Python calls the Alibaba Cloud interface to implement data cleaning and visualization functions

Python calls the Alibaba Cloud interface to implement data cleaning and visualization functions

Introduction:
With the rapid development of the Internet, data has become an indispensable part of today's society. However, the original data is usually messy and requires a data cleaning process to obtain useful information. In order to solve this problem, Alibaba Cloud provides a powerful data processing and analysis interface. This article will introduce how to use Python to call the Alibaba Cloud interface and visually display the cleaned data.

1. Preparation

Before starting, you need to complete the following preparations:

1. Register an Alibaba Cloud account and obtain the Access Key ID and Access Key Secret .
2. Install the Alibaba Cloud SDK for Python (aliyun-python-sdk-core and aliyun-python-sdk-ecs).

2. Call Alibaba Cloud interface for data cleaning

Alibaba Cloud provides multiple data processing services, such as: ECS, RDS, OSS, etc. This article uses ECS (Elastic Computing Service) as an example to demonstrate the data cleaning process. The following is a simple Python code example for calling the Alibaba Cloud ECS interface, obtaining the ECS instance list, and cleaning the data.

import json
from aliyunsdkcore import client
from aliyunsdkecs.request.v20140526 import DescribeInstancesRequest

# 阿里云账号信息
access_key_id = "your_access_key_id"
access_key_secret = "your_access_key_secret"

# 创建API客户端实例
clt = client.AcsClient(access_key_id, access_key_secret, 'your_region_id')

# 创建请求对象
request = DescribeInstancesRequest.DescribeInstancesRequest()

# 发起API调用并处理响应
response = clt.do_action_with_exception(request)
result = json.loads(response)
instance_list = result['Instances']['Instance']

# 清洗数据
cleaned_data = []
for instance in instance_list:
    cleaned_data.append({
        'InstanceID': instance['InstanceId'],
        'InstanceName': instance['InstanceName'],
        'Status': instance['Status'],
        'PublicIP': instance['PublicIpAddress']['IpAddress'][0]
    })

# 输出清洗后的数据
for instance in cleaned_data:
    print(instance)

In the above code, you first need to fill in your Access Key ID, Access Key Secret and Region ID. Then, create an API client instance to call the Alibaba Cloud interface. Next, according to the specific interface requirements, create a request object and initiate an API call. Finally, get and clean the returned data and save it into a list.

3. Use visualization tools to display data

After the data cleaning is completed, we can use Python's visualization tools to display the cleaned data. Here, Matplotlib is used as an example to show the status distribution of ECS instances.

import matplotlib.pyplot as plt

# 统计不同状态的ECS实例个数
status_counts = {}
for instance in cleaned_data:
    status = instance['Status']
    if status not in status_counts:
        status_counts[status] = 1
    else:
        status_counts[status] += 1

# 生成饼图
labels = status_counts.keys()
sizes = status_counts.values()
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.axis('equal')  # 使饼图为正圆形
plt.title('ECS Instance Status Distribution')
plt.show()

In the above code, the number of ECS instances in different states is first counted, and then Matplotlib's pie function is used to generate a pie chart. Finally, use the show function to display it.

Conclusion:
This article introduces how to use Python to call the Alibaba Cloud interface to implement data cleaning and visualization functions. Obtain the ECS instance list by calling the Alibaba Cloud ECS interface, clean the returned data, and finally display the distribution of ECS instance status. This example can not only be applied to ECS, but can also be extended to other Alibaba Cloud data processing services to help users better understand and utilize their data.

Reference link:

1. Alibaba Cloud Developer Center: https://developer.aliyun.com/
2. Alibaba Cloud Python SDK Documentation: https:// help.aliyun.com/document_detail/53087.html
3. Matplotlib official documentation: https://matplotlib.org/

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