数值型使用众数填充后类型可能会变成object类型,进而导致建模报错

1import pandas as pd
2import numpy as np
3import warnings
4warnings.filterwarnings('ignore')

1df = pd.read_csv('./titanic_trains.csv',encoding='utf8')
2df.head(5)
3df.shape

1for x in df.columns:
2    zb = df[x].isnull().sum()/df.shape[0]*100
3    sl = df[x].isnull().sum()
4    print('缺失值占比为:',round(zb,2),"%,列名:",x,',缺失值数量:',sl)

1from sklearn.impute  import SimpleImputer
2
3model_si = SimpleImputer(missing_values=np.nan,strategy='mean')  # 定义规则
4df_mean = model_si.fit_transform(df[['Age']])  # 应用于数据
5df_mean = pd.DataFrame(data=df_mean,columns=['Age'])  # 转换数据类型,使用起来简单

1from sklearn.impute  import SimpleImputer
2# 数值型使用众数填充后类型可能会变成object类型,进而导致建模报错
3
4model_si = SimpleImputer(missing_values=np.nan,strategy='most_frequent')  # 定义规则
5df_mode = model_si.fit_transform(df[['Cabin','Embarked']])  # 应用于数据
6df_mode = pd.DataFrame(data=df_mode,columns=['Cabin','Embarked'])  # 转换数据类型,使用起来简单

1# 覆盖掉原本的数据
2df['Age'] = df_mean
3df[['Cabin','Embarked']] = df_mode

1df.isnull().sum()

1# 删除缺失值超过20%的列
2df_drop = df.dropna(axis=1,thresh=df.shape[0]*0.8)

1df_drop.shape

1# 填充,可以使用repalce
2df_drop['Age'] = df_drop['Age'].replace(np.nan,df_drop['Age'].mean())
3df_drop['Embarked'] = df_drop['Embarked'].replace(np.nan,df_drop['Embarked'].mode()[0])

1df_drop.isnull().sum()

1# 绘制箱线图
2import matplotlib.pyplot as plt
3plt.figure(figsize=(8,6))
4plt.boxplot(df_drop['Age'])
5plt.show()  # 绘图时一定加上show方法

1list_yc = [] # 存储异常值索引位置

 1# list_yc = []
 2# 箱线图检测
 3# 1.取四分位数
 4sfw = df_drop['Age'].describe()
 5Q1 = sfw['25%']
 6Q3 = sfw['75%']
 7# 2.计算IQR
 8IQR = Q3-Q1
 9# 3.计算上/下边界
10up = Q3+1.5*IQR
11down = Q1-1.5*IQR
12# 4.比较
13for x in range(0,891):
14    if x not in list_yc:
15        if df_drop['Age'][x]>up or df_drop['Age'][x]<down:
16            # 5.处理
17            df_drop['Age'] = df_drop['Age'].replace(x,df_drop['Age'].median())  # 替换
18#             df_drop.drop(axis=0,index=x,inplace=True)
19            print('异常值索引为:',x)
20            list_yc.append(x)

1# 绘制箱线图
2import matplotlib.pyplot as plt
3plt.figure(figsize=(8,6))
4plt.boxplot(df_drop['Age'])
5plt.show()  # 绘图时一定加上show方法

1# 1.计算均值
2mean_age = df_drop['Age'].mean()
3# 2.计算标准差
4std_age = np.std(df_drop['Age'])
5for x in df_drop['Age']:
6    if np.abs(x-mean_age)>3*std_age:
7        # 删除和替换的方式和箱线图一样
8        print('异常值为:',x)

1# 查看皮尔逊相关性系数
2cor = df_drop.corr() # pearson:皮尔逊，spearman：斯皮尔曼，kendall：肯德尔
3cor

1# 绘制相关性热力图
2import matplotlib.pyplot as plt
3import seaborn as sns
4plt.figure(figsize=(8,6))
5sns.heatmap(cor)
6plt.show()

1list_ry = []
2# 剔除相关性大于0.8的属性（冗余数据）
3for x in cor.columns:
4    for y in cor.index:
5        if cor[x][y]>0.8 and x!=y and (y,x)not in list_ry:
6            list_ry.append((x,y))

1# 取出要删除的列并去重
2list_drop =np.unique([col[0] for col in list_ry])

1# 删除列
2df_ry_drop = df_drop.drop(axis=1,columns=list_drop)