NumPy - basics

Quick reference

Here we provide a quick reference guide to the commonly used functions from the NumPy package along with several frequently encountered examples.

NumPy command	Note
a.ndim	returns the num. of dimensions
a.shape	returns the num. of rows and colums
arange(start,stop,step)	returns a sequence vector
linspace(start,stop,steps)	returns a evenly spaced sequence in the specificed interval
dot(a,b)	matrix multiplication
vstack([a,b])	stack arrays a and b vertically
hstack([a,b])	stack arrays a and b horizontally
where(a>x)	returns elements from an array depending on condition
argsort(a)	returns the sorted indices of an input array

Basic operations

Arithmetic operators in NumPy work elementwise.

>>> a = np.array([3,4,5])
>>> b = np.ones(3)
>>> a - b
array([ 2.,  3.,  4.])

Something that can be tricky for people familar with other programming languages is that the * operator does not carry out a matrix product. This is done with the dot function.

>>> a = np.array([[1,2],[3,4]])
>>> b = np.array([[1,2],[3,4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> b
array([[1, 2],
       [3, 4]])
>>> a * b
array([[ 1,  4],
       [ 9, 16]])
>>> np.dot(a,b)
array([[ 7, 10],
       [15, 22]])

Special addition and multiplication operators

>>> a = np.zeros((2,2),dtype='float')
>>> a += 5
>>> a
array([[ 5.,  5.],
       [ 5.,  5.]])
>>> a *= 5
>>> a
array([[ 25.,  25.],
       [ 25.,  25.]])
>>> a + a
array([[ 50.,  50.],
       [ 50.,  50.]])

Concatenation

>>> a = np.array([1,2,3])
>>> b = np.array([4,5,6])
>>> c = np.array([7,8,9])
>>> np.hstack([a,b,c])
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.vstack([a,b,c])
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

Sorting arrays

>>> x.sort()
>>> x
array([0, 0, 1, 3, 4, 5])
>>> x = np.array(([1,3,4,0,0,5]))
array([3, 4, 0, 1, 2, 5])
>>> np.argsort(x)
array([3, 4, 0, 1, 2, 5])

Common math functions

>>> x = np.arange(1,5)
>>> np.sqrt(x) * np.pi
array([ 3.14159265,  4.44288294,  5.44139809,  6.28318531])
>>> 2**4
16
>>> np.power(2,4)
16
>>> np.log(np.e)
1.0
>>> x = np.arange(5)
>>> x.max() - x.min()
4

Basic operations excercise

Exercise

In the following table we have expression values for 5 genes at 4 time points. These are completely made up data. Although, some of the questions can be easily answered by looking at the data, microarray data generally come in much larger tables and if you can figure it out here the same code will work for an entire gene chip.

Gene name	4h	12h	24h	48h
A2M	0.12	0.08	0.06	0.02
FOS	0.01	0.07	0.11	0.09
BRCA2	0.03	0.04	0.04	0.02
CPOX	0.05	0.09	0.11	0.14

1. create a single array for the data (4x4)
2. find the mean expression value per gene
3. find the mean expression value per time point
4. which gene has the maximum mean expression value?
5. sort the gene names by the max expression value

Tip

>>> geneList = np.array(["A2M", "FOS", "BRCA2","CPOX"])
>>> values0  = np.array([0.12,0.08,0.06,0.02])
>>> values1  = np.array([0.01,0.07,0.11,0.09])
>>> values2  = np.array([0.03,0.04,0.04,0.02])
>>> values3  = np.array([0.05,0.09,0.11,0.14])

Additional NumPy

Indexing and Slicing

1D arrays can be indexed in the same way a Python list can.

>>> a = np.arange(10)
>>> a[2:4]
array([2, 3])
>>> a[:10:2]
array([0, 2, 4, 6, 8])
>>> a[::-1]
array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])

Multidimensional arrays can have one index per axis

>>> x = np.arange(12).reshape(3,4)
>>> x
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> x[2,3]
11
>>> x[:,1]                       # everything in the second row
array([1, 5, 9])
>>> x[1,:]                       # everything in the second column
array([4, 5, 6, 7])
>>> x[1:3,:]                     # second and third rows
array([[ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

Where

>>> a = np.array([1,1,1,2,2,2,3,3,3])
>>> a[a>1]
array([2, 2, 2, 3, 3, 3])
>>> a[a==3]
array([3, 3, 3])
>>> np.where(a<3)
(array([0, 1, 2, 3, 4, 5]),)
>>> np.where(a<3)[0]
array([0, 1, 2, 3, 4, 5])
>>> np.where(a>9)
(array([], dtype=int64),)

Printing

>>> for row in x:
...     print row
...
[0 1 2 3]
[4 5 6 7]
[ 8  9 10 11]

>>> for element in x.flat:
...     print element
...
0
1
2
3
4
5
6
7
8
9
10
11

Copying

>>> a = np.array(['a','b','c'])
>>> b = a
>>> b[1] = 'z'
>>> a
array(['a', 'z', 'c'],
      dtype='|S1')

>>> a = np.array(['a','b','c'])
>>> b = a.copy()
>>> b[1] = 'z'
>>> a
array(['a', 'b', 'c'],
      dtype='|S1')

Missing data

>>> import numpy as np
>>> from scipy.stats import nanmean
>>> a = np.array([[1,2,3],[4,5,np.nan],[7,8,9]])
>>> a
array([[  1.,   2.,   3.],
       [  4.,   5.,  nan],
       [  7.,   8.,   9.]])
>>> columnMean = nanmean(a,axis=0)
>>> columnMean
array([ 4.,  5.,  6.])
>>> rowMean = nanmean(a,axis=1)
>>> rowMean
array([ 2. ,  4.5,  8. ])

Generating random numbers

>>> np.random.randint(0,10,5)      # random integers from a closed interval
array([2, 8, 3, 7, 8])
>>> np.random.normal(0,1,5)        # random numbers from a Gaussian
array([ 1.44660159, -0.35625249, -2.09994545,  0.7626487 ,  0.36353648])
>>> np.random.uniform(0,2,5)       # random numbers from a uniform distribution
array([ 0.07477679,  0.36409135,  1.42847035,  1.61242304,  0.54228665])

There are many useful functions in random however we are showing only a few so that they will be familar when we get to plotting.