Lecture08_Pandas-series-VISUALIZEDDATA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    a\n",
      "1    b\n",
      "2    c\n",
      "3    d\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = np.array(['a', 'b', 'c', 'd'])\n",
    "s = pd.Series(data)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1\n",
       "0    a    3\n",
       "1    b    4\n",
       "2  NaN  NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['a3', 'b4','c5'])\n",
    "s.str.extract(r'([ab])(\\d)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# experimenting with file movie data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv(r'C:\\Users\\a_ade\\Documents\\Fall2021\\CSC276\\movies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Film</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Lead Studio</th>\n",
       "      <th>Audience score %</th>\n",
       "      <th>Profitability</th>\n",
       "      <th>Rotten Tomatoes %</th>\n",
       "      <th>Worldwide Gross</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Zack and Miri Make a Porno</td>\n",
       "      <td>Romance</td>\n",
       "      <td>The Weinstein Company</td>\n",
       "      <td>70</td>\n",
       "      <td>1.747542</td>\n",
       "      <td>64</td>\n",
       "      <td>$41.94</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Youth in Revolt</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>The Weinstein Company</td>\n",
       "      <td>52</td>\n",
       "      <td>1.090000</td>\n",
       "      <td>68</td>\n",
       "      <td>$19.62</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>You Will Meet a Tall Dark Stranger</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Independent</td>\n",
       "      <td>35</td>\n",
       "      <td>1.211818</td>\n",
       "      <td>43</td>\n",
       "      <td>$26.66</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>When in Rome</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Disney</td>\n",
       "      <td>44</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>$43.04</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What Happens in Vegas</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Fox</td>\n",
       "      <td>72</td>\n",
       "      <td>6.267647</td>\n",
       "      <td>28</td>\n",
       "      <td>$219.37</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Film    Genre            Lead Studio  \\\n",
       "0          Zack and Miri Make a Porno  Romance  The Weinstein Company   \n",
       "1                     Youth in Revolt   Comedy  The Weinstein Company   \n",
       "2  You Will Meet a Tall Dark Stranger   Comedy            Independent   \n",
       "3                        When in Rome   Comedy                 Disney   \n",
       "4               What Happens in Vegas   Comedy                    Fox   \n",
       "\n",
       "   Audience score %  Profitability  Rotten Tomatoes % Worldwide Gross  Year  \n",
       "0                70       1.747542                 64         $41.94   2008  \n",
       "1                52       1.090000                 68         $19.62   2010  \n",
       "2                35       1.211818                 43         $26.66   2010  \n",
       "3                44       0.000000                 15         $43.04   2010  \n",
       "4                72       6.267647                 28        $219.37   2008  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = movies.rename(columns= {\"Lead Studio\": \"Lead_Studio\", \"Audience score %\": \"Audience_score\",\"Rotten Tomatoes %\": \"Rotten_Tomatoes\",\"Worldwide Gross\": \"Worldwide_Gross\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Film</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Lead_Studio</th>\n",
       "      <th>Audience_score</th>\n",
       "      <th>Profitability</th>\n",
       "      <th>Rotten_Tomatoes</th>\n",
       "      <th>Worldwide_Gross</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Zack and Miri Make a Porno</td>\n",
       "      <td>Romance</td>\n",
       "      <td>The Weinstein Company</td>\n",
       "      <td>70</td>\n",
       "      <td>1.747542</td>\n",
       "      <td>64</td>\n",
       "      <td>$41.94</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Youth in Revolt</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>The Weinstein Company</td>\n",
       "      <td>52</td>\n",
       "      <td>1.090000</td>\n",
       "      <td>68</td>\n",
       "      <td>$19.62</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>You Will Meet a Tall Dark Stranger</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Independent</td>\n",
       "      <td>35</td>\n",
       "      <td>1.211818</td>\n",
