Lab1 Introduction to Python¶
Adapted from the cs228 Python tutorial by Volodymyr Kuleshov and Isaac Caswell (https://github.com/kuleshov/cs228-material/blob/master/tutorials/python/cs228-python-tutorial.ipynb)
Introduction¶
Python is a great general-purpose programming language on its own, but with the help of a few popular libraries (numpy, scipy, matplotlib) it becomes a powerful environment for scientific computing.
We expect that many of you will have some experience with Python and numpy; for the rest of you, this section will serve as a quick crash course both on the Python programming language and on the use of Python for scientific computing.
Some of you may have previous knowledge in Matlab, in which case we also recommend the numpy for Matlab users page ( https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html ).
In this tutorial, we will cover:
- IPython: Creating notebooks, Typical workflows
- Anaconda
- Basic Python: Basic data types (Containers, Lists, Dictionaries, Sets, Tuples), Functions, Classes
- Numpy: Arrays, Array indexing, Datatypes, Array math, Broadcasting
- Matplotlib: Plotting, Subplots, Images
IPython ( Interactive Python ): Jupyter notebook
The Jupyter Notebook ( https://jupyter.org/ ) is an incredibly powerful tool for interactively developing and presenting data science projects. A notebook integrates code and its output into a single document that combines visualisations, narrative text, mathematical equations, and other rich media. The intuitive workflow promotes iterative and rapid development, making notebooks an increasingly popular choice at the heart of contemporary data science, analysis, and increasingly science at large.
Markdown Cells
Text can be added to Jupyter Notebooks using Markdown cells. You can change the cell type to Markdown by using the Cell menu, the toolbar, or the key shortcut m. Markdown is a popular markup language that is a superset of HTML. Its specification can be found here: You can learn about markdown here https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Working%20With%20Markdown%20Cells.html
Latex equation¶
$e^x=\sum_{i=0}^\infty \frac{1}{i!}x^i$
Anaconda
Anaconda ( https://www.anaconda.com/ ) is a free and open-source distribution of the Python and R programming languages for scientific computing (data science, machine learning applications, large-scale data processing, predictive analytics, etc.), that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
Basics of Python¶
Python is a high-level, dynamically typed multiparadigm programming language. Python code is often said to be almost like pseudocode, since it allows you to express very powerful ideas in very few lines of code while being very readable. As an example, here is an implementation of the classic quicksort algorithm in Python:
Python versions¶
There are currently two different supported versions of Python, 2.7 and 3.x+. Python 3.x+ introduced many backwards-incompatible changes to the language, so code written for 2.7 may not work under 3.x+ and vice versa. For this class all code will use Python 3.6/7.
You can check your Python version at the command line by running python --version.
Basic data types¶
Numbers¶
Integers and floats work as you would expect from other languages:
x = 3
print( x, type(x) )
print( x + 1 ) # Addition;
print( x - 1 ) # Subtraction;
print( x * 2 ) # Multiplication;
print( x ** 2 ) # Exponentiation;
x/2
float(x)/2
Integer part
x//2
x += 1
print( x ) # Prints "4"
x *= 2
print( x ) # Prints "8"
y = 2.5
print( type(y) ) # Prints "<type 'float'>"
print( y, y + 1, y * 2, y ** 2 ) # Prints "2.5 3.5 5.0 6.25"
Write code to compute the following expression: $3 \cdot \frac{(8-5)^2 \cdot 3}{5}$
# write code here
#x++
Note that unlike many languages, Python does not have unary increment (x++) or decrement (x--) operators.
Python also has built-in types for long integers and complex numbers; you can find all of the details in the documentation.
