The author describes building a personal, open-source computational engine using Python libraries SymPy, NumPy, pandas, SciPy, statsmodels, Pingouin, Matplotlib, and Seaborn, effectively replicating the functionality of Wolfram Mathematica at no cost.
Pandas 3.0 will significantly boost performance by replacing NumPy with PyArrow as its default engine, enabling faster loading and reading of columnar data.
This article discusses how to improve the performance of Pandas operations by using vectorization with NumPy. It highlights alternatives to the apply() method on larger dataframes and provides examples of using NumPy's lesser-known methods like where and select to handle complex if/then/else conditions efficiently.
>>> from sklearn.neighbors import NearestCentroid
>>> import numpy as np
>>> X = np.array( [-1, -1 » , -2, -1 » , -3, -2 » , 1, 1 » , 2, 1 » , 3, 2 » ])
>>> y = np.array( 1, 1, 1, 2, 2, 2 » )
>>> clf = NearestCentroid()
>>> clf.fit(X, y)
NearestCentroid()
>>> print(clf.predict( [-0.8, -1 » ]))
1 »
>>> import numpy as np
>>> X = np.array( [-1, -1 » , -2, -1 » , -3, -2 » , 1, 1 » , 2, 1 » , 3, 2 » ])
>>> y = np.array( 1, 1, 1, 2, 2, 2 » )
>>> clf = NearestCentroid()
>>> clf.fit(X, y)
NearestCentroid()
>>> print(clf.predict( [-0.8, -1 » ]))
1 »
