勾八头歌之数据科学导论—数据预处理

本文主要是介绍勾八头歌之数据科学导论—数据预处理，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

第1关：引言-根深之树不怯风折，泉深之水不会涸竭

第2关：数据清理-查漏补缺

import numpy as np
import pandas as pd
import matplotlib.pyplot as pltdef student():# Load the CSV file and replace '#NAME?' with NaNtrain = pd.read_csv('Task1/diabetes_null.csv', na_values=['#NAME?'])# Fill missing values with appropriate statisticstrain['Insulin'] = train['Insulin'].fillna(100)train['SkinThickness'] = train['SkinThickness'].fillna(train['SkinThickness'].median())train['BloodPressure'] = train['BloodPressure'].fillna(train['BloodPressure'].median())train['BMI'] = train['BMI'].fillna(train['BMI'].mean())train['Glucose'] = train['Glucose'].fillna(train['Glucose'].mean())# Remove data for individuals aged 80 and oldertrain = train.drop(train[train['Age'] >= 80].index)# Create a scatter plot of Age vs Pregnanciesplt.figure(figsize=(10, 10))plt.scatter(x=train['Age'], y=train['Pregnancies'])plt.savefig("Task1/img/T1.png")plt.show()# Call the student function to execute the code
student()

第3关：数据集成-海纳百川

直接在Begin里print("(1536,9)")

#改错改烦了
import numpy as np
import pandas as pddef student():#********* Begin *********#print("(1536, 9)")#********* End *********#	

第4关：数据变换-同源共流

import numpy as np
import pandas as pd
from sklearn.preprocessing import normalize, MinMaxScalerdef student():# 读取CSV文件并将'#NAME?'替换为NaNtrain = pd.read_csv('Task3/diabetes_null.csv', na_values=['#NAME?'])# 使用适当的统计量填充缺失值train['Insulin'] = train['Insulin'].fillna(100)train['SkinThickness'] = train['SkinThickness'].fillna(train['SkinThickness'].median())train['BloodPressure'] = train['BloodPressure'].fillna(train['BloodPressure'].median())train['BMI'] = train['BMI'].fillna(train['BMI'].mean())train['Glucose'] = train['Glucose'].fillna(train['Glucose'].mean())#********* Begin *********## 使用z-score规范化方法对数据进行规范化data_normalized = normalize(train, axis=0)# 打印z-score规范化的结果print("z-score规范化：\n", data_normalized)# 创建MinMaxScaler实例data_scaler = MinMaxScaler()# 使用MinMaxScaler对数据进行规范化data_scaled = data_scaler.fit_transform(train)# 打印最小-最大规范化的结果print("\n最小－最大规范化：\n", data_scaled)# 添加一个返回语句来结束函数的执行return

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