1. 数据绘图¶

1.1 二列数据绘图¶

根据文件中的数据绘制数据曲线，一般的做法是打开文件，读取文件中的数据到列表中，再绘制数据曲线。

import matplotlib.pyplot as plt


1. 打开文件创建文件对象：
with open('文件路径/文件名', 'r', encoding='utf-8') as 文件对象名:
2. 遍历文件对象
for line in 文件对象名:
3.当前行切分为列表
line.strip().split('分隔符')
4.列表元素转数值类型： float(x)
5.绘制曲线：plt.plot(x, y)       
6. 显示绘制结果： plt.show()                              #

%matplotlib inline  # 使jupyter中可以使用matplotlib绘制图形

实例 9.4 读文件绘制数据曲线¶

有一包含两列数据的9.4 DOS.csv文件，两列数据分别代表x,y坐标，利用文件中的数据绘制数据曲线（数据文件可通过扫描二维码下载）。下面仅给出文件前几行数据：

9.4 dos.csv

欲利用文件中的数据绘制曲线，需打开并逐行读取文件中的数据。
要注意读取每行数据时，行末有一个换行符“\n”需要用replace()替换掉或用strip()函数过滤掉。
读取的数据是字符型，需要转换成float()函数或eval()函数转换为数字型。
再将其附加到列表中，构建分别包含x和y坐标的两个列表，再利用matplotlib绘图。
绘图结果如图所示。

import matplotlib.pyplot as plt

with open('images/ch9/9.4 dos.csv', 'r', encoding='utf-8') as file:  # 打开文件创建文件对象file
    ls = [line.strip().split(',') for line in file]                   # 逐行读取文件中的数据创建二维列表

x_ls = [float(x[0]) for x in ls]        # 列表x_ls为ls子列表中序号为0的元素的浮点类型
y_ls = [float(x[1]) for x in ls]        # 列表y_ls为ls子列表中序号为1的元素的浮点类型
plt.plot(x_ls, y_ls, linewidth=1)       # 绘制数据曲线
plt.show()                              # 显示绘制结果

这种方法容易理解，但稍显繁琐，可利用列表推导式对其进行简化。
打开文件后，先将每行数据去掉行末的换行符后切分为列表，存入列表ls中，再用列表推导式将ls中的元素分别放入lsx、lsy两个列表中。

import matplotlib.pyplot as plt


def read_csv(file):
    with open(file) as data:
        ls = [line.strip().split(',') for line in data]  # 读文件到二维列表
    return ls


def plot_dos(ls):
    x = [float(x[0]) for x in ls]  # 列表推导式
    y = [float(x[1]) for x in ls]
    plt.plot(x, y)  # 绘制数据曲线


if __name__ == '__main__':
    lst = read_csv('images/ch9/9.4 dos.csv')
    plot_dos(lst)
    plt.show()

import matplotlib.pyplot as plt

# 支持中文显示
plt.rcParams['font.sans-serif'] = ['SimSun']    # 宋体
plt.rcParams['axes.unicode_minus'] = False      # 正常显示负号 -


def read_csv(file):
    """接受一个文件名变量为参数，读文件，每行切分为一个子列表。
    返回二维列表。
    """
    with open(file) as data:
        ls = [line.strip().split(',') for line in data]
    return ls


def plot_dos(ls):
    """接受二维列表为参数，绘制曲线，无返回值。"""
    x = [float(x[0]) for x in ls]
    y = [float(x[1]) for x in ls]
    plt.plot(x, y, linestyle='-', linewidth=1)  # 绘制数据曲线


def draw_label():
    """增加图名和轴标签"""
    plt.title('（Density of States）')
    plt.xlabel('eV')
    plt.ylabel('eV')


# 函数调用与输入输出
if __name__ == '__main__':
    lst = read_csv('images/ch9/9.4 dos.csv')
    plot_dos(lst)
    draw_label()
    plt.show()

在NumPy中，使用loadtxt函数可以方便地读取csv或txt文件，delimiter=','表示用逗号作为分隔符自动切分字段，并将数据载入NumPy数组。

# 利用numpy读文件绘制数据曲线
import matplotlib.pyplot as plt
import numpy as np

data = np.loadtxt('images/ch9/9.4 dos.csv', delimiter=',')
print(data)
plt.plot(data[0:, 0], data[0:, 1])  # 切片方法获取两列数据
plt.show()

[[-5.85574280e+01  0.00000000e+00]
 [-5.85469054e+01  0.00000000e+00]
 [-5.74735926e+01  1.11050000e-04]
 ...
 [ 2.08045808e+01  1.29131000e-04]
 [ 2.08151034e+01  1.13849000e-04]
 [ 2.08256261e+01  1.00099000e-04]]

对于有标题行的数据：

plt.plot(data[1:, 0], data[1:, 1])

读数据到dataframe,用数据的列标题绘图

plot('xlabel', 'ylabel', data=obj)

