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ischurov/cpk-python-2018-lecture6.ipynb

Created October 13, 2018 09:12
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cpk-python-2018-lecture6.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import sympy"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x, y = sympy.symbols('x y')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"x**10 + 10*x**9*y + 45*x**8*y**2 + 120*x**7*y**3 + 210*x**6*y**4 + 252*x**5*y**5 + 210*x**4*y**6 + 120*x**3*y**7 + 45*x**2*y**8 + 10*x*y**9 + y**10"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sympy.expand((x + y) ** 10)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[-6, 1]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sympy.solve(x ** 2 + 5 * x - 6, x)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[-sqrt(4*y + 25)/2 - 5/2, sqrt(4*y + 25)/2 - 5/2]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sympy.solve(x ** 2 + 5 * x - y, x)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sympy import init_printing\n",
"init_printing(use_latex='mathjax')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$\\left [ - \\frac{1}{2} \\sqrt{4 y + 25} - \\frac{5}{2}, \\quad \\frac{1}{2} \\sqrt{4 y + 25} - \\frac{5}{2}\\right ]$$"
],
"text/plain": [
"⎡ __________ __________ ⎤\n",
"⎢ ╲╱ 4⋅y + 25 5 ╲╱ 4⋅y + 25 5⎥\n",
"⎢- ──────────── - ─, ──────────── - ─⎥\n",
"⎣ 2 2 2 2⎦"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sympy.solve(x ** 2 + 5 * x - y, x)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$x^{2} - \\frac{x^{6}}{6} + \\frac{x^{10}}{120} - \\frac{x^{14}}{5040} + \\mathcal{O}\\left(x^{15}\\right)$$"
],
"text/plain": [
" 6 10 14 \n",
" 2 x x x ⎛ 15⎞\n",
"x - ── + ─── - ──── + O⎝x ⎠\n",
" 6 120 5040 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sympy.sin(x**2).series(n=15)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$- 2 x + 1$$"
],
"text/plain": [
"-2⋅x + 1"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sympy.Matrix([[1, 2], [x, 1]]).det()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"sympy.core.symbol.Symbol"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(x)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ser = pd.Series([2, 6, 12, 5])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2\n",
"1 6\n",
"2 12\n",
"3 5\n",
"dtype: int64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser[2]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"algebra = pd.Series([4, 5, 3], \n",
" index=['Alice', 'Bob', 'Claudia'])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Alice 4\n",
"Bob 5\n",
"Claudia 3\n",
"dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"algebra"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"algebra['Alice']"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"algebra['Bob']"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"algebra[0]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"algebra[1]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"calculus = pd.Series([5, 2, 4], \n",
" index=['Bill', 'Alice', 'Claudia'])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Bill 5\n",
"Alice 2\n",
"Claudia 4\n",
"dtype: int64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calculus"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Alice 4\n",
"Bob 5\n",
"Claudia 3\n",
"dtype: int64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"algebra"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"jhgfjdjd = calculus + algebra"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Alice 6.0\n",
"Claudia 7.0\n",
"Bill NaN\n",
"Bob NaN\n",
"dtype: float64"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jhgfjdjd.sort_values()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Alice 6.0\n",
"Claudia 7.0\n",
"Bill NaN\n",
"Bob NaN\n",
"dtype: float64"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(calculus + algebra).sort_values()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Claudia 7.0\n",
"Bob NaN\n",
"Bill NaN\n",
"Alice 6.0\n",
"dtype: float64"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jhgfjdjd.sort_index(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ser = pd.Series([0, 10, 20, 30], index=[2, 3, 4, 10])"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2 0\n",
"3 10\n",
"4 20\n",
"10 30\n",
"dtype: int64"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser[3]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"30"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser.iloc[3]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4 20\n",
"10 30\n",
"dtype: int64"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser[2:4]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser.loc[3]"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2 0\n",
"3 10\n",
"4 20\n",
"10 30\n",
"dtype: int64"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2 0\n",
"3 10\n",
"4 20\n",
"dtype: int64"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser.loc[2:4]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Bill 5\n",
"Alice 2\n",
"Claudia 4\n",
"dtype: int64"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calculus"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Bill 5\n",
"Alice 2\n",
"dtype: int64"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calculus['Bill':'Alice']"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.DataFrame([[3, 4, 5], [5, 2, 3]])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
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"metadata": {
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"outputs": [],
"source": [
"df = pd.DataFrame([[3, 4, 5], \n",
" [5, 2, 3],\n",
" [5, 2, 1]], \n",
" index=['Alice', 'Bob', 'Claudia'],\n",
" columns=['Algebra', 'Calculus', 'Macro']\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" Algebra Calculus Macro\n",
"Alice 3 4 5\n",
"Bob 5 2 3\n",
"Claudia 5 2 1"
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"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
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"df"
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"execution_count": 73,
"metadata": {},
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{
"data": {
"text/plain": [
"Alice 3\n",
"Bob 5\n",
"Claudia 5\n",
"Name: Algebra, dtype: int64"
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},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"df['Algebra']"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Algebra 3\n",
"Calculus 4\n",
"Macro 5\n",
"Name: Alice, dtype: int64"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc['Alice']"
]
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{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
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{
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"text/plain": [
"3"
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"execution_count": 75,
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"df.loc['Alice', 'Algebra']"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Algebra 3\n",
"Calculus 4\n",
"Macro 5\n",
"Name: Alice, dtype: int64"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
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"source": [
"df.iloc[0]"
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{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
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"execution_count": 77,
"metadata": {},
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"df.iloc[0, 0]"
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{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Alice 3\n",
"Bob 5\n",
"Claudia 5\n",
"Name: Algebra, dtype: int64"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[:, 0]"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Alice 3\n",
"Bob 5\n",
"Claudia 5\n",
"Name: Algebra, dtype: int64"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Algebra']"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
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" Algebra Calculus Macro\n",
"Alice 3 4 5\n",
"Bob 5 2 3"
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"execution_count": 82,
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"source": [
"df['Alice':'Bob']"
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"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
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"Alice 3 4 5 3\n",
"Bob 5 2 3 2\n",
"Claudia 5 2 1 5\n",
"Julia 5 5 5 5"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['last_name'] = ['Smith', 'Smith', 'Ivanova', 'Petrova']"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Algebra int64\n",
"Calculus int64\n",
"Macro int64\n",
"Micro int64\n",
"last_name object\n",
"dtype: object"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Alice Smith\n",
"Bob Smith\n",
"Claudia Ivanova\n",
"Julia Petrova\n",
"Name: last_name, dtype: object"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.last_name"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Alice False\n",
"Bob False\n",
"Claudia True\n",
"Julia True\n",
"Name: last_name, dtype: bool"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.last_name.str.endswith('va')"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"metadata": {},
"output_type": "execute_result"
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"df[df.last_name.str.endswith('va')]"
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{
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"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"df = pd.read_csv(\"https://bit.ly/2A2zkI6\", )"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$\\left ( 5043, \\quad 28\\right )$$"
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"text/plain": [
"(5043, 28)"
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"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
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"source": [
