From 07fd612f4a46256384ba705dda789b73dcf6061f Mon Sep 17 00:00:00 2001 From: Taegun Harshbarger Date: Wed, 7 Sep 2022 03:04:08 +0000 Subject: [PATCH 1/2] added --- tharshba.ipynb | 382 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 382 insertions(+) create mode 100644 tharshba.ipynb diff --git a/tharshba.ipynb b/tharshba.ipynb new file mode 100644 index 0000000..59b489b --- /dev/null +++ b/tharshba.ipynb @@ -0,0 +1,382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Written text as operational data\n", + "\n", + "Written text is one type of data\n", + "\n", + "### Why people write?\n", + "\n", + " - To communicate: their thoughts, feelings, urgency, needs, information\n", + "\n", + "### Why people communicate?\n", + "\n", + "1. To express emotions\n", + "1. To share information\n", + "1. To enable or elicit an action\n", + "1. ...\n", + "\n", + "### We will use written text for the purpose other than \n", + "1. To experience emotion\n", + "1. To learn something the author intended us to learn\n", + "1. To do what the author intended us to do\n", + "\n", + "### Instead, we will use written text to recognize who wrote it\n", + " - By calculating and comparing word frequencies in written documents\n", + " \n", + "See, for example, likely fictional story https://medium.com/@amuse/how-the-nsa-caught-satoshi-nakamoto-868affcef595" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example 1. Dictionaries in python (associative arrays)\n", + "\n", + "Plot the frequency distribution of words on a web page." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "class=\"menu-item\t54\n", + "\t38\n", + "\t35\n", + "
  • \t28\n", + "\t21\n", + "\t21\n" + ] + } + ], + "source": [ + "import requests, re\n", + "# re is a module for regular expressions: to detect various combinations of characters\n", + "import operator\n", + "\n", + "# Start from a simple document\n", + "r = requests .get('http://eecs.utk.edu')\n", + "\n", + "# What comes back includes headers and other HTTP stuff, get just the body of the response\n", + "t = r.text\n", + "\n", + "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n", + "wds = re.split('\\s+',t)\n", + "\n", + "# now populate a dictionary (wf)\n", + "wf = {}\n", + "for w in wds:\n", + " if w in wf: wf [w] = wf [w] + 1\n", + " else: wf[w] = 1\n", + "\n", + "# dictionaries can not be sorted, so lets get a sorted *list* \n", + "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n", + "\n", + "# lets just have no more than 15 words \n", + "ml = min(len(wfs),15)\n", + "for i in range(1,ml,1):\n", + " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example 2\n", + "\n", + "Lots of markup in the output, lets remove it --- \n", + "\n", + "use BeautifulSoup and nltk modules and practice some regular expressions." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import requests, re, nltk\n", + "from bs4 import BeautifulSoup\n", + "from nltk import clean_html\n", + "from collections import Counter\n", + "import operator\n", + "\n", + "# we may not care about the usage of stop words\n", + "stop_words = nltk.corpus.stopwords.words('english') + [\n", + " 'ut', '\\'re','.', ',', '--', '\\'s', '?', ')', '(', ':', '\\'',\n", + " '\\\"', '-', '}', '{', '&', '|', u'\\u2014' ]\n", + "\n", + "# We most likely would like to remove html markup\n", + "def cleanHtml (html):\n", + " from bs4 import BeautifulSoup\n", + " soup = BeautifulSoup(html, 'html.parser')\n", + " return soup .get_text()\n", + "\n", + "# We also want to remove special characters, quotes, etc. from each word\n", + "def cleanWord (w):\n", + " # r in r'[.,\"\\']' tells to treat \\ as a regular character \n", + " # but we need to escape ' with \\'\n", + " # any character between the brackets [] is to be removed \n", + " wn = re.sub('[,\"\\.\\'&\\|:@>*;/=]', \"\", w)\n", + " # get rid of numbers\n", + " return re.sub('^[0-9\\.]