Coronavirus (COVID-19) Visualization & Prediction 笔记

Coronavirus (COVID-19) Visualization & Prediction 笔记

项目链接：https://www.kaggle.com/therealcyberlord/coronavirus-covid-19-visualization-prediction

总结

写在前面：这个项目本来以为很复杂，会有很复杂的数据处理过程，其实就还好。由于图和表太多，把总结写在前面。

首先，没用到什么比较高级的算法什么的，对于python脚本能力要求不是很高。

其次，对我比较有价值的数据分析部分是该项目对新冠病毒未来数据的预测部分，包括SVM, Polynomial Regression, Bayesian Ridge Regression等回归方法。以及其中sklearn相关函数的使用，如 train_test_split， PolynomialFeatures等。

最后，关于数据的可视化这一部分，基本上和新闻上说的差不多，美国一枝独秀。

图和表巨多，有兴趣可以下去点点。部分结果有注释。

正文

冠状病毒(Coronavirus)是一个病毒家族，是由这些病毒具有的冠状突刺（spiky crown）而命名。新型冠状病毒，也就是SARS-CoV-2，是一种传染性呼吸道病毒，首次在中国武汉报道。2020年11月2日，世界卫生组织将这种新型冠状病毒引起的疾病命名为COVID-19。这篇Notebook旨在通过数据分析和预测来探索COVID-19。

新型冠状病毒病例数据由Johns Hopkins University提供

新型冠状病毒移动数据由Apple提供

从World Health Organization获取更多信息

从Centers for Disease Control and Prevention获取更多信息

从JHU CCSE Dashboard查看地图可视化数据

源代码：my Github

最后更新：美国东部时间2020年10月20日下午5:13 数据表更新

最新更新：10月20日每日报告数据更新
时间序列数据更新为10/19，移动性数据更新为10/19
预测模型2020年3月13日开始训练。因此，较早的日期可能不准确。

目录

• Exploring Global Coronavirus Cases
• Exploring Coronavirus Cases From Different Countries
• Worldwide Confirmed Cases Prediction
• Data Table
• Pie Charts
• Bar Charts
• US Testing Data
• Mobility Data for Hotspots

# 导入模块，都比较常见
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import pandas as pd
import random
import math
import time
from sklearn.linear_model import LinearRegression, BayesianRidge
from sklearn.model_selection import RandomizedSearchCV, train_test_split
from sklearn.preprocessing import PolynomialFeatures
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error, mean_absolute_error
import datetime
import operator
plt.style.use('fivethirtyeight')
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")

读取数据

#也可以直接先下来，再读取
confirmed_df = pd.read_csv('./20201021_data/time_series_covid19_confirmed_global.csv')
deaths_df = pd.read_csv('./20201021_data/time_series_covid19_deaths_global.csv')
recoveries_df = pd.read_csv('./20201021_data/time_series_covid19_recovered_global.csv')
latest_data = pd.read_csv('./20201021_data/10-19-2020-latest.csv')
us_medical_data = pd.read_csv('./20201021_data/10-19-2020-us.csv')
apple_mobility = pd.read_csv('./20201021_data/applemobilitytrends-2020-10-19.csv')

# 这个文件是全球随时间变化的确诊病例
print(confirmed_df.columns)
confirmed_df.head()

Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',
       '1/24/20', '1/25/20', '1/26/20', '1/27/20',
       ...
       '10/11/20', '10/12/20', '10/13/20', '10/14/20', '10/15/20', '10/16/20',
       '10/17/20', '10/18/20', '10/19/20', '10/20/20'],
      dtype='object', length=277)

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	…	10/11/20	10/12/20	10/13/20	10/14/20	10/15/20	10/16/20	10/17/20	10/18/20	10/19/20	10/20/20
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	…	39799	39870	39928	39994	40026	40073	40141	40200	40287	40357
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	…	15399	15570	15752	15955	16212	16501	16774	17055	17350	17651
2	NaN	Algeria	28.03390	1.659600	0	0	0	0	0	0	…	53072	53325	53399	53584	53777	53998	54203	54402	54616	54829
3	NaN	Andorra	42.50630	1.521800	0	0	0	0	0	0	…	2696	2995	2995	3190	3190	3377	3377	3377	3623	3623
4	NaN	Angola	-11.20270	17.873900	0	0	0	0	0	0	…	6366	6488	6680	6846	7096	7222	7462	7622	7829	8049

5 rows × 277 columns

# 这个文件是全球随时间变化的死亡病例
print(deaths_df.columns)
deaths_df.head()

Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',
       '1/24/20', '1/25/20', '1/26/20', '1/27/20',
       ...
       '10/11/20', '10/12/20', '10/13/20', '10/14/20', '10/15/20', '10/16/20',
       '10/17/20', '10/18/20', '10/19/20', '10/20/20'],
      dtype='object', length=277)

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	…	10/11/20	10/12/20	10/13/20	10/14/20	10/15/20	10/16/20	10/17/20	10/18/20	10/19/20	10/20/20
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	…	1477	1479	1480	1481	1481	1485	1488	1492	1497	1499
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	…	420	424	429	434	439	443	448	451	454	458
2	NaN	Algeria	28.03390	1.659600	0	0	0	0	0	0	…	1801	1809	1818	1827	1827	1841	1846	1856	1865	1873
3	NaN	Andorra	42.50630	1.521800	0	0	0	0	0	0	…	55	57	57	59	59	59	59	59	62	62
4	NaN	Angola	-11.20270	17.873900	0	0	0	0	0	0	…	218	219	222	227	228	234	241	247	248	251

5 rows × 277 columns

# 这个文件是全球随时间变化的治愈病例
print(recoveries_df.columns)
recoveries_df.head()

Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',
       '1/24/20', '1/25/20', '1/26/20', '1/27/20',
       ...
       '10/11/20', '10/12/20', '10/13/20', '10/14/20', '10/15/20', '10/16/20',
       '10/17/20', '10/18/20', '10/19/20', '10/20/20'],
      dtype='object', length=277)

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	…	10/11/20	10/12/20	10/13/20	10/14/20	10/15/20	10/16/20	10/17/20	10/18/20	10/19/20	10/20/20
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	…	33114	33118	33308	33354	33447	33516	33561	33614	33760	33790
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	…	9500	9585	9675	9762	9864	9957	10001	10071	10167	10225
2	NaN	Algeria	28.03390	1.659600	0	0	0	0	0	0	…	37170	37382	37492	37603	37603	37856	37971	38088	38215	38346
3	NaN	Andorra	42.50630	1.521800	0	0	0	0	0	0	…	1814	1928	1928	2011	2011	2057	2057	2057	2273	2273
4	NaN	Angola	-11.20270	17.873900	0	0	0	0	0	0	…	2743	2744	2761	2801	2928	3012	3022	3030	3031	3037

