5 Best Ways to Reshape a NetworkX Graph in Python

πŸ’‘ Problem Formulation: When working with NetworkX in Python, data scientists and network analysts may encounter the need to reshape graphs. This could mean altering the layout, adding or subtracting nodes or edges, or changing attributes. For example, one might start with a linear graph but needs to transform it into a circular layout for … Read more

Interlacing Print Statements with Matplotlib Plots Inline in IPython

πŸ’‘ Problem Formulation: When working in an IPython environment, developers might want to display print statements alongside inline Matplotlib plots to provide additional context or data insights. This is especially useful for quick data analysis iterations within Jupyter Notebooks. The challenge arises in organising the outputs so that the print statements and plots are presented … Read more

5 Best Ways to Find Average Salary Excluding the Minimum and Maximum Salary in Python

πŸ’‘ Problem Formulation: When computing the average salary from a set of values, it can be more representative to exclude the highest (maximum) and lowest (minimum) salaries to prevent outliers from skewing the data. Consider a list of salary amounts: [45000, 52000, 60000, 75000, 80000]. The task is to calculate the average salary after excluding … Read more

Effective Gaussian Filtering of Images with NaNs in Python using Matplotlib

πŸ’‘ Problem Formulation: When processing images, NaN (Not a Number) values can pose a problem, especially during Gaussian filteringβ€”a common image smoothing technique. These NaN values may arise from invalid operations or missing data within an image array. The conventional Gaussian filtering functions do not handle NaNs, often resulting in distorted output. In this article, … Read more