Matplotlib学习

决定通过一个个例子来实践并掌握Matplotlib。。

例子1.

画一个散点图，数据分布如下：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline #注意，这个代码意思是在ipython的编译器里，内嵌绘图，这样就不用plt.show()了

import os
path = 'data' + os.sep + 'LogiReg_data.txt'
pdData = pd.read_csv(path, header=None, names=['Exam 1', 'Exam 2', 'Admitted'])
pdData.head()

前两列是成绩，第三列是分类，只有01两类

然后开始画图：

positive = pdData[pdData['Admitted'] == 1] # returns the subset of rows such Admitted = 1, i.e. the set of *positive* examples
negative = pdData[pdData['Admitted'] == 0] # returns the subset of rows such Admitted = 0, i.e. the set of *negative* examples

fig, ax = plt.subplots(figsize=(10,5))
ax.scatter(positive['Exam 1'], positive['Exam 2'], s=30, c='b', marker='o', label='Admitted')
ax.scatter(negative['Exam 1'], negative['Exam 2'], s=30, c='r', marker='x', label='Not Admitted')
ax.legend()
ax.set_xlabel('Exam 1 Score')
ax.set_ylabel('Exam 2 Score')

例子2.

画出函数曲线

函数是下面这个：

def sigmoid(z):
    return 1 / (1 + np.exp(-z)) #注意e的指数次形式的写法

画图：

nums = np.arange(-10, 10, step=1) #creates a vector containing 20 equally spaced values from -10 to 10
fig, ax = plt.subplots(figsize=(12,4))
ax.plot(nums, sigmoid(nums), 'r')