Python机器学习笔记2:简单数据可视化

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摘要：介绍简单的数据可视化方法

pandas.tail()

Return the last n rows.

This function returns last n rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows.

散点分类图

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import matplotlib.pyplot as plt

>>> # plot data
>>> plt.scatter(X[:50, 0], X[:50, 1], ... color='red', marker='o', label='setosa')
>>> plt.scatter(X[50:100, 0], X[50:100, 1], ... color='blue', marker='x', label='versicolor')
>>> plt.xlabel('sepal length [cm]')
>>> plt.ylabel('petal length [cm]')
>>> plt.legend(loc='upper left')
>>> plt.show()
  1. s表示点的大小，
  2. c就是color嘛，
  3. marker就是点的形状，比如o,x,参考文档选择需要的形状
  4. alpha,点点的亮度，label，标签啦
  5. 前两个参数就是点的横纵坐标
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plt.scatter(C1[:,0],C1[:,1],s=30,c='red',marker='o',alpha=0.5,label='C1')
plt.scatter(C2[:,0],C2[:,1],s=30,c='blue',marker='x',alpha=0.5,label='C2')

折线图

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>>> ppn = Perceptron(eta=0.1, n_iter=10)
>>> ppn.fit(X, y)
>>> plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')
>>> plt.xlabel('Epochs')
>>> plt.ylabel('Number of updates')
>>> plt.show()
  1. range(1, len(ppn.errors_) + 1) X轴坐标
  2. ppn.errors_ Y 轴坐标
  3. marker 点的形状

决策面

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def plot_decision_regions(X, y, classifier, resolution=0.02):
    # setup marker generator and color map
    markers = ('s', 'x', 'o', '^', 'v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])

    # plot the decision surface
    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
                           np.arange(x2_min, x2_max, resolution))
    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    Z = Z.reshape(xx1.shape)
    plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())

    # plot class samples
    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0],
                    y=X[y == cl, 1],
                    alpha=0.8,
                    c=colors[idx],
                    marker=markers[idx],
                    label=cl,
                    edgecolor='black')