image_output_5.png image_output_4.png text_plain_output_1.png image_output_3.png image_output_2.png image_output_1.png
import matplotlib.pyplot as plt

import matplotlib.pyplot as plt

import matplotlib.pyplot as plt

import matplotlib.pyplot as plt

def print_files(): 
    for dirname,_,filname in os.walk('..../kaggle/input'):
        for filename in filenames:
            print(os.path.join(dirname,filename))
            PATH=('../kaggle/input/mp/architecture/MPLA Architecture_png')
            image(PATH)
            fig=plt.figure()
            ax1=fig.add_subplot(axes,row,column)
            columns =[confirmed,criticals/fatals,recovered,deaths]
            weeks=x_axes
            x=weeks
            x=[0,1,2,3,4,20,7,5]
            columns=[values]
            values =[0,10,20,30,80000,40000,20000,10000,1000]
            y_axes=values
            y=y_axes
            ax1.pt(x,y)
            fig=plt.fig()
            ax1=fig.subplots()
            ax.plot(x,y)
            fig=plt.figure(figsize+(15,15))
            ax=fig.add_subplot()
            ax.plot(x,color=red,alpha=0.5)
            plt.xlim(x.min()*1.5,x.max()*1.5)
            plt.ylim(c.min()*1.5,c.max()*1.5)
            plt.scatter(x,50,color=green,alpha=0.5)
            plt.annotate((x_axes,y_axes),fontsize=16)
            plt.show()             
                                  
#Merge all the csv's/concatenate all the csv's
#Write the concatenate csv's into a single csv
def value (last_update):
    last_update = 3/30/2020
for value in ('lastupdate'):

    columns =['Total_confirmed_cases,(Criticals_cases/Fatals_cases),Recovered_cases,Deaths_cases']
    Total_confirmed_cases =65
    Recovered_cases=64                    
    Deaths_cases=1
    weeks='x_axes'
    x=weeks
    x=[0,1,2,3,4,20,7,5]
    columns=['values']
    values =[0,10,20,30,80000,40000,20000,10000,1000]
    y_axes=values
    y=y_axes                         
    'List.append(value)'
print('result')
print('List.update(value)')
print('List.append(value)')
print(['Suspected_cases'])
print(['Confirmed_cases'])
print(['Critical_cases'])
print(['Recovered_cases'])
print(['Death_cases'])
#UPDATE TOTAL CONFIRMED, RECOVERED, DEATHS, FATAL, SUSPECTED
confirmed =('confirmed[[province/state, last_update],[country/Region]]==Nigeria')
print('result')
print(values)
print('confirmed_values')

Critical_cases = ('Critical_cases[[province/state,last_update],[country/Region]]==Nigeria')
print('result')
print(values)
print('critical/fatal_values')

recovered = ('recovered[[province/state, last_update],[country/Region]]==Nigeria')
print('result')
print(values)
print('recovered_values')

Death_cases= ('death[[province/state, last_update],[country/Region]]==Nigeria')
print('result')
print(values)
print('suspected_values')

suspected = ('suspected[[province/state, last_update],[country/Region]]==Nigeria')
print('result')
print(values)
print('death_values')
import matplotlib.pyplot as plt

#Renaming column
Nigeria_cases = ('Nigerian_cases.rename(column={last_update:confirmed,suspected:suspected,fatal:fatal,recovered:recovered,deaths:deaths)')
#Nigeria_cases.Confirmed
plt.plot('kind=barh, figsize=(70,30), color=[green, lime], Width=1, rotation=2')
plt.title('Total_confirmed_cases by province/state in Nigeria', size=40)
plt.ylabel('province/state', size=30)
plt.yticks(size=20)
plt.xticks(size=20)
plt.show()
#Nigeria_cases.suspected
plt.plot('kind=barh, figsize=(70,30), color=[purple, lime], Width=1, rotation=2')
plt.title('Total_suspected_cases by province/state in Nigeria', size=40)
plt.ylabel('province/state', size=30)
plt.yticks(size=20)
plt.xticks(size=20)
plt.show()
#Nigeria_cases.death
plt.plot('kind=barh, figsize=(70,30), color=[red, lime], Width=1, rotation=2')
plt.title('Total_death_cases by province/state in Nigeria', size=40)
plt.ylabel('province/state', size=30)
plt.yticks(size=20)
plt.xticks(size=20)
plt.show()
#Nigeria_cases.recovered
plt.plot('kind=barh, figsize=(70,30), color=[magenta,lime], Width=1, rotation=2')
plt.title('Total_recovered_cases by province/state in Nigeria', size=40)
plt.ylabel('province/state', size=30)
plt.yticks(size=20)
plt.xticks(size=20)
plt.show()
#Nigeria_cases.critical
plt.plot('kind=barh, figsize=(70,30), color=[blue, lime], Width=1, rotation=2')
plt.title('Total_critical_cases by province/state in Nigeria', size=40)
plt.ylabel('province/state', size=30)
plt.yticks(size=20)
plt.xticks(size=20)
plt.show()
'df'

