<h1 id="about-spacy">About spaCy</h1>
<p><a href="https://spacy.io">spaCy</a> is a free, open-source library for advanced Natural
Language Processing (NLP) in Python. It&#39;s designed
specifically for production use and helps you build
applications that process and &quot;understand&quot; large volumes
of text.</p>
<p>To learn more about spaCy, take my DataCamp course
<a href="https://www.datacamp.com/courses/advanced-nlp-with-spacy">&quot;Advanced NLP with spaCy&quot;</a>. You can download the Cheat Sheet <a href="http://datacamp-community-prod.s3.amazonaws.com/b5685b85-c4de-4987-926d-b999d0f5a8b6">here</a>!</p>
<p><center><a href="http://datacamp-community-prod.s3.amazonaws.com/b5685b85-c4de-4987-926d-b999d0f5a8b6"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625838105/spaCy_Cheat_Sheet-_Advanced_NLP_in_Python_tdcgca.png" /></a></p>

<h2 id="getting-started">Getting started</h2>
<pre><code class="lang-python">$ pip install spacy
</code></pre>
<pre><code class="lang-python">import spacy
</code></pre>
<h2 id="statistical-models">Statistical models</h2>
<h3 id="download-statistical-models">Download statistical models</h3>
<p>Predict part-of-speech tags, dependency labels, named entities and more.
<a href="https://spacy.io/models">See here</a> for available models.</p>
<pre><code class="lang-bash">$ python -m spacy download en_core_web_sm
</code></pre>
<h3 id="check-that-your-installed-models-are-up-to-date">Check that your installed models are up to date</h3>
<pre><code class="lang-bash">$ python -m spacy validate
</code></pre>
<h3 id="loading-statistical-models">Loading statistical models</h3>
<pre><code class="lang-python">import spacy
# Load the installed model &quot;en_core_web_sm&quot;
nlp = spacy.load(&quot;en_core_web_sm&quot;)
</code></pre>
<h2 id="documents-tokens-and-spans">Documents, tokens and spans</h2>
<h3 id="processing-text">Processing text</h3>
<p>Processing text with the <code>nlp</code> object returns a <code>Doc</code> object that holds all
information about the tokens, their linguistic features and their relationships.</p>
<pre><code class="lang-python">doc = nlp(&quot;This is a text&quot;)
</code></pre>
<h3 id="accessing-token-attributes">Accessing token attributes</h3>
<pre><code class="lang-python">doc = nlp(&quot;This is a text&quot;)
# Token texts
[token.text for token in doc]
# [&#39;This&#39;, &#39;is&#39;, &#39;a&#39;, &#39;text&#39;]
</code></pre>
<h2 id="spans">Spans</h2>
<h3 id="accessing-spans">Accessing spans</h3>
<p>Span indices are <strong>exclusive</strong>. So <code>doc[2:4]</code> is a span starting at token 2, up to – but not including! – token 4.</p>
<pre><code class="lang-python">doc = nlp(&quot;This is a text&quot;)
span = doc[2:4]
span.text
# &#39;a text&#39;
</code></pre>
<h3 id="creating-a-span-manually">Creating a span manually</h3>
<pre><code class="lang-python"># Import the Span object
from spacy.tokens import Span
# Create a Doc object
doc = nlp(&quot;I live in New York&quot;)
# Span for &quot;New York&quot; with label GPE (geopolitical)
span = Span(doc, 3, 5, label=&quot;GPE&quot;)
span.text
# &#39;New York&#39;
</code></pre>
<h2 id="linguistic-features">Linguistic features</h2>
<p>Attributes return label IDs. For string labels, use the attributes with an
underscore. For example, <code>token.pos_</code>.</p>
<h2 id="part-of-speech-tags-predicted-by-statistical-model-">Part-of-speech tags (predicted by statistical model)</h2>
<pre><code class="lang-python">doc = nlp(&quot;This is a text.&quot;)
# Coarse-grained part-of-speech tags
[token.pos_ for token in doc]
# [&#39;DET&#39;, &#39;VERB&#39;, &#39;DET&#39;, &#39;NOUN&#39;, &#39;PUNCT&#39;]
# Fine-grained part-of-speech tags
[token.tag_ for token in doc]
# [&#39;DT&#39;, &#39;VBZ&#39;, &#39;DT&#39;, &#39;NN&#39;, &#39;.&#39;]
</code></pre>
<h2 id="syntactic-dependencies-predicted-by-statistical-model-">Syntactic dependencies (predicted by statistical model)</h2>
<pre><code class="lang-python">doc = nlp(&quot;This is a text.&quot;)
# Dependency labels
[token.dep_ for token in doc]
# [&#39;nsubj&#39;, &#39;ROOT&#39;, &#39;det&#39;, &#39;attr&#39;, &#39;punct&#39;]
# Syntactic head token (governor)
[token.head.text for token in doc]
# [&#39;is&#39;, &#39;is&#39;, &#39;text&#39;, &#39;is&#39;, &#39;is&#39;]
</code></pre>
<h2 id="named-entities-predicted-by-statistical-model-">Named Entities (predicted by statistical model)</h2>
<pre><code class="lang-python">doc = nlp(&quot;Larry Page founded Google&quot;)
# Text and label of named entity span
[(ent.text, ent.label_) for ent in doc.ents]
# [(&#39;Larry Page&#39;, &#39;PERSON&#39;), (&#39;Google&#39;, &#39;ORG&#39;)]
</code></pre>
<h2 id="sentences-usually-needs-the-dependency-parser-">Sentences (usually needs the dependency parser)</h2>
<pre><code class="lang-python">doc = nlp(&quot;This a sentence. This is another one.&quot;)
# doc.sents is a generator that yields sentence spans
[sent.text for sent in doc.sents]
# [&#39;This is a sentence.&#39;, &#39;This is another one.&#39;]
</code></pre>
<h2 id="base-noun-phrases-needs-the-tagger-and-parser-">Base noun phrases (needs the tagger and parser)</h2>
<pre><code class="lang-python">doc = nlp(&quot;I have a red car&quot;)
# doc.noun_chunks is a generator that yields spans
[chunk.text for chunk in doc.noun_chunks]
# [&#39;I&#39;, &#39;a red car&#39;]
</code></pre>
<h2 id="label-explanations">Label explanations</h2>
<pre><code class="lang-python">spacy.explain(&quot;RB&quot;)
# &#39;adverb&#39;
spacy.explain(&quot;GPE&quot;)
# &#39;Countries, cities, states&#39;
</code></pre>
<h2 id="visualizing">Visualizing</h2>
<p>⚠️ If you&#39;re in a Jupyter notebook, use <code>displacy.render</code>. Otherwise, use <code>displacy.serve</code> to start a web server and show the visualization in your browser.</p>
<pre><code class="lang-python">from spacy import displacy
</code></pre>
<h3 id="visualize-dependencies">Visualize dependencies</h3>
<pre><code class="lang-python">doc = nlp(&quot;This is a sentence&quot;)
displacy.render(doc, style=&quot;dep&quot;)
</code></pre>
<p><img width="640" alt="displacy_dep" src="https://user-images.githubusercontent.com/13643239/52234132-38e76a00-28c1-11e9-82e6-e5d6b4a7f339.png"></p>
<h3 id="visualize-named-entities">Visualize named entities</h3>
<pre><code class="lang-python">doc = nlp(&quot;Larry Page founded Google&quot;)
displacy.render(doc, style=&quot;ent&quot;)
</code></pre>
<p><img width="422" alt="displacy_ent" src="https://user-images.githubusercontent.com/13643239/52234151-443a9580-28c1-11e9-831a-9adee3ec87b1.png"></p>
<h2 id="word-vectors-and-similarity">Word vectors and similarity</h2>
<p>⚠️ To use word vectors, you need to install the larger models
ending in <code>md</code> or <code>lg</code> , for <code>example en_core_web_lg</code>.</p>
<h3 id="comparing-similarity">Comparing similarity</h3>
<pre><code class="lang-python">doc1 = nlp(&quot;I like cats&quot;)
doc2 = nlp(&quot;I like dogs&quot;)
# Compare 2 documents
doc1.similarity(doc2)
# Compare 2 tokens
doc1[2].similarity(doc2[2])
# Compare tokens and spans
doc1[0].similarity(doc2[1:3])
</code></pre>
<h3 id="accessing-word-vectors">Accessing word vectors</h3>
<pre><code class="lang-python"># Vector as a numpy array
doc = nlp(&quot;I like cats&quot;)
# The L2 norm of the token&#39;s vector
doc[2].vector
doc[2].vector_norm
</code></pre>
<h2 id="pipeline-components">Pipeline components</h2>
<p>Functions that take a <code>Doc</code> object, modify it and return it.</p>
<p><img width="682" alt="pipeline" src="https://user-images.githubusercontent.com/13643239/55229632-dbff9480-521d-11e9-8499-efb2a9c948db.png"></p>
<h3 id="pipeline-information">Pipeline information</h3>
<pre><code class="lang-python">nlp = spacy.load(&quot;en_core_web_sm&quot;)
nlp.pipe_names
# [&#39;tagger&#39;, &#39;parser&#39;, &#39;ner&#39;]
nlp.pipeline
# [(&#39;tagger&#39;, &lt;spacy.pipeline.Tagger&gt;),
# (&#39;parser&#39;, &lt;spacy.pipeline.DependencyParser&gt;),
# (&#39;ner&#39;, &lt;spacy.pipeline.EntityRecognizer&gt;)]
</code></pre>
<h3 id="custom-components">Custom components</h3>
<pre><code class="lang-python"># Function that modifies the doc and returns it
def custom_component(doc):
 print(&quot;Do something to the doc here!&quot;)
 return doc

