contextrecommendationvisual.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data=pd.read_csv(r'C:\\Users\\kshitij\\Downloads\\ContextuaRecommendationSystem.csv')\n",
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime And Thriller\":0,\"Documentary\":0}\n",
    "data.head()\n",
    "data[\"Would you like to get recommended Holiday Theme Movies On Holidays (Ex : Border on Independence Day and Dangal /Marry Kom on Women's Day) -:\"]=data[\"Would you like to get recommended Holiday Theme Movies On Holidays (Ex : Border on Independence Day and Dangal /Marry Kom on Women's Day) -:\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 22)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in data.iterrows():\n",
    "    for k in j[2].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 45, 'Romance': 17, 'Drama': 17, 'Sci-Fi': 20, 'Action': 13, 'Crime And Thriller': 12, 'Documentary': 18}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function dict.values>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(genre)\n",
    "genre.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre preference in morning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 45, 'Romance': 17, 'Drama': 17, 'Sci-Fi': 20, 'Action': 13, 'Crime And Thriller': 12, 'Documentary': 18}\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime And Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[2].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre preference during afternoon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 34, 'Romance': 13, 'Drama': 25, 'Sci-Fi': 25, 'Action': 31, 'Crime And Thriller': 35, 'Documentary': 17}\n"
     ]
    }
   ],
   "source": [
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime And Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[3].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genre preference in evening"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 34, 'Romance': 28, 'Drama': 21, 'Sci-Fi': 23, 'Action': 24, 'Crime And Thriller': 28, 'Documentary': 17}\n"
     ]
    }
   ],
   "source": [
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime And Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[4].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genre preference at night"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 30, 'Romance': 40, 'Drama': 26, 'Sci-Fi': 29, 'Action': 26, 'Crime And Thriller': 38, 'Documentary': 22}\n"
     ]
    }
   ],
   "source": [
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime And Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[5].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genre Preference during Winter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 36, 'Romance': 38, 'Drama': 30, 'Sci-Fi': 23, 'Action': 32, 'Crime And Thriller': 35, 'Documentary': 15}\n"
     ]
    }
   ],
   "source": [
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime And Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[6].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre Preference during Summer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 40, 'Romance': 23, 'Drama': 35, 'Sci-Fi': 33, 'Action': 34, 'Crime And Thriller': 42, 'Documentary': 19}\n"
     ]
    }
   ],
   "source": [
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime And Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[7].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genre Preference during Rainy/Monsoon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 37, 'Romance': 40, 'Drama': 35, 'Sci-Fi': 23, 'Action': 21, 'Crime And Thriller': 29, 'Documentary': 10}\n"
     ]
    }
   ],
   "source": [
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime And Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[8].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre Preference during Autumn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 35, 'Romance': 29, 'Drama': 34, 'Sci-Fi': 31, 'Action': 26, 'Crime And Thriller': 37, 'Documentary': 22}\n"
     ]
    }
   ],
   "source": [
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime And Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[9].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre Preference when on Large Screen Device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 38, 'Romance': 29, 'Drama': 27, 'Sci-Fi': 29, 'Action': 34, 'Crime and Thriller': 49, 'Documentary': 20}\n"
     ]
    }
   ],
   "source": [
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime and Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[10].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre Preference when on Tablet-Size Screen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 36, 'Romance': 26, 'Drama': 23, 'Sci-Fi': 27, 'Action': 23, 'Crime and Thriller': 34, 'Documentary': 15}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime and Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[11].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Timestamp', 'Name',\n",
       "       'Genre Preference in Morning  -: ( Choose all that apply )',\n",
       "       'Genre Preference during Afternoon -: ( Choose all that apply )',\n",
       "       'Genre Preference in Evening -: ( Choose all that apply )',\n",
       "       'Genre Preference at Night -: ( Choose all that apply )',\n",
       "       'Genre Preference during Winter -: ( Choose all that apply )',\n",
       "       'Genre Preference during Summer  -: ( Choose all that apply )',\n",
       "       'Genre Preference during Rainy/Monsoon  -: ( Choose all that apply )',\n",
       "       'Genre Preference during Autumn  -: ( Choose all that apply )',\n",
       "       'Genre Preference on Weekend -: ( Choose all that apply )',\n",
       "       'Genre Preference on Weekdays -: ( Choose all that apply )',\n",
       "       'Genre Preference when on Large Screen Device -: ( Choose all that apply )',\n",
       "       'Genre Preference when on Tablet-Size Screen -: ( Choose all that apply )',\n",
       "       'Genre Preference when on Phone-Size Screen -: ( Choose all that apply )',\n",
       "       'Movie Genre when you are Happy  -: ( Choose all that apply )',\n",
       "       'Movie Genre when you are Sad -: ( Choose all that apply )',\n",
       "       'Movie Genre when you are Angry -: ( Choose all that apply )',\n",
       "       'Movie Genre when you are Normal -: ( Choose all that apply )',\n",
       "       'Genre Preference when watching with Family -: ( Choose all that apply )',\n",
       "       'Genre Preference when watching with Friends -: ( Choose all that apply )',\n",
