image_output_5.png image_output_4.png text_plain_output_2.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

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()
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'])
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
Nigeria_cases = 'Nigerian_cases.rename(column={last_update:confirmed,suspected:suspected,fatal:fatal,recovered:recovered,deaths:deaths)'
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()
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()
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()
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()
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'