5 rows × 277 columns

# 截至当前20201021，各国新冠病毒统计情况
print(latest_data.columns)
latest_data.head()

Index(['FIPS', 'Admin2', 'Province_State', 'Country_Region', 'Last_Update',
       'Lat', 'Long_', 'Confirmed', 'Deaths', 'Recovered', 'Active',
       'Combined_Key', 'Incidence_Rate', 'Case-Fatality_Ratio'],
      dtype='object')

	FIPS	Admin2	Province_State	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Recovered	Active	Combined_Key	Incidence_Rate	Case-Fatality_Ratio
0	NaN	NaN	NaN	Afghanistan	2020-10-20 04:24:22	33.93911	67.709953	40287	1497	33760	5030.0	Afghanistan	103.490154	3.715839
1	NaN	NaN	NaN	Albania	2020-10-20 04:24:22	41.15330	20.168300	17350	454	10167	6729.0	Albania	602.891097	2.616715
2	NaN	NaN	NaN	Algeria	2020-10-20 04:24:22	28.03390	1.659600	54616	1865	38215	14536.0	Algeria	124.548919	3.414750
3	NaN	NaN	NaN	Andorra	2020-10-20 04:24:22	42.50630	1.521800	3623	62	2273	1288.0	Andorra	4689.057141	1.711289
4	NaN	NaN	NaN	Angola	2020-10-20 04:24:22	-11.20270	17.873900	7829	248	3031	4550.0	Angola	23.820776	3.167710

# 截至当前20201021，美国新冠病毒统计情况
print(us_medical_data.columns)
us_medical_data.head()

Index(['Province_State', 'Country_Region', 'Last_Update', 'Lat', 'Long_',
       'Confirmed', 'Deaths', 'Recovered', 'Active', 'FIPS', 'Incident_Rate',
       'People_Tested', 'People_Hospitalized', 'Mortality_Rate', 'UID', 'ISO3',
       'Testing_Rate', 'Hospitalization_Rate'],
      dtype='object')

	Province_State	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Recovered	Active	FIPS	Incident_Rate	People_Tested	People_Hospitalized	Mortality_Rate	UID	ISO3	Testing_Rate	Hospitalization_Rate
0	Alabama	US	2020-10-20 04:30:29	32.3182	-86.9023	173485	2789	74238.0	96458.0	1.0	3538.210367	1260100.0	NaN	1.607632	84000001	USA	25699.621776	NaN
1	Alaska	US	2020-10-20 04:30:29	61.3707	-152.4044	11182	67	6516.0	4599.0	2.0	1528.545749	536223.0	NaN	0.599177	84000002	USA	73300.070399	NaN
2	American Samoa	US	2020-10-20 04:30:29	-14.2710	-170.1320	0	0	NaN	0.0	60.0	0.000000	1616.0	NaN	NaN	16	ASM	2904.333136	NaN
3	Arizona	US	2020-10-20 04:30:29	33.7298	-111.4312	231897	5830	38553.0	187514.0	4.0	3185.959833	1639785.0	NaN	2.514047	84000004	USA	22528.489568	NaN
4	Arkansas	US	2020-10-20 04:30:29	34.9697	-92.3731	99597	1714	89217.0	8666.0	5.0	3300.313738	1223914.0	NaN	1.720935	84000005	USA	40556.444355	NaN

## 苹果的移动数据
print(apple_mobility.columns)
apple_mobility.head()

Index(['geo_type', 'region', 'transportation_type', 'alternative_name',
       'sub-region', 'country', '2020-01-13', '2020-01-14', '2020-01-15',
       '2020-01-16',
       ...
       '2020-10-10', '2020-10-11', '2020-10-12', '2020-10-13', '2020-10-14',
       '2020-10-15', '2020-10-16', '2020-10-17', '2020-10-18', '2020-10-19'],
      dtype='object', length=287)

	geo_type	region	transportation_type	alternative_name	sub-region	country	2020-01-13	2020-01-14	2020-01-15	2020-01-16	…	2020-10-10	2020-10-11	2020-10-12	2020-10-13	2020-10-14	2020-10-15	2020-10-16	2020-10-17	2020-10-18	2020-10-19
0	country/region	Albania	driving	NaN	NaN	NaN	100.0	95.30	101.43	97.20	…	144.47	148.87	123.94	111.80	113.31	111.52	117.39	128.99	137.88	114.93
1	country/region	Albania	walking	NaN	NaN	NaN	100.0	100.68	98.93	98.46	…	167.16	142.52	150.36	141.02	155.39	134.41	142.26	142.22	125.67	149.77
2	country/region	Argentina	driving	NaN	NaN	NaN	100.0	97.07	102.45	111.21	…	79.72	49.19	49.69	61.16	65.26	68.45	84.94	88.93	48.76	52.73
3	country/region	Argentina	walking	NaN	NaN	NaN	100.0	95.11	101.37	112.67	…	62.15	36.57	43.66	52.51	56.79	55.10	69.59	62.42	34.40	42.97
4	country/region	Australia	driving	AU	NaN	NaN	100.0	102.98	104.21	108.63	…	83.24	88.85	91.45	93.17	96.06	104.24	99.65	85.42	92.72	94.60

5 rows × 287 columns

获取疫情爆发日期

# 从确诊、死亡、治愈病例中获取时间序列数据，第四列到最后一列
cols = confirmed_df.keys()
confirmed = confirmed_df.loc[:, cols[4]:cols[-1]]
deaths = deaths_df.loc[:, cols[4]:cols[-1]]
recoveries = recoveries_df.loc[:, cols[4]:cols[-1]]

# 获取累计确诊、死亡、治愈病例，然后画图
dates = confirmed.keys()
world_cases = []
total_deaths = []
mortality_rate = []
recovery_rate = []
total_recovered = []
total_active = []

for i in dates:
    confirmed_sum = confirmed[i].sum()
    death_sum = deaths[i].sum()
    recovered_sum = recoveries[i].sum()

    # confirmed, deaths, recovered, and active
    world_cases.append(confirmed_sum)
    total_deaths.append(death_sum)
    total_recovered.append(recovered_sum)
    total_active.append(confirmed_sum-death_sum-recovered_sum)

    # calculate rates
    mortality_rate.append(death_sum/confirmed_sum)
    recovery_rate.append(recovered_sum/confirmed_sum)

# 获取每日每周增加数据，指上面的 确诊数量变化 死亡数量变化 治愈数量变化 confirmed, deaths, recovered, and active ，以及比例
def daily_increase(data):
    d = []
    for i in range(len(data)):
        if i == 0:
            d.append(data[0])
        else:
            d.append(data[i]-data[i-1])
    return d

def moving_average(data, window_size):
    moving_average = []
    for i in range(len(data)):
        if i + window_size < len(data):
            moving_average.append(np.mean(data[i:i+window_size]))
        else:
            moving_average.append(np.mean(data[i:len(data)]))
    return moving_average