# Add the component first in the pipeline
nlp.add_pipe(custom_component, first=True)
</code></pre>
<p>Components can be added <code>first</code>, <code>last</code> (default), or <code>before</code> or <code>after</code> an
existing component.</p>
<h2 id="extension-attributes">Extension attributes</h2>
<p>Custom attributes that are registered on the global <code>Doc</code>, <code>Token</code> and <code>Span</code>
classes and become available as <code>._</code>.</p>
<pre><code class="lang-python">from spacy.tokens import Doc, Token, Span
doc = nlp(&quot;The sky over New York is blue&quot;)
</code></pre>
<h3 id="attribute-extensions-with-default-value-">Attribute extensions (with default value)</h3>
<pre><code class="lang-python"># Register custom attribute on Token class
Token.set_extension(&quot;is_color&quot;, default=False)
# Overwrite extension attribute with default value
doc[6]._.is_color = True
</code></pre>
<h3 id="property-extensions-with-getter-setter-">Property extensions (with getter &amp; setter)</h3>
<pre><code class="lang-python"># Register custom attribute on Doc class
get_reversed = lambda doc: doc.text[::-1]
Doc.set_extension(&quot;reversed&quot;, getter=get_reversed)
# Compute value of extension attribute with getter
doc._.reversed
# &#39;eulb si kroY weN revo yks ehT&#39;
</code></pre>
<h3 id="method-extensions-callable-method-">Method extensions (callable method)</h3>
<pre><code class="lang-python"># Register custom attribute on Span class
has_label = lambda span, label: span.label_ == label
Span.set_extension(&quot;has_label&quot;, method=has_label)
# Compute value of extension attribute with method
doc[3:5].has_label(&quot;GPE&quot;)
# True
</code></pre>
<h2 id="rule-based-matching">Rule-based matching</h2>
<h3 id="using-the-matcher">Using the Matcher</h3>
<pre><code class="lang-python"># Matcher is initialized with the shared vocab
from spacy.matcher import Matcher
# Each dict represents one token and its attributes
matcher = Matcher(nlp.vocab)
# Add with ID, optional callback and pattern(s)
pattern = [{&quot;LOWER&quot;: &quot;new&quot;}, {&quot;LOWER&quot;: &quot;york&quot;}]
matcher.add(&#39;CITIES&#39;, None, pattern)
# Match by calling the matcher on a Doc object
doc = nlp(&quot;I live in New York&quot;)
matches = matcher(doc)
# Matches are (match_id, start, end) tuples
for match_id, start, end in matches:
     # Get the matched span by slicing the Doc
     span = doc[start:end]
     print(span.text)
# &#39;New York&#39;
</code></pre>
<h3 id="token-patterns">Token patterns</h3>
<pre><code class="lang-python"># &quot;love cats&quot;, &quot;loving cats&quot;, &quot;loved cats&quot;
pattern1 = [{&quot;LEMMA&quot;: &quot;love&quot;}, {&quot;LOWER&quot;: &quot;cats&quot;}]
# &quot;10 people&quot;, &quot;twenty people&quot;
pattern2 = [{&quot;LIKE_NUM&quot;: True}, {&quot;TEXT&quot;: &quot;people&quot;}]
# &quot;book&quot;, &quot;a cat&quot;, &quot;the sea&quot; (noun + optional article)
pattern3 = [{&quot;POS&quot;: &quot;DET&quot;, &quot;OP&quot;: &quot;?&quot;}, {&quot;POS&quot;: &quot;NOUN&quot;}]
</code></pre>
<h3 id="operators-and-quantifiers">Operators and quantifiers</h3>
<p>Can be added to a token dict as the <code>&quot;OP&quot;</code> key.</p>
<div class="output_wrapper"><table>
<thead>
<tr>
<th>OP</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>!</code></td>
<td>Negate pattern and match exactly 0 times.</td>
</tr>
<tr>
<td><code>?</code></td>
<td>Make pattern optional and match 0 or 1 times.</td>
</tr>
<tr>
<td><code>+</code></td>
<td>Require pattern to match 1 or more times.</td>
</tr>
<tr>
<td><code>*</code></td>
<td>Allow pattern to match 0 or more times.</td>
</tr>
</tbody>
</table></div>
<h2 id="glossary">Glossary</h2>
<div class="output_wrapper"><table>
<thead>
<tr>
<th>Name</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Tokenization</strong></td>
<td>Segmenting text into words, punctuation etc.</td>
</tr>
<tr>
<td><strong>Lemmatization</strong></td>
<td>Assigning the base forms of words, for example: &quot;was&quot; → &quot;be&quot; or &quot;rats&quot; → &quot;rat&quot;.</td>
</tr>
<tr>
<td><strong>Sentence Boundary Detection</strong></td>
<td>Finding and segmenting individual sentences.</td>
</tr>
<tr>
<td><strong>Part-of-speech (POS) Tagging</strong></td>
<td>Assigning word types to tokens like verb or noun.</td>
</tr>
<tr>
<td><strong>Dependency Parsing</strong></td>
<td>Assigning syntactic dependency labels, describing the relations between individual tokens, like subject or object.</td>
</tr>
<tr>
<td><strong>Named Entity Recognition (NER)</strong></td>
<td>Labeling named &quot;real-world&quot; objects, like persons, companies or locations.</td>
</tr>
<tr>
<td><strong>Text Classification</strong></td>
<td>Assigning categories or labels to a whole document, or parts of a document.</td>
</tr>
<tr>
<td><strong>Statistical model</strong></td>
<td>Process for making predictions based on examples.</td>
</tr>
<tr>
<td><strong>Training</strong></td>
<td>Updating a statistical model with new examples.</td>
</tr>
</tbody>
</table></div>