"df.shape"
]
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{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n",
" 'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n",
" 'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n",
" 'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n",
" 'actor_3_name', 'facenumber_in_poster', 'plot_keywords',\n",
" 'movie_imdb_link', 'num_user_for_reviews', 'language', 'country',\n",
" 'content_rating', 'budget', 'title_year', 'actor_2_facebook_likes',\n",
" 'imdb_score', 'aspect_ratio', 'movie_facebook_likes'],\n",
" dtype='object')"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"color object\n",
"director_name object\n",
"num_critic_for_reviews float64\n",
"duration float64\n",
"director_facebook_likes float64\n",
"actor_3_facebook_likes float64\n",
"actor_2_name object\n",
"actor_1_facebook_likes float64\n",
"gross float64\n",
"genres object\n",
"actor_1_name object\n",
"movie_title object\n",
"num_voted_users int64\n",
"cast_total_facebook_likes int64\n",
"actor_3_name object\n",
"facenumber_in_poster float64\n",
"plot_keywords object\n",
"movie_imdb_link object\n",
"num_user_for_reviews float64\n",
"language object\n",
"country object\n",
"content_rating object\n",
"budget float64\n",
"title_year float64\n",
"actor_2_facebook_likes float64\n",
"imdb_score float64\n",
"aspect_ratio float64\n",
"movie_facebook_likes int64\n",
"dtype: object"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" color director_name num_critic_for_reviews duration \\\n",
"0 Color James Cameron 723.0 178.0 \n",
"1 Color Gore Verbinski 302.0 169.0 \n",
"2 Color Sam Mendes 602.0 148.0 \n",
"\n",
" director_facebook_likes actor_3_facebook_likes actor_2_name \\\n",
"0 0.0 855.0 Joel David Moore \n",
"1 563.0 1000.0 Orlando Bloom \n",
"2 0.0 161.0 Rory Kinnear \n",
"\n",
" actor_1_facebook_likes gross genres \\\n",
"0 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi \n",
"1 40000.0 309404152.0 Action|Adventure|Fantasy \n",
"2 11000.0 200074175.0 Action|Adventure|Thriller \n",
"\n",
" ... num_user_for_reviews language country content_rating \\\n",
"0 ... 3054.0 English USA PG-13 \n",
"1 ... 1238.0 English USA PG-13 \n",
"2 ... 994.0 English UK PG-13 \n",
"\n",
" budget title_year actor_2_facebook_likes imdb_score aspect_ratio \\\n",
"0 237000000.0 2009.0 936.0 7.9 1.78 \n",
"1 300000000.0 2007.0 5000.0 7.1 2.35 \n",
"2 245000000.0 2015.0 393.0 6.8 2.35 \n",
"\n",
" movie_facebook_likes \n",
"0 33000 \n",
"1 0 \n",
"2 85000 \n",
"\n",
"[3 rows x 28 columns]"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"data": {
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" <td>302.0</td>\n",
" <td>169.0</td>\n",
" <td>563.0</td>\n",
" <td>1000.0</td>\n",
" <td>Orlando Bloom</td>\n",
" <td>40000.0</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>Color</td>\n",
" <td>Sam Mendes</td>\n",
" <td>602.0</td>\n",
" <td>148.0</td>\n",
" <td>0.0</td>\n",
" <td>161.0</td>\n",
" <td>Rory Kinnear</td>\n",
" <td>11000.0</td>\n",
" <td>200074175.0</td>\n",
" <td>Action|Adventure|Thriller</td>\n",
" <td>...</td>\n",
" <td>994.0</td>\n",
" <td>English</td>\n",
" <td>UK</td>\n",
" <td>PG-13</td>\n",
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" <td>393.0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 28 columns</p>\n",
"</div>"
],
"text/plain": [
" color director_name num_critic_for_reviews duration \\\n",
"0 Color James Cameron 723.0 178.0 \n",
"1 Color Gore Verbinski 302.0 169.0 \n",
"2 Color Sam Mendes 602.0 148.0 \n",
"\n",
" director_facebook_likes actor_3_facebook_likes actor_2_name \\\n",
"0 0.0 855.0 Joel David Moore \n",
"1 563.0 1000.0 Orlando Bloom \n",
"2 0.0 161.0 Rory Kinnear \n",
"\n",
" actor_1_facebook_likes gross genres \\\n",
"0 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi \n",
"1 40000.0 309404152.0 Action|Adventure|Fantasy \n",
"2 11000.0 200074175.0 Action|Adventure|Thriller \n",
"\n",
" ... num_user_for_reviews language country content_rating \\\n",
"0 ... 3054.0 English USA PG-13 \n",
"1 ... 1238.0 English USA PG-13 \n",
"2 ... 994.0 English UK PG-13 \n",
"\n",
" budget title_year actor_2_facebook_likes imdb_score aspect_ratio \\\n",
"0 237000000.0 2009.0 936.0 7.9 1.78 \n",
"1 300000000.0 2007.0 5000.0 7.1 2.35 \n",
"2 245000000.0 2015.0 393.0 6.8 2.35 \n",
"\n",
" movie_facebook_likes \n",
"0 33000 \n",
"1 0 \n",
"2 85000 \n",
"\n",
"[3 rows x 28 columns]"
]
},
"execution_count": 132,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[:3]"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Color 4815\n",
" Black and White 209\n",
"Name: color, dtype: int64"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.color.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Color', nan, ' Black and White'], dtype=object)"
]
},
"execution_count": 135,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.color.unique()"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['color'] = df['color'].str.strip()"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Color 4815\n",
"Black and White 209\n",
"Name: color, dtype: int64"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.color.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Color 0.954789\n",
"Black and White 0.041444\n",
"NaN 0.003768\n",
"Name: color, dtype: float64"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.color.value_counts(dropna=False, normalize=True)"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xdbc5b70>"
]
},
"execution_count": 142,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/qr7Ya2MaS5I/AH4M+DPgsdl6VV3bW1M9MwCmLMkrgI3AvQyujroKWFtVn+6xLY0hyVHA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\nRhkAktQoA0CSGmUASFKjDABJatT/BzI62SKylm6fAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd250c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.color.value_counts(dropna=False).plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xe2d8978>"
]
},
"execution_count": 144,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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BZcDJ+e0V4KJmhWoQFZOI1EvFVCD1/s+Y7Jw7sc/nXzezBc0I1ECayhOReqmYCqTeEdMq\nMztkzSdmNh1Y1ZxIDaMRk4jUS8VUIPX+z/gUcHF+XMmAJcDpzQrVIBoxiUi9xgz3hWYWAH8E/kS2\ngvlJsnM/TwPOBMYCjwIfdM6tNLOfkh0O2Y9sNeBZzrkrRpC97dS7Km8BsJeZbZx//kpTUzWGlorL\n63TQ0z29suD+48bctGRqZdGEHnN0WqW22iq9qyuV2moqtdVmtVVmtdWVilttFddpVlttRqdVXJfB\najO6ss/pqmBdZtZlWDdm3YZ1G5UeM+s1Kj3mqr1Q6TWqNaNaw1VrRtVBFZy+RwvC9U5cAeFIvsQu\nwKnOuY+b2WXAicBvnHMXAJjZN4GPAufmz98aOATYHfg9oGLqY73FZGb/OMDjADjnzmlCpkZZMfhT\npGx66BhzU22/fW7q3I+NWLH0+OrcB0+uzuk9wB7fuWq1UT37vwu6u826esy6u4yeLrPuLrPuTrOe\nLrOezuzW2+dW68ru15Sl66xYrdOM/LG8MLP7boMurNJtZt1mlbwwKz1Q6TGr9kK116xag44aVGtm\nHTUY46DDZfdjyH7bH/vqP/o2ZdXOkc6wPJb/Ag9wJxAAU/NC2oRsBuf/93n+b51zNWChmb1xhO/d\ndgYbMbXycZrlvgNIsS1jwqRLeo8+8JLeowGYZosemVG94cmjqndtsinLppgNf3qnHmNhzFjnxuBc\nM9+mIXqgt9usq8vo7sG6uvLyXFOknWa9XWuKtJIV6eqsSGtrirTTzK3OPnZdZnRWjM6sPOnKypO8\nQLMixSo9RqXXrNqTl2hvnxJ12cdrinQsa4rUrDq8v+KIdPb5uBfYEPgpcLxz7h4zOx04bIDnt3Xp\nD8d6i8k59/XRCtIEKiYZknvd5F3u7Zm8y7/0wARWLTuuOvfOU6pzuqdYOrnDatv4zudTB1Q7nNtw\nQ8eG2aYvxVWDWrfR3cWa0ajlo1F6BhqNdsCLTYiyEfC0mY0BZpAde5I61HWMycy2JZsbnU72Xfkn\n4HPOuSeamG2kVEwybCvYcKNf9h554C97jwRgij326GnVG548unrHxptlo6mxniPKACpQGecYNw43\nbgij0V80IcpXgFuBxcB9tPYM1KgyV8f/ODO7HvglcEn+0GnADOfcUU3MNiJBlGxP9g0h0lATWLU8\nrM5f+P7qjaun2V936rDatr4zyYh9kplLz/cdQjL1LhffwjnXd6eHn5rZ55sRqIE0YpKmWMGGEy/r\nPfyAy3qzLST3sMWLZlRveOJd1ds32pxXpmo01ZKKfl5mqdRbTC+Y2WnApfnnp9KcOdlGUjHJqHjQ\n7TD5yz0fnfzlno8yntUrjqneuuD91RtX72WLdhxjvdv5zid10SreAql3Km974DzgILJjTPOAf3DO\nPd7ceCMTREkXIzhxTmSkdrPHH5tRnfX4u6q3TdyCpVPM2MB3JlmntzNz6c2+Q0im3mK6GPi8c+6l\n/PPNgO865z7S5HwjEkTJErSdvRTEhnSufFfltgdO7Zi9ch97NBhjvTv4ziSv2p2ZSx/2HUIy9U7l\nTVtTSgDOuSVmtk+TMjXSclRMUhCrGDf+qtqh+1/VdSgAO9sTiz9QnZ0eU711wht5aYoZG3qOWGbP\n+A4gr6l3xHQPcFi/EdNNzrk3NznfiARRshDYw3cOkcFsQOfqd1buuP/Ujtkr9rVHdhhrPYHvTCWy\nmplL9UtBgdQ7YjobmGdmV5AdYzoZ+FbTUjVO0RdoiACwmnEb/K42fb/fdU0HYEd76vEZ1dlpWJ2/\n4VYsmWLGeM8R29mzvgPI2uoaMQGY2Z7AEWTbZ8xyzi1sZrBGCKLkIoq/C7rIeo2lu/Poyh0PnFqd\nvWy/ysPbjbOenXxnajO3MXPpW32HkNfUfQ2SvIgKX0b9POI7gMhIdTFm3NW1g/a9unYQADvYM098\noDrrr++pzt9gG16cYsYEzxFbnUZMBdPuF8dSMUnbWey22vbbPTO2/XbPDMbQ0/WOyp13zajOXrZ/\n5aFtN7Duyb7ztSAtfCgYFZNIC+umY+y1tbfue20tm4nazp578tTq7L8eW5k3dlt7YU8z7c9Wh0Kf\nj1lGdR9jakVBlEwElvnOIeJDBz3dR1Tuvn9GddbSt1YefNMG1r2L70wFdQozl17mO4S8puI7QDOl\ncbgcDdOlpHroGHNdbf99PtwdHbZ758W7TF/9X0+f23P8LY/XtpzvHK1wFerR8pfhvMjMes1sgZnd\nY2Z3mdnB+eOBmd0/zK85x8z2G85rR/IeZnacmf22z+dfNLNH+3x+rJn9Pv94ndu9mdknzexD+cen\nm9mwLxXT7lN5kE3nbeU7hIhvT7LF1mf3nLz12ZxMBz3db6/cs2BGddbSAysLtxpvXbv5zufRcKf8\nVznn9gYws3cC3wbe3rBUo2se8JM+nx8EvGJmWzrnngMOBuau7ws4537c59PTgfuBp4YTpizFdKjv\nECJF0kPHmFm1t+w9q/YWALbmxWdO6bjxkeMrczt2sGf3NGOS54ij5QlmLm3EBq4bAy/1f9DMArLL\nBa1ZOfkZ59y8/M/OAj4I1IA/OueiPq+rABcBf3POfbnf1/wqcCzZVXLnAZ9wzjkzm0N2/afDyS7n\n/lHn3C1mtmH+tfYEHsxftxbn3PNmttTMdnbOPQq8CbiSrJB+m9+/msPMvgW8h2xX9uOcc8+a2Uyy\n3XZSYD/gF2a2iqzk9gTOIbvE/AvA6c65pwf6j9nWU3k5LYAQGcTTbL7V93tOOvSwru8dtHPnJRPO\n6Pp/917fu++clW7cQ84V/JK1IzOsKbfchvlU3kPA/wD/to7nPAcc5ZzbFzgF+AGAmb0bOB54q3Nu\nL+A/+7ymg+zChX/pX0q585xz+zvnppKVzHv6vtY5dwDweeBr+WOfAlY656aRbYzwlgH+PvOAg81s\nN7Kfm/PzzzuAacDt+fMmAPPz3DcDH+/7RZxzVwB3kF2zb2+yy9afC5zknHsLcCGDbNBQlhGTiNSp\nl2rHjbV9pt1Yy7bDfCNLnjulOucvx1f/VA3smT0qxiaeIzbSAyN4bd+pvIOAn5nZ1H7PGQOcZ2Z7\nA73ArvnjRwIXOedWQrb/aJ/XnA9c5pwb6If34floazywWf53+EP+Z7/J7+8Egvzjt5EXonPuXjO7\nd4CvO5dsZFQF/gzcBnwV2Ad42Dm3On9eF3B1n/cZ7IKxuwFTgevNjPzrDzhaAhWTiAziWTbb8ge9\nJ2z5g94TqFDrnV65/77Tqje8eEjl/jeOZ/XuZpjvjCMwkhHTq5xzfzazNwBb9PujL5CdwLsX2QzV\nmh/uBgOOROeRlc/Zfcoge5HZBsCPgP2cc3/Lp8/6XkqlM7/vZe2f7/WMeucBnyUrjgucc8vy9zuM\ntY8vdbvXlnP3f591MeAB59xBdWQAyjOV1+M7hEg7qFGp3lKb9uZPdP/jYVM6L9zjrZ0/fOE73SfP\nfbS2zbyasyWDf4XCGWj0MCRmtjvZD/T++3NOAp52ztXIjidV88evAz5iZuPz12/W5zX/C1wDXJ5P\no/W1poReMLOJwEl1xLsZmJG/z1Syabl1WQhsQ3ZM/u78sQXAJ8lKayiWwavn0D0MbJGPKjGzMWY2\nZX0vbvtiSuNwFQ365hORtT3Hplv8sPf46Ud2fffgyZ2XbDKj61/vv6b3gDnL3QYLnaPmO98gVjCy\nnw1rjjEtAH4NfNg519vvOT8CPmxm88mm8VYAOOeuBX4P3JG//p/7vsg5dw5wF3BJvhBizeMvAxcA\n95EtSridwf03MDGfwjuLbIrudfJR0K3AC8657vzhPwM7MfRi+inw4/zvViUr0P/Ir1SxgGzKcEBt\nfYLtGkGUnAt8xncOkTLZnKUvnFS9+eETqzezsz21e8Xc5r4z9XMjM5ce4TuEvF4ZjjFB1vYqJpFR\n9CKT3nB+77FvOL/3WIxa7a2VBx84rTrrhbdV7tl8I1btaeZ9xma95+WIP2UqJhHxxFGpzK9NmTK/\nlh1a2JRXlpxUveWhk6o3uV3syV0r5vovGhgN+rlQUKWYygMIouQJspPGRKRQnNvfHn7otI4bnj2s\nsmDzjVm5p9mriwSa9qbAZsxc+nKT30eGoSwjJoA/kZ3gJiKFYna7232P27t33wNgEstfPrF6y8KT\nqje53exvu1TNbdmEN12oUiquMhXTbFRMIoW3lImbXNj77oMv7H034Ny+9shDp3Xc8MwRlbs3m8SK\nKQ0aTen4UoGVrZhEpKWY3eV23f2u7l13B9iY5UuPr85deHL1pp49bPEuVXPD3aBZxVRgpTnGBBBE\nyWJge985RKQx9rZHHz6t44an31G5a9NNWD7FrK5fth3wJmYuXe+2OOJPmUZMALOAM3yHEJHGWOB2\n3m1B9867AUxk5SvZaGpO9xRbvEvVagONphaolIqtbMU0GxWTSFtazviNf9571IE/7832FJ1mix75\nQHXWk0dV75y0GcummjEmf+of/aWUepStmK4j23Sw2UtRRcSze93kXe7tmbxL1AMTWLXsvdV5d51S\nndO1jb14dTOW+UnjlOoYE0AQJdeTbTkvIuXzHLB1GodF38ev1HxvCeLDpb4DiIg3v1cpFV8Zi+k3\nZBe6EpHyucp3ABlc6YopjcOX0cFPkTJaRrYyVwqudMWU03SeSPlck8Zh5+BPE9/KWkx/IL9gl4iU\nxpW+A0h9SllMaRyuBH7nO4eIjJrn0b/5llHKYsr90ncAERk1F6VxqEVPLaLMxXQdsMR3CBFpOgec\n7zuE1K+0xZTGYTdwhe8cItJ016dx+FffIaR+pS2mnFbnibS///YdQIam7MV0E/CA7xAi0jRPkq3C\nlRZS6mJK49AB3/WdQ0Sa5oI0Dnt9h5ChKXUx5X5B9luViLSXHuAC3yFk6EpfTPkiiP/ynUNEGu7q\nNA6f8h1Chq70xZQ7H3jFdwgRaSgtemhRKiYgjcNX0HkOIu1kEXC97xAyPCqm13wfXQ5DpF18J1/c\nJC1IxZTL56K1TZFI61sE/K/vEDJ8Kqa1fYds+xIRaV1fS+Owx3cIGT4VUx9pHC4ErvGdQ0SG7T60\no0vLUzG93n/6DiAiw/aVNA5rvkPIyJhzmrnqL4iSa4F3+s4hIkNyaxqHB/oOISOnEdO6fYHsrHER\naR1f8h1AGkPFtA5pHD4I/NB3DhGp2+w0Dmf5DiGNoWIa2EzgBd8hRKQuGi21ERXTANI4fBn4iu8c\nIjKoP6RxON93CGkcFdP6/QS4x3cIERlQDY2W2o6KaT3yZaef851DRAZ0XhqH9/kOIY2lYhpEGoc3\nAVf4ziEir/MY8K++Q0jjqZjq88/AKt8hRGQtZ6ZxuMJ3CGk8FVMd0jhcjC7BLlIkF6ZxeIPvENIc\nKqb6xcDjvkOICE8D/+Q7hDSPiqlOaRyuBM5Au4+L+Pap/HQOaVMqpiFI43A28APfOURK7LI0Dn/n\nO4Q0l4pp6CLgQd8hREroReCzvkNI86mYhiiNw9XAB9EmryKj7XNpHD7nO4Q0n4ppGNI4vBP4hu8c\nIiWSpHH4C98hZHSomIbv34GbfYcQKYHngTN9h5DRo2IapjQOe4EPoB3IRZqpBpyaxuFTvoPI6FEx\njUAah08CH0ZLyEWa5au6zlL5qJhGKI3Da4BzfOcQaUMJ2ZS5lIyKqTG+CNzqO4RIG0mBD6ZxqNmI\nElIxNUAah93A8cBi31lE2sBK4IQ0Dl/yHUT8UDE1SBqHzwDHANoqRWRkTk/j8G7fIcQfFVMDpXG4\nEDgB6PadRaRFfTONw8t9hxC/VEwNlsbhjcDHfOcQaUG/Bb7qO4T4p2JqgjQOfwZ83XcOkRZyH1rs\nIDlzTt8HzRJEycXAh3znECm4RcDbdBKtrKERU3N9DJjtO4RIgT0OHKFSkr40YmqyIEomAXOBKb6z\niBTMU2QjpUW+g0ixaMTUZGkcLgVC4BnfWUQK5HngSJWSrIuKaRSkcbgYOBKVkwjAS8BRaRzqgpuy\nTiqmUZLG4QPA24C/+c4i4tErwDvTOLzHdxApLhXTKErj8BHgULJVSCJlsxII0zi83XcQKTYV0yjL\np/UOBRb6ziIyilYD703j8E++g0jxqZg8SOPwaeDtgPYDkzLoAk7SdZWkXiomT9I4fAE4ApjvO4tI\nEy0hW+iQ+A4irUPnMXkWRMlE4A/AYZ6jiDTaIuCYNA7/4juItBaNmDxL43A52eUy/ug7i0gDzQMO\nVCnJcKiYCiCNw1VkFxr8te+sifO8AAAEzklEQVQsIg3wa7Jthl7wHURak6byCiaIkn8BvgVUfWcR\nGYZvA1/SLuEyEiqmAgqi5EjgV8DmvrOI1KkH+GQah//rO4i0PhVTQQVRsgPwG2Bf31lEBrGUbDn4\nDb6DSHvQMaaCyk/EnQ5c7DuLyHosBqarlKSRNGJqAUGUfBr4HjDGdxaRPq4APpHG4RLfQaS9qJha\nRBAlB5P9INjadxYpveXA59I4vNB3EGlPKqYWEkTJ1sDlZFN8Ij7cBsxI4/BR30GkfekYUwvJ99g7\nHPgPoNdzHCmXGtlpDNNVStJsGjG1qCBKDgR+CuzmOYq0v8XAB9M4vMV3ECkHjZhaVBqH84G9gXPI\nfpsVaYZLgb1USjKaNGJqA0GUTAcuAnbxnUXaxivAp9M4/LnvIFI+GjG1gTQO5wLTyI4BdHuOI63v\nKuDNKiXxRSOmNhNEyZ7AT9DKPRm6R4B/SOPwWt9BpNw0YmozaRwuJLt0+yeAlz3HkdawEvgy2ShJ\npSTeacTUxoIo2QL4EvBJYJznOFJMvwb+Jd8CS6QQVEwlEETJdsDXgNPR5TQk82fgn9I4/LPvICL9\nqZhKJIiS3YBvAO8DzHMc8eMxIErj8DLfQUQGomIqoSBK9iFbwfdu31lk1PwNOBv4cRqHnb7DiKyP\niqnEgig5FPh34BDfWaRp7ge+A1yaxqFOJZCWoGISgih5N/BNdFHCdnIz8B9pHF7jO4jIUKmY5FX5\nCOrvgROAsZ7jyNDVgN+RFdKtvsOIDJeKSV4niJItgY8BZwI7eI4jg+sEfgZ8N43Dv/gOIzJSKiYZ\nUBAlFSAkG0W9E63kK5qngIuBH6Rx+IzvMCKNomKSugRRshPZibofATb3HKfMlgJXAr8A5qRxqJ3l\npe2omGRIgigZB5wMnEG29VGH30Sl0AlcQ1ZGSRqHqz3nEWkqFZMMWxAlmwHHAMcB7wIm+k3UVmrA\nTWRldGUah9r3UEpDxSQNkY+kjiArqfcCW/tN1JJqwN3Ar8jOO3rScx4RL1RM0nBBlBiwP1lJHQdM\n8ZuosBzwAHBjfrspjcMlfiOJ+KdikqYLomRnslV904GDKfcS9Id4rYjmpHH4vOc8IoWjYpJRF0TJ\nNmQFdTBwILAXMN5rqOZwwCJgDnkZpXH4tNdEIi1AxSTeBVFSBXYj2xJpzW0vYBOfuYboBeA+sr3p\n7stvD6RxuMxrKpEWpGKSwgqiZBKwPbBdft//9iZGb7n6auAl4AmyS5A/mt8/AjyiY0MijaNikpaV\n70yxDa8V1yZke/yN63c/0GNVshNWXxrspnOHREaPiklERAql4juAiIhIXyomEREpFBWTiIgUiopJ\nREQKRcUkIiKFomISEZFCUTGJiEihqJhERKRQVEwiIlIoKiYRESkUFZOIiBSKiklERApFxSQiIoWi\nYhIRkUJRMYmISKGomEREpFBUTCIiUigqJhERKRQVk4iIFIqKSURECkXFJCIihaJiEhGRQlExiYhI\noaiYRESkUFRMIiJSKComEREpFBWTiIgUiopJREQKRcUkIiKFomISEZFCUTGJiEihqJhERKRQVEwi\nIlIoKiYRESkUFZOIiBSKiklERApFxSQiIoWiYhIRkUJRMYmISKGomEREpFBUTCIiUigqJhERKZT/\nA8cccgezRyGWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xdf3bd68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.figure(figsize=(6, 6))\n",
"df.color.value_counts(dropna=False).plot.pie()"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"country\n",
"Kyrgyzstan 8.700000\n",
"Libya 8.400000\n",
"United Arab Emirates 8.200000\n",
"Soviet Union 8.100000\n",
"Egypt 8.100000\n",
"Iran 7.725000\n",
"Poland 7.620000\n",
"Indonesia 7.600000\n",
"Israel 7.525000\n",
"Sweden 7.516667\n",
"Colombia 7.500000\n",
"Cameroon 7.500000\n",
"Argentina 7.500000\n",
"Kenya 7.400000\n",
"Afghanistan 7.400000\n",
"Iceland 7.333333\n",
"New Zealand 7.280000\n",
"Brazil 7.275000\n",
"West Germany 7.266667\n",
"Panama 7.200000\n",
"Finland 7.200000\n",
"Denmark 7.172727\n",
"Taiwan 7.150000\n",
"Greece 7.000000\n",
"Pakistan 7.000000\n",
"Czech Republic 6.966667\n",
"Japan 6.952174\n",
"Netherlands 6.940000\n",
"Chile 6.900000\n",
"Dominican Republic 6.900000\n",
" ... \n",
"Hong Kong 6.741176\n",
"Norway 6.737500\n",
"France 6.678571\n",
"China 6.623333\n",
"Romania 6.600000\n",
"India 6.532353\n",
"Australia 6.514545\n",
"Hungary 6.450000\n",
"South Africa 6.437500\n",
"Slovenia 6.400000\n",
"USA 6.367428\n",
"Germany 6.340206\n",
"Official site 6.300000\n",
"Philippines 6.300000\n",
"South Korea 6.257143\n",
"Canada 6.161905\n",
"Bulgaria 6.100000\n",
"Russia 6.081818\n",
"Thailand 6.080000\n",
"Turkey 6.000000\n",
"Slovakia 6.000000\n",
"Switzerland 5.900000\n",
"Nigeria 5.600000\n",
"Georgia 5.600000\n",
"Cambodia 5.600000\n",
"Belgium 5.600000\n",
"Peru 5.400000\n",
"Aruba 4.800000\n",
"New Line 4.400000\n",
"Bahamas 4.400000\n",
"Name: imdb_score, Length: 65, dtype: float64"
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(df.\n",
" groupby('country')\n",
" ['imdb_score']\n",
" .mean()\n",
" .sort_values(ascending=False))"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$1916.0$$"
],
"text/plain": [
"1916.0"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['title_year'].min()"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$2016.0$$"
],
"text/plain": [
"2016.0"
]
},
"execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['title_year'].max()"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n",
" 'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n",
" 'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n",
" 'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n",
" 'actor_3_name', 'facenumber_in_poster', 'plot_keywords',\n",
" 'movie_imdb_link', 'num_user_for_reviews', 'language', 'country',\n",
" 'content_rating', 'budget', 'title_year', 'actor_2_facebook_likes',\n",
" 'imdb_score', 'aspect_ratio', 'movie_facebook_likes'],\n",
" dtype='object')"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title_year</th>\n",
" <th>movie_title</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4468</th>\n",
" <td>2014.0</td>\n",
" <td>Queen of the Mountains</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title_year movie_title\n",
"4468 2014.0 Queen of the Mountains "
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['country'] == 'Kyrgyzstan'][['title_year', 'movie_title']]"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$2014.0$$"
],
"text/plain": [
"2014.0"
]
},
"execution_count": 169,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(df.query('country == \"USA\" and color == \"Black and White\"')\n",
" ['title_year'].max())"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>director_name</th>\n",
" <th>num_critic_for_reviews</th>\n",
" <th>duration</th>\n",
" <th>director_facebook_likes</th>\n",
" <th>actor_3_facebook_likes</th>\n",
" <th>actor_2_name</th>\n",
" <th>actor_1_facebook_likes</th>\n",
" <th>gross</th>\n",
" <th>genres</th>\n",
" <th>...</th>\n",
" <th>num_user_for_reviews</th>\n",
" <th>language</th>\n",
" <th>country</th>\n",
" <th>content_rating</th>\n",
" <th>budget</th>\n",
" <th>title_year</th>\n",
" <th>actor_2_facebook_likes</th>\n",
" <th>imdb_score</th>\n",
" <th>aspect_ratio</th>\n",
" <th>movie_facebook_likes</th>\n",
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" <tbody>\n",
" <tr>\n",
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" <td>29.0</td>\n",
" <td>8.1</td>\n",
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"</div>"
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"text/plain": [
" color director_name num_critic_for_reviews duration \\\n",
"1061 Black and White Andrei Tarkovsky 144.0 115.0 \n",
"\n",
" director_facebook_likes actor_3_facebook_likes actor_2_name \\\n",
"1061 0.0 12.0 Anatoliy Solonitsyn \n",
"\n",
" actor_1_facebook_likes gross genres \\\n",
"1061 29.0 NaN Drama|Mystery|Sci-Fi \n",
"\n",
" ... num_user_for_reviews language country \\\n",
"1061 ... 236.0 Russian Soviet Union \n",
"\n",
" content_rating budget title_year actor_2_facebook_likes imdb_score \\\n",
"1061 PG 1000000.0 1972.0 29.0 8.1 \n",
"\n",
" aspect_ratio movie_facebook_likes \n",
"1061 2.35 0 \n",
"\n",
"[1 rows x 28 columns]"
]
},
"execution_count": 165,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query('country == \"Soviet Union\"')"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [],
"source": [
"df.rename(columns={'title_year': 'year'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n",
" 'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n",
" 'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n",
" 'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n",
" 'actor_3_name', 'facenumber_in_poster', 'plot_keywords',\n",
" 'movie_imdb_link', 'num_user_for_reviews', 'language', 'country',\n",
" 'content_rating', 'budget', 'year', 'actor_2_facebook_likes',\n",