# window size
window = 7

# confirmed cases
world_daily_increase = daily_increase(world_cases)
world_confirmed_avg= moving_average(world_cases, window)
world_daily_increase_avg = moving_average(world_daily_increase, window)

# deaths
world_daily_death = daily_increase(total_deaths)
world_death_avg = moving_average(total_deaths, window)
world_daily_death_avg = moving_average(world_daily_death, window)


# recoveries
world_daily_recovery = daily_increase(total_recovered)
world_recovery_avg = moving_average(total_recovered, window)
world_daily_recovery_avg = moving_average(world_daily_recovery, window)


# active
world_active_avg = moving_average(total_active, window)

# 从1月22日开始计数，并将数据整理为n*1矩阵
days_since_1_22 = np.array([i for i in range(len(dates))]).reshape(-1, 1)
world_cases = np.array(world_cases).reshape(-1, 1)
total_deaths = np.array(total_deaths).reshape(-1, 1)
total_recovered = np.array(total_recovered).reshape(-1, 1)
print(days_since_1_22.shape)

(273, 1)

# 未来预测，获取未来10天的编号，从1月22日开始计数
days_in_future = 10
future_forcast = np.array([i for i in range(len(dates)+days_in_future)]).reshape(-1, 1)
adjusted_dates = future_forcast[:-10]

# 把上一步得到的编号（int）转化为日期格式，便于可视化。 模块 datetime
start = '1/22/2020'
start_date = datetime.datetime.strptime(start, '%m/%d/%Y')
future_forcast_dates = []
for i in range(len(future_forcast)):
    future_forcast_dates.append((start_date + datetime.timedelta(days=i)).strftime('%m/%d/%Y'))

# 正式预测：train_test_split sklearn.model_selection 从世界确诊病例中挑选第50天后的所有数据，test数据比例为0.15，不打乱
X_train_confirmed, X_test_confirmed, y_train_confirmed, y_test_confirmed = train_test_split(days_since_1_22[50:], world_cases[50:], test_size=0.15, shuffle=False)
print(X_train_confirmed.shape,X_test_confirmed.shape)

(189, 1) (34, 1)

# 利用支持向量机、贝叶斯岭和线性回归模型来预测确诊病例
# SVM sklearn.svm
svm_confirmed = SVR(shrinking=True, kernel='poly',gamma=0.01, epsilon=1,degree=3, C=0.1) #定义模型
svm_confirmed.fit(X_train_confirmed, y_train_confirmed) #训练
svm_pred = svm_confirmed.predict(future_forcast) #预测

# 画个图，看一下
# mean_absolute_error sklearn.metrics 平均绝对误差(MAE) https://blog.csdn.net/StupidAutofan/article/details/79556087
svm_test_pred = svm_confirmed.predict(X_test_confirmed)
plt.plot(y_test_confirmed)
plt.plot(svm_test_pred)
plt.legend(['Test Data', 'SVM Predictions'])
print('MAE:', mean_absolute_error(svm_test_pred, y_test_confirmed))
print('MSE:',mean_squared_error(svm_test_pred, y_test_confirmed))

MAE: 4400479.84939107
MSE: 21084654795537.24

# transform our data for polynomial regression
# 将数据转化为多项式回归，这一步是生成不同指数的特征项
# 用到的函数sklearn.preprocessing.PolynomialFeatures，如果有a，b两个特征，那么它的2次多项式为（1,a,b,a^2,ab, b^2）
poly = PolynomialFeatures(degree=4)
poly_X_train_confirmed = poly.fit_transform(X_train_confirmed)## 即生成单个x的不同乘方【0-4】
poly_X_test_confirmed = poly.fit_transform(X_test_confirmed)
poly_future_forcast = poly.fit_transform(future_forcast)

# 贝叶斯
bayesian_poly = PolynomialFeatures(degree=5)
bayesian_poly_X_train_confirmed = bayesian_poly.fit_transform(X_train_confirmed)
bayesian_poly_X_test_confirmed = bayesian_poly.fit_transform(X_test_confirmed)
bayesian_poly_future_forcast = bayesian_poly.fit_transform(future_forcast)

# polynomial regression
# 开始预测

linear_model = LinearRegression(normalize=True, fit_intercept=False)
linear_model.fit(poly_X_train_confirmed, y_train_confirmed)
test_linear_pred = linear_model.predict(poly_X_test_confirmed)
linear_pred = linear_model.predict(poly_future_forcast)
print('MAE:', mean_absolute_error(test_linear_pred, y_test_confirmed))
print('MSE:',mean_squared_error(test_linear_pred, y_test_confirmed))

MAE: 1125828.6734114974
MSE: 2107924255435.714

# 画个图，比之前的单纯SVR要好点
plt.plot(y_test_confirmed)
plt.plot(test_linear_pred)
plt.legend(['Test Data', 'Polynomial Regression Predictions'])

<matplotlib.legend.Legend at 0x234c8336588>

​

​

# bayesian ridge polynomial regression
# 贝叶斯岭回归多项式回归计算
tol = [1e-6, 1e-5, 1e-4, 1e-3, 1e-2]
alpha_1 = [1e-7, 1e-6, 1e-5, 1e-4, 1e-3]
alpha_2 = [1e-7, 1e-6, 1e-5, 1e-4, 1e-3]
lambda_1 = [1e-7, 1e-6, 1e-5, 1e-4, 1e-3]
lambda_2 = [1e-7, 1e-6, 1e-5, 1e-4, 1e-3]
normalize = [True, False]

bayesian_grid = {'tol': tol, 'alpha_1': alpha_1, 'alpha_2' : alpha_2, 'lambda_1': lambda_1, 'lambda_2' : lambda_2,
                 'normalize' : normalize}

bayesian = BayesianRidge(fit_intercept=False)
bayesian_search = RandomizedSearchCV(bayesian, bayesian_grid, scoring='neg_mean_squared_error', cv=3, return_train_score=True, n_jobs=-1, n_iter=40, verbose=1)
bayesian_search.fit(bayesian_poly_X_train_confirmed, y_train_confirmed)

Fitting 3 folds for each of 40 candidates, totalling 120 fits


[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 120 out of 120 | elapsed:    1.2s finished





