sentimental analysis twitter streaming.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "37f45603",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "from tweepy import API\n",
    "from tweepy import Cursor\n",
    "from tweepy.streaming import StreamListener\n",
    "from tweepy import OAuthHandler \n",
    "from tweepy import Stream\n",
    "import numpy as np \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e379fd66",
   "metadata": {},
   "outputs": [],
   "source": [
    "access_token= '1261021151754682375-WFJgum4dUxrErkxUzLabrGitnYQPgj'\n",
    "access_token_secret = 'sBiyzO60LzMwDC7X6x2XaJBRcoPOzxT7JDypbcptHD1Ob'\n",
    "consumer_key = 'i73CZaLFIv2iuzUSWwAZ4n3qj'\n",
    "consumer_secret = 'Za4BuBZEL8sGPwKq2kDZsS5vieG8qvH0bdgBWM2OdbjOi5Root'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "acac2ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "auth= tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "api=tweepy.API(auth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e6c3a25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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for iPhone</a>', 'in_reply_to_status_id': None, 'in_reply_to_status_id_str': None, 'in_reply_to_user_id': None, 'in_reply_to_user_id_str': None, 'in_reply_to_screen_name': None, 'user': {'id': 1175801461298479104, 'id_str': '1175801461298479104', 'name': 'fira ◡̈ rest buying !!!', 'screen_name': 'jhyunfiles', 'location': 'SHE/HER - 7TEEN', 'description': '🛒 . . . selling, buying, && trading acc for #JAEHYUN ‹𝟹', 'url': None, 'entities': {'description': {'urls': []}}, 'protected': False, 'followers_count': 423, 'friends_count': 430, 'listed_count': 3, 'created_at': 'Sun Sep 22 15:58:17 +0000 2019', 'favourites_count': 7533, 'utc_offset': None, 'time_zone': None, 'geo_enabled': True, 'verified': False, 'statuses_count': 7053, 'lang': None, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'F5F8FA', 'profile_background_image_url': None, 'profile_background_image_url_https': None, 'profile_background_tile': False, 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&amp; \\n\\n@cryptotahid\\n\\n@cryptovenizo… https://t.co/s46MXu8gXB', 'truncated': True, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'cryptotahid', 'name': 'Crypto Giveaways 🐺', 'id': 1949559619, 'id_str': '1949559619', 'indices': [90, 102]}, {'screen_name': 'cryptovenizo', 'name': 'Crypto Venizo 🚀', 'id': 1072139057986039808, 'id_str': '1072139057986039808', 'indices': [104, 117]}], 'urls': [{'url': 'https://t.co/s46MXu8gXB', 'expanded_url': 'https://twitter.com/i/web/status/1423895911323770885', 'display_url': 'twitter.com/i/web/status/1…', 'indices': [119, 142]}]}, 'metadata': {'iso_language_code': 'en', 'result_type': 'recent'}, 'source': '<a href=\"http://twitter.com/download/android\" rel=\"nofollow\">Twitter for Android</a>', 'in_reply_to_status_id': None, 'in_reply_to_status_id_str': None, 'in_reply_to_user_id': None, 'in_reply_to_user_id_str': None, 'in_reply_to_screen_name': None, 'user': {'id': 1334937336690327552, 'id_str': '1334937336690327552', 'name': 'chita🍒', 'screen_name': 'chitaglorya', 'location': '', 'description': 'Social media & crypto project promoter 📈 ||#cutetesty vouch @chita_winner || 🅒🅡🅨🅟🅣🅞 paypal gcash ewallet bank ||', 'url': 'https://t.co/93eccdgkxf', 'entities': {'url': {'urls': [{'url': 'https://t.co/93eccdgkxf', 'expanded_url': 'https://twitter.com/chitaglorya/status/1420055005264900096?s=19', 'display_url': 'twitter.com/chitaglorya/st…', 'indices': [0, 23]}]}, 'description': {'urls': []}}, 'protected': False, 'followers_count': 128629, 'friends_count': 849, 'listed_count': 739, 'created_at': 'Fri Dec 04 19:07:33 +0000 2020', 'favourites_count': 16137, 'utc_offset': None, 'time_zone': None, 'geo_enabled': False, 'verified': False, 'statuses_count': 12587, 'lang': None, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'F5F8FA', 'profile_background_image_url': None, 'profile_background_image_url_https': None, 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/1401926808694530050/24e7VnCP_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1401926808694530050/24e7VnCP_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/1334937336690327552/1623831381', 'profile_link_color': '1DA1F2', 'profile_sidebar_border_color': 'C0DEED', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'profile_use_background_image': True, 'has_extended_profile': False, 'default_profile': True, 'default_profile_image': False, 'following': False, 'follow_request_sent': False, 'notifications': False, 'translator_type': 'none', 'withheld_in_countries': []}, 'geo': None, 'coordinates': None, 'place': None, 'contributors': None, 'is_quote_status': False, 'retweet_count': 20898, 'favorite_count': 8552, 'favorited': False, 'retweeted': False, 'possibly_sensitive': False, 'lang': 'en'}, 'is_quote_status': False, 'retweet_count': 20898, 'favorite_count': 0, 'favorited': False, 'retweeted': False, 'lang': 'en'}, created_at=datetime.datetime(2021, 8, 10, 8, 48, 44), id=1425016252238471170, id_str='1425016252238471170', text='RT @chitaglorya: giveaway 300$ | 4,200,000 IDR \\niphone 8plus for one winner 💥\\n\\n- retweet follow me &amp; \\n\\n@cryptotahid\\n\\n@cryptovenizo\\n\\nends in…', truncated=False, entities={'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'chitaglorya', 'name': 'chita🍒', 'id': 1334937336690327552, 'id_str': '1334937336690327552', 'indices': [3, 15]}, {'screen_name': 'cryptotahid', 'name': 'Crypto Giveaways 🐺', 'id': 1949559619, 'id_str': '1949559619', 'indices': [107, 119]}, {'screen_name': 'cryptovenizo', 'name': 'Crypto Venizo 🚀', 'id': 1072139057986039808, 'id_str': '1072139057986039808', 'indices': [121, 134]}], 'urls': []}, metadata={'iso_language_code': 'en', 'result_type': 'recent'}, source='Twitter for iPhone', source_url='http://twitter.com/download/iphone', in_reply_to_status_id=None, in_reply_to_status_id_str=None, in_reply_to_user_id=None, in_reply_to_user_id_str=None, in_reply_to_screen_name=None, author=User(_api=<tweepy.api.API object at 0x000001DFFAE06430>, _json={'id': 1175801461298479104, 'id_str': '1175801461298479104', 'name': 'fira ◡̈ rest buying !!!', 'screen_name': 'jhyunfiles', 'location': 'SHE/HER - 7TEEN', 'description': '🛒 . . . selling, buying, && trading acc for #JAEHYUN ‹𝟹', 'url': None, 'entities': {'description': {'urls': []}}, 'protected': False, 'followers_count': 423, 'friends_count': 430, 'listed_count': 3, 'created_at': 'Sun Sep 22 15:58:17 +0000 2019', 'favourites_count': 7533, 'utc_offset': None, 'time_zone': None, 'geo_enabled': True, 'verified': False, 'statuses_count': 7053, 'lang': None, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'F5F8FA', 'profile_background_image_url': None, 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profile_text_color='333333', profile_use_background_image=True, has_extended_profile=True, default_profile=True, default_profile_image=False, following=False, follow_request_sent=False, notifications=False, translator_type='none', withheld_in_countries=[]), geo=None, coordinates=None, place=None, contributors=None, is_quote_status=True, quoted_status_id=1414927176655269893, quoted_status_id_str='1414927176655269893', quoted_status=Status(_api=<tweepy.api.API object at 0x000001DFFAE06430>, _json={'created_at': 'Tue Jul 13 12:38:21 +0000 2021', 'id': 1414927176655269893, 'id_str': '1414927176655269893', 'text': 'Coffee Delivery by Driverless @Tesla car. Wow!!! https://t.co/5ZA4OpR1yy', 'truncated': False, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'Tesla', 'name': 'Tesla', 'id': 13298072, 'id_str': '13298072', 'indices': [30, 36]}], 'urls': [], 'media': [{'id': 1414927111530315776, 'id_str': '1414927111530315776', 'indices': [49, 72], 'media_url': 'http://pbs.twimg.com/ext_tw_video_thumb/1414927111530315776/pu/img/DtSKVcI8wwrMDlkk.jpg', 'media_url_https': 'https://pbs.twimg.com/ext_tw_video_thumb/1414927111530315776/pu/img/DtSKVcI8wwrMDlkk.jpg', 'url': 'https://t.co/5ZA4OpR1yy', 'display_url': 'pic.twitter.com/5ZA4OpR1yy', 'expanded_url': 'https://twitter.com/jvidyasagar/status/1414927176655269893/video/1', 'type': 'photo', 'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'large': {'w': 480, 'h': 848, 'resize': 'fit'}, 'small': {'w': 385, 'h': 680, 'resize': 'fit'}, 'medium': {'w': 480, 'h': 848, 'resize': 'fit'}}}]}, 'extended_entities': {'media': [{'id': 1414927111530315776, 'id_str': '1414927111530315776', 'indices': [49, 72], 'media_url': 'http://pbs.twimg.com/ext_tw_video_thumb/1414927111530315776/pu/img/DtSKVcI8wwrMDlkk.jpg', 'media_url_https': 'https://pbs.twimg.com/ext_tw_video_thumb/1414927111530315776/pu/img/DtSKVcI8wwrMDlkk.jpg', 'url': 'https://t.co/5ZA4OpR1yy', 'display_url': 'pic.twitter.com/5ZA4OpR1yy', 'expanded_url': 'https://twitter.com/jvidyasagar/status/1414927176655269893/video/1', 'type': 'video', 'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'large': {'w': 480, 'h': 848, 'resize': 'fit'}, 'small': {'w': 385, 'h': 680, 'resize': 'fit'}, 'medium': {'w': 480, 'h': 848, 'resize': 'fit'}}, 'video_info': {'aspect_ratio': [30, 53], 'duration_millis': 57000, 'variants': [{'content_type': 'application/x-mpegURL', 'url': 'https://video.twimg.com/ext_tw_video/1414927111530315776/pu/pl/XY28Kwv5kdrewIBL.m3u8?tag=12&container=fmp4'}, {'bitrate': 632000, 'content_type': 'video/mp4', 'url': 'https://video.twimg.com/ext_tw_video/1414927111530315776/pu/vid/320x564/Zy1n2hhW-tmpynUy.mp4?tag=12'}, {'bitrate': 950000, 'content_type': 'video/mp4', 'url': 'https://video.twimg.com/ext_tw_video/1414927111530315776/pu/vid/480x848/oYIF1Vug3ppZMVOq.mp4?tag=12'}]}, 'additional_media_info': {'monetizable': False}}]}, 'metadata': {'iso_language_code': 'in', 'result_type': 'recent'}, 'source': '<a href=\"http://twitter.com/download/android\" rel=\"nofollow\">Twitter for Android</a>', 'in_reply_to_status_id': None, 'in_reply_to_status_id_str': None, 'in_reply_to_user_id': None, 'in_reply_to_user_id_str': None, 'in_reply_to_screen_name': None, 'user': {'id': 74205752, 'id_str': '74205752', 'name': '🇮🇳 Vidyasagar 🇮🇳', 'screen_name': 'jvidyasagar', 'location': 'Chennai | Hyderabad | Mumbai', 'description': 'Traffic Volunteer with @CybTraffic Honoured 2 be flwd by @nsitharamanoffc @rajksinghIndia @smritiiranioffc @mafoikprajan (Tweets are Personal)', 'url': 'https://t.co/XjkZKokpvU', 'entities': {'url': {'urls': [{'url': 'https://t.co/XjkZKokpvU', 'expanded_url': 'http://local360.in', 'display_url': 'local360.in', 'indices': [0, 23]}]}, 'description': {'urls': []}}, 'protected': False, 'followers_count': 4846, 'friends_count': 2201, 'listed_count': 60, 'created_at': 'Mon Sep 14 17:10:25 +0000 2009', 'favourites_count': 150987, 'utc_offset': None, 'time_zone': None, 'geo_enabled': True, 'verified': False, 'statuses_count': 77693, 'lang': None, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': '000000', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme7/bg.gif', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme7/bg.gif', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/1237018314922012673/PjHnKFU1_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1237018314922012673/PjHnKFU1_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/74205752/1627824563', 'profile_link_color': 'FF691F', 'profile_sidebar_border_color': '000000', 'profile_sidebar_fill_color': '000000', 'profile_text_color': '000000', 'profile_use_background_image': False, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': False, 'follow_request_sent': False, 