A Review of Basic Algorithms and Data Structures in Python: Graph Algorithms

A Review of Basic Algorithms and Data Structures in Python: Graph Algorithms

<strong>Originally published by </strong><a href="https://medium.com/@diogoribeiro_94486" target="_blank">Diogo Ribeiro</a> <em>at&nbsp;</em><a href="https://medium.com/@diogoribeiro_94486/a-review-of-basic-algorithms-and-data-structures-in-python-graph-algorithms-d73691d86211" target="_blank"><em>Medium</em></a>

Introduction

Recently, while reviewing basic graph algorithms, I decided to write down my study notes as an article in case someone else finds them useful. To verify my understanding, I wrote minimal implementations of the algorithms in Python which make up the bulk of this article. Simple unit tests accompany the code. The unit tests can also be used as examples of using the code.

I’m hoping to write at least a few follow-up posts, focusing on combinatorial algorithms, string algorithms, and maybe even one on computational geometry.

Most of the code was written to be easy to understand without having to reference much else (with a few exceptions, for example, Kruskal’s algorithm uses the disjoint set structure defined in another section). This results in some duplication, especially in the unit tests. I consider this to be acceptable, given that the purpose of the code is to be used as educational material and not as code in production use that needs a day to day maintenance.

One last thing before we start: I wrote the article and all the code relatively quickly. Mistakes and bugs are definitely possible. Corrections are appreciated; please comment below if you find any.

Table Of Contents

Algorithms and data structures in this article:

  • Disjoint Set (Union-Find)
  • Kruskal’s Minimum Spanning Tree (MST)
  • Depth First Search (DFS)
  • Breadth First Search (BFS)
  • Kahn’s Topological Sort Algorithm
  • Dijkstra’s Shortest Path Algorithm
  • Bellman-Ford Shortest Path Algorithm

Disjoint Set (Union-Find)

The disjoint set structure is used to keep track of a partitioning of a set of objects into subsets. The main question it needs to answer is “do X and Y belong to the same subset?” and the main operation it needs to support is joining two subsets so that elements in either of the subsets will belong to the same larger subset afterward.

Quick and minimal implementation is provided below. The implementation below uses a forest to keep track of the subsets in the partition. Each tree in the forest is one subset, and the root of the tree is the “representative” element of the subset. To check if two elements belong to the same subset, we check if they have the same representative element.

Noting that the ideal tree in this implementation is a star (this minimizes the number of recursive find calls), we "compress" the paths on each call to find. That is, we set the parent of all the elements on the path to the representative to the representative as we unwind down the recursive call stack.

class DisjointSet(object):
  def __init__(self, n):
    """
    Initializes a disjoint set structure consisting of n disjoint sets.
    """
    self.parent = list(range(n))
  def find(self, x):
    """Returns the representative element of the set x belongs to."""
    if self.parent[x] != x:
      self.parent[x] = self.find(self.parent[x])
    return self.parent[x]
  def union(self, x, y):
    """Joins the sets containing x and y."""
    self.parent[self.find(x)] = self.find(y)

And the accompanied unit test:

import unittest
from union_find import DisjointSet

class DisjointSetTest(unittest.TestCase):
  def test_initialized_state(self):
    d = DisjointSet(3)
    self.assertEqual(d.find(0), 0)
    self.assertEqual(d.find(1), 1)
    self.assertEqual(d.find(2), 2)
  def test_basic_union(self):
    d = DisjointSet(3)
    d.union(0, 1)
    self.assertEqual(d.find(0), d.find(1))
    self.assertNotEqual(d.find(1), d.find(2))
  def test_basic_union_idempotent(self):
    d = DisjointSet(2)
    d.union(0, 1)
    d.union(0, 1)
    self.assertEqual(d.find(0), d.find(1))
  def test_union_all(self):
    d = DisjointSet(100)
    for i in range(1, 100):
      d.union(i - 1, i)
    for i in range(1, 100):
      self.assertEqual(d.find(0), d.find(i))

Kruskal’s Minimum Spanning Tree (MST)

Kruskal’s minimum spanning tree algorithm is a good example of a greedy algorithm. Starting with a forest consisting of individual disjoint vertices, at each step we pick the next best edge (one with minimal weight) provided it does not introduce a cycle into the forest, and continue until the forest becomes a tree. It’s rather easy to prove that the resulting tree is a minimum spanning tree.

Using the disjoint set structure shown above to keep track of the minimum spanning forest, the implementation below is very simple:

from collections import namedtuple
from union_find import DisjointSet

# Putting weight as the first element means Edges will sort by weight first,
# then source and target (lexicographically).
Edge = namedtuple('Edge', ['weight', 'source', 'target'])

def kruskal_mst(n, edges):
  """
  Given a positive integer n (number of vertices) and a collection of Edge
  namedtuple objects representing the undirected edges of a graph, returns a
  list of edges forming a minimal spanning tree of the graph. Assumes the
  vertices are numbers in the range 0 to n - 1.  Also assumes input is a
  valid connected undirected graph and that for two vertices v and w only one
  of (v, w) or (w, v) is an edge in the input. Output is undefined if these
  assumptions are not satisfied.
  """
  d = DisjointSet(n)
  mst_tree = []
  for edge in sorted(edges):
    if d.find(edge.source) != d.find(edge.target):
      mst_tree.append(edge)
      if len(mst_tree) == n - 1:
        break
      d.union(edge.source, edge.target)
  return mst_tree

