python extra tasks+hive assignment

no_of_words.ipynb

@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count of alphabets\n",
+      "[3771, 3375, 2689, 2637, 2553, 2524, 2220, 2058, 1578, 1494, 1489, 1112, 926, 914, 839, 676, 602, 555, 468, 446, 378, 281, 150, 30, 21, 18]\n",
+      "top 5 alphabets with maximum\n",
+      "[3771, 3375, 2689, 2637, 2553]\n",
+      "graph of all alphabets and their count\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 requests\n",
+    "import webbrowser\n",
+    "from bs4 import BeautifulSoup\n",
+    "import re\n",
+    "from matplotlib import pyplot as plt\n",
+    "url=\"https://www.cricbuzz.com/\"\n",
+    "\n",
+    "page=requests.get(url)\n",
+    "page=page.text\n",
+    "\n",
+    "content=BeautifulSoup(page,\"html.parser\")\n",
+    "#print(content.text)\n",
+    "ll=[]\n",
+    "l2=[\"?\",\"\\xa0•\\xa0\\xa0GMT\",\"►\",\"✖ \",\"suggest.tag\",\"SEARCH  .cb-srch-ico{margin: 0} \",\"{{}} \",\"\"\"CBQueue.push(function(){ if((_GEO.country == \"US\" || _GEO.country == \"CA\") && false){ _ele('#video_playlist').html('<div class=\"cb-bg-white\" style=\"margin:0 5px 10px;\"><div class=\"kaltura-playlist\" uiconf_id=\"33885652\" entry_id=\"0_lvphia47\" uiautoplay=\"false\" style=\"width:420px;height:385px;padding:10px 15px;\"></div></div>'); }else if([\"AE\",\"SA\",\"KW\",\"BH\",\"QA\",\"OM\",\"GB\"].indexOf(_GEO.country) != -1 && false){ var eplr = document.createElement('script'); eplr.type = 'text/javascript'; eplr.async = true; eplr.src = 'https://player.performgroup.com/eplayer.js#ccb175de91b4f3c5c4bbed6a35.qjdsydmry69m1je82zk8il975'; var vpl = document.getElementById('video_playlist'); vpl.className = \"cb-bg-white\"; vpl.style.margin = \"0px 5px 10px\"; vpl.appendChild(eplr); } }); CBQueue.push(function(){\tif(_GEO.country == \"US\" || _GEO.country == \"CA\"){\t_ele('#kaltura-video-player').removeClass('disp-none');\t}\tif(_GEO.country == \"IN\" ){\t_ele('specials-home-module').removeClass('disp-none');\t}\"\"\",\n",
+    "   \"\"\" CBQueue.push(function(){ if((_GEO.country == \"US\" || _GEO.country == \"CA\") && false){ _ele('#video_playlist').html('<div class=\"cb-bg-white\" style=\"margin:0 5px 10px;\"><div class=\"kaltura-playlist\" uiconf_id=\"33885652\" entry_id=\"0_lvphia47\" uiautoplay=\"false\" style=\"width:420px;height:385px;padding:10px 15px;\"></div></div>'); }else if([\"AE\",\"SA\",\"KW\",\"BH\",\"QA\",\"OM\",\"GB\"].indexOf(_GEO.country) != -1 && false){ var eplr = document.createElement('script'); eplr.type = 'text/javascript'; eplr.async = true; eplr.src = 'https://player.performgroup.com/eplayer.js#ccb175de91b4f3c5c4bbed6a35.qjdsydmry69m1je82zk8il975'; var vpl = document.getElementById('video_playlist'); vpl.className = \"cb-bg-white\"; vpl.style.margin = \"0px 5px 10px\"; vpl.appendChild(eplr); } });\"\"\"]\n",
+    "content2=content.find_all(\"div\")\n",
+    "for i in content2:\n",
+    "    ll.append(i.text)\n",
+    "    \n",
+    "#print(len(ll))\n",
+    "\n",
+    "def list_to_str(s):\n",
+    "    str = \"\"\n",
+    "    for ele in s:\n",
+    "        str=str+ele\n",
+    "    return str\n",
+    "\n",
+    "raw_material=list_to_str(ll)\n",
+    "#print(raw_material)   \n",
+    "for i in l2:\n",
+    "    raw_material=raw_material.replace(i,\"\")\n",
+    "#print(raw_material)\n",
+    "total_len=[]\n",
+    "list_of_alphabets=[]\n",
+    "for i in range(97,123):\n",
+    "    list_of_alphabets.append(chr(i))\n",
+    "    x = re.findall(f\"{chr(i)}\", raw_material)\n",
+    "    total_len.append(len(x))\n",
+    "\n",
+    "#print(total_len)\n",
+    "\n",
+    "total_len.sort(reverse=True)\n",
+    "print(\"count of alphabets\")\n",
+    "print(total_len)\n",
+    "max_list=[]\n",
+    "for i in range(0,5):\n",
+    "    max_list.append(total_len[i])\n",
+    "print(\"top 5 alphabets with maximum count\")\n",
+    "print(max_list)\n",
+    "print(\"graph of all alphabets and their count\")\n",
+    "\n",
+    "plt.bar(list_of_alphabets,total_len)\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

sentimental_analysis.ipynb

@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sentiment analysis is basically the process of determining the attitude or the emotion of the writer, \n",
+    "#i.e., whether it is positive or negative or neutral.\n",
+    "#polarity stands for negative and positive.range[-1,1]\n",
+    "#Subjective sentences generally refer to personal opinion, emotion or judgment[0,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sentiment(polarity=-0.8, subjectivity=0.9)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pyaudio\n",
+    "import speech_recognition as sr\n",
+    "from textblob import TextBlob\n",
+    "# from textblob.sentiments import NaiveBayesAnalyzer\n",
+    "r=sr.Recognizer()\n",
+    "sr.Microphone.list_microphone_names()\n",
+    "mic=sr.Microphone(device_index=1)\n",
+    "with mic as source:\n",
+    "    audio=r.listen(source) \n",
+    "r.recognize_google(audio)\n",
+    "# audio=str(audio)\n",
+    "\n",
+    "TextBlob(str(r.recognize_google(audio))).sentiment\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TextBlob(\"naly\")"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}