Numpy Cheat Sheet

Posted : admin | On 01-05-2021

SciPy Cheat Sheet: Linear Algebra in Python February 7th, 2017 This Python cheat sheet is a handy reference for doing linear algebra with SciPy and interacting with NumPy! This beautiful cheat sheet by Justin covers all the primary syntactical techniques used in Numpy. It includes all the primary array operations, multidimensional access, etc. A quick view of the ordinary and binomial distribution is also provided. The Numpy machine learning cheat sheet can be accessed here. Pandas Cheat Sheet by Sanjeev.

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2 Python For Data Science Cheat Sheet NumPy Basics Learn Python for Data Science Interactively at www.DataCamp.com NumPy DataCamp Learn Python for Data Science Interactively The NumPy library is the core library for scientific computing in Python. It provides a high-performance multidimensional array. View 11-Numpy-Cheat-Sheet.pdf from CS 1A at LNCT University. NumPy Cheat Sheets: Tips and Tricks What‘s the Meaning of Axes and Shape properties? Axis 0 3D NumPy Array Axis 0 2D NumPy Array Axis.

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 whichmany important Python data science libraries are built, including Pandas, SciPy and scikit-learn.

Key and Imports

In this cheat sheet, we use the following shorthand:

arr A NumPy Array object

Numpy Cheat Sheet Github

You’ll also need to import numpy to get started:

Importing/exporting

`np.loadtxt('file.txt')`	From a text file
`np.genfromtxt('file.csv',delimiter=',')`	From a CSV file
`np.savetxt('file.txt',arr,delimiter=' ')`	Writes to a text file
`np.savetxt('file.csv',arr,delimiter=',')`	Writes to a CSV file

Creating Arrays

np.empty((1, 2)) | create an empty 1x2 array. The value at each position is uninitialized (random value depending on the memory location).np.array([1,2,3]) | One dimensional array. Keyword argument dtype converts elements into specified type.np.array([(1,2,3),(4,5,6)]) | Two dimensional array
np.zeros(3) | 1D array of length 3 all values 0
np.ones((3,4)) | 3x4 array with all values 1
np.eye(5) | 5x5 array of 0 with 1 on diagonal (Identity matrix)
np.linspace(0,100,6) | Array of 6 evenly divided values from 0 to 100
np.arange(0,10,3) | Array of values from 0 to less than 10 with step 3 (eg [0,3,6,9])
np.full((2,3),8) | 2x3 array with all values 8
np.random.rand(4,5) | 4x5 array of random floats between 0-1
np.random.rand(6,7)*100 | 6x7 array of random floats between 0-100
np.random.randint(5,size=(2,3)) | 2x3 array with random ints between 0-4

Inspecting Properties

`arr.size`	Returns number of elements in `arr`
`arr.shape`	Returns dimensions of `arr` (rows,columns)
`arr.dtype`	Returns type of elements in `arr`
`arr.astype(dtype)`	Convert `arr` elements to type `dtype`
`arr.tolist()`	Convert `arr` to a Python list
`np.info(np.eye)`	View documentation for `np.eye`
`np.copy(arr)`	Copies `arr` to new memory
`arr.view(dtype)`	Creates view of `arr` elements with type `dtype`
`arr.sort()`	Sorts `arr`
`arr.sort(axis=0)`	Sorts specific axis of `arr`
`two_d_arr.flatten()`	Flattens 2D array `two_d_arr` to 1D
`arr.T`	Transposes `arr` (rows become columns and vice versa)
`arr.reshape(3,4)`	Reshapes `arr` to `3` rows, `4` columns without changing data
`arr.resize((5,6))`	Changes `arr` shape to `5`x`6` and fills new values with `0`

Adding/removing Elements

`np.append(arr,values)`	Appends values to end of `arr`
`np.insert(arr,2,values)`	Inserts values into `arr` before index `2`
`np.delete(arr,3,axis=0)`	Deletes row on index `3` of `arr`
`np.delete(arr,4,axis=1)`	Deletes column on index `4` of `arr`

Combining

`np.vstack((arr1, arr2))`	Vertically stack multiple arrays. Think of it like the second arrays’s items being added as new rows to the first array.
`np.hstack((arr1, arr2))`	horizontally stack multiple arrays.
`np.concatenate((arr1,arr2),axis=0)`	Adds `arr2` as rows to the end of `arr1`. It’s a general-purpose `vstack`.
`np.concatenate((arr1,arr2),axis=1)`	Adds `arr2` as columns to end of `arr1`. It’s a general-purpose `hstack`.
`np.split(arr,3)`	Splits `arr` into `3` sub-arrays
`np.hsplit(arr,5)`	Splits `arr` horizontally on the `5`th index