" 'imdb_score', 'aspect_ratio', 'movie_facebook_likes'],\n",
" dtype='object')"
]
},
"execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [],
"source": [
"df.drop('director_name', axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {
"collapsed": true
},
"outputs": [
{
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" <td>635.0</td>\n",
" <td>141.0</td>\n",
" <td>0.0</td>\n",
" <td>19000.0</td>\n",
" <td>Robert Downey Jr.</td>\n",
" <td>26000.0</td>\n",
" <td>458991599.0</td>\n",
" <td>Action|Adventure|Sci-Fi</td>\n",
" <td>Chris Hemsworth</td>\n",
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" <td>21000.0</td>\n",
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" <td>118000</td>\n",
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" <th>9</th>\n",
" <td>Color</td>\n",
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" <td>153.0</td>\n",
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" <td>673.0</td>\n",
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" <td>0.0</td>\n",
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" <td>Lauren Cohan</td>\n",
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" <td>Action|Adventure|Sci-Fi</td>\n",
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" <td>169.0</td>\n",
" <td>0.0</td>\n",
" <td>903.0</td>\n",
" <td>Marlon Brando</td>\n",
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" <tr>\n",
" <th>12</th>\n",
" <td>Color</td>\n",
" <td>403.0</td>\n",
" <td>106.0</td>\n",
" <td>395.0</td>\n",
" <td>393.0</td>\n",
" <td>Mathieu Amalric</td>\n",
" <td>451.0</td>\n",
" <td>168368427.0</td>\n",
" <td>Action|Adventure</td>\n",
" <td>Giancarlo Giannini</td>\n",
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" <td>English</td>\n",
" <td>UK</td>\n",
" <td>PG-13</td>\n",
" <td>200000000.0</td>\n",
" <td>2008.0</td>\n",
" <td>412.0</td>\n",
" <td>6.7</td>\n",
" <td>2.35</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>Color</td>\n",
" <td>313.0</td>\n",
" <td>151.0</td>\n",
" <td>563.0</td>\n",
" <td>1000.0</td>\n",
" <td>Orlando Bloom</td>\n",
" <td>40000.0</td>\n",
" <td>423032628.0</td>\n",
" <td>Action|Adventure|Fantasy</td>\n",
" <td>Johnny Depp</td>\n",
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" <td>1832.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>225000000.0</td>\n",
" <td>2006.0</td>\n",
" <td>5000.0</td>\n",
" <td>7.3</td>\n",
" <td>2.35</td>\n",
" <td>5000</td>\n",
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" <tr>\n",
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" <td>Color</td>\n",
" <td>450.0</td>\n",
" <td>150.0</td>\n",
" <td>563.0</td>\n",
" <td>1000.0</td>\n",
" <td>Ruth Wilson</td>\n",
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" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>215000000.0</td>\n",
" <td>2013.0</td>\n",
" <td>2000.0</td>\n",
" <td>6.5</td>\n",
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" <th>15</th>\n",
" <td>Color</td>\n",
" <td>733.0</td>\n",
" <td>143.0</td>\n",
" <td>0.0</td>\n",
" <td>748.0</td>\n",
" <td>Christopher Meloni</td>\n",
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" <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
" <td>Henry Cavill</td>\n",
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" <td>225000000.0</td>\n",
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" <td>7.2</td>\n",
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" <td>6.6</td>\n",
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" <td>Color</td>\n",
" <td>703.0</td>\n",
" <td>173.0</td>\n",
" <td>0.0</td>\n",
" <td>19000.0</td>\n",
" <td>Robert Downey Jr.</td>\n",
" <td>26000.0</td>\n",
" <td>623279547.0</td>\n",
" <td>Action|Adventure|Sci-Fi</td>\n",
" <td>Chris Hemsworth</td>\n",
" <td>...</td>\n",
" <td>1722.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>220000000.0</td>\n",
" <td>2012.0</td>\n",
" <td>21000.0</td>\n",
" <td>8.1</td>\n",
" <td>1.85</td>\n",
" <td>123000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Color</td>\n",
" <td>448.0</td>\n",
" <td>136.0</td>\n",
" <td>252.0</td>\n",
" <td>1000.0</td>\n",
" <td>Sam Claflin</td>\n",
" <td>40000.0</td>\n",
" <td>241063875.0</td>\n",
" <td>Action|Adventure|Fantasy</td>\n",
" <td>Johnny Depp</td>\n",
" <td>...</td>\n",
" <td>484.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>250000000.0</td>\n",
" <td>2011.0</td>\n",
" <td>11000.0</td>\n",
" <td>6.7</td>\n",
" <td>2.35</td>\n",
" <td>58000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Color</td>\n",
" <td>451.0</td>\n",
" <td>106.0</td>\n",
" <td>188.0</td>\n",
" <td>718.0</td>\n",
" <td>Michael Stuhlbarg</td>\n",
" <td>10000.0</td>\n",
" <td>179020854.0</td>\n",
" <td>Action|Adventure|Comedy|Family|Fantasy|Sci-Fi</td>\n",
" <td>Will Smith</td>\n",
" <td>...</td>\n",
" <td>341.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>225000000.0</td>\n",
" <td>2012.0</td>\n",
" <td>816.0</td>\n",
" <td>6.8</td>\n",
" <td>1.85</td>\n",
" <td>40000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Color</td>\n",
" <td>422.0</td>\n",
" <td>164.0</td>\n",
" <td>0.0</td>\n",
" <td>773.0</td>\n",
" <td>Adam Brown</td>\n",
" <td>5000.0</td>\n",
" <td>255108370.0</td>\n",
" <td>Adventure|Fantasy</td>\n",
" <td>Aidan Turner</td>\n",
" <td>...</td>\n",
" <td>802.0</td>\n",
" <td>English</td>\n",
" <td>New Zealand</td>\n",
" <td>PG-13</td>\n",
" <td>250000000.0</td>\n",
" <td>2014.0</td>\n",
" <td>972.0</td>\n",
" <td>7.5</td>\n",
" <td>2.35</td>\n",
" <td>65000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Color</td>\n",
" <td>599.0</td>\n",
" <td>153.0</td>\n",
" <td>464.0</td>\n",
" <td>963.0</td>\n",
" <td>Andrew Garfield</td>\n",
" <td>15000.0</td>\n",
" <td>262030663.0</td>\n",
" <td>Action|Adventure|Fantasy</td>\n",
" <td>Emma Stone</td>\n",
" <td>...</td>\n",
" <td>1225.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>230000000.0</td>\n",
" <td>2012.0</td>\n",
" <td>10000.0</td>\n",
" <td>7.0</td>\n",
" <td>2.35</td>\n",
" <td>56000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Color</td>\n",
" <td>343.0</td>\n",
" <td>156.0</td>\n",
" <td>0.0</td>\n",
" <td>738.0</td>\n",
" <td>William Hurt</td>\n",
" <td>891.0</td>\n",
" <td>105219735.0</td>\n",
" <td>Action|Adventure|Drama|History</td>\n",
" <td>Mark Addy</td>\n",
" <td>...</td>\n",
" <td>546.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>200000000.0</td>\n",
" <td>2010.0</td>\n",
" <td>882.0</td>\n",
" <td>6.7</td>\n",
" <td>2.35</td>\n",
" <td>17000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Color</td>\n",
" <td>509.0</td>\n",
" <td>186.0</td>\n",
" <td>0.0</td>\n",
" <td>773.0</td>\n",
" <td>Adam Brown</td>\n",
" <td>5000.0</td>\n",
" <td>258355354.0</td>\n",
" <td>Adventure|Fantasy</td>\n",
" <td>Aidan Turner</td>\n",
" <td>...</td>\n",
" <td>951.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>225000000.0</td>\n",
" <td>2013.0</td>\n",
" <td>972.0</td>\n",
" <td>7.9</td>\n",
" <td>2.35</td>\n",
" <td>83000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Color</td>\n",
" <td>251.0</td>\n",
" <td>113.0</td>\n",
" <td>129.0</td>\n",
" <td>1000.0</td>\n",
" <td>Eva Green</td>\n",
" <td>16000.0</td>\n",
" <td>70083519.0</td>\n",
" <td>Adventure|Family|Fantasy</td>\n",
" <td>Christopher Lee</td>\n",
" <td>...</td>\n",
" <td>666.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>180000000.0</td>\n",
" <td>2007.0</td>\n",
" <td>6000.0</td>\n",
" <td>6.1</td>\n",
" <td>2.35</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Color</td>\n",
" <td>446.0</td>\n",
" <td>201.0</td>\n",
" <td>0.0</td>\n",
" <td>84.0</td>\n",
" <td>Thomas Kretschmann</td>\n",
" <td>6000.0</td>\n",
" <td>218051260.0</td>\n",
" <td>Action|Adventure|Drama|Romance</td>\n",
" <td>Naomi Watts</td>\n",
" <td>...</td>\n",
" <td>2618.0</td>\n",
" <td>English</td>\n",
" <td>New Zealand</td>\n",
" <td>PG-13</td>\n",
" <td>207000000.0</td>\n",
" <td>2005.0</td>\n",
" <td>919.0</td>\n",
" <td>7.2</td>\n",
" <td>2.35</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Color</td>\n",
" <td>315.0</td>\n",
" <td>194.0</td>\n",
" <td>0.0</td>\n",
" <td>794.0</td>\n",
" <td>Kate Winslet</td>\n",
" <td>29000.0</td>\n",
" <td>658672302.0</td>\n",
" <td>Drama|Romance</td>\n",
" <td>Leonardo DiCaprio</td>\n",
" <td>...</td>\n",
" <td>2528.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>200000000.0</td>\n",
" <td>1997.0</td>\n",
" <td>14000.0</td>\n",
" <td>7.7</td>\n",
" <td>2.35</td>\n",
" <td>26000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Color</td>\n",
" <td>516.0</td>\n",
" <td>147.0</td>\n",
" <td>94.0</td>\n",
" <td>11000.0</td>\n",
" <td>Scarlett Johansson</td>\n",
" <td>21000.0</td>\n",
" <td>407197282.0</td>\n",
" <td>Action|Adventure|Sci-Fi</td>\n",
" <td>Robert Downey Jr.</td>\n",
" <td>...</td>\n",
" <td>1022.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>250000000.0</td>\n",
" <td>2016.0</td>\n",
" <td>19000.0</td>\n",
" <td>8.2</td>\n",
" <td>2.35</td>\n",
" <td>72000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Color</td>\n",
" <td>377.0</td>\n",
" <td>131.0</td>\n",
" <td>532.0</td>\n",
" <td>627.0</td>\n",
" <td>Alexander Skarsgård</td>\n",
" <td>14000.0</td>\n",
" <td>65173160.0</td>\n",
" <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
" <td>Liam Neeson</td>\n",
" <td>...</td>\n",
" <td>751.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>209000000.0</td>\n",
" <td>2012.0</td>\n",
" <td>10000.0</td>\n",
" <td>5.9</td>\n",
" <td>2.35</td>\n",
" <td>44000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Color</td>\n",
" <td>644.0</td>\n",
" <td>124.0</td>\n",
" <td>365.0</td>\n",
" <td>1000.0</td>\n",
" <td>Judy Greer</td>\n",
" <td>3000.0</td>\n",
" <td>652177271.0</td>\n",
" <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
" <td>Bryce Dallas Howard</td>\n",
" <td>...</td>\n",
" <td>1290.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>150000000.0</td>\n",
" <td>2015.0</td>\n",
" <td>2000.0</td>\n",
" <td>7.0</td>\n",
