{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "6d35ef96", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello world!\n" ] } ], "source": [ "print(\"Hello world!\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "3c5d4d19", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'SpongebobSpongebobSpongebobSpongebobSpongebob'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def repeatNtimes(string, n):\n", " return string * n\n", "repeatNtimes(\"Spongebob\",5)\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "34232df7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "25" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "repeatNtimes(5,5)" ] }, { "cell_type": "code", "execution_count": 4, "id": "50af27ea", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'SquarepantsSquarepantsSquarepantsSquarepantsSquarepants'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "repeatNtimes2 = lambda string, n: string*n\n", "repeatNtimes2('Squarepants', 5)" ] }, { "cell_type": "code", "execution_count": 6, "id": "cc3c49c1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This!\n", "is!\n", "awesome!\n", "---\n", "\n", "0 This***\n", "1 is***\n", "2 awesome***\n", "---\n", "\n", "This\n", "is\n", "awesome\n" ] } ], "source": [ "list_sentence = [\"This\", \"is\", \"awesome\"]\n", "\n", "for idx in range(len(list_sentence)):\n", " word = list_sentence[idx]\n", " print(word + \"!\")\n", "\n", "print('---\\n')\n", "\n", "for idx, word in enumerate(list_sentence):\n", " print(idx, word + '***')\n", " \n", "print('---\\n')\n", "\n", "for word in list_sentence:\n", " print(word)" ] }, { "cell_type": "code", "execution_count": 7, "id": "7a6db787", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['awesome!', 'and!', 'amazing!']" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list_sentence = [\"This\", \"is\", \"awesome\", \"and\", \"amazing\"]\n", "list2 = [x + '!' for x in list_sentence if x.startswith(\"a\")]\n", "list2" ] }, { "cell_type": "code", "execution_count": 8, "id": "4f750d55", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['awesome', 'and', 'amazing']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list_sentence = [\"This\", \"is\", \"awesome\", \"and\", \"amazing\"]\n", "list2 = []\n", "for x in list_sentence:\n", " if x.startswith(\"a\"):\n", " list2.append(x)\n", "list2" ] }, { "cell_type": "code", "execution_count": 9, "id": "6c7ceba8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['D!', 't!'], ['S!', 'c!', 'i!', 'e!', 'n!', 'c!', 'e!']]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[[letter + '!' for letter in string if letter != 'a']\n", " for string in ['Data', 'Science']]" ] }, { "cell_type": "code", "execution_count": 12, "id": "39fd5f2f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['D?', 'a!', 't?', 'a!'], ['S?', 'c?', 'i?', 'e?', 'n?', 'c?', 'e?']]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[[letter + '!' if letter == 'a' else letter + '?' for letter in string]\n", " for string in ['Data', 'Science']]" ] }, { "cell_type": "code", "execution_count": 13, "id": "07794ec7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2550, 17)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "ted_main = pd.read_csv('https://raw.githubusercontent.com/cpethe/TED_Talks/master/ted_main.csv')\n", "\n", "ted_main.shape" ] }, { "cell_type": "code", "execution_count": 14, "id": "cb248817", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2467, 2)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transcripts = pd.read_csv('https://raw.githubusercontent.com/cpethe/TED_Talks/master/transcripts.csv')\n", "transcripts.shape" ] }, { "cell_type": "code", "execution_count": 15, "id": "a945c9f3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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https://www.ted.com/talks/ken_robinson_says_schools_kill_creativity\\n4553Sir Ken Robinson makes an entertaining and pro...1164TED2006114082560060Ken RobinsonKen Robinson: Do schools kill creativity?11151367060[{'id': 7, 'name': 'Funny', 'count': 19645}, {...[{'id': 865, 'hero': 'https://pe.tedcdn.com/im...Author/educator['children', 'creativity', 'culture', 'dance',...Do schools kill creativity?47227110
https://www.ted.com/talks/al_gore_on_averting_climate_crisis\\n265With the same humor and humanity he exuded in ...977TED2006114082560043Al GoreAl Gore: Averting the climate crisis11151367060[{'id': 7, 'name': 'Funny', 'count': 544}, {'i...[{'id': 243, 'hero': 'https://pe.tedcdn.com/im...Climate advocate['alternative energy', 'cars', 'climate change...Averting the climate crisis3200520