Indexing

`arr[5]`	Returns the element at index `5`
`arr[2,5]`	Returns the 2D array element on index `[2][5]`
`arr[1]=4`	Assigns array element on index `1` the value `4`
`arr[1,3]=10`	Assigns array element on index `[1][3]` the value `10`
`arr[0:3]`	Returns the elements at indices `0,1,2` (On a 2D array: returns rows `0,1,2`)
`arr[0:3,4]`	Returns the elements on rows `0,1,2` at column `4`
`arr[:2]`	Returns the elements at indices `0,1` (On a 2D array: returns rows `0,1`)
`arr[:,1]`	Returns the elements at index `1` on all rows
`arr<5`	Returns an array with boolean values
`(arr1<3) & (arr2>5)`	Returns an array with boolean values
`~arr`	Inverts a boolean array
`arr[arr<5]`	Returns array elements smaller than `5`

Conditional Selecting

NumPy makes it possible to test to see if rows match certain values usingmathematical comparison operations like <, >, >=, <=, and . Forexample, if we want to see which wines have a quality rating higher than 5,we can do this:

We get a Boolean array that tells us which of the wines have a quality ratinggreater than 5. We can do something similar with the other operators. Forinstance, we can see if any wines have a quality rating equal to 10:

One of the powerful things we can do with a Boolean array and a NumPy array isselect only certain rows or columns in the NumPy array. For example, the belowcode will only select rows in wines where the quality is over 7:

We select only the rows where high_quality contains a True value, and allof the columns. This subsetting makes it simple to filter arrays for certaincriteria. For example, we can look for wines with a lot of alcohol and highquality. In order to specify multiple conditions, we have to place eachcondition in parentheses, and separate conditions with an ampersand (&):

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We can combine subsetting and assignment to overwrite certain values in anarray:

Reshaping NumPy Arrays

`numpy.transpose(arr)`	Transpose the array.
`numpy.ravel(arr)`	Turn an array into a one-dimensional representation.
`numpy.reshape(arr)`	Reshape an array to a certain shape we specify.

Scalar Math

If you do any of the basic mathematical operations (/, *, -, +, ^) with an array and a value, it will apply the operation to each of the elements in the array.

`np.add(arr,1)` or `arr + 1`	Add `1` to each array element
`np.subtract(arr,2)` or `arr - 2`	Subtract `2` from each array element
`np.multiply(arr,3)` or `arr * 3`	Multiply each array element by `3`
`np.divide(arr,4)` or `arr / 4`	Divide each array element by `4` (returns `np.nan` for division by zero)
`np.power(arr,5)` or `arr ^ 5`	Raise each array element to the `5`th power

Note that the above operation won’t change the wines array – it will return a new 1-dimensional array where 10 has been added to each element in the quality column of wines.

If we instead did +=, we’d modify the array in place.

Vector Math

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All of the common operations (/, *, -, +, ^) will work between arrays.

`np.add(arr1,arr2)`	Elementwise add `arr2` to `arr1`
`np.subtract(arr1,arr2)`	Elementwise subtract `arr2` from `arr1`
`np.multiply(arr1,arr2)`	Elementwise multiply `arr1` by `arr2`
`np.divide(arr1,arr2)`	Elementwise divide `arr1` by `arr2`
`np.power(arr1,arr2)`	Elementwise raise `arr1` raised to the power of `arr2`
`np.array_equal(arr1,arr2)`	Returns `True` if the arrays have the same elements and shape
`np.sqrt(arr)`	Square root of each element in the array
`np.sin(arr)`	Sine of each element in the array
`np.log(arr)`	Natural log of each element in the array
`np.abs(arr)`	Absolute value of each element in the array
`np.ceil(arr)`	Rounds up to the nearest int
`np.floor(arr)`	Rounds down to the nearest int
`np.round(arr)`	Rounds to the nearest int

Statistics

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`np.mean(arr,axis=0)`	Returns mean along specific axis
`arr.sum()`	Returns sum of `arr`
`arr.min()`	Returns minimum value of `arr`
`arr.max(axis=0)`	Returns maximum value of specific axis
`np.var(arr)`	Returns the variance of array
`np.std(arr,axis=1)`	Returns the standard deviation of specific axis
`arr.corrcoef()`	Returns correlation coefficient of array

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Acknowledgement

Python Data Analysis Cheat Sheet

The original post can be found at dataquest.io.