And the accompanied unit test:

import unittest
from kruskal import kruskal_mst, Edge

class KruskalMSPTest(unittest.TestCase):
  def test_single_vertex_graph(self):
    self.assertEqual(kruskal_mst(1, []), [])
  def test_single_edge_graph(self):
    edges = [Edge(source=0, target=1, weight=10)]
    self.assertEqual(kruskal_mst(2, edges), edges)
  def test_cycle_5(self):
    edges = [
      Edge(source=0, target=1, weight=50),
      Edge(source=1, target=2, weight=30),
      Edge(source=2, target=3, weight=60),
      Edge(source=3, target=4, weight=20),
      Edge(source=4, target=0, weight=10),
    ]
    # Everything except the heaviest edge. Output sorted by weight.
    self.assertEqual(kruskal_mst(5, edges), [
      Edge(source=4, target=0, weight=10),
      Edge(source=3, target=4, weight=20),
      Edge(source=1, target=2, weight=30),
      Edge(source=0, target=1, weight=50),
    ])
  def test_complete_graph_4(self):
    edges = [
      Edge(source=0, target=1, weight=10),
      Edge(source=0, target=2, weight=30),
      Edge(source=0, target=3, weight=40),
      Edge(source=1, target=2, weight=20),
      Edge(source=1, target=3, weight=50),
      Edge(source=2, target=3, weight=60),
    ]
    self.assertEqual(kruskal_mst(4, edges), [
      Edge(source=0, target=1, weight=10),
      Edge(source=1, target=2, weight=20),
      Edge(source=0, target=3, weight=40),
    ])

Depth First Search (DFS)

Depth-first search is arguably the simplest graph traversal algorithm. It’s a simple recursive algorithm that just needs to keep track of which vertices have already been processed. In fact, many other recursive algorithms can be thought of as a DFS on some underlying graph (e.g. binary search is guided DFS on the binary search tree). DFS can be used to determine if there is a path from a vertex to another and to visit every vertex starting from a source vertex. Variations of DFS can be used for determining connected components and doing topological sorting. The code below simply uses DFS to return all vertices reachable from a starting vertex.

def dfs(graph, source):
  """
  Given a directed graph (format described below), and a source vertex,
  returns a set of vertices reachable from source.
  The graph parameter is expected to be a dictionary mapping each vertex to a
  list of vertices indicating outgoing edges. For example if vertex v has
  outgoing edges to u and w we have graph[v] = [u, w].
  """
  visited = set()
  def _recurse(v):
    if v in visited:
      return
    visited.add(v)
    for w in graph[v]:
      _recurse(w)
  _recurse(source)
  return visited

And the accompanied unit test:

import unittest
from dfs import dfs

class DFSTest(unittest.TestCase):
  def test_single_vertex(self):
    graph = {0: []}
    self.assertEqual(dfs(graph, 0), {0})
  def test_single_vertex_with_loop(self):
    graph = {0: [0]}
    self.assertEqual(dfs(graph, 0), {0})
  def test_two_vertices_no_path(self):
    graph = {
      0: [],
      1: [],
    }
    self.assertEqual(dfs(graph, 0), {0})
    self.assertEqual(dfs(graph, 1), {1})
  def test_two_vertices_with_simple_path(self):
    graph = {
      0: [1],
      1: [],
    }
    self.assertEqual(dfs(graph, 0), {0, 1})
    self.assertEqual(dfs(graph, 1), {1})
  def test_complete_graph(self):
    def _complete_graph(n):
      return {v: list(set(range(n)) - {v}) for v in range(n)}
    for n in range(2, 10):
      graph = _complete_graph(n)
      for v in range(n):
        self.assertEqual(dfs(graph, v), set(range(n)))
  def test_cycle_5(self):
    graph = {
      0: [1],
      1: [2],
      2: [3],
      3: [4],
      4: [0],
    }
    for v in range(5):
      self.assertEqual(dfs(graph, v), {0, 1, 2, 3, 4})

Breadth First Search (BFS)

BFS is one of the simplest graph algorithms and a good algorithm to understand prior to Dijkstra’s, which is coming up next. It can be used to simply traverse a graph and visit every vertex, to search for a particular vertex, or find the shortest path (assuming edges don’t have weights) to every vertex starting from a single vertex.

from collections import deque

def bfs(graph, source, target):
  """
  Given a directed graph (format described below), and source and target
  vertices, returns a shortest unweighted path as a list of vertices going
  from source to target, or None if no such path exists. Returned path will
  not include the source vertex in it.
  The graph parameter is expected to be a dictionary mapping each vertex to a
  list of vertices indicating outgoing edges. For example if vertex v has
  outgoing edges to u and w we have graph[v] = [u, w].
  """
  q = deque([source])
  # previous_vertex[v] holds the immediate vertex before v in the shortest
  # path from source to v. This dictionary also acts as our "visited" set
  # since we set previous_vertex[v] as soon as the vertex enters our queue.
  previous_vertex = {source: source}
  while q:
    v = q.popleft()
    if v == target:
      return _construct_path(previous_vertex, source, target)
    for w in graph[v]:
      if w not in previous_vertex:
        previous_vertex[w] = v
        q.append(w)
  return None

def _construct_path(previous_vertex, source, target):
  if source == target:
    return []
  return _construct_path(previous_vertex, source,
               previous_vertex[target]) + [target]

