python学习笔记

二. 变量和简单数据类型

命名只能包含字母、数字和下划线，不能以数字开头，不能包含空格

英文引号引起的都是字符串，三个引号可以创建跨行字符串

要在字符串中插入变量的值，可在前引号前加上字母f，再将要插入的变量放在花括号内

三.列表

四.操作列表

五.if语句

六.字典

七.用户输入和while循环

八.函数

1.导入整个模块

import module_name

moudule_name.function_name()

使用import语句导入模块，并通过模块名.函数名的语法，来使用其中的任意一个函数

2.导入特定的函数

from module_name import function_name
from module_name import function_0, function_1, function_2

此种语法调用函数时无须点号

3.使用as给函数指定别名

from module_name import function_name as fn

4..使用as给模块指定别名

import module_name as mn

九.类

根据约定，首字母大写的名称指的是类，小写的名称指的是根据类创建的实例，类中的函数称为方法

__ init _ _函数，此函数开头和末尾各有两个下划线

self形参：指向实例本身的引用，实例的每次调用都会自动传入自身，这样使得实例能够访问类中的属性和方法

class Dog:
    ...

my_dog = Dog('Willie', 6)
print(f"My dog's name is {my_dog.name}.")
print(f"My dog is {my_dog.age} years old.")

my_dog.sit()
my_dog.roll_over()

接着可以通过点号语法，来访问实例的属性

执行调用时，Python 首先会找到 my_dog 实例，随后通过在类中使用 self.name 和 self.age 来引用与这个实例相关联的属性，以获取名字和年龄

与访问属性类似，同样使用点号语法来调用实例的方法，从而让实例“动起来”

Python 会在类中查找调用的方法，然后运行方法体中的语句

十.文件和异常

pathlib模块：帮助我们在各种操作系统中处理文件和目录

splitlines方法：获得把文本按行分割所得的列表

json.dumps( )进行存储，json.loads( )进行读取

十五.生成数据

import matplotlib.pyplot as plt

input_values = [1, 2, 3, 4, 5]
squares = [1, 4, 9, 16, 25]

plt.style.use('seaborn')           使用内置样式
fig, ax = plt.subplots()           subplot()可在一张图片中绘制一个或多个图表，fig表示整张图
ax.plot(input_values, squares, linewidth=3)

# Set chart title and label axes.   标题及坐标轴
ax.set_title("Square Numbers", fontsize=24)
ax.set_xlabel("Value", fontsize=14)
ax.set_ylabel("Square of Value", fontsize=14)

# Set size of tick labels.    刻度标记大小
ax.tick_params(axis='both', labelsize=14)

plt.show()

import matplotlib.pyplot as plt

x_values = range(1, 1001)
y_values = [x**2 for x in x_values]

plt.style.use('seaborn')
fig, ax = plt.subplots()
ax.scatter(x_values, y_values, c=y_values, cmap=plt.cm.Blues, s=10)     绘制点scatter() 参数c设置颜色 s设置点大小
                                颜色映射 c设置成y值列表  cmap选择映射颜色
# Set chart title and label axes.
ax.set_title("Square Numbers", fontsize=24)
ax.set_xlabel("Value", fontsize=14)
ax.set_ylabel("Square of Value", fontsize=14)

# Set size of tick labels.
ax.tick_params(axis='both', which='major', labelsize=14)

# Set the range for each axis.
ax.axis([0, 1100, 0, 1100000])

plt.show()

随机漫步

from random import choice

class RandomWalk:
    """A class to generate random walks."""
    
    def __init__(self, num_points=5000):
        """Initialize attributes of a walk."""
        self.num_points = num_points
        
        # All walks start at (0, 0).
        self.x_values = [0]
        self.y_values = [0]

    def fill_walk(self):
        """Calculate all the points in the walk."""
    
        # Keep taking steps until the walk reaches the desired length.
        while len(self.x_values) < self.num_points:
        
            # Decide which direction to go and how far to go in that direction.
            x_direction = choice([1, -1])
            x_distance = choice([0, 1, 2, 3, 4])
            x_step = x_direction * x_distance
        
            y_direction = choice([1, -1])
            y_distance = choice([0, 1, 2, 3, 4])
            y_step = y_direction * y_distance
        
            # Reject moves that go nowhere.
            if x_step == 0 and y_step == 0:
                continue
        
            # Calculate the new position.
            x = self.x_values[-1] + x_step
            y = self.y_values[-1] + y_step
        
            self.x_values.append(x)
            self.y_values.append(y)

import matplotlib.pyplot as plt

from random_walk import RandomWalk

# Keep making new walks, as long as the program is active.
while True:
    # Make a random walk.
    rw = RandomWalk(50_000)
    rw.fill_walk()

    # Plot the points in the walk.
    plt.style.use('classic')
    fig, ax = plt.subplots(figsize=(15, 9))     指定生成的图形尺寸
    point_numbers = range(rw.num_points)
    ax.scatter(rw.x_values, rw.y_values, c=point_numbers, cmap=plt.cm.Blues,
        edgecolors='none', s=1)
        删除点的黑色轮廓
    # Emphasize the first and last points.绘制起点和终点
    ax.scatter(0, 0, c='green', edgecolors='none', s=100)
    ax.scatter(rw.x_values[-1], rw.y_values[-1], c='red', edgecolors='none',
        s=100)

    # Remove the axes.隐藏坐标轴
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

    plt.show()

    keep_running = input("Make another walk? (y/n): ")
    if keep_running == 'n':
        break

掷骰子

from random import randint

class Die:
    """A class representing a single die."""
    
    def __init__(self, num_sides=6):
        """Assume a six-sided die."""
        self.num_sides = num_sides
        
    def roll(self):
        """"Return a random value between 1 and number of sides."""
        return randint(1, self.num_sides)

from plotly.graph_objs import Bar, Layout
from plotly import offline

from die import Die

# Create two D6 dice.
die_1 = Die()
die_2 = Die()

# Make some rolls, and store results in a list.
results = []
for roll_num in range(1000):
    result = die_1.roll() + die_2.roll()
    results.append(result)
    
# Analyze the results.  计算每个点出现的次数
frequencies = []
max_result = die_1.num_sides + die_2.num_sides
for value in range(2, max_result+1):
    frequency = results.count(value)
    frequencies.append(frequency)
    
# Visualize the results.
x_values = list(range(2, max_result+1))   list转换成列表
data = [Bar(x=x_values, y=frequencies)]   bar（）表示用于绘制条形图的数据集

x_axis_config = {'title': 'Result', 'dtick': 1}
y_axis_config = {'title': 'Frequency of Result'}
my_layout = Layout(title='Results of rolling two D6 dice 1000 times',
        xaxis=x_axis_config, yaxis=y_axis_config)           布局对象
offline.plot({'data': data, 'layout': my_layout}, filename='d6_d6.html')

十六.下载数据

CSV文件：数据作为一系列以逗号分隔的值写入的文件