RandomizedSearchCV(cv=3, estimator=BayesianRidge(fit_intercept=False),
                   n_iter=40, n_jobs=-1,
                   param_distributions={'alpha_1': [1e-07, 1e-06, 1e-05, 0.0001,
                                                    0.001],
                                        'alpha_2': [1e-07, 1e-06, 1e-05, 0.0001,
                                                    0.001],
                                        'lambda_1': [1e-07, 1e-06, 1e-05,
                                                     0.0001, 0.001],
                                        'lambda_2': [1e-07, 1e-06, 1e-05,
                                                     0.0001, 0.001],
                                        'normalize': [True, False],
                                        'tol': [1e-06, 1e-05, 0.0001, 0.001,
                                                0.01]},
                   return_train_score=True, scoring='neg_mean_squared_error',
                   verbose=1)

# 同样，画个图 not bad
bayesian_confirmed = bayesian_search.best_estimator_
test_bayesian_pred = bayesian_confirmed.predict(bayesian_poly_X_test_confirmed)
bayesian_pred = bayesian_confirmed.predict(bayesian_poly_future_forcast)
print('MAE:', mean_absolute_error(test_bayesian_pred, y_test_confirmed))
print('MSE:',mean_squared_error(test_bayesian_pred, y_test_confirmed))

plt.plot(y_test_confirmed)
plt.plot(test_bayesian_pred)
plt.legend(['Test Data', 'Bayesian Ridge Polynomial Predictions'])

MAE: 607921.3030146289
MSE: 390856736680.26746





<matplotlib.legend.Legend at 0x234c8396fc8>

​

​

Graphing the number of confirmed cases, active cases, deaths, recoveries, mortality rate (CFR), and recovery rate

画图关于确诊病例，现存病例，死亡数，治愈数，死亡率，治愈率

# helper method for flattening the data, so it can be displayed on a bar graph
# 定义便于bar图示的flatten数据函数 感觉可以直接用np.adarray.reshape，或者np.flatten
def flatten(arr):
    a = []
    arr = arr.tolist()
    for i in arr:
        a.append(i[0])
    return a

# 画图，感觉没啥说的，很基础
adjusted_dates = adjusted_dates.reshape(1, -1)[0]#拍扁
plt.figure(figsize=(16, 10))
plt.plot(adjusted_dates, world_cases)
plt.plot(adjusted_dates, world_confirmed_avg, linestyle='dashed', color='orange')
plt.title('# of Coronavirus Cases Over Time', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('# of Cases', size=30)
plt.legend(['Worldwide Coronavirus Cases', 'Moving Average {} Days'.format(window)], prop={'size': 20})
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

plt.figure(figsize=(16, 10))
plt.plot(adjusted_dates, total_deaths)
plt.plot(adjusted_dates, world_death_avg, linestyle='dashed', color='orange')
plt.title('# of Coronavirus Deaths Over Time', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('# of Cases', size=30)
plt.legend(['Worldwide Coronavirus Deaths', 'Moving Average {} Days'.format(window)], prop={'size': 20})
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

plt.figure(figsize=(16, 10))
plt.plot(adjusted_dates, total_recovered)
plt.plot(adjusted_dates, world_recovery_avg, linestyle='dashed', color='orange')
plt.title('# of Coronavirus Recoveries Over Time', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('# of Cases', size=30)
plt.legend(['Worldwide Coronavirus Recoveries', 'Moving Average {} Days'.format(window)], prop={'size': 20})
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

plt.figure(figsize=(16, 10))
plt.plot(adjusted_dates, total_active)
plt.plot(adjusted_dates, world_active_avg, linestyle='dashed', color='orange')
plt.title('# of Coronavirus Active Cases Over Time', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('# of Active Cases', size=30)
plt.legend(['Worldwide Coronavirus Active Cases', 'Moving Average {} Days'.format(window)], prop={'size': 20})
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

​

​

plt.figure(figsize=(16, 10))
plt.bar(adjusted_dates, world_daily_increase)
plt.plot(adjusted_dates, world_daily_increase_avg, color='orange', linestyle='dashed')
plt.title('World Daily Increases in Confirmed Cases', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('# of Cases', size=30)
plt.legend(['Moving Average {} Days'.format(window), 'World Daily Increase in COVID-19 Cases'], prop={'size': 20})
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

plt.figure(figsize=(16, 10))
plt.bar(adjusted_dates, world_daily_death)
plt.plot(adjusted_dates, world_daily_death_avg, color='orange', linestyle='dashed')
plt.title('World Daily Increases in Confirmed Deaths', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('# of Cases', size=30)
plt.legend(['Moving Average {} Days'.format(window), 'World Daily Increase in COVID-19 Deaths'], prop={'size': 20})
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

plt.figure(figsize=(16, 10))
plt.bar(adjusted_dates, world_daily_recovery)
plt.plot(adjusted_dates, world_daily_recovery_avg, color='orange', linestyle='dashed')
plt.title('World Daily Increases in Confirmed Recoveries', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('# of Cases', size=30)
plt.legend(['Moving Average {} Days'.format(window), 'World Daily Increase in COVID-19 Recoveries'], prop={'size': 20})
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

​

​

plt.figure(figsize=(16, 10))
plt.plot(adjusted_dates, np.log10(world_cases))
plt.title('Log of # of Coronavirus Cases Over Time', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('# of Cases', size=30)
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

plt.figure(figsize=(16, 10))
plt.plot(adjusted_dates, np.log10(total_deaths))
plt.title('Log of # of Coronavirus Deaths Over Time', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('# of Cases', size=30)
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

plt.figure(figsize=(16, 10))
plt.plot(adjusted_dates, np.log10(total_recovered))
plt.title('Log of # of Coronavirus Recoveries Over Time', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('# of Cases', size=30)
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

​

​

# 根据国家画图
def country_plot(x, y1, y2, y3, y4, country):
    # window is set as 14 in in the beginning of the notebook
    confirmed_avg = moving_average(y1, window)
    confirmed_increase_avg = moving_average(y2, window)
    death_increase_avg = moving_average(y3, window)
    recovery_increase_avg = moving_average(y4, window)

    plt.figure(figsize=(16, 10))
    plt.plot(x, y1)
    plt.plot(x, confirmed_avg, color='red', linestyle='dashed')
    plt.legend(['{} Confirmed Cases'.format(country), 'Moving Average {} Days'.format(window)], prop={'size': 20})
    plt.title('{} Confirmed Cases'.format(country), size=30)
    plt.xlabel('Days Since 1/22/2020', size=30)
    plt.ylabel('# of Cases', size=30)
    plt.xticks(size=20)
    plt.yticks(size=20)
    plt.show()

    plt.figure(figsize=(16, 10))
    plt.bar(x, y2)
    plt.plot(x, confirmed_increase_avg, color='red', linestyle='dashed')
    plt.legend(['Moving Average {} Days'.format(window), '{} Daily Increase in Confirmed Cases'.format(country)], prop={'size': 20})
    plt.title('{} Daily Increases in Confirmed Cases'.format(country), size=30)
    plt.xlabel('Days Since 1/22/2020', size=30)
    plt.ylabel('# of Cases', size=30)
    plt.xticks(size=20)
    plt.yticks(size=20)
    plt.show()

