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ium_444018/script.ipynb
s444018 3c4fcc3642 update
2022-03-21 09:24:22 +01:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "academic-calvin",
"metadata": {},
"source": [
"### Skrypt do ściagnięcia zbiory danych"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "compound-politics",
"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",
"execution_count": null,
"id": "hundred-limitation",
"metadata": {},
"outputs": [],
"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",
"execution_count": null,
"id": "provincial-circuit",
"metadata": {},
"outputs": [],
"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",
"execution_count": 81,
"id": "armed-brisbane",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"data=pd.read_csv('imdb_top_1000.csv')\n",
"# data"
]
},
{
"cell_type": "code",
"execution_count": 82,
"id": "nominated-grenada",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1001 imdb_top_1000.csv\n"
]
}
],
"source": [
"#Wielkosc zbioru\n",
"!wc -l imdb_top_1000.csv"
]
},
{
"cell_type": "markdown",
"id": "generic-success",
"metadata": {},
"source": [
"## Usuwanie kolumn\n",
"- Poster_Link: kolumna zawierająca linki do plakatów promujących film\n",
"- Overview: kolumna zawierająca recenzje poszczególnych filmów"
]
},
{
"cell_type": "code",
"execution_count": 83,
"id": "compliant-synthesis",
"metadata": {},
"outputs": [],
"source": [
"data.drop(columns=[\"Poster_Link\"], inplace=True)\n",
"data.drop(columns=[\"Overview\"], inplace=True)\n",
"\n",
"# data"
]
},
{
"cell_type": "code",
"execution_count": 84,
"id": "reserved-whole",
"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",
"data[\"Gross\"] = pd.to_numeric(data[\"Gross\"], errors='coerce')\n",
"\n",
"data = data.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "given-sodium",
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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"<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",
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" <th>No_of_Votes</th>\n",
" <th>Gross</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>831</td>\n",
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" <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",
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" <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",
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" <th>freq</th>\n",
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" <td>13</td>\n",
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" <td>7</td>\n",
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" <td>4</td>\n",
" <td>NaN</td>\n",
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" <th>mean</th>\n",
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" <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",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</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>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",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>2.343110e+06</td>\n",
" <td>9.366622e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 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 "
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe(include='all')"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "effective-treasury",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"IMDB_Rating 7.9\n",
"No_of_Votes 186734.0\n",
"Gross 23530892.0\n",
"dtype: float64"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.median()"
]
},
{
"cell_type": "code",
"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",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(601, 14)\n",
"(115, 14)\n",
"(115, 14)\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"import sklearn\n",
"\n",
"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",
"print(data_train.shape)\n",
"print(data_test.shape)\n",
"print(data_dev.shape)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "little-gravity",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.1913477537437604"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_test.size/data_train.size"
]
},
{
"cell_type": "code",
"execution_count": 89,
"id": "executive-canada",
"metadata": {},
"outputs": [
{
"data": {
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" <td>601</td>\n",
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" <td>601</td>\n",
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" <th>top</th>\n",
" <td>what ever happened to baby jane?</td>\n",
" <td>2014</td>\n",
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" <td></td>\n",
" <td>martin scorsese</td>\n",
" <td>clint eastwood</td>\n",
" <td>emma watson</td>\n",
" <td>joe pesci</td>\n",
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" <td>3.174649e+05</td>\n",
" <td>6.775699e+07</td>\n",
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" <tr>\n",
" <th>std</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>min</th>\n",
" <td>NaN</td>\n",
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" </tr>\n",
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" <th>50%</th>\n",
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" <td>NaN</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",
" <td>1.897160e+05</td>\n",
" <td>2.365000e+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>4.622520e+05</td>\n",
" <td>7.891296e+07</td>\n",
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" <th>max</th>\n",
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" Series_Title Released_Year Certificate Runtime \\\n",
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"count 601 601.000000 601 601 601 \n",
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{
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"execution_count": 78,
"id": "alert-campus",
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{
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" </tr>\n",
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"text/plain": [
" Series_Title Released_Year Certificate Runtime Genre IMDB_Rating \\\n",
"count 115 115 115 115 115 115.000000 \n",
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{
"cell_type": "code",
"execution_count": 79,
"id": "little-mathematics",
"metadata": {},
"outputs": [
{
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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"text/plain": [
" Series_Title Released_Year Certificate Runtime \\\n",
"count 115 115 115 115 \n",
"unique 115 56 8 72 \n",
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"freq 1 6 28 5 \n",
"mean NaN NaN NaN NaN \n",
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"count 115 115.000000 115 115 115 \n",
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"\n",
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]
},
{
"cell_type": "code",
"execution_count": 80,
"id": "sufficient-parade",
"metadata": {},
"outputs": [],
"source": [
"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)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "accompanied-virtue",
"metadata": {},
"outputs": [],
"source": []
}
],
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"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
}
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