" <td>2.00</td>\n",
" <td>150000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5013</th>\n",
" <td>Color</td>\n",
" <td>28.0</td>\n",
" <td>79.0</td>\n",
" <td>3.0</td>\n",
" <td>42.0</td>\n",
" <td>Panchito Gómez</td>\n",
" <td>93.0</td>\n",
" <td>NaN</td>\n",
" <td>Drama|Family</td>\n",
" <td>Franky G</td>\n",
" <td>...</td>\n",
" <td>21.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>NaN</td>\n",
" <td>24000.0</td>\n",
" <td>2002.0</td>\n",
" <td>46.0</td>\n",
" <td>7.0</td>\n",
" <td>1.78</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5014</th>\n",
" <td>Color</td>\n",
" <td>58.0</td>\n",
" <td>80.0</td>\n",
" <td>892.0</td>\n",
" <td>492.0</td>\n",
" <td>Katharine Isabelle</td>\n",
" <td>986.0</td>\n",
" <td>NaN</td>\n",
" <td>Action|Crime|Thriller</td>\n",
" <td>Matt Frewer</td>\n",
" <td>...</td>\n",
" <td>129.0</td>\n",
" <td>English</td>\n",
" <td>Canada</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>2009.0</td>\n",
" <td>918.0</td>\n",
" <td>6.3</td>\n",
" <td>2.35</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5015</th>\n",
" <td>Black and White</td>\n",
" <td>61.0</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Richard Linklater</td>\n",
" <td>5.0</td>\n",
" <td>1227508.0</td>\n",
" <td>Comedy|Drama</td>\n",
" <td>Tommy Pallotta</td>\n",
" <td>...</td>\n",
" <td>80.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>R</td>\n",
" <td>23000.0</td>\n",
" <td>1991.0</td>\n",
" <td>0.0</td>\n",
" <td>7.1</td>\n",
" <td>1.37</td>\n",
" <td>2000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5016</th>\n",
" <td>Color</td>\n",
" <td>NaN</td>\n",
" <td>90.0</td>\n",
" <td>0.0</td>\n",
" <td>9.0</td>\n",
" <td>Mikaal Bates</td>\n",
" <td>313.0</td>\n",
" <td>NaN</td>\n",
" <td>Crime|Drama|Thriller</td>\n",
" <td>Tjasa Ferme</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>NaN</td>\n",
" <td>25000.0</td>\n",
" <td>2015.0</td>\n",
" <td>25.0</td>\n",
" <td>4.8</td>\n",
" <td>NaN</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5017</th>\n",
" <td>Color</td>\n",
" <td>1.0</td>\n",
" <td>90.0</td>\n",
" <td>138.0</td>\n",
" <td>138.0</td>\n",
" <td>Suzi Lorraine</td>\n",
" <td>370.0</td>\n",
" <td>NaN</td>\n",
" <td>Comedy|Romance</td>\n",
" <td>Kristen Seavey</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>NaN</td>\n",
" <td>22000.0</td>\n",
" <td>2013.0</td>\n",
" <td>184.0</td>\n",
" <td>3.3</td>\n",
" <td>1.78</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5018</th>\n",
" <td>Color</td>\n",
" <td>5.0</td>\n",
" <td>120.0</td>\n",
" <td>589.0</td>\n",
" <td>4.0</td>\n",
" <td>Lisa Arnold</td>\n",
" <td>51.0</td>\n",
" <td>NaN</td>\n",
" <td>Drama</td>\n",
" <td>Shannen Fields</td>\n",
" <td>...</td>\n",
" <td>49.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>NaN</td>\n",
" <td>20000.0</td>\n",
" <td>2003.0</td>\n",
" <td>49.0</td>\n",
" <td>6.9</td>\n",
" <td>1.85</td>\n",
" <td>725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5019</th>\n",
" <td>Color</td>\n",
" <td>43.0</td>\n",
" <td>91.0</td>\n",
" <td>158.0</td>\n",
" <td>265.0</td>\n",
" <td>Brittany Curran</td>\n",
" <td>630.0</td>\n",
" <td>NaN</td>\n",
" <td>Horror|Mystery|Thriller</td>\n",
" <td>Ashley Tramonte</td>\n",
" <td>...</td>\n",
" <td>33.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>R</td>\n",
" <td>NaN</td>\n",
" <td>2015.0</td>\n",
" <td>512.0</td>\n",
" <td>4.6</td>\n",
" <td>1.85</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5020</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>143.0</td>\n",
" <td>8.0</td>\n",
" <td>8.0</td>\n",
" <td>Alana Kaniewski</td>\n",
" <td>720.0</td>\n",
" <td>NaN</td>\n",
" <td>Drama|Horror|Thriller</td>\n",
" <td>Robbie Barnes</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>NaN</td>\n",
" <td>17350.0</td>\n",
" <td>2011.0</td>\n",
" <td>19.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5021</th>\n",
" <td>Color</td>\n",
" <td>51.0</td>\n",
" <td>85.0</td>\n",
" <td>157.0</td>\n",
" <td>10.0</td>\n",
" <td>Katie Aselton</td>\n",
" <td>830.0</td>\n",
" <td>192467.0</td>\n",
" <td>Comedy|Drama|Romance</td>\n",
" <td>Mark Duplass</td>\n",
" <td>...</td>\n",
" <td>71.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>R</td>\n",
" <td>15000.0</td>\n",
" <td>2005.0</td>\n",
" <td>224.0</td>\n",
" <td>6.6</td>\n",
" <td>NaN</td>\n",
" <td>297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5022</th>\n",
" <td>Black and White</td>\n",
" <td>6.0</td>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>Olwenya Maina</td>\n",
" <td>147.0</td>\n",
" <td>NaN</td>\n",
" <td>Drama</td>\n",
" <td>Paul Ogola</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>Swahili</td>\n",
" <td>Kenya</td>\n",
" <td>NaN</td>\n",
" <td>15000.0</td>\n",
" <td>2014.0</td>\n",
" <td>19.0</td>\n",
" <td>7.4</td>\n",
" <td>NaN</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5023</th>\n",
" <td>Color</td>\n",
" <td>22.0</td>\n",
" <td>88.0</td>\n",
" <td>38.0</td>\n",
" <td>211.0</td>\n",
" <td>Heather Burns</td>\n",
" <td>331.0</td>\n",
" <td>76382.0</td>\n",
" <td>Romance</td>\n",
" <td>Zoe Lister-Jones</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>NaN</td>\n",
" <td>15000.0</td>\n",
" <td>2009.0</td>\n",
" <td>212.0</td>\n",
" <td>6.2</td>\n",
" <td>2.35</td>\n",
" <td>324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5024</th>\n",
" <td>Color</td>\n",
" <td>42.0</td>\n",
" <td>78.0</td>\n",
" <td>91.0</td>\n",
" <td>86.0</td>\n",
" <td>Jason Trost</td>\n",
" <td>407.0</td>\n",
" <td>NaN</td>\n",
" <td>Sci-Fi|Thriller</td>\n",
" <td>Sean Whalen</td>\n",
" <td>...</td>\n",
" <td>35.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>Unrated</td>\n",
" <td>20000.0</td>\n",
" <td>2011.0</td>\n",
" <td>91.0</td>\n",
" <td>4.0</td>\n",
" <td>2.35</td>\n",
" <td>835</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5025</th>\n",
" <td>Color</td>\n",
" <td>73.0</td>\n",
" <td>108.0</td>\n",
" <td>0.0</td>\n",
" <td>105.0</td>\n",
" <td>Mink Stole</td>\n",
" <td>462.0</td>\n",
" <td>180483.0</td>\n",
" <td>Comedy|Crime|Horror</td>\n",
" <td>Divine</td>\n",
" <td>...</td>\n",
" <td>183.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>NC-17</td>\n",
" <td>10000.0</td>\n",
" <td>1972.0</td>\n",
" <td>143.0</td>\n",
" <td>6.1</td>\n",
" <td>1.37</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5026</th>\n",
" <td>Color</td>\n",
" <td>81.0</td>\n",
" <td>110.0</td>\n",
" <td>107.0</td>\n",
" <td>45.0</td>\n",
" <td>Béatrice Dalle</td>\n",
" <td>576.0</td>\n",
" <td>136007.0</td>\n",
" <td>Drama|Music|Romance</td>\n",
" <td>Maggie Cheung</td>\n",
" <td>...</td>\n",
" <td>39.0</td>\n",
" <td>French</td>\n",
" <td>France</td>\n",
" <td>R</td>\n",
" <td>4500.0</td>\n",
" <td>2004.0</td>\n",
" <td>133.0</td>\n",
" <td>6.9</td>\n",
" <td>2.35</td>\n",
" <td>171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5027</th>\n",
" <td>Color</td>\n",
" <td>64.0</td>\n",
" <td>90.0</td>\n",
" <td>397.0</td>\n",
" <td>0.0</td>\n",
" <td>Nargess Mamizadeh</td>\n",
" <td>5.0</td>\n",
" <td>673780.0</td>\n",
" <td>Drama</td>\n",
" <td>Fereshteh Sadre Orafaiy</td>\n",
" <td>...</td>\n",
" <td>26.0</td>\n",
" <td>Persian</td>\n",
" <td>Iran</td>\n",
" <td>Not Rated</td>\n",
" <td>10000.0</td>\n",
" <td>2000.0</td>\n",
" <td>0.0</td>\n",
" <td>7.5</td>\n",
" <td>1.85</td>\n",
" <td>697</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5028</th>\n",
" <td>Black and White</td>\n",
" <td>12.0</td>\n",
" <td>83.0</td>\n",
" <td>18.0</td>\n",
" <td>0.0</td>\n",
" <td>Michael Parle</td>\n",
" <td>10.0</td>\n",
" <td>NaN</td>\n",
" <td>Horror</td>\n",
" <td>Patrick O'Donnell</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>English</td>\n",
" <td>Ireland</td>\n",
" <td>NaN</td>\n",
" <td>10000.0</td>\n",
" <td>2007.0</td>\n",
" <td>5.0</td>\n",
" <td>6.7</td>\n",
" <td>1.33</td>\n",
" <td>105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5029</th>\n",
" <td>Color</td>\n",
" <td>78.0</td>\n",
" <td>111.0</td>\n",
" <td>62.0</td>\n",
" <td>6.0</td>\n",
" <td>Anna Nakagawa</td>\n",
" <td>89.0</td>\n",
" <td>94596.0</td>\n",
" <td>Crime|Horror|Mystery|Thriller</td>\n",
" <td>Kôji Yakusho</td>\n",
" <td>...</td>\n",
" <td>50.0</td>\n",
" <td>Japanese</td>\n",
" <td>Japan</td>\n",
" <td>NaN</td>\n",
" <td>1000000.0</td>\n",
" <td>1997.0</td>\n",
" <td>13.0</td>\n",
" <td>7.4</td>\n",
" <td>1.85</td>\n",
" <td>817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5030</th>\n",
" <td>Color</td>\n",
" <td>NaN</td>\n",
" <td>84.0</td>\n",
" <td>5.0</td>\n",
" <td>12.0</td>\n",
" <td>Michael Cortez</td>\n",
" <td>21.0</td>\n",
" <td>NaN</td>\n",
" <td>Drama</td>\n",
" <td>Tatiana Suarez-Pico</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2004.0</td>\n",
" <td>20.0</td>\n",
" <td>6.1</td>\n",
" <td>NaN</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5031</th>\n",
" <td>Color</td>\n",
" <td>13.0</td>\n",
" <td>82.0</td>\n",
" <td>120.0</td>\n",
" <td>84.0</td>\n",
" <td>Joe Coffey</td>\n",
" <td>785.0</td>\n",
" <td>NaN</td>\n",
" <td>Comedy|Horror|Thriller</td>\n",
" <td>Julianna Pitt</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>NaN</td>\n",
" <td>200000.0</td>\n",
" <td>2012.0</td>\n",
" <td>98.0</td>\n",
" <td>5.4</td>\n",
" <td>16.00</td>\n",
" <td>424</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5032</th>\n",
" <td>Color</td>\n",
" <td>10.0</td>\n",
" <td>98.0</td>\n",
" <td>3.0</td>\n",
" <td>152.0</td>\n",
" <td>Stanley B. Herman</td>\n",
" <td>789.0</td>\n",
" <td>NaN</td>\n",
" <td>Crime|Drama</td>\n",
" <td>Peter Greene</td>\n",
" <td>...</td>\n",
" <td>14.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1995.0</td>\n",
" <td>194.0</td>\n",
" <td>6.4</td>\n",
" <td>NaN</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5033</th>\n",
" <td>Color</td>\n",
" <td>143.0</td>\n",
" <td>77.0</td>\n",
" <td>291.0</td>\n",
" <td>8.0</td>\n",
" <td>David Sullivan</td>\n",
" <td>291.0</td>\n",
" <td>424760.0</td>\n",