    plt.figure(figsize=(16, 10))
    plt.bar(x, y3)
    plt.plot(x, death_increase_avg, color='red', linestyle='dashed')
    plt.legend(['Moving Average {} Days'.format(window), '{} Daily Increase in Confirmed Deaths'.format(country)], prop={'size': 20})
    plt.title('{} Daily Increases in Deaths'.format(country), size=30)
    plt.xlabel('Days Since 1/22/2020', size=30)
    plt.ylabel('# of Cases', size=30)
    plt.xticks(size=20)
    plt.yticks(size=20)
    plt.show()

    plt.figure(figsize=(16, 10))
    plt.bar(x, y4)
    plt.plot(x, recovery_increase_avg, color='red', linestyle='dashed')
    plt.legend(['Moving Average {} Days'.format(window), '{} Daily Increase in Confirmed Recoveries'.format(country)], prop={'size': 20})
    plt.title('{} Daily Increases in Recoveries'.format(country), size=30)
    plt.xlabel('Days Since 1/22/2020', size=30)
    plt.ylabel('# of Cases', size=30)
    plt.xticks(size=20)
    plt.yticks(size=20)
    plt.show()

# helper function for getting country's cases, deaths, and recoveries
def get_country_info(country_name):
    country_cases = []
    country_deaths = []
    country_recoveries = []

    for i in dates:
        country_cases.append(confirmed_df[confirmed_df['Country/Region']==country_name][i].sum())
        country_deaths.append(deaths_df[deaths_df['Country/Region']==country_name][i].sum())
        country_recoveries.append(recoveries_df[recoveries_df['Country/Region']==country_name][i].sum())
    return (country_cases, country_deaths, country_recoveries)


def country_visualizations(country_name):
    country_info = get_country_info(country_name)
    country_cases = country_info[0]
    country_deaths = country_info[1]
    country_recoveries = country_info[2]

    country_daily_increase = daily_increase(country_cases)
    country_daily_death = daily_increase(country_deaths)
    country_daily_recovery = daily_increase(country_recoveries)

    country_plot(adjusted_dates, country_cases, country_daily_increase, country_daily_death, country_daily_recovery, country_name)

# 每个国家的country_cases, country_deaths, country_recoveries，本来太多了，这里只显示中美
# countries = ['US', 'Russia', 'India', 'Brazil', 'South Africa', 'China', 'Italy',
#              'Germany', 'Spain', 'France', 'United Kingdom', 'Peru', 'Mexico', 'Colombia', 'Saudi Arabia', 'Iran', 'Bangladesh',
#             'Pakistan', 'Turkey', 'Philippines', 'Iraq', 'Indonesia', 'Israel', 'Ukraine', 'Ecuador', 'Bolivia', 'Netherlands']
countries = ['US','China']
for country in countries:
    country_visualizations(country)

​

​

# Country Comparison
# removed redundant code
# 下面这几个国家比较
compare_countries = ['US', 'Brazil', 'India', 'Russia', 'South Africa']
graph_name = ['Coronavirus Confirmed Cases', 'Coronavirus Confirmed Deaths', 'Coronavirus Confirmed Recoveries']

for num in range(3):
    plt.figure(figsize=(16, 10))
    for country in compare_countries:
        plt.plot(get_country_info(country)[num])
    plt.legend(compare_countries, prop={'size': 20})
    plt.xlabel('Days since 3/1', size=30)
    plt.ylabel('# of Cases', size=30)
    plt.title(graph_name[num], size=30)
    plt.xticks(size=20)
    plt.yticks(size=20)
    plt.show()

​

​

## Predictions for confirmed coronavirus cases worldwide
## 预测画图
def plot_predictions(x, y, pred, algo_name, color):
    plt.figure(figsize=(16, 10))
    plt.plot(x, y)
    plt.plot(future_forcast, pred, linestyle='dashed', color=color)
    plt.title('Worldwide Coronavirus Cases Over Time', size=30)
    plt.xlabel('Days Since 1/22/2020', size=30)
    plt.ylabel('# of Cases', size=30)
    plt.legend(['Confirmed Cases', algo_name], prop={'size': 20})
    plt.xticks(size=20)
    plt.yticks(size=20)
    plt.show()

plot_predictions(adjusted_dates, world_cases, svm_pred, 'SVM Predictions', 'purple')

​

​

plot_predictions(adjusted_dates, world_cases, linear_pred, 'Polynomial Regression Predictions', 'orange')
plot_predictions(adjusted_dates, world_cases, bayesian_pred, 'Bayesian Ridge Regression Predictions', 'green')

​

​

# Future predictions using SVM
# SVM对未来数据的预测
svm_df = pd.DataFrame({'Date': future_forcast_dates[-10:], 'SVM Predicted # of Confirmed Cases Worldwide': np.round(svm_pred[-10:])})
svm_df.style.background_gradient(cmap='Reds')

	Date	SVM Predicted # of Confirmed Cases Worldwide
0	10/21/2020	47874251.000000
1	10/22/2020	48390574.000000
2	10/23/2020	48910680.000000
3	10/24/2020	49434582.000000
4	10/25/2020	49962294.000000
5	10/26/2020	50493831.000000
6	10/27/2020	51029205.000000
7	10/28/2020	51568431.000000
8	10/29/2020	52111522.000000
9	10/30/2020	52658492.000000

# Future predictions using polynomial regression
linear_pred = linear_pred.reshape(1,-1)[0]
linear_df = pd.DataFrame({'Date': future_forcast_dates[-10:], 'Polynomial Predicted # of Confirmed Cases Worldwide': np.round(linear_pred[-10:])})
linear_df.style.background_gradient(cmap='Reds')

	Date	Polynomial Predicted # of Confirmed Cases Worldwide
0	10/21/2020	37750457.000000
1	10/22/2020	37917642.000000
2	10/23/2020	38080320.000000
3	10/24/2020	38238382.000000
4	10/25/2020	38391715.000000
5	10/26/2020	38540207.000000
6	10/27/2020	38683745.000000
7	10/28/2020	38822215.000000
8	10/29/2020	38955502.000000
9	10/30/2020	39083490.000000

# Future predictions using Bayesian Ridge
bayesian_df = pd.DataFrame({'Date': future_forcast_dates[-10:], 'Bayesian Ridge Predicted # of Confirmed Cases Worldwide': np.round(bayesian_pred[-10:])})
bayesian_df.style.background_gradient(cmap='Reds')

