Toolbox: Name and Frequency of unique elements from a List in Python
Count unique categories in a list...
Create Travel Map with Python and PyGMT
Prepare map for Instagram post...
Get more from Crime Rate data and other socio-economic indicators with Pyinterpolate
Transform low-resolution county aggregates into high-resolution input for your machine learning models and analysis....
Toolbox: Get date n weeks from another date in Python
If you want to trim your weekly time series into training and test set, or create / check the date n weeks ahead or back then this function is for you!...
Spatial Epidemiology: Baltic Sea and Algal Blooms – how to get data?
Dangerous and toxic algal blooms are a worldwide issue. Where do we get data to analyze those events?...
Data Science: Moving Average or Moving Median for Data Filtering – Time Series
Moving Mean or Moving Median for Time Series filtering?...
Geostatistics: Theoretical Variogram Models
Comparison of different semivariogram models...
Data Science: The four (and a half) metrics to understand your model
Forecast Bias, Mean Absolute Error, Mean Squared Error and Root of it, Symmetric Mean Absolute Percentage Error: use them and be sure that you produce the best models...
Toolbox: K-means algorithm
K-means clustering class for local experiments...
Spatial Interpolation 101: Variance and Dataset Dimensions
The semivariance is a crucial concept of spatial statistics. We’ve made initial steps to understand it in the previous article when we discovered basic statistical parameters: the mean and the standard deviation. Here we are going a step further, and we look into the variance....
- 1
- 2