" <td>Drama|Sci-Fi|Thriller</td>\n",
" <td>Shane Carruth</td>\n",
" <td>...</td>\n",
" <td>371.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>7000.0</td>\n",
" <td>2004.0</td>\n",
" <td>45.0</td>\n",
" <td>7.0</td>\n",
" <td>1.85</td>\n",
" <td>19000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5034</th>\n",
" <td>Color</td>\n",
" <td>35.0</td>\n",
" <td>80.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Edgar Tancangco</td>\n",
" <td>0.0</td>\n",
" <td>70071.0</td>\n",
" <td>Thriller</td>\n",
" <td>Ian Gamazon</td>\n",
" <td>...</td>\n",
" <td>35.0</td>\n",
" <td>English</td>\n",
" <td>Philippines</td>\n",
" <td>Not Rated</td>\n",
" <td>7000.0</td>\n",
" <td>2005.0</td>\n",
" <td>0.0</td>\n",
" <td>6.3</td>\n",
" <td>NaN</td>\n",
" <td>74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5035</th>\n",
" <td>Color</td>\n",
" <td>56.0</td>\n",
" <td>81.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>Peter Marquardt</td>\n",
" <td>121.0</td>\n",
" <td>2040920.0</td>\n",
" <td>Action|Crime|Drama|Romance|Thriller</td>\n",
" <td>Carlos Gallardo</td>\n",
" <td>...</td>\n",
" <td>130.0</td>\n",
" <td>Spanish</td>\n",
" <td>USA</td>\n",
" <td>R</td>\n",
" <td>7000.0</td>\n",
" <td>1992.0</td>\n",
" <td>20.0</td>\n",
" <td>6.9</td>\n",
" <td>1.37</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5036</th>\n",
" <td>Color</td>\n",
" <td>NaN</td>\n",
" <td>84.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>John Considine</td>\n",
" <td>45.0</td>\n",
" <td>NaN</td>\n",
" <td>Crime|Drama</td>\n",
" <td>Richard Jewell</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>3250.0</td>\n",
" <td>2005.0</td>\n",
" <td>44.0</td>\n",
" <td>7.8</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5037</th>\n",
" <td>Color</td>\n",
" <td>14.0</td>\n",
" <td>95.0</td>\n",
" <td>0.0</td>\n",
" <td>133.0</td>\n",
" <td>Caitlin FitzGerald</td>\n",
" <td>296.0</td>\n",
" <td>4584.0</td>\n",
" <td>Comedy|Drama</td>\n",
" <td>Kerry Bishé</td>\n",
" <td>...</td>\n",
" <td>14.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>Not Rated</td>\n",
" <td>9000.0</td>\n",
" <td>2011.0</td>\n",
" <td>205.0</td>\n",
" <td>6.4</td>\n",
" <td>NaN</td>\n",
" <td>413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5038</th>\n",
" <td>Color</td>\n",
" <td>1.0</td>\n",
" <td>87.0</td>\n",
" <td>2.0</td>\n",
" <td>318.0</td>\n",
" <td>Daphne Zuniga</td>\n",
" <td>637.0</td>\n",
" <td>NaN</td>\n",
" <td>Comedy|Drama</td>\n",
" <td>Eric Mabius</td>\n",
" <td>...</td>\n",
" <td>6.0</td>\n",
" <td>English</td>\n",
" <td>Canada</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2013.0</td>\n",
" <td>470.0</td>\n",
" <td>7.7</td>\n",
" <td>NaN</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5039</th>\n",
" <td>Color</td>\n",
" <td>43.0</td>\n",
" <td>43.0</td>\n",
" <td>NaN</td>\n",
" <td>319.0</td>\n",
" <td>Valorie Curry</td>\n",
" <td>841.0</td>\n",
" <td>NaN</td>\n",
" <td>Crime|Drama|Mystery|Thriller</td>\n",
" <td>Natalie Zea</td>\n",
" <td>...</td>\n",
" <td>359.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>TV-14</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>593.0</td>\n",
" <td>7.5</td>\n",
" <td>16.00</td>\n",
" <td>32000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5040</th>\n",
" <td>Color</td>\n",
" <td>13.0</td>\n",
" <td>76.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>Maxwell Moody</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>Drama|Horror|Thriller</td>\n",
" <td>Eva Boehnke</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>NaN</td>\n",
" <td>1400.0</td>\n",
" <td>2013.0</td>\n",
" <td>0.0</td>\n",
" <td>6.3</td>\n",
" <td>NaN</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5041</th>\n",
" <td>Color</td>\n",
" <td>14.0</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>489.0</td>\n",
" <td>Daniel Henney</td>\n",
" <td>946.0</td>\n",
" <td>10443.0</td>\n",
" <td>Comedy|Drama|Romance</td>\n",
" <td>Alan Ruck</td>\n",
" <td>...</td>\n",
" <td>9.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>NaN</td>\n",
" <td>2012.0</td>\n",
" <td>719.0</td>\n",
" <td>6.3</td>\n",
" <td>2.35</td>\n",
" <td>660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5042</th>\n",
" <td>Color</td>\n",
" <td>43.0</td>\n",
" <td>90.0</td>\n",
" <td>16.0</td>\n",
" <td>16.0</td>\n",
" <td>Brian Herzlinger</td>\n",
" <td>86.0</td>\n",
" <td>85222.0</td>\n",
" <td>Documentary</td>\n",
" <td>John August</td>\n",
" <td>...</td>\n",
" <td>84.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG</td>\n",
" <td>1100.0</td>\n",
" <td>2004.0</td>\n",
" <td>23.0</td>\n",
" <td>6.6</td>\n",
" <td>1.85</td>\n",
" <td>456</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5043 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" color num_critic_for_reviews duration \\\n",
"0 Color 723.0 178.0 \n",
"1 Color 302.0 169.0 \n",
"2 Color 602.0 148.0 \n",
"3 Color 813.0 164.0 \n",
"4 NaN NaN NaN \n",
"5 Color 462.0 132.0 \n",
"6 Color 392.0 156.0 \n",
"7 Color 324.0 100.0 \n",
"8 Color 635.0 141.0 \n",
"9 Color 375.0 153.0 \n",
"10 Color 673.0 183.0 \n",
"11 Color 434.0 169.0 \n",
"12 Color 403.0 106.0 \n",
"13 Color 313.0 151.0 \n",
"14 Color 450.0 150.0 \n",
"15 Color 733.0 143.0 \n",
"16 Color 258.0 150.0 \n",
"17 Color 703.0 173.0 \n",
"18 Color 448.0 136.0 \n",
"19 Color 451.0 106.0 \n",
"20 Color 422.0 164.0 \n",
"21 Color 599.0 153.0 \n",
"22 Color 343.0 156.0 \n",
"23 Color 509.0 186.0 \n",
"24 Color 251.0 113.0 \n",
"25 Color 446.0 201.0 \n",
"26 Color 315.0 194.0 \n",
"27 Color 516.0 147.0 \n",
"28 Color 377.0 131.0 \n",
"29 Color 644.0 124.0 \n",
"... ... ... ... \n",
"5013 Color 28.0 79.0 \n",
"5014 Color 58.0 80.0 \n",
"5015 Black and White 61.0 100.0 \n",
"5016 Color NaN 90.0 \n",
"5017 Color 1.0 90.0 \n",
"5018 Color 5.0 120.0 \n",
"5019 Color 43.0 91.0 \n",
"5020 NaN NaN 143.0 \n",
"5021 Color 51.0 85.0 \n",
"5022 Black and White 6.0 60.0 \n",
"5023 Color 22.0 88.0 \n",
"5024 Color 42.0 78.0 \n",
"5025 Color 73.0 108.0 \n",
"5026 Color 81.0 110.0 \n",
"5027 Color 64.0 90.0 \n",
"5028 Black and White 12.0 83.0 \n",
"5029 Color 78.0 111.0 \n",
"5030 Color NaN 84.0 \n",
"5031 Color 13.0 82.0 \n",
"5032 Color 10.0 98.0 \n",
"5033 Color 143.0 77.0 \n",
"5034 Color 35.0 80.0 \n",
"5035 Color 56.0 81.0 \n",
"5036 Color NaN 84.0 \n",
"5037 Color 14.0 95.0 \n",
"5038 Color 1.0 87.0 \n",
"5039 Color 43.0 43.0 \n",
"5040 Color 13.0 76.0 \n",
"5041 Color 14.0 100.0 \n",
"5042 Color 43.0 90.0 \n",
"\n",
" director_facebook_likes actor_3_facebook_likes actor_2_name \\\n",
"0 0.0 855.0 Joel David Moore \n",
"1 563.0 1000.0 Orlando Bloom \n",
"2 0.0 161.0 Rory Kinnear \n",
"3 22000.0 23000.0 Christian Bale \n",
"4 131.0 NaN Rob Walker \n",
"5 475.0 530.0 Samantha Morton \n",
"6 0.0 4000.0 James Franco \n",
"7 15.0 284.0 Donna Murphy \n",
"8 0.0 19000.0 Robert Downey Jr. \n",
"9 282.0 10000.0 Daniel Radcliffe \n",
"10 0.0 2000.0 Lauren Cohan \n",
"11 0.0 903.0 Marlon Brando \n",
"12 395.0 393.0 Mathieu Amalric \n",
"13 563.0 1000.0 Orlando Bloom \n",
"14 563.0 1000.0 Ruth Wilson \n",
"15 0.0 748.0 Christopher Meloni \n",
"16 80.0 201.0 Pierfrancesco Favino \n",
"17 0.0 19000.0 Robert Downey Jr. \n",
"18 252.0 1000.0 Sam Claflin \n",
"19 188.0 718.0 Michael Stuhlbarg \n",
"20 0.0 773.0 Adam Brown \n",
"21 464.0 963.0 Andrew Garfield \n",
"22 0.0 738.0 William Hurt \n",
"23 0.0 773.0 Adam Brown \n",
"24 129.0 1000.0 Eva Green \n",
"25 0.0 84.0 Thomas Kretschmann \n",
"26 0.0 794.0 Kate Winslet \n",
"27 94.0 11000.0 Scarlett Johansson \n",
"28 532.0 627.0 Alexander Skarsgård \n",
"29 365.0 1000.0 Judy Greer \n",
"... ... ... ... \n",
"5013 3.0 42.0 Panchito Gómez \n",
"5014 892.0 492.0 Katharine Isabelle \n",
"5015 0.0 0.0 Richard Linklater \n",
"5016 0.0 9.0 Mikaal Bates \n",
"5017 138.0 138.0 Suzi Lorraine \n",
"5018 589.0 4.0 Lisa Arnold \n",
"5019 158.0 265.0 Brittany Curran \n",
"5020 8.0 8.0 Alana Kaniewski \n",
"5021 157.0 10.0 Katie Aselton \n",
"5022 0.0 4.0 Olwenya Maina \n",
"5023 38.0 211.0 Heather Burns \n",
"5024 91.0 86.0 Jason Trost \n",
"5025 0.0 105.0 Mink Stole \n",
"5026 107.0 45.0 Béatrice Dalle \n",
"5027 397.0 0.0 Nargess Mamizadeh \n",
"5028 18.0 0.0 Michael Parle \n",
"5029 62.0 6.0 Anna Nakagawa \n",
"5030 5.0 12.0 Michael Cortez \n",
"5031 120.0 84.0 Joe Coffey \n",
"5032 3.0 152.0 Stanley B. Herman \n",
"5033 291.0 8.0 David Sullivan \n",
"5034 0.0 0.0 Edgar Tancangco \n",
"5035 0.0 6.0 Peter Marquardt \n",
"5036 2.0 2.0 John Considine \n",
"5037 0.0 133.0 Caitlin FitzGerald \n",
"5038 2.0 318.0 Daphne Zuniga \n",
"5039 NaN 319.0 Valorie Curry \n",
"5040 0.0 0.0 Maxwell Moody \n",
"5041 0.0 489.0 Daniel Henney \n",
"5042 16.0 16.0 Brian Herzlinger \n",
"\n",
" actor_1_facebook_likes gross \\\n",
"0 1000.0 760505847.0 \n",
"1 40000.0 309404152.0 \n",
"2 11000.0 200074175.0 \n",
"3 27000.0 448130642.0 \n",
"4 131.0 NaN \n",
"5 640.0 73058679.0 \n",
"6 24000.0 336530303.0 \n",
"7 799.0 200807262.0 \n",
"8 26000.0 458991599.0 \n",
"9 25000.0 301956980.0 \n",
"10 15000.0 330249062.0 \n",
"11 18000.0 200069408.0 \n",
"12 451.0 168368427.0 \n",
"13 40000.0 423032628.0 \n",
"14 40000.0 89289910.0 \n",
"15 15000.0 291021565.0 \n",
"16 22000.0 141614023.0 \n",
"17 26000.0 623279547.0 \n",
"18 40000.0 241063875.0 \n",
"19 10000.0 179020854.0 \n",
"20 5000.0 255108370.0 \n",
"21 15000.0 262030663.0 \n",
"22 891.0 105219735.0 \n",
"23 5000.0 258355354.0 \n",
"24 16000.0 70083519.0 \n",
"25 6000.0 218051260.0 \n",
"26 29000.0 658672302.0 \n",
"27 21000.0 407197282.0 \n",
"28 14000.0 65173160.0 \n",
"29 3000.0 652177271.0 \n",
"... ... ... \n",
"5013 93.0 NaN \n",
"5014 986.0 NaN \n",
"5015 5.0 1227508.0 \n",
"5016 313.0 NaN \n",
"5017 370.0 NaN \n",
"5018 51.0 NaN \n",
"5019 630.0 NaN \n",
"5020 720.0 NaN \n",
"5021 830.0 192467.0 \n",
"5022 147.0 NaN \n",
"5023 331.0 76382.0 \n",
"5024 407.0 NaN \n",
"5025 462.0 180483.0 \n",
"5026 576.0 136007.0 \n",
"5027 5.0 673780.0 \n",
"5028 10.0 NaN \n",