	Date	Bayesian Ridge Predicted # of Confirmed Cases Worldwide
0	10/21/2020	41655203.000000
1	10/22/2020	42000051.000000
2	10/23/2020	42345665.000000
3	10/24/2020	42692026.000000
4	10/25/2020	43039116.000000
5	10/26/2020	43386918.000000
6	10/27/2020	43735412.000000
7	10/28/2020	44084580.000000
8	10/29/2020	44434403.000000
9	10/30/2020	44784863.000000

# 死亡率的预测
mean_mortality_rate = np.mean(mortality_rate)
plt.figure(figsize=(16, 10))
plt.plot(adjusted_dates, mortality_rate, color='orange')
plt.axhline(y = mean_mortality_rate,linestyle='--', color='black')
plt.title('Worldwide Mortality Rate of Coronavirus Over Time', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('Case Mortality Rate', size=30)
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

​

​

# 治愈率的预测
mean_recovery_rate = np.mean(recovery_rate)
plt.figure(figsize=(16, 10))
plt.plot(adjusted_dates, recovery_rate, color='blue')
plt.title('Worldwide Recovery Rate of Coronavirus Over Time', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('Case Recovery Rate', size=30)
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

​

​

# 两个对比
plt.figure(figsize=(16, 10))
plt.plot(adjusted_dates, total_deaths, color='r')
plt.plot(adjusted_dates, total_recovered, color='green')
plt.legend(['death', 'recoveries'], loc='best', fontsize=25)
plt.title('Worldwide Coronavirus Cases', size=30)
plt.xlabel('Days Since 1/22/2020', size=30)
plt.ylabel('# of Cases', size=30)
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

​

​

# 两个对比
plt.figure(figsize=(16, 10))
plt.plot(total_recovered, total_deaths)
plt.title('# of Coronavirus Deaths vs. # of Coronavirus Recoveries', size=30)
plt.xlabel('# of Coronavirus Recoveries', size=30)
plt.ylabel('# of Coronavirus Deaths', size=30)
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

​

​

# 获取国家地区关于确诊病例的信息
unique_countries =  list(latest_data['Country_Region'].unique())

country_confirmed_cases = []
country_death_cases = []
country_active_cases = []
country_recovery_cases = []
country_incidence_rate = []
country_mortality_rate = []

no_cases = []
for i in unique_countries:
    cases = latest_data[latest_data['Country_Region']==i]['Confirmed'].sum()
    if cases > 0:
        country_confirmed_cases.append(cases)
    else:
        no_cases.append(i)

for i in no_cases:
    unique_countries.remove(i)

# sort countries by the number of confirmed cases
unique_countries = [k for k, v in sorted(zip(unique_countries, country_confirmed_cases), key=operator.itemgetter(1), reverse=True)]
for i in range(len(unique_countries)):
    country_confirmed_cases[i] = latest_data[latest_data['Country_Region']==unique_countries[i]]['Confirmed'].sum()
    country_death_cases.append(latest_data[latest_data['Country_Region']==unique_countries[i]]['Deaths'].sum())
    country_recovery_cases.append(latest_data[latest_data['Country_Region']==unique_countries[i]]['Recovered'].sum())
    country_active_cases.append(latest_data[latest_data['Country_Region']==unique_countries[i]]['Active'].sum())
    country_incidence_rate.append(latest_data[latest_data['Country_Region']==unique_countries[i]]['Incidence_Rate'].sum())
    country_mortality_rate.append(country_death_cases[i]/country_confirmed_cases[i])

country_df = pd.DataFrame({'Country Name': unique_countries, 'Number of Confirmed Cases': country_confirmed_cases,
                          'Number of Deaths': country_death_cases, 'Number of Recoveries' : country_recovery_cases,
                          'Number of Active Cases' : country_active_cases, 'Incidence Rate' : country_incidence_rate,
                          'Mortality Rate': country_mortality_rate})
# number of cases per country/region

country_df.style.background_gradient(cmap='Oranges')

	Country Name	Number of Confirmed Cases	Number of Deaths	Number of Recoveries	Number of Active Cases	Incidence Rate	Mortality Rate
0	US	8212981	220119	3272603	4720260.000000	7590091.746066	0.026801
1	India	7597063	115197	6733328	748538.000000	26859.671287	0.015163
2	Brazil	5250727	154176	4526393	570158.000000	90720.527859	0.029363
3	Russia	1406667	24205	1070920	311542.000000	83205.304230	0.017207
4	Argentina	1002662	26716	803965	171981.000000	2218.486032	0.026645
5	Spain	974449	33992	150376	790081.000000	38939.187511	0.034883
6	Colombia	965883	29102	867961	68820.000000	55190.061088	0.030130
7	France	952600	33647	109611	809342.000000	13307.247668	0.035321
8	Peru	868675	33759	784056	50860.000000	65217.342575	0.038863
9	Mexico	854926	86338	727759	40829.000000	22262.251352	0.100989
10	United Kingdom	744122	43816	2613	697693.000000	10559.859426	0.058883

unique_provinces =  list(latest_data['Province_State'].unique())
province_confirmed_cases = []
province_country = []
province_death_cases = []
# province_recovery_cases = []
province_active = []
province_incidence_rate = []
province_mortality_rate = []

no_cases = []
for i in unique_provinces:
    cases = latest_data[latest_data['Province_State']==i]['Confirmed'].sum()
    if cases > 0:
        province_confirmed_cases.append(cases)
    else:
        no_cases.append(i)

# remove areas with no confirmed cases
for i in no_cases:
    unique_provinces.remove(i)

unique_provinces = [k for k, v in sorted(zip(unique_provinces, province_confirmed_cases), key=operator.itemgetter(1), reverse=True)]
for i in range(len(unique_provinces)):
    province_confirmed_cases[i] = latest_data[latest_data['Province_State']==unique_provinces[i]]['Confirmed'].sum()
    province_country.append(latest_data[latest_data['Province_State']==unique_provinces[i]]['Country_Region'].unique()[0])
    province_death_cases.append(latest_data[latest_data['Province_State']==unique_provinces[i]]['Deaths'].sum())
#     province_recovery_cases.append(latest_data[latest_data['Province_State']==unique_provinces[i]]['Recovered'].sum())
    province_active.append(latest_data[latest_data['Province_State']==unique_provinces[i]]['Active'].sum())
    province_incidence_rate.append(latest_data[latest_data['Province_State']==unique_provinces[i]]['Incidence_Rate'].sum())
    province_mortality_rate.append(province_death_cases[i]/province_confirmed_cases[i])

