<p>By now, you will have already learned that NumPy, one of the fundamental packages for scientific computing, forms at least for a part&nbsp;the fundament of other important&nbsp;packages that you might use used for data manipulation and machine learning with Python. One of those packages is SciPy, another one&nbsp;of the core packages for scientific computing in Python that provides mathematical algorithms and convenience functions built on the&nbsp;NumPy extension of Python.&nbsp;</p>

<p>You might now wonder why this library might come in handy for data science.&nbsp;</p>

<p>Well, SciPy has many modules that will help you to understand some of the basic components that you need to master when you&#39;re learning data science, namely, math, stats and machine learning. You can find out what other things you need to tackle to learn data science <a href="https://www.datacamp.com/community/tutorials/learn-data-science-infographic/">here</a>. You&#39;ll see that for statistics, for example, a module like&nbsp;<code>scipy.stats</code>, etc. will definitely be of interest to you.</p>

<p>The other topic that was mentioned was machine learning: here, the&nbsp;<code>scipy.linalg</code>&nbsp;and <code>scipy.sparse</code> modules will offer everything that you&#39;re looking for to understand machine learning concepts such as eigenvalues, regression, and matrix multiplication...</p>

<p>But, what is maybe the most obvious is that most machine learning techniques deal with high-dimensional data and that data is often represented as matrices. What&#39;s more, you&#39;ll need to understand how to manipulate these matrices. &nbsp;</p>

<p>That is why DataCamp has made a SciPy cheat sheet that will help you to master linear algebra with Python.&nbsp;</p>

<p>Take a look by clicking on the button below:</p>

<p><a href="http://datacamp-community-prod.s3.amazonaws.com/dfdb6d58-e044-4b38-bab3-5de0b825909b"><img alt="python scipy cheat sheet" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625838166/SciPy_Cheat_Sheet-_Linear_Algebra_in_Python_ff0oa8.png" style="width: 800px; height: 551px;" /></a></p>

<p>&nbsp;</p>

<p>You&#39;ll see that this SciPy cheat sheet covers the basics of linear algebra&nbsp;that you need to get started: it provides a brief explanation of what the&nbsp;library has to offer and how you can use it to interact with NumPy, and goes on to summarize topics in linear algebra, such as matrix creation, matrix functions,&nbsp;basic routines that you can perform with matrices, and matrix decompositions from <code>scipy.linalg</code>. Sparse matrices are also included, with their own routines, functions, and decompositions from the <code>scipy.sparse</code> module.&nbsp;</p>
<p><strong>(Above is the printable version of this cheat sheet)</strong></p>

