pandas

Analyzing correlations is a critical step in understanding complex data relationships. It’s a fast way to find how similar two time series are. Python offers a wide range of libraries that make calculating correlations between two time series a breeze. In this tutorial, we’ll explore some of the most popular libraries for correlation analysis, including NumPy, Pandas, Scipy, Polars, CuPy, CuDF, PyTorch, and Dask. Let’s get started! Correlation Between Two Time Series Using NumPy NumPy is the most popular Python library for numerical computing....

When working with time series data, differencing is a common technique used to make the data stationary. Stationary data is important because it allows us to apply statistical models that assume constant parameters (like the mean and standard deviation) over time, and this can improve the accuracy of our predictions. Let’s see how we can easily perform differencing in Python using Pandas, Numpy, and Polars. First-order Differencing First-order differencing involves subtracting each value in the time series from its previous value....