https://www.ted.com/talks/david_pogue_says_simplicity_sells\\n124New York Times columnist David Pogue takes aim...1286TED2006114073920026David PogueDavid Pogue: Simplicity sells11151367060[{'id': 7, 'name': 'Funny', 'count': 964}, {'i...[{'id': 1725, 'hero': 'https://pe.tedcdn.com/i...Technology columnist['computers', 'entertainment', 'interface desi...Simplicity sells1636292
https://www.ted.com/talks/majora_carter_s_tale_of_urban_renewal\\n200In an emotionally charged talk, MacArthur-winn...1116TED2006114091200035Majora CarterMajora Carter: Greening the ghetto11151367060[{'id': 3, 'name': 'Courageous', 'count': 760}...[{'id': 1041, 'hero': 'https://pe.tedcdn.com/i...Activist for environmental justice['MacArthur grant', 'activism', 'business', 'c...Greening the ghetto1697550
https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen\\n593You've never seen data presented like this. Wi...1190TED2006114056640048Hans RoslingHans Rosling: The best stats you've ever seen11151440680[{'id': 9, 'name': 'Ingenious', 'count': 3202}...[{'id': 2056, 'hero': 'https://pe.tedcdn.com/i...Global health expert; data visionary['Africa', 'Asia', 'Google', 'demo', 'economic...The best stats you've ever seen12005869
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['MacArthur grant', 'activism', 'business', 'c... \n", "https://www.ted.com/talks/hans_rosling_shows_th... ['Africa', 'Asia', 'Google', 'demo', 'economic... \n", "\n", " title \\\n", "url \n", "https://www.ted.com/talks/ken_robinson_says_sch... Do schools kill creativity? \n", "https://www.ted.com/talks/al_gore_on_averting_c... Averting the climate crisis \n", "https://www.ted.com/talks/david_pogue_says_simp... Simplicity sells \n", "https://www.ted.com/talks/majora_carter_s_tale_... Greening the ghetto \n", "https://www.ted.com/talks/hans_rosling_shows_th... The best stats you've ever seen \n", "\n", " views \n", "url \n", "https://www.ted.com/talks/ken_robinson_says_sch... 47227110 \n", "https://www.ted.com/talks/al_gore_on_averting_c... 3200520 \n", "https://www.ted.com/talks/david_pogue_says_simp... 1636292 \n", "https://www.ted.com/talks/majora_carter_s_tale_... 1697550 \n", "https://www.ted.com/talks/hans_rosling_shows_th... 12005869 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main.set_index('url').head(5)" ] }, { "cell_type": "code", "execution_count": 29, "id": "187e62aa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "comments 4553\n", "description Sir Ken Robinson makes an entertaining and pro...\n", "duration 1164\n", "event TED2006\n", "film_date 1140825600\n", "languages 60\n", "main_speaker Ken Robinson\n", "name Ken Robinson: Do schools kill creativity?\n", "num_speaker 1\n", "published_date 1151367060\n", "ratings [{'id': 7, 'name': 'Funny', 'count': 19645}, {...\n", "related_talks [{'id': 865, 'hero': 'https://pe.tedcdn.com/im...\n", "speaker_occupation Author/educator\n", "tags ['children', 'creativity', 'culture', 'dance',...\n", "title Do schools kill creativity?\n", "views 47227110\n", "Name: https://www.ted.com/talks/ken_robinson_says_schools_kill_creativity\\n, dtype: object" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main.set_index('url').iloc[0]" ] }, { "cell_type": "code", "execution_count": 30, "id": "293bb1e8", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "0", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2897\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-> 2898\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\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 2899\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 0", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mted_main\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'url'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\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[0m", "\u001b[1;32m~\\anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 877\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 878\u001b[0m \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 879\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\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 880\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 