浙大疏锦行
学会了绘制两个图：
热力图：表示每个特征之间的影响，颜色越深数值越大表示这两个特征的关系越紧密
箱线图：表示每个特征的数据分布情况
箱体（Box）：
箱体的上下边界分别表示第一四分位数（Q1）和第三四分位数（Q3），即数据的25%和75%分位数。
箱体内的水平线表示中位数（Median），即数据的50%分位数。
须（Whiskers）：
须的上下端点通常表示数据的最小值和最大值，但不包括异常值。
在这个图中，须的下端点接近0，上端点大约在200,000左右。
异常值（Outliers）：
图中箱体外的圆点表示异常值，即显著偏离其他数据点的值。
在这个图中，可以看到许多异常值，这些值远高于第三四分位数（Q3）。
数据分布：
从图中可以看出，年收入的中位数较低，大部分数据集中在较低的收入范围内。
然而，存在一些高收入的异常值，这些值显著高于大多数数据点
# 首先走一遍完整的之前的流程
# 1. 读取数据
import pandas as pd
data = pd.read_csv('data.csv')
# 2. 查看数据
data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7500 entries, 0 to 7499
Data columns (total 18 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Id 7500 non-null int64
1 Home Ownership 7500 non-null object
2 Annual Income 5943 non-null float64
3 Years in current job 7129 non-null object
4 Tax Liens 7500 non-null float64
5 Number of Open Accounts 7500 non-null float64
6 Years of Credit History 7500 non-null float64
7 Maximum Open Credit 7500 non-null float64
8 Number of Credit Problems 7500 non-null float64
9 Months since last delinquent 3419 non-null float64
10 Bankruptcies 7486 non-null float64
11 Purpose 7500 non-null object
12 Term 7500 non-null object
13 Current Loan Amount 7500 non-null float64
14 Current Credit Balance 7500 non-null float64
15 Monthly Debt 7500 non-null float64
16 Credit Score 5943 non-null float64
17 Credit Default 7500 non-null int64
dtypes: float64(12), int64(2), object(4)
memory usage: 1.0+ MB
data["Years in current job"].value_counts()
Years in current job
10+ years 2332
2 years 705
3 years 620
< 1 year 563
5 years 516
1 year 504
4 years 469
6 years 426
7 years 396
8 years 339
9 years 259
Name: count, dtype: int64
data["Home Ownership"].value_counts()
Home Ownership
Home Mortgage 3637
Rent 3204
Own Home 647
Have Mortgage 12
Name: count, dtype: int64
# 创建嵌套字典用于映射
mappings = {
"Years in current job": {
"10+ years": 10,
"2 years": 2,
"3 years": 3,
"< 1 year": 0,
"5 years": 5,
"1 year": 1,
"4 years": 4,
"6 years": 6,
"7 years": 7,
"8 years": 8,
"9 years": 9
},
"Home Ownership": {
"Home Mortgage": 0,
"Rent": 1,
"Own Home": 2,
"Have Mortgage": 3
}
}
# 使用映射字典进行转换
data["Years in current job"] = data["Years in current job"].map(mappings["Years in current job"])
data["Home Ownership"] = data["Home Ownership"].map(mappings["Home Ownership"])
data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7500 entries, 0 to 7499
Data columns (total 18 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Id 7500 non-null int64
1 Home Ownership 7500 non-null int64
2 Annual Income 5943 non-null float64
3 Years in current job 7129 non-null float64
4 Tax Liens 7500 non-null float64
5 Number of Open Accounts 7500 non-null float64
6 Years of Credit History 7500 non-null float64
7 Maximum Open Credit 7500 non-null float64
8 Number of Credit Problems 7500 non-null float64
9 Months since last delinquent 3419 non-null float64
10 Bankruptcies 7486 non-null float64
11 Purpose 7500 non-null object
12 Term 7500 non-null object
13 Current Loan Amount 7500 non-null float64
