Udemy Courses Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data5=pd.read_csv('C:\\\\Users\\\\Rohith varma\\\\Downloads\\\\archive (5)\\\\udemy_courses.csv',parse_dates=['published_timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "course_id                            int64\n",
       "course_title                        object\n",
       "url                                 object\n",
       "is_paid                               bool\n",
       "price                                int64\n",
       "num_subscribers                      int64\n",
       "num_reviews                          int64\n",
       "num_lectures                         int64\n",
       "level                               object\n",
       "content_duration                   float64\n",
       "published_timestamp    datetime64[ns, UTC]\n",
       "subject                             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Any missing value? False\n"
     ]
    }
   ],
   "source": [
    "print(\"Any missing value?\",data5.isnull().values.any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for duplicate data and drop them \n",
    "data5.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data5=data5.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#find no of courses per subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Web Development        1199\n",
       "Business Finance       1191\n",
       "Musical Instruments     680\n",
       "Graphic Design          602\n",
       "Name: subject, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5['subject'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohith varma\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\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 matplotlib.pyplot as plt\n",
    "sns.countplot(data5['subject']) \n",
    "plt.xlabel(\"Subject\",fontsize=13) \n",
    "plt.ylabel(\"No of courses per subject\") \n",
    "plt.xticks(rotation=65) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "All Levels            1925\n",
       "Beginner Level        1268\n",
       "Intermediate Level     421\n",
       "Expert Level            58\n",
       "Name: level, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for which levels udemy provide the courses \n",
    "data5['level'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohith varma\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='level', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "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": [
    "sns.countplot(data5['level'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     3362\n",
       "False     310\n",
       "Name: is_paid, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the count of free and paid courses \n",
    "data5['is_paid'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohith varma\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='is_paid', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSepkWEiSOs1JWCR5JsmuJDuTTLTaeUkeSvLNdn9u3/ibkuxN8lSSq+aiZ0layOZyz+JtVbWqqsbb4xuBbVW1AtjWHpNkJbAOuBhYA9yeZNFcNCxJC9V8Ogy1FtjctjcDV/fV76uqF6rqaWAvsHr07UnSwjVXYVHAHyV5LMnGVruwqg4AtPsLWn0J8Fzf3H2tdpwkG5NMJJmYnJwcUuuStPCcPkfve3lV7U9yAfBQkm9MMzYDajVoYFXdCdwJMD4+PnCMJOnkzcmeRVXtb/cHgc/SO6z0fJLFAO3+YBu+D1jWN30psH903UqSRh4WSV6d5DXHtoGfB54AtgIb2rANwANteyuwLsmZSS4CVgA7Rtu1JC1sc3EY6kLgs0mOvf9/rar/nuRRYEuSa4FngXcDVNXuJFuAPcAR4PqqOjoHfUvSgjXysKiqPwPePKD+HeDKE8zZBGwacmuSpBOYT6fOSpLmKcNCktTJsJAkdTIsJEmdDAtJUifDQpLUaa4u9zHvXfor98x1C5qHHvvN9XPdgjQn3LOQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVKnUyYskqxJ8lSSvUlunOt+JGkhOSXCIski4HeBfwisBK5JsnJuu5KkheOUCAtgNbC3qv6sqv4vcB+wdo57kqQF41T5WdUlwHN9j/cBf3vqoCQbgY3t4eEkT42gt4XgfODbc93EfJBbNsx1Czqen89jPpLZeJWfGlQ8VcJi0H+BOq5QdSdw5/DbWViSTFTV+Fz3IQ3i53M0TpXDUPuAZX2PlwL756gXSVpwTpWweBRYkeSiJK8A1gFb57gnSVowTonDUFV1JMkNwBeARcBdVbV7jttaSDy0p/nMz+cIpOq4Q/+SJP01p8phKEnSHDIsJEmdTok1C82+JEeBXX2lq6vqmROMPVxVZ4+kMQlI8jeAbe3hTwJHgcn2eHX7cq5GyDWLBepkAsCw0FxKcjNwuKpu6audXlVH5q6rhcfDUAIgydlJtiV5PMmuJMddTiXJ4iQPJ9mZ5Ikkf7/Vfz7J9jb395MYLJp1Se5O8rEkXwT+U5Kbk/y7vuefSLK8bb8nyY72Wf29dn05/RgMi4XrrPYPaWeSzwL/B3hnVb0VeBtwa5Kp35z/JeALVbUKeDOwM8n5wIeBt7e5E8C/HdlfoYXmjfQ+ax860YAkPwP8M+Dy9lk9CvzyaNp7+XLNYuH63+0fEgBJzgD+Y5KfBV6kdz2uC4G/6JvzKHBXG/sHVbUzyT+gdyXg/9my5RXA9tH8CVqAfr+qjnaMuRK4FHi0fSbPAg4Ou7GXO8NCx/wyMAZcWlU/TPIM8Mr+AVX1cAuTfwTcm+Q3gb8EHqqqa0bdsBakH/RtH+GvHx059nkNsLmqbhpZVwuAh6F0zE8AB1tQvI0BV55M8lNtzMeBTwBvBb4KXJ7kp9uYVyV54wj71sL1DL3PIEneClzU6tuAdyW5oD13Xvvs6sfgnoWO+RTwh0kmgJ3ANwaMuQL4lSQ/BA4D66tqMsk/Bz6d5Mw27sPAnw69Yy109wPrk+ykd4j0TwGqak+SDwN/lOQ04IfA9cCfz1WjLweeOitJ6uRhKElSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQTlKSr4zgPf5VkvUD6suTPDHs95em8nsW0imkXVX1c1V1yVz3ooXFPQvpJCU53O4HXrL9RHOS3Nou474tyVir/8skjyb5WpL7k7yq1f//5beTXNqe307vm8jSyBkW0kt33CXbpxn7auDxdhn3LwEfafXPVNXfqqo3A08C1w6Y+0ng31TV35mtxqWTZVhIL92jwPvaL7m9qaoOTTP2ReC/te3/Avy9tn1Jkv+RZBe9K/9e3D8pyU8Ar62qL7XSvbPVvHQyDAvpJaqqh4GfBb5F75Ltxy1ITze93d8N3FBVbwI+ypTLwtO73LYLi5pzhoX0Ep3gku0nchrwrrb9S8CX2/ZrgAPtB6WO+zW3qvoe8FdJju2J+ItvmhNeolx66a5gyiXbpxn7A+DiJI8Bf0XvZz8B/j3wCL3LZ++iFx5TvY/eLxT+L+ALs9O6dHI8dVYagSSHq+rsue5Deqk8DCVJ6uRhKGkWJXkEOHNK+b3uVehU52EoSVInD0NJkjoZFpKkToaFJKmTYSFJ6vT/ABN1v/KMs31MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data5['is_paid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['course_id', 'course_title', 'url', 'is_paid', 'price',\n",
       "       'num_subscribers', 'num_reviews', 'num_lectures', 'level',\n",
       "       'content_duration', 'published_timestamp', 'subject'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>course_id</th>\n",
       "      <th>price</th>\n",
       "      <th>num_subscribers</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>num_lectures</th>\n",
       "      <th>content_duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_paid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>691446.970968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11533.951613</td>\n",
       "      <td>425.929032</td>\n",
       "      <td>21.416129</td>\n",
       "      <td>2.210753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>674463.946163</td>\n",
       "      <td>72.198096</td>\n",
       "      <td>2421.270077</td>\n",
       "      <td>131.516359</td>\n",
       "      <td>41.866746</td>\n",
       "      <td>4.271584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             course_id      price  num_subscribers  num_reviews  num_lectures  \\\n",
       "is_paid                                                                         \n",
       "False    691446.970968   0.000000     11533.951613   425.929032     21.416129   \n",
       "True     674463.946163  72.198096      2421.270077   131.516359     41.866746   \n",
       "\n",
       "         content_duration  \n",
       "is_paid                    \n",
       "False            2.210753  \n",
       "True             4.271584  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#which course has more lectures free or paid\n",
    "data5.groupby(['is_paid']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#which courses have a higher no of subsribers (free or paid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>course_id</th>\n",
       "      <th>price</th>\n",
       "      <th>num_subscribers</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>num_lectures</th>\n",
       "      <th>content_duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_paid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>691446.970968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11533.951613</td>\n",
       "      <td>425.929032</td>\n",
