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创图表.py

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+# -*- coding:utf-8 -*-
+import argparse
+import os.path
+import matplotlib
+matplotlib.use('TkAgg')  # 例如使用TkAgg后端
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+def set_outputfolder(output_path):
+    if not os.path.exists(output_path):
+        os.makedirs(output_path)
+
+def basic_show(train_1):
+    print('----------------------------------------------------')
+    print("数据分布情况：")
+    print(train_1.describe())
+    print('----------------------------------------------------')
+    print("数据类型观察：")
+    print(train_1.info())
+    print('----------------------------------------------------')
+    print("数据内容：")
+    print(train_1.head())  
+    print('----------------------------------------------------')
+    print("原始数据缺失情况：")
+    print(train_1.isnull().sum())
+
+def basic_show1(df, path):
+    # 性别对于生还的影响
+    sns.barplot(x="Sex", y="Survived", data=df)
+    plt.savefig(path + '/sex.jpg')
+    plt.show()
+
+    # 船舱等级对生还的影响
+    sns.barplot(x="Pclass", y="Survived", data=df)
+    plt.savefig(path + '/Pclass.jpg')
+    plt.show()
+
+    # 配偶及兄弟姐妹数适中的乘客幸存率更高
+    sns.barplot(x="SibSp", y="Survived", data=df)
+    plt.savefig(path + '/SibSp.jpg')
+    plt.show()
+
+    # 父母与子女数适中的乘客幸存率更高
+    sns.barplot(x="Parch", y="Survived", data=df)
+    plt.savefig(path + '/Parch.jpg')
+    plt.show()
+
+    # Embarked登港港口与生存情况的分析
+    sns.countplot(x='Embarked', hue='Survived', data=df, fill=True)
+    plt.savefig(path + '/Embarked.jpg')
+    plt.show()
+
+def p_class_sex(train_1, path):
+   plt.rcParams['font.sans-serif'] = ['SimHei']
+   plt.rcParams['axes.unicode_minus'] = False  
+   pd.set_option('display.max_columns', None)  
+   pd.set_option('display.max_rows', None)
+   pd.set_option('max_colwidth', 100)
+   pd.set_option('display.width', 1000)
+   fig = plt.figure()  #图形对象（Figure），并将其保存在变量 fig 中
+   fig.set(alpha=0.5)  
+
+   fig = plt.figure()
+   fig.set(alpha=0.65)  # 设置图像透明度，无所谓
+   plt.title(u"根据舱等级和性别的获救情况")
+
+   ax1 = fig.add_subplot(141)#1*4
+   train_1.Survived[train_1.Sex == 'female'][train_1.Pclass != 3].value_counts().plot(kind='bar',label="female highclass",color='#FA2479')
+   ax1.set_xticklabels([u"获救", u"未获救"], rotation=0)#x标签
+   ax1.legend([u"女性/高级舱"], loc='best')
+
+   ax2 = fig.add_subplot(142, sharey=ax1)
+   train_1.Survived[train_1.Sex == 'female'][train_1.Pclass == 3].value_counts().plot(kind='bar',label='female, low class',color='pink')
+   ax2.set_xticklabels([u"未获救", u"获救"], rotation=0)
+   plt.legend([u"女性/低级舱"], loc='best')
+
+   ax3 = fig.add_subplot(143, sharey=ax1)
+   train_1.Survived[train_1.Sex == 'male'][train_1.Pclass != 3].value_counts().plot(kind='bar',label='male, high class',color='lightblue')
+   ax3.set_xticklabels([u"未获救", u"获救"], rotation=0)
+   plt.legend([u"男性/高级舱"], loc='best')
+
+   ax4 = fig.add_subplot(144, sharey=ax1)
+   train_1.Survived[train_1.Sex == 'male'][train_1.Pclass == 3].value_counts().plot(kind='bar', label='male low class',color='steelblue')
+   ax4.set_xticklabels([u"未获救", u"获救"], rotation=0)
+   plt.legend([u"男性/低级舱"], loc='best')
+   plt.savefig(path + '/p_class_set.jpg')
+   plt.show()
+
+def age(train_1, path):
+    plt.rcParams['font.sans-serif'] = ['SimHei']
+    plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
+    pd.set_option('display.max_columns', None)
+    pd.set_option('display.max_rows', None)
+    pd.set_option('max_colwidth', 100)
+    pd.set_option('display.width', 1000)
+    fig = plt.figure(figsize=(8, 5))
+    fig.suptitle(u"各等级的乘客年龄分布")
+    ax = fig.add_subplot(111)
+    for pclass in [1, 2, 3]:
+        sns.kdeplot(train_1[train_1['Pclass'] == pclass]['Age'], ax=ax, label=f'Pclass {pclass}')
+    ax.set_xlabel(u"年龄")
+    ax.set_ylabel(u"密度")
+    plt.legend(loc='best')
+    plt.savefig(path + '/age_distribution.jpg')
+    plt.show()
+
+def carbin(full_1, path):
+    full_1['Has_Cabin'] = full_1['Cabin'].apply(lambda x: 0 if pd.isna(x) else 1)
+    sns.barplot(x='Has_Cabin', y='Survived', data=full_1)
+    plt.title('Survival Rate')
+    plt.savefig(path + '/survival_rate.jpg')
+    plt.show()
+
+    cabin = pd.crosstab(full_1['Has_Cabin'], full_1['Survived'])
+    cabin.rename(index={0: 'no cabin', 1: 'have cabin'}, columns={0: 'Dead', 1: 'Survived'}, inplace=True)
+    cabin.plot.bar(figsize=(8, 5))
+    plt.xticks(rotation=0, size='large')
+    plt.title('Survived Count')
+    plt.xlabel('')
+    plt.legend()
+    plt.savefig(path + '/carbin.jpg')
+    plt.show()
+
+def age_scatter_plot(train_1, path):
+    plt.scatter(train_1['Age'], train_1['Survived'])
+    plt.ylabel(u"获救情况 (1为获救)")
+    plt.xlabel(u"年龄")
+    plt.title(u"年龄与获救情况散点图")
+    plt.savefig(path + '/age_scatter_plot.jpg')
+    plt.show()
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dest_path", default='E:/python1/123', help="files save path")
+    parser.add_argument('--train', default='train.csv', help="train data")
+    parser.add_argument('--test', default='test.csv', help="test data")
+    args = parser.parse_args()
+
+    output_path = args.dest_path + '/data_output/'
+    set_outputfolder(output_path)
+
+    # 拿到测试集和训练集
+    train = pd.read_csv(args.train)
+    test = pd.read_csv(args.test)
+    full = pd.concat([train, test], ignore_index=True)
+
+    basic_show(train)
+    basic_show1(train, output_path)
+    age(train, output_path)
+    p_class_sex(train, output_path)
+    carbin(full, output_path)
+    age_scatter_plot(train, output_path)