NLTKing
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"https://www.nltk.org/book/\n",
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"\n",
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"https://www.nltk.org/book/ch00.html#natural-language-toolkit-nltk\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import nltk"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"nltk.download(\"book\", download_dir=\"/usr/local/share/nltk_data\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from nltk.book import *"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"text1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"type(text1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from nltk.text import Text"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"Text?"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Reading Words for the Future texts\n",
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"\n",
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"[Chapter 3 of the NLTK book](https://www.nltk.org/book/ch03.html) discusses using your own texts using urlopen and the nltk.text.Text class.\n",
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"\n",
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"We can use [urllib.request.urlopen](https://docs.python.org/3/library/urllib.request.html?highlight=urlopen#urllib.request.urlopen) + pull the \"raw\" URLs of materials from the [SI13 materials on git.xpub.nl](https://git.xpub.nl/XPUB/S13-Words-for-the-Future-materials)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"url = \"https://git.xpub.nl/XPUB/S13-Words-for-the-Future-materials/raw/branch/master/txt-essays/RESURGENCE%20Isabelle%20Stengers.txt\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from urllib.request import urlopen"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"text = urlopen(url).read().decode()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from nltk import word_tokenize"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"tokens = word_tokenize(text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stengers = nltk.text.Text(tokens)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stengers"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stengers.concordance(\"power\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stengers.similar(\"power\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stengers.common_contexts([\"power\", \"victims\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stengers.dispersion_plot([\"power\", \"freedom\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"freq = FreqDist(stengers)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"freq"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"freq['power']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"freq.plot(50)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"freq.plot(50, cumulative=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Counting Vocabulary\n",
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"\n",
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"## Making a function\n",
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"Investigating a text as a list of words, we discover that we can compare the count of the total number of words, with the number of unique words. If we compare "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"len(stengers)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"len(set(stengers))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def lexical_diversity(text):\n",
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" return len(text) / len(set(text))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"lexical_diversity(stengers)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def percentage (count, total):\n",
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" return 100 * count / total"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"percentage(4, 5)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"NB: BE CAREFUL RUNNING THE FOLLOWING LINE ... IT'S REALLY SLOW...\n",
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"Not all code is equal, and just because two different methods produce the same result\n",
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"doesn't mean they're equally usable in practice\n",
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"\n",
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"Why? because text1 (Moby Dick) is a list\n",
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"and checking if (x not in text1)\n",
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"has to scan the whole list of words\n",
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"AND THEN this scan is done FOR EVERY WORD in the stengers text\n",
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"The result is called \"order n squared\" execution, as the number of words in each text increases\n",
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"the time to perform the code get EXPONENTIALLY slower\n",
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"it's basically the phenomenon of nested loops on large lists.... SSSSSSSSSLLLLLLLLLOOOOOOOOOOOWWWWWWWWWWW"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# stengers_unique = []\n",
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"# for word in stengers.tokens:\n",
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"# if word not in text1:\n",
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"# stengers_unique.append(word)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# stengers_unique = [x for x in stengers.tokens if x not in text1]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"FIX: make a set based on the Moby Dick text, checking if something is in a set is VERY FAST compared to scanning a list (Order log(n) instead of n)..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"moby = set(text1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\"the\" in moby"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Rather than n\\*n (n squared), the following is just n * log(n) which is *not* exponential as n gets big"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stengers_unique = []\n",
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"for word in stengers.tokens:\n",
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" if word not in moby:\n",
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" stengers_unique.append(word)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The above can also be expressed using the more compact form of a list comprehension"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stengers_unique = [word for word in stengers.tokens if word not in moby]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"len(stengers_unique)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stengers_unique"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stengers_unique_text = Text(stengers_unique)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"freq = FreqDist(stengers_unique)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"freq.plot(50)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stengers_unique_text.concordance(\"witches\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Increasing the default figure size"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from IPython.core.pylabtools import figsize"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"figsize(20.0,4.8)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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Loading…
Reference in New Issue