和exception异常要求用户立刻进行处理不同，warning通常用于提示用户一些错误或者过时的用法。
case
scrapy源码中用到了继承了Warning类创建了一个提醒对象ScrapyDeprecationWarning，用于提醒过时的用户操作，在新版本可能会直接去除支持。

用户感知warnings
python参数控制warning输出
 python3 -W all example.py
 python3 -W error example.py
 python3 -W ignore example.py

python脚本控制
import warnings
warnings.simplefilter('ignore')
warnings.simplefilter('always')
warnings.simplefilter('error')

开发输出warnings
import warnings
warnings.warn('warning message', OwnDeprecationWarning)

import matplotlib as mpl
import matplotlib.pyplot as plt

import numpy as np

from sklearn import datasets
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import StratifiedKFold

print(__doc__)
#为IPython交互环境自动创建模块

colors = ['navy', 'turquoise', 'darkorange']


def make_ellipses(gmm, ax):
    for n, color in enumerate(colors):
        if gmm.covariance_type == 'full':
            covariances = gmm.covariances_[n][:2, :2]
        elif gmm.covariance_type == 'tied':
            covariances = gmm.covariances_[:2, :2]
        elif gmm.covariance_type == 'diag':
            covariances = np.diag(gmm.covariances_[n][:2])
        elif gmm.covariance_type == 'spherical':
            covariances = np.eye(gmm.means_.shape[1]) * gmm.covariances_[n]
        v, w = np.linalg.eigh(covariances)
        u = w[0] / np.linalg.norm(w[0])
        angle = np.arctan2(u[1], u[0])
        angle = 180 * angle / np.pi  # convert to degrees
        v = 2. * np.sqrt(2.) * np.sqrt(v)
        ell = mpl.patches.Ellipse(gmm.means_[n, :2], v[0], v[1],
                                  180 + angle, color=color)
        ell.set_clip_box(ax.bbox)
        ell.set_alpha(0.5)
        ax.add_artist(ell)
        ax.set_aspect('equal', 'datalim')

iris = datasets.load_iris()

# Break up the dataset into non-overlapping training (75%) and testing
# (25%) sets.
skf = StratifiedKFold(n_splits=4)
# Only take the first fold.
train_index, test_index = next(iter(skf.split(iris.data, iris.target)))


X_train = iris.data[train_index]
y_train = iris.target[train_index]
X_test = iris.data[test_index]
y_test = iris.target[test_index]

n_classes = len(np.unique(y_train))

# Try GMMs using different types of covariances.
estimators = {cov_type: GaussianMixture(n_components=n_classes,
              covariance_type=cov_type, max_iter=20, random_state=0)
              for cov_type in ['spherical', 'diag', 'tied', 'full']}

n_estimators = len(estimators)

plt.figure(figsize=(3 * n_estimators // 2, 6))
plt.subplots_adjust(bottom=.01, top=0.95, hspace=.15, wspace=.05,
                    left=.01, right=.99)


for index, (name, estimator) in enumerate(estimators.items()):
    # Since we have class labels for the training data, we can
    # initialize the GMM parameters in a supervised manner.
    estimator.means_init = np.array([X_train[y_train == i].mean(axis=0)
                                    for i in range(n_classes)])

    # Train the other parameters using the EM algorithm.
    estimator.fit(X_train)

    h = plt.subplot(2, n_estimators // 2, index + 1)
    make_ellipses(estimator, h)

    for n, color in enumerate(colors):
        data = iris.data[iris.target == n]
        plt.scatter(data[:, 0], data[:, 1], s=0.8, color=color,
                    label=iris.target_names[n])
    # Plot the test data with crosses
    for n, color in enumerate(colors):
        data = X_test[y_test == n]
        plt.scatter(data[:, 0], data[:, 1], marker='x', color=color)

    y_train_pred = estimator.predict(X_train)
    train_accuracy = np.mean(y_train_pred.ravel() == y_train.ravel()) * 100
    plt.text(0.05, 0.9, 'Train accuracy: %.1f' % train_accuracy,
             transform=h.transAxes)

    y_test_pred = estimator.predict(X_test)
    test_accuracy = np.mean(y_test_pred.ravel() == y_test.ravel()) * 100
    plt.text(0.05, 0.8, 'Test accuracy: %.1f' % test_accuracy,
             transform=h.transAxes)

    plt.xticks(())
    plt.yticks(())
    plt.title(name)

plt.legend(scatterpoints=1, loc='lower right', prop=dict(size=12))


plt.show()

# 先将.xls文件导出成.csv文件
# 注意：以下一切代码均为英文输入法，包括标点符号等！！

# 加载csv数据，并且将数据赋予data变量
# 这里用到了pandas的工具包，读取数据之后的格式为Dataframe，是一种矩阵格式（execl的数据表格其实也是一种矩阵格式）
# pyhton中，创造一个变量的方法就是给其赋值，而“=”代表 变量赋值
# print()是python最基本的语句之一，代表“打印内容”
# import语句：加载工具包
import numpy as np 	 # numpy：科学计算工具包
import pandas as pd 	# pandas：数据分析工具包
import matplotlib.pyplot as plt	# pandas：数据分析工具包

# data = pd.read_csv("C:\\Users\\Hjx\\Desktop\\地市级党委书记数据库（2000-10）.csv",encoding = "gbk")
data = pd.read_csv("书记.csv")


print(data)