Data Analyst Guide Line

Creating a well-organized FastAPI application involves structuring your project in a way that makes it scalable, maintainable, and understandable. Below is a simplified example of how to structure a FastAPI project and write an API within it. Project Structure: A recommended project structure for a FastAPI application might look like this: my_fastapi_project/ │ ├── app/ # Application module │ ├── __init__.py # Initializes Python package │ ├── main.py # FastAPI app creation and configuration │ ├── dependencies.py # Dependency injection (DB connections, etc.) │ ├── models.py # Pydantic models for request/response schemas │ ├── schemas.py # Pydantic schemas for request/response validation │ ├── crud.py # CRUD operations (interact with the database) │ └── routers/ # Router modules for different endpoints │ ├── __init__.py │ ├── items...

In the world of data science, data cleaning is a crucial step to ensure data accuracy, reliability, and usefulness. Inconsistent or unclean data can lead to faulty analyses and misinformed conclusions. This blog will walk you through a systematic approach to cleaning data based on field practices and industry recommendations. Why Clean Data? Cleaning data is vital because it: Prevents Wasted Time : Wobbly or faulty analyses are often due to poorly managed data. Prevents Wrong Conclusions : Incorrect data leads to incorrect insights. Speed-Up Analysis : Clean data allows for faster computation and advanced analytics. Key Steps in Data Cleaning Step 1: Find the Dirt Before any cleaning, it’s important to understand what’s wrong with the data. This could range from missing values and duplicate entries to invalid data types and inconsistencies. Step 2: Scrub the Dirt The cleaning step will depend on the specific type of dirt found in the dataset: Standardizing Strings : Uniform casing, re...