Booleans¶
Python implements all of the usual operators for Boolean logic, but uses English words rather than symbols (&&, ||, etc.):
t, f = True, False
print( type(t) ) # Prints "<type 'bool'>"
Now we let's look at the operations:
print( t and f ) # Logical AND;
print( t or f ) # Logical OR;
print( not t ) # Logical NOT;
print( t != f ) # Logical XOR;
Be aware not to confuse "and" or "or" expressions with "|" or "&" bitwise operators
( 1 and 3 and 5)
( 1 & 3 )
( 1 or 3 or 5 )
( 1 | 4 )
What is the result of the expression $(True \enspace AND \enspace False) \enspace OR \enspace (True \enspace OR \enspace False)$?
# write code here
Strings¶
hello = 'hello' # String literals can use single quotes
world = "world" # or double quotes; it does not matter.
print( hello, len(hello) )
hw = hello + ' ' + world # String concatenation
print( hw ) # prints "hello world"
hw12 = '%s %s %d' % (hello, world, 12) # sprintf style string formatting
print( hw12 ) # prints "hello world 12"
String objects have a bunch of useful methods; for example:
s = "hello"
print( s.capitalize() ) # Capitalize a string; prints "Hello"
print( s.upper() ) # Convert a string to uppercase; prints "HELLO"
print( s.rjust(7) ) # Right-justify a string, padding with spaces; prints " hello"
print( s.center(7) ) # Center a string, padding with spaces; prints " hello "
print( s.replace('l', '(ell)') ) # Replace all instances of one substring with another;
# prints "he(ell)(ell)o"
print( ' world '.strip() ) # Strip leading and trailing whitespace; prints "world"
Create two strings $s1$='you are very tired' and $s2$='fight for the best'. Concatenate the first $8$ characters of $s1$ with the last $8$ characters of $s2$ into a new string $s3$ and print it.
# write code here
You can find a list of all string methods in the documentation.
Containers¶
Python includes several built-in container types: lists, dictionaries, sets, and tuples.
Lists¶
A list is the Python equivalent of an array, but is resizeable and can contain elements of different types:
xs = [3, 1, 2]#Create a list
print( xs, xs[2] )
print( xs[-1] )#Negative indices count from the end of the list; prints "2"
xs[2] = 'foo'# Lists can contain elements of different types
print( xs )
xs.append('bar')# Add a new element to the end of the list
print( xs )
x = xs.pop()# Remove and return the last element of the list
print( x, xs )
As usual, you can find all the gory details about lists in the documentation.
Slicing¶
In addition to accessing list elements one at a time, Python provides concise syntax to access sublists; this is known as slicing:
nums = list( range(5) ) # range is a built-in function that creates a list of integers
print( nums ) # Prints "[0, 1, 2, 3, 4]"
print( nums[2:4] ) # Get a slice from index 2 to 4 (exclusive); prints "[2, 3]"
print( nums[2:] ) # Get a slice from index 2 to the end; prints "[2, 3, 4]"
print( nums[:2] ) # Get a slice from the start to index 2 (exclusive); prints "[0, 1]"
print( nums[:] ) # Get a slice of the whole list; prints ["0, 1, 2, 3, 4]"
print( nums[:-1] ) # Slice indices can be negative; prints ["0, 1, 2, 3]"
nums[2:4] = [8, 9] # Assign a new sublist to a slice
print( nums ) # Prints "[0, 1, 8, 8, 4]"
From decimal to binary
bin( 358 )
To ommit '0b'
bin( 358 )[2:]
Loops¶
You can loop over the elements of a list like this:
animals = ['cat', 'dog', 'monkey']
for animal in animals:
print( animal )
If you want access to the index of each element within the body of a loop, use the built-in enumerate function:
animals = ['cat', 'dog', 'monkey']
for idx, animal in enumerate(animals):
print( '#%d: %s' % (idx + 1, animal) )
List comprehensions:¶
A a simple example, consider the following code that computes square numbers:
nums = [0, 1, 2, 3, 4]
squares = []
for x in nums:
squares.append(x ** 2)
print( squares )
You can make this code simpler using a list comprehension. List comprehensions provide a concise way to create lists.
