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| # 导入warnings包，利用过滤器来实现忽略警告语句
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
## pd.set_option('display.max_columns', None)# 显示所有列
## pd.set_option('display.max_row', None)# 显示所有行
## 1)载入训练集和测试集
Train_data = pd.read_csv("./datalab/used_car_train_20200313.csv", sep = " ")
Test_data = pd.read_csv("./datalab/used_car_testA_20200313.csv", sep = " ")
## 2)简略观察数据（head()+shape)
#print(Train_data.head().append(Train_data.tail()))
#print(Train_data.shape)
#
# ## 3)通过describe()来熟悉相关统计量
# print(Train_data.describe())
#
# ## 4)通过info()来熟悉数据类型
# print(Train_data.info())
#
# ## 5)判断数据缺失和异常
# print(Train_data.isnull().sum())
#
#nan可视化
# missing = Train_data.isnull().sum()
# missing = missing[missing > 0]
# missing.sort_values(inplace=True) # 排序
# missing.plot.bar() # 绘柱状图
# plt.tight_layout() # 自动调整子图参数
# plt.show()
# # # 可视化看下缺省值
# msno.matrix(Train_data.sample(250))
# # plt.show()
# msno.bar(Train_data.sample(1000)) # 条形图
# plt.show()
## 6)查看异常值检测
# Train_data.info()
## print(Train_data["notRepairedDamage"].value_counts()) # 返回包含值和count
Train_data["notRepairedDamage"].replace("-", np.nan, inplace=True) # 将数据中‘-’替换成nan值
# print(Train_data.isnull().sum())
#print(Train_data["notRepairedDamage"].value_counts())
#Test_data.info()
##print(Test_data["notRepairedDamage"].value_counts())
#Test_data["notRepairedDamage"].replace("-", np.nan, inplace=True)
##print(Test_data["notRepairedDamage"].value_counts())
# 删除严重倾斜的数据
#print(Train_data["seller"].value_counts())
#print(Train_data["offerType"].value_counts())
# print(Test_data["seller"].value_counts())
# print(Test_data["offerType"].value_counts())
del Train_data["seller"]
del Train_data["offerType"]
# print(Train_data.info())
# print(Train_data.shape)
#del Test_data["seller"]
#del Test_data["offerType"]
# 了解预测值的分布
# print(Train_data["price"])
# print(Train_data["price"].value_counts())
## 1)总体分布情况(无界约翰逊分布等）
import scipy.stats as st
# y = Train_data["price"]
# plt.figure(1); plt.title("Johnson SU") # 创建新图
# sns.distplot(y, kde=False, fit=st.johnsonsu)
# plt.figure(2); plt.title("Normal")
# sns.distplot(y, kde=False, fit=st.norm)
# plt.figure(3); plt.title("Log Normal")
# sns.distplot(y, kde=False, fit=st.lognorm)
# plt.show() # 最佳拟合是无界约翰逊分布
## 2)查看skewness and kurtosis
# sns.distplot(Train_data["price"])
# print("Skewness: %f" % Train_data["price"].skew()) # 偏度
# print("Kurtosis: %f" % Train_data["price"].kurt()) # 峰度
# plt.show()
# print(Train_data.skew())
# print(Train_data.kurt())
# sns.distplot(Train_data.skew(), color="blue", axlabel="Skewness")
# plt.show()
# sns.distplot(Train_data.kurt(), color="orange", axlabel="Kurtness")
# plt.show()
# 3)查看预测值的具体频数
# plt.hist(Train_data["price"], orientation="vertical", histtype="bar", color="red")
# plt.show() # 直方图
# log变换之后的分布比较均匀，可以进行log变换进行预测，这也是预测问题常用的trick
# plt.hist(np.log(Train_data["price"]), orientation="vertical", histtype="bar", color="red")
# plt.show()
## 查看特征
# 分离label即预测值
Y_train = Train_data["price"]