       "      <td>21.416129</td>\n",
       "      <td>2.210753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>674463.946163</td>\n",
       "      <td>72.198096</td>\n",
       "      <td>2421.270077</td>\n",
       "      <td>131.516359</td>\n",
       "      <td>41.866746</td>\n",
       "      <td>4.271584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             course_id      price  num_subscribers  num_reviews  num_lectures  \\\n",
       "is_paid                                                                         \n",
       "False    691446.970968   0.000000     11533.951613   425.929032     21.416129   \n",
       "True     674463.946163  72.198096      2421.270077   131.516359     41.866746   \n",
       "\n",
       "         content_duration  \n",
       "is_paid                    \n",
       "False            2.210753  \n",
       "True             4.271584  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5.groupby(['is_paid']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='is_paid', ylabel='num_subscribers'>"
      ]
     },
     "execution_count": 19,
     "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": [
    "sns.barplot(x=\"is_paid\",y=\"num_subscribers\",data=data5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='level', ylabel='num_subscribers'>"
      ]
     },
     "execution_count": 20,
     "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": [
    "#which level have higher no of subscribers\n",
    "sns.barplot(x=\"level\",y=\"num_subscribers\",data=data5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>course_id</th>\n",
       "      <th>course_title</th>\n",
       "      <th>url</th>\n",
       "      <th>is_paid</th>\n",
       "      <th>price</th>\n",
       "      <th>num_subscribers</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>num_lectures</th>\n",
       "      <th>level</th>\n",
       "      <th>content_duration</th>\n",
       "      <th>published_timestamp</th>\n",
       "      <th>subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2827</th>\n",
       "      <td>41295</td>\n",
       "      <td>Learn HTML5 Programming From Scratch</td>\n",
       "      <td>https://www.udemy.com/learn-html5-programming-...</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>268923</td>\n",
       "      <td>8629</td>\n",
       "      <td>45</td>\n",
       "      <td>All Levels</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2013-02-14 07:03:41+00:00</td>\n",
       "      <td>Web Development</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      course_id                          course_title  \\\n",
       "2827      41295  Learn HTML5 Programming From Scratch   \n",
       "\n",
       "                                                    url  is_paid  price  \\\n",
       "2827  https://www.udemy.com/learn-html5-programming-...    False      0   \n",
       "\n",
       "      num_subscribers  num_reviews  num_lectures       level  \\\n",
       "2827           268923         8629            45  All Levels   \n",
       "\n",
       "      content_duration       published_timestamp          subject  \n",
       "2827              10.5 2013-02-14 07:03:41+00:00  Web Development  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find most popular course title \n",
    "data5[data5['num_subscribers'].max()==data5['num_subscribers']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#display 10 most popular courses as per no of subscribers \n",
    "top_10=data5.sort_values(by=\"num_subscribers\",ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_subscribers', ylabel='course_title'>"
      ]
     },
     "execution_count": 23,
     "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": [