And the accompanied unit test:

import unittest
from bfs import bfs

class BFSTest(unittest.TestCase):
  def test_single_vertex(self):
    graph = {0: []}
    self.assertEqual(bfs(graph, 0, 0), [])
  def test_single_vertex_with_loop(self):
    graph = {0: [0]}
    self.assertEqual(bfs(graph, 0, 0), [])
  def test_two_vertices_no_path(self):
    graph = {
      0: [],
      1: [],
    }
    self.assertEqual(bfs(graph, 0, 1), None)
  def test_two_vertices_with_simple_path(self):
    graph = {
      0: [1],
      1: [],
    }
    self.assertEqual(bfs(graph, 0, 1), [1])
  def test_complete_graph(self):
    def _complete_graph(n):
      return {v: list(set(range(n)) - {v}) for v in range(n)}
    for n in range(2, 10):
      graph = _complete_graph(n)
      for v in range(n):
        for w in range(n):
          self.assertEqual(bfs(graph, v, w),
                   [] if v == w else [w])
  def test_cycle_5(self):
    graph = {
      0: [4, 1],
      1: [0, 2],
      2: [1, 3],
      3: [2, 4],
      4: [3, 0],
    }
    self.assertEqual(bfs(graph, 0, 2), [1, 2])
    self.assertEqual(bfs(graph, 0, 3), [4, 3])

Kahn’s Topological Sort Algorithm

Given a directed acyclic graph (DAG) representing a set of, say, tasks and their dependencies, the topological sort is the problem of finding an order of task execution that will satisfy all the dependencies. This problem arises in a variety of applications. Examples include task scheduling, build systems (e.g. Bazel), parallel pipelines (e.g. Hadoop), and formula evaluation (e.g. in spreadsheets).

While a variation of DFS can be used for topological sorting, my personal favorite algorithm for doing topological sorts is Kahn’s algorithm, due to its intuitiveness. The idea behind the algorithm is simple: start with vertices with no incoming edges, process them, and then remove them and all their outgoing edges from the graph and continue until there’s nothing left in the graph.

In the code below, instead of returning a particular topological sort, the algorithm assigns a “sequence” to each vertex, such that if sequence[v] < sequence[w] then v should be before w in any topological sort of the graph. This simplifies unit testing, and also allows for easier use of the output in cases where parallelization is possible (since all tasks with the same sequence number can be executed in parallel).

from collections import deque, namedtuple
Vertex = namedtuple('Vertex', ['name', 'incoming', 'outgoing'])

def build_doubly_linked_graph(graph):
  """
  Given a graph with only outgoing edges, build a graph with incoming and
  outgoing edges. The returned graph will be a dictionary mapping vertex to a
  Vertex namedtuple with sets of incoming and outgoing vertices.
  """
  g = {v:Vertex(name=v, incoming=set(), outgoing=set(o))
     for v, o in graph.items()}
  for v in g.values():
    for w in v.outgoing:
      if w in g:
        g[w].incoming.add(v.name)
      else:
        g[w] = Vertex(name=w, incoming={v}, outgoing=set())
  return g

def kahn_top_sort(graph):
  """
  Given an acyclic directed graph (format described below), returns a
  dictionary mapping vertex to sequence such that sorting by the sequence
  component will result in a topological sort of the input graph. Output is
  undefined if input is a not a valid DAG.
  The graph parameter is expected to be a dictionary mapping each vertex to a
  list of vertices indicating outgoing edges. For example if vertex v has
  outgoing edges to u and w we have graph[v] = [u, w].
  """
  g = build_doubly_linked_graph(graph)
  # sequence[v] < sequence[w] implies v should be before w in the topological
  # sort.
  q = deque(v.name for v in g.values() if not v.incoming)
  sequence = {v: 0 for v in q}
  while q:
    v = q.popleft()
    for w in g[v].outgoing:
      g[w].incoming.remove(v)
      if not g[w].incoming:
        sequence[w] = sequence[v] + 1
        q.append(w)
  return sequence

And the accompanied unit test:

import unittest
from kahn import kahn_top_sort

class KahnTopSortTest(unittest.TestCase):
  def test_single_vertex(self):
    graph = {
      0: [],
    }
    self.assertEqual(kahn_top_sort(graph), {
      0: 0,
    })
  def test_total_order_2(self):
    graph = {
      0: [1],
      1: [],
    }
    self.assertEqual(kahn_top_sort(graph), {
      0: 0,
      1: 1,
    })
  def test_total_order_3(self):
    graph = {
      0: [1],
      1: [2],
      2: [],
    }
    self.assertEqual(kahn_top_sort(graph), {
      0: 0,
      1: 1,
      2: 2,
    })
  def test_two_independent_total_orders(self):
    # 0 -> 1 -> 2
    # 3 -> 4 -> 5
    graph = {
      0: [1],
      1: [2],
      2: [],
      3: [4],
      4: [5],
      5: [],
    }
    self.assertEqual(kahn_top_sort(graph), {
      0: 0,
      3: 0,
      1: 1,
      4: 1,
      2: 2,
      5: 2,
    })
  def test_simple_dag_1(self):
    # 0 -> 1 -> 2
    #   \ /
    #  3
    graph = {
      0: [1, 3],
      1: [2],
      2: [],
      3: [1],
    }
    self.assertEqual(kahn_top_sort(graph), {
      0: 0,
      3: 1,
      1: 2,
      2: 3,
    })

Dijkstra’s Shortest Path Algorithm

Dijkstra’s shortest path algorithm is very similar to BFS, except a priority queue is used instead of a regular queue. A proper implementation would use a priority queue with an “update key” operation which would reduce the redundant items in the queue. The implementation below, for the sake of simplicity, uses the built-in Python PriorityQueue which does not support "update key".

