如何用Python进行股票数据分析？yfinance与Pandas结合！

pip install yfinance pandas

import yfinance as yf

# 下载苹果公司过去一年的股票数据
aapl = yf.Ticker("AAPL")
data = aapl.history(period="1y")

print(data.head())

import pandas as pd

# 将数据转换为DataFrame
df = pd.DataFrame(data)

# 检查缺失值
print(df.isnull().sum())

# 处理缺失值（这里简单地用0填充）
df = df.fillna(0)

# 打印处理后的数据信息
print(df.info())

import matplotlib.pyplot as plt
import seaborn as sns

# 设置绘图风格
sns.set_style("darkgrid")

# 绘制收盘价的折线图
plt.figure(figsize=(12, 6))
plt.plot(df['Close'], label='AAPL Close Price')
plt.title('Apple Stock Close Price Over Time')
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.legend()
plt.show()

# 绘制成交量的柱状图
plt.figure(figsize=(12, 6))
plt.bar(df.index, df['Volume'], label='AAPL Volume')
plt.title('Apple Stock Volume Over Time')
plt.xlabel('Date')
plt.ylabel('Volume')
plt.legend()
plt.show()

# 计算50日移动平均线
df['MA50'] = df['Close'].rolling(window=50).mean()

# 绘制收盘价和移动平均线
plt.figure(figsize=(12, 6))
plt.plot(df['Close'], label='AAPL Close Price')
plt.plot(df['MA50'], label='50-day Moving Average')
plt.title('Apple Stock Close Price with 50-day Moving Average')
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.legend()
plt.show()