       "       'Would you like to get recommended Holiday Theme Movies On Holidays (Ex : Border on Independence Day and Dangal /Marry Kom on Women's Day) -:'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 22)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genre Preference when on Phone-Size Screen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 14, 'Romance': 22, 'Drama': 17, 'Sci-Fi': 36, 'Action': 39, 'Crime and Thriller': 42, 'Documentary': 11}\n"
     ]
    }
   ],
   "source": [
    "data.dropna(subset=[\"Genre Preference when on Phone-Size Screen -: ( Choose all that apply )\",inplace=True)\n",
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime and Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[12].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65, 22)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genre Preference when you are happy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'float' object has no attribute 'split'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-157-18707678e921>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mgenre\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"Comedy\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Romance\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Drama\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Sci-Fi\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Action\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Crime and Thriller\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Documentary\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\";\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m         \u001b[0mgenre\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgenre\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'float' object has no attribute 'split'"
     ]
    }
   ],
   "source": [
    "data.dropna(subset=[\"Movie Genre when you are Happy  -: ( Choose all that apply )\"],inplace=True)\n",
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime and Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[13].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movie Genre when you are Sad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T+srAABAJrOucW1w8+PTwQylMgjXJrEnFjMAcwm8Glv7pEx1ERfJ/2yt40aQhAze+npub9LCytxwxzVmTx8RQaaUp13IAY2Ae42ggcPaN/U9LelhpklmTSi/gDLdF+AHfGFjRvKKhWGuQ+58UhJUBtE82jabFWvIwYaQD+MbAyMBvtGSGlSIEBsg+k8yaPMa5LcAP+MnAhrstwC2iwsrFBwMBx1ZTDcnufU6dy/AhBrktwA8YA/MLVlg5Zcrg0yMPOZQEe4bsOOqENEOLeD5fMR0wBuYz7LCy2ImwcrjsNMmsyWOg2wL8gDEwn9IUVn6hf8GSjoaVQ2WX07IMJ+kfKq300+fPFfz0BzRzCs0RCVR1zi3uaFg5QEwyaxLJBk5zW4TX8YWBFc0rygKCbutIVzoaVppk1qRj5sESxBcGhpX/ZTKb2yHesLILJ/JToSuDMQaWIH4xMNM0NFZOhpX6YK+2w8oAeloWYdNiLXkYA0sQvxhYL7cFeA0V6f27nlZYubJz7saW9jHJrEmnv9sCvE67BiYiYRFZIyIbRGStiHxHRNLN+IyBdZCjgcDZN/U7beR1rYSVIbMyazIxN0kSJBYjOq6qY1T1bOBy4GPAj5vvJCLZTouLg24uXtv7iATWtxJWjpAdR9yU5nPc/Mz4grhGUqr6HvA14BticaOIPCEizwEviEg3EXlRRFaJSJWIfBJAREIiUiMij4jIehF5VESmicgyEXlTRCba+00UkZdFZLX971kxSsuK53UYWqalsNKszJpUjIElSNx/QFXdbIeQTTksk4BzVHW/PQq7WlUPi0gf4BURedbebwRwLZYBvgZ8AbgIuAr4H6xuLTXAFFVtFJFpwN3E1gzUGJiD2GFlZHRd/ZKrdx1swEzjJ4tObgvwOh39BohOWfi3qu6P2n63iEwBIlh3WQrs595R1SoAEdkAvKiqKiJVQMjeJwjME5EzsMIY8x/sFiKBzts6dX9/66jdj/Z+/z+D8oadi5gvCidpgDq3NXiduA1MRIYBYaDp7lR0we/1QF9gvKo2iMgWoLP9XPR/ViTq90iUjp8CL6nq1SISAhbGKCsc+yswtEf+Yd39w8fDm7vVh/JXFJ07/JU9fx+3KafgreKCaw7lZXcb77Y+v5BrchcTJi4DE5G+wFzg1/boqfkuQeA927wuBYbEqScI7LB/vjGO4yJxXsfQAoGINt48P7Js2hodJ3DhkgtvWSWBup4AB+r3jHj23dmEuo1ecV6fK/tmSfZQt/X6ALNgZILEYmB5IrIGK5xrBP4C/KqVfR8FnhORlcAarDmteLgXK4T8DrAgjuOOxXkdQzPOeyOy5o5nIt1yGrkYYFfBxNcacnpMkMjxU5at3lK7fuLW2o0N4/IvWzS8+9gxImJKuDqOMbAEEVXXeqc6RtG8oknAy27r8CJN4eLp+052jI5IoHFx8a+2RgKdhgOcOPCreiCn+bG5gS77Lir49Ib83AGTRcTMj8XPnQNnFZe5LcLL+OU2rkm2jJPm4WL0c5uHXvVyJNBpysktsg/0Q1njdZFj+S/u+uuUXjn93pzS75ojnbO6mmWS48OkqCSIXwzMsWWUM4Hm4WI0jVmdD28bdNmoU7dmH4SGVsteDtTvPuOZbb9mWLdzVozrc0VBlmTFO/eZqex0W4DXMQaWQeQf1t0/fCy8+fT9p464otkw6qbVSOBUY5PcWrT96ZrNtesmbqldXz8u//JFw7qfO1ZEeiSu2tdsdluA10m3msYOUTWjqgGodVtHuhKIaOPXng8vmjM73LUt8zreuc/2fb3PvqD5dgl0ORHrtSJEclbum3/xs+/OrttXt3OJqpo7xK3zjtsCvI5fRmAA24GRbotIN9oKF5uztujWbYh8aK12CXQLazi+RSlOhI/2/c/Ov/Ttndt/U3HBNcc6Z3UZG9cJ/E89J1OGDB3ETwb2NsbAPiCWcDGaAz3P2HCsS8Gklp6TQI8OJ1zur9t11jPbHmJ49zGvjMufNiAgWYM7ei6fsXXgrGIzOk0QvxlYxhOIaONX5keWXdbC3cW2qDr7a2FayEwGkEAw4ZKut4+sueCdI1V14/tcsXBot6LxIpLpy1Wb8NEB/GRgGT8hOmFTZPW3non0yAm3Hy5Gs/30KcsbO3VpcfQFIIFgXuLqIEI497W9z19SdWDx+8UF16zplVMwOQ3XlksVGf9+dQI/GVjGjsDyD+vuHz0WfmfAflo1odaISFb9m8M/0+bSxhLo4ejdxBPho33/vXNe3/zc02uKCz5Tl5uVd66T5/cIZgTmAH4ysLfcFpBqssLacPP8yMuXrdXxQvzmBfDGGdcu10B2myM2CXRLyoq3++p2jPzHtgcZ0X3c8rH5UwcFJCuTmr2aEZgD+MnA3sSqiezitpBU0NFwMZr6Tl337+x/0Zh2d5S8Xlh1sEl5v7x1ZNWkzUfW1p3X5yMLQ93OPk9EMmGFXTMCcwDfzD9UzagKA6vd1pFs8g/r7vsfblz+vaciY3PCiXUjX3/2V6uIoRhbRARkbyLXao8I4dwVeysvee7dOUcP1O1Zqn4o0m0bMwJzAD+NwMBa6XWy2yKSQVZYG74yP/Ly1ATCxWiOdum35WBwRMx3KSH7ADT0S/S67XE8XFvwws4/FfTJHVhdXPDphpysvHOSfU0X2DpwVrGpHnEAPxqY75i4KbL69gTDxeasPefr72EtGhkbknM0lnIip9hbt73w6W0PcmaP85af2/sSv82PLXZbgF/wm4GtdFuAkyRyd7Et3s8vWnOic/7EeI6RQJcTGj7a/o4O88bhlZPeOrL6xMQ+H1s4uGvhBBHpmnIRzrPIbQF+wTdzYDZv4oOldbLC2nDLP8OL5swOd3PavBR0Y+GNcfcjFOnmWmuPiIY7v/L+c5c89+5vjhysf88P82NmBOYQvjKwqhlVCvzHbR2JMHFTZPW8X4a3XbZWL5Yk9LvcOvjKl8PZnQvjPU6yOl5O5BTHw0f6zd/xx4sW7n58Y334RJXbejrIroGzit9sbyePNJRuEREZIyIfS8W1/BZCArwAfNZtEfGSf0h3/ejx8DsDYqxd7AjhQKfjm4dOH9aRYyXQI23eK++d2Hb209se4KzgxJfP6XVxKCCBAW5rioOFMe53XFXHAIjIacDfsHpGfKipdBoyBjgP+GesB4hItqrGPcr3hKPHyXy3BcSDHS4unDMn3D2Z5gVQc9b1ryJZrS5M2BZOlRM5yaZDKy58aut9PbfVVi9UVa/0RfhXvAe00FC6s4j80W4evdpuoIOIZInI/7O3rxORb9rbt9h9WhGR80Rkof1zmYjME5EX7H0+LSL32sf/S0Q62fuNF5FFIvK6iMwXkf729oUico+IrBCRN0SkWERygJ8An7NHkJ9rrWF1C42x/9LUDNt+/lERuaqtv43vDKxqRtV2YKPbOmIhKly8JBnhYjR1OcH39px23nkdPV4CwbQsvg5rY5fl7z97ScX2uYcO1e9dlubzYwo836EDVTdjfV5PA26ztxUB12E1wumMZXJDgbGqeg5Wk532GA5MBz4J/BWrrWERcByYbpvYQ8A1qjoe+ANwV9Tx2ao6EbgD+LGq1gP/C5Sr6hhVLedkw+qx9nN3Rx0/CZihqlOBR4CbAOxmMRfSzigubcICh5kPjGp3L5eww8UtTk/Qt8W60bdsQqS4o8dLoFtvJ/U4zbHGw/3/teP3/Qs6D1k/ueBq6RTIPdttTS2wcuCs4vcTOL5pHvIiLFNBVWtEZCtwJjANmNsUikU1nG6L5+02iFVYHe6bRohNDafPAkYD/7YXK8kCdkUd/5T97+ucbFDdnLYaVn/QGFtVF4nIbDtk/jTwZHthpV8N7Bng226LaI6djLps6lo9z4lk1Fg53H3wm0e6D04swVe69CaJ5UROsefE1tFPbb1fC4MXLBvdq3hYQAIdCpmTRGVHD2zWULq1GyqCZRDNaeRktNW52XN1AKoaEZGGqBFsU8NpATaoamvv16YG1WFaf2+01bC6eW7OX7AaZH8e+HIr5/sA34WQNkuwVmhNG1IZLjZn3eiZh0nwDpZdTrTPKU1JRqoPvTL5qa3393j36KZFqnrcbUE2MU9qR9O8oTRWGsb19nNnAoOBTVg3sGaKSLb9XNOoeQvQ1FH9M3FefhPQV0Qm2efsJCLtjW6PANFTDvE0rP4TVjiKqm5oT5wvDaxqRlUEKHdbB1jh4v0PNy7/rgO1ix1h92kTVtbnBse3v2csZB1sf5/0IawNXV9+7x8XV25/eP/h+n1u9w2tGTirOJ5KkbymNAqs1KAXgDvt5+YAWXbYVw7cqKp1WHNI24B1IrIW+IK9/53AAyKyBGukFDP2nNY1wD32OdfQ/kKZLwGjmibxsRpW/1xElmGFoG1dbw9QDfwxFn2+aGzbEkXzisZhxeWukBXWhq/+K/Lypet0fKpHXE0oEl5UfN+WSFYnR4zzxMGHV6JHO3wjwG0K8kJVk0+7OqtTIMeN+dE7Bs4qfsCF63oKEemCNf82TlUPtbe/L0dgAFUzqlZh3f1IOU3h4tR1yUlGjZW3h121zCnzApBAXszdidKRPce3FD219b7CqgOLl6pG9qTw0sewQiNDG4jINKzP7EOxmBek+YSsAzyKNYGYEty4u9gajVm5R7YNmhZ3xn1biHRrVJK6qk4qkI0Hl1/0xqGVtRf0/cSiAV1GnG+nICSTxwbOKo7pA5nJqOp/sObzYsa3IzCbR4CkL6GQFdaGmZXhRXYyquvmBbCh8MZVSKCvk+eUQHfXy4mcolEbui1976mL/7n9t3uPNOxfnuTLzUny+TMWXxtY1Yyq3cATybzG+TWRVekQLkZzvHP+jn35Rec7fV7JCvpuxF7beHDgP7f/btLi3U+sbYjUVyfhEisGzipelYTzGvC5gdk8lIyT5h/SXffPbVxe8nRknBt3F9tibdGtW0hCWJSO5UROsev45nOf2nrfWesPLF2qGomvi2/b/MbBcxma4XsDq5pR9Qqwwqnz2eGiVbt4ID3CxWgOBM/YeKxLv6TUVKZrOZGDBDYcXHbRU1sfyNt57K2FdmpCIuwnTdJ5/IrvQoJWeAgrwzchzq+JrLr92UjPTmEuSVxScqga/dXG1hrUJooEuvdMxnnTjUat775kz5OXdMvu9e7F/a7d0a1Trws6eKo/DZxVnC5JtL4kUwysHKtCfmhHDs4/pLt+9Fh4SzqOuKLZPqD4lcZOXTv6YWsf6ZKPlQjZZjKiX6htPDCocvtvBw3oMmLNpL6fyMsO5JwVx+GKlT1vSCK+TWRtTtG8opuwKuljxk5GXXbpOj0vXSboWyMigYZFxfft1ED2kGRe58SB+94HdfTupkeIFPUqXlYYvGCkxHZ3t3LgrOKPJ11VhuP7ObAo/oy15HRMnF8TWfXnX4bfnbou9bWLHeHNEde+nGzzssjK1G46gaoDS4qf3vpA7q5jmxfZJTatEQH+O1XCMpmMGYEBFM0r+iLtzIX1scPF/mkeLkbTkN31wJLJ9wgiSZ+jOnFw7uvoMYdqK71L9069t15ccO3urp16tpSu8qeBs4pvSrmoDCSTRmBgLcvbYq5PVlgbbq0ML5o9J9zDS+YFUHX2zetSYV4AIt4uJ3KKIw37h1Rsf/j8pXueXt0YaYge2R8HfuSWrkwjowzMXqXiB823N4WLl1rJqJ5q23W0S8HWgz3PTOpS1NFIoKtr3YnSkR3H3hj71Nb7hlUffGWxqu4FHhw4qzitlnLyMxllYABVM6qeBl4EK1x8wE5G7RSmQ80u3GZt0dd3Y69dngok0CNVl/IMimatO7BoytPbHjxU23Dw7vaPMDhFpqRRNOf2rz0ffuiyNXp+KldGdZq9+aPXnsjr43jJUFtIwH/lRE7REDlx18hffuKw2zoyiYwbgQFUzajaOG2NrvRauBiNgm4ovCllI68mJCuY7JUbvMpyzJI5KScjDczmTuAdt0V0lG2DLl8ezu6c8oX5JBBM+5QSFwgDt5WUV2TOLf00IWMNrLCm+hhwq9s6OkI40On428M+EXLj2hLo3suN66Y5c0vKK1a7LSITyVgDAyisqZ4PPOa2jnipOfO6V5Esd7pRW+VEEVeunZ5UA//ltohMJaMNzOYOIGLIXLQAAAwvSURBVJFefSmlLqfH+3sKJrqWSCoSyMJaZcFg5Xx9tqS8onlrMEOKyHgDK6ypfg/4HHF2a3GLqtG31CDi8rI22ZlaTtSc20vKK9a7LSKTyXgDAyisqX4JD4QBR7oNeutw9yEpS1ptFel0xG0JacDfSsorHnFbRKZjDMymsKb6l8Djbutoi7VFMw8i4vpSNqaciDeAW9wWYTAG1pybsXrSpR17Thu/sj63Z1r0ZJRAt6Q3SkljTmDNe9W6LcRgDOwU7NSKq4G0muNRJFx91heDbutoQgLdMznf6Tsl5RVr3RZhsDAG1ozCmuq3getJo1SBzUM//nIkK+cMt3U0kcHlRE+UlFeYJh1phDGwFiisqX4eKHNbB0BjVm7t1sFXjHRbRzQSyMhyotexphgMaYQxsNb5GfCM2yI2jpzxutMNahNFsjKunGg9cGVJeYW5+5pmGANrhcKaagW+BKxzS8Px3N679vY5Z6Jb12+NTOlOZPMmcHlJecU+t4UYPowxsDYorKk+DFyG9Q2cctYV3boZkfRrJitd+2B13fE724BpJeUVu90WYmgZY2DtUFhTvRfLxDam8roHg8Orj3bt737SaguIBLJJszu1SWA3cFlJecU2t4UYWscYWAzY5UZTgZpUXXPd6Fvqk9Wg1hl83Z1oH9bI6y23hRjaxhhYjBTWVO8BLqWVpiBOsqP/5FcbO3U9N9nXSQjJ8evKo4exJuw3uC3E0D7GwOKgsKZ6N1AMvJasa0Qk0PDGGZ8tSNb5nUIk77jbGpLAEeBjJeUVr7stxBAbxsDipLCmeh/WnNhLyTj/W8M/s1wD2aFknNtJJNDVb+VE7wCTSsorlrktxBA7njQwEfmBiGwQkXUiskZEWmxsISLniciDrTy3UEQ22cevEZFr7O0vt3f9wprqI8BHcThPrCG7y8Htp19c5OQ5k4a/yokWAhNM2Og9PGdgIjIJ+DgwTlXPAaYB77a0r6quVNXb2zjd9ao6xn783T4mpjt/hTXVdcBngIfiegFtsH7UzWsR8cSSzQH/lBP9FrjC5Hl5Ey++CfsDe1W1DsBuJoqITAAewOo0VIcV5o0HvquqH4/15CJSq6oxZZoX1lSHgdurRxYuBH4PdDjB82hewdYDvc7yTIs3CQRz3daQII3At0vKK37tthBDx/HcCAx4ARgkIm+IyBwRuVhEcoBy4Fuqei7WqCyWSeZHo0LI/I4KKqypfgoYB6zo6DnWnXPrLqzX4Qkk0MPL5UQHgI8a8/I+njMwVa3FGll9DWst+3KsxeV2qepr9j6HVbUxhtNFh5AJhRCFNdXvABcB98V77L7eo9Ydz+t7QSLXTzUS6OHVcqIa4PyS8or/uC3EkDieMzAAVQ2r6kJV/THwDeDTtFPaIiLz7ZFW0pYBLqypbiisqf4OcBUxNr5Q0PWjvuz6KqtxE+iaj/fKiZ4ALigpr3jTbSEGZ/DcHJiInAVEVLXpTTgGK7n0IyIyQVVfE6vpxSkhpKpemSqNhTXVz1WPLByL1bKtzZsC2wZdtjycnZeWJUNtIZKVgxWKeeGmw17g6yXlFU+4LcTgLF4cgXUD5onIRhFZB4wC/hers9BDIrIW+Dfg6ppVhTXV24CLgXtoZaQSDmSf2Dz0k0NSKsxRsrzQXu3vwChjXv5EVL0WBXiP6pGFU7DSLc6J3r5x5A0Ld/e74BJXRDnAiYNz1qAnxritoxV2AncY4/I3XhyBeY7CmurFWHcpv4G9ikNdp+7v7y44f5yrwhJEJO+Y2xpaIIyVTjPSmJf/8dwcmFexc8ZmV48sfBy4q6rolpGIXOy2roQIdG0gklaLUrwK3FpSXrHabSGG1GBGYCmmsKZ6X2FN9czDPYZ+E3jebT2JkEbdiVYD12DVMrZrXiLST0QeF5G37bnUf4rIma3s225pWaoRkUtEpKLZtiujchpro8rk/iwiN4pITDlvIvKIiIyyf94iIn3sn9OyjZwZgbnEbXOnVgEfmz1zwaXAT7ByyDyFBIJup38sB35WUl7xz1gPEGuNtaeBear6eXvbGKAAq2Ft035ZdrqOJ+4Qq+p8YD5Ydb5YFSgr7d9vjOUc9mv+SqJa7L+xqGrSO3uZEZjL3DZ36ku3zZ1aDEwGnsVDuVUulhMtwFot9cJ4zMvmUqBBVec2bVDVNaq6xB7ZvCQif8NucNw08rCfWyQi/2dXgcwSketFZIWIVInIcHu/viLypIi8Zj8mNxcgIiERWSIiq+zHhVHXWCgifxeRGhF51DYDROQj9ralWHmP8TJARP4lIm+KyL1RWmpF5Cci8iowyb5+mw2UReR79mtbJyJ3Rr2mahGZA6wCBnVAY9yYEViacNvcqS8Dn5w9c8Eo4HtYvSk7uauqbQKpLyeqBO4qKa9YnsA5RmO1SGuNicBoVX2nhefOBQqxkpQ3A4+o6kQR+RbwTeAOrBsI96nqUhEZjDUqKmx2nveAy1X1hIicgZUv2GQaY4Gzse6iLgMmi8hK4HdYqwK/hVV9Ei9j7HPXAZtE5CFVfRerdni9qv4v0O4iwCJyBXAG1t9JgGdFZApW/4CzgJtU9esd0NchjIGlGbfNnboRuGn2zAU/Ar4CzABCropqjdSUE9UC/wB+laLJ+RWtmBfAa6q6C0BE3saqywVrtHap/fM0YFSUEfQQke6qGt2SrRPwazt0DQPR828rVHW7fY01WP/3tcA7TcnbIvJXrFK6eHhRVQ/Zx28EhmCt4hIGnozjPFfYj6b/i25YhrYN2Kqqr8SpKyGMgaUpt82duh0omz1zwZ3AJcCNWBPVXVyUdQpilRMlg+NYo61yoLKkvMLJ1V83YP0dW+NoG8/VRf0cifo9wsnPUgCYpKptaf42sAdrRBcATrRyjXDUeROdWmjtvCdUNRzHeQT4uao+fMpGkRBt/+2SgjGwNOe2uVMVa/XXl2bPXPAN4Frgs1im5uqSNiLZucAhIOjA6eqxRjSPA8+UlFck667XAuBuEfmqqv4OPliKyakvhhew8v1+YZ97jKquabZPENiuqhERmQG0dzOkBhgqIsNV9W3gOoe0doT5wE9F5FFVrRWR0wHXVuc1BuYhbps79QjwB+APs2cu6Iq15tl0+3G6O6qy9kO4owbWiGXOjwNPlZRXHHROV8uoqorI1cD9IlKKNfrZgjV/5cTf8HZgtl3mlg0sBmY222cO8KSIXIv1+tscudhzZV8DKkVkL7AUay4v5ajqCyJSCCy3w+Ra4ItYo7qUY0qJfMLsmQvOBa7Eups5CeibiuueODhnLXoi1g5KW7HWTHvVfqwqKa9Ix2x+g0cwBuZTZs9cMALrztZ4+3EmMABrDsMx6g79YblGDra0kuwhrO5Nr2KbVkl5xR4nr20wGAPLIGbPXJAHDAWG249hWPk6QfvRM+rn5tMLihUmNGJNsu8F9tbXPrMy0vB2NtYdre32YxuwuaS8wry5DEnFGJihRWbPXNA0qd0IhG+bO9WVOQ6DoS2MgRkMBs9iSomSiIhcLSIqIiPb2e9GERkQ9fsHBbUGg6F1jIEll+uwbnl/vp39bsSaYAdAVb+iqhuTqMtg8AXGwJKEiHTDSmm4mSgDE5Hv28W/a+2C4Guw7hY2tXjLiy6oFZHr7P3Xi8g9UeepFZG77PO8IiIFKX6JBoPrGANLHp8C/qWqbwD7RWSciHzU3n6+3b/yXrsj+EpOtnj7oATFDivvwSriHQNMEJFP2U93BV6xz7MY+GrKXpnBkCYYA0se12FlmGP/ex1Woe8fVfUYgKq21xRjArBQVd+3+1w+Ckyxn6sHmha1e510Lfg2GJKIKSVKAmJ1+Z4KjBYRxap1U6yq/3hu+7aVdNqgJ28hRxfnGgwZgxmBJYdrgD+r6hBVDanqIOAdrHWkviwiXQBEpLe9/xGgewvneRW4WET6iEgW1ihuUfLlGwzewBhYcrgOa9niaJ7EutP4LLDSXuvpu/ZzfwLmNk3iNx1grz3131gFv2uBVar6TJK1GwyewSSyGgwGz2JGYAaDwbMYAzMYDJ7FGJjBYPAsxsAMBoNnMQZmMBg8izEwg8HgWYyBGQwGz2IMzGAweBZjYAaDwbMYAzMYDJ7FGJjBYPAsxsAMBoNnMQZmMBg8izEwg8HgWYyBGQwGz2IMzGAweBZjYAaDwbMYAzMYDJ7l/wM6DtCpo3mHCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 42, 'Romance': 23, 'Drama': 27, 'Sci-Fi': 13, 'Action': 15, 'Crime and Thriller': 24, 'Documentary': 13}\n"
     ]
    }
   ],
   "source": [
    "data.dropna(subset=['Movie Genre when you are Sad -: ( Choose all that apply )','Movie Genre when you are Angry -: ( Choose all that apply )','Movie Genre when you are Angry -: ( Choose all that apply )','Movie Genre when you are Normal -: ( Choose all that apply )','Genre Preference when watching with Family -: ( Choose all that apply )','Genre Preference when watching with Friends -: ( Choose all that apply )'],inplace=True)\n",