       "      <td>43</td>\n",
       "      <td>$26.66</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>When in Rome</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Disney</td>\n",
       "      <td>44</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>$43.04</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What Happens in Vegas</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Fox</td>\n",
       "      <td>72</td>\n",
       "      <td>6.267647</td>\n",
       "      <td>28</td>\n",
       "      <td>$219.37</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Film    Genre            Lead_Studio  \\\n",
       "0          Zack and Miri Make a Porno  Romance  The Weinstein Company   \n",
       "1                     Youth in Revolt   Comedy  The Weinstein Company   \n",
       "2  You Will Meet a Tall Dark Stranger   Comedy            Independent   \n",
       "3                        When in Rome   Comedy                 Disney   \n",
       "4               What Happens in Vegas   Comedy                    Fox   \n",
       "\n",
       "   Audience_score  Profitability  Rotten_Tomatoes Worldwide_Gross  Year  \n",
       "0              70       1.747542               64         $41.94   2008  \n",
       "1              52       1.090000               68         $19.62   2010  \n",
       "2              35       1.211818               43         $26.66   2010  \n",
       "3              44       0.000000               15         $43.04   2010  \n",
       "4              72       6.267647               28        $219.37   2008  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Film                  object\n",
       "Genre                 object\n",
       "Lead Studio           object\n",
       "Audience score %       int64\n",
       "Profitability        float64\n",
       "Rotten Tomatoes %      int64\n",
       "Worldwide Gross       object\n",
       "Year                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         77\n",
       "unique        10\n",
       "top       Comedy\n",
       "freq          41\n",
       "Name: Genre, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.Genre.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Comedy       41\n",
       "Romance      13\n",
       "Drama        13\n",
       "Animation     4\n",
       "Action        1\n",
       "Fantasy       1\n",
       "Comdy         1\n",
       "romance       1\n",
       "Romence       1\n",
       "comedy        1\n",
       "Name: Genre, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "movies.Genre.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to see percentage of existing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Comedy       0.532468\n",
       "Romance      0.168831\n",
       "Drama        0.168831\n",
       "Animation    0.051948\n",
       "Action       0.012987\n",
       "Fantasy      0.012987\n",
       "Comdy        0.012987\n",
       "romance      0.012987\n",
       "Romence      0.012987\n",
       "comedy       0.012987\n",
       "Name: Genre, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "movies.Genre.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(movies.Genre.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3khzdPX40sKOv+ibgmcA5Se5msAvxtCQfYrr6AAZ/A9ur6sZu+UoGQTFN/fA84K6q2llVPwauAp7BdPXBfIu975E+L5djMEzllBpJwmCf8raqetu8h64BLujuXwB8YtK1TUpVXVRVx1TVLIN/989V1flMUR8AVNW3gXuTnNitOh24jenqh3uApydZ0/1tnM7guNs09cF8i73va4DzkjwyyXHACcBN+9xaVS27G3AW8DXgP4A39l3PhN7zsxgMAb8K3NLdzgJ+hsFZCHd2P4/su9YJ9cevAp/s7k9dHwC/CGzufh+uBo6Ytn4A/hK4HdgKXAY8chr6ALiCwXGVHzMYEbxib+8beGP3WXkH8Pxh2nBKDElSYznuSpIkjZHBIElqGAySpIbBIElqGAySpIbBIElqGAySpMb/Aa718YfO4G4LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies.Rotten_Tomatoes.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "movies.Rotten_Tomatoes.value_counts().plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=['51','52','53','54','55','56','57']\n",
    "y=[71,95,90,70,55,50,50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x222bf270b20>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.xlabel('Student')\n",
    "plt.ylabel('Greads')\n",
    "plt.title('Students final grade report')\n",
    "plt.plot(x,y,color='green', linewidth=5 , linestyle='dotted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate list (not \"str\") to list",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-1d4d756821fc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\"g+\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: can only concatenate list (not \"str\") to list"
     ]
    }
   ],
   "source": [
    "plt.plot(x,y+\"g+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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