如何在Python中進行資料分析與挖掘

# 使用requests库获取网络上的数据
import requests

url = "http://example.com/data.csv"
response = requests.get(url)
data = response.content

# 使用pandas库读取本地的数据文件
import pandas as pd

data = pd.read_csv("data.csv")

# 使用MySQLdb库连接数据库并获取数据
import MySQLdb

# 连接数据库
conn = MySQLdb.connect(host="localhost", user="root", passwd="password", db="database")
cursor = conn.cursor()

# 执行查询语句
cursor.execute("SELECT * FROM table")

# 获取查询结果
data = cursor.fetchall()

# 关闭数据库连接
conn.close()

import pandas as pd

# 去除重复数据
data = data.drop_duplicates()

# 处理缺失值
data = data.dropna()

# 标准化数据
data['column'] = (data['column'] - data['column'].mean()) / data['column'].std()

# 数据类型转换
data['column'] = data['column'].astype(int)

# 去除异常值
q1 = data['column'].quantile(0.25)
q3 = data['column'].quantile(0.75)
iqr = q3 - q1
data = data[(data['column'] > q1 - 1.5*iqr) & (data['column'] < q3 + 1.5*iqr)]

import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

# 描述性统计分析
data.describe()

# 数据关联分析
data.corr()

# 数据聚类分析
kmeans = KMeans(n_clusters=3).fit(data)
labels = kmeans.labels_
centroids = kmeans.cluster_centers_

# 数据预测和分类
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

# 数据可视化
data.plot(kind='scatter', x='column1', y='column2')
plt.show()