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime and Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[14].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movie Genre when you are Angry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 41, 'Romance': 32, 'Drama': 23, 'Sci-Fi': 16, 'Action': 15, 'Crime And Thriller': 25, 'Documentary': 7}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime And Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[15].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movie Genre when you are Normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 31, 'Romance': 16, 'Drama': 17, 'Sci-Fi': 15, 'Action': 17, 'Crime And Thriller': 22, 'Documentary': 11}\n"
     ]
    }
   ],
   "source": [
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime And Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[16].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genre Preference when watching with Friends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 21, 'Romance': 11, 'Drama': 15, 'Sci-Fi': 19, 'Action': 29, 'Crime And Thriller': 26, 'Documentary': 11}\n"
     ]
    }
   ],
   "source": [
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime And Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[17].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genre Preference when watching with Family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Comedy': 32, 'Romance': 30, 'Drama': 29, 'Sci-Fi': 32, 'Action': 31, 'Crime And Thriller': 40, 'Documentary': 17}\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "for j in data['Genre Preference when watching with Family -: ( Choose all that apply )'].isnull():\n",
    "    if(j):\n",
    "        data=data.drop(i)\n",
    "    i=i+1\n",
    "genre={\"Comedy\":0,\"Romance\":0,\"Drama\":0,\"Sci-Fi\":0,\"Action\":0,\"Crime And Thriller\":0,\"Documentary\":0}\n",
    "for i,j in data.iterrows():\n",
    "    for k in j[18].split(\";\"):\n",
    "        genre[k]=genre[k]+1\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pie(genre.values(),labels=genre.keys())\n",
    "#plt.legend(loc=\"right\")\n",
    "plt.show()\n",
    "print(genre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 22)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Would you like to get recommended Holiday Theme Movies On Holidays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average rating :\n",
      "7.285714285714286\n",
      "YES , users would like to get recommended holiday theme movies in holidays\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import statistics\n",
    "x=np.arange(1,64)\n",
    "y=[]\n",
    "import matplotlib.pyplot as plt\n",
    "for i,j in data.iterrows():\n",
    "    y.append(j[21])\n",
    "plt.bar(x,y)\n",
    "print(\"Average rating :\")\n",
    "print(statistics.mean(y))\n",
    "if(statistics.mean(y)>=5):\n",
    "    print(\"YES , users would like to get recommended holiday theme movies in holidays\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chi Square Test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "genremorning=[]\n",
    "genreafternoon=[]\n",
    "genreevening=[]\n",
    "genrenight=[]\n",
    "genrewinter,genresummer,genrerainy,genreautumn,genreweekend,genreweekdays,genrelargescreen,genresmallscreen,genretabletscreen,genrehappy,genresad,genreangry,genrenormal,genrewithfamily,genrewithoutfamily=[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]\n",
    "genre=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"]\n",
    "for i,j in data.iterrows():\n",
    "    genremorning=genremorning+j[2].split(\";\")\n",
    "    genreafternoon=genreafternoon+j[3].split(\";\")\n",
    "    genreevening=genreevening+j[4].split(\";\")\n",
    "    genrenight=genrenight+j[5].split(\";\")\n",
    "    genrewinter=genrewinter+j[6].split(\";\")\n",
    "    genresummer=genresummer+j[7].split(\";\")\n",
    "    genrerainy=genrerainy+j[8].split(\";\")\n",
    "    genreautumn=genreautumn+j[9].split(\";\")\n",
    "    genreweekend=genreweekend+j[10].split(\";\")\n",
    "    genreweekdays=genreweekdays+j[11].split(\";\")\n",
    "    genrelargescreen=genrelargescreen+j[12].split(\";\")\n",
    "    #genresmallscreen=genresmallscreen+j[13].split(\";\")\n",
    "    genretabletscreen=genretabletscreen+j[14].split(\";\")\n",
    "    genrehappy=genrehappy+j[15].split(\";\")\n",
    "    genresad=genresad+j[16].split(\";\")\n",
    "    genreangry=genreangry+j[17].split(\";\")\n",
    "    genrenormal=genrenormal+j[18].split(\";\")\n",
    "    genrewithfamily=genrewithfamily+j[19].split(\";\")\n",
    "    genrewithoutfamily=genrewithoutfamily+j[20].split(\";\")\n",
    "df=pd.DataFrame(pd.Series(genremorning).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"afternooncount\"]=pd.Series(pd.Series(genreafternoon).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"eveningnooncount\"]=pd.Series(pd.Series(genreevening).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"nightooncount\"]=pd.Series(pd.Series(genrenight).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"winter\"]=pd.Series(pd.Series(genrewinter).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"summer\"]=pd.Series(pd.Series(genresummer).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"rainy\"]=pd.Series(pd.Series(genrerainy).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"autumn\"]=pd.Series(pd.Series(genreautumn).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"weekend\"]=pd.Series(pd.Series(genreweekend).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"weekdays\"]=pd.Series(pd.Series(genreweekdays).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"largscreen\"]=pd.Series(pd.Series(genrelargescreen).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"tablet\"]=pd.Series(pd.Series(genretabletscreen).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"happy\"]=pd.Series(pd.Series(genrehappy).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"sad\"]=pd.Series(pd.Series(genresad).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"angry\"]=pd.Series(pd.Series(genreangry).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"normal\"]=pd.Series(pd.Series(genrenormal).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"with family\"]=pd.Series(pd.Series(genrewithfamily).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "df[\"without family\"]=pd.Series(pd.Series(genrewithoutfamily).value_counts(),index=[\"Comedy\",\"Romance\",\"Drama\",\"Sci-Fi\",\"Action\",\"Crime And Thriller\",\"Documentary\"])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contingency Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Morning</th>\n",
