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update textblob examples
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‎Chapter5/natural_language_processing.ipynb

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"### TextBlob: Processing Text in One Line of Code"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "955a5dec",
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"metadata": {},
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"source": [
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"Processing text doesn’t need to be hard. If you want to find the sentiment of the text, tokenize text, find noun phrase and word frequencies, correct spelling, etc in one line of code, try TextBlob.\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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"!python -m textblob.download_corpora"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "955a5dec",
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"metadata": {},
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"source": [
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"To quickly analyze text, including determining its sentiment, tokenization, noun phrase and word frequency analysis, and spelling correction, use TextBlob.\n",
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"\n",
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"To use TextBlob, start with creating a new instance of the TextBlob class with the text \"Today is a beautiful day\"."
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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": 4,
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"execution_count": 1,
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"id": "d288b6e4",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-09-11T19:52:20.151345Z",
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"start_time": "2021-09-11T19:52:19.550236Z"
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}
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},
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"outputs": [],
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"source": [
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"from textblob import TextBlob\n",
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"\n",
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"text = \"Today is a beautiful day\"\n",
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"blob = TextBlob(text)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "097352f8",
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"metadata": {},
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"source": [
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"Tokenize words:"
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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": 6,
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"id": "8fec9b0b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"WordList(['Today', 'is', 'a', 'beautiful', 'day'])"
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]
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},
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"execution_count": 4,
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from textblob import TextBlob\n",
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"\n",
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"text = \"Today is a beautiful day\"\n",
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"blob = TextBlob(text)\n",
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"\n",
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"blob.words # Word tokenization"
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"blob.words"
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]
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},
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{
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"cell_type": "markdown",
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"id": "26117ce3",
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"metadata": {},
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"source": [
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"Extract noun phrases:"
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]
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},
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{
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}
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],
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"source": [
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"blob.noun_phrases # Noun phrase extraction"
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"blob.noun_phrases"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b438b4a5",
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"metadata": {},
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"source": [
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"Analyze sentiment:"
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]
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},
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{
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}
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],
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"source": [
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"blob.sentiment # Sentiment analysis"
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"blob.sentiment"
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]
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},
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{
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"cell_type": "markdown",
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"id": "51743664",
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"metadata": {},
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"source": [
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"Count words:"
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]
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},
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{
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}
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],
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"source": [
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"blob.word_counts # Word counts"
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"blob.word_counts"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0bead4df",
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"metadata": {},
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"source": [
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"Correct spelling:"
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]
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},
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{
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}
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],
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"source": [
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"# Spelling correction\n",
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"text = \"Today is a beutiful day\"\n",
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"blob = TextBlob(text)\n",
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"blob.correct()"
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"id": "004126f8",
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"metadata": {},
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"source": [
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"[Link to TextBlob](https://textblob.readthedocs.io/en/dev/).\n",
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"[Link to TextBlob](https://bit.ly/465oJK0).\n",
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"\n",
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"[Link to my article about TextBlob](https://towardsdatascience.com/supercharge-your-python-string-with-textblob-2d9c08a8da05?sk=b9de5981cf74c0adf8d9f2a913e3ca05)."
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]

‎Chapter6/better_outputs.ipynb

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"id": "cce6ec0b",
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"metadata": {},
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"source": [
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"To see how Camelot works, start with reading the PDF file named 'foo.pdf' and extracts all the tables present in the file."
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"To see how Camelot works, start by reading the PDF file named 'foo.pdf' and extracts all the tables present in the file."
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]
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},
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{