https://www.datacamp.com/community/blog/spacy-cheatsheet

Check out the first official spaCy cheat sheet! A handy two-page reference to the most important concepts and features.

spaCy Cheat Sheet: Advanced NLP in Python

<section>
<p>Most of you who are learning data science with Python will&nbsp;have definitely heard already about <code>scikit-learn</code>, the open source Python library that implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.&nbsp;</p>

<p>If you&#39;re still quite new to the field, you should be aware that machine learning, and thus also this Python library, belong to the must-knows for every aspiring data scientist.&nbsp;</p>

<p>That&#39;s why DataCamp has created a <code>scikit-learn</code> cheat sheet for those of you who have already started learning about the Python package, but that still want a handy reference sheet. Or, if you still have no idea about how <code>scikit-learn</code> works, this machine learning cheat sheet might come in handy to get a quick first idea of the basics that you need to know to get started.&nbsp;</p>

<p>Either way, we&#39;re sure that you&#39;re going to find it useful when you&#39;re tackling machine learning problems! &nbsp;</p>

<p>This&nbsp;<code>scikit-learn</code> cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you&#39;ll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.&nbsp;</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/eb807da5-dce5-4b97-a54d-74e89f14266b" target="_blank"><img alt="Scikit-Learn Cheat Sheet" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625837937/Scikit-Learn_Cheat_Sheet-_Python_Machine_Learning_wxjgwe.png" style="width: 800px; height: 551px;" /></a></p>

<p>In short, this cheat sheet will kickstart your data science&nbsp;projects: with the help of code examples, you&#39;ll have created, validated and tuned your machine learning models in no time. &nbsp;</p>

<p>So what are you waiting for? Time to get started!</p>

<p><strong>(Click above to download a printable version or read the online version below.)&nbsp;</strong></p>

<div id="scoped-content">
<style type="text/css">:target:before {
content:"";
display:block;
height:150px;
margin:-150px 0 0;
}

h3 {font-weight:normal;
}
h4 {
	font-weight: lighter;
}
</style>
<h2>Python For Data Science Cheat Sheet: Scikit-learn</h2>

<p>Scikit-learn is an open source Python library that implements a range of machine learning, preprocessing, cross-validation and visualization algorithms using a unified interface.</p>

<h3>A Basic Example</h3>

<pre>
<code>&gt;&gt;&gt; from sklearn import neighbors, datasets, preprocessing
&gt;&gt;&gt; from sklearn.model_selection import train_test_split
&gt;&gt;&gt; from sklearn.metrics import accuracy_score
&gt;&gt;&gt; iris = datasets.load_iris()
&gt;&gt;&gt; X, y = iris.data[:, :2], iris.target
&gt;&gt;&gt; X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)
&gt;&gt;&gt; scaler = preprocessing.StandardScaler().fit(X_train)
&gt;&gt;&gt; X_train = scaler.transform(X_train)
&gt;&gt;&gt; X_test = scaler.transform(X_test)
&gt;&gt;&gt; knn = neighbors.KNeighborsClassifier(n_neighbors=5)
&gt;&gt;&gt; knn.fit(X_train, y_train)
&gt;&gt;&gt; y_pred = knn.predict(X_test)
&gt;&gt;&gt; accuracy_score(y_test, y_pred)</code></pre>

<h3>Loading The Data</h3>

<p>Your data needs to be numeric and stored as NumPy arrays or SciPy sparse matrices. Other types that are convertible to numeric arrays, such as Pandas DataFrame, are also acceptable.</p>

<pre>
<code>&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; X = np.random.random((10,5))
&gt;&gt;&gt; y = np.array(['M','M','F','F','M','F','M','M','F','F','F'])
&gt;&gt;&gt; X[X &lt; 0.7] = 0</code></pre>

<h3>Preprocessing The Data</h3>

<h4>Standardization</h4>

<pre>
<code>&gt;&gt;&gt; from sklearn.preprocessing import StandardScaler
&gt;&gt;&gt; scaler = StandardScaler().fit(X_train)
&gt;&gt;&gt; standardized_X = scaler.transform(X_train)
&gt;&gt;&gt; standardized_X_test = scaler.transform(X_test)</code></pre>

<h4>Normalization</h4>

<pre>
<code>&gt;&gt;&gt; from sklearn.preprocessing import Normalizer
&gt;&gt;&gt; scaler = Normalizer().fit(X_train)
&gt;&gt;&gt; normalized_X = scaler.transform(X_train)
&gt;&gt;&gt; normalized_X_test = scaler.transform(X_test)</code></pre>

<h4>Binarization</h4>

<pre>
<code>&gt;&gt;&gt; from sklearn.preprocessing import Binarizer
&gt;&gt;&gt; binarizer = Binarizer(threshold=0.0).fit(X)
&gt;&gt;&gt; binary_X = binarizer.transform(X)</code></pre>

