In [ ]:
# import statement
import numpy
In [ ]:
# create sample 1-dimensional array
a = numpy.array([2, 3, -5, 21, -2, 1, 10, 100, 200])
a.shape
Let's create 2 dimensional array
In [ ]:
# create sample 2-dimensional array
a = numpy.array([[2, 3, -5],[21, -2, 1]])
a.shape
Lets print it...
In [ ]:
print a
What is data type of above 'a' variable
In [ ]:
type(a) # returns type of object 'a'
In [ ]:
a.dtype # returns type of array 'a' elements
How to create float / double type variable
In [ ]:
# To force a certain numerical type for the array, set the "dtype" keyword to a type code
a = numpy.array([[2, 3, -5],[21, -2, 1]], dtype='i')
a.dtype
Lets print it again ...
In [ ]:
print a
Some common typecodes:
To create an array of a given shape filled with zeros, use the zeros function (with dtype being optional)
In [ ]:
a = numpy.zeros((3,2), dtype='d')
print a
To create an array the same as range, use the arange function (again dtype is optional)
In [ ]:
a = numpy.arange(10)
print a
Note : its arange function in numpy, range function can be used in normal list
Like lists, element addresses start with zero, so the first element of 1‐D array a is a[0], the second is a[1], etc.
In [ ]:
a[0]
Like lists, you can reference elements starting from the end, e.g., element a[-1] is the last element in a 1‐D array.
In [ ]:
a[-1]
In [ ]:
a = numpy.array([2, 3.2, 5.5, -6.4, -2.2, 2.4])
a[1:4] # what it will return ? ...
In [ ]:
a = numpy.array([[2, 3.2, 5.5, -6.4, -2.2, 2.4],
[1, 22, 4, 0.1, 5.3, -9],
[3, 1, 2.1, 21, 1.1, -2]])
How can we access the number 4 from the abvoe numpy array variable a ?
In [ ]:
a[1][2] #normal index access using multi square brackets []
Alternatively ...
In [ ]:
a[1,2]
How can we aceess the full row / column ?
In [ ]:
# get the 2nd row of the a numpy array variable
a[1,:] # what about a[1] ?
Tell me answers for the following two statements...
In [ ]:
a[1:3,0]
In [ ]:
a[1:3,0:2]
In [ ]:
# Will it throw an error? why?
a[1:4,0:2]
Some information about arrays comes through functions on the array, others through attributes attached to the array.
In [ ]:
print a
In [ ]:
# Shape of the array:
numpy.shape(a) # same as a.shape
In [ ]:
# Rank of the array:
numpy.rank(a)
In [ ]:
# Number of elements in the array
numpy.size(a) # same as a.size
Why shouldn't we use len(a) function ?
In [ ]:
len(a)
Reshape
In [ ]:
print a
a.shape
In [ ]:
# Reshape the array:
numpy.reshape(a, (9, 2))
In [ ]:
b = a.reshape((6, 3)) # reshape array a and store into variable b
print "b shape", b.shape
print "a shape", a.shape # still array a shape is not changed
Transpose
In [ ]:
numpy.transpose(a) # same as a.T
In [ ]:
b = a.T
b.shape
Convert multi dimension into single dimension array
In [ ]:
numpy.ravel(a)
Repeat every elements in an array
In [ ]:
numpy.repeat(a,3)
Convert array a to another type
In [ ]:
b = a.astype('i') # here the argument is the typecode for b.
b
Multiply two arrays together, element by element
In [ ]:
a = numpy.array([[2, 3.2, 5.5, -6.4],
[3, 1, 2.1, 21]])
b = numpy.array([[4, 1.2, -4, 9.1],
[6, 21, 1.5, -27]])
shape_a = numpy.shape(a)
product = numpy.zeros(shape_a, dtype='f')
for i in xrange(shape_a[0]):
for j in xrange(shape_a[1]):
product[i,j] = a[i,j] * b[i,j]
# end of for j in xrange(shape_a[1]):
# end of for i in xrange(shape_a[0]):
# print the result
print product
Multiply two arrays together, element by element
In [ ]:
import numpy
a = numpy.array([[2, 3.2, 5.5, -6.4],
[3, 1, 2.1, 21]])
b = numpy.array([[4, 1.2, -4, 9.1],
[6, 21, 1.5, -27]])
