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 man importany Python data science libraries are built, including Pandas, SciPy and scikit-learn.
The printable version of this cheat sheet
It’s common when first learning NumPy to have trouble remembering all the functions and methods that you need, and while at Dataquest we advocate getting used to consulting the NumPy documentation, sometimes it’s nice to have a handy reference, so we’ve put together this cheat sheet to help you out!
If you’re interested in learning pandas, you can consult our NumPy tutorial blog post, or you can signup for free and start learning NumPy through our interactive python data science course.
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Key and Imports
In this cheat sheet, we use the following shorthand:
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.array([1,2,3]) |
One dimensional array |
np.array([(1,2,3),(4,5,6)]) |
Two dimensional array |
np.zeros(3) |
3 x1 array with all values 0 |
np.ones((3,4)) |
3 x4 array with all values 1 |
np.eye(5) |
5 x5 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) |
2 x3 array with all values 8 |
np.random.rand(4,5) |
4 x5 array of random floats between 0 –1 |
np.random.rand(6,7)*100 |
6 x7 array of random floats between 0 –100 |
np.random.randint(5,size=(2,3)) |
2 x3 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 |
Copying/sorting/reshaping
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 x6 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/splitting
np.concatenate((arr1,arr2),axis=0) |
Adds arr2 as rows to the end of arr1 |
np.concatenate((arr1,arr2),axis=1) |
Adds arr2 as columns to end of arr1 |
np.split(arr,3) |
Splits arr into 3 sub-arrays |
np.hsplit(arr,5) |
Splits arr horizontally on the 5 th index |
Indexing/slicing/subsetting
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 |
Scalar Math
np.add(arr,1) |
Add 1 to each array element |
np.subtract(arr,2) |
Subtract 2 from each array element |
np.multiply(arr,3) |
Multiply each array element by 3 |
np.divide(arr,4) |
Divide each array element by 4 (returns np.nan for division by zero) |
np.power(arr,5) |
Raise each array element to the 5 th power |
Vector Math
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
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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