Python is a highly powerful general purpose programming language which can be easily learned and provides data scientists a wide variety of tools and packages. Amid this pandemic period, I decided to do an analysis on this novel coronavirus.

Python是一種功能強大的通用編程語言，可以輕松學習，并為數據科學家提供各種工具和軟件包。 在這個大流行時期，我決定對這種新型冠狀病毒進行分析。

In this article, I am going to walk you through the steps I undertook for this analysis with visuals and code snippets.

在本文中，我將通過視覺和代碼片段逐步指導您進行此分析。

數據分析涉及的步驟： (Steps involved in Data Analysis:)

1. Importing required packages
   導入所需的軟件包

2. Gathering Data

2.收集數據

3. Transforming Data to our needs (Data Wrangling)

3.將數據轉變為我們的需求(數據整理)

4. Exploratory Data Analysis (EDA) and Visualization

4.探索性數據分析(EDA)和可視化

步驟— 1：導入所需的軟件包 (Step — 1: Importing required Packages)

Importing our required packages is the starting point of all data analysis programming in python. As I’ve said, python provides a wide variety of packages for data scientists and in this analysis I used python’s most popular data science packages Pandas and NumPy for Data Wrangling and EDA. When coming to Data Visualization, I used python’s interactive packages Plotly and Matplotlib. It’s very simple to import packages in python code:

導入所需的軟件包是python中所有數據分析編程的起點。 就像我說過的那樣，python為數據科學家提供了各種各樣的軟件包，在此分析中，我使用了python最受歡迎的數據科學軟件包Pandas和NumPy進行數據整理和EDA。 進行數據可視化時，我使用了python的交互式軟件包Plotly和Matplotlib。 用python代碼導入軟件包非常簡單：

This is the code for importing our primary packages to perform data analysis but still, we need to add some more packages to our code which we will see in step-2. Yay! We successfully finished our first step.

這是用于導入主要程序包以執行數據分析的代碼，但是仍然需要向代碼中添加更多程序包，我們將在步驟2中看到這些代碼。 好極了！ 我們成功地完成了第一步。

步驟2：收集數據 (Step — 2: Gathering Data)

For a clean and perfect data analysis, the foremost important element is collecting quality Data. For this analysis, I’ve collected many data from various sources for better accuracy.

對于 干凈，完美的數據分析，最重要的元素是收集高質量的數據。 為了進行此分析，我從各種來源收集了許多數據，以提高準確性。

Our primary dataset is extracted from esri (a website which provides updated data on coronavirus) using a query url (click here to view the website). Follow the code snippets to extract the data from esri:

我們的主要數據集是使用查詢網址從esri(提供有關冠狀病毒的最新數據的網站)中提取的( 請單擊此處查看該網站 )。 按照代碼片段從esri中提取數據：

Requests is a python packages used to extract data from a given json file. In this code I used requests to extract data from the given query url by esri. We are now ready to do some Data Wrangling! (Note : We will be importing many data in step-4 of our analysis)

Requests是一個python軟件包，用于從給定的json文件中提取數據。 在這段代碼中，我使用了esri的請求從給定的查詢URL中提取數據。 現在，我們準備進行一些數據整理！ (注意：我們將在分析的第4步中導入許多數據)

步驟— 3：數據整理 (Step — 3: Data Wrangling)

Data Wrangling is a process where we will transform and clean our data to our needs. We can’t do analysis with our raw extracted data. So, we have to transform the data to proceed our analysis. Here’s the code for our Data Wrangling:

數據整理是一個過程，在此過程中，我們將根據需要轉換和清理數據。 我們無法使用原始提取的數據進行分析。 因此，我們必須轉換數據以進行分析。 這是我們的數據整理的代碼：

Note that, we have imported a new python package, ‘datetime’, which helps us to work with dates and times in a dataset. Now, get ready to see the big picture of our analysis -’ EDA and Data Visualization’.

請注意，我們已經導入了一個新的python包“ datetime”，它可以幫助我們處理數據集中的日期和時間。 現在，準備看一下我們分析的大圖-“ EDA和數據可視化”。

步驟— 4：探索性數據分析和數據可視化 (Step — 4: Exploratory Data Analysis and Data Visualization)

This process is quite long as it is the heart and soul of data analysis. So, I’ve divided this process into three steps:

這個過程很長，因為它是數據分析的心臟和靈魂。 因此，我將這一過程分為三個步驟：

a. Ranking countries and provinces (based on COVID-19 aspects)

一個。 對國家和省進行排名(基于COVID-19方面)

b. Time Series on COVID-19 Cases

b。 COVID-19病例的時間序列

c. Classification and Distribution of cases

C。 案件分類和分布

Ranking countries and provinces

排名國家和省

From our previously extracted data we are going to rank countries and provinces based on confirmed, deaths, recovered and active cases by doing some EDA and Visualization. Follow the code snippets for the upcoming visuals (Note : Every visualizations are interactive and you can hover them to see their data points)

從我們先前提取的數據中，我們將通過進行一些EDA和可視化，根據確診，死亡，康復和活著的病例對國家和省進行排名。 請遵循即將出現的視覺效果的代碼片段(注意：每個視覺效果都是交互式的，您可以將它們懸停以查看其數據點)

Part 1 — Ranking Most affected countries

第1部分-排名受影響最大的國家

i) Top 10 Confirmed Cases Countries:

i)十大確診病例國家：

The following code will produce a plot ranking top 10 countries based on confirmed cases.