XRD_AFOtxtd.csv

在Pandas库中，使用read_csv()函数可以方便地读取csv或txt文件，delimiter=','表示用逗号作为分隔符自动切分字段，并将数据载入为dataframe类型。
可通过列标签获取其中第 i 列数据。

import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv('images/ch9/XRD_AFOtxtd.csv')
plt.plot('2d', 'Intensity', data=df)   # 根据列标题获取数据
plt.show()   # 显示绘制结果

/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1377: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
  x[:, None]
/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_base.py:237: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
  x = x[:, np.newaxis]
/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_base.py:239: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.
  y = y[:, np.newaxis]

在Pandas库中，使用read_csv()函数可以方便地读取csv或txt文件，delimiter=','表示用逗号作为分隔符自动切分字段，并将数据载入为dataframe类型。
可通过dataframe.iloc[:,i]获取其中第 i 列数据。

# 利用pandas读文件绘制数据曲线
import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv('images/ch9/XRD_AFOtxtd.csv')
plt.plot(df.iloc[1:, 0], df.iloc[1:, 1])  # 索引两列数据为数组
plt.show()  # 显示绘制结果

1.2 多列数据绘图¶

当数据文件中包括多列数据时，可用循环的方法，每次读两列数据绘图，重复执行，完成所有数据的读取和绘制。

实例 9.5 多列数据绘图¶

有一包含多列数据的PDOS.csv文件，8列数据分别代表4组x,y坐标，利用文件中的数据绘制数据曲线（数据文件可通过扫描二维码下载）。
下面仅给出文件部分数据：

PDOS.csv

用循环的方法读取数据比较容易理解，每次读取相邻的2列数据作为一组坐标，取消10和11行的注释，可获得区域放大的效果，绘图结果如图9.11所示：

import matplotlib.pyplot as plt


with open('images/ch9/PDOS.csv', 'r', encoding='utf-8') as file:
    ls = [line.strip().split(',') for line in file]  # 逐行读取文件中的数据创建二维列表

    


for i in range(0, 8, 2):                # i取偶数0，2，4，6，i+1的值分别为1，3，5，7
    x_ls = [float(x[i]) for x in ls]    # 列表x_ls为ls子列表中序号为i的元素的浮点类型
    y_ls = [float(x[i+1]) for x in ls]  # 列表y_ls为ls子列表中序号为i+1的元素的浮点类型
    plt.plot(x_ls, y_ls, linewidth=2)  # 绘制当前数据的曲线，颜色通过索引获得

plt.show()

10和11行的的作用是获得区域放大的效果：

import matplotlib.pyplot as plt


with open('images/ch9/PDOS.csv', 'r', encoding='utf-8') as file:
    ls = [line.strip().split(',') for line in file]  # 逐行读取文件中的数据创建二维列表
for i in range(0, 8, 2):                # i取偶数0，2，4，6，i+1的值分别为1，3，5，7
    x_ls = [float(x[i]) for x in ls]    # 列表x_ls为ls子列表中序号为i的元素的浮点类型
    y_ls = [float(x[i+1]) for x in ls]  # 列表y_ls为ls子列表中序号为i+1的元素的浮点类型
    plt.plot(x_ls, y_ls,  linewidth=2)  # 绘制当前数据的曲线，颜色通过索引获得
    
plt.xlim(-5, 5)                         # 设置x轴的上下限，获得区域放大的曲线
plt.ylim(-0.5, 8)                       # 设置y轴的上下限，获得区域放大的曲线

plt.show()

9.5 PDOS.csv

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示
plt.rcParams['axes.unicode_minus'] = False


def read_csv(filename):
    """接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。"""
    with open(filename, 'r', encoding='utf-8') as fr:
        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表
    return data_lst


def draw_dos(data_lst):
    """接收二维列表为参数，绘制数据曲线。"""
    cl = ['cyan', 'green', 'red', 'blue']  # 创建颜色列表
    sty = ['-', '--', ':', '-.']
    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线
        x = [float(ls[2 * i]) for ls in data_lst]  # 生成x的列表
        y = [float(ls[2 * i + 1]) for ls in data_lst]  # 生成y的列表
        plt.plot(x, y, color=cl[i], linestyle=sty[i],linewidth=3)  # cl[i]根据i值取颜色


def draw_label():  # 加图名和轴标签
    plt.title('态密度（Density of States）曲线')
    plt.xlabel('能量/eV')
    plt.ylabel('分波态密度/eV')
    
    
def zoom_area():
    """只显示横坐标在-5到5之间的区域，局部放大，纵坐标根据曲线的最大值来设定为-0.5到8"""
    plt.xlim(-5, 5)                         # 设置x轴的上下限，获得区域放大的曲线
    plt.ylim(-0.5, 8)                       # 设置y轴的上下限，获得区域放大的曲线

    
if __name__ == '__main__':
    file = 'images/ch9/9.5 PDOS.csv'
    dos_in_lst = read_csv(file)
    draw_dos(dos_in_lst)
    draw_label()
    zoom_area()
    plt.show()

