NumPy is the fundamental package for scientific computing with Python.
This cheat sheet acts as a intro to Python for data science.
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) |
Arange array | link |
| Operators | Description | Documentation |
|---|---|---|
np.linspace(0,2,9) |
Add evenly spaced values btw intervall 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 |
# 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])| 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 |
| 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 |
# 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]| 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 |
array.resize((2,4)) |
Resize array to shape(2,4) | link |
np.delete(array,1,axis) |
Deletes items from array | link |
| 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 |
| 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 |
| Operator | Description | Documentation |
|---|---|---|
other = ndarray.flatten() |
Flattens a 2d array to 1d | link |
array = np.transpose(other)array.T |
Transpose array | link |
| Operator | Description | Documentation |
|---|---|---|
np.add(x,y) |
Addition | link |
np.substract(x,y) |
Subtraction | link |
np.divide(x,y) |
Division | link |
np.multiply(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) |
Dot product | link |
Remember: NumPy array operations work element-wise.
# 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]]| 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 |
# 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]| Operator | Description | Documentation |
|---|---|---|
array.mean()np.mean(array) |
Mean | link |
np.median(array) |
Median | link |
array.corrcoef() |
Correlation Coefficient | link |
array.std(array) |
Standard Deviation | link |
| 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 |
| Operator | Description | Documentation |
|---|---|---|
array[i] |
1d array at index i | link |
array[i,j] |
2d array at index[i][j] | see above |
array[i<2] |
Boolean Indexing | see above |
array[0:2] |
Select items of index 0 and 1 | 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,...] |
equals array[1,:,:] | see above |
array[ : :-1] |
Reverses array |
see above |
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]This is a growing list of examples
"SQL WHERE trick", couldn't come up with a better title...
# 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']x = np.array(4,6,8,1,2,6,9)
y = x > 5
print(y[x])
>>> [9 6]