'notifications': False, 'translator_type': 'none', 'withheld_in_countries': []}, 'geo': None, 'coordinates': None, 'place': None, 'contributors': None, 'is_quote_status': False, 'retweet_count': 2, 'favorite_count': 14, 'favorited': False, 'retweeted': False, 'possibly_sensitive': False, 'lang': 'en'}, created_at=datetime.datetime(2021, 7, 13, 12, 38, 21), id=1414927176655269893, id_str='1414927176655269893', text='Coffee Delivery by Driverless @Tesla car. Wow!!! https://t.co/5ZA4OpR1yy', truncated=False, entities={'hashtags': [], 'symbols': [], 'user_mentions': [{'screen_name': 'Tesla', 'name': 'Tesla', 'id': 13298072, 'id_str': '13298072', 'indices': [30, 36]}], 'urls': [], 'media': [{'id': 1414927111530315776, 'id_str': '1414927111530315776', 'indices': [49, 72], 'media_url': 'http://pbs.twimg.com/ext_tw_video_thumb/1414927111530315776/pu/img/DtSKVcI8wwrMDlkk.jpg', 'media_url_https': 'https://pbs.twimg.com/ext_tw_video_thumb/1414927111530315776/pu/img/DtSKVcI8wwrMDlkk.jpg', 'url': 'https://t.co/5ZA4OpR1yy', 'display_url': 'pic.twitter.com/5ZA4OpR1yy', 'expanded_url': 'https://twitter.com/jvidyasagar/status/1414927176655269893/video/1', 'type': 'photo', 'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'large': {'w': 480, 'h': 848, 'resize': 'fit'}, 'small': {'w': 385, 'h': 680, 'resize': 'fit'}, 'medium': {'w': 480, 'h': 848, 'resize': 'fit'}}}]}, extended_entities={'media': [{'id': 1414927111530315776, 'id_str': '1414927111530315776', 'indices': [49, 72], 'media_url': 'http://pbs.twimg.com/ext_tw_video_thumb/1414927111530315776/pu/img/DtSKVcI8wwrMDlkk.jpg', 'media_url_https': 'https://pbs.twimg.com/ext_tw_video_thumb/1414927111530315776/pu/img/DtSKVcI8wwrMDlkk.jpg', 'url': 'https://t.co/5ZA4OpR1yy', 'display_url': 'pic.twitter.com/5ZA4OpR1yy', 'expanded_url': 'https://twitter.com/jvidyasagar/status/1414927176655269893/video/1', 'type': 'video', 'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'large': {'w': 480, 'h': 848, 'resize': 'fit'}, 'small': {'w': 385, 'h': 680, 'resize': 'fit'}, 'medium': {'w': 480, 'h': 848, 'resize': 'fit'}}, 'video_info': {'aspect_ratio': [30, 53], 'duration_millis': 57000, 'variants': [{'content_type': 'application/x-mpegURL', 'url': 'https://video.twimg.com/ext_tw_video/1414927111530315776/pu/pl/XY28Kwv5kdrewIBL.m3u8?tag=12&container=fmp4'}, {'bitrate': 632000, 'content_type': 'video/mp4', 'url': 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'https://t.co/XjkZKokpvU', 'entities': {'url': {'urls': [{'url': 'https://t.co/XjkZKokpvU', 'expanded_url': 'http://local360.in', 'display_url': 'local360.in', 'indices': [0, 23]}]}, 'description': {'urls': []}}, 'protected': False, 'followers_count': 4846, 'friends_count': 2201, 'listed_count': 60, 'created_at': 'Mon Sep 14 17:10:25 +0000 2009', 'favourites_count': 150987, 'utc_offset': None, 'time_zone': None, 'geo_enabled': True, 'verified': False, 'statuses_count': 77693, 'lang': None, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': '000000', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme7/bg.gif', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme7/bg.gif', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/1237018314922012673/PjHnKFU1_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1237018314922012673/PjHnKFU1_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/74205752/1627824563', 'profile_link_color': 'FF691F', 'profile_sidebar_border_color': '000000', 'profile_sidebar_fill_color': '000000', 'profile_text_color': '000000', 'profile_use_background_image': False, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': False, 'follow_request_sent': False, 'notifications': False, 'translator_type': 'none', 'withheld_in_countries': []}, id=74205752, id_str='74205752', name='🇮🇳 Vidyasagar 🇮🇳', screen_name='jvidyasagar', location='Chennai | Hyderabad | Mumbai', description='Traffic Volunteer with @CybTraffic Honoured 2 be flwd by @nsitharamanoffc @rajksinghIndia @smritiiranioffc @mafoikprajan (Tweets are Personal)', url='https://t.co/XjkZKokpvU', entities={'url': {'urls': [{'url': 'https://t.co/XjkZKokpvU', 'expanded_url': 'http://local360.in', 'display_url': 'local360.in', 'indices': [0, 23]}]}, 'description': {'urls': []}}, protected=False, followers_count=4846, friends_count=2201, listed_count=60, created_at=datetime.datetime(2009, 9, 14, 17, 10, 25), favourites_count=150987, utc_offset=None, time_zone=None, geo_enabled=True, verified=False, statuses_count=77693, lang=None, contributors_enabled=False, is_translator=False, is_translation_enabled=False, profile_background_color='000000', profile_background_image_url='http://abs.twimg.com/images/themes/theme7/bg.gif', profile_background_image_url_https='https://abs.twimg.com/images/themes/theme7/bg.gif', profile_background_tile=False, profile_image_url='http://pbs.twimg.com/profile_images/1237018314922012673/PjHnKFU1_normal.jpg', profile_image_url_https='https://pbs.twimg.com/profile_images/1237018314922012673/PjHnKFU1_normal.jpg', profile_banner_url='https://pbs.twimg.com/profile_banners/74205752/1627824563', profile_link_color='FF691F', profile_sidebar_border_color='000000', profile_sidebar_fill_color='000000', profile_text_color='000000', profile_use_background_image=False, has_extended_profile=True, default_profile=False, default_profile_image=False, following=False, follow_request_sent=False, notifications=False, translator_type='none', withheld_in_countries=[]), user=User(_api=<tweepy.api.API object at 0x000001DFFAE06430>, _json={'id': 74205752, 'id_str': '74205752', 'name': '🇮🇳 Vidyasagar 🇮🇳', 'screen_name': 'jvidyasagar', 'location': 'Chennai | Hyderabad | Mumbai', 'description': 'Traffic Volunteer with @CybTraffic Honoured 2 be flwd by @nsitharamanoffc @rajksinghIndia @smritiiranioffc @mafoikprajan (Tweets are Personal)', 'url': 'https://t.co/XjkZKokpvU', 'entities': {'url': {'urls': [{'url': 'https://t.co/XjkZKokpvU', 'expanded_url': 'http://local360.in', 'display_url': 'local360.in', 'indices': [0, 23]}]}, 'description': {'urls': []}}, 'protected': False, 'followers_count': 4846, 'friends_count': 2201, 'listed_count': 60, 'created_at': 'Mon Sep 14 17:10:25 +0000 2009', 'favourites_count': 150987, 'utc_offset': None, 'time_zone': None, 'geo_enabled': True, 'verified': False, 'statuses_count': 77693, 'lang': None, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': '000000', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme7/bg.gif', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme7/bg.gif', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/1237018314922012673/PjHnKFU1_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1237018314922012673/PjHnKFU1_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/74205752/1627824563', 'profile_link_color': 'FF691F', 'profile_sidebar_border_color': '000000', 'profile_sidebar_fill_color': '000000', 'profile_text_color': '000000', 'profile_use_background_image': False, 'has_extended_profile': True, 'default_profile': False, 'default_profile_image': False, 'following': False, 'follow_request_sent': False, 'notifications': False, 'translator_type': 'none', 'withheld_in_countries': []}, id=74205752, id_str='74205752', name='🇮🇳 Vidyasagar 🇮🇳', screen_name='jvidyasagar', location='Chennai | Hyderabad | Mumbai', description='Traffic Volunteer with @CybTraffic Honoured 2 be flwd by @nsitharamanoffc @rajksinghIndia @smritiiranioffc @mafoikprajan (Tweets are Personal)', url='https://t.co/XjkZKokpvU', entities={'url': {'urls': [{'url': 'https://t.co/XjkZKokpvU', 'expanded_url': 'http://local360.in', 'display_url': 'local360.in', 'indices': [0, 23]}]}, 'description': {'urls': []}}, protected=False, followers_count=4846, friends_count=2201, listed_count=60, created_at=datetime.datetime(2009, 9, 14, 17, 10, 25), favourites_count=150987, utc_offset=None, time_zone=None, geo_enabled=True, verified=False, statuses_count=77693, lang=None, contributors_enabled=False, is_translator=False, is_translation_enabled=False, profile_background_color='000000', profile_background_image_url='http://abs.twimg.com/images/themes/theme7/bg.gif', profile_background_image_url_https='https://abs.twimg.com/images/themes/theme7/bg.gif', profile_background_tile=False, profile_image_url='http://pbs.twimg.com/profile_images/1237018314922012673/PjHnKFU1_normal.jpg', profile_image_url_https='https://pbs.twimg.com/profile_images/1237018314922012673/PjHnKFU1_normal.jpg', profile_banner_url='https://pbs.twimg.com/profile_banners/74205752/1627824563', profile_link_color='FF691F', profile_sidebar_border_color='000000', profile_sidebar_fill_color='000000', profile_text_color='000000', profile_use_background_image=False, has_extended_profile=True, default_profile=False, default_profile_image=False, following=False, follow_request_sent=False, notifications=False, translator_type='none', withheld_in_countries=[]), geo=None, coordinates=None, place=None, contributors=None, is_quote_status=False, retweet_count=2, favorite_count=14, favorited=False, retweeted=False, possibly_sensitive=False, lang='en'), retweet_count=0, favorite_count=0, favorited=False, retweeted=False, possibly_sensitive=False, lang='in')\n"
     ]
    }
   ],
   "source": [
    "for tweet in api.search('iPhone'):\n",
    "    print(tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "07c86d21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RT @jiims: GIVEAWAY BRANDNEW IPHONE | 10 DAYS ! 😻✨\n",
      "\n",
      "must be following me &amp;\n",
      "@cryptonning , @cryptoethereal\n",
      "RT + tag mutuals! https://t.co/40…\n",
      "RT @chitaglorya: giveaway 300$ | 4,200,000 IDR \n",
      "iphone 8plus for one winner 💥\n",
      "\n",
      "- retweet follow me &amp; \n",
      "\n",
      "@cryptotahid\n",
      "\n",
      "@cryptovenizo\n",
      "\n",
      "ends in…\n",
      "マイクの件なんだけど昨日ようやくマイク正常に動きまして...\n",
      "それまでマイク認識してなかった笑。iPhoneの内蔵マイクをずっと使ってたという。切ない。\n",
      "\n",
      "配線も複雑で助けてもらいながらなんとか完成してよかった...\n",
      "RT @takegiveson: 📱第21回 iPhone勢支援企画📱\n",
      "\n",
      "夏の還元祭 vol.2🌻\n",
      "\n",
      "iPad Pro 1TB+Magic keybordかpencil\n",
      "→2名🎁\n",
      "\n",
      "iTunesカード10000円分\n",
      "→1名🎁\n",
      "\n",
      "・@takegiveson をフォロー\n",
      "・このツイ…\n",
      "RT @DIPROMOSIKAN: 【ＲＥＴＷＥＥＴ】 #TFBJP\n",
      "#TEAMFOLLOWBACK\n",
      "#TEAMFOLLOWWACK #IPHONE\n",
      "#1000ADAY #TEAMHITFOLLOW #Follow\n",
      "#OPENFOLLOW #Mustfollow #IPOD…\n",
      "RT @jaemcityx: GIVEAWAY $300 | 4.1 JUTA RUPIAH | IPHONE 8 FOR ONE WINNER !!\n",
      "\n",
      "- rt follow me and\n",
      "\n",
      "@promotionfairy \n",
      "@YnahGives \n",
      "@RemiPromotes…\n",
      "モイ！iPhoneからキャス配信中 - / ぱお https://t.co/7citL1ImMm\n",
      "青スライドが速すぎるからiPhoneでやったほうが繋ぎやすいかもなあ\n",
      "RT @OneMinuteBriefs: One Minute Brief of the Day: \n",
      "Create billboard-style posters to show how you can share LIVE the big, small and silly m…\n",
      "RT @LochengJM: IPhone spark 2 at mutasa\n",
      "RT @chitaglorya: giveaway 300$ | 4,200,000 IDR \n",
      "iphone 8plus for one winner 💥\n",
      "\n",
      "- retweet follow me &amp; \n",
      "\n",
      "@cryptotahid\n",
      "\n",
      "@cryptovenizo\n",
      "\n",
      "ends in…\n",
      "RT @livedoornews: 【便利】nanacoが「Apple Pay」に対応へ iPhoneかざして決済可能に\n",
      "https://t.co/wmyYMUw4Lj\n",
      "\n",
      "電子マネー「WAON」と「nanaco」がApple Payに対応することが発表された。いずれも年内のサー…\n",
      "RT @xionback: GIVEAWAY $200 || 2.800.000 IDR || IPHONE 6s PLUS FOR ONE WINNER !! \n",
      "\n",
      "- RT\n",
