Python Collections module: Practical Use-Cases
Did you know that Python has a built-in module for collections of data? Let's take a look at examples of its classes: Counter, DefaultDict, Deque and NamedTuple.
Did you know that Python has a built-in module for collections of data? Let's take a look at examples of its classes: Counter, DefaultDict, Deque and NamedTuple.
Every programming language has its own guidelines and best practices. The official style guide in Python is defined in PEP 8. In this tutorial, I will list the most common and practical rules from it.
We'll build a chatbot JS widget that would call the Python API for transforming the human question into an SQL query and getting back the results in human language, with the help of OpenAI API and GPT-4.
While using Pandas in Python, it's common to filter data with `df.query()`. But there are so many options of the syntax and parameters!
When working on Pandas DataFrame, it is common to find empty columns or even rows. This tutorial will show you how to search for them and process them by deleting or replacing them with new values.
In this tutorial, we will perform Excel data transformation with Python, based on the actual job from Upwork, which would earn $150.
When working with OpenAI and GPT models, it's crucial to calculate the costs. In this tutorial, I will show you how to check the costs of the API calls, both humanly and automatically, with Python and LangChain.
After building and training your Machine Learning model, the next step is to publish it as an API or a web page for others to make predictions.
One of the most typical Machine Learning tasks is reading structured text from images with OCR. We experimented with 5 sample invoices and a few Python libraries.
Customer support chatbot: get the answers based on your FAQ documents.
In this tutorial, I will show you the most often-used functions from Pandas to get a general overview of the data.
Three real-life practical projects, with the Random Forest algorithm explained.
I recently saw a few jobs on Upwork that involve splitting large CSV files into smaller ones. So, I decided to write this tutorial and show how to do it in Python.
Python `pandas` library has a great method `df.describe()`, to show some numbers about the dataframe, but do you understand the meaning behind all of them?
If you watch any video tutorials about Machine Learning and Python, you will see that most developers use Jupyter Notebook for ML projects, not IDE like PyCharm. Why? What are the benefits?