在 plt.plot() 方法中传递的 arguments 是什么意思？

Question

I was trying to plot a linear regression model in python using scikit learn and matplotlib. However, the code got confusing when I was plotting the data using plt.scatter() and plt.plot()

Here's my code which models the data using sklearn:-

from sklearn import linear_model
regr = linear_model.LinearRegression()
train_x = np.asanyarray(train[['ENGINESIZE']])
train_y = np.asanyarray(train[['CO2EMISSIONS']])
regr.fit (train_x, train_y)
# The coefficients
print ('Coefficients: ', regr.coef_)
print ('Intercept: ',regr.intercept_)

Here's my code which plots the linear regression model on a graph:-

plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')
plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-y')
plt.xlabel("Engine size")
plt.ylabel("Emission")

I don't understand the arguments passed in plt.scatter() and plt.plot() . I noticed that when I remove the method plt.plot() , the line of best fit is not plotted on the graph.

Answer 1

The plt library plots data. The first entry is the x data, the second the y data. Other inputs can be used to add colour, linewidth or marker type, as seen in the documentation .

plt.scatter adds a scatter plot of your data. Guessing by the name of your variables I suspect:

plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')

Makes a scatter plot of all the engine sizes and their corresponding CO2 emmisions using blue markers.

The plt.plot draws a line. Guessing by the name of your variables I suspect

plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-y')

Will plot the linear regression of your training data in yellow. train_x is the x data and regr.coef_[0][0]*train_x + regr.intercept_[0] the y data (it follows the formula y = a*x + b).