可视化库-Matplotlib-Pandas与sklearn结合(第四天)

缺乏、安全感 2021-09-20 10:06 294阅读 0赞

1. 计算每一种的比例的百分比

import pandas as pd
    from matplotlib.ticker import FuncFormatter
    
    
    np.random.seed(0)
    df = pd.DataFrame({
        'Condition 1':np.random.rand(20),
                       'Condition 2':np.random.rand(20)*0.9,
                       'Condtion 3':np.random.rand(20)*1.1})
    
    print(df.head())
    
    fig, ax = plt.subplots()
    # stacked 进行堆叠操作
    df.plot.bar(ax=ax, stacked=True)
    plt.show()
    
    # 设置百分比
    df_ratio = df.div(df.sum(axis=1), axis=0)
    fig, ax = plt.subplots()
    df_ratio.plot.bar(ax=ax, stacked=True)
    ax.yaxis.set_major_formatter(FuncFormatter(lambda y,_:'{:.0%}'.format(y)))
    plt.show()

![1396837-20190108170553981-213314715.png][]![1396837-20190108170650832-277158991.png][]

2. 通过pd将数据导入，进行缺失值补充，画出特征的PCA图

# 1 下载数据
    url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00383/risk_factors_cervical_cancer.csv'
    df = pd.read_csv(url, na_values='?')
    print(df.head())
    # 2.对缺失值进行补充
    from sklearn.preprocessing import Imputer
    # 引入inputer() 使用均值对缺失值进行填充
    impute = pd.DataFrame(Imputer().fit_transform(df))
    print(impute.head())
    impute.columns = df.columns
    impute.index = df.index
    
    
    import seaborn as sns
    from sklearn.decomposition import  PCA
    from mpl_toolkits.mplot3d import Axes3D
    
    # 3.取出样品特征， 取出Dx:Cancer 
    features = impute.drop('Dx:Cancer', axis=1)
    y = impute['Dx:Cancer']
    # 4进行PCA操作
    pca = PCA(n_components=3)
    X_r = pca.fit_transform(features)
    # '{:.2%}'表示保留两位小数, pca.explained_variabce_ratio表示所占的比例
    print('Explained variance:\nPC1{:.2%}\nPC2{:.2%}\nPC3{:.2%}'
        .format(pca.explained_variance_ratio_[0],
                pca.explained_variance_ratio_[1],
                pca.explained_variance_ratio_[2],))
    # 构造三维坐标系
    fig = plt.figure()
    ax = Axes3D(fig)
    # 画散点图
    ax.scatter(X_r[:, 0], X_r[:, 1], X_r[:, 2], c='r', cmap=plt.cm.coolwarm)
    # 对三个维度的坐标进行标注
    ax.set_xlabel('PC1')
    ax.set_ylabel('PC2')
    ax.set_zlabel('PC3')
    
    plt.show()

![1396837-20190108172234002-2072594814.png][]

转载于:https://www.cnblogs.com/my-love-is-python/p/10240187.html

[1396837-20190108170553981-213314715.png]: https://img2018.cnblogs.com/blog/1396837/201901/1396837-20190108170553981-213314715.png
[1396837-20190108170650832-277158991.png]: /images/20210726/2e518684eee14c06ade7ceb807d90ae9.png
[1396837-20190108172234002-2072594814.png]: /images/20210726/1d95efe4be26439fa2814cc06aba71e7.png