1.3 numpy读文件，利用数组绘图¶

# 利用numpy读文件绘制数据曲线
import matplotlib.pyplot as plt
import numpy as np

data = np.loadtxt('images/ch9/9.5 PDOS.csv',delimiter=',')

print(len(data[0]))
print(data)
for i in range(4):
    plt.plot(data[0:, 2*i], data[0:, 2*i+1])  # 切片方法获取两列数据
plt.show()

8
[[-5.85574280e+01  0.00000000e+00 -5.85574280e+01 ...  0.00000000e+00
  -5.85574280e+01  0.00000000e+00]
 [-5.85469054e+01  0.00000000e+00 -5.85469054e+01 ...  0.00000000e+00
  -5.85469054e+01  0.00000000e+00]
 [-5.74735926e+01  1.10663000e-04 -5.71684350e+01 ...  1.02657000e-04
  -5.74735926e+01  1.11050000e-04]
 ...
 [ 1.81739122e+01  2.81961870e-02  1.77003919e+01 ...  1.02179000e-04
   1.61640814e+01  6.77418280e-01]
 [ 1.81844349e+01  2.56184650e-02  1.77109145e+01 ...  9.39000000e-05
   1.61746041e+01  6.83354996e-01]
 [ 1.81949576e+01  2.32660780e-02  1.77214372e+01 ...  0.00000000e+00
   1.61851268e+01  6.90765294e-01]]

1.4 pandas读文件，利用dataframe绘图¶

# 利用pandas读文件绘制数据曲线
import matplotlib.pyplot as plt
import pandas as pd

data = pd.read_csv('images/ch9/9.5 PDOS.csv')
for i in range(4):
    plt.plot(data.iloc[0:, 2*i], data.iloc[0:, 2*i+1])  # 切片方法获取两列数据
plt.show()

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示
plt.rcParams['axes.unicode_minus'] = False


def read_csv(filename):
    """接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。"""
    with open(filename, 'r', encoding='utf-8') as fr:
        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表
    return data_lst


def draw_dos(data_lst):
    """接收二维列表为参数，绘制数据曲线。"""
    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线
        x = [float(ls[2 * i]) for ls in data_lst]  # 生成x的列表
        y = [float(ls[2 * i + 1]) for ls in data_lst]  # 生成y的列表
        plt.subplot(211)  # 分2x1，小于10的数可合成3位数
        plt.plot(x, y)
    plt.title('态密度（Density of States）曲线')
    plt.ylabel('分波态密度/eV')


def zoom_in_dos(data_lst):
    """接收二维列表为参数，绘制数据曲线。"""
    sty = ['-', '--', ':', '-.']
    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线
        x = [float(ls[2 * i]) for ls in data_lst if -5 < float(ls[2 * i]) < 5]  # 生成x的列表
        y = [float(ls[2 * i + 1]) for ls in data_lst if -5 < float(ls[2 * i]) < 5]  # 生成y的列表
        plt.subplot(212)  # 分2x1，小于10的数可合成3位数
        plt.plot(x, y, linestyle=sty[i])  # cl[i]根据i值取颜色
    plt.xlabel('能量/eV')


if __name__ == '__main__':
    file = 'images/ch9/9.5 PDOS.csv'
    dos_in_lst = read_csv(file)
    draw_dos(dos_in_lst)
    zoom_in_dos(dos_in_lst)
    plt.show()

/usr/local/lib/python3.7/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.7/dist-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.
  (prop.get_family(), self.defaultFamily[fontext]))

def zoom_in_dos(data_lst):
    """接收二维列表为参数，绘制数据曲线。"""
    sty = ['-', '--', ':', '-.']
    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线
        x = [float(ls[2 * i]) for ls in data_lst]  # 生成x的列表
        y = [float(ls[2 * i + 1]) for ls in data_lst]  # 生成y的列表
        plt.subplot(212)  # 分2x1，小于10的数可合成3位数
        plt.plot(x, y, color=cl[i], linestyle=sty[i])  # cl[i]根据i值取颜色
    plt.xlim(-5, 5)  # 设置横轴的上下限
    plt.ylim(-0.5, 7.5)  # 设置纵轴的上下限
    plt.xlabel('能量/eV')

1.5 两列数据分段绘图¶

实例 9.7 两列数据绘制多条曲线¶

有一个数据文件band.txt，其中包括两列数据，分别代表坐标x,y的值，数据间用制表符分隔（'\t'），其中x值数据从0变化到1时表示一条曲线，整个文件包含多组这样的数据，请读取这个文件的数据并绘制相应的曲线。

9.7 band.txt

参考前述方法，将一条曲线的数据附加到一组列表中。
因每条曲线的x值都是从0逐渐增加到1为止，因此可查看保存x值数据的列表的最后一个元素值是否是1，当该元素值为1时，利用列表中的数据绘制一条曲线。然后清空列表，读取并附加下一组数据。
重复操作，直至所有数据读取完毕。