    "sns.barplot(x=\"num_subscribers\",y=\"course_title\",data=top_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>course_id</th>\n",
       "      <th>course_title</th>\n",
       "      <th>url</th>\n",
       "      <th>is_paid</th>\n",
       "      <th>price</th>\n",
       "      <th>num_subscribers</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>num_lectures</th>\n",
       "      <th>level</th>\n",
       "      <th>content_duration</th>\n",
       "      <th>published_timestamp</th>\n",
       "      <th>subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>625204</td>\n",
       "      <td>The Web Developer Bootcamp</td>\n",
       "      <td>https://www.udemy.com/the-web-developer-bootcamp/</td>\n",
       "      <td>True</td>\n",
       "      <td>200</td>\n",
       "      <td>121584</td>\n",
       "      <td>27445</td>\n",
       "      <td>342</td>\n",
       "      <td>All Levels</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2015-11-02 21:13:27+00:00</td>\n",
       "      <td>Web Development</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      course_id                course_title  \\\n",
       "3230     625204  The Web Developer Bootcamp   \n",
       "\n",
       "                                                    url  is_paid  price  \\\n",
       "3230  https://www.udemy.com/the-web-developer-bootcamp/     True    200   \n",
       "\n",
       "      num_subscribers  num_reviews  num_lectures       level  \\\n",
       "3230           121584        27445           342  All Levels   \n",
       "\n",
       "      content_duration       published_timestamp          subject  \n",
       "3230              43.0 2015-11-02 21:13:27+00:00  Web Development  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find the course which is having the highest no of reviews \n",
    "data5[data5['num_reviews'].max()==data5['num_reviews']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='subject', ylabel='num_reviews'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### to visualize it the above  \n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,4))\n",
    "\n",
    "sns.barplot(x=\"subject\",y='num_reviews',data=data5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price', ylabel='num_reviews'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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REZGZoKLCWNHWNCUn8LhSVanF5RNIRoHNZvYrxraR3DDpuRIRkZzFq9TGt5EUu0otLp9A8tPgISIiZaRUVWpx+QwjryHiRUTKVCmq1BKfnesbzWynme0Y/8iyzHfM7ICZbUlKm2tmj5jZ9uDvnKTXbjGzLjN72czem5R+rpm9ELx2l5lZkF5rZvcH6U+a2bK8vr2IiBQsn+6/ncBbgsf5wF3AP2ZZ5nvAxnFpnwIedfdVwKPB/5jZauBKYE2wzNfMrDJY5uvAdcCq4BFf57XAEXdfCXwZuD2P7yMiIpMg50Di7j1Jjzfc/e/IMs2uuz8OjL9h8XIgXk12D3BFUvp97j7k7juBLmC9mS0EWtz9CXd34N5xy8TX9SPgwnhpRUREpkY+gzaek/RvBbESSvMJfGa7u+8DcPd9ZjY/SF8E/D7pfXuCtJHg+fj0+DKvB+uKmFkv0ApMuNfFzK4jVqqho6PjBLItIiKp5NNr6w6OT14VAXYBH57EvKQqSXiG9EzLTEx0vxu4G6Czs1OTcImITJJ82kjeB3ybWLvGb4E3iLVp5Ks7qK4i+HsgSN8DLEl632Jgb5C+OEX6mGXMrAqYxcSqNBERKaJ8AslPgUuJVTUdCx4nMpDLQ8DVwfOrgQeT0q8MemItJ9ao/lRQDdZvZhuC9o+rxi0TX9eHgMeCdhQREZki+VRtLXb38T2wMjKzfwbeDcwzsz3AbcAXgQfM7FpgN0H1mLtvNbMHgBeJVZ1d7+6jwao+TqwHWD3wi+ABsRLS982si1hJ5ERKSCIiUgDL9QLezO4GvuruLxQ3S8XX2dnpmzZp4GIRkXyY2TPu3jk+PZ8SyXnAn5nZTmJjbRng7n7mJOVRRESmoXwCyfuKlotpoJSzj4mIlLN8xtp6rZgZKWelnn1MRKSc5dNr66RV6tnHRGSsaNTZcfAYT7x6iB0HjxGNqrNmKeVTtXXSKvXsYyJynGoIyo9KJDmIzz6WbCpnHxOR41RDUH4USHIQn30sHkymevYxETkuUw2BlIaqtnJQ6tnHROS4Us9PLhOpRJKj+OxjG1bMY0Vbk4KISImohuDEFLODgkokIjKtqIYgf8XuoKASiYhMO6ohyE+xOygokIiIzHDF7qCgqi0RkRkg0zBOxe6goBKJiMg0F28DueSuX/PRbz7JJXf9moe37k80qBe7g0LOw8jPJBpGXkRmkh0Hj3HJXb+eUOL4+Q3nJ0bfiJdYCumgMBnDyIuISBnKZRineAeFYgzrpECSIw0jLyLlqtQ3aaqNJAfZ6h9FRIot0w2Fpb5JU20kOcil/lFEpFhyuaFwMtpAsknXRqISSQ40SJyIlFIuNxSW8iZNtZHkoL2ljqWt9bz/zEVYsG9+9twbGiRORKZEuc+JpECSg445Dfz5Bav4q59uSRQrv3DFWjrmNJQ6ayJyEih1Y3o2CiQ52H1kgK8+tp1rz1uRKJF89bHtnNMxpyyuBkRkZos3po9vIymXEY8VSHLQExrimrcvp2dgmKhDpcE1b1/O4dCQAomIFF25j3isQJKD+upKBkZGufvxHYmrgRsvXEVddWWpsyYiJ4li3lBYKPXaykFoaJSvPLp9TI+Jrzy6ndDQaIlzJiJSegokOQgNR1L2mBgYjpQoRyIi5aNkgcTMdpnZC2a22cw2BWlzzewRM9se/J2T9P5bzKzLzF42s/cmpZ8brKfLzO4ys0mvNFw6tzFxx2hcXXUFHXPLo6FLRKSUSl0ieY+7r0u6U/JTwKPuvgp4NPgfM1sNXAmsATYCXzOzeAPF14HrgFXBY+NkZ3L5vNTDDyyfp0AiIlJuje2XA+8Ont8D/DvwP4L0+9x9CNhpZl3AejPbBbS4+xMAZnYvcAXwi8nMVLn3mBCRma+cB44tZSBx4Jdm5sA/uPvdQLu77wNw931mNj947yLg90nL7gnSRoLn49MnMLPriJVc6OjoOPFMn3xDk4lIieUy1lYplTKQvMPd9wbB4hEzeynDe1NtKc+QPjExFqjuhtigjflktNx3oojMbOnG2jojj4Fji1miKVkbibvvDf4eAH4CrAe6zWwhQPD3QPD2PcCSpMUXA3uD9MUp0ifVzkOpd+LOQ6EsS4qIFC6XgWMzDTNf7KkwShJIzKzRzJrjz4GLgS3AQ8DVwduuBh4Mnj8EXGlmtWa2nFij+lNBNVi/mW0IemtdlbTMpHntcCjlTtx9WIFERIovPtZWsuSxtrIFimJfDJeqRNIO/MbMngOeAv63uz8MfBG4yMy2AxcF/+PuW4EHgBeBh4Hr3T1+N+DHgW8BXcCrTHJDO0BjTVXKndhQU259FURkJso2cVW2YeaLfTFckjOhu+8AzkqR3gNcmGaZvwH+JkX6JmDtZOcx2YJZtdx26Ro+97OtiTaS2y5dw4JZtcX8WBERIHvP0WzDzMcvhsePHjxZF8O6pM5BZBS+8R9didF/3WP/r1+2vtRZE5GTRKaxtuY3px5mvq0pVvXV3lLLjReuSgz1FB8vsL1lci6GFUhycKA/zGs9g/yvX3WNST94LMyp88tvADUROblUVpAyUFQGNfIdcxtZ1d7Ede9cQdShwmBVe9Okjc6hQJKDcp9URkRObvt6w9z7xGtjak3ufeI1zu6YzbJ5sWl3Lzi9nRXzmopyU7UCSQ7KfVIZkemonO/Unm7aW+o4MjA8ptZk/MVuMYehVyDJwXQeIkU/VilHhd7kq+N6rFJf7JqfhGN+dHZ2+qZNm0qdjaLTHflSrnYcPMYld/16QnXxz3O4U1vHdWrx4FrMi10zeyZpkN2EUo/+K0WUrW+5SKnkcqd2OjquU4tXXW1YMY8VbU1TGlQVSHKUafiBclXIj/VkNR3383SU7U5tSL8vdFynVspjV20kOYhGnd/vPEhkFA6HRhgZjbK/b4ANy9vKuiit3mb5UZXJ1MlWp59pX+i4nqjUx67aSHKw+/Axnnj1MLc9dPzO9s9dtoa3nTqXjrnlex9JJBLlp8+9wV/9dEsi31+4Yi1XnLWIqioVRscrpN5e8pepTj/TvljW2qiAP85UHbvp2khUIsnB/qNDiSACsWL0bQ9t5d7/sr6sA8nuIwN89bHtY/qWf/Wx7ZzTMUcnxhSyDTMxU5WqB1Sm7qjZ9sXFb2rn/us2sK83zMJZdaxZOOukDSIQ215zGmr44DmLiU82/uNn9tDdNzXHrgJJDg70D6U+qI8NlShHuenuS31H/lScGMu1e2amfJ2MVSalrhJJJ9O+iEadX27rLrs8l1JzXRVXvW3phDvbm+um5hSv+o0cLJxVx9LWeq5/z0o+cUHssbS1ngUt5X2CyaVBsxiKPfdBsfKVbYTVmahce0Bl2hflmudSGhgeTQQRiG2Trzy6ncHh0SxLTg6VSHIwf1YNN190Gl0HQ0QdKg1uvug02mfVlDprGZXqJqXJmM2tWPm6/eFtiao+gNsf3sYZC5oT3SWn642nJ6pcq/MqKmxc9VU9axa2UFFhaatxSp3nUjo2FEm5H48NRRL/F7OWQIEkBwd6h3njaJi7H98xpti4aPYwS+aUOnfplerEWK4/9J7QEH/c2cFdjx0v/t9wwSoOh4YS+SrmMBKZlKoqsFyr8zJVXy2cVcfH37WCQ6HhxIXdx9+1ouxrCIpp6dxGlrbW8/4zFyV+cz977o3EoIzFrsJUIMlBODLKfU/vHnMle9/Tuzlz8azSZiwHpTgxLpxVl7K+ttQ/9JrKikQQgdgV212Pbef+6zaUNF+lbKco9dAa6WQq1RoQGh6dcGF3EnZATVg6t4E/v2DVhB6aS+c2AOlnSDz9z8+flBHMFUhyEBmNprySjYxGsy98EhqNkrK+9uLVC0qar9DQaMri/8AU1SOnMxlVgSdaoinX6rxMVW6GpTy+zumYw/IZXLWVaR/vPjKQCCIQ2yZ/9dMtiR6amWZIVCCZIvU1VSmvZO+9RhNbpXKgP3XVVj7ztxSjqqexNt0scZUFrbdQhbZTFFqiKcfqvExVbrt6Up8UB4YjzFTZ9rFmSJwG+gZHUu6kvsGREuWovBVatVWsqp7h0VFuuGDVhJLlSIlLloW2U2Qr0ZRjV+xs+zhTlZs7GdsDZqJs+7i9pS7lNokfQwtn1/KFK9ZOqPpaOFszJE6Z2Q3VKX/osxuqp+TzM50IyvEkUWjVVrF6fbU21nL/pt1jbtC8f9NuNq4tbZVboe0Uma5Gy/Uu8Gz7OFOVW7b2gJkoW4mjY07qbdIxJ7ZNQuEIlRU2ZobEygojFJ6cUpwCSQ4qzVJPY2nF/yFmunIDynJOhwP9qQ/6XKu2itUldVlrI595/2qe39NL1KGqAj7z/tUlb1gGqKka+yOvqcp9P2SrBirHrti57ON0VW7jR2yA6TNiw4n+5rKVWrO1kRwdjHDLv7wwYfnv/tlbJuV7KZDkoLt/KOU0lsvnFf8ElOlEAJzwSaKYPYXmNdWmPOhbG3MrRherS2o06hwdGBnT2+cLV6wlGvUpuTqPRKJs3dc75r6IqqoKdvWE+MQ//eGEx0laPKuev758LZ958PjV6F9fvpbFs+p55vUjZXmfSCH7OJdu3OWokN/cstZG/v4/n524CKo0ePPiWYmLoGxd7o8MDKc8Do4MDE/Kd1MgyUFDTWXKaSynopF2f2/6MXSArCeJdFdAuVypnujVU2gokrIEFxrKrRjdMaeBv/3QmWw/cCzxo1k5vylRTE93Qs5m677elFdtq+Y3cdYk3BCUaXtlGkCzuy/MafOb+K/vPJXBoQgNtVV88/FXx4yTlGndrxzsxz3K//zQWYSGIzTWVDEwPMIrB/tzOmGXonq0kOq8YnfjLtb2KOQ3l+0iaH5zbcp2ybam2MVbuou7eTle3GWjQJKDxprKlCfGqQgkjbWVKQ+QhppKmutSt93ETxKZroCyVS0UcvW05+hgyhLckrkNrOuYk8hbuh/rnqMD9BwbHvOj+dTGM9hzdIDFsxtOeETjfb2pv/P+3jBnLcltf2T6oWfaXlv39aasjlk1v4nZDVX8nxuW8pc/ei6x7OcvW8Pshqqs+7Giwjg2FKEnNMKtD7045hg5NhRh9cJZWYdr/3XXAfoHRwkNRegJDfP6kRDnr5xf1GBSSLfjgeHs3bhPNBgUs6Sey2/usZe7J5Q6Lji9PetFUF94JOW9bhtWzAUgMjqastQ66pPT9V2BJAcNtZWsaGuMXfENRWisq6K60misLX4gGRqJ8ui2/XzpQ2clrlbv+d0Ozu2Yw5sXNXLHh9dx8w+PH/R3fPj4SSLTkCDZrlR39YT4zm9eHfO53/nNq4nhRCD9j7W9pZY3L2pi/fI5HAmNMLexmu3dR2lvrk0sl+nHeujYEN/93c4x+f7u73aydlELvYMjWUsV6fJ1yuz6lD1bFs4qvDdZtqvNTNUxzXU13DpudOlbH9rKD659a2JfZFp3ZNRTdm741lWdWU/Yuw+H2Ht0iM/97PgUCbdduobdh0Msm9d0wqW/XGTrdpxuP6a9um7K7fjKpJhtSlnbOQ6H2N59bMKNlivbmrJeBB0dHE55fPUGPUtb6mvoC0fGnMOqKqC5bnKGeVIgycFIxBmORCdUtYxEin8r7eBIhD/ZsIyuA/2Jz/6TDcsYHIkQjTrVlTYhwMWLuz2hIT7xnpXUV1clXl86dyWHQ0OsWzwnY/VR7+AwH+nsmHCV3DcYq1PNdPU0u76Sj751Kck/2Y++dSmzG2KBN9uYV8eGRlL+KOLjCaWq6tsX/KCiUecXW/ZPCK7vW7uA2Q1VXP/ulYmTduLKv/F477vh4VGe39vL/r4wC1vqePMps6ipOZ7vdCeZbFebs+pqxvQYg1iPsTs+vI7DoeGU36knFNvW2dY9HImmfH04Ek3sq/7wCEcHRqivrhrTJrS/L8w3/qNrTL6+8R9dLJ93ZkGlv7hilAyGR1NXnQ6PRrLup3gwSBcgJ2N4n3THULbqvO6+obQ3Wi6cVZ8yCC0ILoLqq6tSHl+3/6czgdgF6e7Dg9z5yCuJz77potNoa5qc0SYUSHLgDnuODE64UuiYgu6GLXXVhEcivG1FKwf7h2hrrqVvcIiWumpe7u4jPDLC7IZaIlGnpa6KowNDvNzdx5pFs2muraShpoqXu48HodPam2mqrWTP0QGWz6ujvbmO7v7Yj7yhJlattGxeE+4QiQxz739Zn3i9a/8R4gP4vtYTYnZ9JeedOi/xeoVFea0nxHAE5tRXMRwxKitGqKuupKaygvj9YtkaS2urqtj8eg//8KfnJko0//j7nZzdMZs59TXcfPEq6qqOB8ebLz5+j8qOg8cSQQRiP8abf7iZ09vP4/DAMJt2HeI7f/YWDgXb8ifP7mbZvEY65jYxPDzKb3cepNIqiIw6oeEIv915kHcsb6OmppLuvjDXnbeUd6xq50DwnX/zSnfiSr9z6SyuevuKMSXH+A81PDLCZy9bTUN1deL95y6dxVBkhFn1VXzywpW80RtO7KdPXriSWfWxn2d7Sx0Xr57Hn2xYPmZ7xK9kl7Y2pjzJLG1tJBKJ8vCL+3klOAa27evj9SMDbFy9gKqqCvrCEa5/16k01FYntmfH7FPpC0fYuq+XX27dO2E/5NqmlK1kkC1oHz0WmnD87eoJ8caRMNv3943Zjz98ejentjWx5pTsgTdTe1Uu90BlKqUND4/y8Lb9dAUXaNu7+3mjd5CNb1pATU0l717Zyr3XrKe7b4j2llrWLmhOBNbQcCRlEBsYjnBux5yU94GsWTgr+H6j3Pb+N1FfU504T5yzpIWhkVjV1eDIKJt2HZqwL1cvbMm6H3MxIwKJmW0EvgJUAt9y9y9O5vr7hyIprxTOXFT8sbaqKmF4FJ7Y0UPU4dWDx1g5v4mqSohEoyyb10B4mMQJe9m8BiLRWD4jo05oaOxNk6GhESKjjtVE2LZvYEKd6frlsR9yc10FDXV1XPXdpxKvf/GDb6a5Ln4lGmE44pg57jAadSLuQITGWnhx7zAvBSevrgPHOL29mdWnxA63mooKaipGx5wknt99iOqK2LorK5yPrh9Xolm/lMqK2JV0bVUlrySV0FbOb6Iq+DHuPBRK+WPceSjErIZKLly9kMPHhhkcHqUnNMyFqxcCse3Vdaif6gqjsqKCCoPqygoqok7XoX5WnzKbJa11vH64mauTtsnnL1vL4rl1uMMfv2VsO8dfX76WmqCwM7+llqHIKAPBFDZm0NZUTW1VJQPDzqI5tSyf15TYHsOR2P4EOKW5jkvPWswzr8UCeeUhuPSsxZwSBJLl8xq548NncfMPj3/2HR8+i+XzGtm2v5dD/UMT2ptePtDHmlNm095cS12VUV1ZSdSd1sYaWuoqmVVfQzgywgfPWUL/YIShkVH6wsYHz1lCOHK808SJluAWz6rnp8/v5dak4+/zl6/lijNPoaamEvcIVdW1Y46/z1++FvcIbc01vOuM+fw+6TfxrjPm09Yc29jZbs7L1N7QUFOVsq3hj97UHjvqI1F+svmNCb+bD6yLldJe3N9HeDjCafObE4F5YGiEF/f3cXpbE7/YdoBP/+SFxLJ/84E3c8mb2qmvr2Z5ayM3XHAq+/qGEsf2DRecerxqdlYtd//puRwZGGFOQzVVFSSC0MJZtbzcHWLznuPb5NT5TZzeXg9AbTW8d+0iPvb9Z8Z8du0k3Qo37QOJmVUC/wu4CNgDPG1mD7n7i5P1GeGR1I17gyPFH6Mp6pa2NDS7wXhyR9+Eqpq3rohdZUSiTk9oZMKykahzsC+a+DHEv89nHtzCP16znqWtMDgS5VNJ/c7Dwf8PfOytwbphz5HwhM9ua65hKOK8dnhgYp5bYwd1XQ20NNSPPUlctoa6mtiPorGmmp2HBidMbby2uYVI1DOWDpvr00zwU19FfVUlr4YGJ7QHnBJcbRqw9+jQhO8U77a898gQtz40dpvd+tAW7r1mPZVmKbfnD659K0vmQHUl/GF3aMK61y9voa7aeHn/MLc+9Icxr521JJav7Yf6U37n7Yf6efPiOUQiUUbdx9yHMupOJBIlNBThiw+/NCZfX3z4Je69Jnb/QE2lsb934neO9fap5shAeML2Wjgrth+Hh0czBoO9RwdS/m72HR3kcGg4sVxiWz64hRXzGulcNpfDodGUr3//mvVUVhh7U4zGvSSols12c96BvjQT1fUNUVOVuq1h79EBVrU3s2Vvb8r9vGp+E+s65jASHWXUjb9IuqC47dI1jERHeelgfyKIxJf99E9eYMW8Bs7umEvUnaODkQnfK+rOrkPHeG5PX4qSUj0r5jczMBTljRTHyJLZsX1VaZUpP/uf/utbczoPZTMTJrZaD3S5+w53HwbuAy6fzA9oqY/1jkpWV11BS13x72zvG0xdGuoLR+juHU3ZSNvdGwtw/UOpJ7s5NjTKwWOp+5UfPBarl993NPWPbf/R2OtHQqk/+0holCMDqfN8dCB2Jds7GE25bO9ALN/HhkZTTm18bGiUvnDqdfcHd+hWWuoB/SrNODY8mjgpxl/73M+2cizo7dMfTv2d+sOx17vTnIC6+4bSzqJ5MJhF80Bf6nUf6BuldyD1a/HtcTTNMXB0MPadn9/by1/+6HnuerSLv3+si7se7eIvf/Q8z+/t5Ugo9fA+R0ORrN85lGZ7xXtHPb+3N+XJ/vm9vQDUVFWm/N1UV1WwP0310/6gW3t3mu3Z3T+UtoYgPvdGupvzdh8ZAI6PVDE+X7MbqqmpqkzZtbimKlbKeuPoYMp8vXF0EIDIKCm3WWQ0fQA72Bf85nrDKb/Xvt4we9O8trc3tr3SbZP+YJscTLM9D/VPzn0kMyGQLAJeT/p/T5A2hpldZ2abzGzTwYMH8/qAIwMj3HDBqsTBF79KOTIFY22lnbAmHMn4YwM4Fk69bH84QlNdVcofU1MwNWd9TeqTQG1NLC3TZ2f6XMhwQg7ynXZq41zWnWHZnjTBM9GonWV7zm+uTblN5jfX0lSbens2BoPiZVr3ie7HY8F3TndS7u4Lp92PdTnsx8Oh1NvrcLC9Mn0uZPjdDIzQ2liTMl+tjbFeRJleH0zT/XdwOJaWqY0EoH8odb76h0boC6cZVy8c+63PTZOvuUG+e9Jss57QcNbfVH+6Y3sokvE1yN4lOttnF2omBJJUXUAmdKdy97vdvdPdO9va2