correlationMatrix is a Python powered library for the statistical analysis and visualization of correlation phenomena. It can be used to analyze any dataset that captures timestamped values (timeseries) The present use cases focus on typical analysis of market correlations, e.g., via factor models...
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more - pandas-dev/pandas
Scikit-learn, often abbreviated as sklearn, is an open-source machine-learning library for Python. It is built on top of other popular Python libraries such as NumPy, SciPy, and matplotlib. Scikit-learn provides simple and efficient tools for data analysis and modeling, making it one of the ...
LibraryCore FeaturesBest Used For Pandas DataFrame operations, data analysis Tabular data processing NumPy Array operations, mathematical functions Scientific computing Dask Parallel processing Large dataset handling Polars Fast DataFrame operations High performance analytics Vaex Out-of-memory processing Big data...
As a comparison, we use thecorrfunction in the xarray library,corrcoeffunction in numpy library, cdist inscipy, apply_func in xarray andfor-loop. The time required to calculate the correlation coefficient between SSTA and nino3.4 for 50 times is shown in the figure below. ...
Python’s vast library of tools and packages makes it an excellent choice for data analysis and visualization. Furthermore, the flexibility, ease of use, detailed documentation hub, community support, and open-source nature make Python the most reliable language and an all-in-one so...
Making informative visualizations (sometimes calledplots) is one of the most important tasks in data analysis. It may be a part of the exploratory process—for example, to help identify outliers or needed data transformations, or as a way of generating ideas for models. For others, building an...
Method for statistical analysis is more aligned with the R programming language, making it a suitable library for data scientists already familiar with R and who want to transition to Python. This beginning statsmodels course is an excellent place to start if you'd like to learn more. ⭐ ...
In addition to these libraries, there are many other Python libraries that can be helpful for quantitative investing, such as matplotlib for data visualization, statsmodels for statistical modelling, and pyfolio for portfolio analysis. The choice of library depends on the specific needs and preferences...
PySal: A python spatial analysis library for open source and crossed platform Geospatial Data Science Shapely: Shapely is a Python package for manipulation and analysis of planar geometric objects. It is based on the widely deployedGEOS(the engine of PostGIS) andJTSlibraries. ...