<h4>Encoding Categorical Features</h4>

<pre>
<code>&gt;&gt;&gt; from sklearn.preprocessing import LabelEncoder
&gt;&gt;&gt; enc = LabelEncoder()
&gt;&gt;&gt; y = enc.fit_transform(y)</code></pre>

<h4>Imputing Missing Values</h4>

<pre>
<code>&gt;&gt;&gt;from sklearn.preprocessing import Imputer
&gt;&gt;&gt;imp = Imputer(missing_values=0, strategy='mean', axis=0)
&gt;&gt;&gt;imp.fit_transform(X_train)</code></pre>

<h4>Generating Polynomial Features</h4>

<pre>
<code>&gt;&gt;&gt; from sklearn.preprocessing import PolynomialFeatures
&gt;&gt;&gt; poly = PolynomialFeatures(5)
&gt;&gt;&gt; oly.fit_transform(X)</code></pre>

<h3>Training And Test Data</h3>

<pre>
<code>&gt;&gt;&gt; from sklearn.model_selection import train_test_split
&gt;&gt;&gt; X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0)</code></pre>

<h3>Create Your Model</h3>

<h4>Supervised Learning Estimators</h4>

<p><strong>Linear Regression</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn.linear_model import LinearRegression
&gt;&gt;&gt; lr = LinearRegression(normalize=True)</code></pre>

<p><strong>Support Vector Machines (SVM)</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn.svm import SVC
&gt;&gt;&gt; svc = SVC(kernel='linear')</code></pre>

<p><strong>Naive Bayes</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn.naive_bayes import GaussianNB
&gt;&gt;&gt; gnb = GaussianNB()</code></pre>

<p><strong>KNN</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn import neighbors
&gt;&gt;&gt; knn = neighbors.KNeighborsClassifier(n_neighbors=5)</code></pre>

<p><strong>Unsupervised Learning Estimators</strong></p>

<p><strong>Principal Component Analysis (PCA)</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn.decomposition import PCA
&gt;&gt;&gt; pca = PCA(n_components=0.95)</code></pre>

<p><strong>K Means</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn.cluster import KMeans
&gt;&gt;&gt; k_means = KMeans(n_clusters=3, random_state=0)</code></pre>

<h3>Model Fitting</h3>

<h4>Supervised learning</h4>

<pre>
<code>&gt;&gt;&gt; lr.fit(X, y)
&gt;&gt;&gt; knn.fit(X_train, y_train)
&gt;&gt;&gt; svc.fit(X_train, y_train)</code></pre>
Unsupervised Learning

<pre>
<code>&gt;&gt;&gt; k_means.fit(X_train)
&gt;&gt;&gt; pca_model = pca.fit_transform(X_train)</code></pre>

<h3>Prediction</h3>

<p><strong>Supervised Estimators</strong></p>

<pre>
<code>&gt;&gt;&gt; y_pred = svc.predict(np.random.random((2,5)))</code></pre>

<pre>
<code>&gt;&gt;&gt; y_pred = lr.predict(X_test)</code></pre>

<pre>
<code>&gt;&gt;&gt; y_pred = knn.predict_proba(X_test))</code></pre>

<p><strong>Unsupervised Estimators</strong></p>

<pre>
<code>&gt;&gt;&gt; y_pred = k_means.predict(X_test)</code></pre>

<h3>Evaluate Your Model&#39;s Performance</h3>

<h4>Classification Metrics</h4>

<p><strong>Accuracy Score</strong></p>

<pre>
<code>&gt;&gt;&gt; knn.score(X_test, y_test)
&gt;&gt;&gt; from sklearn.metrics import accuracy_score
&gt;&gt;&gt; accuracy_score(y_test, y_pred)</code></pre>

<p><strong>Classification Report</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn.metrics import classification_report
&gt;&gt;&gt; print(classification_report(y_test, y_pred)))</code></pre>

<p><strong>Confusion Matrix</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn.metrics import confusion_matrix
&gt;&gt;&gt; print(confusion_matrix(y_test, y_pred)))</code></pre>

<h4>Regression Metrics</h4>

<p><strong>Mean Absolute Error</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn.metrics import mean_absolute_error
&gt;&gt;&gt; y_true = [3, -0.5, 2])
&gt;&gt;&gt; mean_absolute_error(y_true, y_pred))</code></pre>

<p><strong>Mean Squared Error</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn.metrics import mean_squared_error
&gt;&gt;&gt; mean_squared_error(y_test, y_pred))</code></pre>

<p><strong>R<sup>2</sup> Score</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn.metrics import r2_score
&gt;&gt;&gt; r2_score(y_true, y_pred))</code></pre>

<h4>Clustering Metrics</h4>

<p><strong>Adjusted Rand Index</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn.metrics import adjusted_rand_score
&gt;&gt;&gt; adjusted_rand_score(y_true, y_pred))</code></pre>

<p><strong>Homogeneity</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn.metrics import homogeneity_score
&gt;&gt;&gt; homogeneity_score(y_true, y_pred))</code></pre>

<p><strong>V-measure</strong></p>

<pre>
<code>&gt;&gt;&gt; from sklearn.metrics import v_measure_score
&gt;&gt;&gt; metrics.v_measure_score(y_true, y_pred))</code></pre>

<h4>Cross-Validation</h4>

<pre>
<code>&gt;&gt;&gt; print(cross_val_score(knn, X_train, y_train, cv=4))
&gt;&gt;&gt; print(cross_val_score(lr, X, y, cv=2))</code></pre>

<h3>Tune Your Model</h3>

<h4>Grid Search</h4>

<pre>
<code>&gt;&gt;&gt; from sklearn.grid_search import GridSearchCV</code></pre>

<pre>
<code>&gt;&gt;&gt; params = {"n_neighbors": np.arange(1,3), "metric": ["euclidean", "cityblock"]}</code></pre>

<pre>
<code>&gt;&gt;&gt; grid = GridSearchCV(estimator=knn,param_grid=params)</code></pre>

<pre>
<code>&gt;&gt;&gt; grid.fit(X_train, y_train)</code></pre>

<pre>
<code>&gt;&gt;&gt; print(grid.best_score_)</code></pre>

<pre>
<code>&gt;&gt;&gt; print(grid.best_estimator_.n_neighbors)</code></pre>

<h4>Randomized Parameter Optimization</h4>

<pre>
<code>&gt;&gt;&gt; from sklearn.grid_search import RandomizedSearchCV</code></pre>

<pre>
<code>&gt;&gt;&gt; params = {"n_neighbors": range(1,5), "weights": ["uniform", "distance"]}</code></pre>

<pre>
<code>&gt;&gt;&gt; rsearch = RandomizedSearchCV(estimator=knn,
   param_distributions=params,
   cv=4,
   n_iter=8,
   random_state=5)</code></pre>