The basic syntax is
[ expression for item in list if conditional ]
nums = [0, 1, 2, 3, 4]
squares = [x ** 2 for x in nums]
print( squares )
List comprehensions can also contain conditions:
nums = [0, 1, 2, 3, 4]
even_squares = [x ** 2 for x in nums if x % 2 == 0]
print( even_squares )
Furthermore when cleaning data, frequently we want to transform one type of data into another.
print( even_squares )
print( type(even_squares[0]) )
Convert from int to float
float_even_squares = [ float(i) for i in even_squares ]
print( float_even_squares )
print( type( float_even_squares[0] ) )
Or with map function, which apply 'float' to every element
fes = list( map( float, even_squares ) )
print( fes )
print( type(fes[0]) )
def f(i):
return ( i*i + 5 )
a = list( map( f, even_squares ) )
print( a )
print( type(a[0]) )
Delete an element from the list. Let's say that we wanted to erase the 2nd element.
print( a )
del a[1]
print( a )
Create a list containing integers from $2$ to $100$. Use list comprehension to create a new list containing only the prime numbers of the initial list. Print the new list.
A prime number n is a number whose divisors are {1,n}
def is_prime( n ):
i = 2
while i * i <= n:
if n % i == 0:
return ( False )
i += 1
return ( True )
The prime numbers at the range [ 2, 100 ] are
for i in range( 2, 101 ):
if is_prime(i):
print( i, end=' ' )
# write your code here
Dictionaries¶
A dictionary stores (key, value) pairs, similar to a Map in Java or an object in Javascript.
** Optional ( Dictionaries are helpful in dynamic programming and the technique is called memoization. We just store subproblems we have solved in order not to recalculate when needed https://en.wikipedia.org/wiki/Memoization E.x. Calculate n-th fibonacci, Calculate n-th factorial
What is the profit? Exponential time complexity now reduces to Linear ) **
You can use it like this:
d = { 'cat':'cute',
'dog': 'furry'} # Create a new dictionary with some data
print( d['cat'] ) # Get an entry from a dictionary; prints "cute"
print( 'cat' in d ) # Check if a dictionary has a given key; prints "True"
d['fish'] = 'wet' # Set an entry in a dictionary
print( d['fish'] ) # Prints "wet"
print( d['monkey'] ) # KeyError: 'monkey' not a key of d
print( d.get('monkey', 'N/A') ) # Get an element with a default; prints "N/A"
print( d.get('fish', 'N/A') ) # Get an element with a default; prints "wet"
del d['fish'] # Remove an element from a dictionary
print( d.get('fish', 'N/A') ) # "fish" is no longer a key; prints "N/A"
You can find all you need to know about dictionaries in the documentation.
It is easy to iterate over the keys in a dictionary:
d = {'person': 2,
'cat': 4,
'spider': 8}
for animal in d:
legs = d[animal]
print( 'A %s has %d legs' % (animal, legs) )
If you want access to keys and their corresponding values, use the iteritems method:
d = {'person': 2, 'cat': 4, 'spider': 8}
for animal, legs in d.items():
print( 'A %s has %d legs' % (animal, legs) )
Dictionary comprehensions: These are similar to list comprehensions, but allow you to easily construct dictionaries. For example:
nums = [0, 1, 2, 3, 4]
even_num_to_square = { x: x ** 2 for x in nums if x % 2 == 0 }
print( even_num_to_square )
Sets¶
A set is an unordered collection of distinct elements. As a simple example, consider the following:
animals = {'cat', 'dog'}#equivalent set( ['cat','dog'] )
print( 'cat' in animals ) # Check if an element is in a set; prints "True"
print( 'fish' in animals ) # prints "False"
animals.add('fish') # Add an element to a set
print( 'fish' in animals )
print( len(animals) ) # Number of elements in a set;
animals.add('cat') # Adding an element that is already in the set does nothing
print( len(animals) )
animals.remove('cat') # Remove an element from a set
print( len(animals) )
Loops: Iterating over a set has the same syntax as iterating over a list; however since sets are unordered, you cannot make assumptions about the order in which you visit the elements of the set:
animals = {'cat', 'dog', 'fish'}
for idx, animal in enumerate(animals):
print( '#%d: %s' % (idx + 1, animal) )
# Prints "#1: fish", "#2: dog", "#3: cat"
Set comprehensions: Like lists and dictionaries, we can easily construct sets using set comprehensions:
from math import sqrt
print( { int(sqrt(x)) for x in range(30) } )
Tuples¶
A tuple is an (immutable) ordered list of values. Here is a trivial example:
d = { (x, x + 1): x for x in range(10) } # Create a dictionary with tuple keys
t = (5, 6) # Create a tuple
print( type(t) )
print( d )
print( d[t] )
print( d[(1, 2)] )
A tuple is in many ways similar to a list; one of the most important differences is that tuples can be used as keys in dictionaries and as elements of sets, while lists cannot.