# We didn't create the result 'product' matrix yet !
product = a * b # just normal multiplication
product
How about matrix addition, subtraction, division
In [ ]:
addition = a + b # element-wise addition
subtraction = a - b # element-wise subtraction
division = a / b # element-wise division
In [ ]:
division
How about Matrix Multiplication (say dot product)
In [ ]:
numpy.dot(a, b)
In [ ]:
numpy.dot(a, b.T) # equivalent to a.dot(b.T)
Say you have a 2‐D array a and you want to return an array answer which is square the value when the element in a is greater than 5 and less than 10, and output zero when it is not.
In [ ]:
a = numpy.array([[2, 3.2, 5.5, -6.4, 5],
[30, 7, 2.1, 4.9, 6]])
answer = numpy.zeros(numpy.shape(a), dtype='f')
for i in xrange(numpy.shape(a)[0]):
for j in xrange(numpy.shape(a)[1]):
if (a[i,j] > 5) and (a[i,j] < 10):
answer[i,j] = a[i,j] * a[i,j]
else:
pass
# end of if if (a[i,j] > 5) and (a[i,j] < 10):
# end of for j in xrange(numpy.shape(a)[1]):
# end of for i in xrange(numpy.shape(a)[0]):
answer
Comparison operators (implemented either as operators or functions) act element‐wise, and return a boolean array.
In [ ]:
a = numpy.arange(10)
print a
a > 5 # What do you expect the ans
In [ ]:
numpy.greater(a, 5) # same as 'a > 5'
Boolean operators are implemented as functions that also act element‐wise (e.g., logical_and, logical_or).
The where function tests any condition and applies operations for true and false cases, as specified, on an element‐wise basis.
In [ ]:
# share your view on this
a = numpy.arange(11)
condition = numpy.logical_and(a>5, a<10)
answer = numpy.where(condition, a*2, 0)
What is condition? answer?
In [ ]:
condition
In [ ]:
answer
In [ ]:
condition = numpy.logical_and(a>5, a<10)
answer = ((a*2)*condition) + (0*numpy.logical_not(condition))
In [ ]:
* Use time module, learn it yourself # import time
Use help(numpy), as well as help(numpy.x), where x is the name of a function, to get more information.
In [ ]:
help(numpy)
In [ ]:
help(numpy.histogram)
In [ ]:
print numpy.fft.__doc__
For the file two‐col_rad_sine.txt in files, write code to read the two columns of data into two arrays, one for angle in radians (column 1) and the other for the sine of the angle (column 2).
The two columns are separated by tabs. The file's newline character is just '\n' (though this isn't something you'll need to know to do the exercise).
Exercise 1 : Reading a multi‐column text file Simple Solution
In [ ]:
import numpy
# data path
DATAPATH = ‘./sample_cdat_data/’
# absolute file path
fileobj = open(DATAPATH + 'two-col_rad_sine.txt', 'r')
data_str = fileobj.readlines()
fileobj.close()
# it just declared the arrays with needed length, shape
radians = numpy.array(len(data_str), 'f')
sines = numpy.array(len(data_str), 'f')
# loop through no of lines
for i in xrange(len(data_str)):
# split the line string w.r.t tab character
split_istr = data_str[i].split('\t') # split line and returns as list
radians[i] = float(split_istr[0]) # convert string no into float data type
sines[i] = float(split_istr[1])
Exercise 1 : Reading a multi‐column text file NumPy Powered Solution
In [ ]:
import numpy, os
# data path
DATAPATH = ‘./sample_cdat_data/’
# absolute file path
filepath = os.path.join(DATAPATH, 'two-col_rad_sine.txt')
# read two columns from datafile and load it into two variables
radians, sines = numpy.loadtxt(filepath, delimiter='\t', unpack=True) # to unpack all columns