What are the advantages and disadvantages of the Python data mining toolkit?
[Guide] The python data mining toolkit is scikit-learn. scikit-learn is an open source machine learning toolkit based on NumPy, SciPy, and Matplotlib. It mainly covers classification, regression and clustering algorithms, such as SVM, logistic regression, and naive Baye. The algorithms, code and documentation are all very good, such as S., random forest, k-means, etc., and have been applied in many Python projects.
advantage:
1. Complete documentation: complete official documentation and timely updates.
2. The interface is easy to use: Provides consistent interface calling rules for all algorithms, whether it is KNN, K-Means or PCA.
3. Comprehensive algorithms: algorithms covering mainstream machine learning tasks, including regression algorithms, classification algorithms, clustering analysis, and data dimensionality reduction processing.
Disadvantages:
The disadvantage is that scikit-learn does not support distributed computing and is not suitable for processing very large data.
Pandas is a powerful time series data processing toolkit. Pandas is built on Numpy and is simpler to use than Numpy. The original purpose of development was to analyze financial data, and it is now widely used in the field of Python data analysis. Pandas, the most basic data structure is Series, which can be used to express a row of data, which can be understood as a one-dimensional array. Another key data structure is DataFrame, which represents a two-dimensional array
Pandas is developed based on NumPy and Matplotlib. It is mainly used for data analysis and data visualization. Its data structure DataFrame is very similar to data.frame in R language, especially for time series data. It has its own set of analysis mechanisms. There is a book "Python for Data Analysis", the author is the main development of Pandas, and in turn introduces related functions in iPython, NumPy, Pandas, data visualization, data cleaning and processing, time data processing, etc. Cases include financial stock data mining, etc. ,Pretty good.
Mlpy is a Python machine learning module based on NumPy/SciPy, and it is an extended application of Cython.
The advantages and disadvantages of the python data mining toolkit are here. Scikit-learn provides a consistent calling interface. It is based on Python numerical calculation libraries such as Numpy and scipy, and provides efficient algorithm implementation, so if you want to learn python, you have to learn the above content.