-SQLlite-with-Python

For Data Analysts and Data Scientists, Python has many advantages. A huge range of open-source libraries make it an incredibly useful tool for any Data Analyst. We have pandas, NumPy and Vaex for data analysis, Matplotlib, seaborn and Bokeh for visualisation, and TensorFlow, scikit-learn and PyTorch for machine learning applications (plus many, many more). With its (relatively) easy learning curve and versatility, it's no wonder that Python is one of the fastest-growing programming languages out there. So if we're using Python for data analysis, it's worth asking - where does all this data come from? While there is a massive variety of sources for datasets, in many cases - particularly in enterprise businesses - data is going to be stored in a relational database. Relational databases are an extremely efficient, powerful and widely-used way to create, read, update and delete data of all kinds. The most widely used relational database management systems (RDBMSs) - Oracle, MySQL, Microsoft SQL Server, PostgreSQL, IBM DB2 - all use the Structured Query Language (SQL) to access and make changes to the data. Note that each RDBMS uses a slightly different flavour of SQL, so SQL code written for one will usually not work in another without (normally fairly minor) modifications. But the concepts, structures and operations are largely identical. This means for a working Data Analyst, a strong understanding of SQL is hugely important. Knowing how to use Python and SQL together will give you even more of an advantage when it comes to working with your data.