881\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\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~\\anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1108\u001b[0m \u001b[1;31m# fall thru to straight lookup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1109\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1110\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\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 1111\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1112\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_slice_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\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~\\anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m 1057\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\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 1058\u001b[0m \u001b[1;31m# GH#5667 this will fail if the label is not present in the axis.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1059\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\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 1060\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1061\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_handle_lowerdim_multi_index_axis0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\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~\\anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mxs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m 3491\u001b[0m \u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc_level\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdrop_level\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3492\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-> 3493\u001b[1;33m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\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 3494\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3495\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\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~\\anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2898\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2899\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2900\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2901\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2902\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 0" ] } ], "source": [ "ted_main.set_index('url').loc[0]" ] }, { "cell_type": "code", "execution_count": 35, "id": "4ca14fb3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "comments 4553\n", "description Sir Ken Robinson makes an entertaining and pro...\n", "duration 1164\n", "event TED2006\n", "film_date 1140825600\n", "languages 60\n", "main_speaker Ken Robinson\n", "name Ken Robinson: Do schools kill creativity?\n", "num_speaker 1\n", "published_date 1151367060\n", "ratings [{'id': 7, 'name': 'Funny', 'count': 19645}, {...\n", "related_talks [{'id': 865, 'hero': 'https://pe.tedcdn.com/im...\n", "speaker_occupation Author/educator\n", "tags ['children', 'creativity', 'culture', 'dance',...\n", "title Do schools kill creativity?\n", "views 47227110\n", "Name: https://www.ted.com/talks/ken_robinson_says_schools_kill_creativity\\n, dtype: 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529248David Logan talks about the five kinds of trib...999TEDxUSC123776640025David LoganDavid Logan: Tribal leadership11254790800[{'id': 22, 'name': 'Fascinating', 'count': 33...[{'id': 538, 'hero': 'https://pe.tedcdn.com/im...Professor of management['TEDx', 'business', 'communication', 'culture...Tribal leadershiphttps://www.ted.com/talks/david_logan_on_triba...1404586
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53989Computer graphics trailblazer Paul Debevec exp...366TEDxUSC123776640024Paul DebevecPaul Debevec: Animating a photo-real digital face11256029020[{'id': 23, 'name': 'Jaw-dropping', 'count': 1...[{'id': 469, 'hero': 'https://pe.tedcdn.com/im...Computer graphics pioneer['TEDx', 'animation', 'art', 'computers', 'des...Animating a photo-real digital facehttps://www.ted.com/talks/paul_debevec_animate...748862
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25342What the astronauts felt when they saw Earth f...725TEDxSkoll14915232001Benjamin GrantBenjamin Grant: What it feels like to see Eart...11504814438[{'id': 10, 'name': 'Inspiring', 'count': 46},...[{'id': 2511, 'hero': 'https://pe.tedcdn.com/i...Author['TEDx', 'art', 'climate change', 'environment...What it feels like to see Earth from spacehttps://www.ted.com/talks/benjamin_grant_what_...646174