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<h2>Python for Data-Science <em>Cheat Sheet</em>&#58; SciPy - Linear Algebra</h2>
<h2>SciPy</h2>
<p>The SciPy library is one of the core packages for scientific computing that provides mathematical algorithms and convenience functions built on the NumPy extension of Python.</p>
<h2>Asking For Help</h2>
<tr><td><code>&#62;&#62;&#62; help&#40;scipy.linalg.diagsvd&#41;</code></td></tr>
<h2>Interacting With NumPy</h2>
<table>
<tr><td><code>&#62;&#62;&#62; import numpy as np </code></td></tr>
<tr><td><code>&#62;&#62;&#62; a &#58; np.array&#40;&#91;1&#44;2&#44;3&#93;&#41;</code></td></tr>
<tr><td><code>&#62;&#62;&#62; b &#58; np.array&#40;&#91;&#40;1+5j&#44;2j&#44;3j&#41;&#44; &#40;4j&#44;5j&#44;6j&#41;&#93;&#41;</code></td></tr>
<tr><td><code>&#62;&#62;&#62; c &#58; np.array&#40;&#91;&#91;&#40;1.5&#44;2&#44;3&#41;&#44; &#40;4&#44;5&#44;6&#41;&#93;&#44; &#91;&#40;3&#44;2&#44;1&#41;&#44; &#40;4&#44;5&#44;6&#41;&#93;&#93;&#41;</code></td></tr>
</table>
<h3>Index Tricks</h3>
<table>
<tr><td><code>&#62;&#62;&#62; np.mgrid&#91;0&#58;5&#44;0&#58;5&#93;</code></td><td> Create a dense meshgrid</td></tr>
<tr><td><code>&#62;&#62;&#62; np.ogrid&#91;0&#58;2&#44;0&#58;2&#93; </code></td></tr>
<tr><td><code>&#62;&#62;&#62; np.r_&#91;3&#44;&#91;0&#93;*5&#44;-1&#58;1&#58;10j&#93;</code></td><td>Stack arrays vertically &#40;row-wise&#41;</td></tr>
<tr><td><code>&#62;&#62;&#62; np.c_&#91;b&#44;c&#93;</code></td></tr>
</table>
<h3>Shape Manipulation</h3>
<table>
<tr><td><code>&#62;&#62;&#62; np.transpose&#40;b&#41;</code></td><td> Permute array dimensions</td></tr>
<tr><td><code>&#62;&#62;&#62; b.flatten&#40;&#41;</code></td><td>Flatten the array</td></tr>
<tr><td><code>&#62;&#62;&#62; np.hstack&#40;&#40;b&#44;c&#41;&#41;</code></td><td>Stack arrays horizontally &#40;column-wise&#41;</td></tr>
<tr><td><code>&#62;&#62;&#62; np.vstack&#40;&#40;a&#44;b&#41;&#41;</code></td><td>Stack arrays vertically &#40;row-wise&#41;</td></tr>
<tr><td><code>&#62;&#62;&#62; np.hsplit&#40;c&#44;2&#41;</code></td><td>Split the array horizontally at the 2nd index</td></tr>
<tr><td><code>&#62;&#62;&#62; np.vpslit&#40;d&#44;2&#41;</code></td></tr>
</table>
<h3>Polynomials </h3>
<table>
<tr><td><code>&#62;&#62;&#62; from numpy import poly1d</code></td></tr>
<tr><td><code>&#62;&#62;&#62; p &#58; poly1d&#40;&#91;3&#44;4&#44;5&#93;&#41;</code></td><td>Create a polynomial object</td></tr>
</table>
<h3>Vectorizing Functions</h3>
<table>