       "      <th>Afternoon</th>\n",
       "      <th>Evening</th>\n",
       "      <th>Night</th>\n",
       "      <th>Winter</th>\n",
       "      <th>Summer</th>\n",
       "      <th>Rainy</th>\n",
       "      <th>Autumn</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Weekdays</th>\n",
       "      <th>Largscreen</th>\n",
       "      <th>Tablet</th>\n",
       "      <th>Happy</th>\n",
       "      <th>Sad</th>\n",
       "      <th>Angry</th>\n",
       "      <th>Normal</th>\n",
       "      <th>With Family</th>\n",
       "      <th>Without Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>41</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>31</td>\n",
       "      <td>33.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>41</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>42</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romance</th>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>35</td>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>36</td>\n",
       "      <td>26</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sci-Fi</th>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>18</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>29.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Morning  Afternoon  Evening  Night  Winter  Summer  Rainy  Autumn  \\\n",
       "Comedy        41         30       30     25      32      36     33      31   \n",
       "Romance       16         13       24     35      34      19     36      26   \n",
       "Drama         16         23       17     20      26      32     29      28   \n",
       "Sci-Fi        19         23       21     24      19      28     19      27   \n",
       "Action        13         27       22     21      27      30     17      22   \n",
       "\n",
       "         Weekend  Weekdays  Largscreen  Tablet  Happy  Sad  Angry  Normal  \\\n",
       "Comedy      33.0      32.0        14.0    42.0     41   31     21      32   \n",
       "Romance     24.0      22.0        20.0    23.0     32   16     11      30   \n",
       "Drama       22.0      19.0        16.0    27.0     23   17     15      29   \n",
       "Sci-Fi      24.0      22.0        35.0    13.0     16   15     19      32   \n",
       "Action      29.0      19.0        37.0    15.0     15   17     29      31   \n",
       "\n",
       "         With Family  Without Family  \n",
       "Comedy            42              34  \n",
       "Romance            6              20  \n",
       "Drama             32              21  \n",
       "Sci-Fi            18              33  \n",
       "Action            27              39  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns=[\"Morning\",\"Afternoon\",\"Evening\",\"Night\",\"Winter\",\"Summer\",\"Rainy\",\"Autumn\",\"Weekend\",\"Weekdays\",\"Largscreen\",\"Tablet\",\"Happy\",\"Sad\",\"Angry\",\"Normal\",\"With Family\",\"Without Family\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc['Total',:]= df.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,'Total'] = df.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Morning</th>\n",
       "      <th>Afternoon</th>\n",
       "      <th>Evening</th>\n",
       "      <th>Night</th>\n",
       "      <th>Winter</th>\n",
       "      <th>Summer</th>\n",
       "      <th>Rainy</th>\n",
       "      <th>Autumn</th>\n",
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       "      <th>Largscreen</th>\n",
       "      <th>Tablet</th>\n",
       "      <th>Happy</th>\n",
       "      <th>Sad</th>\n",
       "      <th>Angry</th>\n",
       "      <th>Normal</th>\n",
       "      <th>With Family</th>\n",
       "      <th>Without Family</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>41.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romance</th>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>412.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sci-Fi</th>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>13.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime And Thriller</th>\n",
       "      <td>11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Documentary</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>129.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>2868.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Morning  Afternoon  Evening  Night  Winter  Summer  Rainy  \\\n",
       "Comedy                 41.0       30.0     30.0   25.0    32.0    36.0   33.0   \n",
       "Romance                16.0       13.0     24.0   35.0    34.0    19.0   36.0   \n",
       "Drama                  16.0       23.0     17.0   20.0    26.0    32.0   29.0   \n",
       "Sci-Fi                 19.0       23.0     21.0   24.0    19.0    28.0   19.0   \n",
       "Action                 13.0       27.0     22.0   21.0    27.0    30.0   17.0   \n",
       "Crime And Thriller     11.0       32.0     25.0   33.0    30.0    36.0   25.0   \n",
       "Documentary            13.0       14.0     14.0   17.0    10.0    15.0    7.0   \n",
       "Total                 129.0      162.0    153.0  175.0   178.0   196.0  166.0   \n",
       "\n",
       "                    Autumn  Weekend  Weekdays  Largscreen  Tablet  Happy  \\\n",
       "Comedy                31.0     33.0      32.0        14.0    42.0   41.0   \n",
       "Romance               26.0     24.0      22.0        20.0    23.0   32.0   \n",
       "Drama                 28.0     22.0      19.0        16.0    27.0   23.0   \n",
       "Sci-Fi                27.0     24.0      22.0        35.0    13.0   16.0   \n",
       "Action                22.0     29.0      19.0        37.0    15.0   15.0   \n",
       "Crime And Thriller    32.0      NaN       NaN         NaN     NaN   25.0   \n",
       "Documentary           18.0     14.0      10.0         9.0    13.0    7.0   \n",
       "Total                184.0    146.0     124.0       131.0   133.0  159.0   \n",
       "\n",
       "                      Sad  Angry  Normal  With Family  Without Family   Total  \n",
       "Comedy               31.0   21.0    32.0         42.0            34.0   580.0  \n",
       "Romance              16.0   11.0    30.0          6.0            20.0   407.0  \n",
       "Drama                17.0   15.0    29.0         32.0            21.0   412.0  \n",
       "Sci-Fi               15.0   19.0    32.0         18.0            33.0   407.0  \n",
       "Action               17.0   29.0    31.0         27.0            39.0   437.0  \n",