vL6gLkN1YkeEZ+4YCXXnreC+zftZs4UjLXV1pz6wG1rqqG9JfWJrb2lNuOy85pqaKmt4sYLx/6YbrxwFc21sRNfU5bXM312ps/Ntmyh6053sm9rrk2/3ubsnwvQXFfJ5y5bM2abfO6yNTTXVtJcV5lyezUFXcQzrbuQ/QiwMM2Uyu0tdWn3Y1MO+3FBmtfmB9sr0+cCzMnwu2msqeS2S8duy9suXUNj0L6S6fV0+3hec/z4yjzFdGtjXcp8tTbWsrClPuWy8cb25tqqlPlKbM8sFxsn8puan+HYje+LtqY02yToEp3tswtlPs1ngzGztwGfdff3Bv/fAuDu/2+6ZTo7O33Tpk05f8bRwTC/3HIwUT8eq0dey8Vr25hdX9zJmo4Ohnlk68EJjXsXrYkFw0z5yrbsr1/pSXT/rTBYNb+J809rTSz7+Ms9dB08/vrKtibeefrx19N9NpD2c7MtW8x157K9sr3+h91HqbSKRIPnqEc5u2M2AI+/0pPorVMRdAJ452m5ba9MnxsaHOLnWw9M+M6XrJlPY31txraK8OgI/55iP7779FZacsnX1oMT1ntxsK2ztZGEBof431sOTFj3/7F2PhVU8PirhxgJesc11sS6rr/z1HnU11dzdDDMk68emfD6W0+N9Rb7/148OKEN5I9Wx/KVrbdYpl5bQMYuz8PDo/xq+wFGIkn5qjLes2p+1u88imfcF8PDozz4/N4J+/nyM08BSPtaTU0lRwfDGbdJtt9zrszsGXfvnJA+AwJJFfAKcCHwBvA08J/dfWu6ZfINJBA7ob+yP5TotnfagsaiB5FcPjtbvoq17HRd93TNV2hwiK37jyVeX7Ogicb648NbxHtPxe/XODOp91TfYJiXktZ9xoJGWiYpX5k+N1u+BwdHeGF/X+K1Ny9oob6+elLyFb9/Jd1d8/EuvPt7wyyYVceahbMSXXgzvVbod862LzKtO9vnFnps52LGBhIAM7sE+Dti3X+/4+5/k+n9JxJIREROdukCybTv/gvg7j8Hfl7qfIiInIxmQmO7iIiUkAKJiIgURIFEREQKokAiIiIFmRG9tvJlZgeB105w8XnAoUnMzmRRvvKjfOVH+cpfueatkHwtdfcJd3SflIGkEGa2KVX3t1JTvvKjfOVH+cpfueatGPlS1ZaIiBREgURERAqiQJK/u0udgTSUr/woX/lRvvJXrnmb9HypjURERAqiEomIiBREgURERAqiQJIHM9toZi+bWZeZfaqE+VhiZr8ys21mttXMbgzSP2tmb5jZ5uBxSQnytsvMXgg+f1OQNtfMHjGz7cHfOVOcp9OTtslmM+szs0+WYnuZ2XfM7ICZbUlKS7t9zOyW4Hh72czeO8X5+lsze8nMnjezn5jZ7CB9mZkNJm23b0xxvtLutxJvr/uT8rTLzDYH6VO5vdKdG4p7jLm7Hjk8iA1R/yqwAqgBngNWlygvC4FzgufNxOZjWQ18FviLEm+nXcC8cWlfAj4VPP8UcHuJ9+N+YGkpthfwTuAcYEu27RPs0+eAWmB5cPxVTmG+Lgaqgue3J+VrWfL7SrC9Uu63Um+vca/fAdxagu2V7txQ1GNMJZLcrQe63H2Huw8D9wGXlyIj7r7P3Z8NnvcD20gxT30ZuRy4J3h+D3BF6bLChcCr7n6iIxsUxN0fBw6PS063fS4H7nP3IXffCXQROw6nJF/u/kt3jwT//h5YXIzPzjdfGZR0e8WZmQEfAf65GJ+dSYZzQ1GPMQWS3C0CXk/6fw9lcPI2s2XA2cCTQdIngqqI70x1FVLAgV+a2TNmdl2Q1u7u+yB2oAPzS5CvuCsZ+wMv9faC9NunnI65a4BfJP2/3Mz+YGb/YWbnlyA/qfZbuWyv84Fud9+elDbl22vcuaGox5gCSe4sRVpJ+06bWRPwY+CT7t4HfB04FVgH7CNWvJ5q73D3c4D3Adeb2TtLkIeUzKwGuAz4YZBUDtsrk7I45szs00AE+EGQtA/ocPezgZuAfzKzlinMUrr9VhbbC/goYy9Wpnx7pTg3pH1rirS8t5kCSe72AEuS/l8M7C1RXjCzamIHyg/c/V8A3L3b3UfdPQp8kyIV6zNx973B3wPAT4I8dJvZwiDfC4EDU52vwPuAZ929O8hjybdXIN32KfkxZ2ZXA+8H/sSDSvWgGqQneP4MsXr106YqTxn2Wzlsryrgg8D98bSp3l6pzg0U+RhTIMnd08AqM1seXNleCTxUiowEdbDfBra5+51J6QuT3vYBYMv4ZYucr0Yza44/J9ZYu4XYdro6eNvVwINTma8kY64US729kqTbPg8BV5pZrZktB1YBT01VpsxsI/A/gMvcfSApvc3MKoPnK4J87ZjCfKXbbyXdXoE/Al5y9z3xhKncXunODRT7GJuKngQz5QFcQqwXxKvAp0uYj/OIFT+fBzYHj0uA7wMvBOkPAQunOF8riPUAeQ7YGt9GQCvwKLA9+Du3BNusAegBZiWlTfn2IhbI9gEjxK4Gr820fYBPB8fby8D7pjhfXcTqz+PH2DeC9/6nYP8+BzwLXDrF+Uq730q5vYL07wH/fdx7p3J7pTs3FPUY0xApIiJSEFVtiYhIQRRIRESkIAokIiJSEAUSEREpiAKJiIgURIFEpEyY2efN7I9KnQ+RfKn7r0gZMLNKdx8tdT5EToRKJCJFFsxH8ZKZ3RMMNPgjM2sI5qy41cx+A3zYzL5nZh8KlnmLmf3OzJ4zs6fMrNnMKi02R8jTwXo+VuKvJgIokIhMldOBu939TKAP+L+C9LC7n+fu98XfGAzBcz9wo7ufRWzYjUFid3X3uvtbgLcA/y0Y1kKkpBRIRKbG6+7+2+D5PxIbygKSBvdLcjqwz92fBnD3Po/NC3IxcFUw896TxIa9WFXUXIvkoKrUGRA5SYxvjIz/H0rxXkvx/nj6n7v7v01mxkQKpRKJyNToMLO3Bc8/Cvwmw3tfAk4xs7cABO0jVcC/AR8PhgnHzE4LRlkWKSkFEpGpsQ242syeB+YSm5wpJY9N5fzHwFfN7DngEaAO+BbwIvCsmW0B/gHVKkgZUPdfkSILpjz9V3dfW+q8iBSDSiQiIlIQlUhERKQgKpGIiEhBFEhERKQgCiQiIlIQBRIRESmIAomIiBTk/wc54HJbICoFpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#does price effect no of reviews \n",
    "#we use scatter plot for it \n",
    "sns.scatterplot(x=\"price\",y=\"num_reviews\",data=data5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find total no of courses related to python\n",
    "\n",
    "len(data5[data5['course_title'].str.contains('python',case=False)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#display 10 most popular python courses as per no of subscribers  \n",
    "python=data5[data5['course_title'].str.contains('python',case=False)].sort_values(by=\"num_subscribers\",ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_subscribers', ylabel='course_title'>"
