\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528 Python `pandas` \u5e93\u8fdb\u884c\u6570\u636e\u5904\u7406\u7684\u5b9e\u4f8b\uff0c\u6db5\u76d6\u5e38\u89c1\u64cd\u4f5c\u5982\u6570\u636e\u8bfb\u53d6\u3001\u6e05\u6d17\u3001\u7b5b\u9009\u3001\u805a\u5408\u7b49\u3002
—
### \u793a\u4f8b\u573a\u666f
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u9500\u552e\u6570\u636e\u96c6 `sales_data.csv`\uff0c\u5305\u542b\u4ee5\u4e0b\u5b57\u6bb5\uff1a
– `OrderID` (\u8ba2\u5355ID)
– `Product` (\u4ea7\u54c1\u540d\u79f0)
– `Quantity` (\u8d2d\u4e70\u6570\u91cf)
– `Price` (\u5355\u4ef7)
– `OrderDate` (\u8ba2\u5355\u65e5\u671f)
– `CustomerID` (\u5ba2\u6237ID)
\u76ee\u6807\uff1a\u5206\u6790\u9500\u552e\u6570\u636e\uff0c\u627e\u51fa\u6700\u7545\u9500\u7684\u4ea7\u54c1\u548c\u5ba2\u6237\u6d88\u8d39\u884c\u4e3a\u3002
—
### 1. \u5bfc\u5165\u5e93\u5e76\u8bfb\u53d6\u6570\u636e
“`python
import pandas as pd
# \u8bfb\u53d6 CSV \u6587\u4ef6
df = pd.read_csv(“sales_data.csv”)
# \u67e5\u770b\u524d 5 \u884c\u6570\u636e
print(df.head())
“`
—
### 2. \u6570\u636e\u6e05\u6d17
#### \u5904\u7406\u7f3a\u5931\u503c
“`python
# \u68c0\u67e5\u7f3a\u5931\u503c
print(df.isnull().sum())
# \u586b\u5145\u7f3a\u5931\u503c\uff08\u4f8b\u5982\u7528\u5e73\u5747\u503c\u586b\u5145 “Price” \u5217\u7684\u7f3a\u5931\u503c\uff09
df[“Price”].fillna(df[“Price”].mean(), inplace=True)
# \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\uff08\u53ef\u9009\uff09
df.dropna(inplace=True)
“`
#### \u5904\u7406\u91cd\u590d\u503c
“`python
# \u5220\u9664\u91cd\u590d\u884c
df.drop_duplicates(inplace=True)
“`
—
### 3. \u6570\u636e\u7b5b\u9009\u4e0e\u6392\u5e8f
#### \u7b5b\u9009\u7279\u5b9a\u6761\u4ef6\u7684\u6570\u636e
“`python
# \u7b5b\u9009\u5355\u4ef7\u5927\u4e8e 100 \u7684\u8ba2\u5355
high_price_orders = df[df[“Price”] > 100]
# \u7b5b\u9009 2023 \u5e74\u7684\u8ba2\u5355\uff08\u5047\u8bbe OrderDate \u683c\u5f0f\u4e3a “YYYY-MM-DD”\uff09
df[“OrderDate”] = pd.to_datetime(df[“OrderDate”])
orders_2023 = df[df[“OrderDate”].dt.year == 2023]
“`
#### \u6309\u5217\u6392\u5e8f
“`python
# \u6309\u5355\u4ef7\u964d\u5e8f\u6392\u5217
df_sorted = df.sort_values(“Price”, ascending=False)
“`
—
### 4. \u6570\u636e\u805a\u5408\u4e0e\u5206\u7ec4\u7edf\u8ba1
#### \u8ba1\u7b97\u603b\u9500\u552e\u989d\uff08\u65b0\u589e\u5217\uff09
“`python
df[“TotalSales”] = df[“Quantity”] * df[“Price”]
“`
#### \u6309\u4ea7\u54c1\u7edf\u8ba1\u603b\u9500\u91cf\u548c\u603b\u9500\u552e\u989d
“`python
product_stats = df.groupby(“Product”).agg({
“Quantity”: “sum”,
“TotalSales”: “sum”
}).reset_index()
print(product_stats)
“`
#### \u6309\u5ba2\u6237\u7edf\u8ba1\u6d88\u8d39\u6b21\u6570\u548c\u603b\u6d88\u8d39\u91d1\u989d
“`python
customer_stats = df.groupby(“CustomerID”).agg({
“OrderID”: “count”,
“TotalSales”: “sum”
}).rename(columns={“OrderID”: “PurchaseCount”})
print(customer_stats)
“`
—
### 5. \u6570\u636e\u5408\u5e76
\u5047\u8bbe\u6709\u53e6\u4e00\u4e2a\u5ba2\u6237\u4fe1\u606f\u8868 `customer_info.csv`\uff0c\u5305\u542b `CustomerID` \u548c `CustomerName`\uff1a
“`python
# \u8bfb\u53d6\u5ba2\u6237\u4fe1\u606f\u8868
df_customers = pd.read_csv(“customer_info.csv”)
# \u5408\u5e76\u9500\u552e\u6570\u636e\u4e0e\u5ba2\u6237\u4fe1\u606f\uff08\u7c7b\u4f3c SQL \u7684 JOIN\uff09
merged_df = pd.merge(df, df_customers, on=”CustomerID”, how=”left”)
“`
—
### 6. \u6570\u636e\u4fdd\u5b58
\u5c06\u5904\u7406\u540e\u7684\u6570\u636e\u4fdd\u5b58\u4e3a\u65b0\u6587\u4ef6\uff1a
“`python
# \u4fdd\u5b58\u4e3a CSV
merged_df.to_csv(“processed_sales_data.csv”, index=False)
# \u4fdd\u5b58\u4e3a Excel
merged_df.to_excel(“processed_sales_data.xlsx”, index=False)
“`
—
### 7. \u9ad8\u7ea7\u5206\u6790\u793a\u4f8b
#### \u65f6\u95f4\u5e8f\u5217\u5206\u6790\uff08\u6309\u6708\u7edf\u8ba1\u9500\u552e\u989d\uff09
“`python