d = {}
d[ [1,2] ] = 1
and as we said they are immutable, therefore you can't change an element ( strings are also immutable as well )
t[0] = 1
Functions¶
Python functions are defined using the def keyword. For example:
def sign(x):
if x > 0:
return 'positive'
elif x < 0:
return 'negative'
else:
return 'zero'
for x in [-1, 0, 1]:
print( sign(x) )
We will often define functions to take optional keyword arguments, like this:
def hello(name, loud=False):
if loud:
print( 'HELLO, %s' % name.upper() )
else:
print( 'Hello, %s!' % name )
hello('Bob')
hello('Fred', loud=True)
#Input array
#Output sorted array
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[ len(arr) // 2 ]#pick pivot element, notice that we use // instead of / because // results in a integer value
left = [x for x in arr if x < pivot]#all element less than pivot are placed on left
middle = [x for x in arr if x == pivot]#all emement equal to pivot are placed in middle
right = [x for x in arr if x > pivot]#all elements greater than pivot are placed to right
return quicksort(left) + middle + quicksort(right)
print( quicksort([3,6,8,10,1,2,1]) )
and an implementation of mergesort
#Input array
#Output sorted array
def mergesort( arr ):
#basic cases of 1 and 2 elements
if len(arr) <= 1:
return [ arr[0] ]
if len(arr) <= 2:
if arr[0] > arr[1]:
return ( [ arr[1], arr[0] ] )
return ( [ arr[0], arr[1] ] )
#divide & conquer
#split into halves
#sort until basic case
#then merge sorted arrays
mid = len(arr) // 2
c1 = mergesort( arr[:mid] )
c2 = mergesort( arr[mid:] )
#c1 and c2 now are sorted, merge them into c in O( |n1| + |n2| )
c = []
i, j, n, m = 0, 0, len(c1), len(c2)
while i < n and j < m:
if c1[i] < c2[j]:
c.append( c1[i] )
i += 1
else:
c.append( c2[j] )
j += 1
#append whatever remained ( c1 or c2 now is already in c )
while i < n:
c.append( c1[i] )
i += 1
while j < m:
c.append( c2[j] )
j += 1
#print( c1, c2, c )
return ( c )
a = [38, 27, 43, 3, 9, 82, 10]
print( mergesort( a ) )
print( mergesort( [12, 11, 13, 5, 6, 7] ) )
Classes¶
The syntax for defining classes in Python is straightforward:
class Greeter:
# Constructor
def __init__(self, name):
self.name = name #Create an instance variable, self refers to the object we created, similar to "this->"
# Instance method
def greet(self, loud=False):
if loud:
print( 'HELLO, %s!' % self.name.upper() )
else:
print( 'Hello, %s' % self.name )
g = Greeter('Fred') # Construct an instance of the Greeter class
g.greet() # Call an instance method; prints "Hello, Fred"
g.greet(loud=True) # Call an instance method; prints "HELLO, FRED!"
