NaN NaN NaN \n",
"min NaN 7.600000 NaN NaN NaN \n",
"25% NaN 7.700000 NaN NaN NaN \n",
"50% NaN 7.900000 NaN NaN NaN \n",
"75% NaN 8.100000 NaN NaN NaN \n",
"max NaN 8.800000 NaN NaN NaN \n",
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"\n",
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" Star2 Star3 Star4 No_of_Votes \\\n",
"count 115 115 115 1.150000e+02 \n",
"unique 115 115 112 NaN \n",
"top charlize theron joel edgerton kevin bacon NaN \n",
"freq 1 1 2 NaN \n",
"mean NaN NaN NaN 3.048547e+05 \n",
"std NaN NaN NaN 3.400764e+05 \n",
"min NaN NaN NaN 2.522900e+04 \n",
"25% NaN NaN NaN 8.123350e+04 \n",
"50% NaN NaN NaN 1.820090e+05 \n",
"75% NaN NaN NaN 4.148195e+05 \n",
"max NaN NaN NaN 2.067042e+06 \n",
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"\n",
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" Gross \n",
"count 1.150000e+02 \n",
"unique NaN \n",
"top NaN \n",
"freq NaN \n",
"mean 7.129188e+07 \n",
"std 1.275242e+08 \n",
"min 3.600000e+03 \n",
"25% 3.425538e+06 \n",
"50% 2.018666e+07 \n",
"75% 8.406197e+07 \n",
"max 9.366622e+08 "
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]
},
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"execution_count": 79,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_dev.describe(include=\"all\")"
]
},
{
"cell_type": "code",
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"execution_count": 80,
"id": "sufficient-parade",
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"metadata": {},
"outputs": [],
"source": [
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"data_test.to_csv(\"data_test.csv\", encoding=\"utf-8\", index=False)\n",
"data_dev.to_csv(\"data_dev.csv\", encoding=\"utf-8\", index=False)\n",
"data_train.to_csv(\"data_train.csv\", encoding=\"utf-8\", index=False)"
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]
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},
{
"cell_type": "code",
"execution_count": null,
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"id": "accompanied-virtue",
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"metadata": {},
"outputs": [],
"source": []
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}
],
"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": 5
}
Reference in New Issue Copy Permalink