以下代碼將根據已確認的案例得出前十個國家/地區的地塊。

# a. Top 10 confirmed countries (Bubble plot)top10_confirmed = pd.DataFrame(data.groupby('Country')['Confirmed'].sum().nlargest(10).sort_values(ascending = False))
fig1 = px.scatter(top10_confirmed, x = top10_confirmed.index, y = 'Confirmed', size = 'Confirmed', size_max = 120,color = top10_confirmed.index, title = 'Top 10 Confirmed Cases Countries')
fig1.show()

ii) Top 10 Death Cases Countries:

ii)十大死亡案例國家：

The following code will produce a plot ranking top 10 countries based on death cases.

以下代碼將根據死亡案例產生一個排在前十個國家/地區的地塊。

# b. Top 10 deaths countries (h-Bar plot)top10_deaths = pd.DataFrame(data.groupby('Country')['Deaths'].sum().nlargest(10).sort_values(ascending = True))
fig2 = px.bar(top10_deaths, x = 'Deaths', y = top10_deaths.index, height = 600, color = 'Deaths', orientation = 'h',color_continuous_scale = ['deepskyblue','red'], title = 'Top 10 Death Cases Countries')
fig2.show()

iii) Top 10 Recovered Cases Countries:

iii)十大被追回病例國家：

The following code will produce a plot ranking top 10 countries based on recovered cases.

以下代碼將根據恢復的案例生成一個排在前10個國家/地區的地塊。

# c. Top 10 recovered countries (Bar plot)top10_recovered = pd.DataFrame(data.groupby('Country')['Recovered'].sum().nlargest(10).sort_values(ascending = False))
fig3 = px.bar(top10_recovered, x = top10_recovered.index, y = 'Recovered', height = 600, color = 'Recovered',title = 'Top 10 Recovered Cases Countries', color_continuous_scale = px.colors.sequential.Viridis)
fig3.show()

iv) Top 10 Active Cases Countries:

iv)十大活躍案例國家：

The following code will produce a plot ranking top 10 countries based on recovered cases.

以下代碼將根據恢復的案例生成一個排在前10個國家/地區的地塊。

# d. Top 10 active countriestop10_active = pd.DataFrame(data.groupby('Country')['Active'].sum().nlargest(10).sort_values(ascending = True))
fig4 = px.bar(top10_active, x = 'Active', y = top10_active.index, height = 600, color = 'Active', orientation = 'h',color_continuous_scale = ['paleturquoise','blue'], title = 'Top 10 Active Cases Countries')
fig4.show()

Part 2 — Ranking most affected States in largely affected Countries:

第2部分-在受影響最大的國家中對受影響最大的國家進行排名：

EDA for ranking states in largely affected Countries:

對受災嚴重國家排名的EDA：

# USA
topstates_us = data['Country'] == 'US'
topstates_us = data[topstates_us].nlargest(5, 'Confirmed')
# Brazil
topstates_brazil = data['Country'] == 'Brazil'
topstates_brazil = data[topstates_brazil].nlargest(5, 'Confirmed')
# India
topstates_india = data['Country'] == 'India'
topstates_india = data[topstates_india].nlargest(5, 'Confirmed')
# Russia
topstates_russia = data['Country'] == 'Russia'
topstates_russia = data[topstates_russia].nlargest(5, 'Confirmed')

We are extracting States’ data from USA, Brazil, India and Russia respectively because, these are the countries which are most affected by COVID-19. Now, let’s visualize it!

我們分別從美國，巴西，印度和俄羅斯提取州的數據，因為這是受COVID-19影響最大的國家。 現在，讓我們對其進行可視化！

Visualization of Most affected states in largely affected Countries:

受影響最大的國家中受影響最大的州的可視化：

i) Most affected States in USA:

i)美國受影響最嚴重的州：

The following code will produce a plot ranking top 5 most affected states in the United States of America.

以下代碼將產生一個在美國受災最嚴重的州中排名前5位的地塊。

# USA 
fig5 = go.Figure(data = [go.Bar(name = 'Active Cases', x = topstates_us['Active'], y = topstates_us['State'], orientation = 'h'),go.Bar(name = 'Death Cases', x = topstates_us['Deaths'], y = topstates_us['State'], orientation = 'h')
])
fig5.update_layout(title = 'Most Affected States in USA', height = 600)
fig5.show()

ii) Most affected States in Brazil:

ii)巴西受影響最嚴重的國家：

The following code will produce a plot ranking top 5 most affected states in Brazil.

以下代碼將產生一個在巴西受影響最嚴重的州中排名前5位的地塊。

# Brazil
fig6 = go.Figure(data = [go.Bar(name = 'Recovered Cases', x = topstates_brazil['State'], y = topstates_brazil['Recovered']),go.Bar(name = 'Active Cases', x = topstates_brazil['State'], y = topstates_brazil['Active']),go.Bar(name = 'Death Cases', x = topstates_brazil['State'], y = topstates_brazil['Deaths'])
])
fig6.update_layout(title = 'Most Affected States in Brazil', barmode = 'stack', height = 600)
fig6.show()

iii) Most affected States in India:

iii)印度受影響最大的國家：

The following code will produce a plot ranking top 5 most affected states in India.

以下代碼將產生一個在印度受影響最嚴重的州中排名前5位的地塊。

# India
fig7 = go.Figure(data = [go.Bar(name = 'Recovered Cases', x = topstates_india['State'], y = topstates_india['Recovered']),go.Bar(name = 'Active Cases', x = topstates_india['State'], y = topstates_india['Active']),go.Bar(name = 'Death Cases', x = topstates_india['State'], y = topstates_india['Deaths'])
])
fig7.update_layout(title = 'Most Affected States in India', barmode = 'stack', height = 600)
fig7.show()

iv) Most affected States in Russia:

iv)俄羅斯受影響最嚴重的國家：

The following code will produce a plot ranking top 5 most affected states in Russia.

