请你分别写出这些可复用的python代码，然后咱们逐一讨论

好的，我可以给您提供一些示例代码：

1. 数据可视化：

# 使用matplotlib库进行可视化展示
import matplotlib.pyplot as plt
plt.plot(df['close'])
plt.title('Close Price')
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()

2. 数据统计：

# 计算均值和标准差
import numpy as np
mean = np.mean(df['close'])
std = np.std(df['close'])

# 计算最大值和最小值
max_price = np.max(df['close'])
min_price = np.min(df['close'])

3. 技术指标计算：

# 计算MACD指标
ema12 = df['close'].ewm(span=12, adjust=False).mean()
ema26 = df['close'].ewm(span=26, adjust=False).mean()
dif = ema12 - ema26
dea = dif.ewm(span=9, adjust=False).mean()
macd = (dif - dea) * 2

# 计算RSI指标
up = df['close'].diff(1).clip(lower=0)
down = -1 * df['close'].diff(1).clip(upper=0)
ema_up = up.ewm(com=13, adjust=False).mean()
ema_down = down.ewm(com=13, adjust=False).mean()
rs = ema_up / ema_down
rsi = 100 - (100 / (1 + rs))

# 计算KDJ指标
low_list = df['low'].rolling(window=9, center=False).min()
high_list = df['high'].rolling(window=9, center=False).max()
rsv = (df['close'] - low_list) / (high_list - low_list) * 100
k = rsv.ewm(com=2, adjust=False).mean()
d = k.ewm(com=2, adjust=False).mean()
j = 3 * k - 2 * d

4. 数据挖掘：

# 使用聚类算法挖掘数据
from sklearn.cluster import KMeans
X = df[['open', 'high', 'low', 'close', 'volume']]
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
labels = kmeans.labels_
df['label'] = labels

以上是一些Python代码示例，可以用于行情数据统计分析。需要注意的是，这些代码仅是示例，实际分析中需要根据数据特点和分析目的进行选择和优化。