Example:<\/strong><\/p>\n\n\n\nimportstatsmodels.apiassm<\/p>\n\n\n\n
importnumpyasnp<\/p>\n\n\n\n
X = np.random.rand(100, 2)<\/p>\n\n\n\n
y = X @ np.array([1, 2]) + np.random.rand(100)<\/p>\n\n\n\n
model = sm.OLS(y, sm.add_constant(X)).fit()<\/p>\n\n\n\n
print(model.summary())<\/p>\n\n\n\n
Why R Users Will Love It:<\/strong><\/p>\n\n\n\n\n- Provides detailed regression output.<\/li>\n\n\n\n
- Offers ANOVA, GLM, and statistical tests.<\/li>\n<\/ul>\n\n\n\n
Statsmodels is undoubtedly part of the Top Python Packages For R Users because it mirrors R\u2019s statistical environment.<\/p>\n\n\n\n
6. Scikit-learn (Alternative to R\u2019s caret)<\/h3>\n\n\n\n
R\u2019s caret<\/code> package simplifies machine learning. Python\u2019s Scikit-learn is its closest counterpart.<\/p>\n\n\n\nExample:<\/strong><\/p>\n\n\n\nfromsklearn.linear_modelimportLinearRegression<\/p>\n\n\n\n
importnumpyasnp<\/p>\n\n\n\n
X = np.array([[1], [2], [3], [4]])<\/p>\n\n\n\n
y = np.array([2, 4, 6, 8])<\/p>\n\n\n\n
model = LinearRegression().fit(X, y)<\/p>\n\n\n\n
print(model.coef_, model.intercept_)<\/p>\n\n\n\n
Why R Users Will Love It:<\/strong><\/p>\n\n\n\n\n- Provides regression, classification, clustering, and preprocessing.<\/li>\n\n\n\n
- Easy integration with Pandas and NumPy.<\/li>\n<\/ul>\n\n\n\n
For machine learning, Scikit-learn is among the Top Python Packages For R Users.<\/p>\n\n\n\n
7. SciPy (R\u2019s Advanced Stats Functions)<\/h3>\n\n\n\n
R users often use built-in functions for statistical distributions and hypothesis testing. SciPy in Python offers these and more.<\/p>\n\n\n\n
Example:<\/strong><\/p>\n\n\n\nfromscipyimportstats<\/p>\n\n\n\n
print(stats.ttest_ind([1, 2, 3], [3, 4, 5]))<\/p>\n\n\n\n
Why R Users Will Love It:<\/strong><\/p>\n\n\n\n\n- Includes optimization, integration, interpolation.<\/li>\n\n\n\n
- Comprehensive suite for applied statistics.<\/li>\n<\/ul>\n\n\n\n
SciPy cements its place in the Top Python Packages For R Users.<\/p>\n\n\n\n
8. Plotly (Interactive Visualizations Like R\u2019s Shiny + plotly)<\/h3>\n\n\n\n
R users enjoy interactive plots with Shiny and plotly. Python\u2019s Plotly<\/strong> enables similar interactivity.<\/p>\n\n\n\nExample:<\/strong><\/p>\n\n\n\nimportplotly.expressaspx<\/p>\n\n\n\n
fig = px.scatter(x=[1,2,3,4], y=[10,11,12,13], title=”Interactive Plot”)<\/p>\n\n\n\n
fig.show()<\/p>\n\n\n\n
Why R Users Will Love It:<\/strong><\/p>\n\n\n\n\n- Supports dashboards and interactive plots.<\/li>\n\n\n\n
- Easy integration with Python notebooks.<\/li>\n<\/ul>\n\n\n\n
For dynamic reporting, Plotly ranks high among the Top Python Packages For R Users.<\/p>\n\n\n\n
9. PyCaret (AutoML Like R\u2019s caret)<\/h3>\n\n\n\n
AutoML simplifies machine learning pipelines. R users familiar with caret can switch to PyCaret in Python.<\/p>\n\n\n\n
Example:<\/strong><\/p>\n\n\n\nfrompycaret.classificationimportsetup, compare_models<\/p>\n\n\n\n
fromsklearn.datasetsimportload_iris<\/p>\n\n\n\n
importpandasaspd<\/p>\n\n\n\n
data = load_iris(as_frame=True).frame<\/p>\n\n\n\n
setup(data, target=’target’)<\/p>\n\n\n\n
best = compare_models()<\/p>\n\n\n\n
Why R Users Will Love It:<\/strong><\/p>\n\n\n\n\n- Minimal coding for model building.<\/li>\n\n\n\n
- Automates preprocessing, tuning, and evaluation.<\/li>\n<\/ul>\n\n\n\n
PyCaret is rapidly becoming one of the Top Python Packages For R Users.<\/p>\n\n\n\n
10. TensorFlow and PyTorch (For Advanced ML and AI)<\/h3>\n\n\n\n
R has ML packages, but Python dominates deep learning with TensorFlow and PyTorch.<\/p>\n\n\n\n
Example:<\/strong><\/p>\n\n\n\nimporttensorflowastf<\/p>\n\n\n\n
model = tf.keras.Sequential([<\/p>\n\n\n\n
tf.keras.layers.Dense(10, activation=’relu’),<\/p>\n\n\n\n
tf.keras.layers.Dense(1)<\/p>\n\n\n\n
])<\/p>\n\n\n\n
Why R Users Will Love It:<\/strong><\/p>\n\n\n\n
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