	<table>
	<tr><td><code>&#62;&#62;&#62; def myfunc&#40;a&#41;&#58;</code></td></tr>
	<tr><td><code>&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;if a &#8249; 0&#58; </code></td></tr>
	<tr><td><code>&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;return a*2</code></td></tr>
	<tr><td><code>&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;else&#58;</code></td></tr>
	<tr><td><code>&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;return a/2</code></td></tr>
	<tr><td><code>&#62;&#62;&#62; np.vectorize&#40;myfunc&#41;</code></td><td> Vectorize functions</td></tr>
</table>
<h3>Type Handling </h3>
<table>
<tr><td><code>&#62;&#62;&#62; np.real&#40;b&#41;</code></td><td>Return the real part of the array elements </td></tr>
<tr><td><code>&#62;&#62;&#62; np.imag&#40;b&#41;</code></td><td>Return the imaginary part of the array elements</td></tr>
<tr><td><code>&#62;&#62;&#62; np.real_if_close&#40;c&#44;tol&#58;1000&#41; </code></td><td>Return a real array if complex parts close to 0</td></tr>
<tr><td><code>&#62;&#62;&#62; np.cast&#91;'f'&#93;&#40;np.pi&#41;</code></td><td>Cast object to a data type</td></tr>
</table>
<h3>Other Useful Functions</h3>
<table>
<tr><td><code>&#62;&#62;&#62; np.angle&#40;b&#44;deg&#58;True&#41;</code></td><td>Return the angle of the complex argument</td></tr>
<tr><td><code>&#62;&#62;&#62; g &#58; np.linspace&#40;0&#44;np.pi&#44;num&#58;5&#41;</code></td><td>Create an array of evenly spaced values &#40;number of samples&#41;</td></tr>
<tr><td><code>&#62;&#62;&#62; g &#91;3&#58;&#93; +&#58; np.pi</code></td></tr>
<tr><td><code>&#62;&#62;&#62; np.unwrap&#40;g&#41; </code></td></tr>
<tr><td><code>&#62;&#62;&#62; np.logspace&#40;0&#44;10&#44;3&#41;</code></td><td>Create an array of evenly spaced values &#40;log scale&#41;</td></tr>
<tr><td><code>&#62;&#62;&#62; np.select&#40;&#91;c&#60;4&#93;&#44;&#91;c*2&#93;&#41;</code></td><td>Return values from a list of arrays depending on conditions</td></tr>
<tr><td><code>&#62;&#62;&#62; misc.factorial&#40;a&#41;</code></td><td>Factorial </td></tr>
<tr><td><code>&#62;&#62;&#62; misc.comb&#40;10&#44;3&#44;exact&#58;True&#41;</code></td><td>Combine N things taken at k time</td></tr>
<tr><td><code>&#62;&#62;&#62; misc.central_diff_weights&#40;3&#41;</code></td><td>Weights for Np-point central derivative</td></tr>
<tr><td><code>&#62;&#62;&#62; misc.derivative&#40;myfunc&#44;1.0&#41;</code></td><td>Find the n-th derivative of a function at a point</td></tr>
</table>

