practice for titanic import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.nonparametric.kde import KDEUnivariate
from statsmodels.nonparametric import smoothers_lowess
from pandas import DataFrame
from patsy import dmatrices
df = pd.read_csv('C:/Users/huangshanshan1/downloads/train.csv')
df = df.drop(['Cabin'],axis=1)#而非df.drop(['Cabin'],axis=1) fig = plt.figure(figsize=(18,6),dpi=100) alpha = alpha_scatterplot = 0.2 alpha_bar_chart = 0.55
ax1 = plt.subplot2grid((2,3),(0,0)) df.Survived.value_counts().plot(kind='bar',alpha=alpha_bar_chart) ax1.set_xlim(-1,2) plt.title("Distribution of Survival,(1 = Survived)")
ax2 = plt.subplot2grid((2,3),(0,1)) plt.scatter(df.Survived,df.Age,alpha=alpha_scatterplot) plt.ylabel("Age") plt.grid(b=True,which='major',axis='y') plt.title("Survial by Age,(1 = Survived)")
ax3 = plt.subplot2grid((2,3),(0,2)) df.Pclass.value_counts().plot(kind="barh",alpha=alpha_bar_chart) ax3.set_ylim(-1,len(df.Pclass.value_counts())) plt.title("Class Distribution")
plt.subplot2grid((2,3),(1,0),colspan=2) df.Age[df.Pclass == 1].plot(kind='kde') df.Age[df.Pclass == 2].plot(kind='kde') df.Age[df.Pclass == 3].plot(kind='kde') plt.xlabel("Age") plt.title("Survial by Age,(1 = Survived)") plt.legend(('1st Class','2nd Class','3rd Class'),loc='best')
ax5 = plt.subplot2grid((2,3),(1,2)) df.Embarked.value_counts().plot(kind='bar',alpha=alpha_bar_chart) ax5.set_xlim(-1,len(df.Embarked.value_counts())) plt.title("Passengers per boarding location")
plt.figure(figsize=(6,4)) fig,ax = plt.subplots() df.Survived.value_counts().plot(kind='barh',color="blue",alpha = .65) ax.set_ylim(-1,len(df.Survived.value_counts())) plt.title("Survival (1 = Survived ,0 = Died ）")
ax1 = plt.subplot(1,2,1) df.Survived[df.Sex == 'male'].value_counts(sort=False).plot(kind='bar',label='Male') df.Survived[df.Sex == 'female'].value_counts(sort=False).plot(kind='bar',color='#FA2379',label='Female') ax1.set_xlim(-1,2) plt.title("Sex Survived Distribution");plt.legend(loc='best')
ax2 = plt.subplot(1,2,2) (df.Survived[df.Sex == 'male'].value_counts(sort=False)/float(df.Sex[df.Sex == 'male'].size)).plot(kind='bar',label='Male') (df.Survived[df.Sex == 'female'].value_counts(sort=False)/float(df.Sex[df.Sex == 'female'].size)).plot(kind='bar',color='#FA2379',label='Female') ax2.set_xlim(-1,2) plt.title("Sex Survived Distribution proportion");plt.legend(loc='best')
fig = plt.figure(figsize=(20,7),dpi=100) alpha_level = 0.65 ax1 = fig.add_subplot(141) df.Survived[df.Sex=='female'][df.Pclass!=3].value_counts().plot(kind='bar',label='highclassfemale',color='#FA2479')
ax1.set_xlim(-1,len(df.Survived[df.Sex=='female'][df.Pclass != 3].value_counts())) plt.title("high class female survived");plt.legend(loc="best")
ax2 = fig.add_subplot(142) df.Survived[df.Sex == 'male'][df.Pclass != 3].value_counts().plot(kind='bar',color='steelblue',alpha=alpha_level) ax1.set_xlim(-1,len(df.Survived[df.Sex == 'male'][df.Pclass !=3].value_counts())) plt.title("high class male survived");plt.legend(loc="best")
fig = plt.figure(figsize=(20,7),dpi=100) ax3 = fig.add_subplot(143) df.Survived[df.Sex == 'female'][df.Pclass == 3].value_counts().plot(kind='bar',color='#FA2479',alpha=alpha_level) ax1.set_xlim(-1,len(df.Survived[df.Sex == 'female'][df.Pclass == 3].value_counts())) plt.title("low class female survived");plt.legend(loc="best")
ax4 = fig.add_subplot(144) df.Survived[df.Sex == 'male'][df.Pclass == 3].value_counts().plot(kind='bar',color='steelblue',alpha=alpha_level) ax1.set_xlim(-1,len(df.Survived[df.Sex == 'male'][df.Pclass == 3].value_counts())) plt.title("low class male survived");plt.legend(loc="best")
formula = 'Survived ~ C(Pclass) + C(Sex) + Age +SibSp +C(Embarked)' y,x = dmatrices(formula,data=df,return_type='dataframe') model = sm.Logit(y,x) model = model.fit() model.summary()
plt.figure(figsize=(18,4)); plt.subplot(121,axisbg='#DBDBDB') ypred = model.predict(x) plt.plot(x.index,ypred,'bo',x.index,y,'mo',alpha=.25) plt.grid(color='White',linestyle='dashed') plt.title('Logit predictions,Blue:\nFitted/predicted values:Red');
ax2 = plt.subplot(122,axisbg='#DBDBDB') plt.plot(model.resid_dev,'r-') plt.grid(color='White',linestyle='dashed') ax2.set_xlim(-1,len(model.resid_dev)) plt.title('Logit Residuals');
fig = plt.figure(figsize=(18,9),dpi=1600) a = .2
fig.add_subplot(221,axisbg='#DBDBDB') kde_res = KDEUnivariate(model.predict()) kde_res.fit() plt.plot(kde_res.support,kde_res.density,alpha=a) plt.title("Distribution of our Predictions")
fig.add_subplot(222,axisbg='#DBDBDB') plt.scatter(model.predict(),x['C(Sex)[T.male]'],alpha =a) plt.grid(b=True,which='major',axis='x') plt.xlabel('Predicted chance of survival') plt.ylabel('Gender Bool') plt.title('The Change of Survival Probability by Gender(1=Male)')
fig.add_subplot(223,axisbg='#DBDBDB') plt.scatter(model.predict(),x['C(Pclass)[T.3]'],alpha =a) plt.grid(b=True,which='major',axis='x') plt.xlabel('Predicted chance of survival') plt.ylabel('Class Bool') plt.title('The Change of Survival Probability by Class(1= 3rd Class')
fig.add_subplot(224,axisbg='#DBDBDB') plt.scatter(model.predict(),x.Age,alpha =a) plt.grid(b=True,linewidth=0.15) plt.xlabel('Predicted chance of survival') plt.ylabel('Age') plt.title('The Change of Survival Probability by Age')