<pre>
<code>&gt;&gt;&gt; rsearch.fit(X_train, y_train)</code></pre>

<pre>
<code>&gt;&gt;&gt; print(rsearch.best_score_)</code></pre>

<h3>Going Further</h3>

<p>Begin with&nbsp;<a href="https://www.datacamp.com/community/tutorials/machine-learning-python/">our scikit-learn tutorial for beginners</a>, in which you&#39;ll learn in an easy, step-by-step way&nbsp;how to explore handwritten digits data, how to create a model for it, how to fit your data to your model and how to predict target values. In addition, you&#39;ll make use of Python&#39;s data visualization library matplotlib to visualize your results.</p>

<p>&nbsp;</p>

<p>PS. Don&#39;t miss our <a href="https://www.datacamp.com/community/blog/bokeh-cheat-sheet-python/">Bokeh cheat sheet</a>, the&nbsp;<a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/">Pandas cheat sheet</a>&nbsp;or the&nbsp;<a href="https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics/">Python cheat sheet for data science</a>.&nbsp;</p>
</div>
</section>


https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.

Scikit-Learn Cheat Sheet: Python Machine Learning

<p>Before doing any data cleaning, wrangling, visualizing, ... You&#39;ll need to know how to get data into Python. As you know, there are many ways to import data into Python, depending also on which files you&#39;re dealing with.</p>
<p>However, you&#39;ll most often make use of the pandas and the NumPy libraries: The pandas library is one of the most preferred tools for data scientists to do data manipulation and analysis, next to matplotlib for data visualization and NumPy, the fundamental library for scientific computing in Python on which Pandas was built. </p>
<p>In this importing data in Python cheat sheet, you&#39;ll find some NumPy and pandas functions, together with functions that are built in the Python programming language, that will help you to get your data in Python fast! </p>
<p>This quick guide helps you to learn the basics of importing data in Python that you will need to get started on cleaning and wrangling your data! </p>
<p><a href="http://datacamp-community-prod.s3.amazonaws.com/72e88aa1-b4f2-4658-9d86-15becf8263df"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625837839/Importing_Data_in_Python_Cheat_Sheet_osn6zr.png" alt="data import python cheat sheet"></a></p>
<p>The Importing Data in Python cheat sheet will guide you through the basics of getting your data in your workspace: you&#39;ll not only learn how to import flat files such as text files, but you&#39;ll also see how you can get data from files native to other software such as Excel spreadsheets, Stata, SAS and MATLAB files and relational databases. On top of that, you&#39;ll get some more information on how to ask for help, how to navigate your filesystem and how to start exploring your data.</p>
<p>In short, everything that you need to kickstart your data science learning with Python!</p>
<p>Do you want to learn more? Start the <a href="https://www.datacamp.com/courses/importing-data-in-python-part-1/">Importing Data in Python course</a> for free now or try out our <a href="https://www.datacamp.com/community/tutorials/python-excel-tutorial">Python Excel tutorial</a>! </p>
<p>Also, don&#39;t miss out on our <a href="https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics">Python cheat sheet</a> for data science, or the many others we have <a href="https://www.datacamp.com/community/data-science-cheatsheets">here</a> on our Community! </p>


https://www.datacamp.com/community/blog/importing-data-python-cheat-sheet

With this Python cheat sheet, you'll have a handy reference guide to importing your data, from flat files to files native to other software, and relational databases.

Importing Data in Python Cheat Sheet

<p>Bokeh distinguishes itself from other Python visualization libraries such as Matplotlib or Seaborn&nbsp;in the fact that it is an&nbsp;interactive visualization library that is ideal for anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.&nbsp;</p>

<p>Bokeh is also known for&nbsp;enabling high-performance visual presentation of&nbsp;large data sets in modern web browsers.&nbsp;</p>

<p>For data scientists, Bokeh is the ideal tool to build statistical charts quickly and easily; But there are also other advantages, such as the various output options and the fact that you can embed your visualizations in applications. And let&#39;s not forget that the&nbsp;wide variety of visualization customization&nbsp;options makes this Python library an indispensable tool for your data science toolbox.</p>

<p>As you might know, DataCamp recently launched the&nbsp;<a href="https://www.datacamp.com/courses/interactive-data-visualization-with-bokeh/">Interactive Data Visualization with Bokeh</a> course together with <a href="https://www.continuum.io/people/bryan-van-de-ven">Bryan Van de Ven</a>, Bokeh core contributor.&nbsp;</p>

<p>Now, DataCamp has created a Bokeh cheat sheet for those who have already taken the course and that still want a handy one-page reference&nbsp;or for those who&nbsp;need an extra push to get started.</p>

<p>In short, you&#39;ll see that this cheat sheet not&nbsp;only presents you with the five steps that you can go through to make beautiful plots but will also introduce you to the basics of statistical charts.&nbsp;</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/f0c1e06f-53ba-4f3b-aa9f-b196221f55a3" target="_blank"><img alt="Python Bokeh Cheat Sheet" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625838040/Python_Data_Visualization-_Bokeh_Cheat_Sheet_cjcnps.png" style="width: 400px; height: 276px;" /></a></p>

<p>In no time, this Bokeh cheat sheet will make you familiar&nbsp;with how you can prepare your data, create a new plot, add renderers for your data with custom visualizations, output your plot and save or show it. And the creation of basic statistical charts will hold no secrets for you any longer.&nbsp;</p>

<p>Boost your Python data visualizations now with the help of Bokeh! :)</p>

<p>PS. Check out our&nbsp;<a href="https://www.datacamp.com/courses/interactive-data-visualization-with-bokeh/">Interactive Data Visualization with Bokeh</a> course or visit the <a href="http://bokeh.pydata.org/">Bokeh documentation website</a>. Also,&nbsp;don&#39;t miss our <a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/">Pandas cheat sheet</a>&nbsp;or the <a href="https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics/">Python cheat sheet for data science</a>.&nbsp;</p>


https://www.datacamp.com/community/blog/bokeh-cheat-sheet-python

A handy cheat sheet to interactive plotting and statistical charts with Bokeh.