以下代碼將產生一個在俄羅斯受影響最嚴重的州中排名前5位的地塊。

# Russia
fig8 = go.Figure(data = [go.Bar(name = 'Recovered Cases', x = topstates_russia['State'], y = topstates_russia['Recovered']),go.Bar(name = 'Active Cases', x = topstates_russia['State'], y = topstates_russia['Active']),go.Bar(name = 'Death Cases', x = topstates_russia['State'], y = topstates_russia['Deaths'])
])
fig8.update_layout(title = 'Most Affected States in Russia', barmode = 'stack', height = 600)
fig8.show()

Time Series on COVID-19 Cases

COVID-19病例的時間序列

To perform time series analysis on COVID-19 cases we need a new dataset. https://covid19.who.int/ Follow this link and images shown below for downloading our next dataset.

要對COVID-19案例執行時間序列分析，我們需要一個新的數據集。 https://covid19.who.int/請點擊下面的鏈接和圖像，下載我們的下一個數據集。

After pressing the link mentioned above, you will land into this page. On the bottom right of the represented map, you can find the download button. From there you can download the dataset and save it to your files. Good work! We fetched our Data! Let’s import the data :

按下上述鏈接后，您將進入此頁面。 在所顯示地圖的右下角，您可以找到下載按鈕。 從那里您可以下載數據集并將其保存到文件中。 干得好！ 我們獲取了數據！ 讓我們導入數據：

time_series = pd.read_csv('who_data.csv', encoding = 'ISO-8859-1')
time_series['Date_reported'] = pd.to_datetime(time_series['Date_reported'])

From the above extracted dataset, we are going to perform two types of time series analysis, ‘COVID-19 cases Worldwide’ and ‘Most affected countries over time’.

從上面提取的數據集中，我們將執行兩種類型的時間序列分析：“全球COVID-19病例”和“一段時間內受影響最大的國家”。

i) COVID-19 cases worldwide:

i)全球COVID-19病例：

EDA for COVID-19 cases worldwide:

全球COVID-19案件的EDA：

time_series_dates = time_series.groupby('Date_reported').sum()
time_series_dates

a) Cumulative cases worldwide:

a)全世界的累積病例：

The following code produces a time series chart of cumulative cases worldwide right from the beginning of the outbreak.

以下代碼從爆發開始就生成了全球累積病例的時序圖。

# Cumulative casesfig11 = go.Figure()
fig11.add_trace(go.Scatter(x = time_series_dates.index, y = time_series_dates[' Cumulative_cases'], fill = 'tonexty',line_color = 'blue'))
fig11.update_layout(title = 'Cumulative Cases Worldwide')
fig11.show()

b) Cumulative death cases worldwide:

b)世界范圍內的累積死亡案例：

The following code produces a time series chart of cumulative death cases worldwide right from the beginning of the outbreak.

以下代碼從爆發開始就生成了全球累積死亡病例的時間序列圖。

# Cumulative death casesfig12 = go.Figure()
fig12.add_trace(go.Scatter(x = time_series_dates.index, y = time_series_dates[' Cumulative_deaths'], fill = 'tonexty',line_color = 'red'))
fig12.update_layout(title = 'Cumulative Deaths Worldwide')
fig12.show()

c) Daily new cases worldwide:

c)全世界每天的新病例：

The following code produces a time series chart of daily new cases worldwide right from the beginning of the outbreak.

以下代碼從爆發開始就生成了全球每日新病例的時序圖。

# Daily new casesfig13 = go.Figure()
fig13.add_trace(go.Scatter(x = time_series_dates.index, y = time_series_dates[' New_cases'], fill = 'tonexty',line_color = 'gold'))
fig13.update_layout(title = 'Daily New Cases Worldwide')
fig13.show()

d) Daily death cases worldwide:

d)全世界每日死亡案例：

The following code produces a time series chart of daily death cases worldwide right from the beginning of the outbreak.

以下代碼從爆發開始就生成了全球每日死亡病例的時間序列圖。

# Daily death casesfig14 = go.Figure()
fig14.add_trace(go.Scatter(x = time_series_dates.index, y = time_series_dates[' New_deaths'], fill = 'tonexty',line_color = 'hotpink'))
fig14.update_layout(title = 'Daily Death Cases Worldwide')
fig14.show()

ii) Most affected countries over time:

ii)一段時間內受影響最大的國家：

EDA for Most affected countries over time:

隨時間推移對受影響最嚴重國家的EDA：

# USA 
time_series_us = time_series[' Country'] == ('United States of America')
time_series_us = time_series[time_series_us]# Brazil
time_series_brazil = time_series[' Country'] == ('Brazil')
time_series_brazil = time_series[time_series_brazil]# India
time_series_india = time_series[' Country'] == ('India')
time_series_india = time_series[time_series_india]# Russia
time_series_russia = time_series[' Country'] == ('Russia')
time_series_russia = time_series[time_series_russia]# Peru
time_series_peru = time_series[' Country'] == ('Peru')
time_series_peru = time_series[time_series_peru]

Note that, we have extracted data of countries USA, Brazil, India, Russia and Peru respectively as they are highly affected by COVID-19 in the world.

請注意，我們分別提取了美國，巴西，印度，俄羅斯和秘魯等國家/地區的數據，因為它們在世界范圍內受到COVID-19的高度影響。

a) Most affected Countries’ Cumulative cases over time

a)隨時間推移受影響最嚴重的國家的累計案件

The following code will produce a time series chart of the most affected countries’ cumulative cases right from the beginning of the outbreak.