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commentsdescriptiondurationeventfilm_datelanguagesmain_speakernamenum_speakerpublished_dateratingsrelated_talksspeaker_occupationtagstitleurlviews
23976After speaking at TED2007 on elegance in physi...135TED2007117288000061Murray Gell-MannMurray Gell-Mann: The ancestor of language11213146000[{'id': 22, 'name': 'Fascinating', 'count': 78...[{'id': 161, 'hero': 'https://pe.tedcdn.com/im...Physicist['culture', 'global issues', 'history', 'langu...The ancestor of languagehttps://www.ted.com/talks/murray_gell_mann_on_...785293
24842Singer-songwriter Nellie McKay performs the se...140TED2008120424320039Nellie McKayNellie McKay: \"Clonie\"11214528400[{'id': 25, 'name': 'OK', 'count': 104}, {'id'...[{'id': 325, 'hero': 'https://pe.tedcdn.com/im...Singer/songwriter['culture', 'entertainment', 'genetics', 'live...\"Clonie\"https://www.ted.com/talks/nellie_mckay_sings_c...680591
54746The euphonium, with its sweet brass sound, is ...141TEDGlobal 200912483072000Matthew WhiteMatthew White: The modern euphonium11256864400[{'id': 9, 'name': 'Ingenious', 'count': 43}, ...[{'id': 478, 'hero': 'https://pe.tedcdn.com/im...Brass virtuoso['creativity', 'live music', 'music', 'perform...The modern euphoniumhttps://www.ted.com/talks/matthew_white_gives_...771962
45462Yves Behar and Forrest North unveil Mission On...143TED2009123387840045Yves BeharYves Behar: A supercharged motorcycle design11242982440[{'id': 7, 'name': 'Funny', 'count': 30}, {'id...[{'id': 266, 'hero': 'https://pe.tedcdn.com/im...Designer['cars', 'design', 'engineering', 'green', 'tr...A supercharged motorcycle designhttps://www.ted.com/talks/yves_behar_s_superch...556657
26415In this short, image-packed talk, Marisa Fick-...153TEDGlobal 2007118108800043Marisa Fick-JordanMarisa Fick-Jordan: The wonder of Zulu wire art11216666500[{'id': 1, 'name': 'Beautiful', 'count': 81}, ...[{'id': 127, 'hero': 'https://pe.tedcdn.com/im...Craft artist, product designer['Africa', 'art', 'business', 'design']The wonder of Zulu wire arthttps://www.ted.com/talks/marisa_fick_jordan_s...294961
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59420In the midst of an earlier crisis, Haitian aut...3573University of California10976256000Edwidge DanticatEdwidge Danticat: Stories of Haiti11263481440[{'id': 1, 'name': 'Beautiful', 'count': 25}, ...[{'id': 652, 'hero': 'https://pe.tedcdn.com/im...Author['books', 'disaster relief', 'novel', 'poetry'...Stories of Haitihttps://www.ted.com/talks/edwidge_danticat_sto...50443
2387345How do we make sense of today's political divi...3608TED Dialogues148711680019Yuval Noah HarariYuval Noah Harari: Nationalism vs. globalism: ...21487604924[{'id': 8, 'name': 'Informative', 'count': 583...[{'id': 2625, 'hero': 'https://pe.tedcdn.com/i...Historian['big problems', 'climate change', 'collaborat...Nationalism vs. globalism: the new political d...https://www.ted.com/talks/yuval_noah_harari_na...1514291
629146In this archival footage from BBC TV, celebrat...3955BBC TV4265316000Richard FeynmanRichard Feynman: Physics is fun to imagine11267631820[{'id': 8, 'name': 'Informative', 'count': 324...[{'id': 194, 'hero': 'https://pe.tedcdn.com/im...Physicist['astronomy', 'physics', 'science']Physics is fun to imaginehttps://www.ted.com/talks/richard_feynman\\n521974
588124In 2007, Carnegie Mellon professor Randy Pausc...4587Carnegie Mellon University11900736000Randy PauschRandy Pausch: Really achieving your childhood ...11262950320[{'id': 3, 'name': 'Courageous', 'count': 299}...[{'id': 229, 'hero': 'https://pe.tedcdn.com/im...Professor['culture', 'disease', 'education', 'life', 's...Really achieving your childhood dreamshttps://www.ted.com/talks/randy_pausch_really_...564781
640101Blind river dolphins, reclusive lemurs, a parr...5256University of California9899712000Douglas AdamsDouglas Adams: Parrots, the universe and every...11268762040[{'id': 22, 'name': 'Fascinating', 'count': 29...[{'id': 635, 'hero': 'https://pe.tedcdn.com/im...Author, satirist['biodiversity', 'biology', 'comedy', 'humor',...Parrots, the universe and everythinghttps://www.ted.com/talks/douglas_adams_parrot...473220