*$', \"\", wn)\n", + " \n", + "# define a function to get text/clean/calculate frequency\n", + "def get_wf (URL):\n", + " # first get the web page\n", + " r = requests .get(URL)\n", + " \n", + " # Now clean\n", + " # remove html markup\n", + " t = cleanHtml (r .text) .lower()\n", + " \n", + " # split string into an array of words using any sequence of spaces \"\\s+\" \n", + " wds = re .split('\\s+',t)\n", + " \n", + " # remove periods, commas, etc stuck to the edges of words\n", + " for i in range(len(wds)):\n", + " wds [i] = cleanWord (wds [i])\n", + " \n", + " # If satisfied with results, lets go to the next step: calculate frequencies\n", + " # We can write a loop to create a dictionary, but \n", + " # there is a special function for everything in python\n", + " # in particular for counting frequencies (like function table() in R)\n", + " wf = Counter (wds)\n", + " \n", + " # Remove stop words from the dictionary wf\n", + " for k in stop_words:\n", + " wf. pop(k, None)\n", + " \n", + " #how many regular words in the document?\n", + " tw = 0\n", + " for w in wf:\n", + " tw += wf[w] \n", + " \n", + " \n", + " # Get ordered list\n", + " wfs = sorted (wf .items(), key = operator.itemgetter(1), reverse=True)\n", + " ml = min(len(wfs),15)\n", + "\n", + " #Reverse the list because barh plots items from the bottom\n", + " return (wfs [ 0:ml ] [::-1], tw)\n", + " \n", + "# Now populate two lists \n", + "(wf_ee, tw_ee) = get_wf('http://www.gutenberg.org/ebooks/1342.txt.utf-8')\n", + "(wf_bu, tw_bu) = get_wf('http://www.gutenberg.org/ebooks/76.txt.utf-8')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AOHwz8mL809Q/Bz6PY9nT8X0hFwDrwl6XOOdKgEuAjvgnsR8N1rUjSj5j8A8C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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plot the results: are there striking differences in language?\n", + "import numpy as np\n", + "import pylab\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "def plotTwoLists (wf_ee, wf_bu, title):\n", + " f = plt.figure (figsize=(10, 6))\n", + " # this is painfully tedious....\n", + " f .suptitle (title, fontsize=20)\n", + " ax = f.add_subplot(111)\n", + " ax .spines ['top'] .set_color ('none')\n", + " ax .spines ['bottom'] .set_color ('none')\n", + " ax .spines ['left'] .set_color ('none')\n", + " ax .spines ['right'] .set_color ('none')\n", + " ax .tick_params (labelcolor='w', top='off', bottom='off', left='off', right='off', labelsize=20)\n", + "\n", + " # Create two subplots, this is the first one\n", + " ax1 = f .add_subplot (121)\n", + " plt .subplots_adjust (wspace=.5)\n", + "\n", + " pos = np .arange (len(wf_ee)+1) \n", + " ax1 .tick_params (axis='both', which='major', labelsize=14)\n", + " pylab .yticks (pos, [ x [0] for x in wf_ee ])\n", + " ax1 .barh (range(len(wf_ee)), [ x [1] for x in wf_ee ], align='center')\n", + "\n", + " ax2 = f .add_subplot (122)\n", + " ax2 .tick_params (axis='both', which='major', labelsize=14)\n", + " pos = np .arange (len(wf_bu)+1) \n", + " pylab .yticks (pos, [ x [0] for x in wf_bu ])\n", + " ax2 .barh (range (len(wf_bu)), [ x [1] for x in wf_bu ], align='center')\n", + "\n", + "plotTwoLists (wf_ee, wf_bu, 'Difference between Pride and Prejudice and Huck Finn')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "and\t2836\n", + "of\t2676\n", + "to\t2646\n", + "a\t2217\n", + "in\t1422\n", + "his\t1205\n", + "he\t928\n", + "that\t920\n", + "was\t823\n", + "for\t798\n", + "with\t797\n", + "as\t672\n", + "I\t505\n", + "you\t497\n" + ] + } + ], + "source": [ + "#In case Project gutenberg is blocked you can download text to your laptop and copy to the docker container via scp\n", + "#Assuming the file name you copy is pg4680.txt here is how you change the script\n", + "# Please note the option errors='replace'\n", + "# without it python invariably runs into unicode errors\n", + "f = open ('pg4680.txt', 'r', encoding=\"ascii\", errors='replace')\n", + " \n", + "# What comes back includes headers and other HTTP stuff, get just the body