      "- Follow @DrTokenomics\n",
      "\n",
      " Ends in 24 hours, good luc…\n",
      "昨日の散歩途中、iPhoneでぱしゃり📸\n",
      "\n",
      "#カメラ男子 #写真で伝える私の世界 #写真好きな人と繫がりたい #カコソラ　#写真　#カメラ好きと繋がりたい #カメラ好きな人と繫がりたい #カメラ https://t.co/6SEHi3NaiC\n",
      "RT @Bang_Garr: $300 CRYPTO || 4 JUTA IDR\n",
      "|| one winner || iPhone 8 || Garangan 04 ||\n",
      "\n",
      "Retweet, follow me and \n",
      "\n",
      "@thebossofbali\n",
      "\n",
      "Winner will…\n"
     ]
    }
   ],
   "source": [
    "for tweet in api.search('iPhone'):\n",
    "    print (tweet.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cecb27ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(columns = ['Tweets','Tweet_id','User', 'User_id', 'User_statuses_count', 'User followers', 'User_friends_count', 'User_location', 'fav_count', 'rt_count',\n",
    "'tweet_date', 'User_verified'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "27a2ba8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream(data, file_name):\n",
    "    i=0\n",
    "    for tweet in tweepy. Cursor(api.search, q=data, count=100, lang='en').items():\n",
    "        print (i, end='\\r')\n",
    "        df.loc[i, 'Tweets'] = tweet.text \n",
    "        df.loc[i, 'Tweet_id'] = tweet.id\n",
    "        #df. Loc[i, Hashtags'] = tweet. entities.hashtags\n",
    "        df.loc[i, 'User'] = tweet.user.name\n",
    "        df.loc[i, 'User_id'] = tweet.user.id\n",
    "        df.loc[i, 'User_statuses_count'] = tweet.user.statuses_count \n",
    "        df.loc[i, 'User_followers'] = tweet.user.followers_count\n",
    "        df.loc[i, 'User_friends_count']=tweet.user.friends_count\n",
    "        df.loc[i, 'User_location'] = tweet.user.location\n",
    "        df.loc[i, 'fav_count'] = tweet.favorite_count\n",
    "        df.loc[i, 'rt_count'] = tweet.retweet_count\n",
    "        df.loc[i, 'tweet_date'] = tweet.created_at\n",
    "        df.loc[i, 'User_verified'] = tweet.user.verified\n",
    "        df.to_csv('{}.csv'.format(file_name))\n",
    "        i+=1\n",
    "        if i== 2000:\n",
    "               break\n",
    "        else:\n",
    "               pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4cbb4b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "418\r"
     ]
    }
   ],
   "source": [
    " stream(data = ['iPhoneX'], file_name = 'my_tweets_2k')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3b1809fc",
   "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>Tweets</th>\n",
       "      <th>Tweet_id</th>\n",
       "      <th>User</th>\n",
       "      <th>User_id</th>\n",
       "      <th>User_statuses_count</th>\n",
       "      <th>User followers</th>\n",
       "      <th>User_friends_count</th>\n",
       "      <th>User_location</th>\n",
       "      <th>fav_count</th>\n",
       "      <th>rt_count</th>\n",
       "      <th>tweet_date</th>\n",
       "      <th>User_verified</th>\n",
       "      <th>User_followers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@O629_k_ iPhoneSE＋iPhoneX＝？</td>\n",
       "      <td>1425014174069886985</td>\n",
       "      <td>オーロラリティー</td>\n",
       "      <td>1263153611007115265</td>\n",
       "      <td>275</td>\n",
       "      <td>NaN</td>\n",
       "      <td>201</td>\n",
       "      <td>→waseda?</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-08-10 08:40:29</td>\n",
       "      <td>False</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@kjenslave Legit plus pre debut haruto his own...</td>\n",
       "      <td>1425003186666885120</td>\n",
       "      <td>Prince Haruto🦋</td>\n",
       "      <td>1265638750337429504</td>\n",
       "      <td>6260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>911</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-08-10 07:56:49</td>\n",
       "      <td>False</td>\n",
       "      <td>827.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@Apple hi iam using iPhoneX. I was updated iOS...</td>\n",
       "      <td>1425001795512717317</td>\n",
       "      <td>pasith</td>\n",
       "      <td>1164923482662211584</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-08-10 07:51:17</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT @Qu_entinD: SERIES =&amp;gt; Today is a new wal...</td>\n",
       "      <td>1424989217936060433</td>\n",
       "      <td>DomCub</td>\n",
       "      <td>1238179956611571718</td>\n",
       "      <td>2468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166</td>\n",
       "      <td>Frankfurt am Main, Deutschland</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-08-10 07:01:19</td>\n",
       "      <td>False</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blender Timelapse | modeling and rendering iPh...</td>\n",
       "      <td>1424974306199576577</td>\n",
       "      <td>Bram</td>\n",
       "      <td>3312108100</td>\n",
       "      <td>6410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>860</td>\n",
       "      <td>Rotterdam, Zuid-Holland</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-08-10 06:02:03</td>\n",
       "      <td>False</td>\n",
       "      <td>1308.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets             Tweet_id  \\\n",
       "0                        @O629_k_ iPhoneSE＋iPhoneX＝？  1425014174069886985   \n",
       "1  @kjenslave Legit plus pre debut haruto his own...  1425003186666885120   \n",
       "2  @Apple hi iam using iPhoneX. I was updated iOS...  1425001795512717317   \n",
       "3  RT @Qu_entinD: SERIES =&gt; Today is a new wal...  1424989217936060433   \n",
       "4  Blender Timelapse | modeling and rendering iPh...  1424974306199576577   \n",
       "\n",
       "             User              User_id User_statuses_count User followers  \\\n",
       "0        オーロラリティー  1263153611007115265                 275            NaN   \n",
       "1  Prince Haruto🦋  1265638750337429504                6260            NaN   \n",
       "2          pasith  1164923482662211584                  14            NaN   \n",
       "3          DomCub  1238179956611571718                2468            NaN   \n",
       "4            Bram           3312108100                6410            NaN   \n",
       "\n",
       "  User_friends_count                   User_location fav_count rt_count  \\\n",
       "0                201                        →waseda?         1        0   \n",
       "1                911                                         0        0   \n",
       "2                 16                                         0        0   \n",
       "3                166  Frankfurt am Main, Deutschland         0        3   \n",
       "4                860         Rotterdam, Zuid-Holland         1        0   \n",
       "\n",
       "            tweet_date User_verified  User_followers  \n",
       "0  2021-08-10 08:40:29         False            27.0  \n",
       "1  2021-08-10 07:56:49         False           827.0  \n",
       "2  2021-08-10 07:51:17         False             0.0  \n",
       "3  2021-08-10 07:01:19         False            88.0  \n",
       "4  2021-08-10 06:02:03         False          1308.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3624d9af",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'wordcloud'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-56-e5a01fa5208a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtextblob\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mTextBlob\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mwordcloud\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mWordCloud\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSTOPWORDS\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'wordcloud'"
     ]
    }
   ],
   "source": [
    "from textblob import TextBlob \n",
    "from wordcloud import WordCloud, STOPWORDS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "98927aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "def clean_tweet (tweet):\n",
    "    return ' '.join(re.sub('(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)| (https?:\\/\\/.*[\\r\\n]*)','',tweet).split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "51e14a42",
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_sentiment (tweet):\n",
    "    analysis = TextBlob(tweet)\n",
    "    if analysis. sentiment.polarity > 8: return 4\n",
    "    elif analysis. sentiment.polarity ==0:\n",
    "        return '2'\n",
    "    else:\n",
    "        return 'e'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9aaa634b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['clean_tweet'] = df[ 'Tweets'].apply(lambda x: clean_tweet(x)) \n",
    "df['Sentiment'] = df[ 'clean_tweet'].apply(lambda x: analyze_sentiment (x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f7793762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original tweet: \n",
      "@ItsSKhan22 @Amph1trit3 The iPhone 8 was an outlier. Apples 2017 flagship was the iPhone X (10th anniversary phone)… https://t.co/De1f5Z5Nhd\n",
      "\n",
      "Clean tweet:\n",
      "The iPhone 8 was an outlier Apples 2017 flagship was the iPhone X 10th anniversary phone\n",
      "\n",
      "Sentiment:\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "n=12 \n",
    "print ('Original tweet: \\n' + df['Tweets'][n]) \n",
    "print() \n",
    "print('Clean tweet:\\n'+df['clean_tweet'][n]) \n",
    "print() \n",
    "print('Sentiment:\\n'+df[ 'Sentiment' ][n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5d435d72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2' 'e']\n",
      "['2' 'e']\n",
      "2    235\n",
      "e    184\n",
      "Name: Sentiment, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['Sentiment'].unique ())\n",
    "#df['Sentiment']=df['Sentiment'].astype(int) \n",
    "print(df['Sentiment'].unique())\n",
    "print(df['Sentiment'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "372e348b",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_tweets=''.join(tweet for tweet in df['clean_tweet'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8569d42b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#wordcloud WordCloud (stopwords=STOPWORDS).generate (all_tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5019e264",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c3c9525a",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOL while scanning string literal (<ipython-input-88-c0064d309ac0>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-88-c0064d309ac0>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    plt.imshow (wordcloud, interpolation='bilinear\")\u001b[0m\n\u001b[1;37m                                                    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m EOL while scanning string literal\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize (10,10))\n",
    "plt.imshow (wordcloud, interpolation='bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show("
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "42b03dc5",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-89-09a7d35b8d3e>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-89-09a7d35b8d3e>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    df_freq pd.DataFrame.from_dict(data=wordcloud.word_,orinet='index')\u001b[0m\n\u001b[1;37m            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "df_freq pd.DataFrame.from_dict(data=wordcloud.word_,orinet='index')\n",
    "df_freq=df_freq.head (20) \n",