monthly_sales = merged_df.resample(“M”, on=”OrderDate”)[“TotalSales”].sum()
print(monthly_sales)
“`
#### \u6700\u7545\u9500\u4ea7\u54c1 Top 5
“`python
top_products = product_stats.sort_values(“TotalSales”, ascending=False).head(5)
print(top_products)
“`
—
### 8. \u6570\u636e\u53ef\u89c6\u5316\uff08\u7ed3\u5408 `matplotlib`\uff09
“`python
import matplotlib.pyplot as plt
# \u7ed8\u5236\u6708\u5ea6\u9500\u552e\u989d\u8d8b\u52bf\u56fe
monthly_sales.plot(kind=”line”, title=”Monthly Sales Trend”)
plt.xlabel(“Month”)
plt.ylabel(“Total Sales”)
plt.show()
# \u7ed8\u5236\u4ea7\u54c1\u9500\u91cf\u67f1\u72b6\u56fe
top_products.plot(kind=”bar”, x=”Product”, y=”TotalSales”, title=”Top 5 Products by Sales”)
plt.ylabel(“Total Sales”)
plt.show()
“`
—
### \u603b\u7ed3
\u901a\u8fc7\u4e0a\u8ff0\u64cd\u4f5c\uff0c\u53ef\u4ee5\u5b9e\u73b0\uff1a
1. **\u6570\u636e\u6e05\u6d17**\uff1a\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u503c\u3002
2. **\u6570\u636e\u7b5b\u9009**\uff1a\u6309\u6761\u4ef6\u8fc7\u6ee4\u548c\u6392\u5e8f\u3002
3. **\u6570\u636e\u805a\u5408**\uff1a\u5206\u7ec4\u7edf\u8ba1\u5173\u952e\u6307\u6807\u3002
4. **\u6570\u636e\u5408\u5e76**\uff1a\u5173\u8054\u591a\u5f20\u8868\u3002
5. **\u53ef\u89c6\u5316**\uff1a\u76f4\u89c2\u5c55\u793a\u5206\u6790\u7ed3\u679c\u3002
\u6839\u636e\u5b9e\u9645\u9700\u6c42\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u6269\u5c55\u66f4\u590d\u6742\u7684\u903b\u8f91\uff08\u5982\u7279\u5f81\u5de5\u7a0b\u3001\u673a\u5668\u5b66\u4e60\u96c6\u6210\u7b49\uff09\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528 Python `pandas` \u5e93\u8fdb\u884c\u6570\u636e\u5904\u7406\u7684\u5b9e\u4f8b\uff0c\u6db5\u76d6\u5e38\u89c1\u64cd\u4f5c\u5982\u6570\u636e\u8bfb\u53d6\u3001\u6e05\u6d17\u3001\u7b5b\u9009\u3001\u805a\u5408\u7b49\u3002 — ### \u793a\u4f8b\u573a\u666f\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u9500\u552e\u6570\u636e\u96c6 `sales_data.csv`\uff0c\u5305\u542b\u4ee5\u4e0b\u5b57\u6bb5\uff1a– `OrderID` (\u8ba2\u5355ID)– `Product` (\u4ea7\u54c1\u540d\u79f0)– `Quantity` (\u8d2d\u4e70\u6570\u91cf)– `Price` (\u5355\u4ef7)– `OrderDate` (\u8ba2\u5355\u65e5\u671f)– `CustomerID` (\u5ba2\u6237ID) \u76ee\u6807\uff1a\u5206\u6790\u9500\u552e\u6570\u636e\uff0c\u627e\u51fa\u6700\u7545\u9500\u7684\u4ea7\u54c1\u548c\u5ba2\u6237\u6d88\u8d39\u884c\u4e3a\u3002 — ### 1. \u5bfc\u5165\u5e93\u5e76\u8bfb\u53d6\u6570\u636e“`pythonimport pandas as pd # \u8bfb\u53d6…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[],"class_list":["post-704","post","type-post","status-publish","format-standard","hentry","category-python"],"_links":{"self":[{"href":"http:\/\/www.cnitw.com\/index.php?rest_route=\/wp\/v2\/posts\/704","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.cnitw.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.cnitw.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.cnitw.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.cnitw.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=704"}],"version-history":[{"count":1,"href":"http:\/\/www.cnitw.com\/index.php?rest_route=\/wp\/v2\/posts\/704\/revisions"}],"predecessor-version":[{"id":705,"href":"http:\/\/www.cnitw.com\/index.php?rest_route=\/wp\/v2\/posts\/704\/revisions\/705"}],"wp:attachment":[{"href":"http:\/\/www.cnitw.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=704"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.cnitw.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=704"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.cnitw.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=704"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}