of the response\n", + "t = f.read()\n", + "\n", + "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n", + "wds = re.split('\\s+',t)\n", + "\n", + "# now populate a dictionary (wf)\n", + "wf = {}\n", + "for w in wds:\n", + " if w in wf: wf [w] = wf [w] + 1\n", + " else: wf [w] = 1\n", + "\n", + "# dictionaries can not be sorted, so lets get a sorted *list* \n", + "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n", + "\n", + "# lets just have no more than 15 words \n", + "ml = min(len(wfs),15)\n", + "for i in range(1,ml,1):\n", + " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment 1\n", + "\n", + "1. Compare word frequencies between two works of a single author.\n", + "1. Compare word frequencies between works of two authors.\n", + "1. Are there some words preferred by one author but used less frequently by another author?\n", + "\n", + "Extra credit\n", + "\n", + "1. The frequency of a specific word, e.g., \"would\" should follow a binomial distribution (each regular word in a document is a trial and with probability p that word is \"would\". The estimate for p is N(\"would\")/N(regular word)). Do these binomial distributions for your chosen word differ significantly between books of the same author or between authors? \n", + "\n", + "Project Gutenberg is a good source of for fiction and non-fiction.\n", + "\n", + "E.g below are two most popular books from Project Gutenberg:\n", + "- Pride and Prejudice at http://www.gutenberg.org/ebooks/1342.txt.utf-8\n", + "- Adventures of Huckleberry Finn at http://www.gutenberg.org/ebooks/76.txt.utf-8" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import requests, re, nltk\n", + "#In case your text is not on Project Gutenberg but at some other URL\n", + "#http://www.fullbooks.com/Our-World-or-The-Slaveholders-Daughter2.html\n", + "# that contains 12 parts\n", + "t = \"\"\n", + "for i in range(2,13):\n", + " r = requests .get('http://www.fullbooks.com/Our-World-or-The-Slaveholders-Daughter' + str(i) + '.html')\n", + " t = t + r.text" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1323653" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(t)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 3194fade0155f956cd613a6ef8fd3a7a1ba7d633 Mon Sep 17 00:00:00 2001 From: Taegun Harshbarger Date: Tue, 13 Sep 2022 19:49:43 +0000 Subject: [PATCH 2/2] Finished Project --- tharshba.ipynb | 242 ++++++++++++------------------------------------- 1 file changed, 57 insertions(+), 185 deletions(-) diff --git a/tharshba.ipynb b/tharshba.ipynb index 59b489b..ccc1253 100644 --- a/tharshba.ipynb +++ b/tharshba.ipynb @@ -4,113 +4,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Written text as operational data\n", + "# Comparing two works by Charles Dickens\n", "\n", - "Written text is one type of data\n", + "- A Tale of Two Cities\n", + "- Bleak House\n", "\n", - "### Why people write?\n", - "\n", - " - To communicate: their thoughts, feelings, urgency, needs, information\n", - "\n", - "### Why people communicate?\n", - "\n", - "1. To express emotions\n", - "1. To share information\n", - "1. To enable or elicit an action\n", - "1. ...\n", - "\n", - "### We will use written text for the purpose other than \n", - "1. To experience emotion\n", - "1. To learn something the author intended us to learn\n", - "1. To do what the author intended us to do\n", - "\n", - "### Instead, we will use written text to recognize who wrote it\n", - " - By calculating and comparing word frequencies in written documents\n", - " \n", - "See, for example, likely fictional story https://medium.com/@amuse/how-the-nsa-caught-satoshi-nakamoto-868affcef595" + "#### These texts are two very different stories written by the same author. The purpose is to see the difference in Dickens's vocabulary between the two stories.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Example 1. Dictionaries in python (associative arrays)\n", - "\n", - "Plot the frequency distribution of words on a web page." + "### Extract the text from the two novels" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "class=\"menu-item\t54\n", - "\t38\n", - "\t35\n", - "
