7 Tips for making your code more ‘pythonic’ and elegant

7 use-cases where you can make your python code more nifty, concise, and elegant — without compromising readability.

Original image from unsplash.com ( https://unsplash.com/@thisisramiro )

About this article

Use Case 1 :

When you have to change the type of many of the attributes of your pandas columns / attributes, use a dictionary to do it more elegantly

df_vin = df_vin.astype(
df_box[‘Height’]: df_box[‘Height’].astype(float)
df_vin = df_vin.astype(
{‘Length’: float,
‘Width’: int,
‘Height’: float})

Use Case 2 :

When writing a function that returns a value based on a condition, try writing in simple one line conditions for easy readability.

def is_greater (a,b):
if a > b :
return 1
return 0
def is_greater(): return 1 if a > b else 0
# Checks if m is an even number 
is_even=1 if m%2 == 0 else 0

Use Case 3:

When you want to do any list operations, try using List comprehensions.

list1  = [4,2,3,5,7,8,11,16]
list1 = [4,2,3,5,7,8,11,16]list_odd = [] 
for n in list1 :
if n%2 ==1 :
list_odd = [n for n in list1 if n%2 == 1 ]

Use Case 4:

Use the walrus operator when you want to assign the result of a condition-statement to a Boolean in a nifty manner.

Image courtesy : unsplash.com
my_list = [1,2,3,4,5]
if len(my_list) > 3 : 
n = True
else :
n= False

if n := len(my_list) > 3:
using walrus operator v/s traditional way of coding

Use Case 5:

lambda arguments: expression
traditional way of using a function
pythonic way of executing the same code using a lambda function

Use Case 6:

making a key value type of list from two different lists
using the zip function to combine two lists

Use Case 7:

Output of the above code

4 ways to filter numeric values in dataframes using pandas

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