Accessing data means bringing data into your Python workflow so you can inspect, clean, analyse, and visualise it. In data analysis, your data can come from many sources such as CSV files, Excel sheets, databases, APIs, or online repositories. Learning how to access data correctly is important because analysis becomes easier when your data is loaded in the right format and structure.
The most common way to access data is through files. CSV files are widely used because they are simple and lightweight, and you can load them easily into a DataFrame using Pandas. Excel files are also common in business settings, and they may contain multiple sheets, so you often need to specify the sheet name you want. You may also work with JSON files, especially when data comes from APIs or web sources. In those cases, the data may be nested and may need extra steps to flatten it into a table.
Another important source is databases. Many organisations store data in systems like MySQL, PostgreSQL, or SQL Server. In Python, you can connect to a database, run a query, and load the results into a DataFrame for analysis. This is useful when datasets are too large for manual exports, or when you need live data.
APIs are another common method. APIs allow you to request data from a service and receive it in JSON format. This is useful for pulling data from tools like CRMs, payment systems, analytics platforms, or public datasets.
Good practices while accessing data include checking file paths, understanding file encoding, confirming the correct sheet or table is selected, and previewing the dataset immediately after loading. Once data is accessed properly, the next steps like cleaning and analysis become much smoother.