      ]
     },
     "execution_count": 29,
     "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": [
    "sns.barplot(x=\"num_subscribers\",y=\"course_title\",data=python)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "###\n",
    "#In which year the highest number of courses were posted\n",
    "data5['Year']=data5['published_timestamp'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>course_id</th>\n",
       "      <th>course_title</th>\n",
       "      <th>url</th>\n",
       "      <th>is_paid</th>\n",
       "      <th>price</th>\n",
       "      <th>num_subscribers</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>num_lectures</th>\n",
       "      <th>level</th>\n",
       "      <th>content_duration</th>\n",
       "      <th>published_timestamp</th>\n",
       "      <th>subject</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1070968</td>\n",
       "      <td>Ultimate Investment Banking Course</td>\n",
       "      <td>https://www.udemy.com/ultimate-investment-bank...</td>\n",
       "      <td>True</td>\n",
       "      <td>200</td>\n",
       "      <td>2147</td>\n",
       "      <td>23</td>\n",
       "      <td>51</td>\n",
       "      <td>All Levels</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2017-01-18 20:58:58+00:00</td>\n",
       "      <td>Business Finance</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   course_id                        course_title  \\\n",
       "0    1070968  Ultimate Investment Banking Course   \n",
       "\n",
       "                                                 url  is_paid  price  \\\n",
       "0  https://www.udemy.com/ultimate-investment-bank...     True    200   \n",
       "\n",
       "   num_subscribers  num_reviews  num_lectures       level  content_duration  \\\n",
       "0             2147           23            51  All Levels               1.5   \n",
       "\n",
       "        published_timestamp           subject  Year  \n",
       "0 2017-01-18 20:58:58+00:00  Business Finance  2017  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rohith varma\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='count'>"
      ]
     },
     "execution_count": 32,
     "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": [
    "sns.countplot('Year',data=data5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  subject            \n",
       "2011  Web Development          5\n",
       "2012  Web Development         19\n",
       "      Graphic Design          10\n",
       "      Musical Instruments     10\n",
       "      Business Finance         6\n",
       "2013  Business Finance        84\n",
       "      Web Development         55\n",
       "      Musical Instruments     39\n",
       "      Graphic Design          23\n",
       "2014  Business Finance       192\n",
       "      Musical Instruments    120\n",
       "      Web Development        113\n",
       "      Graphic Design          65\n",
       "2015  Business Finance       339\n",
       "      Web Development        336\n",
       "      Musical Instruments    171\n",
       "      Graphic Design         168\n",
       "2016  Web Development        448\n",
       "      Business Finance       347\n",
       "      Musical Instruments    228\n",
       "      Graphic Design         181\n",
       "2017  Business Finance       223\n",
       "      Web Development        223\n",
       "      Graphic Design         155\n",
       "      Musical Instruments    112\n",
       "Name: subject, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display category wise count of posted subjects[year-wise] \n",
    "data5.groupby('Year')['subject'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
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}