#前100数据
province_limit = 100
province_df = pd.DataFrame({'Province/State Name': unique_provinces[:province_limit], 'Country': province_country[:province_limit], 'Number of Confirmed Cases': province_confirmed_cases[:province_limit],
                          'Number of Deaths': province_death_cases[:province_limit],'Number of Active Cases' : province_active[:province_limit],
                            'Incidence Rate' : province_incidence_rate[:province_limit], 'Mortality Rate': province_mortality_rate[:province_limit]})
# number of cases per country/region

province_df.style.background_gradient(cmap='Oranges')

	Province/State Name	Country	Number of Confirmed Cases	Number of Deaths	Number of Active Cases	Incidence Rate	Mortality Rate
0	Maharashtra	India	1601365	42240	174246.000000	1300.397989	0.026377
1	Sao Paulo	Brazil	1064039	38035	117805.000000	2317.206090	0.035746
2	California	US	879645	16982	862663.000000	101246.390300	0.019306
3	Texas	US	856948	17481	839467.000000	635233.906196	0.020399
4	Andhra Pradesh	India	786050	6453	35065.000000	1458.256997	0.008209
5	Karnataka	India	770604	10542	106233.000000	1140.576323	0.013680
6	Florida	US	756727	16021	740706.000000	265466.585181	0.021171
7	Tamil Nadu	India	690936	10691	38093.000000	887.621729	0.015473
8	England	United Kingdom	629211	38783	590428.000000	1124.048720	0.061638
9	New York	US	485279	33366	451913.000000	74530.743741	0.068756
10	Uttar Pradesh	India	456865	6685	31495.000000	192.054719	0.014632

# return the data table with province/state info for a given country
def country_table(country_name):
    states = list(latest_data[latest_data['Country_Region']==country_name]['Province_State'].unique())
    state_confirmed_cases = []
    state_death_cases = []
    # state_recovery_cases = []
    state_active = []
    state_incidence_rate = []
    state_mortality_rate = []

    no_cases = []
    for i in states:
        cases = latest_data[latest_data['Province_State']==i]['Confirmed'].sum()
        if cases > 0:
            state_confirmed_cases.append(cases)
        else:
            no_cases.append(i)

    # remove areas with no confirmed cases
    for i in no_cases:
        states.remove(i)

    states = [k for k, v in sorted(zip(states, state_confirmed_cases), key=operator.itemgetter(1), reverse=True)]
    for i in range(len(states)):
        state_confirmed_cases[i] = latest_data[latest_data['Province_State']==states[i]]['Confirmed'].sum()
        state_death_cases.append(latest_data[latest_data['Province_State']==states[i]]['Deaths'].sum())
    #     state_recovery_cases.append(latest_data[latest_data['Province_State']==states[i]]['Recovered'].sum())
        state_active.append(latest_data[latest_data['Province_State']==states[i]]['Active'].sum())
        state_incidence_rate.append(latest_data[latest_data['Province_State']==states[i]]['Incidence_Rate'].sum())
        state_mortality_rate.append(state_death_cases[i]/state_confirmed_cases[i])


    state_df = pd.DataFrame({'State Name': states, 'Number of Confirmed Cases': state_confirmed_cases,
                              'Number of Deaths': state_death_cases, 'Number of Active Cases' : state_active,
                             'Incidence Rate' : state_incidence_rate, 'Mortality Rate': state_mortality_rate})
    # number of cases per country/region
    return state_df

#美国的数据
us_table = country_table('US')
us_table.style.background_gradient(cmap='Oranges')

	State Name	Number of Confirmed Cases	Number of Deaths	Number of Active Cases	Incidence Rate	Mortality Rate
0	California	879645	16982	862663.000000	101246.390300	0.019306
1	Texas	856948	17481	839467.000000	635233.906196	0.020399
2	Florida	756727	16021	740706.000000	265466.585181	0.021171
3	New York	485279	33366	451913.000000	74530.743741	0.068756
4	Illinois	350744	9496	341248.000000	222737.038481	0.027074
5	Georgia	341310	7657	333653.000000	545676.734025	0.022434
6	North Carolina	247172	3939	243233.000000	241343.144415	0.015936
7	Tennessee	232061	2922	229139.000000	349619.782941	0.012592
8	Arizona	231897	5830	226068.000000	49228.321751	0.025140
9	New Jersey	221205	16214	204991.000000	46343.379793	0.073299
10	Pennsylvania	188381	8475	179906.000000	66883.526616	0.044989

#中国的数据
china_table = country_table('China')
china_table.style.background_gradient(cmap='Oranges')

	State Name	Number of Confirmed Cases	Number of Deaths	Number of Active Cases	Incidence Rate	Mortality Rate
0	Hubei	68139	4512	0.000000	115.158019	0.066218
1	Hong Kong	5256	105	169.000000	70.108155	0.019977
2	Guangdong	1889	8	40.000000	1.664904	0.004235
3	Zhejiang	1283	1	3.000000	2.236360	0.000779
4	Henan	1281	22	3.000000	1.333680	0.017174
5	Shanghai	1095	7	75.000000	4.517327	0.006393
6	Hunan	1019	4	0.000000	1.477026	0.003925
7	Anhui	991	6	0.000000	1.567046	0.006054
8	Heilongjiang	948	13	0.000000	2.512589	0.013713
9	Beijing	938	9	2.000000	4.354689	0.009595
10	Jiangxi	935	1	0.000000	2.011618	0.001070

total_world_cases = np.sum(country_confirmed_cases)
us_confirmed = latest_data[latest_data['Country_Region']=='US']['Confirmed'].sum()
outside_us_confirmed = total_world_cases - us_confirmed

plt.figure(figsize=(16, 9))
plt.barh('United States', us_confirmed)
plt.barh('Outside United States', outside_us_confirmed)
plt.title('# of Total Coronavirus Confirmed Cases', size=20)
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()


plt.figure(figsize=(16, 9))
plt.barh('United States', us_confirmed/total_world_cases)
plt.barh('Outside United States', outside_us_confirmed/total_world_cases)
plt.title('# of Coronavirus Confirmed Cases Expressed in Percentage', size=20)
plt.xticks(size=20)
plt.yticks(size=20)
plt.show()

​

​

# Only show 15 countries with the most confirmed cases, the rest are grouped into the other category
visual_unique_countries = []
visual_confirmed_cases = []
others = np.sum(country_confirmed_cases[10:])

for i in range(len(country_confirmed_cases[:10])):
    visual_unique_countries.append(unique_countries[i])
    visual_confirmed_cases.append(country_confirmed_cases[i])

visual_unique_countries.append('Others')
visual_confirmed_cases.append(others)

def plot_bar_graphs(x, y, title):
    plt.figure(figsize=(16, 12))
    plt.barh(x, y)
    plt.title(title, size=20)
    plt.xticks(size=20)
    plt.yticks(size=20)
    plt.show()

