鍦≒ython涓紝鏈夊緢澶氭暟鎹垎鏋愬簱鍙互浣跨敤锛屽叾涓渶娴佽鐨勫寘鎷琍andas銆丯umPy銆丮atplotlib鍜孲eaborn銆備互涓嬫槸濡備綍浣跨敤杩欎簺鏁版嵁鍒嗘瀽搴撶殑绠€鍗曠ず渚嬶細

1. Pandas锛歅andas鏄竴涓己澶х殑鏁版嵁鍒嗘瀽搴擄紝鍙互鐢ㄤ簬鏁版嵁娓呮礂銆佹暟鎹垎鏋愬拰鏁版嵁鍙鍖栥€備笅闈㈡槸涓€涓娇鐢≒andas鍔犺浇鍜屾煡鐪嬫暟鎹殑绀轰緥浠ｇ爜锛?/li>

import pandas as pd

# 璇诲彇CSV鏂囦欢
data = pd.read_csv('data.csv')

# 鏌ョ湅鏁版嵁鐨勫墠鍑犺
print(data.head())

2. NumPy锛歂umPy鏄疨ython涓敤浜庣瀛﹁绠楃殑鍩虹搴擄紝鎻愪緵浜嗗缁存暟缁勫璞″拰鍚勭鏁板鍑芥暟銆備笅闈㈡槸涓€涓娇鐢∟umPy璁＄畻鏁扮粍鐨勫钩鍧囧€煎拰鏍囧噯宸殑绀轰緥浠ｇ爜锛?/li>

import numpy as np

# 鍒涘缓涓€涓狽umPy鏁扮粍
arr = np.array([1, 2, 3, 4, 5])

# 璁＄畻鏁扮粍鐨勫钩鍧囧€煎拰鏍囧噯宸?/span>
mean = np.mean(arr)
std = np.std(arr)

print('Mean:', mean)
print('Standard Deviation:', std)

3. Matplotlib锛歁atplotlib鏄竴涓敤浜庣粯鍒跺浘琛ㄥ拰鍙鍖栨暟鎹殑搴撱€備笅闈㈡槸涓€涓娇鐢∕atplotlib缁樺埗鎶樼嚎鍥剧殑绀轰緥浠ｇ爜锛?/li>

import matplotlib.pyplot as plt

# 鍒涘缓鏁版嵁
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# 缁樺埗鎶樼嚎鍥?/span>
plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot')
plt.show()

4. Seaborn锛歋eaborn鏄竴涓熀浜嶮atplotlib鐨勬暟鎹彲瑙嗗寲搴擄紝鎻愪緵浜嗘洿楂樼骇鐨勭粯鍥惧姛鑳藉拰鏇寸編瑙傜殑鍥捐〃鏍峰紡銆備笅闈㈡槸涓€涓娇鐢⊿eaborn缁樺埗鐩存柟鍥剧殑绀轰緥浠ｇ爜锛?/li>

import seaborn as sns
import numpy as np

# 鐢熸垚闅忔満鏁版嵁
data = np.random.normal(size=1000)

# 缁樺埗鐩存柟鍥?/span>
sns.histplot(data, kde=True)
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Histogram')
plt.show()

閫氳繃浣跨敤杩欎簺鏁版嵁鍒嗘瀽搴擄紝鎮ㄥ彲浠ユ洿杞绘澗鍦板鐞嗗拰鍒嗘瀽鏁版嵁锛屼粠鑰屾洿濂藉湴鐞嗚В鏁版嵁骞跺仛鍑烘洿濂界殑鍐崇瓥銆?/p>