＃xiaobai_0 import numpy as py import pandas as pd from pandas import Series, DataFrame

import matplotlib.pyplot as plt import seaborn as sns

from sklearn.linear_model import LogisticRegression from sklearn.model_selection import KFold from sklearn.metrics import accuracy_score from sklearn.preprocessing import Imputer

train_data = pd.read_csv('./data/train.csv') train_data.head()

train_data.describe()

sns.barplot(x='Sex',y='Survived',data=train_data) #<matplotlib.axes._subplots.AxesSubplot at 0x16501a1ce10> plt.show()

sns.barplot(x='Embarked',y='Survived',hue='Sex',data=train_data) plt.show()

sns.pointplot(x='Pclass',y='Survived',hue='Sex',data=train_data, palette={'male':'blue','female':'pink'}, markers=['*','o'],linestyles=['-','--']) plt.show()

grid = sns.FacetGrid(train_data,col='Survived',row='Sex', size=2.2,aspect=1.6) grid.map(plt.hist,'Age',alpha=.5,bins=20) grid.add_legend() plt.show()

sns.barplot(x='SibSp',y='Survived',data=train_data) plt.show()

sns.barplot(x='Parch',y='Survived',data=train_data) plt.show()

train_data.Sex.unique() array(['male', 'female'], dtype=object) train_data.loc[train_data.Sex == 'male','Sex'] = 1 train_data.loc[train_data.Sex == 'female','Sex'] = 0 train_data.head()