Merge b7e8e52 into e5c906f

docs/sources/CHANGELOG.md

@@ -23,7 +23,7 @@ The CHANGELOG for the current development version is available at
 - The `bias_variance_decomp` function now supports Keras estimators. ([#725](https://github.com/rasbt/mlxtend/pull/725) via [@hanzigs](https://github.com/hanzigs))
 - Adds new `mlxtend.classifier.OneRClassifier` (One Rule Classfier) class, a simple rule-based classifier that is often used as a performance baseline or simple interpretable model. ([#726](https://github.com/rasbt/mlxtend/pull/726)
 - Adds new `create_counterfactual` method for creating counterfactuals to explain model predictions.  ([#740](https://github.com/rasbt/mlxtend/pull/740))
-
+- Adds new `scatter_hist` method for generating scattered histogram. ([#596](https://github.com/rasbt/mlxtend/issues/596))
 
 ##### Changes
 

mlxtend/plotting/__init__.py

@@ -19,8 +19,7 @@
 from .ecdf import ecdf
 from .scatterplotmatrix import scatterplotmatrix
 from .pca_correlation_graph import plot_pca_correlation_graph
-
-
+from .scatter_hist import scatter_hist
 __all__ = ["plot_learning_curves",
            "plot_decision_regions",
            "plot_confusion_matrix",
@@ -34,4 +33,5 @@
            "checkerboard_plot",
            "ecdf",
            "scatterplotmatrix",
-           "plot_pca_correlation_graph"]
+           "plot_pca_correlation_graph",
+           "scatter_hist"]

mlxtend/plotting/scatter_hist.py

@@ -0,0 +1,71 @@
+# Sebastian Raschka 2014-2020
+# mlxtend Machine Learning Library Extensions
+# Author: Sebastian Raschka <sebastianraschka.com>
+#
+# License: BSD 3 clause
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def scatter_hist(x, y, data):
+    """
+    Scatter plot and individual feature histograms along axes.
+
+    Parameters
+    ----------
+    x : str or int
+        DataFrame column name of the x-axis values or
+        integer for the numpy ndarray column index.
+    y : str or int
+        DataFrame column name of the y-axis values or
+        integer for the numpy ndarray column index
+    data : Pandas DataFrame object or NumPy ndarray.
+    
+    Returns
+    ---------
+    plot : matplotlib pyplot figure object
+    
+    """
+    left, width = 0.1, 0.65
+    bottom, height = 0.1, 0.65
+    spacing = 0.001
+    rect_scatter = [left, bottom, width, height]
+    rect_histx = [left, bottom + height + spacing, width, 0.2]
+    rect_histy = [left + width + spacing, bottom, 0.2, height]
+
+    if isinstance(data, pd.DataFrame):
+        for i in (x, y):
+            assert (isinstance(i, str))
+        frame = True
+        xlabel = x
+        ylabel = y
+        x = data[x]
+        y = data[y]
+
+    elif isinstance(data, np.ndarray):
+        for i in (x, y):
+            assert (isinstance(i, int))
+        frame = False
+        x = data[:, x]
+        y = data[:, y]
+
+    else:
+        raise ValueError('df must be pandas.DataFrame or numpy.ndarray object')
+
+    fig = plt.figure(figsize=(5, 5))
+    ax = fig.add_axes(rect_scatter)
+    if frame:
+        ax.set_xlabel(xlabel)
+        ax.set_ylabel(ylabel)
+
+    ax_histx = fig.add_axes(rect_histx, sharex=ax)
+    ax_histy = fig.add_axes(rect_histy, sharey=ax)
+    ax_histx.tick_params(axis="x", labelbottom=False)
+    ax_histy.tick_params(axis="y", labelleft=False)
+    ax_histx.axis("off")
+    ax_histy.axis("off")
+    ax_histx.hist(x, edgecolor='white', bins='auto')
+    ax_histy.hist(y, edgecolor='white', orientation='horizontal', bins='auto')
+    plot = ax.scatter(x, y)
+    return plot

mlxtend/plotting/tests/test_scatter_hist.py

@@ -0,0 +1,31 @@
+from mlxtend.data import iris_data
+from mlxtend.plotting import scatter_hist
+import pandas as pd
+import pytest
+
+X, y = iris_data()
+df = pd.DataFrame(X)
+df.columns = (['sepal length [cm]',
+               'sepal width [cm]',
+               'petal length [cm]',
+               'petal width [cm]'])
+
+
+def test_pass_data_as_dataframe():
+    scatter_hist("sepal length [cm]", "sepal width [cm]", df)
+
+
+def test_pass_data_as_numpy_array():
+    scatter_hist(0, 1, X)
+
+
+def test_incorrect_x_or_y_data_as_dataframe():
+    with pytest.raises(AssertionError) as execinfo:
+        scatter_hist(0, "sepal width [cm]", df)
+        assert execinfo.value.message == 'Assertion failed'
+
+
+def test_incorrect_x_or_y_data_as_numpy_array():
+    with pytest.raises(AssertionError) as execinfo:
+        scatter_hist("sepal length [cm]", 1, X)
+        assert execinfo.value.message == 'Assertion failed'