以下代碼將從疫情爆發之初就產生受影響最嚴重國家累計病例的時序圖。

# Cumulative casesfig15 = go.Figure()fig15.add_trace(go.Line(x = time_series_us['Date_reported'], y = time_series_us[' Cumulative_cases'], name = 'USA'))
fig15.add_trace(go.Line(x = time_series_brazil['Date_reported'], y = time_series_brazil[' Cumulative_cases'], name = 'Brazil'))
fig15.add_trace(go.Line(x = time_series_india['Date_reported'], y = time_series_india[' Cumulative_cases'], name = 'India'))
fig15.add_trace(go.Line(x = time_series_russia['Date_reported'], y = time_series_russia[' Cumulative_cases'], name = 'Russia'))
fig15.add_trace(go.Line(x = time_series_peru['Date_reported'], y = time_series_peru[' Cumulative_cases'], name = 'Peru'))fig15.update_layout(title = 'Time Series of Most Affected countries"s Cumulative Cases')fig15.show()

b) Most affected Countries’ cumulative death cases over time:

b)隨著時間的推移，大多數受影響國家的累計死亡病例：

The following code will produce a time series chart of the most affected countries’ cumulative death cases right from the beginning of the outbreak.

以下代碼將從疫情爆發之初就繪制出受影響最嚴重國家累計死亡病例的時間序列圖。

# Cumulative death casesfig16 = go.Figure()fig16.add_trace(go.Line(x = time_series_us['Date_reported'], y = time_series_us[' Cumulative_deaths'], name = 'USA'))
fig16.add_trace(go.Line(x = time_series_brazil['Date_reported'], y = time_series_brazil[' Cumulative_deaths'], name = 'Brazil'))
fig16.add_trace(go.Line(x = time_series_india['Date_reported'], y = time_series_india[' Cumulative_deaths'], name = 'India'))
fig16.add_trace(go.Line(x = time_series_russia['Date_reported'], y = time_series_russia[' Cumulative_deaths'], name = 'Russia'))
fig16.add_trace(go.Line(x = time_series_peru['Date_reported'], y = time_series_peru[' Cumulative_deaths'], name = 'Peru'))fig16.update_layout(title = 'Time Series of Most Affected countries"s Cumulative Death Cases')fig16.show()

c) Most affected Countries’ daily new cases over time:

c)一段時間以來受影響最嚴重的國家的每日新病例：

The following code will produce a time series chart of the most affected countries’ daily new cases right from the beginning of the outbreak.

以下代碼將從疫情爆發之初就產生受影響最嚴重國家的每日新病例的時序圖。

# Daily new casesfig17 = go.Figure()fig17.add_trace(go.Line(x = time_series_us['Date_reported'], y = time_series_us[' New_cases'], name = 'USA'))
fig17.add_trace(go.Line(x = time_series_brazil['Date_reported'], y = time_series_brazil[' New_cases'], name = 'Brazil'))
fig17.add_trace(go.Line(x = time_series_india['Date_reported'], y = time_series_india[' New_cases'], name = 'India'))
fig17.add_trace(go.Line(x = time_series_russia['Date_reported'], y = time_series_russia[' New_cases'], name = 'Russia'))
fig17.add_trace(go.Line(x = time_series_peru['Date_reported'], y = time_series_peru[' New_cases'], name = 'Peru'))fig17.update_layout(title = 'Time Series of Most Affected countries"s Daily New Cases')fig17.show()

d) Most affected Countries’ daily death cases:

d)最受影響國家的每日死亡案例：

The following code will produce a time series chart of the most affected countries’ daily death cases right from the beginning of the outbreak.

以下代碼將從疫情爆發之初就產生受影響最嚴重國家的每日死亡病例的時序圖。

# Daily death casesfig18 = go.Figure()fig18.add_trace(go.Line(x = time_series_us['Date_reported'], y = time_series_us[' New_deaths'], name = 'USA'))
fig18.add_trace(go.Line(x = time_series_brazil['Date_reported'], y = time_series_brazil[' New_deaths'], name = 'Brazil'))
fig18.add_trace(go.Line(x = time_series_india['Date_reported'], y = time_series_india[' New_deaths'], name = 'India'))
fig18.add_trace(go.Line(x = time_series_russia['Date_reported'], y = time_series_russia[' New_deaths'], name = 'Russia'))
fig18.add_trace(go.Line(x = time_series_peru['Date_reported'], y = time_series_peru[' New_deaths'], name = 'Peru'))fig18.update_layout(title = 'Time Series of Most Affected countries"s Daily Death Cases')fig18.show()

Case Classification and Distribution

病例分類與分布

Here we are going to analyze how COVID-19 cases are distributed. For this, we need a new dataset. https://www.kaggle.com/imdevskp/corona-virus-report Follow this link for our new dataset.

在這里，我們將分析COVID-19案例的分布方式。 為此，我們需要一個新的數據集。 https://www.kaggle.com/imdevskp/corona-virus-report請點擊此鏈接以獲取新數據集。

i) WHO Region-Wise Case Distribution:

i)世衛組織區域明智病例分布：

For this analysis, we are going to use ‘country_wise_latest.csv’ dataset which will come along with the downloaded kaggle dataset. The following code produces a pie chart representing case distribution among WHO Region classification.