"5029 89.0 94596.0 \n",
"5030 21.0 NaN \n",
"5031 785.0 NaN \n",
"5032 789.0 NaN \n",
"5033 291.0 424760.0 \n",
"5034 0.0 70071.0 \n",
"5035 121.0 2040920.0 \n",
"5036 45.0 NaN \n",
"5037 296.0 4584.0 \n",
"5038 637.0 NaN \n",
"5039 841.0 NaN \n",
"5040 0.0 NaN \n",
"5041 946.0 10443.0 \n",
"5042 86.0 85222.0 \n",
"\n",
" genres \\\n",
"0 Action|Adventure|Fantasy|Sci-Fi \n",
"1 Action|Adventure|Fantasy \n",
"2 Action|Adventure|Thriller \n",
"3 Action|Thriller \n",
"4 Documentary \n",
"5 Action|Adventure|Sci-Fi \n",
"6 Action|Adventure|Romance \n",
"7 Adventure|Animation|Comedy|Family|Fantasy|Musi... \n",
"8 Action|Adventure|Sci-Fi \n",
"9 Adventure|Family|Fantasy|Mystery \n",
"10 Action|Adventure|Sci-Fi \n",
"11 Action|Adventure|Sci-Fi \n",
"12 Action|Adventure \n",
"13 Action|Adventure|Fantasy \n",
"14 Action|Adventure|Western \n",
"15 Action|Adventure|Fantasy|Sci-Fi \n",
"16 Action|Adventure|Family|Fantasy \n",
"17 Action|Adventure|Sci-Fi \n",
"18 Action|Adventure|Fantasy \n",
"19 Action|Adventure|Comedy|Family|Fantasy|Sci-Fi \n",
"20 Adventure|Fantasy \n",
"21 Action|Adventure|Fantasy \n",
"22 Action|Adventure|Drama|History \n",
"23 Adventure|Fantasy \n",
"24 Adventure|Family|Fantasy \n",
"25 Action|Adventure|Drama|Romance \n",
"26 Drama|Romance \n",
"27 Action|Adventure|Sci-Fi \n",
"28 Action|Adventure|Sci-Fi|Thriller \n",
"29 Action|Adventure|Sci-Fi|Thriller \n",
"... ... \n",
"5013 Drama|Family \n",
"5014 Action|Crime|Thriller \n",
"5015 Comedy|Drama \n",
"5016 Crime|Drama|Thriller \n",
"5017 Comedy|Romance \n",
"5018 Drama \n",
"5019 Horror|Mystery|Thriller \n",
"5020 Drama|Horror|Thriller \n",
"5021 Comedy|Drama|Romance \n",
"5022 Drama \n",
"5023 Romance \n",
"5024 Sci-Fi|Thriller \n",
"5025 Comedy|Crime|Horror \n",
"5026 Drama|Music|Romance \n",
"5027 Drama \n",
"5028 Horror \n",
"5029 Crime|Horror|Mystery|Thriller \n",
"5030 Drama \n",
"5031 Comedy|Horror|Thriller \n",
"5032 Crime|Drama \n",
"5033 Drama|Sci-Fi|Thriller \n",
"5034 Thriller \n",
"5035 Action|Crime|Drama|Romance|Thriller \n",
"5036 Crime|Drama \n",
"5037 Comedy|Drama \n",
"5038 Comedy|Drama \n",
"5039 Crime|Drama|Mystery|Thriller \n",
"5040 Drama|Horror|Thriller \n",
"5041 Comedy|Drama|Romance \n",
"5042 Documentary \n",
"\n",
" actor_1_name ... num_user_for_reviews \\\n",
"0 CCH Pounder ... 3054.0 \n",
"1 Johnny Depp ... 1238.0 \n",
"2 Christoph Waltz ... 994.0 \n",
"3 Tom Hardy ... 2701.0 \n",
"4 Doug Walker ... NaN \n",
"5 Daryl Sabara ... 738.0 \n",
"6 J.K. Simmons ... 1902.0 \n",
"7 Brad Garrett ... 387.0 \n",
"8 Chris Hemsworth ... 1117.0 \n",
"9 Alan Rickman ... 973.0 \n",
"10 Henry Cavill ... 3018.0 \n",
"11 Kevin Spacey ... 2367.0 \n",
"12 Giancarlo Giannini ... 1243.0 \n",
"13 Johnny Depp ... 1832.0 \n",
"14 Johnny Depp ... 711.0 \n",
"15 Henry Cavill ... 2536.0 \n",
"16 Peter Dinklage ... 438.0 \n",
"17 Chris Hemsworth ... 1722.0 \n",
"18 Johnny Depp ... 484.0 \n",
"19 Will Smith ... 341.0 \n",
"20 Aidan Turner ... 802.0 \n",
"21 Emma Stone ... 1225.0 \n",
"22 Mark Addy ... 546.0 \n",
"23 Aidan Turner ... 951.0 \n",
"24 Christopher Lee ... 666.0 \n",
"25 Naomi Watts ... 2618.0 \n",
"26 Leonardo DiCaprio ... 2528.0 \n",
"27 Robert Downey Jr. ... 1022.0 \n",
"28 Liam Neeson ... 751.0 \n",
"29 Bryce Dallas Howard ... 1290.0 \n",
"... ... ... ... \n",
"5013 Franky G ... 21.0 \n",
"5014 Matt Frewer ... 129.0 \n",
"5015 Tommy Pallotta ... 80.0 \n",
"5016 Tjasa Ferme ... 2.0 \n",
"5017 Kristen Seavey ... 3.0 \n",
"5018 Shannen Fields ... 49.0 \n",
"5019 Ashley Tramonte ... 33.0 \n",
"5020 Robbie Barnes ... 8.0 \n",
"5021 Mark Duplass ... 71.0 \n",
"5022 Paul Ogola ... 1.0 \n",
"5023 Zoe Lister-Jones ... 8.0 \n",
"5024 Sean Whalen ... 35.0 \n",
"5025 Divine ... 183.0 \n",
"5026 Maggie Cheung ... 39.0 \n",
"5027 Fereshteh Sadre Orafaiy ... 26.0 \n",
"5028 Patrick O'Donnell ... 1.0 \n",
"5029 Kôji Yakusho ... 50.0 \n",
"5030 Tatiana Suarez-Pico ... 3.0 \n",
"5031 Julianna Pitt ... 8.0 \n",
"5032 Peter Greene ... 14.0 \n",
"5033 Shane Carruth ... 371.0 \n",
"5034 Ian Gamazon ... 35.0 \n",
"5035 Carlos Gallardo ... 130.0 \n",
"5036 Richard Jewell ... 1.0 \n",
"5037 Kerry Bishé ... 14.0 \n",
"5038 Eric Mabius ... 6.0 \n",
"5039 Natalie Zea ... 359.0 \n",
"5040 Eva Boehnke ... 3.0 \n",
"5041 Alan Ruck ... 9.0 \n",
"5042 John August ... 84.0 \n",
"\n",
" language country content_rating budget year \\\n",
"0 English USA PG-13 237000000.0 2009.0 \n",
"1 English USA PG-13 300000000.0 2007.0 \n",
"2 English UK PG-13 245000000.0 2015.0 \n",
"3 English USA PG-13 250000000.0 2012.0 \n",
"4 NaN NaN NaN NaN NaN \n",
"5 English USA PG-13 263700000.0 2012.0 \n",
"6 English USA PG-13 258000000.0 2007.0 \n",
"7 English USA PG 260000000.0 2010.0 \n",
"8 English USA PG-13 250000000.0 2015.0 \n",
"9 English UK PG 250000000.0 2009.0 \n",
"10 English USA PG-13 250000000.0 2016.0 \n",
"11 English USA PG-13 209000000.0 2006.0 \n",
"12 English UK PG-13 200000000.0 2008.0 \n",
"13 English USA PG-13 225000000.0 2006.0 \n",
"14 English USA PG-13 215000000.0 2013.0 \n",
"15 English USA PG-13 225000000.0 2013.0 \n",
"16 English USA PG 225000000.0 2008.0 \n",
"17 English USA PG-13 220000000.0 2012.0 \n",
"18 English USA PG-13 250000000.0 2011.0 \n",
"19 English USA PG-13 225000000.0 2012.0 \n",
"20 English New Zealand PG-13 250000000.0 2014.0 \n",
"21 English USA PG-13 230000000.0 2012.0 \n",
"22 English USA PG-13 200000000.0 2010.0 \n",
"23 English USA PG-13 225000000.0 2013.0 \n",
"24 English USA PG-13 180000000.0 2007.0 \n",
"25 English New Zealand PG-13 207000000.0 2005.0 \n",
"26 English USA PG-13 200000000.0 1997.0 \n",
"27 English USA PG-13 250000000.0 2016.0 \n",
"28 English USA PG-13 209000000.0 2012.0 \n",
"29 English USA PG-13 150000000.0 2015.0 \n",
"... ... ... ... ... ... \n",
"5013 English USA NaN 24000.0 2002.0 \n",
"5014 English Canada R NaN 2009.0 \n",
"5015 English USA R 23000.0 1991.0 \n",
"5016 English USA NaN 25000.0 2015.0 \n",
"5017 English USA NaN 22000.0 2013.0 \n",
"5018 English USA NaN 20000.0 2003.0 \n",
"5019 English USA R NaN 2015.0 \n",
"5020 English USA NaN 17350.0 2011.0 \n",
"5021 English USA R 15000.0 2005.0 \n",
"5022 Swahili Kenya NaN 15000.0 2014.0 \n",
"5023 English USA NaN 15000.0 2009.0 \n",
"5024 English USA Unrated 20000.0 2011.0 \n",
"5025 English USA NC-17 10000.0 1972.0 \n",
"5026 French France R 4500.0 2004.0 \n",
"5027 Persian Iran Not Rated 10000.0 2000.0 \n",
"5028 English Ireland NaN 10000.0 2007.0 \n",
"5029 Japanese Japan NaN 1000000.0 1997.0 \n",
"5030 English USA NaN NaN 2004.0 \n",
"5031 English USA NaN 200000.0 2012.0 \n",
"5032 English USA NaN NaN 1995.0 \n",
"5033 English USA PG-13 7000.0 2004.0 \n",
"5034 English Philippines Not Rated 7000.0 2005.0 \n",
"5035 Spanish USA R 7000.0 1992.0 \n",
"5036 English USA PG-13 3250.0 2005.0 \n",
"5037 English USA Not Rated 9000.0 2011.0 \n",
"5038 English Canada NaN NaN 2013.0 \n",
"5039 English USA TV-14 NaN NaN \n",
"5040 English USA NaN 1400.0 2013.0 \n",
"5041 English USA PG-13 NaN 2012.0 \n",
"5042 English USA PG 1100.0 2004.0 \n",
"\n",
" actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes \n",
"0 936.0 7.9 1.78 33000 \n",
"1 5000.0 7.1 2.35 0 \n",
"2 393.0 6.8 2.35 85000 \n",
"3 23000.0 8.5 2.35 164000 \n",
"4 12.0 7.1 NaN 0 \n",
"5 632.0 6.6 2.35 24000 \n",
"6 11000.0 6.2 2.35 0 \n",
"7 553.0 7.8 1.85 29000 \n",
"8 21000.0 7.5 2.35 118000 \n",
"9 11000.0 7.5 2.35 10000 \n",
"10 4000.0 6.9 2.35 197000 \n",
"11 10000.0 6.1 2.35 0 \n",
"12 412.0 6.7 2.35 0 \n",
"13 5000.0 7.3 2.35 5000 \n",
"14 2000.0 6.5 2.35 48000 \n",
"15 3000.0 7.2 2.35 118000 \n",
"16 216.0 6.6 2.35 0 \n",
"17 21000.0 8.1 1.85 123000 \n",
"18 11000.0 6.7 2.35 58000 \n",
"19 816.0 6.8 1.85 40000 \n",
"20 972.0 7.5 2.35 65000 \n",
"21 10000.0 7.0 2.35 56000 \n",
"22 882.0 6.7 2.35 17000 \n",
"23 972.0 7.9 2.35 83000 \n",
"24 6000.0 6.1 2.35 0 \n",
"25 919.0 7.2 2.35 0 \n",
"26 14000.0 7.7 2.35 26000 \n",
"27 19000.0 8.2 2.35 72000 \n",
"28 10000.0 5.9 2.35 44000 \n",
"29 2000.0 7.0 2.00 150000 \n",
"... ... ... ... ... \n",
"5013 46.0 7.0 1.78 61 \n",
"5014 918.0 6.3 2.35 0 \n",
"5015 0.0 7.1 1.37 2000 \n",
"5016 25.0 4.8 NaN 33 \n",
"5017 184.0 3.3 1.78 200 \n",
"5018 49.0 6.9 1.85 725 \n",
"5019 512.0 4.6 1.85 0 \n",
"5020 19.0 3.0 NaN 33 \n",
"5021 224.0 6.6 NaN 297 \n",
"5022 19.0 7.4 NaN 45 \n",
"5023 212.0 6.2 2.35 324 \n",
"5024 91.0 4.0 2.35 835 \n",
"5025 143.0 6.1 1.37 0 \n",
"5026 133.0 6.9 2.35 171 \n",
"5027 0.0 7.5 1.85 697 \n",
"5028 5.0 6.7 1.33 105 \n",
"5029 13.0 7.4 1.85 817 \n",
"5030 20.0 6.1 NaN 22 \n",
"5031 98.0 5.4 16.00 424 \n",
"5032 194.0 6.4 NaN 20 \n",
"5033 45.0 7.0 1.85 19000 \n",
"5034 0.0 6.3 NaN 74 \n",
"5035 20.0 6.9 1.37 0 \n",
"5036 44.0 7.8 NaN 4 \n",
"5037 205.0 6.4 NaN 413 \n",
"5038 470.0 7.7 NaN 84 \n",
"5039 593.0 7.5 16.00 32000 \n",
"5040 0.0 6.3 NaN 16 \n",
"5041 719.0 6.3 2.35 660 \n",
"5042 23.0 6.6 1.85 456 \n",
"\n",
"[5043 rows x 27 columns]"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xe3c4c50>"
]
},
"execution_count": 179,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0xe3d6160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.duration[df.duration < 300].hist()"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xe3aa7b8>"
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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oKAiKx8NUmqNR79TWSiglUNZbwKzW+UZzHJv8ZZRL23v4xcgUanM9o9zspdTr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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xf4ce5c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(x='duration', y='imdb_score', kind='scatter')"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Trapped'"
]
},
"execution_count": 188,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[df.duration.idxmax(), 'movie_title'].strip()"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xf60a940>"
]
},
