NumPy Cheat Sheet
Numpy API: https://numpy.org/doc/stable/reference/index.html
NumPy is the fundamental package for scientific computing with Python.
Installation
If you don't already have it installed, you can do so using Pip or Anaconda:
$ pip install numpy
or
$ conda install numpy
This cheat sheet acts as a intro to Python for data science.
Index
- Basics
- Arrays
- Mathematics
- Slicing and Subsetting
- Tricks
- Credits
Basics
One of the most commonly used functions of NumPy are NumPy arrays: The essential difference between lists and NumPy arrays is functionality and speed. lists give you basic operation, but NumPy adds FFTs, convolutions, fast searching, basic statistics, linear algebra, histograms, etc.
The most important difference for data science is the ability to do element-wise calculations with NumPy arrays.
axis 0 always refers to row
axis 1 always refers to column
| Operator |
Description |
Documentation |
np.array([1,2,3]) |
1d array |
link |
np.array([(1,2,3),(4,5,6)]) |
2d array |
see above |
np.arange(start,stop,step) |
range array |
link |
Placeholders
| Operators |
Description |
Documentation |
np.linspace(0,2,9) |
Add evenly spaced values btw interval to array of length |
link |
np.zeros((1,2)) |
Create and array filled with zeros |
link |
np.ones((1,2)) |
Creates an array filled with ones |
link |
np.random.random((5,5)) |
Creates random array |
link |
np.empty((2,2)) |
Creates an empty array |
link |
Examples
import numpy as np
# 1 dimensional
x = np.array([1,2,3])
# 2 dimensional
y = np.array([(1,2,3),(4,5,6)])
x = np.arange(3)
>>> array([0, 1, 2])
y = np.arange(3.0)
>>> array([ 0., 1., 2.])
x = np.arange(3,7)
>>> array([3, 4, 5, 6])
y = np.arange(3,7,2)
>>> array([3, 5])
Array
Array Properties
| Syntax |
Description |
Documentation |
array.shape |
Dimensions (Rows,Columns) |
link |
len(array) |
Length of Array |
link |
array.ndim |
Number of Array Dimensions |
link |
array.size |
Number of Array Elements |
link |
array.dtype |
Data Type |
link |
array.astype(type) |
Converts to Data Type |
link |
type(array) |
Type of Array |
link |
Copying/Sorting
| Operators |
Descriptions |
Documentation |
np.copy(array) |
Creates copy of array |
link |
other = array.copy() |
Creates deep copy of array |
see above |
array.sort() |
Sorts an array |
link |
array.sort(axis=0) |
Sorts axis of array |
see above |
Examples
import numpy as np
# Sort sorts in ascending order
y = np.array([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])
y.sort()
print(y)
>>> [ 1 2 3 4 5 6 7 8 9 10]
Array Manipulation Routines
Adding or Removing Elements
| Operator |
Description |
Documentation |
np.append(a,b) |
Append items to array |
link |
np.insert(array, 1, 2, axis) |
Insert items into array at axis 0 or 1 |
link |
np.resize((2,4)) |
Resize array to shape(2,4) |
link |
np.delete(array,1,axis) |
Deletes items from array |
link |
Example
import numpy as np
# Append items to array
a = np.array([(1, 2, 3),(4, 5, 6)])
b = np.append(a, [(7, 8, 9)])
print(b)
>>> [1 2 3 4 5 6 7 8 9]
# Remove index 2 from previous array
print(np.delete(b, 2))
>>> [1 2 4 5 6 7 8 9]
Combining Arrays
| Operator |
Description |
Documentation |
np.concatenate((a,b),axis=0) |
Concatenates 2 arrays, adds to end |
link |
np.vstack((a,b)) |
Stack array row-wise |
link |
np.hstack((a,b)) |
Stack array column wise |
link |
Example
import numpy as np
a = np.array([1, 3, 5])
b = np.array([2, 4, 6])
# Stack two arrays row-wise
print(np.vstack((a,b)))
>>> [[1 3 5]
[2 4 6]]
# Stack two arrays column-wise
print(np.hstack((a,b)))
>>> [1 3 5 2 4 6]
Splitting Arrays
| Operator |
Description |
Documentation |
numpy.split() |
|
link |
np.array_split(array, 3) |
Split an array in sub-arrays of (nearly) identical size |
link |
numpy.hsplit(array, 3) |
Split the array horizontally at 3rd index |
link |
Example
# Split array into groups of ~3
a = np.array([1, 2, 3, 4, 5, 6, 7, 8])
print(np.array_split(a, 3))
>>> [array([1, 2, 3]), array([4, 5, 6]), array([7, 8])]
Shaping Arrays
TODO
| Operator |
Description |
Documentation |
other = ndarray.flatten() |
Flattens a 2d array to 1d |
link |
| numpy.flip() |
Flips order of elements in 1D array |
|
| np.ndarray[::-1] |
Same as above |
|
| reshape |
|
|
| squeeze |
|
|
| expand_dims |
|
|
Misc
| Operator |
Description |
Documentation |
other = ndarray.flatten() |
Flattens a 2d array to 1d |
link |
array = np.transpose(other)
array.T |
Transpose array |
link |
inverse = np.linalg.inv(matrix) |
Inverse of a given matrix |
link |
Example
# Find inverse of a given matrix
>>> np.linalg.inv([[3,1],[2,4]])
array([[ 0.4, -0.1],
[-0.2, 0.3]])
Mathematics
Operations
| Operator |
Description |
Documentation |
np.add(x,y)
x + y |
Addition |
link |
np.substract(x,y)
x - y |
Subtraction |
link |
np.divide(x,y)
x / y |
Division |
link |
np.multiply(x,y)
x @ y |
Multiplication |
link |
np.sqrt(x) |
Square Root |
link |
np.sin(x) |
Element-wise sine |
link |
np.cos(x) |
Element-wise cosine |
link |
np.log(x) |
Element-wise natural log |
link |
np.dot(x,y) |
Dot product |
link |
np.roots([1,0,-4]) |
Roots of a given polynomial coefficients |
link |
Remember: NumPy array operations work element-wise.