Numpy¶
Numpy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. If you are already familiar with MATLAB, you might find this tutorial useful to get started with Numpy.
To use Numpy, we first need to import the numpy package:
import numpy as np
Arrays¶
A numpy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension.
We can initialize numpy arrays from nested Python lists, and access elements using square brackets:
a = np.array( [1, 2, 3] ) # Create a rank 1 array
print( type(a), a.shape, a[0], a[1], a[2] )
a[0] = 5 # Change an element of the array
print( a )
b = np.array( [ [1,2,3], [4,5,6] ] ) # Create a rank 2 array
print( b )
print( b.shape )
print( b[0, 0], b[0, 1], b[1, 0] )
Numpy also provides many functions to create arrays:
a = np.zeros( (2,2) )# Create an array of all zeros
print( a )
b = np.ones( (1,2) )# Create an array of all ones
print( b )
c = np.full( (2,2), 7 )# Create a 2x2 array and sets 7 as default value
print( c )
d = np.eye(2)# Create a 2x2 identity matrix
print( d )
e = np.random.random( (2,2) ) # Create an array filled with random values
print( e )
Create a $3 \times 2$ array and fill it with values of your preference. Then create a $4 \times 3$ array of all $8$s.
# write your code here
Array indexing¶
Numpy offers several ways to index into arrays.
Slicing: Similar to Python lists, numpy arrays can be sliced. Since arrays may be multidimensional, you must specify a slice for each dimension of the array:
# Create the following array with shape (3, 4)
# [[ 1 2 3 4]
# [ 5 6 7 8]
# [ 9 10 11 12]]
a = np.array( [ [1,2,3,4], [5,6,7,8], [9,10,11,12] ] )
print( a )
#alternative way to create array
a_2 = np.arange(1, 13).reshape(3,4)
print( a )
# Use slicing to pull out the subarray consisting of the first 2 rows
# and columns 1 and 2; b is the following array of shape (2, 2):
# [[2 3]
# [6 7]]
b = a[:2, 1:3]
print( b )
A slice of an array is a view into the same data, so modifying it will not modify the original array.
print( a[0, 1] )
b[0, 0] = 77 # b[0, 0] is the same piece of data as a[0, 1]
print( a[0, 1] )
Be carefull of dimension (,)¶
You can also mix integer indexing with slice indexing. However, doing so will yield an array of lower rank than the original array. Note that this is quite different from the way that MATLAB handles array slicing:
# Create the following array with shape (3, 4)
a = np.arange(1, 13).reshape(3,4)
print( a )
print( a.shape )
Two ways of accessing the data in the middle row of the array. Mixing integer indexing with slices yields an array of lower rank, while using only slices yields an array of the same rank as the original array:
row_r1 = a[ 1, : ]# view of the second row of a -- constant leads to a lower rank
row_r2 = a[ 1:2, : ] # view of the second row of a
row_r3 = a[ [1], : ] # view of the second row of a
print( row_r1, row_r1.shape )
print( row_r2, row_r2.shape )
print( row_r3, row_r3.shape )
# We can make the same distinction when accessing columns of an array:
col_r1 = a[:, 1]
col_r2 = a[:, 1:2]
print( col_r1, col_r1.shape )
print()
print( col_r2, col_r2.shape )
Transpose matrix: A problem with the col_r1 previously is that numpy does not know how to handle col_r1 when we call the transpose function, since it shape is (3,) ( is it row or column vector? )
print( col_r1.transpose() )
print( col_r1.transpose().shape )
print( col_r1.transpose().transpose() )
print( col_r1.transpose().transpose().shape )
On the contrary col_r2 is well-defined
print( col_r2.transpose() )
print( col_r2.transpose().shape )
print( col_r2.transpose().transpose() )
print( col_r2.transpose().transpose().shape )
Boolean array indexing
Boolean array indexing lets you pick out arbitrary elements of an array. Frequently this type of indexing is used to select the elements of an array that satisfy some condition. Here is an example:
a = np.array( [ [1,2], [3, 4], [5, 6] ] )
bool_idx = (a > 2) # Find the elements of a that are bigger than 2;