對于此分析，我們將使用“ country_wise_latest.csv”數據集，該數據集將與下載的kaggle數據集一起提供。 以下代碼生成了一個餅圖，代表了WHO區域分類之間的病例分布。

who = pd.read_csv('country_wise_latest.csv')
who_region = pd.DataFrame(who.groupby('WHO Region')['Confirmed'].sum())labels = who_region.index
values = who_region['Confirmed']fig9 = go.Figure(data=[go.Pie(labels = labels, values = values, pull=[0, 0, 0, 0, 0.2, 0])])fig9.update_layout(title = 'WHO Region-Wise Case Distribution', width = 700, height = 400, margin = dict(t = 0, l = 0, r = 0, b = 0))fig9.show()

ii) Most affected Countries’ case distribution:

ii)最受影響國家的案件分布：

For this analysis we are going to use the same ‘country_wise_latest.csv’ dataset which we imported for the previous analysis.

對于此分析，我們將使用為先前的分析導入的相同“ country_wise_latest.csv”數據集。

EDA for Most affected countries’ case distribution:

受影響最嚴重國家的EDA分布：

case_dist = who# US
dist_us = case_dist['Country/Region'] == 'US'
dist_us = case_dist[dist_us][['Country/Region','Deaths','Recovered','Active']].set_index('Country/Region')# Brazil
dist_brazil = case_dist['Country/Region'] == 'Brazil'
dist_brazil = case_dist[dist_brazil][['Country/Region','Deaths','Recovered','Active']].set_index('Country/Region')# India
dist_india = case_dist['Country/Region'] == 'India'
dist_india = case_dist[dist_india][['Country/Region','Deaths','Recovered','Active']].set_index('Country/Region')# Russia
dist_russia = case_dist['Country/Region'] == 'Russia'
dist_russia = case_dist[dist_russia][['Country/Region','Deaths','Recovered','Active']].set_index('Country/Region')

The following code will produce a pie chart representing the case classification on Most affected Countries.

以下代碼將生成一個餅狀圖，代表大多數受影響國家/地區的案件分類。

fig = plt.figure(figsize = (22,14))
colors_series = ['deepskyblue','gold','springgreen','coral']
explode = (0,0,0.1)plt.subplot(221)
plt.pie(dist_us, labels = dist_us.columns, colors = colors_series, explode = explode,startangle = 90,autopct = '%.1f%%', shadow = True)
plt.title('USA', fontsize = 16)plt.subplot(222)
plt.pie(dist_brazil, labels = dist_brazil.columns, colors = colors_series, explode = explode,startangle = 90,autopct = '%.1f%%',shadow = True)
plt.title('Brazil', fontsize = 16)plt.subplot(223)
plt.pie(dist_india, labels = dist_india.columns, colors = colors_series, explode = explode, startangle = 90, autopct = '%.1f%%',shadow = True)
plt.title('India', fontsize = 16)plt.subplot(224)
plt.pie(dist_russia, labels = dist_russia.columns, colors = colors_series, explode = explode, startangle = 90,autopct = '%.1f%%', shadow = True)
plt.title('Russia', fontsize = 16)plt.suptitle('Case Classification of Most Affected Countries', fontsize = 20)

iii) Most affected continents’ Negative case vs Positive case percentage composition:

iii)受災最嚴重的大陸的消極案例與積極案例的百分比構成：

For this analysis we need a new dataset. https://ourworldindata.org/coronavirus-source-data Follow this link to get our next dataset.

對于此分析，我們需要一個新的數據集。 https://ourworldindata.org/coronavirus-source-data單擊此鏈接以獲取我們的下一個數據集。

EDA for Negative case vs Positive case percentage composition :

負面案例與正面案例所占百分比的EDA：

negative_positive = pd.read_csv('owid-covid-data.csv')
negative_positive = negative_positive.groupby('continent')[['total_cases','total_tests']].sum()explode = (0,0.1)
labels = ['Postive','Negative']
colors = ['beige','paleturquoise']

The following code will produce a pie chart illustrating the percentage composition of Negative cases and Positive cases in most affected Continents.

以下代碼將產生一個餅圖，說明在受影響最大的大陸中，陰性案例和陽性案例的百分比構成。

fig = plt.figure(figsize = (20,20))plt.subplot(321)
plt.pie(negative_positive[negative_positive.index == 'Asia'],labels = labels, explode = explode, autopct = '%.1f%%', startangle = 90, colors = colors, shadow = True)
plt.title('Asia', fontsize = 15)plt.subplot(322)
plt.pie(negative_positive[negative_positive.index == 'North America'],labels = labels, explode = explode, autopct = '%.1f%%', startangle = 90, colors = colors, shadow = True)
plt.title('North America', fontsize = 15)plt.subplot(323)
plt.pie(negative_positive[negative_positive.index == 'South America'],labels = labels, explode = explode, autopct = '%.1f%%', startangle = 90, colors = colors, shadow = True)
plt.title('South America', fontsize = 15)plt.subplot(324)
plt.pie(negative_positive[negative_positive.index == 'Europe'],labels = labels, explode = explode, autopct = '%.1f%%', startangle = 90, colors = colors, shadow = True)
plt.title('Europe', fontsize = 15)plt.suptitle('Continent-Wise Tested Positive & Negative Percentage Composition', fontsize = 20)

結論 (Conclusion)

Hurrah! We successfully completed creating our own COVID-19 report with Python. If you forgot to follow any above mentioned steps I have provided the full code for this analysis below. Apart from our analysis, there are much more you can do with Python and its powerful packages. So don’t stop exploring and create your own reports and dashboards.