  • \t28\n", - "\t21\n", - "\t21\n" - ] - } - ], - "source": [ - "import requests, re\n", - "# re is a module for regular expressions: to detect various combinations of characters\n", - "import operator\n", - "\n", - "# Start from a simple document\n", - "r = requests .get('http://eecs.utk.edu')\n", - "\n", - "# What comes back includes headers and other HTTP stuff, get just the body of the response\n", - "t = r.text\n", - "\n", - "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n", - "wds = re.split('\\s+',t)\n", - "\n", - "# now populate a dictionary (wf)\n", - "wf = {}\n", - "for w in wds:\n", - " if w in wf: wf [w] = wf [w] + 1\n", - " else: wf[w] = 1\n", - "\n", - "# dictionaries can not be sorted, so lets get a sorted *list* \n", - "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n", - "\n", - "# lets just have no more than 15 words \n", - "ml = min(len(wfs),15)\n", - "for i in range(1,ml,1):\n", - " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) " - ] - }, - { - "cell_type": "markdown", + "execution_count": 11, "metadata": {}, - "source": [ - "### Example 2\n", - "\n", - "Lots of markup in the output, lets remove it --- \n", - "\n", - "use BeautifulSoup and nltk modules and practice some regular expressions." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, "outputs": [], "source": [ "import requests, re, nltk\n", @@ -179,8 +91,8 @@ " return (wfs [ 0:ml ] [::-1], tw)\n", " \n", "# Now populate two lists \n", - "(wf_ee, tw_ee) = get_wf('http://www.gutenberg.org/ebooks/1342.txt.utf-8')\n", - "(wf_bu, tw_bu) = get_wf('http://www.gutenberg.org/ebooks/76.txt.utf-8')" + "(wf_ee, tw_ee) = get_wf('https://gutenberg.org/files/98/98-0.txt') #Tale of Two Cities\n", + "(wf_bu, tw_bu) = get_wf('https://gutenberg.org/cache/epub/1023/pg1023.txt') #Bleak House" ] }, { @@ -190,9 +102,9 @@ "outputs": [ { "data": { - "image/png": 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AOHwz8mL809Q/Bz6PY9nT8X0hFwDrwl6XOOdKgEuAjvgnsR8N1rUjSj5j8A8C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\nCF5HRCRWvP322zRq1IiDBw8CsGHDBsyMm2++uSTNPffcw0UXXQTAmjV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", "text/plain": [ - "" + "
    " ] }, "metadata": {}, @@ -204,6 +116,7 @@ "import numpy as np\n", "import pylab\n", "import matplotlib.pyplot as plt\n", + "plt.rcParams[\"font.family\"] = \"serif\"\n", "\n", "%matplotlib inline\n", "def plotTwoLists (wf_ee, wf_bu, title):\n", @@ -221,72 +134,18 @@ " ax1 = f .add_subplot (121)\n", " plt .subplots_adjust (wspace=.5)\n", "\n", - " pos = np .arange (len(wf_ee)+1) \n", + " pos = np .arange (len(wf_ee)) \n", " ax1 .tick_params (axis='both', which='major', labelsize=14)\n", " pylab .yticks (pos, [ x [0] for x in wf_ee ])\n", " ax1 .barh (range(len(wf_ee)), [ x [1] for x in wf_ee ], align='center')\n", "\n", " ax2 = f .add_subplot (122)\n", " ax2 .tick_params (axis='both', which='major', labelsize=14)\n", - " pos = np .arange (len(wf_bu)+1) \n", + " pos = np .arange (len(wf_bu)) \n", " pylab .yticks (pos, [ x [0] for x in wf_bu ])\n", " ax2 .barh (range (len(wf_bu)), [ x [1] for x in wf_bu ], align='center')\n", "\n", - "plotTwoLists (wf_ee, wf_bu, 'Difference between Pride and Prejudice and Huck Finn')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "and\t2836\n", - "of\t2676\n", - "to\t2646\n", - "a\t2217\n", - "in\t1422\n", - "his\t1205\n", - "he\t928\n", - "that\t920\n", - "was\t823\n", - "for\t798\n", - "with\t797\n", - "as\t672\n", - "I\t505\n", - "you\t497\n" - ] - } - ], - "source": [ - "#In case Project gutenberg is blocked you can download text to your laptop and copy to the docker container via scp\n", - "#Assuming the file name you copy is pg4680.txt here is how you change the script\n", - "# Please note