14 Current Credit Balance 7500 non-null float64
15 Monthly Debt 7500 non-null float64
16 Credit Score 5943 non-null float64
17 Credit Default 7500 non-null int64
dtypes: float64(13), int64(3), object(2)
memory usage: 1.0+ MB
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# 提取连续值特征
continuous_features = [
'Annual Income', 'Years in current job', 'Tax Liens',
'Number of Open Accounts', 'Years of Credit History',
'Maximum Open Credit', 'Number of Credit Problems',
'Months since last delinquent', 'Bankruptcies',
'Current Loan Amount', 'Current Credit Balance', 'Monthly Debt',
'Credit Score'
]
# 计算相关系数矩阵
correlation_matrix = data[continuous_features].corr()
# 设置图片清晰度
plt.rcParams['figure.dpi'] = 300
# 绘制热力图
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1)
plt.title('Correlation Heatmap of Continuous Features')
plt.show()
import pandas as pd
import matplotlib.pyplot as plt
# 定义要绘制的特征
features = ['Annual Income', 'Years in current job', 'Tax Liens', 'Number of Open Accounts']
# 随便选的4个特征，不要在意对不对
# 设置图片清晰度
plt.rcParams['figure.dpi'] = 300
# 创建一个包含 2 行 2 列的子图布局
fig, axes = plt.subplots(2, 2, figsize=(12, 8))
# 手动指定特征索引进行绘图，仔细观察下这个坐标
i = 0
feature = features[i]
axes[0, 0].boxplot(data[feature].dropna())
axes[0, 0].set_title(f'Boxplot of {feature}')
axes[0, 0].set_ylabel(feature)
i = 1
feature = features[i]
axes[0, 1].boxplot(data[feature].dropna())
axes[0, 1].set_title(f'Boxplot of {feature}')
axes[0, 1].set_ylabel(feature)
i = 2
feature = features[i]
axes[1, 0].boxplot(data[feature].dropna())
axes[1, 0].set_title(f'Boxplot of {feature}')
axes[1, 0].set_ylabel(feature)
i = 3
feature = features[i]
axes[1, 1].boxplot(data[feature].dropna())
axes[1, 1].set_title(f'Boxplot of {feature}')
axes[1, 1].set_ylabel(feature)
# 调整子图之间的间距
plt.tight_layout()
# 显示图形
plt.show()
# 定义要绘制的特征
features = ['Annual Income', 'Years in current job', 'Tax Liens', 'Number of Open Accounts']
# 设置图片清晰度
plt.rcParams['figure.dpi'] = 300
# 创建一个包含 2 行 2 列的子图布局，其中
fig, axes = plt.subplots(2, 2, figsize=(12, 8))#返回一个Figure对象和Axes对象
# 这里的axes是一个二维数组，包含2行2列的子图
# 这里的fig是一个Figure对象，表示整个图形窗口
# 你可以把fig想象成一个画布，axes就是在这个画布上画的图形
# 遍历特征并绘制箱线图
for i, feature in enumerate(features):
row = i // 2
col = i % 2
axes[row, col].boxplot(data[feature].dropna())
axes[row, col].set_title(f'Boxplot of {feature}')
axes[row, col].set_ylabel(feature)
# 调整子图之间的间距
plt.tight_layout()
# 显示图形
plt.show()
# 定义要绘制的特征
features = ['Annual Income', 'Years in current job', 'Tax Liens', 'Number of Open Accounts']
# 设置图片清晰度
plt.rcParams['figure.dpi'] = 300
# 创建一个包含 2 行 2 列的子图布局，其中
fig, axes = plt.subplots(2, 2, figsize=(12, 8))#返回一个Figure对象和Axes对象
# 这里的axes是一个二维数组，包含2行2列的子图
# 这里的fig是一个Figure对象，表示整个图形窗口
# 你可以把fig想象成一个画布，axes就是在这个画布上画的图形
# 遍历特征并绘制箱线图
for i, feature in enumerate(features):
row = i // 2
col = i % 2
axes[row, col].boxplot(data[feature].dropna())
axes[row, col].set_title(f'Boxplot of {feature}')
axes[row, col].set_ylabel(feature)
# 调整子图之间的间距
plt.tight_layout()
# 显示图形
plt.show()