The invariant in the algorithm is that each time we get an item from the queue, we know that we have the shortest path from source to it already (this is where the guarantee of non-negative weights is key, as this invariant can fail if we have negative weights.)

from collections import namedtuple, defaultdict
from Queue import PriorityQueue
Edge = namedtuple('Edge', ['target', 'weight'])

def dijkstra(graph, source, target):
  """
  Given a directed graph (format described below), and source and target
  vertices, returns a shortest path as a list of vertices going from source
  to target, along with the distance of the shortest path, or None if no such
  path exists. Returned path will not include the source vertex in it.
  Assumes non-negative weights.
  The graph parameter is expected to be a dictionary mapping each vertex to a
  list of Edge named tuples indicating the vertex's outgoing edges. For
  example if vertex v has outgoing edges to u and w with weights 10 and 20
  respectively, we have graph[v] = [Edge(u, 10), Edge(w, 20)].
  """
  q = PriorityQueue()
  q.put((0, source))
  # previous_vertex[v] holds the immediate vertex before v in the shortest
  # path from source to v. This dictionary also acts as our "visited" set
  # since we set previous_vertex[v] as soon as the vertex enters our queue.
  previous_vertex = {source: source}
  # Arguably not the best way to represent infinity but it works for the sake
  # of learning the algorithm.
  shortest_distance = defaultdict(lambda: float('inf'))
  shortest_distance[source] = 0
  while not q.empty():
    (distance, v) = q.get()
    if v == target:
      return (distance, _construct_path(previous_vertex, source, target))
    for edge in graph[v]:
      alt_distance = edge.weight + distance
      if alt_distance < shortest_distance[edge.target]:
        shortest_distance[edge.target] = alt_distance
        q.put((alt_distance, edge.target))
        previous_vertex[edge.target] = v
  return None

def _construct_path(previous_vertex, source, target):
  if source == target:
    return []
  return _construct_path(previous_vertex, source,
               previous_vertex[target]) + [target]

And the accompanied unit test:

import unittest
from dijkstra import dijkstra, Edge

class DijkstraTest(unittest.TestCase):
  def test_single_vertex(self):
    graph = {0: []}
    self.assertEqual(dijkstra(graph, 0, 0), (0, []))
  def test_two_vertices_no_path(self):
    graph = {
      0: [],
      1: [],
    }
    self.assertEqual(dijkstra(graph, 0, 1), None)
  def test_two_vertices_with_path(self):
    graph = {
      0: [Edge(target=1, weight=10)],
      1: [],
    }
    self.assertEqual(dijkstra(graph, 0, 1), (10, [1]))
  def test_cycle_3(self):
    graph = {
      0: [Edge(target=1, weight=10), Edge(target=2, weight=30)],
      1: [Edge(target=0, weight=10), Edge(target=2, weight=10)],
      2: [Edge(target=0, weight=30), Edge(target=1, weight=30)],
    }
    self.assertEqual(dijkstra(graph, 0, 2), (20, [1, 2]))
  def test_clrs_example(self):
    graph = {
      's': [
        Edge(target='t', weight=3),
        Edge(target='y', weight=5),
      ],
      't': [
        Edge(target='x', weight=6),
        Edge(target='y', weight=2),
      ],
      'y': [
        Edge(target='t', weight=1),
        Edge(target='z', weight=6),
      ],
      'x': [
        Edge(target='z', weight=2),
      ],
      'z': [
        Edge(target='x', weight=7),
        Edge(target='s', weight=3),
      ],
    }
    distance, path = dijkstra(graph, 's', 'z')
    self.assertEqual(distance, 11)
    self.assertIn(path, [
      ['y', 'z'],
      ['t', 'y', 'x', 'z'],
    ])
    distance, path = dijkstra(graph, 's', 'x')
    self.assertEqual(distance, 9)
    self.assertIn(path, [
      ['t', 'x'],
      ['y', 'x'],
    ])

Bellman-Ford Shortest Path Algorithm

Bellman-Ford is another single-source shortest path algorithm. It’s very easy to implement but has worse running time than Dijkstra’s. While in Dijkstra’s we relax edges greedily based on the next closest vertex to the source, in Bellman-Ford we relax every edge exactly n-1 times. Each such iteration guarantees to increase the number of vertices for which we have the shortest path by at least one, and hence after n-1 iterations, we have the shortest path to every vertex. We then do a final loop over all the edges and try to relax further. If we succeed, we know a negative cycle exists. This is the key advantage of Bellman-Ford as compared to Dijkstra’s (Dijkstra’s algorithm does not work if negative weights exist.)