Python Data Visualization: Bokeh Cheat Sheet

<p><a href="http://keras.io/">Keras</a> is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models.</p>

<p>We recently launched one of the first online interactive deep learning course using Keras 2.0, called &quot;<a href="https://www.datacamp.com/courses/deep-learning-in-python/">Deep Learning in Python</a>&quot;.</p>

<p>Now, DataCamp has created a Keras cheat sheet for those who have already taken the course and that still want a handy one-page reference or for those who need an extra push to get started.</p>

<p>In no time, this Keras cheat sheet will make you familiar with how you can load datasets from the library itself, preprocess the data, build up a model architecture, and compile, train, and evaluate it. As there is a considerable amount of freedom in how you build up your models, you&#39;ll see that the cheat sheet uses some of the simple&nbsp;key&nbsp;code examples of the Keras library that you need to know to get started with building your own neural networks in Python.</p>

<p>Furthermore, you&#39;ll also see some examples of how to inspect your model, and how you can save and reload it. Lastly, you&rsquo;ll also find examples of how you can predict values for test data and how you can fine tune your models by adjusting the optimization parameters and early stopping.</p>

<p>In short, you&#39;ll see that this cheat sheet not only presents you with the six steps that you can go through to make neural networks in Python with the Keras library.</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/af9bb467-170d-41c9-a0bd-26e675384c4e" target="_blank"><img alt="keras cheat sheet" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625839502/Keras_Cheat_Sheet-_Neural_Networks_in_Python_tjhi1q.png" style="width: 800px; height: 551px;" /></a></p>

<p>In short, this cheat sheat will boost your journey with deep learning in Python: you&#39;ll have preprocessed, created, validated and tuned your deep learning models in no time thanks to the code examples! &nbsp; &nbsp;</p>

<p><strong>(Click above to download a printable version or read the online version below.) </strong></p>

<div id="scoped-content">
<style type="text/css">:target:before { content:""; display:block; height:150px; margin:-150px 0 0; } h3 {font-weight:normal; } h4 { 	font-weight: lighter; }
</style>
<h2>Python For Data Science Cheat Sheet: Keras</h2>

<p>Keras is a powerful and easy-to-use deep learning library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models.</p>

<h3>A Basic Example</h3>

<pre>
<code>&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; from keras.models import Sequential
&gt;&gt;&gt; from keras.layers import Dense
&gt;&gt;&gt; data = np.random.random((1000,100))
&gt;&gt;&gt; labels = np.random.randint(2,size=(1000,1))
&gt;&gt;&gt; model = Sequential()
&gt;&gt;&gt; model.add(Dense(32, activation='relu', input_dim=100))
&gt;&gt;&gt; model.add(Dense(1, activation='sigmoid'))
&gt;&gt;&gt; model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
&gt;&gt;&gt;  model.fit(data,labels,epochs=10,batch_size=32)
&gt;&gt;&gt;  predictions = model.predict(data)</code></pre>

<h3>Data</h3>

<p>Your data needs to be stored as NumPy arrays or as a list of NumPy arrays. Ideally, you split the data in training and test sets, for which you can also resort to the <code>train_test_split</code> module of <code>sklearn.cross_validation</code>.</p>

<h4>Keras Data Sets</h4>

<pre>
<code>&gt;&gt;&gt; from keras.datasets import boston_housing, mnist, cifar10, imdb
&gt;&gt;&gt; (x_train,y_train),(x_test,y_test) = mnist.load_data()
&gt;&gt;&gt; (x_train2,y_train2),(x_test2,y_test2) = boston_housing.load_data()
&gt;&gt;&gt; (x_train3,y_train3),(x_test3,y_test3) = cifar10.load_data()
&gt;&gt;&gt; (x_train4,y_train4),(x_test4,y_test4) = imdb.load_data(num_words=20000)
&gt;&gt;&gt; num_classes = 10</code></pre>

<h4>Other</h4>

<pre>
<code>&gt;&gt;&gt; from urllib.request import urlopen
&gt;&gt;&gt; data = np.loadtxt(urlopen("http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"),delimiter=",")
&gt;&gt;&gt; X = data[:,0:8]
&gt;&gt;&gt; y = data [:,8]</code></pre>

<h3>Preprocessing</h3>

<h4>Sequence Padding</h4>

<pre>
<code>&gt;&gt;&gt; from keras.preprocessing import sequence
&gt;&gt;&gt; x_train4 = sequence.pad_sequences(x_train4,maxlen=80)
&gt;&gt;&gt; x_test4 = sequence.pad_sequences(x_test4,maxlen=80)</code></pre>

<h4>One-Hot Encoding</h4>

<pre>
<code>&gt;&gt;&gt; from keras.utils import to_categorical
&gt;&gt;&gt; Y_train = to_categorical(y_train, num_classes)
&gt;&gt;&gt; Y_test = to_categorical(y_test, num_classes)
&gt;&gt;&gt; Y_train3 = to_categorical(y_train3, num_classes)
&gt;&gt;&gt; Y_test3 = to_categorical(y_test3, num_classes)</code></pre>

<h4>Train And Test Sets</h4>

<pre>
<code>&gt;&gt;&gt; from sklearn.model_selection import train_test_split
&gt;&gt;&gt; X_train5, X_test5, y_train5, y_test5 = train_test_split(X, y, test_size=0.33, random_state=42)</code></pre>

<h3>Standardization/Normalization</h3>

<pre>
<code>&gt;&gt;&gt; from sklearn.preprocessing import StandardScaler
&gt;&gt;&gt; scaler = StandardScaler().fit(x_train2)
&gt;&gt;&gt; standardized_X = scaler.transform(x_train2)
&gt;&gt;&gt; standardized_X_test = scaler.transform(x_test2)</code></pre>

<h3>Model Architecture</h3>

<h4>Sequential Model</h4>

<pre>
<code>&gt;&gt;&gt; from keras.models import Sequential
&gt;&gt;&gt; model = Sequential()
&gt;&gt;&gt; model2 = Sequential()
&gt;&gt;&gt; model3 = Sequential()</code></pre>

<h4>Multi-Layer Perceptron (MLP)</h4>

<p><strong>Binary Classification</strong></p>

<pre>
<code>&gt;&gt;&gt; from keras.layers import Dense
&gt;&gt;&gt; model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu'))
&gt;&gt;&gt; model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
&gt;&gt;&gt; model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))</code></pre>

<p><strong>Multi-Class Classification</strong></p>

<pre>
<code>&gt;&gt;&gt; from keras.layers import Dropout
&gt;&gt;&gt; model.add(Dense(512,activation='relu',input_shape=(784,)))
&gt;&gt;&gt; model.add(Dropout(0.2))
&gt;&gt;&gt; model.add(Dense(512,activation='relu'))
&gt;&gt;&gt; model.add(Dropout(0.2))
&gt;&gt;&gt; model.add(Dense(10,activation='softmax'))</code></pre>

<p><strong>Regression</strong></p>

<pre>
<code>&gt;&gt;&gt; model.add(Dense(64, activation='relu', input_dim=train_data.shape[1]))
&gt;&gt;&gt; model.add(Dense(1))</code></pre>