<h3>﻿Linear Algebra</h3>
<p>You&#39;ll use the linalg and sparse modules. Note thatscipy.linalg contains and expands onnumpy.linalg. </p>
<table>
<tr><td><code>&#62;&#62;&#62; from scipy import linalg&#44; sparse</code></td></tr>
</table>
<h3>Creating Matrices</h3>
<table>
<tr><td><code>&#62;&#62;&#62; A &#58; np.matrix&#40;np.random.random&#40;&#40;2&#44;2&#41;&#41;&#41;</code></td></tr>
<tr><td><code>&#62;&#62;&#62; B &#58; np.asmatrix&#40;b&#41;</code></td></tr>
<tr><td><code>&#62;&#62;&#62; C &#58; np.mat&#40;np.random.random&#40;&#40;10&#44;5&#41;&#41;&#41;</code></td></tr>
<tr><td><code>&#62;&#62;&#62; D &#58; np.mat&#40;&#91;&#91;3&#44;4&#93;&#44; &#91;5&#44;6&#93;&#93;&#41;</code></td></tr>
</table>



<table>
</table>
<h3>Basic Matrix Routines</h3>
<table>
</table>
<h4>Inverse</h4>
<table>
<tr><td><code>&#62;&#62;&#62; A.I</code></td><td>Inverse</td></tr>
<tr><td><code>&#62;&#62;&#62; linalg.inv&#40;A&#41;</code></td><td>Inverse</td></tr>
</table>
<h4>Transposition</h4>
<table>
<tr><td><code>&#62;&#62;&#62; A.T</code></td><td>Tranpose matrix</td></tr>
<tr><td><code>&#62;&#62;&#62; A.H</code></td><td>Conjugate transposition</td></tr>
</table>
<h4>Trace</h4>
<table>
<tr><td><code>&#62;&#62;&#62; np.trace&#40;A&#41;</code></td><td>Trace</td></tr>
</table>
<h4>Norm</h4>
<table>
<tr><td><code>&#62;&#62;&#62; linalg.norm&#40;A&#41;</code></td><td> Frobenius norm</td></tr>
<tr><td><code>&#62;&#62;&#62; linalg.norm&#40;A&#44;1&#41;</code></td><td> L1 norm &#40;max column sum&#41;</td></tr>
<tr><td><code>&#62;&#62;&#62; linalg.norm&#40;A&#44;np.inf&#41;</code></td><td> L inf norm &#40;max row sum&#41;</td></tr>
</table>
<h4>Rank</h4>
<table>
<tr><td><code>&#62;&#62;&#62; np.linalg.matrix_rank&#40;C&#41;</code></td><td> Matrix rank</td></tr>
</table>
<h4>Determinant</h4>
<table>
<tr><td><code>&#62;&#62;&#62; linalg.det&#40;A&#41;</code></td><td>Determinant</td></tr>
</table>
<h4>Solving linear problems</h4>
<table>
<tr><td><code>&#62;&#62;&#62; linalg.solve&#40;A&#44;b&#41;</code></td><td>Solver for dense matrices</td></tr>
<tr><td><code>&#62;&#62;&#62; E &#58; np.mat&#40;a&#41;.T</code></td><td>Solver for dense matrices</td></tr>
<tr><td><code>&#62;&#62;&#62; linalg.lstsq&#40;F&#44;E&#41;</code></td><td>Least-squares solution to linear matrix equation</td></tr>
</table>
<h4> Generalized inverse</h4>
<table>
<tr><td><code>&#62;&#62;&#62; linalg.pinv&#40;C&#41;</code></td><td> Compute the pseudo-inverse of a matrix &#40;least-squares solver</td></tr>
<table>
<tr><td><code>&#62;&#62;&#62; linalg.pinv2&#40;C&#41;</code></td><td>Compute the pseudo-inverse of a matrix &#40;SVD&#41;</td></tr>
</table>

<h3>Creating Sparse Matrices</h3>
<table>
<tr><td><code>&#62;&#62;&#62; F &#58; np.eye&#40;3&#44; k&#58;1&#41;</code></td><td> Create a 2X2 identity matrix</td></tr>
<tr><td><code>&#62;&#62;&#62; G &#58; np.mat&#40;np.identity&#40;2&#41;&#41; </code></td><td>Create a 2x2 identity matrix </td></tr>
<tr><td><code>&#62;&#62;&#62; C&#91;C &#62; 0.5&#93; &#58; 0 </code></td></tr>
<tr><td><code>&#62;&#62;&#62; H &#58; sparse.csr_matrix&#40;C&#41; </code></td><td>Compressed Sparse Row matrix</td></tr>
<tr><td><code>&#62;&#62;&#62; I &#58; sparse.csc_matrix&#40;D&#41; </code></td><td>Compressed Sparse Column matrix</td></tr>
<tr><td><code>&#62;&#62;&#62; J &#58; sparse.dok_matrix&#40;A&#41; </code></td><td>Dictionary Of Keys matrix</td></tr>
<tr><td><code>&#62;&#62;&#62; E.todense&#40;&#41;</code></td><td>Sparse matrix to full matrix</td></tr>
<tr><td><code>&#62;&#62;&#62; sparse.isspmatrix_csc&#40;A&#41; </code></td><td>Identify sparse matrix</td></tr>
</table>
<h3>Sparse Matrix Routines</h3>
<table>
</table>
<h4>Inverse</h4>
<table>
<tr><td><code>&#62;&#62;&#62; sparse.linalg.inv&#40;I&#41;</code></td><td>Inverse</td></tr>
</table>
<h4>Norm </h4>
<table>
<tr><td><code>&#62;&#62;&#62; sparse.linalg.norm&#40;I&#41; </code></td><td>Norm</td></tr>
</table>
<h4> Solving linear problems </h4>
<table>
<tr><td><code>&#62;&#62;&#62; sparse.linalg.spsolve&#40;H&#44;I&#41;</code></td><td>Solver for sparse matrices</td></tr>
</table>
<h3>Sparse Matrix Functions</h3>
<table>
<tr><td><code>&#62;&#62;&#62; la&#44; v &#58; sparse.linalg.eigs&#40;F&#44;1&#41;</code></td><td>Eigenvalues and eigenvectors</td></tr>
<tr><td><code>&#62;&#62;&#62; sparse.linalg.svds&#40;H&#44; 2&#41;</code></td><td> SVD</td></tr>
<tr><td><code>&#62;&#62;&#62; sparse.linalg.expm&#40;I&#41; </code></td><td>Sparse matrix exponential</td></tr>
</table>