"execution_count": 193,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Rz1jlzcONPPruSX777qmoK6k1iZGQ0AshvgX4gCeMQxGKRTTPpZQPSymXSymX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\nWumK6x3uUDx3XwwrfHllLla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JTUJCL4T4shBinxBirxDiD0IIuxBiqhDifSHEESHEU0KI\nCbXJ6KVziwlIePPw0FbJ7qlupzI/LbT9X7JISzFjNqlFUnoyVqOZ2MQt9EKIMuAfgeVSygWAGbgZ\n+BHwMynlTKAVuD0ZDT1XWFSWTUFGypCzWe6paWdheU7S2yGECLlv9H6xGs3EJlHXjQVIFUJYgDSg\nFrgUeCb4+mPA9Qle45zCZBJ8YHYRGw814PMHBizb4fZR09bNnEmZw9IWQ+i1Ra/RTGziFnopZQ3w\nU+A0SuDbge1Am5TS2C27GiiLdL4Q4k4hxDYhxLbGxviTgY1F1s0twuHysf1U64DljgXDMGcUxZeK\neDCMjJU66kajmdgk4rrJBa4DpgKlQDpwVYSiEVcPSSkfllIul1IuLywsjLcZY5ILZxaSajXzlad3\n8fe9tVEXUB1rVEI/Pc6c84NhxNLrqBuNZmKTiOvmMuCElLJRSukFngVWAzlBVw5AOXA2wTaec2TY\nLDx++0oy7RY+/7sdfPo3WzjR1Nmv3LHGDiwmwZT8tGFphxFLry16jWZik4jQnwbOF0KkCZUDdx2w\nH3gD+EiwzK3A84k18dxkeWUef737Qu67Zh5VZ9q4/bGt/cocbehgSn4aVvPwRLkaIZY6vFKjmdgk\n4qN/HzXpugPYE6zrYeDrwFeEEEeBfOCRJLTznMRiNvGZNVO5+9IZHG/spLnD3ev1Y42dw+a2AT0Z\nq9FoFAkpgJTyPuC+PoePAysTqXe8sSC4GGpPTTtrZ6s0CV5/gJNNnayfVzxs1y3LSSXLbiHNah62\na2g0mrGPXhk7AoSEPpiOGOB0Sxe+gBxWi/7Tq6fw8pcvxmTSu0tpNBMZLfQjQJbdyrSC9FC6Axj+\n0EoAm8VMSXbqsNWv0WjODbTQjxALy7N7Cf3RYGjltML00WqSRqOZIGihHyEWlmVT2+6iwekC4FhD\nJ8VZtl7phDUajWY40EI/Qhgbi+wNWvXHGjuG1W2j0Wg0BlroR4j5ZdkIAXuqHUgpOdbQMawTsRqN\nRmOghX6EyLBZmF6YwZ6aNhqdbpxun7boNRrNiKCFfgRZVJbN7ur20J6y2qLXaDQjgRb6EWRBWTYN\nTjfvHmsGhje0UqPRaAy00I8gi4IbgD9XVUOGzUJRpm2UW6TRaCYCWuhHkHmlWZgEVLd2M70wHZUL\nTqPRaIYXLfQjSFqKhZlFajep6dpto9FoRggt9COMkfdGT8RqNJqRQgv9CGP46fVErEajGSm00I8w\nl80r5qKZBayszBvtpmg0mgmC3pFihCnLSeXx21eNdjM0Gs0EQlv0Go1GM85JSOiFEDlCiGeEEAeF\nEAeEEBcIIfKEEK8KIY4E/+Ymq7EajUajiZ1ELfoHgb9LKecAi4EDwL3ABinlTGBD8LlGo9FoRom4\nhV4IkQVcTHDzbymlR0rZBlwHPBYs9hhwfaKN1Gg0Gk38JGLRTwMagf8TQuwUQvyvECIdKJZS1gIE\n/xYloZ0ajUajiZNEhN4CLAV+KaU8D+gkBjeNEOJOIcQ2IcS2xsbGBJqh0Wg0moFIROirgWop5fvB\n58+ghL9eCFECEPzbEOlkKeXDUsrlUsrlhYWFCTRDo9FoNAMRt9BLKeuAM0KI2cFD64D9wAvArcFj\ntwLPJ9RCjUaj0SREogum7gaeEEKkAMeBz6A6j6eFELcDp4GPJngNjUaj0SRAQkIvpawClkd4aV0i\n9Wo0Go0meeiVsRqNRjPO0UKv0Wg04xwt9BqNRjPO0UKv0Wg04xwt9BqNRjPO0UKv0Wg04xwt9BqN\nRjPO0UKv0Wg04xwt9BqNRjPO0UKv0Wg04xwt9BqNRjPO0UKv0Wg04xwt9BqNRjPO0UKv0Wg04xwt\n9BqNRjPO0UKv0Wg04xwt9BqNRjPO0UKv0Wg045yEhV4IYRZC7BRC/DX4fKoQ4n0hxBEhxFPB/WQ1\nGo1GM0okw6L/EnAg7PmPgJ9JKWcCrcDtSbiGRqPRaOIkIaEXQpQDHwL+N/hcAJcCzwSLPAZcn8g1\nNBqNRpMYiVr0/wl8DQgEn+cDbVJKX/B5NVAW6UQhxJ1CiG1CiG2NjY0JNkOj0Wg00Yhb6IUQVwMN\nUsrt4YcjFJWRzpdSPiylXC6lXF5YWBhvMzQajUYzCJYEzl0DXCuE+CBgB7JQFn6OEMIStOrLgbOJ\nN1Oj0Wg08RK3RS+l/IaUslxKWQncDLwupfwE8AbwkWCxW4HnE26lRqPRaOJmOOLovw58RQhxFOWz\nf2QYrqHRaDSaIZKI6yaElHIjsDH4/3FgZTLq1Wg0Gk3i6JWxGo1GM87RQq/RaDTjHC30Go1GM87R\nQq/RaDTjHC30Go1GM87RQq/RaDTjHC30Go1GM87RQq/RaDTjHC30Go1GM87RQq/RaDTjHC30Go1G\nM87RQq/RaDTjHC30Go1GM87RQq/RaDTjHC30Go1GM87RQq/RaDTjHC30Go1GM86JW+iFEBVCiDeE\nEAeEEPuEEF8KHs8TQrwqhDgS/JubvOZqNBqNJlYSseh9wD9LKecC5wN3CSHmAfcCG6SUM4ENweca\njUajGSXiFnopZa2Ucv4VPwYAAA+DSURBVEfwfydwACgDrgMeCxZ7DLg+0UZqNBqNJn6S4qMXQlQC\n5wHvA8VSylpQnQFQlIxraDQajSY+EhZ6IUQG8Cfgn6SUjhjOu1MIsU0Isa2xsTHRZmg0Go0mCgkJ\nvRDCihL5J6SUzwYP1wshSoKvlwANkc6VUj4spVwupVxeWFiYSDM0Go1GMwCJRN0I4BHggJTyP8Je\negG4Nfj/rcDz8TdPo9FoNIliSeDcNcCngD1CiKrgsW8CDwBPCyFuB04DH02siRqNRqNJhLiFXkr5\nNiCivLwu3no1Go1Gk1z0yliNRqMZ52ih12g0mnGOFnqNRqMZ52ih12g0mnGOFnqNRqMZ52ih12g0\nmnGOFnqNRqMZ52ih12g0mnGOFnqNRqMZ5ySSAiF5yMDIXs/bDafegdrd0a+dXggVq6BgFpiS2B+6\nnXDyHcgqgfyZkJIW2/lSQstx6GqB0vPAPMBX6HOr8lZ7Ym0ebQ6+CO89BFmlUDQPihdA+XJIzeld\nzlmvvtfcSiieDxbbqDRXoxlrjA2hr9sDT3wM5l4Nsz8I6QXJrV9KaDwIRzfAsQ1w6l3wuYZ2rj0H\nKlYGH+crcW07BSc2qYe3Sx2fcgGUr4CU9Mj1tJyALQ/Dzt+B28jmLCC7Qp0T8ILfqw5b08Caqo5n\nFEHGJPW3+SgcfxPaTwfblg0zLoOpF0N3GzQfgeZj4KyFzmbwOMFsg3nXwbLbYMpqEH2yVkgJNdvh\n8N+h9RS0nVbnlyxS38XM9er78Lqgs0FdR5jAZAaTRXWI4YLr90LrSfV+u1uguxVcDiXSkxZC0VwQ\nZmg6DHW7oemI6nj9bnXupIWqvRlFqlN8+Zuw47eQM1m9t91PBT86M0w+H2ZeDrYs2PdnJfJGx21O\nUWKfPxOyyyCrTNVpSVUdnzCp+hoPQsMB9blkV0BOBWSWgj1L1WvPgvQiyCju6VQ9ndBeA95O1fEM\npUMJBKCjHhw1qo1mK5isqjPuqFefbUcjdDUHP7c2SMuD3KmQN1UZHMXz1XkaTYwIKeVot4Hls0rk\nti8UKAETJph8Acy5Wgl/zuT4Ku1qgeMblbAffR2cZ9XxgtkwYx1MXweTVykh7IeEtjNw5n048x6c\n2aIEoS+5U5UY1+9T55gsULJYtb9iFbjaobYKzlYpMTWZYf4NsOTj6rWmI0rwfC4lTCarqsfbpcTP\n3aEEwFmnjtmzlahPWwtp+XDkNTjyiioDSpDyZ0B2uXo9LR866mD3H8Hdrl6bejGULFFCXrcXtv4a\nancp4cwug5wp6rwz7yvBRyjBc7dH/6xtWUokAz5oOab+RsNkUdfyu9VzYVadmjlFiW1Xs7oHKi9U\nnU7baVjzJVj7DSWoXS1Qv1d9t4dfgfo9qp78GbDgw6pjaq+GszvVo/UEOGpVRxoJa5oSUSHUd97V\nFKXhQnUUfq8SYgNzCkxaBKVLwNMF7WfUw9Op7i2zVQm742z0NoSTkglpueq77mzuuW9BdVJlS1Vn\n6PeCp0PdI2ar6pDs2erzdDvU/eVzQ+FsdU+WLFbtaK9W79PtUJ+nNU29B78XfN3qHGECi109bJmq\nk84qUx1PuKHg86jPoqul99/uNpD+nnLG/ZFdrkawjYehYb8yXCy24L1aoNpvy1DXtKarkbQIGhQp\n6cHXs9TvyO/tMQ7CMaeodg800h0qPo/6LCLVJWXvz8xiU23ra0gNM0KI7VLK5YOWGxNCv3y53LZ1\nq7LwDvwVDv5V3QigfkRzr1HCXzQ3+gfp9ykxPbZBWe5nd6gb256thHH6Oph+qbLY4qGrBaq3qXqz\nK5RgGnW52lVncOpdOL1ZtcPvUa+lZKofWeUaZVVnlcZ+bSnVj9qapm7ycAIB5cpJL+jvyjDwdMH+\n55Q1XLMjbEQBFM6FlXfAopvUDyz8mrVVcOjv6sebEbRq7TmAhIBf3egd9Uo82s+oH0XBLPXIn65+\nwKm5qt620+r7rd2tBG/SIiVY+TN7fkhSKut637PKQjdZ4JoHleUejfYaZfkXzo5+bwQC0NmoHj6X\n6kQDXtVR50zp7ZrzdKn35Hb2CGZHvepsnbWqTdkV6mG2qM6kept6X7ZMdU9kVyjB8vuCHZpQ33tO\nhRJMkzU4gvOozsD4bNMLwZLSu+1elxpB1u2B6q3q0XBQjUpSMtTD7wm21aE6WXu2epgsg3e8sWCy\nqvtPSkD23OPxkjFJfQ5dLaq+ISMGLy/MPZ2DPUd9HwGfarPfq+5f6KlHmHvem6tdjUS9neo1c0qw\nQ7Qq8fd71Pfa1+1rsvTc78Ks7kdhUseNEbCI4gaWAdVheDrV/Wkyq3psmaoDMQw3e7a6D1uOQ/Mx\nxFcPnmNCv21b74PNx5TgH/grVG9Rx/KmBUX/GihbBo7qoDvmdeXScLerD7JsmRL2GeugdGlyevdY\n8LrUDzM1V7U5mT7+RAkElJVbuwsyS5SIjrAVohlBvC5lNNXtVkKdE7Ss7TlKWAyL1LCELfag6LjU\nw9WuRiOOs2p0GPAH7xehOpm0XEjNU9a+8dee0+NiMoTTMAbcTmUIFM1RogWqTlc7uNqCHWyHGsEG\nfOq1gE89dzlUh+b3qg7RGDGFkugaVrZbtd3TEay3XdVrtgZHzkHhDU++K/2qrVKqdqXm9hhOnk51\nfb9XWe5mq7q21a5GWRabuqbhdnM7g3UFeh4BX3D0Eaa3Uvb+7VlS1YjHmqrOcTlUXa72nhGT26FG\nP/nTIW864sZfneNCH46jFg79TYn+ybfUh5aSqXzQoHyqMy4N+qsvUTebRqPRjDcC/l6j+qG6bsbG\nZOxgZJXAijvUo7tV+WZPvgWFc5TVXjhHW6UajWb809d1O0TODaEPJzUXFt+kHhqNRqMZlDHkPNZo\nNBrNcDBsQi+EuFIIcUgIcVQIce9wXUej0Wg0AzMsQi+EMAO/AK4C5gG3CCHmDce1NBqNRjMww2XR\nrwSOSimPSyk9wJPAdcN0LY3m/7d3vzFylVUcx78/W7DSCm2xxfJHCwmoaEDaRUqCQECL1kQgSELQ\ntLomaGICJoJC5IWVoNIYNYYXSqRJCQYSQbBGpTYEbCSl9p8sXYq0a1BXG1pbFKrBknp88Zx1L+vu\nlq2dnZl7f5/kZu4895nZe888OXvnmTtnzGwcrUr0JwF/qtwfzLb/knSdpE2SNu3Zs6dFu2FmZq1K\n9KNd6/iaC/Yj4q6I6ImInjlz5rRoN8zMrFWJfhCo1ho4GfjLGH3NzKyFWpXoNwKnSzpV0tHANcDq\nFv0tMzMbR8tKIEhaAnwHmAKsjIjbx+m7B/jDYfyZtwBjlRtsGsdimGNROA7D6hqLt0fEIee+O6LW\nzeGStOn11HloAsdimGNROA7Dmh4LfzPWzKzmnOjNzGqu2xP9Xe3egQ7iWAxzLArHYVijY9HVc/Rm\nZnZo3X5Gb2Zmh9BRiV7SSkm7JW2rtJ0tab2kpyX9VNKx2f5BSZuzfbOkSyqPWZjtOyV9V+q+XyWZ\nSCwq298mab+kGyttXV9FdKKxkHRWbuvP7dOyvVHjQtJRklZl+3ZJt1QeU4dxcYqkx/LY+iXdkO2z\nJa2VtCNvZ2W78nXfKalP0oLKcy3L/jskLWvXMbVMRHTMAlwILAC2Vdo2Ahflei9wW66fA5yY6+8B\n/lx5zG+A8ymlGH4BfLjdx9bKWFS2Pwj8CLgx708BBoDTgKOBp4Az231sLR4XU4E+4Oy8fzwwpYnj\nArgWuD/XjwGeB+bXaFzMAxbk+puB5yjVclcAN2f7zcAdub4kX3cBi4AN2T4b+H3ezsr1We0+viO5\ndNQZfUSsA/aNaH4HsC7X1wJXZd+tETFUVqEfmCbpjZLmAcdGxPoor+I9wBWt3/sjayKxAJB0BWWA\n9lf616KK6ARjsRjoi4in8rF7I+JgQ8dFANMlTQXeBBwAXqI+42JXRGzJ9ZeB7ZTiiZcDq7LbKoZf\n58uBe6J4EpiZ4+IyYG1E7IuIFykx/NAkHkrLdVSiH8M24KO5fjWvraEz5Cpga0T8i/JCD1a2/U/l\nzC42aiwkTQe+BCwf0f+QVUS72Fjj4gwgJK2RtEXSF7O9ceMCeAD4B7AL+CPwzYjYRw3HhaT5lHf5\nG4ATImIXlH8GwNzsNtZx1y4eI3VDou8FPidpM+Xt2YHqRknvBu4APjPUNMpz1OXSorFisRz4dkTs\nH9G/ibGYClwAfDxvr5R0Kc2MxfuAg8CJwKnAFySdRs1iIWkGZdry8xHx0nhdR2mLcdpro+N/HDwi\nnqW8HUfSGcBHhrZJOhl4CFgaEQPZPEipljmkNpUzx4nFecDHJK0AZgL/lvQKsJmaVhEdJxaDwK8i\n4q+57eeUOe17ad64uBZ4JCJeBXZLegLooZy91mJcSDqKkuR/GBE/zuYXJM2LiF05NbM728eqqjsI\nXDyi/fFW7vdk6/gzeklz8/YNwK3A9/L+TOBnwC0R8cRQ/3yr9rKkRXlVxVLgJ5O+4y0wViwi4v0R\nMT8i5lMKyX0tIu6kxlVEx4oFsAY4S9IxOTd9EfBME8cFZbrmkrzaZDrlA8hnqcm4yNfxbmB7RHyr\nsmk1MHTlzDKGX+fVwNKMxyLg7zku1gCLJc3KK3QWZ1t9tPvT4OoC3EeZT3yV8l/208ANlE/TnwO+\nwfCXvG6lzD/+trLMzW09lHnLAeDOocd00zKRWIx43FfIq27y/pLsPwB8ud3HNRmxAD5B+VB6G7Ci\n0t6ocQHMoFyF1Q88A9xUs3FxAWWKpa+SA5ZQrrR6FNiRt7Ozvyi/ZT0APA30VJ6rF9iZy6fafWxH\nevE3Y83Maq7jp27MzOz/40RvZlZzTvRmZjXnRG9mVnNO9GZmNedEb2ZWc070ZkeIpCnt3gez0TjR\nWyNJum2ofnnev13S9ZJukrQx65Uvr2x/WOV3D/olXVdp3y/pq5I2UEogm3UcJ3prqrvJr8ln6YBr\ngBeA0ynFwN4LLJR0YfbvjYiFlG/XXi/p+GyfTqkNf15E/HoyD8Ds9er4omZmrRARz0vaK+kc4ARg\nK3Aupc7J1uw2g5L411GS+5XZfkq276VUh3xwMvfdbKKc6K3JfgB8EngrsBK4FPh6RHy/2knSxcAH\ngPMj4p+SHgem5eZXIuLgZO2w2eHw1I012UOUXxI6l1KtcA3Qm/XNkXRSVoY8Dngxk/w7KVUgzbqG\nz+itsSLigKTHgL/lWfkvJb0LWF8q4LKfUgnzEeCzkvqA3wFPtmufzQ6Hq1daY+WHsFuAqyNiR7v3\nx6xVPHVjjSTpTErt8Ued5K3ufEZvZlZzPqM3M6s5J3ozs5pzojczqzknejOzmnOiNzOrOSd6M7Oa\n+w+uuI39DXlA+gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xf5e8f28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.groupby('year')[['duration', 'imdb_score']].mean().plot()"
]
},
{
"cell_type": "code",
"execution_count": 222,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(1950,175,'$x^2+\\\\sqrt{y}$')"
]
},
"execution_count": 222,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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pvt5aAH9ljHkAHCOiowBOA7Al0ZP8wSAu//1HuO/KhbhmZe207XoQCDIQINu7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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1252a080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(df.query('imdb_score > 8')\n",
" .groupby('year')['duration']\n",
" .mean().plot(title=\"Some graph\"))\n",
"plt.tight_layout()\n",
"plt.annotate('$x^2+\\\\sqrt{y}$', (1940, 150), xytext = (1950, 175),\n",
" arrowprops={'arrowstyle': '->'})\n",
"# plt.savefig(\"years2.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'C:\\\\Users\\\\student'"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\\left[\\begin{matrix}x^{2} + 2 y & 2 x y\\\\4 x & x^{2} + 2 y\\end{matrix}\\right]\n"
]
}
],
"source": [
"print(sympy.latex(sympy.Matrix([[x, y], [2, x]]) ** 2))"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$\\left[\\begin{matrix}x^{2} + 2 y & 2 x y\\\\4 x & x^{2} + 2 y\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡ 2 ⎤\n",
"⎢x + 2⋅y 2⋅x⋅y ⎥\n",
"⎢ ⎥\n",
"⎢ 2 ⎥\n",
"⎣ 4⋅x x + 2⋅y⎦"
]
},
"execution_count": 206,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sympy.Matrix([[x, y], [2, x]]) ** 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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