       "Crime And Thriller   22.0   26.0    40.0         18.0            40.0   395.0  \n",
       "Documentary          11.0   11.0    17.0         21.0             9.0   230.0  \n",
       "Total               129.0  132.0   211.0        164.0           196.0  2868.0  "
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
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      "  32.15481172 26.08786611 26.69456067 42.67085077 33.16596932 39.63737796]\n",
      " [18.30648536 22.98953975 21.7123431  24.83437936 25.26011158 27.81450488\n",
      "  23.55718271 26.11157601 20.71896792 17.59693166 18.59030683 18.87412831\n",
      "  22.56380753 18.30648536 18.73221757 29.94316597 23.27336123 27.81450488]\n",
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      "  23.84658298 26.43235704 20.9735007  17.81311018 18.81868898 19.10599721\n",
      "  22.84100418 18.53138075 18.9623431  30.31101813 23.55927476 28.15620642]\n",
      " [18.30648536 22.98953975 21.7123431  24.83437936 25.26011158 27.81450488\n",
      "  23.55718271 26.11157601 20.71896792 17.59693166 18.59030683 18.87412831\n",
      "  22.56380753 18.30648536 18.73221757 29.94316597 23.27336123 27.81450488]\n",
      " [19.65585774 24.68410042 23.31276151 26.66492329 27.12203626 29.86471409\n",
      "  25.29358438 28.0362622  22.24616457 18.89400279 19.96059972 20.2653417\n",
      "  24.22698745 19.65585774 20.11297071 32.15027894 24.9888424  29.86471409]\n",
      " [17.7667364  22.31171548 21.07217573 24.10216179 24.5153417  26.9944212\n",
      "  22.86262204 25.34170153 20.10808926 17.07810321 18.04218968 18.31764296\n",
      "  21.89853556 17.7667364  18.17991632 29.06032078 22.58716876 26.9944212 ]\n",
      " [10.34518828 12.9916318  12.26987448 14.03417015 14.27475593 15.71827057\n",
      "  13.31241283 14.75592748 11.70850767  9.94421199 10.5055788  10.66596932\n",
      "  12.75104603 10.34518828 10.58577406 16.92119944 13.15202232 15.71827057]]\n"
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       "      <th>5</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "      <th>12</th>\n",
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       "      <td>26.087866</td>\n",
       "      <td>32.761506</td>\n",
       "      <td>30.941423</td>\n",
       "      <td>35.390516</td>\n",
       "      <td>35.997211</td>\n",
       "      <td>39.637378</td>\n",
       "      <td>33.570432</td>\n",
       "      <td>37.210600</td>\n",
       "      <td>29.525802</td>\n",
       "      <td>25.076709</td>\n",
       "      <td>26.492329</td>\n",
       "      <td>26.896792</td>\n",
       "      <td>32.154812</td>\n",
       "      <td>26.087866</td>\n",
       "      <td>26.694561</td>\n",
       "      <td>42.670851</td>\n",
       "      <td>33.165969</td>\n",
       "      <td>39.637378</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.306485</td>\n",
       "      <td>22.989540</td>\n",
       "      <td>21.712343</td>\n",
       "      <td>24.834379</td>\n",
       "      <td>25.260112</td>\n",
       "      <td>27.814505</td>\n",
       "      <td>23.557183</td>\n",
       "      <td>26.111576</td>\n",
       "      <td>20.718968</td>\n",
       "      <td>17.596932</td>\n",
       "      <td>18.590307</td>\n",
       "      <td>18.874128</td>\n",
       "      <td>22.563808</td>\n",
       "      <td>18.306485</td>\n",
       "      <td>18.732218</td>\n",
       "      <td>29.943166</td>\n",
       "      <td>23.273361</td>\n",
       "      <td>27.814505</td>\n",
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       "      <th>2</th>\n",
       "      <td>18.531381</td>\n",
       "      <td>23.271967</td>\n",
       "      <td>21.979079</td>\n",
       "      <td>25.139470</td>\n",
       "      <td>25.570432</td>\n",
       "      <td>28.156206</td>\n",
       "      <td>23.846583</td>\n",
       "      <td>26.432357</td>\n",
       "      <td>20.973501</td>\n",
       "      <td>17.813110</td>\n",
       "      <td>18.818689</td>\n",
       "      <td>19.105997</td>\n",
       "      <td>22.841004</td>\n",
       "      <td>18.531381</td>\n",
       "      <td>18.962343</td>\n",
       "      <td>30.311018</td>\n",
       "      <td>23.559275</td>\n",
       "      <td>28.156206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.306485</td>\n",
       "      <td>22.989540</td>\n",
       "      <td>21.712343</td>\n",
       "      <td>24.834379</td>\n",
       "      <td>25.260112</td>\n",
       "      <td>27.814505</td>\n",
       "      <td>23.557183</td>\n",
       "      <td>26.111576</td>\n",
       "      <td>20.718968</td>\n",
       "      <td>17.596932</td>\n",
       "      <td>18.590307</td>\n",
       "      <td>18.874128</td>\n",
       "      <td>22.563808</td>\n",
       "      <td>18.306485</td>\n",
       "      <td>18.732218</td>\n",
       "      <td>29.943166</td>\n",
       "      <td>23.273361</td>\n",
       "      <td>27.814505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.655858</td>\n",
       "      <td>24.684100</td>\n",
       "      <td>23.312762</td>\n",
       "      <td>26.664923</td>\n",
       "      <td>27.122036</td>\n",
       "      <td>29.864714</td>\n",
       "      <td>25.293584</td>\n",
       "      <td>28.036262</td>\n",
       "      <td>22.246165</td>\n",
       "      <td>18.894003</td>\n",
       "      <td>19.960600</td>\n",
       "      <td>20.265342</td>\n",
       "      <td>24.226987</td>\n",
       "      <td>19.655858</td>\n",
       "      <td>20.112971</td>\n",
       "      <td>32.150279</td>\n",
       "      <td>24.988842</td>\n",
       "      <td>29.864714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17.766736</td>\n",
       "      <td>22.311715</td>\n",
       "      <td>21.072176</td>\n",
       "      <td>24.102162</td>\n",
       "      <td>24.515342</td>\n",
       "      <td>26.994421</td>\n",
       "      <td>22.862622</td>\n",
       "      <td>25.341702</td>\n",
       "      <td>20.108089</td>\n",
       "      <td>17.078103</td>\n",
       "      <td>18.042190</td>\n",
       "      <td>18.317643</td>\n",
       "      <td>21.898536</td>\n",
       "      <td>17.766736</td>\n",
       "      <td>18.179916</td>\n",
       "      <td>29.060321</td>\n",
       "      <td>22.587169</td>\n",
       "      <td>26.994421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.345188</td>\n",
       "      <td>12.991632</td>\n",
       "      <td>12.269874</td>\n",
       "      <td>14.034170</td>\n",
       "      <td>14.274756</td>\n",
       "      <td>15.718271</td>\n",
       "      <td>13.312413</td>\n",