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" ], "text/plain": [ " comments description duration event film_date languages \\\n", "0 False False False False False False \n", "1 False False False False False False \n", "2 False False False False False False \n", "3 False False False False False False \n", "4 False False False False False False \n", "... ... ... ... ... ... ... \n", "2545 False False False False False False \n", "2546 False False False False False False \n", "2547 False False False False False False \n", "2548 False False False False False False \n", "2549 False False False False False False \n", "\n", " main_speaker name num_speaker published_date ratings \\\n", "0 False False False False False \n", "1 False False False False False \n", "2 False False False False False \n", "3 False False False False False \n", "4 False False False False False \n", "... ... ... ... ... ... \n", "2545 False False False False False \n", "2546 False False False False False \n", "2547 False False False False False \n", "2548 False False False False False \n", "2549 False False False False False \n", "\n", " related_talks speaker_occupation tags title url views \n", "0 False False False False False False \n", "1 False False False False False False \n", "2 False False False False False False \n", "3 False False False False False False \n", "4 False False False False False False \n", "... ... ... ... ... ... ... \n", "2545 False False False False False False \n", "2546 False False False False False False \n", "2547 False False False False False False \n", "2548 False False False False False False \n", "2549 False False False False False False \n", "\n", "[2550 rows x 17 columns]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main.isnull()" ] }, { "cell_type": "code", "execution_count": 49, "id": "6ee0026d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp = ted_main.fillna('Not specified')\n", "temp2 = temp[temp['speaker_occupation'] == 'Not specified']\n", "len(temp2)" ] }, { "cell_type": "code", "execution_count": 50, "id": "46bf042b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2544, 17)" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main.dropna(how = 'any', inplace = True)\n", "ted_main.shape" ] }, { "cell_type": "code", "execution_count": 51, "id": "115f7afc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"[{'id': 7, 'name': 'Funny', 'count': 19645}, {'id': 1, 'name': 'Beautiful', 'count': 4573}, {'id': 9, 'name': 'Ingenious', 'count': 6073}, {'id': 3, 'name': 'Courageous', 'count': 3253}, {'id': 11, 'name': 'Longwinded', 'count': 387}, {'id': 2, 'name': 'Confusing', 'count': 242}, {'id': 8, 'name': 'Informative', 'count': 7346}, {'id': 22, 'name': 'Fascinating', 'count': 10581}, {'id': 21, 'name': 'Unconvincing', 'count': 300}, {'id': 24, 'name': 'Persuasive', 'count': 10704}, {'id': 23, 'name': 'Jaw-dropping', 'count': 4439}, {'id': 25, 'name': 'OK', 'count': 1174}, {'id': 26, 'name': 'Obnoxious', 'count': 209}, {'id': 10, 'name': 'Inspiring', 'count': 24924}]\"" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main['ratings'].iloc[0]" ] }, { "cell_type": "code", "execution_count": 52, "id": "42949d7e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1140825600" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main['film_date'].iloc[0]" ] }, { "cell_type": "code", "execution_count": 53, "id": "24ccb497", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1151367060" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main['published_date'].iloc[0]" ] }, { "cell_type": "code", "execution_count": 54, "id": "a170fc86", "metadata": {}, "outputs": [], "source": [ "from ast import literal_eval" ] }, { "cell_type": "code", "execution_count": 55, "id": "6241fd02", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 [{'id': 7, 'name': 'Funny', 'count': 19645}, {...\n", "1 [{'id': 7, 'name': 'Funny', 'count': 544}, {'i...\n", "2 [{'id': 7, 'name': 'Funny', 'count': 964}, {'i...\n", "3 [{'id': 3, 'name': 'Courageous', 'count': 760}...\n", "4 [{'id': 9, 'name': 'Ingenious', 'count': 3202}...\n", " ... \n", "2545 [{'id': 3, 'name': 'Courageous', 'count': 24},...\n", "2546 [{'id': 22, 'name': 'Fascinating', 'count': 32...\n", "2547 [{'id': 1, 'name': 'Beautiful', 'count': 14}, ...\n", "2548 [{'id': 11, 'name': 'Longwinded', 'count': 3},...\n", "2549 [{'id': 21, 'name': 'Unconvincing', 'count': 2...