    "df_freq.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ef9f209f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 419 entries, 0 to 418\n",
      "Data columns (total 15 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Tweets               419 non-null    object \n",
      " 1   Tweet_id             419 non-null    object \n",
      " 2   User                 419 non-null    object \n",
      " 3   User_id              419 non-null    object \n",
      " 4   User_statuses_count  419 non-null    object \n",
      " 5   User followers       0 non-null      object \n",
      " 6   User_friends_count   419 non-null    object \n",
      " 7   User_location        419 non-null    object \n",
      " 8   fav_count            419 non-null    object \n",
      " 9   rt_count             419 non-null    object \n",
      " 10  tweet_date           419 non-null    object \n",
      " 11  User_verified        419 non-null    object \n",
      " 12  User_followers       419 non-null    float64\n",
      " 13  clean_tweet          419 non-null    object \n",
      " 14  Sentiment            419 non-null    object \n",
      "dtypes: float64(1), object(14)\n",
      "memory usage: 68.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8a6cf8d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['→waseda?' '' 'Frankfurt am Main, Deutschland' 'Rotterdam, Zuid-Holland'\n",
      " 'North Western, Zambia' 'Los Angeles, CA' 'USA' 'Japan' 'ilesha,Nigeria'\n",
      " 'Ghana' 'Greater Accra, Ghana' 'Prampram ' '♡🐈🐈♡' 'New York' 'Canada'\n",
      " 'Richmond Hill Ontario, Canada' 'The Phynest' 'Mind Your Business'\n",
      " 'Greater Accra, Ghana ' 'Near us' 'IDIOT,GO AHEAD AND BLOCK ME'\n",
      " 'Adenta, Accra ' 'United States' 'France' 'Bengaluru, India' 'usa'\n",
      " 'Indonesia' '八王子の玉座' 'Auckland, New Zealand' 'Kumasi, Ghana📍'\n",
      " 'Taichung City, Taiwan' 'Ahmadabad City, Gujarat' '新潟'\n",
      " 'Dilli hai meri jaan' 'Edinburgh, Scotland' 'Shenzhen,China'\n",
      " 'आज़मगढ़, भारत' 'Turín - Italia' 'Edinburgh ' 'Seoul, Korea'\n",
      " 'Stockholm, Suède' 'Buston, USA' 'California, USA' 'Chicago, USA' 'Earth'\n",
      " 'Lagos, Nigeria ' 'Multan, Pakistan' 'Montevideo, Uruguay' 'Florida, USA'\n",
      " 'Bedford, MA' 'Bwari, Nigeria' 'EARTH' 'Toronto, Ontario' 'shah alam'\n",
      " '#LindseyGrahamIsGAY' 'Dhaka, Bangladesh' 'Rio de janeiro'\n",
      " 'Lagos, Nigeria' 'World' 'India' ' 🇬🇭🇳🇬' 'Washington, DC, USA'\n",
      " 'rightchyear' 'Abuja, Nigeria' 'Sportybet' 'Happiness'\n",
      " 'Abuja, Lagos, Makurdi' 'Johannesburg' 'Nigeria' 'Probably ahead of you'\n",
      " 'Accra, Ghana' 'ebe obula ego di' 'Poland, OH' 'Massachusetts, USA ijn'\n",
      " 'Ijoko, Nigeria' 'South Africa' 'Pretoria, South Africa' 'Alkebulan'\n",
      " 'Port Elizabeth || Pretoria🇿🇦' 'Johannesburg, South Africa|PTA'\n",
      " 'Middelburg, South Africa' 'on her mind ' 'Manila City' 'Taiwan.'\n",
      " 'Houston, TX' 'Sydney' 'AC - GH' 'Uganda'\n",
      " 'he/him | 🏳️\\u200d⚧️ | 🏳️\\u200d🌈' '29 Kotobabi RD' 'Kampala Uganda' '日本 '\n",
      " 'Christ ' 'Ho' 'in Christ Jesus.' 'Kampala, Uganda' 'CometLakeおじさんファンクラブ'\n",
      " 'Cologne, Germany' 'UK' 'السيب, سلطنة عمان' '東京 中央区' 'Italia' '日本 東京'\n",
      " 'Vandoies' 'DISCOVER JAPAN 2' 'SF, California' 'Delhi' 'Global'\n",
      " 'Santa Ana, CA' '大阪〜埼玉' 'jakarta timur ' '北陸' '@joosehni' '岩手県'\n",
      " 'お天気ならどこかで風に乗ってますっ' '湯長谷藩' 'Internet' 'Somewhere' 'Abuja'\n",
      " 'Shadow Of His Wings' 'Lagos, PH, Abuja,Enugu Nigeria' 'Lagos'\n",
      " 'Ontario, Canada In Jesus Name' 'Newcastle' 'Nord-Ouest, Cameroon'\n",
      " 'Osun state' 'Ibadan, Nigeria' 'Lagos, Ibadan Nigeria' 'Ilorin, Nigeria'\n",
      " 'where there is money' 'Ghana,Nigeria ' 'Manchester, England'\n",
      " 'LA 𝘰𝘧 GOS.' 'Kasland City' 'Jacksonville Beach, FL' 'Ogun, Nigeria'\n",
      " 'Somewhere in the 🌍' 'Twitter NG' 'Canada in jesus name' 'Lagos, Enugu '\n",
      " '(#abeg) #whoizezay' 'Planet Earth' '🌍' 'Here and There ' 'PH CITY'\n",
      " 'Computer Village,Ikeja' 'Federal Capital Territory, Nig' 'Ikeja, Lagos'\n",
      " 'London' 'Whitby, Ontario' '浜松町文化放送の隣の1番線京浜東北線(東京・上野・大宮方面)' 'Web'\n",
      " 'Verona, Veneto' 'Agra, India' 'Melbourne, Victoria' '127.0.0.1'\n",
      " 'Based in Dubai' '\\uf8ff' 'Newcastle, South Africa' 'São Paulo - Brasil '\n",
      " 'Tahiti' 'La planète Terre' 'Klang MYS' 'WorldWide 🌎'\n",
      " 'Nachikatsuura-cho, Wakayama' 'Guangdong,China' 'Rossia'\n",
      " '@xxaphrodite_07' '埼玉県' 'Asheville, NC' 'Seattle' 'Nairobi, Kenya'\n",
      " 'influencer ' 'Free Shipping Worldwide' '4 Oshitelu Street Comp Village'\n",
      " 'Luswaaa_ville,Uganda' 'North Carolina' 'New York, USA' 'Mbarara' 'Пермь']\n"
     ]
    }
   ],
   "source": [
    "print (df['User_location'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "f46c90d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['User_statuses_count'] = df['User_statuses_count'].astype (int)\n",
    "df['User_followers'] = df['User_followers'].astype(int)\n",
    "df[ 'User_friends_count'] = df [ 'User_friends_count'].astype (int) \n",
    "df['rt_count'] = df['rt_count'].astype (int)\n",
    "df[ 'fav_count'] = df['fav_count'].astype (int)\n",
    "df['tweet_date_mod'] = pd.to_datetime(df['tweet_date'], format='[%d/%b/%Y: %H:%M:%S]') \n",
    "# set date time to pandas datatime obj\n",
    "df =df.drop(['tweet_date'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "20b07e35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 419 entries, 0 to 418\n",
      "Data columns (total 15 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   Tweets               419 non-null    object        \n",
      " 1   Tweet_id             419 non-null    object        \n",
      " 2   User                 419 non-null    object        \n",
      " 3   User_id              419 non-null    object        \n",
      " 4   User_statuses_count  419 non-null    int32         \n",
      " 5   User followers       0 non-null      object        \n",
      " 6   User_friends_count   419 non-null    int32         \n",
      " 7   User_location        419 non-null    object        \n",
      " 8   fav_count            419 non-null    int32         \n",
      " 9   rt_count             419 non-null    int32         \n",
      " 10  User_verified        419 non-null    object        \n",
      " 11  User_followers       419 non-null    int32         \n",
      " 12  clean_tweet          419 non-null    object        \n",
      " 13  Sentiment            419 non-null    object        \n",
      " 14  tweet_date_mod       419 non-null    datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int32(5), object(9)\n",
      "memory usage: 60.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c029c914",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[    275    6260      14    2468    6410     290  302068    4496   67982\n",
      "    4094    3587       1    1011  121912   11371    1682  118876    9112\n",
      "       5   33648     845   45637  105884  151964   77483     760  216743\n",
      "   58312    2053   85204      12   16849    4151    3762  436032  152457\n",
      "      10     167   34876      11    1014   10765     270   14232     190\n",
      "    2691   17909 1545538    2479   25950  584826   71303      88   50281\n",
      "      59    1663   76943    3934      60   11787   48360    5032    1101\n",
      "    6958      78      40    1683      97    8552     139     450       3\n",
      "  138899    1258   91795     206     981   77952  108589    6765   49098\n",
      "    2250   22919    4070      86  494164    4026     476   11243      39\n",
      "     209  108382      61    2278   62886   20193   43669   10851    1967\n",
      "   95382  201864     214   18394   38206   39410    7373   13051   15500\n",
      "   70852    7101  225247   12519    9472   84229   11960     991   16760\n",
      "  112709    1313   17999     934   13203     917     147      57     358\n",
      "    2069     972   13149    6818  283338  102860    6636     929    9707\n",
      "   36284      15   12885   45847   39925   62800    9447   55656   18347\n",
      "    9041      29   25271    7299     296   24166  105830   28577   72792\n",
      "   20970    1669   29546   43097   23605    4549   92263  120359    4052\n",
      "  153022   11418   13703   27577    3111     252  283042   89648      19\n",
      "    7482   10147   61935    6228    1332    6694   18542   27870  256365\n",
      "   29668   45320    1448   10184    1210  318223    5544    1916   27749\n",
      "    4107   24682   40722  106661   15127  109528   24160   73247   19246\n",
      "    9426   36078   24370   92972      71   11988   60615  184583    3630\n",
      "   50757    6156   10574    1536   73346      24   15656   25387    1375\n",
      "   20496   27362   49806   43273   13688   49652    2864    9755   17267\n",
      "   41863    7182  109682   43353    1846   51379   41464   15309  432915\n",
      "   55299   11966    2916   27169   18057   31669   13229   37867   13119\n",
      "   66448    3399    7918   67465   36585     302    7954    2519   34201\n",
      "    1414   76795     128    1394    3715    1389  271420    1361    1520\n",
      "   67896    1726    7588    1000   21641     183      76   19304   16098\n",
      "     107  201319   12824   49903  299183   67133   51021    3705    2530\n",
      "    6053     649      99   66796   35401   18429    1089     641    3194\n",
      "  487533     447    2786   25757   12415      55      17     254  115511\n",
      "    7287    1528   21073      42   17068    1372    7364   47392      31\n",
      "     303   12280    2064    8944]\n",
      "152457    18\n",
      "2053       9\n",
      "15         7\n",
      "2691       6\n",
      "88         5\n",
      "          ..\n",
      "206        1\n",
      "70852      1\n",
      "209        1\n",
      "9426       1\n",
      "6156       1\n",
      "Name: User_statuses_count, Length: 310, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['User_statuses_count'].unique()) \n",
    "print(df['User_statuses_count'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e604a418",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\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 seaborn as sns\n",
    "dg=sns.distplot(df['rt_count'].dropna ())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1ff159bc",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "dg=sns.countplot(data=df, x='User_statuses_count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "88556824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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.boxplot(x=\"Sentiment\", y=\"fav_count\", data=df)\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(x=\"Sentiment\", y=\"rt_count\", data=df) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "45c28154",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pyspark modules\n",
    "from pyspark.context import SparkContext\n",
    "from pyspark.sql.session import SparkSession\n",
    "from pyspark.sql.types import *\n",
    "from pyspark.sql.functions import *\n",
    "from pyspark.ml.classification import LogisticRegression\n",
    "from pyspark.ml.feature import HashingTF, Tokenizer, StopWordsRemover\n",
    "#create Spark session\n",
    "appName = \"Sentiment Analysis in Spark\"\n",
    "spark = SparkSession \\\n",
    "  .builder \\\n",
    "  .appName(appName) \\\n",