       "      <td>14.755927</td>\n",
       "      <td>11.708508</td>\n",
       "      <td>9.944212</td>\n",
       "      <td>10.505579</td>\n",
       "      <td>10.665969</td>\n",
       "      <td>12.751046</td>\n",
       "      <td>10.345188</td>\n",
       "      <td>10.585774</td>\n",
       "      <td>16.921199</td>\n",
       "      <td>13.152022</td>\n",
       "      <td>15.718271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0          1          2          3          4          5  \\\n",
       "0  26.087866  32.761506  30.941423  35.390516  35.997211  39.637378   \n",
       "1  18.306485  22.989540  21.712343  24.834379  25.260112  27.814505   \n",
       "2  18.531381  23.271967  21.979079  25.139470  25.570432  28.156206   \n",
       "3  18.306485  22.989540  21.712343  24.834379  25.260112  27.814505   \n",
       "4  19.655858  24.684100  23.312762  26.664923  27.122036  29.864714   \n",
       "5  17.766736  22.311715  21.072176  24.102162  24.515342  26.994421   \n",
       "6  10.345188  12.991632  12.269874  14.034170  14.274756  15.718271   \n",
       "\n",
       "           6          7          8          9         10         11  \\\n",
       "0  33.570432  37.210600  29.525802  25.076709  26.492329  26.896792   \n",
       "1  23.557183  26.111576  20.718968  17.596932  18.590307  18.874128   \n",
       "2  23.846583  26.432357  20.973501  17.813110  18.818689  19.105997   \n",
       "3  23.557183  26.111576  20.718968  17.596932  18.590307  18.874128   \n",
       "4  25.293584  28.036262  22.246165  18.894003  19.960600  20.265342   \n",
       "5  22.862622  25.341702  20.108089  17.078103  18.042190  18.317643   \n",
       "6  13.312413  14.755927  11.708508   9.944212  10.505579  10.665969   \n",
       "\n",
       "          12         13         14         15         16         17  \n",
       "0  32.154812  26.087866  26.694561  42.670851  33.165969  39.637378  \n",
       "1  22.563808  18.306485  18.732218  29.943166  23.273361  27.814505  \n",
       "2  22.841004  18.531381  18.962343  30.311018  23.559275  28.156206  \n",
       "3  22.563808  18.306485  18.732218  29.943166  23.273361  27.814505  \n",
       "4  24.226987  19.655858  20.112971  32.150279  24.988842  29.864714  \n",
       "5  21.898536  17.766736  18.179916  29.060321  22.587169  26.994421  \n",
       "6  12.751046  10.345188  10.585774  16.921199  13.152022  15.718271  "
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expected=np.outer(df[\"Total\"][0:7],\n",
    "                     df.loc[\"Total\"][0:18])/2868\n",
    "print(expected)\n",
    "expected_df=pd.DataFrame(expected)\n",
    "expected_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.087866</td>\n",
       "      <td>32.761506</td>\n",
       "      <td>30.941423</td>\n",
       "      <td>35.390516</td>\n",
       "      <td>35.997211</td>\n",
       "      <td>39.637378</td>\n",
       "      <td>33.570432</td>\n",
       "      <td>37.210600</td>\n",
       "      <td>29.525802</td>\n",
       "      <td>25.076709</td>\n",
       "      <td>26.492329</td>\n",
       "      <td>26.896792</td>\n",
       "      <td>32.154812</td>\n",
       "      <td>26.087866</td>\n",
       "      <td>26.694561</td>\n",
       "      <td>42.670851</td>\n",
       "      <td>33.165969</td>\n",
       "      <td>39.637378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.306485</td>\n",
       "      <td>22.989540</td>\n",
       "      <td>21.712343</td>\n",
       "      <td>24.834379</td>\n",
       "      <td>25.260112</td>\n",
       "      <td>27.814505</td>\n",
       "      <td>23.557183</td>\n",
       "      <td>26.111576</td>\n",
       "      <td>20.718968</td>\n",
       "      <td>17.596932</td>\n",
       "      <td>18.590307</td>\n",
       "      <td>18.874128</td>\n",
       "      <td>22.563808</td>\n",
       "      <td>18.306485</td>\n",
       "      <td>18.732218</td>\n",
       "      <td>29.943166</td>\n",
       "      <td>23.273361</td>\n",
       "      <td>27.814505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.531381</td>\n",
       "      <td>23.271967</td>\n",
       "      <td>21.979079</td>\n",
       "      <td>25.139470</td>\n",
       "      <td>25.570432</td>\n",
       "      <td>28.156206</td>\n",
       "      <td>23.846583</td>\n",
       "      <td>26.432357</td>\n",
       "      <td>20.973501</td>\n",
       "      <td>17.813110</td>\n",
       "      <td>18.818689</td>\n",
       "      <td>19.105997</td>\n",
       "      <td>22.841004</td>\n",
       "      <td>18.531381</td>\n",
       "      <td>18.962343</td>\n",
       "      <td>30.311018</td>\n",
       "      <td>23.559275</td>\n",
       "      <td>28.156206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.306485</td>\n",
       "      <td>22.989540</td>\n",
       "      <td>21.712343</td>\n",
       "      <td>24.834379</td>\n",
       "      <td>25.260112</td>\n",
       "      <td>27.814505</td>\n",
       "      <td>23.557183</td>\n",
       "      <td>26.111576</td>\n",
       "      <td>20.718968</td>\n",
       "      <td>17.596932</td>\n",
       "      <td>18.590307</td>\n",
       "      <td>18.874128</td>\n",
       "      <td>22.563808</td>\n",
       "      <td>18.306485</td>\n",
       "      <td>18.732218</td>\n",
       "      <td>29.943166</td>\n",
       "      <td>23.273361</td>\n",
       "      <td>27.814505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.655858</td>\n",
       "      <td>24.684100</td>\n",
       "      <td>23.312762</td>\n",
       "      <td>26.664923</td>\n",
       "      <td>27.122036</td>\n",
       "      <td>29.864714</td>\n",
       "      <td>25.293584</td>\n",
       "      <td>28.036262</td>\n",
       "      <td>22.246165</td>\n",
       "      <td>18.894003</td>\n",
       "      <td>19.960600</td>\n",
       "      <td>20.265342</td>\n",
       "      <td>24.226987</td>\n",
       "      <td>19.655858</td>\n",
       "      <td>20.112971</td>\n",
       "      <td>32.150279</td>\n",
       "      <td>24.988842</td>\n",
       "      <td>29.864714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17.766736</td>\n",
       "      <td>22.311715</td>\n",
       "      <td>21.072176</td>\n",
       "      <td>24.102162</td>\n",
       "      <td>24.515342</td>\n",
       "      <td>26.994421</td>\n",
       "      <td>22.862622</td>\n",
       "      <td>25.341702</td>\n",
       "      <td>20.108089</td>\n",
       "      <td>17.078103</td>\n",
       "      <td>18.042190</td>\n",
       "      <td>18.317643</td>\n",