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示
plt.rcParams['axes.unicode_minus'] = False


def read_csv(filename):
    """接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。"""
    with open(filename, 'r', encoding='utf-8') as fr:
        data_lst = [line.strip().split('\t') for line in fr]  # 数据转列表
        # data_lst = [line.strip().split() for line in fr]  # 数据转列表
    return data_lst


def draw_label():  # 加图名和轴标签
    plt.title('能带曲线')
    plt.ylabel('Energy/eV')


def draw_band(data_lst):
    """接收二维列表为参数，绘制数据曲线。"""
    x, y = [], []
    for xy in data_lst:
        x.append(float(xy[0]))  # 将数据转为浮点型附加到列表中
        y.append(float(xy[1]))
        if x[-1] == 1:  # 列表最后一个元素为1时
            plt.plot(x, y, '-',linewidth=2)  # 绘制曲线
            x, y = [], []  # 清空列表，准备接收下一条曲线的数据


if __name__ == '__main__':
    file = 'images/ch9/9.7 band.txt'
    data_in_lst = read_csv(file)
    draw_band(data_in_lst)
    draw_label()
    plt.show()

/usr/local/lib/python3.7/dist-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.
  (prop.get_family(), self.defaultFamily[fontext]))

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示
plt.rcParams['axes.unicode_minus'] = False


def read_csv(filename):
    """接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。"""
    with open(filename, 'r', encoding='utf-8') as fr:
        data_lst = [line.strip().split() for line in fr]  # 数据转列表
    return data_lst


def draw_label():
    """加图名和轴标签"""
    plt.title('能带曲线')
    plt.ylabel('Energy/eV')


def draw_band(data_lst):
    """接收二维列表为参数，绘制数据曲线。"""
    x, y = [], []
    for xy in data_lst:
        x.append(float(xy[0]))  # 将数据转为浮点型附加到列表中
        y.append(float(xy[1]))
        if x[-1] == 1:  # 列表最后一个元素为1时
            plt.plot(x, y, '-')  # 绘制曲线
            x, y = [], []  # 清空列表，准备接收下一条曲线的数据
    
def draw_line():
    """绘制区间线"""
    xtick = [0, 0.25, 0.43, 0.69, 1]
    plt.xticks(xtick)  # 横轴刻度
    for i in xtick:    # 在刻度处绘制纵向破折线
        plt.axvline(i, linestyle='--', c='gray')
    plt.axhline(0, linestyle='--', c='gray')  # 绘制水平线
    

if __name__ == '__main__':
    file = 'images/ch9/9.7 band.txt'
    data_in_lst = read_csv(file)
    plt.subplot(121)        # 一行两列第一个位置绘图
    draw_band(data_in_lst)  # 绘制完整数据图
    draw_label()
    draw_line()
    plt.subplot(122)        # 一行两列第二个位置绘图
    draw_band(data_in_lst)  # 绘制完整数据图
    plt.ylim(-5, 5)         # 显示纵坐标值在-5到5之间部分
    draw_label()
    draw_line()
    plt.show()

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示
plt.rcParams['axes.unicode_minus'] = False


def read_csv(filename):
    """接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。"""
    with open(filename, 'r', encoding='utf-8') as fr:
        data_lst = [line.strip().split() for line in fr]  # 数据转列表
    return data_lst


def draw_label():
    """加图名和轴标签"""
    plt.title('能带曲线')
    plt.ylabel('Energy/eV')


def draw_band(data_lst):
    """接收二维列表为参数，绘制数据曲线。"""
    x, y = [], []
    for xy in data_lst:
        x.append(float(xy[0]))  # 将数据转为浮点型附加到列表中
        y.append(float(xy[1]))
        if x[-1] == 1:  # 列表最后一个元素为1时
            plt.plot(x, y, '-')  # 绘制曲线
            x, y = [], []  # 清空列表，准备接收下一条曲线的数据
    
def draw_line():
    """绘制区间线"""
    xtick = [0, 0.25, 0.43, 0.69, 1]
    plt.xticks(xtick)  # 横轴刻度
    for i in xtick:    # 在刻度处绘制纵向破折线
        plt.axvline(i, linestyle='--', c='gray')
    plt.axhline(0, linestyle='--', c='gray')  # 绘制水平线
    
def zoom_area(m,n,data_lst):
    """接收二维列表为参数，只绘制满足区间条件的数据曲线，避免绘制的曲线不完整。"""
    lsx, lsy = [], []
    for x,y in data_lst:
        if m<=float(y)<=n:
            lsx.append(float(x))  # 将数据转为浮点型附加到列表中
            lsy.append(float(y))
            if lsx[-1] == 1:  # 列表最后一个元素为1时
                plt.plot(lsx, lsy, '-')  # 绘制曲线
                lsx, lsy = [], []  # 清空列表，准备接收下一条曲线的数据
    

if __name__ == '__main__':
    file = 'images/ch9/9.7 band.txt'
    data_in_lst = read_csv(file)
    plt.subplot(121)        # 一行两列第一个位置绘图
    draw_band(data_in_lst)  # 绘制完整数据图
    draw_label()
    draw_line()
    plt.subplot(122)        # 一行两列第二个位置绘图
    zoom_area(-5,5,data_in_lst)
    draw_label()
    draw_line()
    plt.show()