歡呼！ 我們已經成功地使用Python創建了自己的COVID-19報告。 如果您忘記了執行上述任何步驟，則在下面提供了此分析的完整代碼。 除了我們的分析之外，Python及其強大的軟件包還可以做更多的事情。 因此，不要停止探索并創建自己的報告和儀表板。

You can find many useful resources on internet based on data science in python for example edX, Coursera, Udemy and so on but, never ever stop learning. Hope you find this article useful and knowledgeable. Happy Analyzing!

您可以在python中基于數據科學在Internet上找到許多有用的資源，例如edX，Coursera，Udemy等，但永遠都不要停止學習。 希望您發現本文有用且知識淵博。 分析愉快！

FULL CODE:

完整代碼：

import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
import numpy as np
import plotly
import plotly.graph_objects as go
import datetime as dt
import requests
from plotly.subplots import make_subplots# Getting Dataurl_request = requests.get("https://services1.arcgis.com/0MSEUqKaxRlEPj5g/arcgis/rest/services/Coronavirus_2019_nCoV_Cases/FeatureServer/1/query?where=1%3D1&outFields=*&outSR=4326&f=json")
url_json = url_request.json()
df = pd.DataFrame(url_json['features'])
df['attributes'][0]# Data Wrangling# a. transforming datadata_list = df['attributes'].tolist()
data = pd.DataFrame(data_list)
data.set_index('OBJECTID')
data = data[['Province_State','Country_Region','Last_Update','Lat','Long_','Confirmed','Recovered','Deaths','Active']]
data.columns = ('State','Country','Last Update','Lat','Long','Confirmed','Recovered','Deaths','Active')
data['State'].fillna(value = '', inplace = True)
data# b. cleaning datadef convert_time(t):t = int(t)return dt.datetime.fromtimestamp(t)data = data.dropna(subset = ['Last Update'])
data['Last Update'] = data['Last Update']/1000
data['Last Update'] = data['Last Update'].apply(convert_time)
data# Exploratory Data Analysis & Visualization# Our analysis contains 'Ranking countries and provinces', 'Time Series' and 'Classification and Distribution'# 1. Ranking countries and provinces
# a. Top 10 confirmed countries (Bubble plot)top10_confirmed = pd.DataFrame(data.groupby('Country')['Confirmed'].sum().nlargest(10).sort_values(ascending = False))
fig1 = px.scatter(top10_confirmed, x = top10_confirmed.index, y = 'Confirmed', size = 'Confirmed', size_max = 120,color = top10_confirmed.index, title = 'Top 10 Confirmed Cases Countries')
fig1.show()# b. Top 10 deaths countries (h-Bar plot)top10_deaths = pd.DataFrame(data.groupby('Country')['Deaths'].sum().nlargest(10).sort_values(ascending = True))
fig2 = px.bar(top10_deaths, x = 'Deaths', y = top10_deaths.index, height = 600, color = 'Deaths', orientation = 'h',color_continuous_scale = ['deepskyblue','red'], title = 'Top 10 Death Cases Countries')
fig2.show()# c. Top 10 recovered countries (Bar plot)top10_recovered = pd.DataFrame(data.groupby('Country')['Recovered'].sum().nlargest(10).sort_values(ascending = False))
fig3 = px.bar(top10_recovered, x = top10_recovered.index, y = 'Recovered', height = 600, color = 'Recovered',title = 'Top 10 Recovered Cases Countries', color_continuous_scale = px.colors.sequential.Viridis)
fig3.show()# d. Top 10 active countriestop10_active = pd.DataFrame(data.groupby('Country')['Active'].sum().nlargest(10).sort_values(ascending = True))
fig4 = px.bar(top10_active, x = 'Active', y = top10_active.index, height = 600, color = 'Active', orientation = 'h',color_continuous_scale = ['paleturquoise','blue'], title = 'Top 10 Active Cases Countries')
fig4.show()# e. Most affected states/provinces in largely affected countries
# Here we are going to extract top 4 affected countries' states data and plot it!# Firstly, aggregating data with our dataset :
# USA
topstates_us = data['Country'] == 'US'
topstates_us = data[topstates_us].nlargest(5, 'Confirmed')
# Brazil
topstates_brazil = data['Country'] == 'Brazil'
topstates_brazil = data[topstates_brazil].nlargest(5, 'Confirmed')
# India
topstates_india = data['Country'] == 'India'
topstates_india = data[topstates_india].nlargest(5, 'Confirmed')
# Russia
topstates_russia = data['Country'] == 'Russia'
topstates_russia = data[topstates_russia].nlargest(5, 'Confirmed')# Let's plot!
# USA 
fig5 = go.Figure(data = [go.Bar(name = 'Active Cases', x = topstates_us['Active'], y = topstates_us['State'], orientation = 'h'),go.Bar(name = 'Death Cases', x = topstates_us['Deaths'], y = topstates_us['State'], orientation = 'h')
])
fig5.update_layout(title = 'Most Affected States in USA', height = 600)
fig5.show()
# Brazil
fig6 = go.Figure(data = [go.Bar(name = 'Recovered Cases', x = topstates_brazil['State'], y = topstates_brazil['Recovered']),go.Bar(name = 'Active Cases', x = topstates_brazil['State'], y = topstates_brazil['Active']),go.Bar(name = 'Death Cases', x = topstates_brazil['State'], y = topstates_brazil['Deaths'])
])
fig6.update_layout(title = 'Most Affected States in Brazil', barmode = 'stack', height = 600)
fig6.show()
# India
fig7 = go.Figure(data = [go.Bar(name = 'Recovered Cases', x = topstates_india['State'], y = topstates_india['Recovered']),go.Bar(name = 'Active Cases', x = topstates_india['State'], y = topstates_india['Active']),go.Bar(name = 'Death Cases', x = topstates_india['State'], y = topstates_india['Deaths'])