       "      <td>21.898536</td>\n",
       "      <td>17.766736</td>\n",
       "      <td>18.179916</td>\n",
       "      <td>29.060321</td>\n",
       "      <td>22.587169</td>\n",
       "      <td>26.994421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.345188</td>\n",
       "      <td>12.991632</td>\n",
       "      <td>12.269874</td>\n",
       "      <td>14.034170</td>\n",
       "      <td>14.274756</td>\n",
       "      <td>15.718271</td>\n",
       "      <td>13.312413</td>\n",
       "      <td>14.755927</td>\n",
       "      <td>11.708508</td>\n",
       "      <td>9.944212</td>\n",
       "      <td>10.505579</td>\n",
       "      <td>10.665969</td>\n",
       "      <td>12.751046</td>\n",
       "      <td>10.345188</td>\n",
       "      <td>10.585774</td>\n",
       "      <td>16.921199</td>\n",
       "      <td>13.152022</td>\n",
       "      <td>15.718271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0          1          2          3          4          5  \\\n",
       "0  26.087866  32.761506  30.941423  35.390516  35.997211  39.637378   \n",
       "1  18.306485  22.989540  21.712343  24.834379  25.260112  27.814505   \n",
       "2  18.531381  23.271967  21.979079  25.139470  25.570432  28.156206   \n",
       "3  18.306485  22.989540  21.712343  24.834379  25.260112  27.814505   \n",
       "4  19.655858  24.684100  23.312762  26.664923  27.122036  29.864714   \n",
       "5  17.766736  22.311715  21.072176  24.102162  24.515342  26.994421   \n",
       "6  10.345188  12.991632  12.269874  14.034170  14.274756  15.718271   \n",
       "\n",
       "           6          7          8          9         10         11  \\\n",
       "0  33.570432  37.210600  29.525802  25.076709  26.492329  26.896792   \n",
       "1  23.557183  26.111576  20.718968  17.596932  18.590307  18.874128   \n",
       "2  23.846583  26.432357  20.973501  17.813110  18.818689  19.105997   \n",
       "3  23.557183  26.111576  20.718968  17.596932  18.590307  18.874128   \n",
       "4  25.293584  28.036262  22.246165  18.894003  19.960600  20.265342   \n",
       "5  22.862622  25.341702  20.108089  17.078103  18.042190  18.317643   \n",
       "6  13.312413  14.755927  11.708508   9.944212  10.505579  10.665969   \n",
       "\n",
       "          12         13         14         15         16         17  \n",
       "0  32.154812  26.087866  26.694561  42.670851  33.165969  39.637378  \n",
       "1  22.563808  18.306485  18.732218  29.943166  23.273361  27.814505  \n",
       "2  22.841004  18.531381  18.962343  30.311018  23.559275  28.156206  \n",
       "3  22.563808  18.306485  18.732218  29.943166  23.273361  27.814505  \n",
       "4  24.226987  19.655858  20.112971  32.150279  24.988842  29.864714  \n",
       "5  21.898536  17.766736  18.179916  29.060321  22.587169  26.994421  \n",
       "6  12.751046  10.345188  10.585774  16.921199  13.152022  15.718271  "
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expected_df=expected_df.fillna(0)\n",
    "df=df.fillna(0)\n",
    "expected_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "287.68726414733504\n"
     ]
    }
   ],
   "source": [
    "chisq=0\n",
    "for i in range(0,7):\n",
    "    for j in range(0,18):\n",
    "        x=(df.iloc[i,j]-expected_df.iloc[i,j])*(df.iloc[i,j]-expected_df.iloc[i,j])\n",
    "        x=x/expected_df.iloc[i,j]\n",
    "        chisq=chisq+x\n",
    "print(chisq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126.57414819149433\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "dfreedom=17*6\n",
    "crit = stats.chi2.ppf(q = 0.95, df = dfreedom)\n",
    "print(crit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "observe=df.iloc[0:7,0:18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(287.68726414733493,\n",
       " 1.330651152097142e-19,\n",
       " 102,\n",
       " array([[26.08786611, 32.76150628, 30.94142259, 35.39051604, 35.9972106 ,\n",
       "         39.63737796, 33.57043236, 37.21059972, 29.52580195, 25.07670851,\n",
       "         26.49232915, 26.89679219, 32.15481172, 26.08786611, 26.69456067,\n",
       "         42.67085077, 33.16596932, 39.63737796],\n",
       "        [18.30648536, 22.98953975, 21.7123431 , 24.83437936, 25.26011158,\n",
       "         27.81450488, 23.55718271, 26.11157601, 20.71896792, 17.59693166,\n",
       "         18.59030683, 18.87412831, 22.56380753, 18.30648536, 18.73221757,\n",
       "         29.94316597, 23.27336123, 27.81450488],\n",
       "        [18.53138075, 23.27196653, 21.9790795 , 25.13947001, 25.57043236,\n",
       "         28.15620642, 23.84658298, 26.43235704, 20.9735007 , 17.81311018,\n",
       "         18.81868898, 19.10599721, 22.84100418, 18.53138075, 18.9623431 ,\n",
       "         30.31101813, 23.55927476, 28.15620642],\n",
       "        [18.30648536, 22.98953975, 21.7123431 , 24.83437936, 25.26011158,\n",
       "         27.81450488, 23.55718271, 26.11157601, 20.71896792, 17.59693166,\n",
       "         18.59030683, 18.87412831, 22.56380753, 18.30648536, 18.73221757,\n",
       "         29.94316597, 23.27336123, 27.81450488],\n",
       "        [19.65585774, 24.68410042, 23.31276151, 26.66492329, 27.12203626,\n",
       "         29.86471409, 25.29358438, 28.0362622 , 22.24616457, 18.89400279,\n",
       "         19.96059972, 20.2653417 , 24.22698745, 19.65585774, 20.11297071,\n",
       "         32.15027894, 24.9888424 , 29.86471409],\n",
       "        [17.7667364 , 22.31171548, 21.07217573, 24.10216179, 24.5153417 ,\n",
       "         26.9944212 , 22.86262204, 25.34170153, 20.10808926, 17.07810321,\n",
       "         18.04218968, 18.31764296, 21.89853556, 17.7667364 , 18.17991632,\n",
       "         29.06032078, 22.58716876, 26.9944212 ],\n",
       "        [10.34518828, 12.9916318 , 12.26987448, 14.03417015, 14.27475593,\n",
       "         15.71827057, 13.31241283, 14.75592748, 11.70850767,  9.94421199,\n",
       "         10.5055788 , 10.66596932, 12.75104603, 10.34518828, 10.58577406,\n",
       "         16.92119944, 13.15202232, 15.71827057]]))"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.chi2_contingency(observed=observe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chisquare is greater than pvalue which indicates attributes are corelated\n"
     ]
    }
   ],
   "source": [
    "print(\"chisquare is greater than pvalue which indicates attributes are corelated\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "version": 3
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 },
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