## 这个区别方式适用于没有直接label coding的数据
## 这里不适用，需要人为根据实际含义来区分
## 数字特征
## numeric_features = Train_data.select_dtypes(include=[np.number])
## numeric_features.columns
## # 类型特征
## categorical_features = Train_data.select_dtypes(include=[np.object])
## categorical_features.columns
# 数字特征
numeric_features = ['power', 'kilometer', 'v_0', 'v_1', 'v_2', 'v_3', 'v_4', 'v_5', 'v_6', 'v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12', 'v_13','v_14' ]
# 类别特征
categorical_features = ['name', 'model', 'brand', 'bodyType', 'fuelType', 'gearbox', 'notRepairedDamage', 'regionCode']
## 类别特征nunique分布——Train_data
# for cat_fea in categorical_features:
# print(cat_fea+"的特征分布如下：")
# print("{}特征有{}个不同的值".format(cat_fea, Train_data[cat_fea].nunique()))
# print(Train_data[cat_fea].value_counts())
## 类别特征nunique分布——Test_data
# for cat_fea in categorical_features:
# print(cat_fea+"的特征分布如下：")
# print("{}特征有{}个不同的值".format(cat_fea, Test_data[cat_fea].nunique()))
# print(Test_data[cat_fea].value_counts())
## 数字特征分析
numeric_features.append("price")
# print(numeric_features)
#print(Train_data.head())
## 1)相关性分析
price_numeric = Train_data[numeric_features]
correlation = price_numeric.corr() # 返回一个相关系数的矩阵
# print(correlation["price"].sort_values(ascending=False),"\n") # 降序排序
# f , ax = plt.subplots(figsize = (7, 7))
# plt.title("Correlation of Numeric Features with Price")
# sns.heatmap(correlation, square=True, vmax=0.8) # 热图（显示相关系数）
# plt.show()
## 2)查看几个特征的偏度和峰度
# for col in numeric_features:
# print("{:15}".format(col),"Skewness:{:05.2f}".format(Train_data[col].skew()),
# " ",
# "Kurtosis:{:06.2f}".format(Train_data[col].kurt()))
## 3)每个数字特征得分布可视化
# f = pd.melt(Train_data, value_vars=numeric_features) # 转换
# g = sns.FacetGrid(f,col="variable", col_wrap=2, sharex=False,sharey=False) # 以”variable“作“格子"绘图
# # plt.show()
# g = g.map(sns.distplot, "value") # 以”value“绘制到”格子”图中
# plt.show()
## 4）数字特征相互之间的关系可视化
# sns.set() # 风格设置
# colunms = ["price", "v_12", "v_8", "v_0", "power", "v_5", "v_2", "v_6", "v_1", "v_14"]
# sns.pairplot(Train_data[colunms],size=2, kind="scatter", diag_kind="kde") # 多变量图
# plt.show()
# print(Train_data.columns)
# print(Y_train)
## 5)多变量互相关系回归关系可视化
# fig,((ax1, ax2), (ax3, ax4), (ax5, ax6), (ax7, ax8), (ax9, ax10)) = plt.subplots(nrows=5, ncols=2, figsize=(24, 20)) # 生成5行2列十个子图
# # ['v_12', 'v_8' , 'v_0', 'power', 'v_5', 'v_2', 'v_6', 'v_1', 'v_14']
# v_12_scatter_plot = pd.concat([Y_train,Train_data["v_12"]], axis=1) # 合并成一列
# #print(v_12_scatter_plot)
# sns.regplot(x="v_12", y="price", data=v_12_scatter_plot,scatter=True,fit_reg=True,ax=ax1) # 数据与回归模型拟合
#
# v_8_scatter_plot = pd.concat([Y_train,Train_data['v_8']],axis = 1)
# sns.regplot(x='v_8',y = 'price',data = v_8_scatter_plot,scatter= True, fit_reg=True, ax=ax2)
#
# v_0_scatter_plot = pd.concat([Y_train,Train_data['v_0']],axis = 1)
# sns.regplot(x='v_0',y = 'price',data = v_0_scatter_plot,scatter= True, fit_reg=True, ax=ax3)
#
# power_scatter_plot = pd.concat([Y_train,Train_data['power']],axis = 1)
# sns.regplot(x='power',y = 'price',data = power_scatter_plot,scatter= True, fit_reg=True, ax=ax4)
#
# v_5_scatter_plot = pd.concat([Y_train,Train_data['v_5']],axis = 1)
# sns.regplot(x='v_5',y = 'price',data = v_5_scatter_plot,scatter= True, fit_reg=True, ax=ax5)
#
# v_2_scatter_plot = pd.concat([Y_train,Train_data['v_2']],axis = 1)
# sns.regplot(x='v_2',y = 'price',data = v_2_scatter_plot,scatter= True, fit_reg=True, ax=ax6)
#
# v_6_scatter_plot = pd.concat([Y_train,Train_data['v_6']],axis = 1)
# sns.regplot(x='v_6',y = 'price',data = v_6_scatter_plot,scatter= True, fit_reg=True, ax=ax7)
#
# v_1_scatter_plot = pd.concat([Y_train,Train_data['v_1']],axis = 1)
# sns.regplot(x='v_1',y = 'price',data = v_1_scatter_plot,scatter= True, fit_reg=True, ax=ax8)
#
# v_14_scatter_plot = pd.concat([Y_train,Train_data['v_14']],axis = 1)
# sns.regplot(x='v_14',y = 'price',data = v_14_scatter_plot,scatter= True, fit_reg=True, ax=ax9)
#
# v_13_scatter_plot = pd.concat([Y_train,Train_data['v_13']],axis = 1)
# sns.regplot(x='v_13',y = 'price',data = v_13_scatter_plot,scatter= True, fit_reg=True, ax=ax10)
# plt.show()
# 类别特征分析
## 1）nunique分布
# for fea in categorical_features:
# print(Train_data[fea].nunique())
#
# print(categorical_features)
## 2)类别箱形图可视化
# 因为 name和 regionCode的类别太稀疏了，这里我们把不稀疏的几类画一下
categorical_features = ["model",
"brand",
"bodyType",
"fuelType",
"gearbox",
"notRepairedDamage"]
for c in categorical_features:
Train_data[c] = Train_data[c].astype("category") # 强制转换数据类型
if Train_data[c].isnull().any(): # 检查字段缺失
Train_data[c] = Train_data[c].cat.add_categories(["MISSING"]) # 添加新类别
Train_data[c] = Train_data[c].fillna("MISSING") # 填充为NAN的值
# def boxplot(x, y, **kwargs):
# sns.boxplot(x=x, y=y) # 箱形图
# x=plt.xticks(rotation=90) # 设置坐标轴
#
# f = pd.melt(Train_data, id_vars=["price"], value_vars=categorical_features)
# g = sns.FacetGrid(f,col="variable", col_wrap=2, sharex=False,sharey=False,size=5)
# g = g.map(boxplot, "value", "price")
# plt.show()
## 3)类别特征的小提琴图可视化
#print(Train_data.columns)
# catg_list = categorical_features
# target = "price"
# for catg in catg_list:
# sns.violinplot(x=catg,y=target,data=Train_data)
# plt.show()
# print(categorical_features)
## 4)类别特征的柱形图可视化
# def bar_plot(x,y,**kwargs): # 柱形图
# sns.barplot(x=x,y=y)
# x=plt.xticks(rotation=90)
# f = pd.melt(Train_data, id_vars=["price"], value_vars=categorical_features)
# g = sns.FacetGrid(f, col="variable",col_wrap=2,sharex=False,sharey=False,size=5)
# g = g.map(bar_plot, "value", "price")
# plt.show()
## 5)类别特征的每个类别频数可视化
# def count_plot(x,**kwargs): # 计数直方图
# sns.countplot(x=x)
# x=plt.xticks(rotation=90)
# f = pd.melt(Train_data,value_vars=categorical_features)
# g = sns.FacetGrid(f,col="variable", col_wrap=2,sharex=False,sharey=False,size=5)
# g = g.map(count_plot,"value")
# plt.show()
## 生成数据报告
import pandas_profiling
#
# pfr = pandas_profiling.ProfileReport(Train_data)
# pfr.to_file("./example.html")
|