<h3>Matrix Functions</h3>
<h4>Addition</h4>
<table>
<tr><td><code>&#62;&#62;&#62; np.add&#40;A&#44;D&#41;</code></td><td>Addition</td></tr>
</table>
<h4> Subtraction</h4>
<table>
<tr><td><code>&#62;&#62;&#62; np.subtract&#40;A&#44;D&#41;</code></td><td> Subtraction</td></tr>
</table>
<h4> Division</h4>
<table>
<tr><td><code>&#62;&#62;&#62; np.divide&#40;A&#44;D&#41;</code></td><td> Division</td></tr>
</table>
<h4> Multiplication</h4>
<table>
<tr><td><code>&#62;&#62;&#62; A @ D</code></td><td>Multiplication operator &#40;Python 3&#41;</td></tr>

<tr><td><code>&#62;&#62;&#62; np.multiply&#40;D&#44;A&#41;</code></td><td> Multiplication</td></tr>
<tr><td><code>&#62;&#62;&#62; np.dot&#40;A&#44;D&#41;</code></td><td>Dot product</td></tr>
<tr><td><code>&#62;&#62;&#62; np.vdot&#40;A&#44;D&#41;</code></td><td> Vector dot product</td></tr>
<tr><td><code>&#62;&#62;&#62; np.inner&#40;A&#44;D&#41;</code></td><td>Inner product</td></tr>
<tr><td><code>&#62;&#62;&#62; np.outer&#40;A&#44;D&#41;</code></td><td>Outer product</td></tr>
<tr><td><code>&#62;&#62;&#62; np.tensordot&#40;A&#44;D&#41;</code></td><td>Tensor dot product</td></tr>
<tr><td><code>&#62;&#62;&#62; np.kron&#40;A&#44;D&#41;</code></td><td> Kronecker product</td></tr>
</table>
<h4>Exponential Functions</h4>
<table>
<tr><td><code>&#62;&#62;&#62; linalg.expm&#40;A&#41;</code></td><td> Matrix exponential</td></tr>
<tr><td><code>&#62;&#62;&#62; linalg.expm2&#40;A&#41;</code></td><td>Matrix exponential &#40;Taylor Series&#41;</td></tr>
<tr><td><code>&#62;&#62;&#62; linalg.expm3&#40;D&#41;</code></td><td>Matrix exponential &#40;eigenvalue decomposition&#41;</td></tr>
</table>
<h4>Logarithm Function</h4>
<table>
<tr><td><code>&#62;&#62;&#62; linalg.logm&#40;A&#41;</code></td><td> Matrix logarithm </td></tr>
</table>
<h4>Trigonometric Functions</h4>
<table>
<tr><td><code>&#62;&#62;&#62; linalg.sinm&#40;D&#41;</code></td><td> Matrix sine</td></tr>
<tr><td><code>&#62;&#62;&#62; linalg.cosm&#40;D&#41;</code></td><td> Matrix cosine</td></tr>
<tr><td><code>&#62;&#62;&#62; linalg.tanm&#40;A&#41;</code></td><td> Matrix tangent</td></tr>
</table>
<h4>Hyperbolic Trigonometric Functions</h4>
<table>
<tr><td><code>&#62;&#62;&#62; linalg.sinhm&#40;D&#41;</code></td><td>Hypberbolic matrix sine</td></tr>
<tr><td><code>&#62;&#62;&#62; linalg.coshm&#40;D&#41;</code></td><td>Hyperbolic matrix cosine</td></tr>
<tr><td><code>&#62;&#62;&#62; linalg.tanhm&#40;A&#41;</code></td><td>Hyperbolic matrix tangent</td></tr>
</table>
<h4>Matrix Sign Function </h4>
<table>
<tr><td><code>&#62;&#62;&#62; np.signm&#40;A&#41;</code></td><td>Matrix sign function</td></tr>
</table>
<h4>Matrix Square Root</h4>
<table>
<tr><td><code>&#62;&#62;&#62; linalg.sqrtm&#40;A&#41;</code></td><td>Matrix square root</td></tr>
</table>
<h4>Arbitrary Functions</h4>
<table>
<tr><td><code>&#62;&#62;&#62; linalg.funm&#40;A&#44; lambda x&#58; x*x&#41;</code></td><td>Evaluate matrix function</td></tr>
</table>