    "  .config(\"spark.some.config.option\", \"some-value\") \\\n",
    "  .getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "44d33ece",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Tweets: string (nullable = true)\n",
      " |-- Tweet_id: long (nullable = true)\n",
      " |-- User: string (nullable = true)\n",
      " |-- User_id: long (nullable = true)\n",
      " |-- User_statuses_count: long (nullable = true)\n",
      " |-- User followers: double (nullable = true)\n",
      " |-- User_friends_count: long (nullable = true)\n",
      " |-- User_location: string (nullable = true)\n",
      " |-- fav_count: long (nullable = true)\n",
      " |-- rt_count: long (nullable = true)\n",
      " |-- User_verified: boolean (nullable = true)\n",
      " |-- User_followers: long (nullable = true)\n",
      " |-- clean_tweet: string (nullable = true)\n",
      " |-- Sentiment: string (nullable = true)\n",
      " |-- tweet_date_mod: timestamp (nullable = true)\n",
      "\n"
     ]
    },
    {
     "ename": "Py4JJavaError",
     "evalue": "An error occurred while calling o173.showString.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 15.0 failed 1 times, most recent failure: Lost task 0.0 in stage 15.0 (TID 25) (Prakanshurockss executor driver): java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 31 more\r\n\nDriver stacktrace:\r\n\tat org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2258)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2207)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2206)\r\n\tat scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)\r\n\tat scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)\r\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2206)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1079)\r\n\tat scala.Option.foreach(Option.scala:407)\r\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2445)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2387)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2376)\r\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2196)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2217)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2236)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:472)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:425)\r\n\tat org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)\r\n\tat org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3696)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2722)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3687)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)\r\n\tat org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)\r\n\tat org.apache.spark.sql.Dataset.withAction(Dataset.scala:3685)\r\n\tat org.apache.spark.sql.Dataset.head(Dataset.scala:2722)\r\n\tat org.apache.spark.sql.Dataset.take(Dataset.scala:2929)\r\n\tat org.apache.spark.sql.Dataset.getRows(Dataset.scala:301)\r\n\tat org.apache.spark.sql.Dataset.showString(Dataset.scala:338)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)\r\n\tat java.lang.reflect.Method.invoke(Unknown Source)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\t... 1 more\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 31 more\r\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPy4JJavaError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-137-de464085d10d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mtweets_csv\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mspark\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreateDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mtweets_csv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprintSchema\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----> 3\u001b[1;33m \u001b[0mtweets_csv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtruncate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pyspark\\sql\\dataframe.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(self, n, truncate, vertical)\u001b[0m\n\u001b[0;32m    484\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshowString\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvertical\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[0;32m    485\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 486\u001b[1;33m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshowString\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtruncate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvertical\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    487\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    488\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\py4j\\java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m   1302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1303\u001b[0m         \u001b[0manswer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1304\u001b[1;33m         return_value = get_return_value(\n\u001b[0m\u001b[0;32m   1305\u001b[0m             answer, self.gateway_client, self.target_id, self.name)\n\u001b[0;32m   1306\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pyspark\\sql\\utils.py\u001b[0m in \u001b[0;36mdeco\u001b[1;34m(*a, **kw)\u001b[0m\n\u001b[0;32m    109\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\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[0;32m    110\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\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    112\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m             \u001b[0mconverted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\py4j\\protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[1;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[0;32m    324\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mOUTPUT_CONVERTER\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0manswer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgateway_client\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    325\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0manswer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mREFERENCE_TYPE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 326\u001b[1;33m                 raise Py4JJavaError(\n\u001b[0m\u001b[0;32m    327\u001b[0m                     \u001b[1;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    328\u001b[0m                     format(target_id, \".\", name), value)\n",
      "\u001b[1;31mPy4JJavaError\u001b[0m: An error occurred while calling o173.showString.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 15.0 failed 1 times, most recent failure: Lost task 0.0 in stage 15.0 (TID 25) (Prakanshurockss executor driver): java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 31 more\r\n\nDriver stacktrace:\r\n\tat org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2258)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2207)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2206)\r\n\tat scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)\r\n\tat scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)\r\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2206)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1079)\r\n\tat scala.Option.foreach(Option.scala:407)\r\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2445)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2387)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2376)\r\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2196)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2217)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2236)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:472)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:425)\r\n\tat org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)\r\n\tat org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3696)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2722)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3687)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)\r\n\tat org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)\r\n\tat org.apache.spark.sql.Dataset.withAction(Dataset.scala:3685)\r\n\tat org.apache.spark.sql.Dataset.head(Dataset.scala:2722)\r\n\tat org.apache.spark.sql.Dataset.take(Dataset.scala:2929)\r\n\tat org.apache.spark.sql.Dataset.getRows(Dataset.scala:301)\r\n\tat org.apache.spark.sql.Dataset.showString(Dataset.scala:338)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)\r\n\tat java.lang.reflect.Method.invoke(Unknown Source)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\t... 1 more\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 31 more\r\n"
     ]
    }
   ],
   "source": [
    "tweets_csv =spark.createDataFrame(df) \n",
    "tweets_csv.printSchema () \n",
    "tweets_csv.show(truncate=False, n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "c98dfa5c",
   "metadata": {},
   "outputs": [
    {
     "ename": "Py4JJavaError",
     "evalue": "An error occurred while calling o142.describe.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 13.0 failed 1 times, most recent failure: Lost task 0.0 in stage 13.0 (TID 21) (Prakanshurockss executor driver): java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 36 more\r\n\nDriver stacktrace:\r\n\tat org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2258)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2207)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2206)\r\n\tat scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)\r\n\tat scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)\r\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2206)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1079)\r\n\tat scala.Option.foreach(Option.scala:407)\r\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2445)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2387)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2376)\r\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2196)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2217)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2236)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2261)\r\n\tat org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1030)\r\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\r\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\r\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:414)\r\n\tat org.apache.spark.rdd.RDD.collect(RDD.scala:1029)\r\n\tat org.apache.spark.sql.execution.stat.StatFunctions$.aggResult$lzycompute$1(StatFunctions.scala:274)\r\n\tat org.apache.spark.sql.execution.stat.StatFunctions$.aggResult$1(StatFunctions.scala:274)\r\n\tat org.apache.spark.sql.execution.stat.StatFunctions$.