<h4>Convolutional Neural Network (CNN)</h4>

<pre>
<code>&gt;&gt;&gt; from keras.layers import Activation, Conv2D, MaxPooling2D, Flatten
&gt;&gt;&gt; model2.add(Conv2D(32, (3,3), padding='same', input_shape=x_train.shape[1:]))
&gt;&gt;&gt; model2.add(Activation('relu'))
&gt;&gt;&gt; model2.add(Conv2D(32, (3,3)))
&gt;&gt;&gt; model2.add(Activation('relu'))
&gt;&gt;&gt; model2.add(MaxPooling2D(pool_size=(2,2)))
&gt;&gt;&gt; model2.add(Dropout(0.25))
&gt;&gt;&gt; model2.add(Conv2D(64, (3,3), padding='same'))
&gt;&gt;&gt; model2.add(Activation('relu'))
&gt;&gt;&gt; model2.add(Conv2D(64, (3, 3)))
&gt;&gt;&gt; model2.add(Activation('relu'))
&gt;&gt;&gt; model2.add(MaxPooling2D(pool_size=(2,2)))
&gt;&gt;&gt; model2.add(Dropout(0.25))
&gt;&gt;&gt; model2.add(Flatten())
&gt;&gt;&gt; model2.add(Dense(512))
&gt;&gt;&gt; model2.add(Activation('relu'))
&gt;&gt;&gt; model2.add(Dropout(0.5))
&gt;&gt;&gt; model2.add(Dense(num_classes))
&gt;&gt;&gt; model2.add(Activation('softmax'))</code></pre>

<h4>Recurrent Neural Network (RNN)</h4>

<pre>
<code>&gt;&gt;&gt; from keras.klayers import Embedding,LSTM
&gt;&gt;&gt; model3.add(Embedding(20000,128))
&gt;&gt;&gt; model3.add(LSTM(128,dropout=0.2,recurrent_dropout=0.2))
&gt;&gt;&gt; model3.add(Dense(1,activation='sigmoid'))</code></pre>

<h3>Inspect Model</h3>

<p>Model output shape</p>

<pre>
<code>&gt;&gt;&gt; model.output_shape</code></pre>

<p>Model summary representation</p>

<pre>
<code>&gt;&gt;&gt; model.summary()</code></pre>

<p>Model configuration</p>

<pre>
<code>&gt;&gt;&gt; model.get_config()</code></pre>

<p>List all weight tensors in the model</p>

<pre>
<code>&gt;&gt;&gt; model.get_weights()</code></pre>

<h3>Compile Model</h3>

<h4>Multi-Layer Perceptron (MLP)</h4>

<p><strong>MLP: Binary Classification</strong></p>

<pre>
<code>&gt;&gt;&gt; model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])</code></pre>

<p><strong>MLP: Multi-Class Classification</strong></p>

<pre>
<code>&gt;&gt;&gt; model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])</code></pre>

<p><strong>MLP: Regression</strong></p>

<pre>
<code>&gt;&gt;&gt; model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])</code></pre>

<h4>Recurrent Neural Network (RNN)</h4>

<pre>
<code>&gt;&gt;&gt; model3.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])</code></pre>

<h3>Model Training</h3>

<pre>
<code>&gt;&gt;&gt; model3.fit(x_train4, y_train4, batch_size=32, epochs=15, verbose=1, validation_data=(x_test4, y_test4))</code></pre>

<h3>Evaluate Your Model&#39;s Performance</h3>

<pre>
<code>&gt;&gt;&gt; score = model3.evaluate(x_test, y_test, batch_size=32)</code></pre>

<h3>Prediction</h3>

<pre>
<code>&gt;&gt;&gt; model3.predict(x_test4, batch_size=32)
&gt;&gt;&gt; model3.predict_classes(x_test4,batch_size=32)</code></pre>

<h3>Save/Reload Models</h3>

<pre>
<code>&gt;&gt;&gt; from keras.models import load_model
&gt;&gt;&gt; model3.save('model_file.h5')
&gt;&gt;&gt; my_model = load_model('my_model.h5')</code></pre>

<h3>Model Fine-Tuning</h3>

<h4>Optimization Parameters</h4>

<pre>
<code>&gt;&gt;&gt; from keras.optimizers import RMSprop
&gt;&gt;&gt; opt = RMSprop(lr=0.0001, decay=1e-6)
&gt;&gt;&gt; model2.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])</code></pre>

<h4>Early Stopping</h4>

<pre>
<code>&gt;&gt;&gt; from keras.callbacks import EarlyStopping
&gt;&gt;&gt; early_stopping_monitor = EarlyStopping(patience=2)
&gt;&gt;&gt; model3.fit(x_train4, y_train4, batch_size=32, epochs=15, validation_data=(x_test4, y_test4), callbacks=[early_stopping_monitor])</code></pre>

<h3>Going Further</h3>

<p>Begin with <a href="https://www.datacamp.com/community/tutorials/deep-learning-python">our Keras tutorial for beginners</a>, in which you&#39;ll learn in an easy, step-by-step way how to explore and preprocess the wine quality data set, build up a multi-layer perceptron for classification and regression tasks, compile, fit and evaluate the model and fine-tune the model that you have built.</p>

<p>Also, don&#39;t miss out on our<a href="https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet/"> Scikit-Learn cheat sheet</a>, <a href="https://www.datacamp.com/community/blog/python-numpy-cheat-sheet/">NumPy cheat sheet</a> and <a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/">Pandas cheat sheet</a>!</p>
</div>


https://www.datacamp.com/community/blog/keras-cheat-sheet

Make your own neural networks with this Keras cheat sheet to deep learning in Python for beginners, with code samples. 

Keras Cheat Sheet: Neural Networks in Python

<p>Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there.&nbsp;</p>

<p>For most beginners, the first package that they use to get in touch with data visualization and storytelling is, naturally, Matplotlib: it is a Python 2D plotting library that enables users to make publication-quality figures. But, what might be even more convincing is the fact that other packages, such as Pandas, intend&nbsp;to build more plotting integration with Matplotlib as time goes on.</p>

<p>However, what might slow down beginners is the fact that this package is pretty extensive. There is so much that you can do with it and it might be hard to still keep a structure when you&#39;re learning how to work with Matplotlib. &nbsp;&nbsp;</p>

<p>DataCamp has created a Matplotlib cheat sheet for those who might already know how to use the package to their advantage to make beautiful plots in Python, but that still want to keep a one-page reference handy. Of course, for those who don&#39;t know how to work with Matplotlib, this might be the extra push be convinced and to finally get started with data visualization in Python.&nbsp;</p>

<p>(By the way, if you want to get started with this Python package, you might want to consider our <a href="https://www.datacamp.com/community/tutorials/matplotlib-tutorial-python/">Matplotlib tutorial</a>.)</p>