Here’s a basic implementation:

from collections import namedtuple, defaultdict
Edge = namedtuple('Edge', ['target', 'weight'])

def bellman_ford(graph, source, target):
  """
  Given a directed graph (format described below), and source and target
  vertices, returns a shortest path as a list of vertices going from source
  to target, along with the distance of the shortest path, or None if no such
  path exists and -1 if a negative loop is found. Returned path will not
  include the source vertex in it. Assumes non-negative weights.
  The graph parameter is expected to be a dictionary mapping each vertex to a
  list of Edge named tuples indicating the vertex's outgoing edges. For
  example if vertex v has outgoing edges to u and w with weights 10 and 20
  respectively, we have graph[v] = [Edge(u, 10), Edge(w, 20)].
  """
  # previous_vertex[v] holds the immediate vertex before v in the shortest
  # path from source to v. This dictionary also acts as our "visited" set
  # since we set previous_vertex[v] as soon as the vertex enters our queue.
  previous_vertex = {source: source}
  # Arguably not the best way to represent infinity but it works for the sake
  # of learning the algorithm.
  shortest_distance = defaultdict(lambda: float('inf'))
  shortest_distance[source] = 0
  # Run n - 1 times. We start by knowing the shortest path to 1 vertex
  # (source itself) and each iteration below increases the vertices for which
  # we have the shortest path to by one. This means at the end we have the
  # shortest path to 1 + (n - 1) = n vertices.
  for i in range(len(graph) - 1):
    for v in graph:
      for edge in graph[v]:
        alt_distance = shortest_distance[v] + edge.weight
        if alt_distance < shortest_distance[edge.target]:
          shortest_distance[edge.target] = alt_distance
          previous_vertex[edge.target] = v
  # Final loop over all edges to check for negative loops. If at this point
  # we find a shorter alternative path it means a negative loop exists.
  for v in graph:
    for edge in graph[v]:
      alt_distance = shortest_distance[v] + edge.weight
      if alt_distance < shortest_distance[edge.target]:
        return -1
  if shortest_distance[target] < float('inf'):
    return (shortest_distance[target],
        _construct_path(previous_vertex, source, target))
  return None

def _construct_path(previous_vertex, source, target):
  if source == target:
    return []
  return _construct_path(previous_vertex, source,
               previous_vertex[target]) + [target]

And as before, accompanied unit test, which is a copy of the one used for Dijkstra’s, with an additional test for negative cycles:

import unittest
from bellman import bellman_ford, Edge

class BellmanFordTest(unittest.TestCase):
  def test_single_vertex(self):
    graph = {0: []}
    self.assertEqual(bellman_ford(graph, 0, 0), (0, []))
  def test_two_vertices_no_path(self):
    graph = {
      0: [],
      1: [],
    }
    self.assertEqual(bellman_ford(graph, 0, 1), None)
  def test_two_vertices_with_path(self):
    graph = {
      0: [Edge(target=1, weight=10)],
      1: [],
    }
    self.assertEqual(bellman_ford(graph, 0, 1), (10, [1]))
  def test_cycle_3(self):
    graph = {
      0: [Edge(target=1, weight=10), Edge(target=2, weight=30)],
      1: [Edge(target=0, weight=10), Edge(target=2, weight=10)],
      2: [Edge(target=0, weight=30), Edge(target=1, weight=30)],
    }
    self.assertEqual(bellman_ford(graph, 0, 2), (20, [1, 2]))
  def test_negative_cycle_3(self):
    graph = {
      0: [Edge(target=1, weight=10), Edge(target=2, weight=30)],
      1: [Edge(target=0, weight=10), Edge(target=2, weight=10)],
      2: [Edge(target=0, weight=-30), Edge(target=1, weight=30)],
    }
    self.assertEqual(bellman_ford(graph, 0, 2), -1)
  def test_clrs_example(self):
    graph = {
      's': [
        Edge(target='t', weight=3),
        Edge(target='y', weight=5),
      ],
      't': [
        Edge(target='x', weight=6),
        Edge(target='y', weight=2),
      ],
      'y': [
        Edge(target='t', weight=1),
        Edge(target='z', weight=6),
      ],
      'x': [
        Edge(target='z', weight=2),
      ],
      'z': [
        Edge(target='x', weight=7),
        Edge(target='s', weight=3),
      ],
    }
    distance, path = bellman_ford(graph, 's', 'z')
    self.assertEqual(distance, 11)
    self.assertIn(path, [
      ['y', 'z'],
      ['t', 'y', 'x', 'z'],
    ])
    distance, path = bellman_ford(graph, 's', 'x')
    self.assertEqual(distance, 9)
    self.assertIn(path, [
      ['t', 'x'],
      ['y', 'x'],
    ])

What's Python IDLE? How to use Python IDLE to interact with Python?

What's Python IDLE? How to use Python IDLE to interact with Python?

In this tutorial, you’ll learn all the basics of using **IDLE** to write Python programs. You'll know what Python IDLE is and how you can use it to interact with Python directly. You’ve also learned how to work with Python files and customize Python IDLE to your liking.

In this tutorial, you'll learn how to use the development environment included with your Python installation. Python IDLE is a small program that packs a big punch! You'll learn how to use Python IDLE to interact with Python directly, work with Python files, and improve your development workflow.

If you’ve recently downloaded Python onto your computer, then you may have noticed a new program on your machine called IDLE. You might be wondering, “What is this program doing on my computer? I didn’t download that!” While you may not have downloaded this program on your own, IDLE comes bundled with every Python installation. It’s there to help you get started with the language right out of the box. In this tutorial, you’ll learn how to work in Python IDLE and a few cool tricks you can use on your Python journey!

In this tutorial, you’ll learn:

  • What Python IDLE is
  • How to interact with Python directly using IDLE
  • How to edit, execute, and debug Python files with IDLE
  • How to customize Python IDLE to your liking

Table of Contents

What Is Python IDLE?

Every Python installation comes with an Integrated Development and Learning Environment, which you’ll see shortened to IDLE or even IDE. These are a class of applications that help you write code more efficiently. While there are many IDEs for you to choose from, Python IDLE is very bare-bones, which makes it the perfect tool for a beginning programmer.