\n", "Name: ratings, Length: 2544, dtype: object" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main['ratings'] = ted_main['ratings'].apply(literal_eval)\n", "ted_main['related_talks'] = ted_main['related_talks'].apply(literal_eval)\n", "ted_main['tags'] = ted_main['tags'].apply(literal_eval)\n", "\n", "ted_main['ratings']" ] }, { "cell_type": "code", "execution_count": 56, "id": "3d7a3392", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1140825600" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main['film_date'].iloc[0]" ] }, { "cell_type": "code", "execution_count": 57, "id": "389c7199", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1151367060" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main['published_date'].iloc[0]" ] }, { "cell_type": "code", "execution_count": 59, "id": "d2dc99ab", "metadata": {}, "outputs": [], "source": [ "ted_main['film_date'] = pd.to_datetime(ted_main['film_date'], unit = 's')\n", "ted_main['published_date'] = pd.to_datetime(ted_main['published_date'], unit = 's')" ] }, { "cell_type": "code", "execution_count": 60, "id": "46af2608", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Timestamp('2006-02-25 00:00:00')" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main['film_date'].iloc[0]" ] }, { "cell_type": "code", "execution_count": 61, "id": "e0f965b2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Timestamp('2006-06-27 00:11:00')" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main['published_date'].iloc[0]" ] }, { "cell_type": "code", "execution_count": 62, "id": "fc20e209", "metadata": {}, "outputs": [], "source": [ "ted_main.loc[(ted_main['languages'] == 0) & (ted_main['comments'] == 'whatever'), 'comments'] = 1" ] }, { "cell_type": "code", "execution_count": 63, "id": "d4b74aa4", "metadata": {}, "outputs": [], "source": [ "ted_main['url'] = ted_main['url'].apply(lambda x: x.replace('\\n', ''))" ] }, { "cell_type": "code", "execution_count": 64, "id": "676e9b79", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://www.ted.com/talks/ken_robinson_says_schools_kill_creativity'" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main['url'].iloc[0]" ] }, { "cell_type": "code", "execution_count": 65, "id": "7519f45b", "metadata": {}, "outputs": [], "source": [ "def removeSpeakerName(row):\n", " return row['name'].replace(\"%s:\" % row['main_speaker'], '').strip()" ] }, { "cell_type": "code", "execution_count": 66, "id": "a496feb0", "metadata": {}, "outputs": [], "source": [ "ted_main['name'] = ted_main.apply(removeSpeakerName, axis = 1)" ] }, { "cell_type": "code", "execution_count": 67, "id": "46876214", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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transcripturl
0Good morning. How are you?(Laughter)It's been ...https://www.ted.com/talks/ken_robinson_says_sc...
1Thank you so much, Chris. And it's truly a gre...https://www.ted.com/talks/al_gore_on_averting_...
2(Music: \"The Sound of Silence,\" Simon & Garfun...https://www.ted.com/talks/david_pogue_says_sim...
3If you're here today — and I'm very happy that...https://www.ted.com/talks/majora_carter_s_tale...
4About 10 years ago, I took on the task to teac...https://www.ted.com/talks/hans_rosling_shows_t...
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" ], "text/plain": [ " transcript \\\n", "0 Good morning. How are you?(Laughter)It's been ... \n", "1 Thank you so much, Chris. And it's truly a gre... \n", "2 (Music: \"The Sound of Silence,\" Simon & Garfun... \n", "3 If you're here today — and I'm very happy that... \n", "4 About 10 years ago, I took on the task to teac... \n", "\n", " url \n", "0 https://www.ted.com/talks/ken_robinson_says_sc... \n", "1 https://www.ted.com/talks/al_gore_on_averting_... \n", "2 https://www.ted.com/talks/david_pogue_says_sim... \n", "3 https://www.ted.com/talks/majora_carter_s_tale... \n", "4 https://www.ted.com/talks/hans_rosling_shows_t... " ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transcripts.head()" ] }, { "cell_type": "code", "execution_count": 68, "id": "6e015619", "metadata": {}, "outputs": [], "source": [ "import re" ] }, { "cell_type": "code", "execution_count": 69, "id": "2f4c3968", "metadata": {}, "outputs": [], "source": [ "transcripts['transcript'] = \\\n", " transcripts['transcript'].apply(lambda x: re.sub(r'\\((.*?)\\)',' ',x))" ] }, { "cell_type": "code", "execution_count": 70, "id": "c1dda3f4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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transcripturl
0Good morning. How are you? It's been great, ha...https://www.ted.com/talks/ken_robinson_says_sc...
