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256 lines
11 KiB
Markdown
256 lines
11 KiB
Markdown
[#]: collector: (lujun9972)
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[#]: translator: ( )
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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[#]: subject: (Using Python to visualize COVID-19 projections)
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[#]: via: (https://opensource.com/article/20/4/python-data-covid-19)
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[#]: author: (AnuragGupta https://opensource.com/users/999anuraggupta)
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Using Python to visualize COVID-19 projections
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======
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I'll demonstrate how to create two visualizations of the spread of a
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virus across the globe, provided open data and using open source
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libraries.
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![Colorful sound wave graph][1]
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Using [Python][2] and some graphing libraries, you can project the total number of confirmed cases of COVID-19, and also display the total number of deaths for a country (this article uses India as an example) on a given date. Humans sometimes need help interpreting and processing the meaning of data, so this article also demonstrates how to create an animated horizontal bar graph for five countries, showing the variation of cases by date.
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### Projecting confirmed cases and deaths for India
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This is done in three steps.
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#### 1\. Download data
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Scientific data isn't always open, but fortunately, many modern science and healthcare organizations are eager to share information with each other and the public. Data about COVID-19 cases is available online, and it's updated frequently.
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To parse the data, you first must download it: <https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv>
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Load the data directly into a Pandas DataFrame. Pandas provides a function, **read_csv()**, which can take a URL and give back a DataFrame object, as shown below:
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```
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import pycountry
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import plotly.express as px
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import pandas as pd
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URL_DATASET = r'<https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv>'
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df1 = pd.read_csv(URL_DATASET)
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print(df1.head(3)) # Get first 3 entries in the dataframe
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print(df1.tail(3)) # Get last 3 entries in the dataframe
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```
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The top row of the data set contains column names:
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1. Date
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2. Country
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3. Confirmed
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4. Recovered
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5. Deaths
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The output of the **head** query includes a unique identifier (not listed as a column) plus an entry for each column:
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```
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0 2020-01-22 Afghanistan 0 0 0
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1 2020-01-22 Albania 0 0 0
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1 2020-01-22 Algeria 0 0 0
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```
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The output of the **tail** query is similar but contains the tail end of the data set:
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```
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12597 2020-03-31 West Bank and Gaza 119 18 1
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12598 2020-03-31 Zambia 35 0 0
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12599 2020-03-31 Zimbabwe 8 0 1
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```
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From the output, you can see that the DataFrame (**df1**) has the following columns:
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1. Date
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2. Country
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3. Confirmed
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4. Recovered
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5. Dead
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Further, you can see that the **Date** column has entries starting from January 22 to March 31. This database is updated daily, so you will have current values.
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#### 2\. Select data for India
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In this step, we will select only those rows in the DataFrame that include India. This is shown in the script below:
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```
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#### ----- Step 2 (Select data for India)----
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df_india = df1[df1['Country'] == 'India']
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print(df_india.head(3))
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```
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#### 3\. Plot data
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Here we create a bar chart. We will put the dates on the X-axis and the number of confirmed cases and the number of deaths on the Y-axis. There are a few noteworthy things about this part of the script which are as follows:
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* The line of code: **plt.rcParams["_figure.figsize"_]=20,20** is meant only for Jupyter. So remove it if you are using some other IDE.
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* Notice the line of code: **ax1 = plt.gca()**. To ensure that both the plots i.e. for confirmed cases as well as for deaths are plotted on the same graph, we need to give to the second graph the **ax** object of the plot. So we use **gca()** to do this. (By the way, 'gca' stands for 'get current axis').
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The complete script is given below:
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```
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# Author:- Anurag Gupta # email:- [999.anuraggupta@gmail.com][3]
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%matplotlib inline
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import matplotlib.pyplot as plt
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import pandas as pd
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#### ----- Step 1 (Download data)----
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URL_DATASET = r'<https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv>'
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df1 = pd.read_csv(URL_DATASET)
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# print(df1.head(3)) # Uncomment to see the dataframe
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#### ----- Step 2 (Select data for India)----
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df_india = df1[df1['Country'] == 'India']
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print(df_india.head(3))
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#### ----- Step 3 (Plot data)----
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# Increase size of plot
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plt.rcParams["figure.figsize"]=20,20 # Remove if not on Jupyter
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# Plot column 'Confirmed'
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df_india.plot(kind = 'bar', x = 'Date', y = 'Confirmed', color = 'blue')
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ax1 = plt.gca()
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df_india.plot(kind = 'bar', x = 'Date', y = 'Deaths', color = 'red', ax = ax1)
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plt.show()
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```
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The entire script is [available on GitHub][4].
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### Creating an animated horizontal bar graph for five countries
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Note for Jupyter: To run this in Jupyter as a dynamic animation rather than as a static png, you need to add a magic command at the beginning of your cell, namely: **%matplotlib notebook**. This will keep the figure alive instead of displaying a static png file and can hence also show animations. If you are on another IDE, remove this line.
