MS系列最后一课 数据的可视化
visualize with matplotlib
Visualization(matplotlib)
Both linear and scatterplot can show how data is distributed and what the correlation between them
import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LinearRegression# 用到matplotlibimport matplotlib.pyplot as plt# 先加载CSVdefer_df = pd.read_csv('Data/Lots_of_flight_data.csv')defer_df.dropna(inplace=True)# 要建立departure delay 和 arrive delay关系x = defer_df.loc[:,['DEP_DELAY']]y = defer_df.loc[:, ['ARR_DELAY']]# train 两者modelx_train,x_test,y_train,y_test = train_test_split(x, y, test_size=0.3, random_state=42)regressor = LinearRegression()regressor.fit(x_train,y_train)# 得到y_predicty_pred = regressor.predict(x_test)# 定义图的x,y,title信息plt.xlabel('Departure Delay(miniutes)')plt.ylabel('Arrive Delay(miniutes)')plt.title('Correlation between Departure delay and Arrive Delay')# 利用x_test, y_pred画一个直线关系图plt.plot(x_test, y_pred, color='RED', linewidth=2)# 画departure delay 和 arrive delay的散点图，alpha表示点的颜色深浅,范围0~1,1表示完全黑点，用浅色更能表现出数据严重程度的高低plt.scatter(x, y, color='blue', alpha=0.3)# 两个图同时显示plt.show()
附上另一种画散点图的方法：
defer_df.plot( kind='scatter', x='DEP_DELAY', y='ARR_DELAY', color='blue', alpha=0.3, title='Correlation between distance and arrive delay')plt.show()