$anonfun$summary$16(StatFunctions.scala:287)\r\n\tat scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:158)\r\n\tat org.apache.spark.sql.execution.stat.StatFunctions$.summary(StatFunctions.scala:286)\r\n\tat org.apache.spark.sql.Dataset.summary(Dataset.scala:2711)\r\n\tat org.apache.spark.sql.Dataset.describe(Dataset.scala:2649)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)\r\n\tat java.lang.reflect.Method.invoke(Unknown Source)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\t... 1 more\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 36 more\r\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPy4JJavaError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-135-4aae0946eb6a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtweets_csv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoPandas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\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[1;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pyspark\\sql\\dataframe.py\u001b[0m in \u001b[0;36mdescribe\u001b[1;34m(self, *cols)\u001b[0m\n\u001b[0;32m   1489\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\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[0;32m   1490\u001b[0m             \u001b[0mcols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcols\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[1;32m-> 1491\u001b[1;33m         \u001b[0mjdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jseq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols\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   1492\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msql_ctx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1493\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\py4j\\java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m   1302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1303\u001b[0m         \u001b[0manswer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1304\u001b[1;33m         return_value = get_return_value(\n\u001b[0m\u001b[0;32m   1305\u001b[0m             answer, self.gateway_client, self.target_id, self.name)\n\u001b[0;32m   1306\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pyspark\\sql\\utils.py\u001b[0m in \u001b[0;36mdeco\u001b[1;34m(*a, **kw)\u001b[0m\n\u001b[0;32m    109\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\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[0;32m    110\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\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    112\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m             \u001b[0mconverted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\py4j\\protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[1;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[0;32m    324\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mOUTPUT_CONVERTER\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0manswer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgateway_client\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    325\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0manswer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mREFERENCE_TYPE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 326\u001b[1;33m                 raise Py4JJavaError(\n\u001b[0m\u001b[0;32m    327\u001b[0m                     \u001b[1;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    328\u001b[0m                     format(target_id, \".\", name), value)\n",
      "\u001b[1;31mPy4JJavaError\u001b[0m: An error occurred while calling o142.describe.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 13.0 failed 1 times, most recent failure: Lost task 0.0 in stage 13.0 (TID 21) (Prakanshurockss executor driver): java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 36 more\r\n\nDriver stacktrace:\r\n\tat org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2258)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2207)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2206)\r\n\tat scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)\r\n\tat scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)\r\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2206)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1079)\r\n\tat scala.Option.foreach(Option.scala:407)\r\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2445)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2387)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2376)\r\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2196)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2217)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2236)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2261)\r\n\tat org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1030)\r\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\r\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\r\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:414)\r\n\tat org.apache.spark.rdd.RDD.collect(RDD.scala:1029)\r\n\tat org.apache.spark.sql.execution.stat.StatFunctions$.aggResult$lzycompute$1(StatFunctions.scala:274)\r\n\tat org.apache.spark.sql.execution.stat.StatFunctions$.aggResult$1(StatFunctions.scala:274)\r\n\tat org.apache.spark.sql.execution.stat.StatFunctions$.$anonfun$summary$16(StatFunctions.scala:287)\r\n\tat scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:158)\r\n\tat org.apache.spark.sql.execution.stat.StatFunctions$.summary(StatFunctions.scala:286)\r\n\tat org.apache.spark.sql.Dataset.summary(Dataset.scala:2711)\r\n\tat org.apache.spark.sql.Dataset.describe(Dataset.scala:2649)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)\r\n\tat java.lang.reflect.Method.invoke(Unknown Source)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\t... 1 more\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 36 more\r\n"
     ]
    }
   ],
   "source": [
    "tweets_csv.describe().toPandas().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "b9e9b256",
   "metadata": {},
   "outputs": [
    {
     "ename": "Py4JJavaError",
     "evalue": "An error occurred while calling o81.showString.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 6.0 failed 1 times, most recent failure: Lost task 0.0 in stage 6.0 (TID 10) (Prakanshurockss executor driver): java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 31 more\r\n\nDriver stacktrace:\r\n\tat org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2258)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2207)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2206)\r\n\tat scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)\r\n\tat scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)\r\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2206)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1079)\r\n\tat scala.Option.foreach(Option.scala:407)\r\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2445)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2387)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2376)\r\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2196)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2217)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2236)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:472)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:425)\r\n\tat org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)\r\n\tat org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3696)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2722)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3687)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)\r\n\tat org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)\r\n\tat org.apache.spark.sql.Dataset.withAction(Dataset.scala:3685)\r\n\tat org.apache.spark.sql.Dataset.head(Dataset.scala:2722)\r\n\tat org.apache.spark.sql.Dataset.take(Dataset.scala:2929)\r\n\tat org.apache.spark.sql.Dataset.getRows(Dataset.scala:301)\r\n\tat org.apache.spark.sql.Dataset.showString(Dataset.scala:338)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)\r\n\tat java.lang.reflect.Method.invoke(Unknown Source)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\t... 1 more\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 31 more\r\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPy4JJavaError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-128-031fb61a70ea>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#selecting clean_tweets and SentimentLabel variables\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtweets_csv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"clean_tweet\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Sentiment\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Int\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malias\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"label\"\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----> 3\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtruncate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pyspark\\sql\\dataframe.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(self, n, truncate, vertical)\u001b[0m\n\u001b[0;32m    484\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshowString\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvertical\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[0;32m    485\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 486\u001b[1;33m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshowString\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtruncate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvertical\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    487\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    488\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\py4j\\java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m   1302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1303\u001b[0m         \u001b[0manswer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1304\u001b[1;33m         return_value = get_return_value(\n\u001b[0m\u001b[0;32m   1305\u001b[0m             answer, self.gateway_client, self.target_id, self.name)\n\u001b[0;32m   1306\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pyspark\\sql\\utils.py\u001b[0m in \u001b[0;36mdeco\u001b[1;34m(*a, **kw)\u001b[0m\n\u001b[0;32m    109\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\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[0;32m    110\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\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    112\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m             \u001b[0mconverted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\py4j\\protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[1;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[0;32m    324\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mOUTPUT_CONVERTER\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0manswer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgateway_client\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    325\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0manswer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mREFERENCE_TYPE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 326\u001b[1;33m                 raise Py4JJavaError(\n\u001b[0m\u001b[0;32m    327\u001b[0m                     \u001b[1;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    328\u001b[0m                     format(target_id, \".\", name), value)\n",
