Correlation is one of the most fundamental statistical concepts used in almost any sectors.
For example, as in portfolio management, correlation is often used to measure the amount of diversification among the assets contained in a portfolio. Choosing assets with low or negative correlation with each other can help to reduce the risk of a portfolio. In addition, correlations give insights about marketing strategies and business outcomes in marketing research, which further help marketers make actionable decisions, and ultimately, grow businesses.
Python is slow.
I bet you might encounter this counterargument many times about using Python, especially from people who come from C or C++ or Java world. This is true in many cases, for instance, looping over or sorting Python arrays, lists, or dictionaries can be sometimes slow. After all, Python is developed to make programming fun and easy. Thus, the improvements of Python code in succinctness and readability have to come with a cost of performance.
The prerequisite for doing any data-related operations in Python, such as data cleansing, data aggregation, data transformation, and data visualisation, is to load data into Python. Depends on the types of data files (e.g. .csv, .txt, .tsv, .html, .json, Excel spreadsheets, relational databases etc.) and their size, different methods should be applied to deal with this initial operation accordingly. In this post, I will list some common methods for importing data in Python.