1.6 网络数据绘图¶

实例9.8 TIOBE 程序语言排行榜曲线¶

TIOBE排行榜是根据互联网上有经验的程序员、课程和第三方厂商的数量，并使用搜索引擎（如Google、Bing、Yahoo!）以及Wikipedia、Amazon、YouTube和Baidu（百度）统计出排名数据，只是反映某个编程语言的热门程度，并不能说明一门编程语言好不好，或者一门语言所编写的代码数量多少。
TIOBE开发语言排行榜每月更新一次，依据的指数是基于世界范围内的资深软件工程师和第三方供应商提供，其结果作为当前业内程序开发语言的流行使用程度的有效指标。
该指数可以用来检阅开发者的编程技能能否跟上趋势，或是否有必要作出战略改变，以及什么编程语言是应该及时掌握的。观察认为，该指数反应的虽并非当前最流行或应用最广的语言，但对世界范围内开发语言的走势仍具有重要参考意义。

网页上右键查看网页源代码，搜索“series: ”，可看到数据：

    series: [
    	{name : 'Python',data : [[Date.UTC(2001, 5, 30), 1.25], [Date.UTC(2001, 6, 30), 1.13], [Date.UTC(2001, 7, 30), 1.20], [Date.UTC(2001, 8, 28), 1.17], [Date.UTC(2001, 9, 26), 1.28], [Date.UTC(2001, 10, 28), 1.23], [Date.UTC(2001, 11, 31), 1.04], [Date.UTC(2002, 0, 30), 1.02], [Date.UTC(2002, 1, 27), 0.99], [Date.UTC(2002, 2, 29), 0.99], [Date.UTC(2002, 3, 29), 1.07], [Date.UTC(2002, 4, 29), 1.06], [Date.UTC(2002, 5, 29), 1.13], [Date.UTC(2002, 6, 31), 1.08], [Date.UTC(2002, 7, 29), 1.22], [Date.UTC(2002, 8, 29), 1.08], [Date.UTC(2002, 9, 30), 1.19], [Date.UTC(2002, 10, 29), 1.00], [Date.UTC(2002, 11, 31), 1.00], [Date.UTC(2003, 0, 31), 1.03], [Date.UTC(2003, 1, 28), 0.97], [Date.UTC(2003, 2, 31), 0.99], [Date.UTC(2003, 3, 30), 1.01], [Date.UTC(2003, 4, 30), 1.16], [Date.UTC(2003, 5, 30), 1.28], [Date.UTC(2003, 6, 31), 1.30], [Date.UTC(2003, 7, 31), 1.42], [Date.UTC(2003, 8, 30), 1.49], [Date.UTC(2003, 9, 31), 1.77], [Date.UTC(2003, 10, 30), 1.10], [Date.UTC(2003, 11, 31), 1.13], [Date.UTC(2004, 0, 31), 1.01], [Date.UTC(2004, 1, 29), 1.02], [Date.UTC(2004, 2, 31), 1.01], [Date.UTC(2004, 3, 30), 4.57], [Date.UTC(2004, 4, 30), 6.58], [Date.UTC(2004, 5, 30), 4.96], [Date.UTC(2004, 6, 31), 4.70], [Date.UTC(2004, 7, 30), 5.60], [Date.UTC(2004, 8, 30), 4.44], [Date.UTC(2004, 9, 31), 5.20], [Date.UTC(2004, 10, 30), 3.03], [Date.UTC(2004, 11, 31), 2.84], [Date.UTC(2005, 0, 31), 2.51], [Date.UTC(2005, 1, 28), 2.42], [Date.UTC(2005, 2, 31), 2.70], [Date.UTC(2005, 3, 30), 2.48], [Date.UTC(2005, 4, 31), 2.80], [Date.UTC(2005, 5, 30), 2.52], [Date.UTC(2005, 6, 31), 2.88], [Date.UTC(2005, 7, 31), 3.03], [Date.UTC(2005, 8, 30), 2.88], [Date.UTC(2005, 11, 3), 2.68], [Date.UTC(2006, 0, 4), 2.60], [Date.UTC(2006, 1, 2), 2.67], [Date.UTC(2006, 2, 1), 3.09], [Date.UTC(2006, 3, 2), 2.76], [Date.UTC(2006, 4, 1), 3.04], [Date.UTC(2006, 5, 1), 3.46], [Date.UTC(2006, 6, 2), 3.02], [Date.UTC(2006, 7, 2), 3.07], [Date.UTC(2006, 8, 2), 3.14], [Date.UTC(2006, 9, 1), 3.47], [Date.UTC(2006, 10, 2), 3.64], [Date.UTC(2006, 11, 1), 3.76], [Date.UTC(2007, 0, 2), 3.50], [Date.UTC(2007, 1, 3), 3.57], [Date.UTC(2007, 2, 3), 3.90], [Date.UTC(2007, 3, 1), 3.81], [Date.UTC(2007, 4, 5), 3.78], [Date.UTC(2007, 5, 2), 3.16], [Date.UTC(2007, 6, 2), 3.02], [Date.UTC(2007, 7, 5), 2.75], [Date.UTC(2007, 8, 2), 3.03], [Date.UTC(2007, 