])
fig7.update_layout(title = 'Most Affected States in India', barmode = 'stack', height = 600)
fig7.show()
# Russia
fig8 = go.Figure(data = [go.Bar(name = 'Recovered Cases', x = topstates_russia['State'], y = topstates_russia['Recovered']),go.Bar(name = 'Active Cases', x = topstates_russia['State'], y = topstates_russia['Active']),go.Bar(name = 'Death Cases', x = topstates_russia['State'], y = topstates_russia['Deaths'])
])
fig8.update_layout(title = 'Most Affected States in Russia', barmode = 'stack', height = 600)
fig8.show()# 2. Time series of top affected countries
# We need a new data for this plot 
# https://covid19.who.int/ follow the link for this link for the next dataset(you can find the download option on the bottomright of the map chart)
time_series = pd.read_csv('who_data.csv', encoding = 'ISO-8859-1')
time_series['Date_reported'] = pd.to_datetime(time_series['Date_reported'])# a. Covid-19 cases worldwide
# Firsty Data
time_series_dates = time_series.groupby('Date_reported').sum()# Let's Plot
# Cumulative cases
fig11 = go.Figure()
fig11.add_trace(go.Scatter(x = time_series_dates.index, y = time_series_dates[' Cumulative_cases'], fill = 'tonexty',line_color = 'blue'))
fig11.update_layout(title = 'Cumulative Cases Worldwide')
fig11.show()
# Cumulative death cases
fig12 = go.Figure()
fig12.add_trace(go.Scatter(x = time_series_dates.index, y = time_series_dates[' Cumulative_deaths'], fill = 'tonexty',line_color = 'red'))
fig12.update_layout(title = 'Cumulative Deaths Worldwide')
fig12.show()
# Daily new cases
fig13 = go.Figure()
fig13.add_trace(go.Scatter(x = time_series_dates.index, y = time_series_dates[' New_cases'], fill = 'tonexty',line_color = 'gold'))
fig13.update_layout(title = 'Daily New Cases Worldwide')
fig13.show()
# Daily death cases
fig14 = go.Figure()
fig14.add_trace(go.Scatter(x = time_series_dates.index, y = time_series_dates[' New_deaths'], fill = 'tonexty',line_color = 'hotpink'))
fig14.update_layout(title = 'Daily Death Cases Worldwide')
fig14.show()# b. Most Affected Countries over the time
# Data
# USA 
time_series_us = time_series[' Country'] == ('United States of America')
time_series_us = time_series[time_series_us]
# Brazil
time_series_brazil = time_series[' Country'] == ('Brazil')
time_series_brazil = time_series[time_series_brazil]
# India
time_series_india = time_series[' Country'] == ('India')
time_series_india = time_series[time_series_india]
# Russia
time_series_russia = time_series[' Country'] == ('Russia')
time_series_russia = time_series[time_series_russia]
# Peru
time_series_peru = time_series[' Country'] == ('Peru')
time_series_peru = time_series[time_series_peru]# Let's plot
# Cumulative cases
fig15 = go.Figure()
fig15.add_trace(go.Line(x = time_series_us['Date_reported'], y = time_series_us[' Cumulative_cases'], name = 'USA'))
fig15.add_trace(go.Line(x = time_series_brazil['Date_reported'], y = time_series_brazil[' Cumulative_cases'], name = 'Brazil'))
fig15.add_trace(go.Line(x = time_series_india['Date_reported'], y = time_series_india[' Cumulative_cases'], name = 'India'))
fig15.add_trace(go.Line(x = time_series_russia['Date_reported'], y = time_series_russia[' Cumulative_cases'], name = 'Russia'))
fig15.add_trace(go.Line(x = time_series_peru['Date_reported'], y = time_series_peru[' Cumulative_cases'], name = 'Peru'))
fig15.update_layout(title = 'Time Series of Most Affected countries"s Cumulative Cases')
fig15.show()
# Cumulative death cases
fig16 = go.Figure()
fig16.add_trace(go.Line(x = time_series_us['Date_reported'], y = time_series_us[' Cumulative_deaths'], name = 'USA'))
fig16.add_trace(go.Line(x = time_series_brazil['Date_reported'], y = time_series_brazil[' Cumulative_deaths'], name = 'Brazil'))
fig16.add_trace(go.Line(x = time_series_india['Date_reported'], y = time_series_india[' Cumulative_deaths'], name = 'India'))
fig16.add_trace(go.Line(x = time_series_russia['Date_reported'], y = time_series_russia[' Cumulative_deaths'], name = 'Russia'))
fig16.add_trace(go.Line(x = time_series_peru['Date_reported'], y = time_series_peru[' Cumulative_deaths'], name = 'Peru'))
fig16.update_layout(title = 'Time Series of Most Affected countries"s Cumulative Death Cases')
fig16.show()
# Daily new cases
fig17 = go.Figure()
fig17.add_trace(go.Line(x = time_series_us['Date_reported'], y = time_series_us[' New_cases'], name = 'USA'))
fig17.add_trace(go.Line(x = time_series_brazil['Date_reported'], y = time_series_brazil[' New_cases'], name = 'Brazil'))
fig17.add_trace(go.Line(x = time_series_india['Date_reported'], y = time_series_india[' New_cases'], name = 'India'))
fig17.add_trace(go.Line(x = time_series_russia['Date_reported'], y = time_series_russia[' New_cases'], name = 'Russia'))
fig17.add_trace(go.Line(x = time_series_peru['Date_reported'], y = time_series_peru[' New_cases'], name = 'Peru'))