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#### 1\. Download the data
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This step is exactly the same as in the previous script, and therefore, it need not be repeated.
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#### 2\. Create a list of all dates
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If you examine the data you downloaded, you notice that it has a column **Date**. Now, this column has a date value for each country. So the same date is occurring a number of times. We need to create a list of dates with only unique values. This will be used on the X-axis of our bar charts. We have a line of code like: **list_dates = df[_‘Date’_].unique()**. The **unique()** method will pick up only the unique values for each date.
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#### 3\. Pick five countries and create an **ax** object
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Take a list of five countries. (You can choose whatever countries you prefer, or even increase or decrease the number of countries). I have also taken a list of five colors for the bars of each country. (You can change this too if you like). One important line of code here is: **fig, ax = plt.subplots(figsize=(15, 8))**. This is needed to create an **ax** object.
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#### 4\. Write the call back function
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If you want to do animation in Matplotlib, you need to create an object of a class called **matplotlib.animation.FuncAnimation**. The signature of this class is available online. The constructor of this class, apart from other parameters, also takes a parameter called **func**, and you have to give this parameter a callback function. So in this step, we will write the callback function, which is repeatedly called in order to render the animation.
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#### 5\. Create **FuncAnimation** object
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This step has partly been explained in the previous step.
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Our code to create an object of this class is:
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```
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my_anim = animation.FuncAnimation(fig = fig, func = plot_bar,
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frames= list_dates, blit=True,
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interval=20)
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```
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The three important parameters to be given are:
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* **fig**, which must be given a fig object, which we created earlier.
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* **func**, which must be the call back function.
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* **frames**, which must contain the variable on which the animation is to be done. Here in our case, it will be the list of dates we created earlier.
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#### 6\. Save the animation to an mp4 file
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You can save the animation created into an mp4 file. But for this you need **ffmpeg**. You can download this using pip by **pip install ffmpeg-python**, or using conda (on Jupyter) **install -c conda-forge ffmpeg**.
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And finally, you can run the animation using **plt.show()**. Please note that on many platforms, the **ffmpeg** may not work properly and may require further "tweaking."
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```
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%matplotlib notebook
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# Author:- Anurag Gupta # email:- [999.anuraggupta@gmail.com][3]
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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from time import sleep
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#### ---- Step 1:- Download data
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URL_DATASET = r'<https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv>'
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df = pd.read_csv(URL_DATASET, usecols = ['Date', 'Country', 'Confirmed'])
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# print(df.head(3)) # uncomment this to see output
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#### ---- Step 2:- Create list of all dates
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list_dates = df['Date'].unique()
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# print(list_dates) # Uncomment to see the dates
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#### --- Step 3:- Pick 5 countries. Also create ax object
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fig, ax = plt.subplots(figsize=(15, 8))
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# We will animate for these 5 countries only
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list_countries = ['India', 'China', 'US', 'Italy', 'Spain']
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# colors for the 5 horizontal bars
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list_colors = ['black', 'red', 'green', 'blue', 'yellow']
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### --- Step 4:- Write the call back function
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# plot_bar() is the call back function used in FuncAnimation class object
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def plot_bar(some_date):
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df2 = df[df['Date'].eq(some_date)]
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ax.clear()
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# Only take Confirmed column in descending order
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df3 = df2.sort_values(by = 'Confirmed', ascending = False)
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# Select the top 5 Confirmed countries
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df4 = df3[df3['Country'].isin(list_countries)]
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# print(df4) # Uncomment to see that dat is only for 5 countries
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sleep(0.2) # To slow down the animation
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# ax.barh() makes a horizontal bar plot.
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return ax.barh(df4['Country'], df4['Confirmed'], color= list_colors)
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###----Step 5:- Create FuncAnimation object---------
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my_anim = animation.FuncAnimation(fig = fig, func = plot_bar,
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frames= list_dates, blit=True,
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interval=20)
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### --- Step 6:- Save the animation to an mp4
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# Place where to save the mp4. Give your file path instead
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path_mp4 = r'C:\Python-articles\population_covid2.mp4'
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# my_anim.save(path_mp4, fps=30, extra_args=['-vcodec', 'libx264'])
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my_anim.save(filename = path_mp4, writer = 'ffmpeg',
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fps=30,
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extra_args= ['-vcodec', 'libx264', '-pix_fmt', 'yuv420p'])
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plt.show()
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```
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The complete script is [available on GitHub][5].
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/20/4/python-data-covid-19
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作者：[AnuragGupta][a]
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选题：[lujun9972][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://opensource.com/users/999anuraggupta
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[b]: https://github.com/lujun9972
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[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/colorful_sound_wave.png?itok=jlUJG0bM (Colorful sound wave graph)
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[2]: https://opensource.com/resources/python
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[3]: mailto:999.anuraggupta@gmail.com
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[4]: https://raw.githubusercontent.com/ag999git/jupyter_notebooks/master/corona_bar_india
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[5]: https://raw.githubusercontent.com/ag999git/jupyter_notebooks/master/corona_bar_animated
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