Python IDLE comes included in Python installations on Windows and Mac. If you’re a Linux user, then you should be able to find and download Python IDLE using your package manager. Once you’ve installed it, you can then use Python IDLE as an interactive interpreter or as a file editor.

An Interactive Interpreter

The best place to experiment with Python code is in the interactive interpreter, otherwise known as a shell. The shell is a basic Read-Eval-Print Loop (REPL). It reads a Python statement, evaluates the result of that statement, and then prints the result on the screen. Then, it loops back to read the next statement.

The Python shell is an excellent place to experiment with small code snippets. You can access it through the terminal or command line app on your machine. You can simplify your workflow with Python IDLE, which will immediately start a Python shell when you open it.

A File Editor

Every programmer needs to be able to edit and save text files. Python programs are files with the .py extension that contain lines of Python code. Python IDLE gives you the ability to create and edit these files with ease.

Python IDLE also provides several useful features that you’ll see in professional IDEs, like basic syntax highlighting, code completion, and auto-indentation. Professional IDEs are more robust pieces of software and they have a steep learning curve. If you’re just beginning your Python programming journey, then Python IDLE is a great alternative!

How to Use the Python IDLE Shell

The shell is the default mode of operation for Python IDLE. When you click on the icon to open the program, the shell is the first thing that you see:

This is a blank Python interpreter window. You can use it to start interacting with Python immediately. You can test it out with a short line of code:

Here, you used print() to output the string "Hello, from IDLE!" to your screen. This is the most basic way to interact with Python IDLE. You type in commands one at a time and Python responds with the result of each command.

Next, take a look at the menu bar. You’ll see a few options for using the shell:

You can restart the shell from this menu. If you select that option, then you’ll clear the state of the shell. It will act as though you’ve started a fresh instance of Python IDLE. The shell will forget about everything from its previous state:

In the image above, you first declare a variable, x = 5. When you call print(x), the shell shows the correct output, which is the number 5. However, when you restart the shell and try to call print(x) again, you can see that the shell prints a traceback. This is an error message that says the variable x is not defined. The shell has forgotten about everything that came before it was restarted.

You can also interrupt the execution of the shell from this menu. This will stop any program or statement that’s running in the shell at the time of interruption. Take a look at what happens when you send a keyboard interrupt to the shell:

A KeyboardInterrupt error message is displayed in red text at the bottom of your window. The program received the interrupt and has stopped executing.

How to Work With Python Files

Python IDLE offers a full-fledged file editor, which gives you the ability to write and execute Python programs from within this program. The built-in file editor also includes several features, like code completion and automatic indentation, that will speed up your coding workflow. First, let’s take a look at how to write and execute programs in Python IDLE.

Opening a File

To start a new Python file, select File → New File from the menu bar. This will open a blank file in the editor, like this:

From this window, you can write a brand new Python file. You can also open an existing Python file by selecting File → Open… in the menu bar. This will bring up your operating system’s file browser. Then, you can find the Python file you want to open.

If you’re interested in reading the source code for a Python module, then you can select File → Path Browser. This will let you view the modules that Python IDLE can see. When you double click on one, the file editor will open up and you’ll be able to read it.

The content of this window will be the same as the paths that are returned when you call sys.path. If you know the name of a specific module you want to view, then you can select File → Module Browser and type in the name of the module in the box that appears.

Editing a File

Once you’ve opened a file in Python IDLE, you can then make changes to it. When you’re ready to edit a file, you’ll see something like this:

The contents of your file are displayed in the open window. The bar along the top of the window contains three pieces of important information:

  1. The name of the file that you’re editing
  2. The full path to the folder where you can find this file on your computer
  3. The version of Python that IDLE is using

In the image above, you’re editing the file myFile.py, which is located in the Documents folder. The Python version is 3.7.1, which you can see in parentheses.

There are also two numbers in the bottom right corner of the window:

  1. Ln: shows the line number that your cursor is on.
  2. Col: shows the column number that your cursor is on.

It’s useful to see these numbers so that you can find errors more quickly. They also help you make sure that you’re staying within a certain line width.

There are a few visual cues in this window that will help you remember to save your work. If you look closely, then you’ll see that Python IDLE uses asterisks to let you know that your file has unsaved changes:

The file name shown in the top of the IDLE window is surrounded by asterisks. This means that there are unsaved changes in your editor. You can save these changes with your system’s standard keyboard shortcut, or you can select File → Save from the menu bar. Make sure that you save your file with the .py extension so that syntax highlighting will be enabled.

Executing a File

When you want to execute a file that you’ve created in IDLE, you should first make sure that it’s saved. Remember, you can see if your file is properly saved by looking for asterisks around the filename at the top of the file editor window. Don’t worry if you forget, though! Python IDLE will remind you to save whenever you attempt to execute an unsaved file.

To execute a file in IDLE, simply press the F5 key on your keyboard. You can also select Run → Run Module from the menu bar. Either option will restart the Python interpreter and then run the code that you’ve written with a fresh interpreter. The process is the same as when you run python3 -i [filename] in your terminal.

When your code is done executing, the interpreter will know everything about your code, including any global variables, functions, and classes. This makes Python IDLE a great place to inspect your data if something goes wrong. If you ever need to interrupt the execution of your program, then you can press Ctrl+C in the interpreter that’s running your code.

How to Improve Your Workflow

Now that you’ve seen how to write, edit, and execute files in Python IDLE, it’s time to speed up your workflow! The Python IDLE editor offers a few features that you’ll see in most professional IDEs to help you code faster. These features include automatic indentation, code completion and call tips, and code context.