fig17.update_layout(title = 'Time Series of Most Affected countries"s Daily New Cases')
fig17.show()
# Daily death cases
fig18 = go.Figure()
fig18.add_trace(go.Line(x = time_series_us['Date_reported'], y = time_series_us[' New_deaths'], name = 'USA'))
fig18.add_trace(go.Line(x = time_series_brazil['Date_reported'], y = time_series_brazil[' New_deaths'], name = 'Brazil'))
fig18.add_trace(go.Line(x = time_series_india['Date_reported'], y = time_series_india[' New_deaths'], name = 'India'))
fig18.add_trace(go.Line(x = time_series_russia['Date_reported'], y = time_series_russia[' New_deaths'], name = 'Russia'))
fig18.add_trace(go.Line(x = time_series_peru['Date_reported'], y = time_series_peru[' New_deaths'], name = 'Peru'))
fig18.update_layout(title = 'Time Series of Most Affected countries"s Daily Death Cases')
fig18.show()# 3. Case Classification and Distribution# For this we need a new dataset 
# https://www.kaggle.com/imdevskp/corona-virus-report follow this link for the next dataset# a. WHO Region-Wise Distribution
# For this plot we are going to use country_wise_latest dataset which will come along with the downloaded kaggle dataset
# Firstly Data
who = pd.read_csv('country_wise_latest.csv')
who_region = pd.DataFrame(who.groupby('WHO Region')['Confirmed'].sum())
labels = who_region.index
values = who_region['Confirmed']
# Let's Plot!
fig9 = go.Figure(data=[go.Pie(labels = labels, values = values, pull=[0, 0, 0, 0, 0.2, 0])])
fig9.update_layout(title = 'WHO Region-Wise Case Distribution', width = 700, height = 400, margin = dict(t = 0, l = 0, r = 0, b = 0))
fig9.show()# b. Most Affected countries case distribution
# For this plot we are going to use the same country_wise_latest dataset# Firstly Data
case_dist = who
# US
dist_us = case_dist['Country/Region'] == 'US'
dist_us = case_dist[dist_us][['Country/Region','Deaths','Recovered','Active']].set_index('Country/Region')
# Brazil
dist_brazil = case_dist['Country/Region'] == 'Brazil'
dist_brazil = case_dist[dist_brazil][['Country/Region','Deaths','Recovered','Active']].set_index('Country/Region')
# India
dist_india = case_dist['Country/Region'] == 'India'
dist_india = case_dist[dist_india][['Country/Region','Deaths','Recovered','Active']].set_index('Country/Region')
# Russia
dist_russia = case_dist['Country/Region'] == 'Russia'
dist_russia = case_dist[dist_russia][['Country/Region','Deaths','Recovered','Active']].set_index('Country/Region')# Let's Plot!
# This plot is produced with matplotlib
fig = plt.figure(figsize = (22,14))
colors_series = ['deepskyblue','gold','springgreen','coral']
explode = (0,0,0.1)plt.subplot(221)
plt.pie(dist_us, labels = dist_us.columns, colors = colors_series, explode = explode,startangle = 90,autopct = '%.1f%%', shadow = True)
plt.title('USA', fontsize = 16)plt.subplot(222)
plt.pie(dist_brazil, labels = dist_brazil.columns, colors = colors_series, explode = explode,startangle = 90,autopct = '%.1f%%',shadow = True)
plt.title('Brazil', fontsize = 16)plt.subplot(223)
plt.pie(dist_india, labels = dist_india.columns, colors = colors_series, explode = explode, startangle = 90, autopct = '%.1f%%',shadow = True)
plt.title('India', fontsize = 16)plt.subplot(224)
plt.pie(dist_russia, labels = dist_russia.columns, colors = colors_series, explode = explode, startangle = 90,autopct = '%.1f%%', shadow = True)
plt.title('Russia', fontsize = 16)plt.suptitle('Case Classification of Most Affected Countries', fontsize = 20)# c. Most affected continents' negative case vs positive case percentage composition
# For this we need a new dataset 
# https://ourworldindata.org/coronavirus-source-data Follow this link for our next dataset# Firstly Data
negative_positive = pd.read_csv('owid-covid-data.csv')
negative_positive = negative_positive.groupby('continent')[['total_cases','total_tests']].sum()
explode = (0,0.1)
labels = ['Postive','Negative']
colors = ['beige','paleturquoise']#Let's Plot!
fig = plt.figure(figsize = (20,20))
plt.subplot(321)
plt.pie(negative_positive[negative_positive.index == 'Asia'],labels = labels, explode = explode, autopct = '%.1f%%', startangle = 90, colors = colors, shadow = True)
plt.title('Asia', fontsize = 15)plt.subplot(322)
plt.pie(negative_positive[negative_positive.index == 'North America'],labels = labels, explode = explode, autopct = '%.1f%%', startangle = 90, colors = colors, shadow = True)
plt.title('North America', fontsize = 15)plt.subplot(323)
plt.pie(negative_positive[negative_positive.index == 'South America'],labels = labels, explode = explode, autopct = '%.1f%%', startangle = 90, colors = colors, shadow = True)
plt.title('South America', fontsize = 15)plt.subplot(324)
plt.pie(negative_positive[negative_positive.index == 'Europe'],labels = labels, explode = explode, autopct = '%.1f%%', startangle = 90, colors = colors, shadow = True)
plt.title('Europe', fontsize = 15)plt.suptitle('Continent-Wise Tested Positive & Negative Percentage Composition', fontsize = 20)