我就废话不多说了，直接上代码吧！

#-*- coding:utf-8 _*- """ @author:Administrator@file: standard_process.py@time: 2018/8/9"""import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport sysimport osimport seaborn as snsfrom sklearn.preprocessing import StandardScaler'''通过box_plot(盒图来确认）异常值'''# 获取项目根目录input_data_path = os.path.dirname(os.path.dirname(os.getcwd())) + '/input/'print(input_data_path)# 获取数据得位置month_6_train_path = input_data_path +'month_6_1.csv'month_6_test_path = input_data_path + 'test_data_6_1.csv'# 读取数据data_train = pd.read_csv(month_6_train_path)data_test = pd.read_csv(month_6_test_path)# print(data_train.head())# print(data_test.head())# 暂时不考虑省份城市地址# 月份只有一个月，暂时不考虑# bedrooms 需要看成分类型得数据# 只取出longitude，latitude，price，buildingTypeId,bedrooms,daysOnMarket# 取出这些数据；# train = data_train[['longitude', 'latitude', 'price', 'buildingTypeId', 'bedrooms', 'daysOnMarket']]# train= train.dropna()train = data_test[['longitude', 'latitude', 'price', 'buildingTypeId', 'bedrooms', 'daysOnMarket']]print(train.head())# print(test.head())# print(train.isna().sum())# sns.pairplot(train)# # sns.pairplot(test)# plt.show()# 特征清洗：异常值清理用用箱图；# 分为两步走，一步是单列异常值处理，# 第二步是多列分组异常值处理def remove_filers_with_boxplot(data): p = data.boxplot(return_type='dict') for index,value in enumerate(data.columns): # 获取异常值 fliers_value_list = p['fliers'][index].get_ydata() # 删除异常值 for flier in fliers_value_list: data = data[data.loc[:,value] != flier] return dataprint(train.shape)train = remove_filers_with_boxplot(train)print(train.shape)'''以上得异常值处理还不够完善，完善的异常值处理是分组判断异常值，也就是他在单独这一列种,还有一种情况是多余不同的分类，他是不是存在异常所以就需要用到分组获取数据再箱图处理掉异常数据；'''train = train[pd.isna(train.buildingTypeId) != True]print(train.shape)print(train['bedrooms'].value_counts())'''3.0 87602.0 57914.0 54421.0 20565.0 18286.0 4290.0 1597.0 82由于样本存在不均衡得问题：所以只采用12345数据：也就是说去掉0，7，6，到时候测试数据也要做相同得操作；还有一种是通过下采样或者是上采样的方式进行，这里暂时不考虑；'''# 只取bedrooms 为1，2，3，4，5 得数据train = train[train['bedrooms'].isin([1,2,3,4,5])]print(train.shape)# 利用pivot分组后去掉异常点def use_pivot_box_to_remove_fliers(data,pivot_columns_list,pivot_value_list): for column in pivot_columns_list: for value in pivot_value_list: # 获取分组的dataframe new_data = data.pivot(columns=column,values=value) p = new_data.boxplot(return_type='dict') for index,value_new in enumerate(new_data.columns): # 获取异常值 fliers_value_list = p['fliers'][index].get_ydata() # 删除异常值 for flier in fliers_value_list: data = data[data.loc[:, value] != flier] return data# train = use_pivot_box_to_remove_fliers(train,['buildingTypeId','bedrooms'],['price','daysOnMarket','longitude','latitude'])print(train.shape)# print(train.isna().sum())# 以上就不考虑longitude和latitude的问题了；应为房屋的类型以及房间个数和经纬度关系不大,但是也不一定，# 实践了一下加上longitude和latitude之后样本数据并没有减少；# sns.pairplot(train)# plt.show()# 先进一步做处理将纬度小于40的去掉train = train[train.latitude>40]# --------------------------------》》》# 对于数值类型得用均值填充，但是在填充之前注意一些原本就是分类型数据得列# def fill_na(data):# for column in data.columns:# if column.dtype != str:# data[column].fillna(data[column].mean())# return data# 以上是异常值，或者是离群点的处理，以及均值填充数据# 下面将根据catter图或者是hist图来处理数据# # 标准化数据# train = StandardScaler().fit_transform(train)# # 标准化之后画图发现数据分布并没有变## sns.pairplot(pd.DataFrame(train))# plt.show()'''1:循环遍历整个散点图用刚才写好的算法去除点；'''# 获取# def get_outlier(x,y,init_point_count ,distance,least_point_count):# x_outliers_list = []# y_outliers_list = []# for i in range(len(x)):# for j in range(len(x)):# d =np.sqrt(np.square(x[i]-x[j])+np.square(y[i]-y[j]))# # print('距离',d)# if d <= distance:# init_point_count +=1# if init_point_count <least_point_count+1:# x_outliers_list.append(x[i])# y_outliers_list.append(y[i])# print(x[i],y[i])# init_point_count =0# return x_outliers_list,y_outliers_list## def circulation_to_remove_outliers(data,list_columns=['longitude','latitude','price','daysOnMarket',]):# for column_row in list_columns:# for column_col in list_columns:# if column_row != column_col:# x = list(data[column_row])# y = list(data[column_col])# x_outliers_list ,y_outliers_list = get_outlier(x,y,0,0.01,2)# for x_outlier in x_outliers_list:# data = data[data.loc[:, column_row] != x_outlier]# for y_outlier in y_outliers_list:# data = data[data.loc[:, column_col] != y_outlier]# return data## train = circulation_to_remove_outliers(train)## print(train.shape)# def get_outlier(x,y,init_point_count ,distance,least_point_count):# for i in range(len(x)):# for j in range(len(x)):# d =np.sqrt(np.square(x[i]-x[j])+np.square(y[i]-y[j]))# # print('距离',d)# if d <= distance:# init_point_count +=1# if init_point_count <least_point_count+1:# print(x[i],y[i])# init_point_count =0## get_outlier(train['longitude'],train['latitude'],0,0.3,1)# sns.pairplot(train)# plt.show()# train = train.dropna()# print(train.tail())# train.to_csv('./finnl_processing_train_data_6_no_remove_outliers_test.csv',index=False)