# this returns a numpy array of Booleans of the same
# shape as a, where each slot of bool_idx tells
# whether that element of a is > 2.
print( bool_idx )
# We use boolean array indexing to construct a rank 1 array
# consisting of the elements of a corresponding to the True values
# of bool_idx
print( a[bool_idx] )
print()
# We can do all of the above in a single concise statement:
print( a[a > 2] )
Boolean array indexing could be useful for threshodling an image, e.x. set a value to high or low pixel-values
For brevity we have left out a lot of details about numpy array indexing; if you want to know more you should read the documentation.
Datatypes¶
Every numpy array is a grid of elements of the same type. Numpy provides a large set of numeric datatypes that you can use to construct arrays. Numpy tries to guess a datatype when you create an array, but functions that construct arrays usually also include an optional argument to explicitly specify the datatype. Here is an example:
x = np.array( [1, 2] ) # Let numpy choose the datatype
y = np.array( [1.0, 2.0] ) # Let numpy choose the datatype
z = np.array( [1, 2], dtype=np.int64 ) # Force a particular datatype
print( x.dtype, y.dtype, z.dtype )
You can read all about numpy datatypes in the documentation.
Array math¶
Basic mathematical functions operate elementwise on arrays, and are available both as operator overloads and as functions in the numpy module:
x = np.array( [ [1,2], [3,4] ], dtype=np.float64)
y = np.array( [ [5,6], [7,8] ], dtype=np.float64)
# Elementwise sum; both produce the array
print( x + y )
print( np.add(x, y) )
# Elementwise difference; both produce the array
print( x - y )
print( np.subtract(x, y) )
# Elementwise product; both produce the array
print( x * y )
print( np.multiply(x, y) )
# Elementwise division; both produce the array
# [[ 0.2 0.33333333]
# [ 0.42857143 0.5 ]]
print( x / y )
print( np.divide(x, y) )
# Elementwise square root; produces the array
# [[ 1. 1.41421356]
# [ 1.73205081 2. ]]
print( np.sqrt(x) )
Note that unlike MATLAB, * is elementwise multiplication, not matrix multiplication. We instead use the dot function to compute inner products of vectors, to multiply a vector by a matrix, and to multiply matrices. dot is available both as a function in the numpy module and as an instance method of array objects:
x = np.array( [ [1,2], [3,4] ] )
y = np.array( [ [5,6], [7,8] ] )
v = np.array( [9,10] )
w = np.array( [11, 12] )
# Inner product of vectors; both produce 219
print( v.dot(w) )
print( np.dot(v, w) )
# Matrix / vector product; both produce the rank 1 array [29 67]
print( x.dot(v) )
print( np.dot(x, v) )
# Matrix / matrix product; both produce the rank 2 array
# [[19 22]
# [43 50]]
print( x.dot(y) )
print( np.dot(x, y) )
Numpy provides many useful functions for performing computations on arrays; one of the most useful is sum:
x = np.array( [ [1,2],[3,4] ] )
print( np.sum(x) ) # Compute sum of all elements; prints "10"
print( np.sum(x, axis=0) ) # Compute sum of each column; prints "[4 6]"
print( np.sum(x, axis=1) ) # Compute sum of each row; prints "[3 7]"
Be careful again about the (,) shape, use keepdim
print( np.sum( x, axis = 0 ).shape )
print( np.sum( x, axis = 1 ).shape )
print( np.sum( x, axis = 0, keepdims=True ).shape )
print( np.sum( x, axis = 1, keepdims=True ).shape )
You can find the full list of mathematical functions provided by numpy in the documentation.