# good for a lot x values
def plot_bar_graphs_tall(x, y, title):
    plt.figure(figsize=(19, 18))
    plt.barh(x, y)
    plt.title(title, size=25)
    plt.xticks(size=25)
    plt.yticks(size=25)
    plt.show()

plot_bar_graphs(visual_unique_countries, visual_confirmed_cases, '# of Covid-19 Confirmed Cases in Countries/Regions')

​

​

log_country_confirmed_cases = [math.log10(i) for i in visual_confirmed_cases]
plot_bar_graphs(visual_unique_countries, log_country_confirmed_cases, 'Common Log # of Coronavirus Confirmed Cases in Countries/Regions')

​

​

# Only show 10 provinces with the most confirmed cases, the rest are grouped into the other category
visual_unique_provinces = []
visual_confirmed_cases2 = []
others = np.sum(province_confirmed_cases[10:])
for i in range(len(province_confirmed_cases[:10])):
    visual_unique_provinces.append(unique_provinces[i])
    visual_confirmed_cases2.append(province_confirmed_cases[i])

visual_unique_provinces.append('Others')
visual_confirmed_cases2.append(others)

plot_bar_graphs(visual_unique_provinces, visual_confirmed_cases2, '# of Coronavirus Confirmed Cases in Provinces/States')

log_province_confirmed_cases = [math.log10(i) for i in visual_confirmed_cases2]
plot_bar_graphs(visual_unique_provinces, log_province_confirmed_cases, 'Log of # of Coronavirus Confirmed Cases in Provinces/States')

​

​

## 检测率
us_medical_data.fillna(value=0, inplace=True)

def plot_us_medical_data():
    states = us_medical_data['Province_State'].unique()
    testing_number = []
    testing_rate = []

    for i in states:
        testing_number.append(us_medical_data[us_medical_data['Province_State']==i]['People_Tested'].sum())
        testing_rate.append(us_medical_data[us_medical_data['Province_State']==i]['Testing_Rate'].max())

    # only show the top 15 states
    testing_states = [k for k, v in sorted(zip(states, testing_number), key=operator.itemgetter(1), reverse=True)]
    testing_rate_states = [k for k, v in sorted(zip(states, testing_rate), key=operator.itemgetter(1), reverse=True)]

    for i in range(len(states)):
        testing_number[i] = us_medical_data[us_medical_data['Province_State']==testing_states[i]]['People_Tested'].sum()
        testing_rate[i] = us_medical_data[us_medical_data['Province_State']==testing_rate_states[i]]['Testing_Rate'].sum()

    top_limit = 30

    plot_bar_graphs_tall(testing_states[:top_limit], testing_number[:top_limit], 'Total Testing per State (Top 30)')
    plot_bar_graphs_tall(testing_rate_states[:top_limit], testing_rate[:top_limit], 'Testing Rate per 100,000 People (Top 30)')


plot_us_medical_data()

​

​

## 移动数据
def get_mobility_by_state(transport_type, state, day):
    return apple_mobility[apple_mobility['sub-region']==state][apple_mobility['transportation_type']==transport_type].sum()[day]

get_mobility_by_state('walking', 'Connecticut', '2020-07-30')
revised_dates = []
for i in range(len(dates)):
    revised_dates.append(datetime.datetime.strptime(dates[i], '%m/%d/%y').strftime('%Y-%m-%d'))

def weekday_or_weekend(date):
    date_obj = datetime.datetime.strptime(date, '%Y-%m-%d')
    day_of_the_week =  date_obj.weekday()
    if (day_of_the_week+1) % 6 == 0 or (day_of_the_week+1) % 7 == 0:
        return True
    else:
        return False

revised_day_since_1_22 = [i for i in range(len(revised_dates))]

import matplotlib.dates as mdates
states = ['New York', 'Connecticut', 'Florida', 'California', 'Texas', 'Georgia', 'Arizona', 'Illinois', 'Louisiana', 'Ohio',
          'Tennessee', 'North Carolina', 'South Carolina', 'Alabama', 'Missouri', 'Kansas', 'Pennsylvania', 'Wisconsin', 'Virginia', 'Massachusetts', 'Utah', 'Minnesota',
         'Oklahoma', 'Iowa', 'Arkansas', 'Kentucky', 'Puerto Rico', 'Colorado', 'New Jersey', 'Idaho', 'New Jersey', 'Nevada', 'Maryland']
states.sort()

# making sure the dates are in sync
mobility_latest_date = apple_mobility.columns[-1]
mobility_latest_index = revised_dates.index(mobility_latest_date)

for state in states:
    # weekend and weekday mobility are separated
    weekday_mobility = []
    weekday_mobility_dates = []
    weekend_mobility = []
    weekend_mobility_dates = []

    for i in range(len(revised_dates)):
        if i <= mobility_latest_index:
            if weekday_or_weekend(revised_dates[i]):
                weekend_mobility.append(get_mobility_by_state('walking', state, revised_dates[i]))
                weekend_mobility_dates.append(i)
            else:
                weekday_mobility.append(get_mobility_by_state('walking', state, revised_dates[i]))
                weekday_mobility_dates.append(i)
        else:
            pass

    # remove null values (they are counted as 0)
    for i in range(len(weekend_mobility)):
        if weekend_mobility[i] == 0 and i != 0:
            weekend_mobility[i] = weekend_mobility[i-1]
        elif weekend_mobility[i] == 0 and i == 0:
            weekend_mobility[i] = weekend_mobility[i+1]
        else:
            pass

    for i in range(len(weekday_mobility)):
        if weekday_mobility[i] == 0 and i != 0:
            weekday_mobility[i] = weekday_mobility[i-1]
        elif weekday_mobility[i] == 0 and i == 0:
            weekday_mobility[i] = weekday_mobility[i+1]
        else:
            pass


    weekday_mobility_average = moving_average(weekday_mobility, 7)
    weekend_mobility_average = moving_average(weekend_mobility, 7)

    plt.figure(figsize=(16, 10))
    plt.bar(weekday_mobility_dates, weekday_mobility, color='cornflowerblue')
    plt.plot(weekday_mobility_dates, weekday_mobility_average, color='green')

    plt.bar(weekend_mobility_dates, weekend_mobility, color='salmon')
    plt.plot(weekend_mobility_dates, weekend_mobility_average, color='black')

    plt.legend(['Moving average (7 days) weekday mobility', 'Moving Average (7 days) weekend mobility', 'Weekday mobility', 'Weekend mobility'], prop={'size': 25})
    plt.title('{} Walking Mobility Data'.format(state), size=25)
    plt.xlabel('Days since 1/22', size=25)
    plt.ylabel('Mobility Value', size=25)
    plt.xticks(size=25)
    plt.yticks(size=25)
    plt.show()

​

​