<h3>Decompositions</h3>
<table>
</table>
<h4>Eigenvalues and Eigenvectors</h4>
<table>
<tr><td><code>&#62;&#62;&#62; la&#44; v &#58; linalg.eig&#40;A&#41; </code></td><td>Solve ordinary or generalized eigenvalue problem for square matrix </td></tr>
<tr><td><code>&#62;&#62;&#62; l1&#44; l2 &#58; la</code></td><td> Unpack eigenvalues</td></tr>
<tr><td><code>&#62;&#62;&#62; v&#91;&#58;&#44;0&#93;</code></td><td>First eigenvector</td></tr>
<tr><td><code>&#62;&#62;&#62; v&#91;&#58;&#44;1&#93;</code></td><td>Second eigenvector</td></tr>
<tr><td><code>&#62;&#62;&#62; linalg.eigvals&#40;A&#41;</code></td><td> Unpack eigenvalues</td></tr>
</table>
<h4>Singular Value Decomposition </h4>
<table>
<tr><td><code>&#62;&#62;&#62; U&#44;s&#44;Vh &#58; linalg.svd&#40;B&#41;</code></td><td>Singular Value Decomposition &#40;SVD&#41;</td></tr>
<tr><td><code>&#62;&#62;&#62; M&#44;N &#58; B.shape </code></td></tr>
<tr><td><code>&#62;&#62;&#62; Sig &#58; linalg.diagsvd&#40;s&#44;M&#44;N&#41;</code></td><td>Construct sigma matrix in SVD</td></tr>
<h3>LU Decomposition</h3>
<table><tr><td><code>&#62;&#62;&#62; P&#44;L&#44;U &#58; linalg.lu&#40;C&#41;</code></td><td>LU Decomposition</td></tr></table>


<h3>Sparse Matrix Decompositions</h3>
<tr><td><code>&#62;&#62;&#62; np.info&#40;np.matrix&#41;</code></td></tr>
<br>
<br>
<br>
<br>
<p>PS.&nbsp;Don&#39;t miss our other Python cheat sheets for data science that cover <a href="https://www.datacamp.com/community/blog/python-numpy-cheat-sheet/">Numpy</a>&#44;&nbsp;<a href="https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet/">Scikit-Learn</a>&#44;&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>

</div>


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

This Python cheat sheet is a handy reference with code samples for doing linear algebra with SciPy and interacting with NumPy.

