NumPy is the library that gives Python its ability to work with data at speed. Originally, launched in 1995 as ‘Numeric,’ NumPy is the foundation on which many important Python data science libraries are built, including Pandas, SciPy and scikit-learn. Download a Printable PDF of this Cheat Sheet. With this, we come to an end of Python Data Structures Basic Cheat sheet. To get in-depth knowledge, check out our Python for Data Science Bootcamp that comes with 24.7 support to guide you throughout your learning period. NumPy / SciPy / Pandas Cheat Sheet Select column. Select row by label. Return DataFrame index. Delete given row or column. Pass axis=1 for columns. Reindex df1 with index of df2. Reset index, putting old index in column named index. Change DataFrame index, new indecies set to NaN. Show first n rows. Show last n rows. MATLAB commands in numerical Python (NumPy) 3 Vidar Bronken Gundersen /mathesaurus.sf.net 2.5 Round off Desc. Matlab/Octave Python R Round round(a) around(a) or math.round(a) round(a). Don’t miss our FREE NumPy cheat sheet at the bottom of this post. NumPy is a commonly used Python data analysis package. By using NumPy, you can speed up your workflow, and interface with other packages in the Python ecosystem, like scikit-learn, that use NumPy under the hood. NumPy was originally developed in the mid 2000s, and arose from an.
- Python cheatsheet
Operators¶
Command | Description |
---|---|
* | multiplication operation: 2*3 returns 6 |
** | power operation: 2**3 returns 8 |
@ | matrix multiplication: returns |
Data Types¶
Command | Description |
---|---|
l=[a1,a2,…,an] | Constructs a list containing the objects (a1, a2,.., an). You can append to the list using l.append() .The (ith) element of (l) can be accessed using l[i] |
t=(a1,a2,…,an) | Constructs a tuple containing the objects (a1, a2,.., an). The (ith) element of (t) can be accessed using t[i] |
![Sheet Sheet](/uploads/1/3/4/8/134840619/215980291.jpg)
Built-In Functions¶
Command | Description |
---|---|
len(iterable) | Reparacao eletronica. len is a function that takes an iterable, such as a list, tuple or numpy array and returns the number of items in that object.For a numpy array, len returns the length of the outermost dimensionreturns 5 . |
zip | Make an iterator that aggregates elements from each of the iterables. returns [(1,4),(2,5),(3,6)] |
Iterating¶
Command | Description |
---|---|
forainiterable: | For loop used to perform a sequence of commands (denoted using tabs) for each element in an iterable object such as a list, tuple, or numpy array.An example code is prints [1,4,9] |
Comparisons and Logical Operators¶
Command | Description |
---|---|
ifcondition: | Performs code if a condition is met (using tabs). For example squares (x) if (x) is (5), otherwise cubes it. |
Python Numpy Cheat Sheet Pdf
User-Defined Functions¶
Command | Description |
---|---|
lambda | Used for create anonymous one line functions of the form: The code after the lambda but before variables specifies the parameters. The code after the colon tells python what object to return. |
def | The def command is used to create functions of more than one line: The code immediately following def names the function, in this example g .The variables in the parenthesis are the parameters of the function. The remaining lines of the function are denoted by tab indents.The return statement specifies the object to be returned. |
Numpy¶
Command | Description |
---|---|
np.array(object,dtype=None) | np.array constructs a numpy array from an object, such as a list or a list of lists.dtype allows you to specify the type of object the array is holding.You will generally note need to specify the dtype .Examples: |
A[i1,i2,…,in] | Access a the element in numpy array A in with index i1 in dimension 1, i2 in dimension 2, etc.Can use : to access a range of indices, where imin:imax represents all (i) such that (imin leq i < imax).Always returns an object of minimal dimension.For example,A[:,2] returns the 2nd column (counting from 0) of A as a 1 dimensional array and A[0:2,:] returns the 0th and 1st rows in a 2 dimensional array. |
np.zeros(shape) | Constructs numpy array of shape shape. Here shape is an integer of sequence of integers. Such as 3, (1, 2), (2, 1), or (5, 5). Thus np.zeros((5,5)) Constructs an (5times 5) array while np.zeros(5,5) will throw an error. |