Automatic Indentation

IDLE will automatically indent your code when it needs to start a new block. This usually happens after you type a colon (:). When you hit the enter key after the colon, your cursor will automatically move over a certain number of spaces and begin a new code block.

You can configure how many spaces the cursor will move in the settings, but the default is the standard four spaces. The developers of Python agreed on a standard style for well-written Python code, and this includes rules on indentation, whitespace, and more. This standard style was formalized and is now known as PEP 8. To learn more about it, check out How to Write Beautiful Python Code With PEP 8.

Code Completion and Call Tips

When you’re writing code for a large project or a complicated problem, you can spend a lot of time just typing out all of the code you need. Code completion helps you save typing time by trying to finish your code for you. Python IDLE has basic code completion functionality. It can only autocomplete the names of functions and classes. To use autocompletion in the editor, just press the tab key after a sequence of text.

Python IDLE will also provide call tips. A call tip is like a hint for a certain part of your code to help you remember what that element needs. After you type the left parenthesis to begin a function call, a call tip will appear if you don’t type anything for a few seconds. For example, if you can’t quite remember how to append to a list, then you can pause after the opening parenthesis to bring up the call tip:

The call tip will display as a popup note, reminding you how to append to a list. Call tips like these provide useful information as you’re writing code.

Code Context

The code context functionality is a neat feature of the Python IDLE file editor. It will show you the scope of a function, class, loop, or other construct. This is particularly useful when you’re scrolling through a lengthy file and need to keep track of where you are while reviewing code in the editor.

To turn it on, select Options → Code Context in the menu bar. You’ll see a gray bar appear at the top of the editor window:

As you scroll down through your code, the context that contains each line of code will stay inside of this gray bar. This means that the print() functions you see in the image above are a part of a main function. When you reach a line that’s outside the scope of this function, the bar will disappear.

How to Debug in IDLE

A bug is an unexpected problem in your program. They can appear in many forms, and some are more difficult to fix than others. Some bugs are tricky enough that you won’t be able to catch them by just reading through your program. Luckily, Python IDLE provides some basic tools that will help you debug your programs with ease!

Interpreter DEBUG Mode

If you want to run your code with the built-in debugger, then you’ll need to turn this feature on. To do so, select Debug → Debugger from the Python IDLE menu bar. In the interpreter, you should see [DEBUG ON] appear just before the prompt (>>>), which means the interpreter is ready and waiting.

When you execute your Python file, the debugger window will appear:

In this window, you can inspect the values of your local and global variables as your code executes. This gives you insight into how your data is being manipulated as your code runs.

You can also click the following buttons to move through your code:

  • Go: Press this to advance execution to the next breakpoint. You’ll learn about these in the next section.
  • Step: Press this to execute the current line and go to the next one.
  • Over: If the current line of code contains a function call, then press this to step over that function. In other words, execute that function and go to the next line, but don’t pause while executing the function (unless there is a breakpoint).
  • Out: If the current line of code is in a function, then press this to step out of this function. In other words, continue the execution of this function until you return from it.

Be careful, because there is no reverse button! You can only step forward in time through your program’s execution.

You’ll also see four checkboxes in the debug window:

  1. Globals: your program’s global information
  2. Locals: your program’s local information during execution
  3. Stack: the functions that run during execution
  4. Source: your file in the IDLE editor

When you select one of these, you’ll see the relevant information in your debug window.

Breakpoints

A breakpoint is a line of code that you’ve identified as a place where the interpreter should pause while running your code. They will only work when DEBUG mode is turned on, so make sure that you’ve done that first.

To set a breakpoint, right-click on the line of code that you wish to pause. This will highlight the line of code in yellow as a visual indication of a set breakpoint. You can set as many breakpoints in your code as you like. To undo a breakpoint, right-click the same line again and select Clear Breakpoint.

Once you’ve set your breakpoints and turned on DEBUG mode, you can run your code as you would normally. The debugger window will pop up, and you can start stepping through your code manually.

Errors and Exceptions

When you see an error reported to you in the interpreter, Python IDLE lets you jump right to the offending file or line from the menu bar. All you have to do is highlight the reported line number or file name with your cursor and select Debug → Go to file/line from the menu bar. This is will open up the offending file and take you to the line that contains the error. This feature works regardless of whether or not DEBUG mode is turned on.

Python IDLE also provides a tool called a stack viewer. You can access it under the Debug option in the menu bar. This tool will show you the traceback of an error as it appears on the stack of the last error or exception that Python IDLE encountered while running your code. When an unexpected or interesting error occurs, you might find it helpful to take a look at the stack. Otherwise, this feature can be difficult to parse and likely won’t be useful to you unless you’re writing very complicated code.

How to Customize Python IDLE

There are many ways that you can give Python IDLE a visual style that suits you. The default look and feel is based on the colors in the Python logo. If you don’t like how anything looks, then you can almost always change it.

To access the customization window, select Options → Configure IDLE from the menu bar. To preview the result of a change you want to make, press Apply. When you’re done customizing Python IDLE, press OK to save all of your changes. If you don’t want to save your changes, then simply press Cancel.

There are 5 areas of Python IDLE that you can customize:

  1. Fonts/Tabs
  2. Highlights
  3. Keys
  4. General
  5. Extensions

Let’s take a look at each of them now.

