Python for循环输出到数据框

我是python的新手，已经开始在COVID-19锁定期间学习编程。我认为这将是一个很好的实践数据集，因此建立了一个程序，可以根据输入参数对数据进行图形处理。我建立了一个for循环，可以遍历数据，为指定的每个国家绘制一条线，然后执行线性回归，输出给定日期的预测值。输出的print语句可以很好地满足我想要的数据，但我想将其转换为称为预测的数据框。我很难做到这一点，并尝试了一个numpy数组以及使用熊猫。我能够输出到数组或数据框，但是它只会从循环迭代的最后一个条目中输入数据。我已经阅读了其他类似问题的帖子，但是我可以 不能足够准确地阅读他们的代码以为我的代码画出类似的解决方案。任何帮助将非常感激。

prediction = pd.DataFrame()

countries = [country for country in covid4]

for country in covid4:
    plt.plot(covid4.index, covid4[country], marker = ',', linestyle = '-', linewidth = 2)

    slope, intercept, rval, pval, stder = linregress(covid4.index, covid4[country])
    y2 = (slope * days_from_covid_start) + intercept
    print(country, y2, (rval**2), pval, stder)

    #prediction = pd.DataFrame({'country': country, 'predicted': y2, 'rval': (rval**2), 'pval': pval, 'stder': stder}, index = [len(country)])
    prediction.append(pd.DataFrame([country,y2, (rval**2), pval, stder], index=[0]), ignore_index=True)

print output = US 5179943.844549271 0.9006676283944509 2.6485459147090845e-114 621.5097854328127
Brazil 2936531.6824269355 0.7832301173896118 2.574767200468765e-76 594.2400982216876
India 2073088.1203383887 0.6345313255372067 7.298314985773991e-51 624.2194955678399
Russia 978300.4286278635 0.9142926929617138 1.7522978381731647e-121 108.50766510347822
Peru 505978.0715319741 0.858987519325372 2.99136171150524e-97 76.57252904070529
Colombia 356724.59233558143 0.6333783192991587 1.0395048549255093e-50 107.7550260556157
South Africa 472308.1410419348 0.6966400875418044 6.071717833597016e-60 122.57207991971943
Mexico 466476.80000000005 0.8003861479274035 2.486332913502741e-80 89.06795808066232
Spain 406391.36529570003 0.9113259379908633 7.934640637615308e-120 39.7913275775704
Argentina 231457.53997115127 0.6268247164508722 7.600868471677846e-50 70.99075237604976
Chile 400397.3610073682 0.8667991679346673 5.033317538752307e-100 58.15733490786391
Iran 361576.26469403406 0.964179243093371 6.270093847731166e-164 23.965905293313885
United Kingdom 396538.81819552195 0.9237619398832538 3.5207778505031074e-127 36.6462363789314
France 307895.94598781073 0.9160909120260833 1.628524516693465e-122 29.273492653868225
Bangladesh 258210.0824529258 0.8134667793006155 1.245704530630672e-83 47.16886076960728
Saudi Arabia 301026.7291359661 0.8720838496759029 5.387824647150151e-102 42.7760188991517

*** edit covid4在每个国家/地区都有一列，并列出了该日期的索引，这些行表示该国家/地区在该日期的案件。

对于上下文，完整代码如下

#specifies countries that have above 'n' number
n = 300000

#search for a specific country's COVID data from the date specified.
country_to_find = ['']
value = 'cases'
today = '7/30/20'
#specifies how many days from covid first onset to map linear regression model for country to find
days_from_covid_start = 230


import matplotlib.pyplot as plt 
import pandas as pd
import numpy as np
from datetime import date
import seaborn as sns
from scipy.stats import linregress

sns.set()