the option errors='replace'\n", - "# without it python invariably runs into unicode errors\n", - "f = open ('pg4680.txt', 'r', encoding=\"ascii\", errors='replace')\n", - " \n", - "# What comes back includes headers and other HTTP stuff, get just the body of the response\n", - "t = f.read()\n", - "\n", - "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n", - "wds = re.split('\\s+',t)\n", - "\n", - "# now populate a dictionary (wf)\n", - "wf = {}\n", - "for w in wds:\n", - " if w in wf: wf [w] = wf [w] + 1\n", - " else: wf [w] = 1\n", - "\n", - "# dictionaries can not be sorted, so lets get a sorted *list* \n", - "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n", - "\n", - "# lets just have no more than 15 words \n", - "ml = min(len(wfs),15)\n", - "for i in range(1,ml,1):\n", - " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) " + "plotTwoLists (wf_ee, wf_bu, 'Difference between A Tale of Two Cities and Bleak House')" ] }, { @@ -296,8 +155,8 @@ "# Assignment 1\n", "\n", "1. Compare word frequencies between two works of a single author.\n", - "1. Compare word frequencies between works of two authors.\n", - "1. Are there some words preferred by one author but used less frequently by another author?\n", + "2. Compare word frequencies between works of two authors.\n", + "3. Are there some words preferred by one author but used less frequently by another author?\n", "\n", "Extra credit\n", "\n", @@ -311,56 +170,64 @@ ] }, { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "import requests, re, nltk\n", - "#In case your text is not on Project Gutenberg but at some other URL\n", - "#http://www.fullbooks.com/Our-World-or-The-Slaveholders-Daughter2.html\n", - "# that contains 12 parts\n", - "t = \"\"\n", - "for i in range(2,13):\n", - " r = requests .get('http://www.fullbooks.com/Our-World-or-The-Slaveholders-Daughter' + str(i) + '.html')\n", - " t = t + r.text" + "# Compare word frequencies between works of two authors." ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "1323653" + "
    " ] }, - "execution_count": 23, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "len(t)" + "(wf_drac, tw_drac) = get_wf('https://gutenberg.org/cache/epub/345/pg345.txt')\n", + "plotTwoLists (wf_ee, wf_drac, 'Difference between A Tale of Two Cities and Dracula')" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Are there some words preferred by one author but used less frequently by another author?\n", + "### Through the analysis, I have found that words like \"shall\" are used a lot more by Bram Stoker. Van also appears on Stoker's a lot since he is a main character. It is odd that his last name Helsing does not appear as well. Also, it seems as though Dickens has a more consisten vocabulary compared to Stoker. The spread is much more even while Stoker uses the top word a lot." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Presentation\n", + "## What is the question?\n", + "- How similar is the language used in Bleak House and A Tale of Two Cities?\n", + "- How similar is the language between Dracula and A Tale of Two Cities? Two books that were written 50 years apart.\n", + "## What was the approach?\n", + "- Make a graph that shows the frequency of certain words between the texts\n", + "## What problems did I encounter?\n", + "- I have blank words appearing in the graphs that I cannot get rid of.\n", + "## What results did I get?\n", + "- Dickens uses a lot of titles throughout.\n", + "- Stoker had no titles in his top words and most of the words were very different to Dickens's.\n", + "## What new ideas did this generate?\n", + "- It made me interested in seeing more authors and how their word frequencies differ." + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -374,7 +241,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.8.10" + }, + "vscode": { + "interpreter": { + "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" + } } }, "nbformat": 4,