Python Tip #9 – sorting

Sorting is simplified in Python with sorted(). You can even sort with complex rules.

>>> strings = ['alice', 'bob', 'donald', 'cathy']
>>> sorted(strings)
['alice', 'bob', 'cathy', 'donald']

>>> sorted(strings, key=len)
['bob', 'alice', 'cathy', 'donald']

>>> def secondchar(word):
...    return word[1]

>>> sorted(strings, key=secondchar)
['cathy', 'alice', 'bob', 'donald']

Python Tip #8 – reducing looping by using dicts

In situations where you have a list of objects and have to retrieve then in random order, dictionaries can act as lookup tables.

users = list of class User 

# Get users one by one by looking up ids
user_1 = next((u for u in users if == user_1_id), None)
user_2 = next((u for u in users if == user_2_id), None)

# Simpler solution using lookup table
lookup = dict((, user) for u in users)
user_1 = lookup[user_1_id]
user_2 = lookup[user_2_id]

This tip is not very obvious, hence this explanation:

user_1 = next((u for u in users if == user_1_id), None)

This method employs a iterator looping through the list of users every time we have to find a user, which means we have to run this loop a hundred times. This poses a complexity of O(N2).

lookup = dict((, user) for u in users)
user_1 = lookup[user_1_id]

This method on the other hand iterates through the users list one time and create a lookup table that we can again and again without having to iterate through the list every time. This reduces the complexity to O(N) which could theoretically lead up to 10 times faster execution of the program.

Python Tip #7 – getattr()

Sometimes we have to deal with external objects and their attributes. getattr() can save you at those times.

# Get the attribute name
name =  # AttributeError if name is not present

# Check if the attribute is present before fetching
    name =
except AttributeError:
    name = "Guest"

# Simpler solution
name = if hasattr(obj, "name") else "Guest"

# Simplest Solution
name = getattr(obj, "name", "Guest")

Python Tip #6 – Merging Dictionaries

Merge or combine dictionaries

d1 = { "a": 1 }
d2 = { "b": 2 }

# Adding elements of one dictionary to another
d1.update(d2)  # d1 => { "a": 1, "b": 2 }

# Create a new dict with values from other dictionaries
d3 = { **d1, **d2 }  # d3 => { "a": 1, "b": 2 }
d4 = { **d3, "c": 3 }  # d4 => { "a": 1, "b": 2, "c": 3 }

** is the unpacking operator

Python Tip #4 – Find an element in a list satisfying condition

What if you want to find the first item that matches the condition instead of getting a list of items?

selected = None
for i in items:
if condition:
selected = i

# Simpler version using next()
selected = next((i for i in items if condition), None)

next() is a built in function which is not that well known.