9, 4), 3.43], [Date.UTC(2007, 10, 4), 4.23], [Date.UTC(2007, 11, 3), 4.70], [Date.UTC(2008, 0, 3), 5.54], [Date.UTC(2008, 1, 7), 4.76], [Date.UTC(2008, 5, 1), 4.90], [Date.UTC(2008, 6, 2), 4.97], [Date.UTC(2008, 7, 3), 4.98], [Date.UTC(2008, 8, 3), 5.01], [Date.UTC(2008, 9, 6), 4.56], [Date.UTC(2008, 10, 2), 5.14], [Date.UTC(2008, 11, 3), 4.17], [Date.UTC(2009, 0, 2), 4.73], [Date.UTC(2009, 1, 1), 4.57], [Date.UTC(2009, 2, 5), 5.18], [Date.UTC(2009, 3, 7), 6.08], [Date.UTC(2009, 4, 1), 5.55], [Date.UTC(2009, 5, 4), 4.76], [Date.UTC(2009, 6, 2), 4.43], [Date.UTC(2009, 7, 1), 4.49], [Date.UTC(2009, 8, 5), 3.93], [Date.UTC(2009, 9, 2), 3.90], [Date.UTC(2009, 10, 2), 4.67], [Date.UTC(2009, 11, 2), 5.19], [Date.UTC(2010, 0, 5), 4.45], [Date.UTC(2010, 1, 7), 4.31], [Date.UTC(2010, 2, 7), 4.23], [Date.UTC(2010, 3, 5), 4.21], [Date.UTC(2010, 4, 15), 4.10], [Date.UTC(2010, 6, 6), 4.22], [Date.UTC(2010, 6, 30), 4.22], [Date.UTC(2010, 8, 11), 4.58], [Date.UTC(2010, 9, 2), 4.86], [Date.UTC(2010, 10, 3), 5.68], [Date.UTC(2010, 11, 7), 6.48], [Date.UTC(2011, 0, 2), 6.26], [Date.UTC(2011, 1, 8), 7.04], [Date.UTC(2011, 2, 8), 5.74], [Date.UTC(2011, 3, 3), 4.93], [Date.UTC(2011, 4, 2), 4.58], [Date.UTC(2011, 5, 5), 3.90], [Date.UTC(2011, 5, 27), 3.90], [Date.UTC(2011, 6, 8), 3.58], [Date.UTC(2011, 7, 3), 3.41], [Date.UTC(2011, 8, 10), 4.00], [Date.UTC(2011, 9, 9), 3.94], [Date.UTC(2011, 10, 7), 3.62], [Date.UTC(2011, 11, 4), 3.49], [Date.UTC(2012, 0, 8), 3.21], [Date.UTC(2012, 1, 5), 3.15], [Date.UTC(2012, 2, 11), 3.29], [Date.UTC(2012, 3, 8), 3.66], [Date.UTC(2012, 4, 9), 3.82], [Date.UTC(2012, 5, 10), 3.85], [Date.UTC(2012, 6, 4), 4.00], [Date.UTC(2012, 7, 10), 3.88], [Date.UTC(2012, 8, 2), 3.86], [Date.UTC(2012, 9, 5), 3.90], [Date.UTC(2012, 10, 4), 4.06], [Date.UTC(2012, 11, 2), 3.85], [Date.UTC(2013, 0, 5), 4.17], [Date.UTC(2013, 1, 8), 4.95], [Date.UTC(2013, 2, 11), 4.39], [Date.UTC(2013, 3, 7), 4.44], [Date.UTC(2013, 4, 8), 4.32], [Date.UTC(2013, 5, 9), 4.18], [Date.UTC(2013, 6, 7), 4.03], [Date.UTC(2013, 6, 12), 4.03], [Date.UTC(2013, 7, 4), 3.60], [Date.UTC(2013, 8, 11), 3.17], [Date.UTC(2013, 9, 10), 3.11], [Date.UTC(2013, 10, 9), 3.11], [Date.UTC(2013, 11, 6), 2.21], [Date.UTC(2014, 0, 1), 2.37], [Date.UTC(2014, 1, 8), 2.16], [Date.UTC(2014, 2, 3), 2.02], [Date.UTC(2014, 3, 10), 1.99], [Date.UTC(2014, 4, 7), 3.06], [Date.UTC(2014, 5, 8), 2.71], [Date.UTC(2014, 6, 6), 2.66], [Date.UTC(2014, 7, 11), 3.12], [Date.UTC(2014, 8, 1), 2.78], [Date.UTC(2014, 9, 3), 2.33], [Date.UTC(2014, 10, 8), 2.59], [Date.UTC(2014, 11, 7), 2.29], [Date.UTC(2015, 0, 6), 2.61], [Date.UTC(2015, 1, 5), 2.88], [Date.UTC(2015, 2, 7), 2.61], [Date.UTC(2015, 3, 13), 2.69], [Date.UTC(2015, 4, 13), 3.72], [Date.UTC(2015, 5, 6), 