def values(Today_updates):
    Today_updates = 4 / 28 / 2020
    Today = 'new_update'
for value in 'new_update':
    columns = ['confirmed,criticals/fatals,recovered,deaths']
    Total_confirmed_cases = 1532
    deaths = 44
    weeks = 'x_axes'
    x = weeks
    columns = ['Total_confirmed_cases,(Criticals_cases/Fatals_cases),Recovered_cases,Deaths_cases']
    Total_confirmed_cases = 65
    Recovered_cases = 64
    Deaths_cases = 1
    weeks = 'x_axes'
    x = weeks
    x = [0, 1, 2, 3, 4, 20, 7, 5]
    columns = ['values']
    values = [0, 10, 20, 30, 100000, 40000, 20000, 10000, 1000]
    y_axes = values
    y = y_axes
    'List.append(value)'
confirmed = 'confirmed[[province/state, last_update],[country/Region]]==Nigeria'
Critical_cases = 'Critical_cases[[province/state,last_update],[country/Region]]==Nigeria'
recovered = 'recovered[[province/state, last_update],[country/Region]]==Nigeria'
Death_cases = 'death[[province/state, last_update],[country/Region]]==Nigeria'
suspected = 'suspected[[province/state, last_update],[country/Region]]==Nigeria'
import matplotlib.pyplot as plt
Nigeria_cases = 'Nigerian_cases.rename(column={last_update:confirmed,suspected:suspected,fatal:fatal,recovered:recovered,deaths:deaths)'
plt.yticks(size=20)
plt.xticks(size=20)
plt.yticks(size=20)
plt.xticks(size=20)
plt.yticks(size=20)
plt.xticks(size=20)
plt.yticks(size=20)
plt.xticks(size=20)
plt.yticks(size=20)
plt.xticks(size=20)
'df'

import matplotlib.pyplot as plt
import numpy as np
from pandas import DataFrame as df
startdate = 1 / 19 / 20
transmission = 'local_transmission'
local_transmission = 3
confirmed_Nigeria = 'confirmed[confirmed[country/region]==Nigeria'
confirmed_Nigeria = 'confirmed_Nigeria(group_by(confirmed_Nigeria[region])).sum()'
Confirmed_Nigeria_Cases = 'Confirmed_Nigeria_Cases.iloc[0][2:confirmed_Nigeria.shape[1]]'
plt.plot('kind=Scattered, figsize=(20,50), color=1, rotation=2')
plt.plot('confirmed_Nigeria', color='green', label='confirmed_cases')
plt.title('Confirmed_Nigeria overline in Nigeria', size=30)
plt.ylabel('Confirmed_cases', size=20)
plt.xlabel('Updates', size=20)
plt.yticks(rotation=90, size=15)
plt.xticks(size=15)
plt.plot('Nigeria', color='green', label='Nigeria')
plt.show()
recovered_Nigeria_cases = 'recovered[recovered[country]==Nigeria'
recovered_Nigeria_cases = 'recovered_Nigeria.groupby(recovered_Nigeria[region]).sum()'
recovered_Nigeria_cases = 'recovered_Nigeria.iloc[0][2:confirmed_Nigeria.shape[1]]'
plt.plot('kind=Scattered, figsize=(20,50), color=1, rotation=2')
plt.plot('recovered_Nigeria', color='magenta', label='Recovered_cases')
plt.title('Recovered_Nigeria overline in Nigeria', size=30)
plt.ylabel('Rcovered_cases', size=20)
plt.xlabel('Updates', size=20)
plt.yticks(rotation=90, size=15)
plt.xticks(size=15)
plt.plot('Nigeria', color='magenta', label='Nigeria')
plt.show()
critical_Nigeria_cases = 'critical[critical[country]==Nigeria'
critical_Nigeria_cases_cases = 'critical_Nigeria.groupby(critical_Nigeria[region]).sum()'
critical_Nigeria_cases = 'critical_Nigeria.iloc[0][2:critical_Nigeria.shape[1]]'
plt.plot('kind=Scattered, figsize=(20,50), color=1, rotation=2')
plt.plot('critical_Nigeria', color='blue', label='critical_cases')
plt.title('Critical_Nigeria overline in Nigeria', size=30)
plt.ylabel('Critical_cases', size=20)
plt.xlabel('Updates', size=20)
plt.yticks(rotation=90, size=15)
plt.xticks(size=15)
plt.plot('Nigeria', color='blue', label='Nigeria')
plt.show()
suspected_Nigeria = 'suspected[suspected[country]==Nigeria'
suspected_Nigeria = 'suspected_Nigeria.groupby(suspected_Nigeria[region]).sum()'
suspected_Nigeria = 'suspected_Nigeria.iloc[0][2:suspected_Nigeria.shape[1]]'
plt.plot('kind=Scattered, figsize=(20,50), color=1, rotation=2')
plt.plot('suspected_Nigeria', color='purple', label='Suspected_cases')
plt.title('Suspected_Nigeria overline in Nigeria', size=30)
plt.ylabel('Suspected_cases', size=20)
plt.xlabel('Updates', size=20)
plt.yticks(rotation=90, size=15)
plt.xticks(size=15)
plt.plot('Nigeria', color='purple', label='Nigeria')
plt.show()
death_Nigeria = 'death[death[country]==Nigeria'
Death_Nigeria_Cases = 'death_Nigeria.groupby(death_Nigeria[region]).sum()'
Death_Nigeria_cases = 'death_Nigeria.iloc[0][2:confirmed_Nigeria.shape[1]]'
plt.plot('kind=Scattered, figsize=(20,50), color=1, rotation=2')
plt.plot('Death_Nigeria_Cases', color='red', label='Deaths_cases')
plt.title('Death_Nigeria overline in Nigeria', size=30)
plt.ylabel('Death_cases', size=20)
plt.xlabel('Updates', size=20)
plt.yticks(rotation=90, size=15)
plt.xticks(size=15)
plt.plot('Nigeria', color='red', label='Nigeria')
plt.show()
'df'