Fonts/Tabs

The first tab allows you to change things like font color, font size, and font style. You can change the font to almost any style you like, depending on what’s available for your operating system. The font settings window looks like this:

You can use the scrolling window to select which font you prefer. (I recommend you select a fixed-width font like Courier New.) Pick a font size that’s large enough for you to see well. You can also click the checkbox next to Bold to toggle whether or not all text appears in bold.

This window will also let you change how many spaces are used for each indentation level. By default, this will be set to the PEP 8 standard of four spaces. You can change this to make the width of your code more or less spread out to your liking.

Highlights

The second customization tab will let you change highlights. Syntax highlighting is an important feature of any IDE that highlights the syntax of the language that you’re working in. This helps you visually distinguish between the different Python constructs and the data used in your code.

Python IDLE allows you to fully customize the appearance of your Python code. It comes pre-installed with three different highlight themes:

  1. IDLE Day
  2. IDLE Night
  3. IDLE New

You can select from these pre-installed themes or create your own custom theme right in this window:

Unfortunately, IDLE does not allow you to install custom themes from a file. You have to create customs theme from this window. To do so, you can simply start changing the colors for different items. Select an item, and then press Choose color for. You’ll be brought to a color picker, where you can select the exact color that you want to use.

You’ll then be prompted to save this theme as a new custom theme, and you can enter a name of your choosing. You can then continue changing the colors of different items if you’d like. Remember to press Apply to see your changes in action!

Keys

The third customization tab lets you map different key presses to actions, also known as keyboard shortcuts. These are a vital component of your productivity whenever you use an IDE. You can either come up with your own keyboard shortcuts, or you can use the ones that come with IDLE. The pre-installed shortcuts are a good place to start:

The keyboard shortcuts are listed in alphabetical order by action. They’re listed in the format Action - Shortcut, where Action is what will happen when you press the key combination in Shortcut. If you want to use a built-in key set, then select a mapping that matches your operating system. Pay close attention to the different keys and make sure your keyboard has them!

Creating Your Own Shortcuts

The customization of the keyboard shortcuts is very similar to the customization of syntax highlighting colors. Unfortunately, IDLE does not allow you to install custom keyboard shortcuts from a file. You must create a custom set of shortcuts from the Keys tab.

Select one pair from the list and press Get New Keys for Selection. A new window will pop up:

Here, you can use the checkboxes and scrolling menu to select the combination of keys that you want to use for this shortcut. You can select Advanced Key Binding Entry >> to manually type in a command. Note that this cannot pick up the keys you press. You have to literally type in the command as you see it displayed to you in the list of shortcuts.

General

The fourth tab of the customization window is a place for small, general changes. The general settings tab looks like this:

Here, you can customize things like the window size and whether the shell or the file editor opens first when you start Python IDLE. Most of the things in this window are not that exciting to change, so you probably won’t need to fiddle with them much.

Extensions

The fifth tab of the customization window lets you add extensions to Python IDLE. Extensions allow you to add new, awesome features to the editor and the interpreter window. You can download them from the internet and install them to right into Python IDLE.

To view what extensions are installed, select Options → Configure IDLE -> Extensions. There are many extensions available on the internet for you to read more about. Find the ones you like and add them to Python IDLE!

Conclusion

In this tutorial, you’ve learned all the basics of using IDLE to write Python programs. You know what Python IDLE is and how you can use it to interact with Python directly. You’ve also learned how to work with Python files and customize Python IDLE to your liking.

You’ve learned how to:

  • Work with the Python IDLE shell
  • Use Python IDLE as a file editor
  • Improve your workflow with features to help you code faster
  • Debug your code and view errors and exceptions
  • Customize Python IDLE to your liking

Now you’re armed with a new tool that will let you productively write Pythonic code and save you countless hours down the road. Happy programming!

Importance of Python Programming skills

Importance of Python Programming skills

Python is one among the most easiest and user friendly programming languages when it comes to the field of software engineering. The codes and syntaxes of python is so simple and easy to use that it can be deployed in any problem solving...

Python is one among the most easiest and user friendly programming languages when it comes to the field of software engineering. The codes and syntaxes of python is so simple and easy to use that it can be deployed in any problem solving challenges. The codes of Python can easily be deployed in Data Science and Machine Learning. Due to this ease of deployment and easier syntaxes, this platform has a lot of real world problem solving applications. According to the sources the companies are eagerly hunting for the professionals with python skills along with SQL. An average python developer in the united states makes around 1 lakh U.S Dollars per annum. In some of the top IT hubs in our country like Bangalore, the demand for professionals in the domains of Data Science and Python Programming has surpassed over the past few years. As a result of which a lot of various python certification courses are available right now.

Array in Python: An array is defined as a data structure that can hold a fixed number of elements that are of the same python data type. The following are some of the basic functions of array in python:

  1. To find the transverse
  2. For insertion of the elements
  3. For deletion of the elements
  4. For searching the elements

Along with this one can easily crack any python interview by means of python interview questions

Tkinter Python Tutorial | Python GUI Programming Using Tkinter Tutorial | Python Training

This video on Tkinter tutorial covers all the basic aspects of creating and making use of your own simple Graphical User Interface (GUI) using Python. It establishes all of the concepts needed to get started with building your own user interfaces while coding in Python.

This video on Tkinter tutorial covers all the basic aspects of creating and making use of your own simple Graphical User Interface (GUI) using Python. It establishes all of the concepts needed to get started with building your own user interfaces while coding in Python.

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Original video source: https://www.youtube.com/watch?v=VMP1oQOxfM0