4.00], [Date.UTC(2015, 6, 12), 4.26], [Date.UTC(2015, 7, 6), 4.07], [Date.UTC(2015, 8, 5), 3.66], [Date.UTC(2015, 9, 4), 4.51], [Date.UTC(2015, 10, 7), 3.77], [Date.UTC(2015, 11, 4), 4.43], [Date.UTC(2016, 0, 2), 3.85], [Date.UTC(2016, 1, 2), 4.18], [Date.UTC(2016, 2, 3), 4.26], [Date.UTC(2016, 3, 7), 3.33], [Date.UTC(2016, 4, 6), 3.79], [Date.UTC(2016, 5, 5), 3.90], [Date.UTC(2016, 6, 4), 4.17], [Date.UTC(2016, 7, 6), 4.40], [Date.UTC(2016, 8, 8), 4.30], [Date.UTC(2016, 9, 7), 3.77], [Date.UTC(2016, 10, 5), 3.57], [Date.UTC(2016, 11, 4), 4.24], [Date.UTC(2017, 0, 7), 3.46], [Date.UTC(2017, 1, 8), 4.04], [Date.UTC(2017, 2, 7), 3.92], [Date.UTC(2017, 3, 9), 3.46], [Date.UTC(2017, 4, 6), 3.55], [Date.UTC(2017, 5, 3), 4.33], [Date.UTC(2017, 6, 7), 3.54], [Date.UTC(2017, 7, 2), 3.69], [Date.UTC(2017, 8, 6), 2.98], [Date.UTC(2017, 9, 5), 3.80], [Date.UTC(2017, 10, 12), 4.48], [Date.UTC(2017, 11, 9), 3.78], [Date.UTC(2018, 0, 3), 4.68], [Date.UTC(2018, 1, 8), 5.17], [Date.UTC(2018, 2, 7), 5.87], [Date.UTC(2018, 3, 1), 5.80], [Date.UTC(2018, 4, 6), 5.19], [Date.UTC(2018, 5, 10), 5.76], [Date.UTC(2018, 6, 7), 6.36], [Date.UTC(2018, 7, 1), 6.99], [Date.UTC(2018, 8, 3), 7.65], [Date.UTC(2018, 9, 5), 7.16], [Date.UTC(2018, 10, 8), 7.68], [Date.UTC(2018, 11, 2), 8.38], [Date.UTC(2019, 0, 4), 8.29], [Date.UTC(2019, 1, 6), 7.57], [Date.UTC(2019, 2, 2), 8.26], [Date.UTC(2019, 3, 7), 8.17], [Date.UTC(2019, 4, 4), 7.83], [Date.UTC(2019, 5, 9), 8.53], [Date.UTC(2019, 6, 6), 9.26], [Date.UTC(2019, 7, 5), 10.02], [Date.UTC(2019, 8, 9), 9.87], [Date.UTC(2019, 9, 5), 9.09], [Date.UTC(2019, 10, 3), 9.84], [Date.UTC(2019, 11, 6), 10.31], [Date.UTC(2020, 0, 5), 9.70], [Date.UTC(2020, 1, 4), 9.35], [Date.UTC(2020, 2, 4), 10.11], [Date.UTC(2020, 3, 2), 9.31], [Date.UTC(2020, 4, 2), 9.12], [Date.UTC(2020, 5, 1), 8.36], [Date.UTC(2020, 6, 4), 9.09], [Date.UTC(2020, 7, 2), 9.69], [Date.UTC(2020, 8, 6), 10.47], [Date.UTC(2020, 9, 4), 11.28], [Date.UTC(2020, 10, 3), 12.12], [Date.UTC(2020, 11, 3), 12.21], [Date.UTC(2021, 0, 2), 11.72], [Date.UTC(2021, 1, 6), 10.86], [Date.UTC(2021, 2, 4), 10.31], [Date.UTC(2021, 3, 4), 11.03], [Date.UTC(2021, 4, 2), 11.87], [Date.UTC(2021, 5, 5), 11.84], [Date.UTC(2021, 6, 4), 10.96], [Date.UTC(2021, 7, 3), 11.86], [Date.UTC(2021, 8, 11), 11.67], [Date.UTC(2021, 9, 6), 11.27], [Date.UTC(2021, 10, 6), 11.77], [Date.UTC(2021, 11, 5), 12.90]]}, {name : 'C',data : [[Date.UTC(2001, 5, 30), 20.24], [Date.UTC(2001, 6, 30), 20.77], [Date.UTC(2001, 7, 30), 20.75], [Date.UTC(2001, 8, 28), 20.77], [Date.UTC(2001, 9, 26), 19.75], [Date.UTC(2001, 10, 28), 19.21], [Date.UTC(2001, 11, 31), 20.14], [Date.UTC(2002, 0, 30), 18.83], [Date.UTC(2002, 1, 27), 19.89], [Date.UTC(2002, 2, 29), 19.85], [Date.UTC(2002, 3, 29), 19.82], [Date.UTC(2002, 