      "\u001b[1;31mPy4JJavaError\u001b[0m: An error occurred while calling o81.showString.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 6.0 failed 1 times, most recent failure: Lost task 0.0 in stage 6.0 (TID 10) (Prakanshurockss executor driver): java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 31 more\r\n\nDriver stacktrace:\r\n\tat org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2258)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2207)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2206)\r\n\tat scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)\r\n\tat scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)\r\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2206)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1079)\r\n\tat scala.Option.foreach(Option.scala:407)\r\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2445)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2387)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2376)\r\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2196)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2217)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2236)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:472)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:425)\r\n\tat org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)\r\n\tat org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3696)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2722)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3687)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)\r\n\tat org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)\r\n\tat org.apache.spark.sql.Dataset.withAction(Dataset.scala:3685)\r\n\tat org.apache.spark.sql.Dataset.head(Dataset.scala:2722)\r\n\tat org.apache.spark.sql.Dataset.take(Dataset.scala:2929)\r\n\tat org.apache.spark.sql.Dataset.getRows(Dataset.scala:301)\r\n\tat org.apache.spark.sql.Dataset.showString(Dataset.scala:338)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)\r\n\tat java.lang.reflect.Method.invoke(Unknown Source)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\t... 1 more\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 31 more\r\n"
     ]
    }
   ],
   "source": [
    "#selecting clean_tweets and SentimentLabel variables\n",
    "data=tweets_csv.select(\"clean_tweet\", col(\"Sentiment\").cast(\"Int\").alias(\"label\"))\n",
    "data.show(truncate=False,n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "bde4005a",
   "metadata": {},
   "outputs": [
    {
     "ename": "Py4JJavaError",
     "evalue": "An error occurred while calling o70.count.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 2 in stage 4.0 failed 1 times, most recent failure: Lost task 2.0 in stage 4.0 (TID 8) (Prakanshurockss executor driver): java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 33 more\r\n\nDriver stacktrace:\r\n\tat org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2258)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2207)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2206)\r\n\tat scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)\r\n\tat scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)\r\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2206)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1079)\r\n\tat scala.Option.foreach(Option.scala:407)\r\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2445)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2387)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2376)\r\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2196)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2217)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2236)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2261)\r\n\tat org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1030)\r\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\r\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\r\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:414)\r\n\tat org.apache.spark.rdd.RDD.collect(RDD.scala:1029)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:390)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$count$1(Dataset.scala:3006)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$count$1$adapted(Dataset.scala:3005)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3687)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)\r\n\tat org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)\r\n\tat org.apache.spark.sql.Dataset.withAction(Dataset.scala:3685)\r\n\tat org.apache.spark.sql.Dataset.count(Dataset.scala:3005)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)\r\n\tat java.lang.reflect.Method.invoke(Unknown Source)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\t... 1 more\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 33 more\r\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPy4JJavaError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-127-eba7289cd88c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mtrainingData\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdividedData\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#index 0 data training\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mtestingData\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdividedData\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#index 1 = data testing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mtrain_rows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrainingData\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\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      6\u001b[0m \u001b[0mtest_rows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtestingData\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\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[0;32m      7\u001b[0m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"Training data rows:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_rows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"; Testing data rows:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_rows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pyspark\\sql\\dataframe.py\u001b[0m in \u001b[0;36mcount\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    662\u001b[0m         \u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    663\u001b[0m         \"\"\"\n\u001b[1;32m--> 664\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\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[0m\u001b[0;32m    665\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    666\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcollect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\py4j\\java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m   1302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1303\u001b[0m         \u001b[0manswer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1304\u001b[1;33m         return_value = get_return_value(\n\u001b[0m\u001b[0;32m   1305\u001b[0m             answer, self.gateway_client, self.target_id, self.name)\n\u001b[0;32m   1306\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pyspark\\sql\\utils.py\u001b[0m in \u001b[0;36mdeco\u001b[1;34m(*a, **kw)\u001b[0m\n\u001b[0;32m    109\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\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[0;32m    110\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\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    112\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m             \u001b[0mconverted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\welcome\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\py4j\\protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[1;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[0;32m    324\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mOUTPUT_CONVERTER\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0manswer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgateway_client\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    325\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0manswer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mREFERENCE_TYPE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 326\u001b[1;33m                 raise Py4JJavaError(\n\u001b[0m\u001b[0;32m    327\u001b[0m                     \u001b[1;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    328\u001b[0m                     format(target_id, \".\", name), value)\n",
      "\u001b[1;31mPy4JJavaError\u001b[0m: An error occurred while calling o70.count.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 2 in stage 4.0 failed 1 times, most recent failure: Lost task 2.0 in stage 4.0 (TID 8) (Prakanshurockss executor driver): java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 33 more\r\n\nDriver stacktrace:\r\n\tat org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2258)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2207)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2206)\r\n\tat scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)\r\n\tat scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)\r\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2206)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1079)\r\n\tat scala.Option.foreach(Option.scala:407)\r\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1079)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2445)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2387)\r\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2376)\r\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\r\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2196)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2217)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2236)\r\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2261)\r\n\tat org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1030)\r\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\r\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\r\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:414)\r\n\tat org.apache.spark.rdd.RDD.collect(RDD.scala:1029)\r\n\tat org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:390)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$count$1(Dataset.scala:3006)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$count$1$adapted(Dataset.scala:3005)\r\n\tat org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3687)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)\r\n\tat org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)\r\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)\r\n\tat org.apache.spark.sql.Dataset.withAction(Dataset.scala:3685)\r\n\tat org.apache.spark.sql.Dataset.count(Dataset.scala:3005)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)\r\n\tat java.lang.reflect.Method.invoke(Unknown Source)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Unknown Source)\r\nCaused by: java.io.IOException: Cannot run program \"python3\": CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessBuilder.start(Unknown Source)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:165)\r\n\tat org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:107)\r\n\tat org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:119)\r\n\tat org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:145)\r\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)\r\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)\r\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:337)\r\n\tat org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)\r\n\tat org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:131)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)\r\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)\r\n\t... 1 more\r\nCaused by: java.io.IOException: CreateProcess error=2, The system cannot find the file specified\r\n\tat java.lang.ProcessImpl.create(Native Method)\r\n\tat java.lang.ProcessImpl.<init>(Unknown Source)\r\n\tat java.lang.ProcessImpl.start(Unknown Source)\r\n\t... 33 more\r\n"
     ]
    }
   ],
