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

#12.1(a)
# x = np.linspace(-10,10, 200)
# y = 1/(1+x**2)
# plt.plot(x,y,'g', label = '1/(1+x^2)')
# plt.legend()
# plt.show()

#12.1(b)
# x = np.linspace(0,4)
# y1 = x**2
# y2 = np.exp(x)
# y3 = x**x
# plt.plot(x,y1, label = 'x^2')
# plt.plot(x,y2, label = 'e^x')
# plt.plot(x,y3, label = 'x^x')
# plt.legend()
# plt.show()

#12.1(c)
# x = np.linspace(0,3)
# # y1 = x**2
# # y2 = np.exp(x)
# # y3 = x**x
# # fig, axs = plt.subplots(3)
# # axs[0].plot(x,y1,'green',linestyle="--", label = 'x^2')
# # axs[0].set_ylim(0,25)
# # axs[1].plot(x,y2,'red',linestyle=':', label = 'e^x')
# # axs[1].set_ylim(0,25)
# # axs[2].plot(x,y3,'blue',  label = 'x^x')
# # axs[2].set_ylim(0,25)
# # fig.legend(title='Wykres',loc = "upper center")
# # plt.show()

#12.2
# titanic = pd.read_csv('titanic.csv', encoding = "UTF-8")
#12.2 (a)
# print(len(titanic[(titanic['Sex']=='female') & (titanic['Pclass']==1)& (titanic['Survived']==1)]))
#12.2 (b)
# print(np.mean(titanic[(titanic['Sex']=='male') &(titanic['Survived']==0)]['Age']))
#12.2 (c)
# colors = np.where(titanic['Sex']=='male','blue','red')
# titanic.plot.scatter(x = 'Age', y = 'Fare', c = colors)
# plt.show()
#12.2 (d)
# ((titanic[titanic['Survived']==1]).groupby(['Pclass']).size()/
#  titanic.groupby(['Pclass']).size()).plot.bar()
# plt.show()

#12.3
dane = sns.load_dataset("iris")
print(dane)
#12.3(a)
#sns.relplot(x = 'sepal_length', y ='sepal_width',data = dane, kind = 'scatter', hue = 'species')
#sns.catplot(x = 'species',y = 'petal_length', data = dane, kind = 'violin')
#plt.show()
#12.3(b)
#sns.pairplot(dane, hue = 'species')
reg = stats.linregress(dane['petal_width'],dane['petal_length'])
print(reg)
x = np.linspace(0,3)
y = reg.intercept + x*reg.slope
plt.plot(x,y,'r')
plt.plot(dane['petal_width'], dane['petal_length'],'o')
plt.show()