Example
# If a 1d array is added to a 2d array (or the other way), NumPy
# chooses the array with smaller dimension and adds it to the one
# with bigger dimension
a = np.array([1, 2, 3])
b = np.array([(1, 2, 3), (4, 5, 6)])
print(np.add(a, b))
>>> [[2 4 6]
[5 7 9]]
# Example of np.roots
# Consider a polynomial function (x-1)^2 = x^2 - 2*x + 1
# Whose roots are 1,1
>>> np.roots([1,-2,1])
array([1., 1.])
# Similarly x^2 - 4 = 0 has roots as x=±2
>>> np.roots([1,0,-4])
array([-2., 2.])
Comparison
| Operator |
Description |
Documentation |
== |
Equal |
link |
!= |
Not equal |
link |
< |
Smaller than |
link |
> |
Greater than |
link |
<= |
Smaller than or equal |
link |
>= |
Greater than or equal |
link |
np.array_equal(x,y) |
Array-wise comparison |
link |
Example
# Using comparison operators will create boolean NumPy arrays
z = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
c = z < 6
print(c)
>>> [ True True True True True False False False False False]
Basic Statistics
| Operator |
Description |
Documentation |
np.mean(array) |
Mean |
link |
np.median(array) |
Median |
link |
array.corrcoef() |
Correlation Coefficient |
link |
np.std(array) |
Standard Deviation |
link |
Example
# Statistics of an array
a = np.array([1, 1, 2, 5, 8, 10, 11, 12])
# Standard deviation
print(np.std(a))
>>> 4.2938910093294167
# Median
print(np.median(a))
>>> 6.5
More
| Operator |
Description |
Documentation |
array.sum() |
Array-wise sum |
link |
array.min() |
Array-wise minimum value |
link |
array.max(axis=0) |
Maximum value of specified axis |
|
array.cumsum(axis=0) |
Cumulative sum of specified axis |
link |
Slicing and Subsetting
| Operator |
Description |
Documentation |
array[i] |
1d array at index i |
link |
array[i,j] |
2d array at index[i][j] |
see above |
array[i<4] |
Boolean Indexing, see Tricks |
see above |
array[0:3] |
Select items of index 0, 1 and 2 |
see above |
array[0:2,1] |
Select items of rows 0 and 1 at column 1 |
see above |
array[:1] |
Select items of row 0 (equals array[0:1, :]) |
see above |
array[1:2, :] |
Select items of row 1 |
see above |
|array[ : :-1]|Reverses array|see above|
Examples
b = np.array([(1, 2, 3), (4, 5, 6)])
# The index *before* the comma refers to *rows*,
# the index *after* the comma refers to *columns*
print(b[0:1, 2])
>>> [3]
print(b[:len(b), 2])
>>> [3 6]
print(b[0, :])
>>> [1 2 3]
print(b[0, 2:])
>>> [3]
print(b[:, 0])
>>> [1 4]
c = np.array([(1, 2, 3), (4, 5, 6)])
d = c[1:2, 0:2]
print(d)
>>> [[4 5]]
Tricks
This is a growing list of examples. Know a good trick? Let me know in a issue or fork it and create a pull request.
boolean indexing (available as separate .py file here
# Index trick when working with two np-arrays
a = np.array([1,2,3,6,1,4,1])
b = np.array([5,6,7,8,3,1,2])
# Only saves a at index where b == 1
other_a = a[b == 1]
#Saves every spot in a except at index where b != 1
other_other_a = a[b != 1]
import numpy as np
x = np.array([4,6,8,1,2,6,9])
y = x > 5
print(x[y])
>>> [6 8 6 9]
# Even shorter
x = np.array([1, 2, 3, 4, 4, 35, 212, 5, 5, 6])
print(x[x < 5])
>>> [1 2 3 4 4]
Credits
Datacamp,
Quandl & Official docs
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