df = pd.read_csv('https://data.humdata.org/hxlproxy/api/data-preview.csv?url=https%3A%2F%2Fraw.githubusercontent.com%2FCSSEGISandData%2FCOVID-19%2Fmaster%2Fcsse_covid_19_data%2Fcsse_covid_19_time_series%2Ftime_series_covid19_confirmed_global.csv&filename=time_series_covid19_confirmed_global.csv')
covid = df.groupby('Country/Region', as_index = False).sum()

date_i = covid[(today)]
G = str(date_i.sum())
covid = covid.sort_values((today), ascending=False)

covid.drop(columns=['Lat', 'Long'], inplace=True)
sig_cases = covid[date_i <= n].index
covid.drop(sig_cases, inplace=True)
plt.figure(figsize=(14,7.2))
sns.barplot(y = covid['Country/Region'], x = date_i, data = covid, )


covid = covid.sort_values((today), ascending=True)
x_values = range(len(covid['Country/Region']))
countries = covid['Country/Region'].map(str)


print(date_i)
#matplotlib graph, shows the same data as previous seaborn plot

'''cases = covid[(today)]
plt.figure(figsize=(14,7.2))
plt.ylabel('Countries')
plt.xlabel('Total Recorded')
plt.title('Covid Cases By Country', size = 10)
group = covid.groupby('Country/Region').sum()
plt.yticks(x_values, countries, rotation= 0, size = 10)
plt.barh(x_values, cases,  color='Red')
plt.grid()'''



covid.sort_values((today), ascending = False, inplace = True)

covid_transposed = covid.transpose()

covid_transposed.columns = covid_transposed.iloc[0]
covid_transposed2 = covid_transposed[1:]

covid_transposed2.to_csv('covid_transposed_new_index.csv', index = False)
covid_transposed2.to_csv('covid_transposed_new.csv')


covid5 = pd.read_csv('covid_transposed_new.csv')
covid5.rename( columns={'Unnamed: 0':'Date'}, inplace=True)
sns.set_style("ticks")
plt.subplot()
plt.figure(figsize=(14,7.2))

for country in covid5:
    if country in country_to_find:
        plt.plot(covid5.index, covid5[country], marker = ',', linestyle = '-', linewidth = 2)
        slope, intercept, rval, pval, stder = linregress(covid5.index, covid5[country])
        y = (slope * days_from_covid_start) + intercept
        print(y, rval **2 , pval, stder)

plt.xticks(covid5.index[::30], covid5.Date[::30])
plt.legend(country_to_find, loc=2, prop={'size': 15})
plt.xticks(rotation=32, size = 15)
plt.yticks(size = 15)
plt.title('Covid ' +str(value)+ ' per selected country', size=20)
plt.ylabel('Num ' +str(value), size=20)
plt.xlabel('Date', size=20)
plt.grid(True)

covid4 = pd.read_csv('covid_transposed_new_index.csv')

plt.figure(figsize=(14,7.2))

plt.subplot()
prediction = pd.DataFrame()

countries = [country for country in covid4]

for country in covid4:
    plt.plot(covid4.index, covid4[country], marker = ',', linestyle = '-', linewidth = 2)
    slope, intercept, rval, pval, stder = linregress(covid4.index, covid4[country])
    y2 = (slope * days_from_covid_start) + intercept
    prediction.append(pd.DataFrame(np.array([[country], [y2], [(rval**2)], [pval], [stder]])))
    print(country, y2, (rval**2), pval, stder)
    

plt.xticks(covid4.index[::30])
plt.legend(countries, loc=2, prop={'size': 9})
plt.xticks(rotation=32, size = 15)
plt.yticks(size = 15)
plt.title('Covid ' +str(value)+ ' per selected country', size=20)
plt.ylabel('Num ' +str(value), size=20)
plt.xlabel('Days from first COVID-19 identification', size=20)
plt.grid(True)

sns.set_style("darkgrid")


plt.show()

print(str('Total cases as of date ' + str(today) + ' is ')+ G)
print(prediction)

ps我知道回归模型不起作用哈哈