Here are a few Python concepts for beginners to explore if you are starting out with the language. In this post, I’ll highlight my favorite “must-learn” tools to master that come with your Python installation. Understanding them will make you a more capable Python programmer and problem solver.
2. String methods. Want to capitalize, lowercase or replace characters in text? How about checking if a str.isdigit()? Get to know Python’s string methods. I use these frequently. Also, the pandas string method implementations are great for applying them to tabular data.
3. Docstrings. I truly enjoy adding docstrings at the beginning of my functions. They add clarity and ease of understanding.
5. List Comprehensions. These allow you to perform transformations on lists in one line of code! I love the feeling when I apply a list comprehension that is concise, yet readable.
6. Lambda Expressions. These can be used to apply a function “on the fly”. I love their succinctness. It took me a few years to become comfortable with them. Sometimes it makes sense to use a lambda expression instead of a regular function to transform data.
7. Date Objects. Wielding date objects and formatting them to your needs is a pivotal Python skill. Once you have it down, it unlocks a lot of automation and scripting abilities when combined with libraries like pathlib, os or glob for reading file metadata and then executing an action based on the date of the file, for example. I use date.today() a lot when I want to fetch today’s date and timedelta to compare two dates. The datetime module is your friend, dive in. Must know for custom date formatting:
strptime() Format Codes
For tabular data, I often use
pd.to_datetime() to convert a series of strings to datetime objects:
""" install pandas with this command: python -m pip install pandas """ import pandas as pd events = [['USA Born','1776-07-04'],['WTC Bombings','2001-09-11'],['Biden Inauguration','2021-01-20']] df = pd.DataFrame(events, columns=['events','dates']) # convert a pandas series of strings to datetime objects df.dates = pd.to_datetime(df.dates) print(df.dtypes) print(df.head())
Go here if you’re having pip installation problems.
Just the tip of the iceberg…
The amazing part of Python is that its community has developed an astonishing plethora of external libraries which can be installed by pip. Usually I’ll learn how to use new libraries after googling to find a well-written README on Github or helpful documentation. The language comes with an impressive line-up of baked-in tools and libraries way beyond what I’ve mentioned here. But I think this is a great start. Get to know these common Python language features and you’ll be surprised how much you can do!
Additional Comprehensive Python Learning Resources
Practical Python Programming (free course)