   "source": [
    "#divide data, 70% for training, 30% for testing\n",
    "dividedData = data.randomSplit([0.7, 0.3])\n",
    "trainingData = dividedData [0] #index 0 data training\n",
    "testingData = dividedData[1] #index 1 = data testing\n",
    "train_rows = trainingData.count()\n",
    "test_rows = testingData.count()\n",
    "print (\"Training data rows:\", train_rows, \"; Testing data rows:\", test_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89634030",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer Tokenizer (inputCol=\"clean_tweet\", output Col=\"Sentimentwords\"\n",
    "tokenizedTrain tokenizer.transform(trainingData) \n",
    "tokenizedTrain.show(truncate=False, n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3f16561",
   "metadata": {},
   "outputs": [],
   "source": [
    "Swr = StopWordsRemover (inputCol=tokenizer.getOutputCol(), outputCol=\"Meaningfulwords\") \n",
    "SwRemoved Train=swr.transform(tokenizedTrain) = SwRemoved \n",
    "rain.show(truncate=False, n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69f1369c",
   "metadata": {},
   "outputs": [],
   "source": [
    "hashTF = HashingTF (inputCol-swr.getOutputCol(), outputCol=\"features\")\n",
    "numericTrainData hashTF.transform(SwRemoved Train).select('label', 'Meaningfulwords', 'features')\n",
    "numericTrainData.show(truncate=False, n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da552132",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression (labelCol=\"label\", featuresCol=\"features\", maxIter-10, regParam=0.01) \n",
    "model = lr.fit(numeric TrainData) \n",
    "print (\"Iraining is done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00290e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized Test = tokenizer.transform(testingData) \n",
    "SwRemoved Test = swr.transform(tokenizedTest) numericTest = hashTF.transform \n",
    "(SwRemovedTest).select( 'label', 'MeaningfulWords', 'features') \n",
    "numericTest.show(truncate=False, n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7195222e",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.transform(numeric Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef19283b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.evaluation import MulticlassClassificationEvaluator \n",
    "evaluator=MulticlassClassificationEvaluator (predictionCol=\"prediction\") \n",
    "evaluator.evaluate(prediction) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de59e96f",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionFinal prediction.select( \"Meaningfulwords\", \"prediction\", \"label\")\n",
    "predictionFinal.show(n=20, truncate = False)\n",
    "correctPrediction = predictionFinal.filter( predictionFinal['prediction'] == prediction Final['label']).count() \n",
    "totalData = predictionFinal.count() \n",
    "print(\"correct prediction:\", correct Prediction,\", total data: \", totalData, accuracy:\", correct Prediction/totalData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c558592b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install --user --upgrade pixiedust\n",
    "import pixiedust\n",
    "from pixiedust.display import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "430f1bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "display (predictionFinal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f82e7687",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import NaiveBayes\n",
    "nb NaiveBayes (smoothing=1)\n",
    "model2 nb.fit(numericTrainData)\n",
    "print (\"Model is trained!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5227f887",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=model2.transform(numeric Test)\n",
    "predictionFinal2= prediction.select\"Meaningfulwords\", \"prediction\", \"label\")\n",
    "predictionFinal2.show(n=20, truncate = False) \n",
    "correctPrediction predictionFinal2.filter(\n",
    "predictionFinal2['prediction'] predictionFinal2['label']).count() \n",
    "totalData predictionFinal2.count()\n",
    "print(\"correct prediction:\", correct Prediction, total data:\", totalData, \", accuracy:\", correctPrediction/totalData)\n",
    "\n",
    "I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ddc82ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "display(predictionFinall2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f6c5abd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier( numTrees=3, maxDepth=5, seed-42, labelcol=\"label\", featuresCol \"features\") \n",
    "model3 = rfc.fit(numeric TrainData)\n",
    "print (\"Model is trained!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1d6a0a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionmodel3.transform(numeric Test) predictionFinal3 = prediction.select(\"Meaningfulwords\", \"prediction\", Tabel\") predictionFinal3.show(n=20, truncate=False)\n",
    "correctPrediction=predictionFinal3.filter(\n",
    "predictionFinal3['prediction'] == predictionFinal3[\"label]).count()\n",
    "totalData predictionFinal3.count()\n",
    "print(\"correct prediction:\", correct Prediction, \", total data\", totalData,\", accuracy:\", correct Prediction/totalData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "787b893d",
   "metadata": {},
   "outputs": [],
   "source": [
    "display(predictionFinall3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec85c380",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.clustering import LDA\n",
    "# Generate 7 Data-Driven Topics:\n",
    "# \"em\" = expectation-maximization\n",
    "lda = LDA (k=7, maxIter-10, featuresCol= \"features\") model = lda. fit (numeric Test)\n",
    "\n",
    "# A model with higher Log-Likelihood and Lower perplexity is considered to be good.\n",
    "ll= model.logLikelihood (numeric Test) lp model.logPerplexity (numeric Test)\n",
    "print(\"The lower bound on the log likelihood of the entire corpus: \" + str(ll)) print(\"The upper bound on perplexity: + str(lp))\n",
    "\n",
    "# Describe topics.\n",
    "topics model.describeTopics (7) =\n",
    "print(\"The topics described by their top-weighted terms:\")\n",
    "topics.show(truncate=False)\n",
    "\n",
    "# Shows the result\n",
    "transformed = model.transform(numericTest)\n",
    "print (transformed.show())\n",
    "print (transformed.show(trincate=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab4663c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "topn_ words 10\n",
    "num_topics = 7\n",
    "topics= model.topicsMatrix().toArray()\n",
    "for topic in range(num_topics):\n",
    "print(\"Topic + str(topic) + \":\") for word in range(0, topn_words):\n",
    "print(\" + str(topics [word] [topic])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d68f7f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.clustering import KMeans from pyspark.ml.feature import VectorAssembler\n",
    "#define assembler\n",
    "assembler VectorAssembler (inputCols ['User_statuses_count', 'User_followers', 'User_friends_count'], = output Col=\"features\")\n",
    "data assembler.transform(tweets_csv).select('User', 'features') \n",
    "data.show(truncate False, n=3) \n",
    "data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06141d02",
   "metadata": {},
   "outputs": [],
   "source": [
    "cost np.zeros(20)\n",
    "for k in range (2,20):\n",
    "kmeans KMeans().setk(k).setSeed (1). setFeatures Col(\"features\")\n",
    "model kmeans.fit (data.sample(False, 0.1, seed=42))\n",
    "cost [k] model.computeCost(data) # requires Spark 2.0 or Later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fabe8bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots (1,1, figsize =(8,6))\n",
    "ax.plot(range (2,20), cost[2:20])\n",
    "ax.set_xlabel('k')\n",
    "ax.set_ylabel('cost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "211688ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#define kMeans clustering algorithm\n",
    "kmeans - KMeans(\n",
    "featuresCol-assembler.getOutputCol(),\n",
    "predictionCol=\"cluster\", k=6)\n",
    "model = kmeans.fit(data)\n",
    "print (\"Model is successfully trained!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12434481",
   "metadata": {},
   "outputs": [],
   "source": [
    "centers = model.clusterCenters()\n",
    "print(\"Cluster Centers: \") \n",
    "for center in centers: \n",
    "    print(center)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8f3afdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed model.transform(data). select('User', 'cluster')\n",
    "rows transformed.collect() \n",
    "print (rows[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3be087ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pred spark.createDataFrame(rows) \n",
    "df_pred.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94b6858c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = tweets_csv[['User', 'User_statuses_count', 'User_followers', 'User_friends_count']]\n",
    "df_pred df_pred.join(df, 'User') \n",
    "df_pred.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7638be61",
   "metadata": {},
   "outputs": [],
   "source": [
    "pddf_pred=df_pred.toPandas().set_index('User')\n",
    "pddf_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8ab6da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "from mp1_toolkits.mplot3d import Axes 3D \n",
    "%matplotlib inline\n",
    "threedee = plt.figure(figsize=(12,10)).gca (projection=\"3d')\n",
    "threedee.scatter(pddf_pred.User_statuses_count, pddf_pred.User_followers, pddf_pred. User_friends_count, c-pddf_pred.cluster) \n",
    "threedee.set_xlabel('User_statuses_count\") \n",
    "threedee.set_ylabel('User_followers) \n",
    "threedee.set_zlabel('User_friends_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6a5dae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "pd.tools.plotting.parallel_coordinates(\n",
    "  pddf_pred[['cluster', 'User_statuses_count', 'User_followers', 'User_friends_count']],'cluster')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "049255cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import plotly\n",
    "plotly.tools.set_credentials_file(username='Drashtiba', api_key='25XpKdAvVmLa63PCm3w2')\n",
    "\n",
    "data = [\n",
    "   go.Parcoords(\n",
    "     line = dict (color = pddf_pred['cluster'],\n",
    "        colorscale = [[0, #D7C16B'],[0.5, #23D8C3'],[1, '#F3F10F']]),\n",
    "     dimensions = list([\n",
    "       dict (range = [0,5000],\n",
    "           constraintrange = [4,8],\n",
    "             label = User_statuses_count', values = pddf_pred['User_statuses_cours']),\n",
    "       dict (range = [0,5000], \n",
    "             label = 'User_followers', values = pddf_pred[ 'User_followers']),\n",
    "        dict (range = [0,5000],\n",
    "            label=\"User friends _count', values pddf_pred[ 'User_friends_count']),\n",
    "      dict (range = [0,5],\n",
    "           label = cluster', values = pddf_pred['cluster'])\n",
    "      ]) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc163500",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pairplot the cluster distribution.\n",
    "cluster_g sns.pairplot (pddf_pred, hue='cluster')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a867eb06",
   "metadata": {},
   "outputs": [],
   "source": [
    "spark.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2247b807",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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