np.ones(shape) | Same as np.zeros but produces an array of ones |
np.linspace(a,b,n) | Returns a numpy array with (n) linearly spaced points between (a) and (b). For example np.linspace(1,2,10) returns |
np.eye(N) | Constructs the identity matrix of size (N). For example np.eye(3) returns the (3times 3) identity matrix: [begin{split}left(begin{matrix}1&0&00&1&0 0&0&1end{matrix}right)end{split}] |
np.diag(a) | np.diag has 2 uses. First if a is a 2 dimensional array then np.diag returns the principle diagonal of the matrix.Thusnp.diag([[1,3],[5,6]]) returns [1,6] .If (a) is a 1 dimensional array then np.diag constructs an array with $a$ as the principle diagonal. Thus,np.diag([1,2]) returns [begin{split}left(begin{matrix}1&00&2end{matrix}right)end{split}] |
np.random.rand(d0,d1,…,dn) | Constructs a numpy array of shape (d0,d1,…,dn) filled with random numbers drawn from a uniform distribution between :math`(0, 1)`.For example, np.random.rand(2,3) returns |
np.random.randn(d0,d1,…,dn) | Same as np.random.rand(d0,d1,…,dn) except that it draws from the standard normal distribution (mathcal N(0, 1))rather than the uniform distribution. |
A.T | Reverses the dimensions of an array (transpose).For example,if (x = left(begin{matrix} 1& 23&4end{matrix}right)) then x.T returns (left(begin{matrix} 1& 32&4end{matrix}right)) |
np.hstack(tuple) | Take a sequence of arrays and stack them horizontally to make a single array. For example returns [1,2,3,2,3,4] whilereturns (left( begin{matrix} 1&22&3 3&4 end{matrix}right)) |
np.vstack(tuple) | Like np.hstack . Takes a sequence of arrays and stack them vertically to make a single array. For examplereturns |
np.amax(a,axis=None) | By default np.amax(a) finds the maximum of all elements in the array (a).Can specify maximization along a particular dimension with axis.Ifa=np.array([[2,1],[3,4]])#createsa2dimarray then np.amax(a,axis=0)#maximizationalongrow(dim0) returns array([3,4]) andnp.amax(a,axis=1)#maximizationalongcolumn(dim1) returns array([2,4]) |
np.amin(a,axis=None) | Same as np.amax except returns minimum element. |
np.argmax(a,axis=None) | Performs similar function to np.amax except returns index of maximal element.By default gives index of flattened array, otherwise can use axis to specify dimension.From the example for np.amax returns array([1,1]) andreturns array([0,1]) |
np.argmin(a,axis=None) | Same as np.argmax except finds minimal index. |
np.dot(a,b) or a.dot(b) | Returns an array equal to the dot product of (a) and (b).For this operation to work the innermost dimension of (a) must be equal to the outermost dimension of (b).If (a) is a ((3, 2)) array and (b) is a ((2)) array then np.dot(a,b) is valid.If (b) is a ((1, 2)) array then the operation will return an error. |
Numpy Reference Sheet
numpy.linalg¶
Command | Description |
---|---|
np.linalg.inv(A) | For a 2-dimensional array (A). np.linalg.inv returns the inverse of (A).For example, for a ((2, 2)) array (A)returns |
np.linalg.eig(A) | Returns a 1-dimensional array with all the eigenvalues of $A$ as well as a 2-dimensional array with the eigenvectors as columns.For example, eigvals,eigvecs=np.linalg.eig(A) returns the eigenvalues in eigvals and the eigenvectors in eigvecs .eigvecs[:,i] is the eigenvector of (A) with eigenvalue of eigval[i] . |
np.linalg.solve(A,b) | Constructs array (x) such that A.dot(x) is equal to (b). Theoretically should give the same answer asbut numerically more stable. |
Pandas¶
Command | Description |
---|---|
pd.Series() | Constructs a Pandas Series Object from some specified data and/or index |
pd.DataFrame() | Constructs a Pandas DataFrame object from some specified data and/or index, column names etc. or alternatively, |
Plotting¶
Command | Description |
---|---|
plt.plot(x,y,s=None) | The plot command is included in matplotlib.pyplot .The plot command is used to plot (x) versus (y) where (x) and (y) are iterables of the same length.By default the plot command draws a line, using the (s) argument you can specify type of line and color.For example ‘-‘, ‘- -‘, ‘:’, ‘o’, ‘x’, and ‘-o’ reprent line, dashed line, dotted line, circles, x’s, and circle with line through it respectively.Color can be changed by appending ‘b’, ‘k’, ‘g’ or ‘r’, to get a blue, black, green or red plot respectively.For example,plots the cosine function on the domain (0, 10) with a green line with circles at the points (x, v) |