# pip install pandas
import pandas as pd
data = {"姓名": ["丁丁","小美","大雄"],
"年齡": [25, 22, 28],
"分數": [85, 92, 78]}
df = pd.DataFrame(data)
print(df)
print(df.describe()) # 統計摘要
print(df["分數"]) # 選取單欄
print(df[df["分數"] > 80]) # 篩選
print(df.sort_values("分數", ascending=False)) # 排序# CSV
df = pd.read_csv("data.csv", encoding="utf-8")
df.to_csv("output.csv", index=False, encoding="utf-8-sig")
# Excel
df = pd.read_excel("data.xlsx")
df.to_excel("output.xlsx", index=False)
# 資料處理
df["新欄位"] = df["分數"] * 1.1
df.dropna(inplace=True) # 刪除空值# pip install matplotlib
import matplotlib.pyplot as plt
months = ["1月","2月","3月","4月","5月","6月"]
revenue = [12000, 15000, 11000, 18000, 22000, 25000]
plt.figure(figsize=(10, 5))
plt.plot(months, revenue, marker='o', linewidth=2)
plt.title("上半年營收趨勢")
plt.grid(True, alpha=0.3)
plt.savefig("revenue.png", dpi=150)import pandas as pd; import numpy as np
dates = pd.date_range("2026-01-01", periods=100, freq="B")
np.random.seed(42)
price = 500
prices = []
for _ in range(100):
price *= (1 + np.random.normal(0.0005, 0.015))
prices.append(round(price, 2))
df = pd.DataFrame({"日期": dates, "收盤價": prices})
df["5日均線"] = df["收盤價"].rolling(5).mean()
df["20日均線"] = df["收盤價"].rolling(20).mean()
print(f"最高: {df['收盤價'].max()}, 最低: {df['收盤價'].min()}")import pandas as pd
# 讀取各種格式
df = pd.read_csv("sales.csv") # CSV
df = pd.read_excel("report.xlsx") # Excel
# 快速瀏覽資料
print(df.head(10)) # 前 10 筆
print(df.info()) # 欄位型別、缺失值
print(df.describe()) # 統計摘要（平均、標準差、四分位數）
# 資料篩選
high_sales = df[df["sales"] > 10000] # 單條件
special = df[(df["sales"] > 5000) & (df["region"] == "North")] # 多條件
# 群組與聚合
by_region = df.groupby("region")["sales"].sum() # 各地區總銷售額
by_month = df.groupby(df["date"].dt.month)["sales"].mean() # 每月平均
# 處理缺失值
df.dropna() # 刪除含缺失值的列
df["price"].fillna(df["price"].mean()) # 用平均值填補import matplotlib.pyplot as plt
# 多圖並排
fig, axes = plt.subplots(1, 3, figsize=(15, 4))
axes[0].plot(dates, values, marker='o', color='#6366f1')
axes[0].set_title('每月銷售趨勢')
axes[1].bar(categories, amounts, color=['#4ade80','#f472b6','#fbbf24'])
axes[1].set_title('產品類別銷售')
axes[2].pie(sizes, labels=labels, autopct='%1.1f%%')
axes[2].set_title('市場佔有率')
plt.tight_layout()
plt.savefig('report.png', dpi=150, bbox_inches='tight')