Python采集全国疫情数据,可视化展视图示各数据数值

1. Introduction

In this article, we will discuss how to use Python to collect and visualize nationwide COVID-19 data in China. We will be using Python to fetch the latest data from a reliable source and then visualize the data in the form of various graphs and charts.

2. Python Libraries

Before we begin, let's first import the necessary Python libraries that we will be using for this project:

import requests

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

3. Data Collection

To collect the nationwide COVID-19 data in China, we will be using the requests library to make HTTP requests to a reliable API endpoint. Let's define a function to fetch the data:

def fetch_covid_data():

url = 'https://api.example.com/covid_data'

response = requests.get(url)

if response.status_code == 200:

data = response.json()

return data['nationwide']

else:

return None

In the above code, we are making a GET request to the API endpoint and checking if the response status code is 200 (indicating a successful request). If so, we are extracting the nationwide COVID-19 data from the response in JSON format.

4. Data Processing

Once we have fetched the data, we need to process it into a suitable format for visualization. We will be using the pandas library to read the JSON data into a DataFrame and perform any required data manipulation:

data = fetch_covid_data()

if data:

df = pd.DataFrame(data)

df['date'] = pd.to_datetime(df['date'])

df.set_index('date', inplace=True)

In the above code, we are creating a DataFrame from the fetched data and converting the 'date' column to a datetime format. We are also setting the 'date' column as the index of the DataFrame for easy manipulation.

5. Data Visualization

5.1 Total Cases

Let's start by visualizing the total number of COVID-19 cases over time. We will use the matplotlib library for this purpose:

plt.figure(figsize=(12, 6))

plt.plot(df.index, df['total_cases'])

plt.title('Total COVID-19 Cases in China')

plt.xlabel('Date')

plt.ylabel('Total Cases')

plt.xticks(rotation=45)

plt.show()

In the code above, we are creating a line plot of the total number of cases over time. We are also adding a title, labels for the x-axis and y-axis, and rotating the x-axis tick labels for better readability.

5.2 Daily New Cases

Next, let's visualize the daily number of new COVID-19 cases using a bar plot:

df['new_cases'] = df['total_cases'].diff()

plt.figure(figsize=(12, 6))

sns.barplot(x=df.index, y=df['new_cases'])

plt.title('Daily New COVID-19 Cases in China')

plt.xlabel('Date')

plt.ylabel('New Cases')

plt.xticks(rotation=45)

plt.show()

In the code above, we are calculating the daily new cases by taking the difference between consecutive total cases. We are then creating a bar plot of the daily new cases over time.

5.3 Recovery and Death Rates

Lastly, let's visualize the recovery and death rates as percentages of the total cases:

df['recovery_rate'] = df['total_recovered'] / df['total_cases'] * 100

df['death_rate'] = df['total_deaths'] / df['total_cases'] * 100

plt.figure(figsize=(12, 6))

plt.plot(df.index, df['recovery_rate'], label='Recovery Rate')

plt.plot(df.index, df['death_rate'], label='Death Rate')

plt.title('Recovery and Death Rates of COVID-19 in China')

plt.xlabel('Date')

plt.ylabel('Rate (%)')

plt.legend()

plt.xticks(rotation=45)

plt.show()

In the code above, we are calculating the recovery and death rates as percentages of the total cases. We are then creating a line plot of both rates over time, with a legend to differentiate the two.

6. Conclusion

In this article, we have discussed how to use Python to collect and visualize nationwide COVID-19 data in China. We used the requests library to fetch the data, the pandas library to process the data, and the matplotlib and seaborn libraries for data visualization.

By visualizing the data, we were able to gain insights into the total number of cases over time, the daily new cases, and the recovery and death rates. These visualizations can help us understand the impact of the COVID-19 pandemic in China and make informed decisions based on the data.

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