1Thank you so much, Chris. And it's truly a gre...https://www.ted.com/talks/al_gore_on_averting_...
2Hello voice mail, my old friend. I've called ...https://www.ted.com/talks/david_pogue_says_sim...
3If you're here today — and I'm very happy that...https://www.ted.com/talks/majora_carter_s_tale...
4About 10 years ago, I took on the task to teac...https://www.ted.com/talks/hans_rosling_shows_t...
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" ], "text/plain": [ " transcript \\\n", "0 Good morning. How are you? It's been great, ha... \n", "1 Thank you so much, Chris. And it's truly a gre... \n", "2 Hello voice mail, my old friend. I've called ... \n", "3 If you're here today — and I'm very happy that... \n", "4 About 10 years ago, I took on the task to teac... \n", "\n", " url \n", "0 https://www.ted.com/talks/ken_robinson_says_sc... \n", "1 https://www.ted.com/talks/al_gore_on_averting_... \n", "2 https://www.ted.com/talks/david_pogue_says_sim... \n", "3 https://www.ted.com/talks/majora_carter_s_tale... \n", "4 https://www.ted.com/talks/hans_rosling_shows_t... " ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transcripts.head()" ] }, { "cell_type": "code", "execution_count": 71, "id": "b8e50dfb", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "hists = ted_main.hist()" ] }, { "cell_type": "code", "execution_count": 72, "id": "2ab569e6", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 73, "id": "515e1c8a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hists = ted_main.hist()\n", "plt.tight_layout()\n", "plt.show()\n", "plt.savefig('plot.png')" ] }, { "cell_type": "code", "execution_count": 74, "id": "4d932b82", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "nums = [1,2,3,4]\n", "plt.plot(nums, [x * x for x in nums], '-o');\n", "plt.plot(nums, [x ** 3 for x in nums], '-o');\n", "plt.legend(['Square', 'Cube']);\n", "plt.xlabel('x');\n", "plt.ylabel('power(x)');" ] }, { "cell_type": "code", "execution_count": 75, "id": "7166934c", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "nums = [1,2,3,4]\n", "plt.figure(figsize=(4,4))\n", "plt.plot(nums, [x * x for x in nums], '-s', color = 'Green');\n", "plt.plot(nums, [x * x * x for x in nums], '-.', color = 'Red');\n", "plt.legend(['Square','Cube'])\n", "plt.xlabel('x');\n", "plt.ylabel('power(x)');\n", "\n", "plt.xticks(nums)\n", "plt.title('A super boring graph')\n", "plt.grid(axis ='y')" ] }, { "cell_type": "code", "execution_count": 76, "id": "626e7842", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " comments duration languages num_speaker views\n", "comments 1.000000 0.140507 0.318831 -0.035564 0.530897\n", "duration 0.140507 1.000000 -0.294214 0.022001 0.048489\n", "languages 0.318831 -0.294214 1.000000 -0.063085 0.378027\n", "num_speaker -0.035564 0.022001 -0.063085 1.000000 -0.026474\n", "views 0.530897 0.048489 0.378027 -0.026474 1.000000" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ted_main.corr()" ] }, { "cell_type": "code", "execution_count": 77, "id": "4640bfd6", "metadata": {}, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 78, "id": "df104834", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.heatmap(ted_main.corr(), vmin=-1, vmax=1, cmap = 'seismic', linewidths = 0.2, annot=True);" ] }, { "cell_type": "code", "execution_count": 80, "id": "09bfac44", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 81, "id": "55a0fa88", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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