SciPy Cheat Sheet: Linear Algebra 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>The tidyverse is a powerful collection of R packages that you can use for data science. They are designed to help you to transform and visualize data. All packages within this collection share an underlying philosophy and common APIs. </p>
<p>As you might know, DataCamp recently launched the <a href="https://www.datacamp.com/courses/introduction-to-the-tidyverse">Introduction to the Tidyverse</a> course together with David Robinson, Data Scientist at Stack Overflow.</p>
<p>Now, DataCamp has created a tidyverse cheat sheet for beginners that 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 on discovering this popular collection of packages. You must have already run into packages such as <code>ggplot2</code> and <code>dplyr</code>, so this cheat sheet will definitely come in handy!</p>
<p>It&#39;s a quick guide through the basics of manipulating and visualizing your data the powerful tools that the tidyverse has to offer in R! </p>
<p><a href="http://datacamp-community-prod.s3.amazonaws.com/c1fae72f-d2d7-4646-9dce-dd0f8fb5c5e8"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625839345/Tidyverse_Cheat_Sheet_For_Beginners_d4lkxp.png"/></a></p>
<p>The tidyverse cheat sheet will guide you through some general information on the tidyverse, and then covers topics such as useful functions, loading in your data, manipulating it with <code>dplyr</code> and lastly, visualize it with <code>ggplot2</code>. </p>
<p>In short, everything that you need to kickstart your data science learning with R!</p>
<p>Do you want to learn more? Also check out and star the <a href="https://www.datacamp.com/courses/exploratory-data-analysis-in-r-case-study">Exploratory Data Analysis in R: Case Study</a> course, also taught by David Robinson, for free now! </p>
<p>Also, don&#39;t miss out on our <a href="https://www.datacamp.com/community/tutorials/data-table-cheat-sheet">data.table cheat sheet</a> for data manipulation in R and our <a href="https://www.datacamp.com/community/data-science-cheatsheets">other cheat sheets for data science</a>.</p>
<p><strong>(Click above to download a printable version or read the online version below)</strong>. </p>


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

This tidyverse cheat sheet will guide you through the basics of the tidyverse, and 2 of its core packages: dplyr and ggplot2! 

Tidyverse Cheat Sheet For Beginners

<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>You&#39;ll probably know the Jupyter notebooks pretty well - it&#39;s one of the most well-known parts of the Jupyter ecosystem! If you haven&#39;t explored the ecosystem yet or if you simply want to know more about it, don&#39;t hesitate to go and explore it <a href="www.jupyter.org">here</a>!.</p>
<p>For those who are new to Project Jupyter, the Jupyter Notebook Application produces documents that contain a mix of executable code, text elements, and even HTML, which makes it thé ideal place to bring together an analysis description and its results as well as to perform data analysis in real time. This, combined with its many useful functionalities, explains why the Jupyter Notebook is a <a href="https://www.datacamp.com/community/tutorials/data-science-python-ide">one of the data scientist&#39;s preferred development environments environments</a> that allows for interactive, reproducible data science analysis, computation and communication.</p>
<p>One of the other great things about the Jupyter notebooks?</p>
<p>They&#39;re super easy to get started with! You might have already noticed this when you read DataCamp&#39;s <a href="https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook">definitive guide to Jupyter Notebook</a>. However, when you first enter in the application, you might have to find your way around the variety of functionalities that are presented to you: from saving your current notebook to adding or moving cells in the notebook or embedding current widgets in your notebook - without a doubt, there&#39;s a lot out there to discover when you first get started! </p>
<p>That&#39;s why DataCamp made a Jupyter Notebook cheat sheet for those who are just starting out and that want to have some help to find their way around. </p>
<p><strong>Note</strong> also that the Jupyter Notebook Application has a handy &quot;Help&quot; menu that includes a full-blown User Interface Tour! -- No worries, we also included this in the cheat sheet :)</p>
<p>Check it out here:</p>
<p><a href="http://datacamp-community-prod.s3.amazonaws.com/21fdc814-3f08-4aa9-90fa-247eedefd655" target="_blank"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625839536/Jupyter_Notebook_Cheat_Sheet_ntmalw.png" alt="Jupyter Notebook cheat sheet"/></a></p>
<p>In short, this cheat sheet will help you to kickstart your data science projects, however small or big they might be: with of some screenshots and explanations, you&#39;ll be a Jupyter Notebook expert in no time!</p>
<p>So what are you waiting for? Time to get started!</p>


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

This Jupyter Notebook cheat sheet will help you to find your way around the well-known Notebook App, a subproject of Project Jupyter.

This Jupyter Notebook cheat sheet will help you to find your way around the well-known Jupyter Notebook App, a subproject of Project Jupyter.

Jupyter Notebook Cheat Sheet

<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

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<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>

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<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>

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<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.

community

datacamp

Official Blog