<p>You&#39;ll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots.&nbsp;</p>

<p>Check out the infographic by clicking on the button below:</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/e1a8f39d-71ad-4d13-9a6b-618fe1b8c9e9" target="_blank"><img alt="Python Matplotlib cheat sheet" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625838238/Matplotlib_Cheat_Sheet-_Plotting_in_Python_htbtut.png" /></a></p>

<p>With this handy reference, you&#39;ll familiarize yourself in no time with the basics of Matplotlib: you&#39;ll learn how you can prepare your data, create a new plot, use some basic plotting routines to your advantage, add customizations to your plots, and save, show and close the plots that you make.</p>

<p>What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the <a href="http://matplotlib.org/gallery.html">Matplotlib Gallery</a>, the <a href="http://matplotlib.org/2.0.0/index.html">documentation</a>&nbsp;and our <a href="https://www.datacamp.com/community/tutorials/matplotlib-tutorial-python/">tutorial</a>. &nbsp;&nbsp;</p>

<p>Also, don&#39;t miss out on our other cheat sheets for data science that cover <a href="https://www.datacamp.com/community/blog/python-scipy-cheat-sheet/">SciPy</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/python-numpy-cheat-sheet/">Numpy</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet/">Scikit-Learn</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/bokeh-cheat-sheet-python/">Bokeh</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/">Pandas</a>&nbsp;and the&nbsp;<a href="https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics/">Python basics</a>.</p>


https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet

This Matplotlib cheat sheet introduces you to the basics that you need to plot your data with Python and includes code samples. 

This CHEAT SHEET introduces you to the basics of MATPLOTLIB that are needed to plot your data with PYTHON. It also includes code samples. 

(Cheat Sheet) Matplotlib: Plotting in Python

Matplotlib Cheat Sheet: Plotting in Python

<p>You most probably will know by now that data storytelling, accomplished by data visualization, amongst other things, is an essential skill for every data scientist: after you have turned the raw data into understanding, insights and knowledge, you also need to communicate these findings effectively to your audience. </p>
<p>For most beginners, the first Python data visualization library that they use is, naturally, Matplotlib. It is a Python 2D plotting library that enables users to make publication-quality figures. It is quite an extensive library where a <a href="https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet">cheat sheet</a> will definitely come in handy when you&#39;re learning, but when you manage to use this library effectively, you&#39;ll also be able to get insights and work better with other packages, such as Pandas, that intend to build more plotting integration with Matplotlib as time goes on. </p>
<p>Another package that you&#39;ll be able to tackle easily is Seaborn, the statistical data visualization library of Python. </p>
<p>DataCamp has created a Seaborn cheat sheet for those who are ready to get started with this data visualization library with the help of a handy one-page reference. </p>
<p>You&#39;ll see that this cheat sheet presents you with the five basic steps that you can go through to make beautiful statistical graphs in Python. </p>
<p>Check out the infographic by clicking on the button below:</p>
<p><a href="http://datacamp-community-prod.s3.amazonaws.com/263130e2-2c92-4348-a356-9ed9b5034247"><img alt="Seaborn Python cheat sheet" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625839419/Python_Seaborn_Cheat_Sheet_q074wv.png" style="width: 800px; height: 419px;" /></a></p>
<p>This cheat sheet will walk you through the five steps that you need to go through to make these plots: you&#39;ll see how you can load in data, set the figure aesthetics, plot, customize and eventually, show or save your plot with Seaborn.</p>
<p>What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the <a href="http://seaborn.pydata.org/examples/index.html">Seaborn Gallery</a>, the <a href="http://seaborn.pydata.org/index.html">documentation</a>, and our <a href="https://www.datacamp.com/community/tutorials/seaborn-python-tutorial">Seaborn tutorial</a>.</p>
<p>Also, don&#39;t miss out on our other cheat sheets for data science that cover <a href="https://www.datacamp.com/community/blog/python-scipy-cheat-sheet/">SciPy</a>, <a href="https://www.datacamp.com/community/blog/python-numpy-cheat-sheet/">NumPy</a>, <a href="https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet/">Scikit-Learn</a>, <a href="https://www.datacamp.com/community/blog/bokeh-cheat-sheet-python/">Bokeh</a>, <a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/">Pandas</a> and the <a href="https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics/">Python basics</a>.</p>


https://www.datacamp.com/community/blog/seaborn-cheat-sheet-python

This Python Seaborn cheat sheet with code samples guides you through the data visualization library that is based on Matplotlib.

This Python Seaborn cheat sheet with code samples guides you through the data visualization library that is based on matplotlib.

Python Seaborn Cheat Sheet

<p>By now, you&#39;ll already know the <a href="http://pandas.pydata.org/">Pandas</a>&nbsp;library is one of the most preferred tools for data manipulation and analysis, and you&#39;ll have explored the fast, flexible, and expressive Pandas data structures, maybe with the help of DataCamp&#39;s <a href="https://www.datacamp.com/community/blog/python-pandas-cheat-sheet/">Pandas Basics cheat sheet</a>.</p>

<p>Yet, there is still much functionality that is built into this package to explore, especially when you get hands-on with the data: you&#39;ll need to reshape or rearrange your data, iterate over DataFrames, visualize your data, and much more. And this might be even more difficult than &quot;just&quot; mastering the basics.&nbsp;</p>

<p>That&#39;s why today&#39;s post introduces a new, more advanced Pandas cheat sheet.&nbsp;</p>

<p>It&#39;s a quick guide through the functionalities that Pandas can offer you when you get into more advanced data wrangling&nbsp;with Python.&nbsp;</p>

<p>(Do you want to learn more? Start our <a href="https://www.datacamp.com/courses/pandas-foundations/">Pandas Foundations</a> course for free now or try out our&nbsp;<a href="https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python/">Pandas DataFrame tutorial</a>!&nbsp;)</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/d4efb29b-f9c6-4f1c-8c98-6f568d88b48f" target="_blank"><img alt="Pandas Cheat Sheet" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625839736/Pandas_Cheat_Sheet-_Data_Wrangling_in_Python_baiwnb.png" style="width: 800px; height: 551px;" /></a></p>

<p>The Pandas cheat sheet will guide you through some more advanced indexing techniques, DataFrame iteration, handling missing values or duplicate data, grouping and combining data, data functionality, and data visualization.&nbsp;</p>

<p>In short, everything that you need to complete your data manipulation with Python!</p>

<p>Don&#39;t miss out on our other cheat sheets for data science that cover <a href="https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet/">Matplotlib</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/python-scipy-cheat-sheet/">SciPy</a>,&nbsp;<a href="https://www.datacamp.com/community/blog/python-numpy-cheat-sheet/">Numpy</a>,&nbsp;and the&nbsp;<a href="https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics/">Python basics</a>.</p>


https://www.datacamp.com/community/blog/pandas-cheat-sheet-python

This cheat sheet is a quick reference for data wrangling with Pandas, complete with code samples.

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