Apart from computing mathematical functions using arrays, we frequently need to reshape or otherwise manipulate data in arrays. The simplest example of this type of operation is transposing a matrix; to transpose a matrix, simply use the T attribute of an array object:
print( x )
print( x.T )
v = np.array([[1,2,3]])
print( v )
print( v.T )
Create the following matrices and do the multiplication? What is the problem? $$ \begin{bmatrix} 1 & 2 & 3 \\ 4 & 5 & 6 \\ 7 & 8 & 9 \\ 10 & 11 & 12 \\ \end{bmatrix} \times \begin{bmatrix} 4 & 5 & 6 \\ 7 & 8 & 9 \\ 10 & 11 & 12 \\ 13 & 14 & 15 \\ \end{bmatrix} = $$
# write code here
Matplotlib¶
Matplotlib is a plotting library. In this section give a brief introduction to the matplotlib.pyplot module, which provides a plotting system similar to that of MATLAB.
import matplotlib.pyplot as plt
By running this special iPython command, we will be displaying plots inline:
%matplotlib inline
Plotting¶
The most important function in matplotlib is plot, which allows you to plot 2D data. Here is a simple example:
# Compute the x and y coordinates for points on a sine curve
x = np.arange( 0, 3 * np.pi, 0.1 )#from 0 up_to 3*3.141592653 with step 0.1
y = np.sin(x)
# Plot the points using matplotlib
plt.plot( x, y )
With just a little bit of extra work we can easily plot multiple lines at once, and add a title, legend, and axis labels:
y_cos = np.cos(x)#calculate points of cos(x)
# Plot the points using matplotlib
plt.plot( x, y )#plot sin(x)
plt.plot( x, y_cos )#plot cos(x)
plt.xlabel('x axis label')#set the label for the x-axis
plt.ylabel('y axis label')#set tha label for the y-axis
plt.title( 'Sine and Cosine' )#set title of plot
plt.legend( ['Sine', 'Cosine'] )#names of each line -- in the same order we called plot function
Subplots¶
You can plot different things in the same figure using the subplot function. Here is an example:
# Compute the x and y coordinates for points on sine and cosine curves
x = np.arange( 0, 3 * np.pi, 0.1 )
y_sin = np.sin(x)
y_cos = np.cos(x)
# Set up a subplot grid that has height 2 and width 1,
# and set the first such subplot as active.
plt.subplot(2, 1, 1)#height, width, which_plot
# Make the first plot
plt.plot( x, y_sin )
plt.title('Sine')
# Set the second subplot as active, and make the second plot.
plt.subplot( 2, 1, 2 )
plt.plot( x, y_cos )
plt.title('Cosine')
# Show the figure.
plt.show()
You can read much more about the subplot function in the documentation.
Scatter plots¶
# Compute the x and y coordinates for randomly generated points
x = np.linspace(-1, 1, 50)
y = np.random.randn(50)
plt.scatter(x,y)
Other helpful functions:
help gives the manual for the function
help( np.random.randn )
dir( np.random )
Compute the $x$ and $y$ coordinates for $50$ points on the line described by $f(x) = 2x + 4$. Plot the line and then make a scatter plot of the $50$ points.
# write your code here
Basic I/O¶
Reading and writing files in python is easy
with open('test_file.txt', 'r') as fp:
for line in fp:
print( line.strip() )
# alternative
with open('test_file.txt', 'r') as fp:
lines = fp.readlines()
print( lines )
# 2nd alternative
f = open('test_file.txt', 'r')
lines = f.readlines()
for line in lines:
print( line.strip() )
f.close()
# write to file
f = open('test_file.txt', 'a')
f.write("\nNew Line")
# f.close()
Handling Exceptions¶
days = {0 : "Monday", 1: "Tuesday", 2: "Wednesday", 3: "Thurdsay", 4: "Friday", 5: "Saturday"}
print( days )
days[0]
days[6]
try:
print( days[6] )
except KeyError:
print( "Sunday" )
a = list( range(10) )
print( a )
print( a[10] )
try:
a[10]
except IndexError:
print( 10 )