python1.爬取主流电商平台的华为手机的销售数据，将数据保存在自己姓名.txt中 2.分析数据，并图形化输出

Sure, I can help you with that. To crawl sales data for Huawei smartphones from popular e-commerce platforms and save it to a file named after your name, you can follow these steps:

1. Import the necessary libraries:

import requests
import json

2. Define a function to crawl the sales data from the e-commerce platform. Here's an example using JD.com (you can modify it for other platforms):

def crawl_sales_data():
    url = "https://api.jd.com/routerjson"
    
    # Construct the request payload
    payload = {
        "method": "jd.union.open.goods.promotiongoodsinfo.query",
        "app_key": "your_app_key",
        "v": "1.0",
        "param_json": {
            "goodsReqDTO": {
                "eliteId": "1",
                "pageIndex": "1",
                "pageSize": "100",
                "sortName": "inOrderCount30DaysSku",
                "sort": "desc",
                "brandCode": "huawei",
                "categoryId": "",
                "owner": "pop"
            }
        }
    }
    
    # Send the request
    response = requests.post(url, json=payload)
    
    # Process the response
    if response.status_code == 200:
        data = json.loads(response.text)
        # Extract and save the sales data
        with open("your_name.txt", "w") as file:
            for item in data["result"]["data"]:
                file.write(f"Product Name: {item['skuName']}, Sales: {item['inOrderCount30DaysSku']}\n")
        print("Sales data saved successfully!")
    else:
        print("Failed to retrieve sales data!")

Note: You need to obtain your own app key from JD.com's developer platform and replace `"your_app_key"` with it.

3. Call the `crawl_sales_data()` function to crawl and save the sales data:

crawl_sales_data()

To analyze the data and visualize it, you can use libraries like pandas and matplotlib. Here's an example of how to do it:

1. Import the necessary libraries:

import pandas as pd
import matplotlib.pyplot as plt

2. Read the saved sales data from the file:

df = pd.read_csv("your_name.txt", delimiter=",", names=["Product Name", "Sales"])

3. Analyze the data:

# Get total sales
total_sales = df["Sales"].sum()

# Get top 5 products by sales
top_products = df.nlargest(5, "Sales")

# Get average sales
average_sales = df["Sales"].mean()

4. Visualize the data:

# Plot a bar chart of top products by sales
plt.bar(top_products["Product Name"], top_products["Sales"])
plt.xlabel("Product")
plt.ylabel("Sales")
plt.title("Top 5 Products by Sales")
plt.xticks(rotation=45)
plt.show()

# Display total sales and average sales
print("Total Sales:", total_sales)
print("Average Sales:", average_sales)

Remember to replace `"your_name.txt"` with the actual filename you used to save the data.

I hope this helps you get started with crawling and analyzing sales data for Huawei smartphones from e-commerce platforms! Let me know if you have any further questions.