‎docs/Chapter5/natural_language_processing.html

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@@ -551,7 +551,6 @@ <h1><span class="section-number">6.6. </span>Natural Language Processing<a class
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<p>This section some tools to process and work with text.</p>
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<section id="textblob-processing-text-in-one-line-of-code">
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<h2><span class="section-number">6.6.1. </span>TextBlob: Processing Text in One Line of Code<a class="headerlink" href="#textblob-processing-text-in-one-line-of-code" title="Permalink to this heading">#</a></h2>
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<p>Processing text doesn’t need to be hard. If you want to find the sentiment of the text, tokenize text, find noun phrase and word frequencies, correct spelling, etc in one line of code, try TextBlob.</p>
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<div class="cell tag_hide-cell docutils container">
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<details class="hide above-input">
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<summary aria-label="Toggle hidden content">
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</div>
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</details>
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</div>
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<p>To quickly analyze text, including determining its sentiment, tokenization, noun phrase and word frequency analysis, and spelling correction, use TextBlob.</p>
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<p>To use TextBlob, start with creating a new instance of the TextBlob class with the text “Today is a beautiful day”.</p>
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<div class="cell docutils container">
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<div class="cell_input docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">textblob</span> <span class="kn">import</span> <span class="n">TextBlob</span>
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<span class="n">text</span> <span class="o">=</span> <span class="s2">&quot;Today is a beautiful day&quot;</span>
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<span class="n">blob</span> <span class="o">=</span> <span class="n">TextBlob</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
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<span class="n">blob</span><span class="o">.</span><span class="n">words</span> <span class="c1"># Word tokenization</span>
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</pre></div>
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</div>
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</div>
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</div>
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<p>Tokenize words:</p>
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<div class="cell docutils container">
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<div class="cell_input docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">blob</span><span class="o">.</span><span class="n">words</span>
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</pre></div>
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</div>
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</div>
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</div>
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</div>
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</div>
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<p>Extract noun phrases:</p>
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<div class="cell docutils container">
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<div class="cell_input docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">blob</span><span class="o">.</span><span class="n">noun_phrases</span> <span class="c1"># Noun phrase extraction</span>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">blob</span><span class="o">.</span><span class="n">noun_phrases</span>
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</pre></div>
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</div>
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</div>
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</div>
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</div>
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<p>Analyze sentiment:</p>
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<div class="cell docutils container">
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<div class="cell_input docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">blob</span><span class="o">.</span><span class="n">sentiment</span> <span class="c1"># Sentiment analysis</span>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">blob</span><span class="o">.</span><span class="n">sentiment</span>
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</pre></div>
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</div>
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</div>
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</div>
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</div>
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<p>Count words:</p>
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<div class="cell docutils container">
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<div class="cell_input docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">blob</span><span class="o">.</span><span class="n">word_counts</span> <span class="c1"># Word counts</span>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">blob</span><span class="o">.</span><span class="n">word_counts</span>
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</pre></div>
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</div>
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</div>
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</div>
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</div>
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</div>
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<p>Correct spelling:</p>
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<div class="cell docutils container">
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<div class="cell_input docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Spelling correction</span>
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<span class="n">text</span> <span class="o">=</span> <span class="s2">&quot;Today is a beutiful day&quot;</span>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">text</span> <span class="o">=</span> <span class="s2">&quot;Today is a beutiful day&quot;</span>
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<span class="n">blob</span> <span class="o">=</span> <span class="n">TextBlob</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
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<span class="n">blob</span><span class="o">.</span><span class="n">correct</span><span class="p">()</span>
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</pre></div>
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</div>
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</div>
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</div>
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<p><a class="reference external" href="https://textblob.readthedocs.io/en/dev/">Link to TextBlob</a>.</p>
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<p><a class="reference external" href="https://bit.ly/465oJK0">Link to TextBlob</a>.</p>
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<p><a class="reference external" href="https://towardsdatascience.com/supercharge-your-python-string-with-textblob-2d9c08a8da05?sk=b9de5981cf74c0adf8d9f2a913e3ca05">Link to my article about TextBlob</a>.</p>
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</section>
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<section id="convert-names-into-a-generalized-format">

‎docs/Chapter6/better_outputs.html

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</details>
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</div>
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<p>With Camelot, you can extract tables from PDFs using Python and convert the data into a more structured format, such as a pandas DataFrame or a CSV file for efficient analysis, manipulation, and integration.</p>
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<p>To see how Camelot works, start with reading the PDF file named ‘foo.pdf’ and extracts all the tables present in the file.</p>
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<p>To see how Camelot works, start by reading the PDF file named ‘foo.pdf’ and extracts all the tables present in the file.</p>
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<div class="cell docutils container">
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<div class="cell_input docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">camelot</span>

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