4, 29), 19.99], [Date.UTC(2002, 5, 29), 19.57], [Date.UTC(2002, 6, 31), 19.25], [Date.UTC(2002, 7, 29), 18.16], [Date.UTC(2002, 8, 29), 18.70], [Date.UTC(2002, 9, 30), 18.73], [Date.UTC(2002, 10, 29), 17.50], [Date.UTC(2002, 11, 31), 17.26], [Date.UTC(2003, 0, 31), 18.25], [Date.UTC(2003, 1, 28), 18.54], [Date.UTC(2003, 2, 31), 17.21], [Date.UTC(2003, 3, 30), 18.02], [Date.UTC(2003, 4, 30), 18.50], [Date.UTC(2003, 5, 30), 18.52], [Date.UTC(2003, 6, 31), 17.97], [Date.UTC(2003, 7, 31), 17.78], [Date.UTC(2003, 8, 30), 18.31], [Date.UTC(2003, 9, 31), 17.00], [Date.UTC(2003, 10, 30), 18.47], [Date.UTC(2003, 11, 31), 18.60], [Date.UTC(2004, 0, 31), 18.20], [Date.UTC(2004, 1, 29), 18.93], [Date.UTC(2004, 2, 31), 17.75], [Date.UTC(2004, 3, 30), 17.95], [Date.UTC(2004, 4, 30), 18.29], [Date.UTC(2004, 5, 30), 17.14], [Date.UTC(2004, 6, 31), 16.33], [Date.UTC(2004, 7, 30), 17.12], [Date.UTC(2004, 8, 30), 18.17], [Date.UTC(2004, 9, 31), 17.99], [Date.UTC(2004, 10, 30), 19.70], [Date.UTC(2004, 11, 31), 20.71], [Date.UTC(2005, 0, 31), 19.82], [Date.UTC(2005, 1, 28), 19.47], [Date.UTC(2005, 2, 31), 18.63], [Date.UTC(2005, 3, 30), 18.52], [Date.UTC(2005, 4, 31), 19.37], [Date.UTC(2005, 5, 30), 19.85], [Date.UTC(2005, 6, 31), 19.47], [Date.UTC(2005, 7, 31), 19.16], [Date.UTC(2005, 8, 30), 18.77], [Date.UTC(2005, 11, 3), 19.63], [Date.UTC(2006, 0, 4), 19.01], [Date.UTC(2006, 1, 2), 18.33], [Date.UTC(2006, 2, 1), 17.79], [Date.UTC(2006, 3, 2), 17.69], [Date.UTC(2006, 4, 1), 17.69], [Date.UTC(2006, 5, 1), 18.25], [Date.UTC(2006, 6, 2), 17.83], [Date.UTC(2006, 7, 2), 17.43], [Date.UTC(2006, 8, 2), 18.06], [Date.UTC(2006, 9, 1), 17.66], [Date.UTC(2006, 10, 2), 17.20], [Date.UTC(2006, 11, 1), 16.62], [Date.UTC(2007, 0, 2), 15.81], [Date.UTC(2007, 1, 3), 16.10], [Date.UTC(2007, 2, 3), 15.63], [Date.UTC(2007, 3, 1), 14.94], [Date.UTC(2007, 4, 5), 15.15], [Date.UTC(2007, 5, 2), 15.97], [Date.UTC(2007, 6, 2), 16.36], [Date.UTC(2007, 7, 5), 15.70], [Date.UTC(2007, 8, 2), 14.91], [Date.UTC(2007, 9, 4), 14.59], [Date.UTC(2007, 10, 4), 13.97], [Date.UTC(2007, 11, 3), 13.17], [Date.UTC(2008, 0, 3), 13.92], [Date.UTC(2008, 1, 7), 14.86], [Date.UTC(2008, 5, 1), 15.51], [Date.UTC(2008, 6, 2), 15.95], [Date.UTC(2008, 7, 3), 16.18], [Date.UTC(2008, 8, 3), 15.38], [Date.UTC(2008, 9, 6), 15.57], [Date.UTC(2008, 10, 2), 15.28], [Date.UTC(2008, 11, 3), 15.02], [Date.UTC(2009, 0, 2), 15.93], [Date.UTC(2009, 1, 1), 15.84], [Date.UTC(2009, 2, 5), 15.86], [Date.UTC(2009, 3, 7), 15.47], [Date.UTC(2009, 4, 1), 16.13], [Date.UTC(2009, 5, 4), 16.78], [Date.UTC(2009, 6, 2), 17.32], [Date.UTC(2009, 7, 1), 17.22], [Date.UTC(2009, 8, 5), 16.86], [Date.UTC(2009, 9, 2), 16.84], [Date.UTC(2009, 10, 2), 17.32], [Date.UTC(2009, 11, 2), 16.28], [Date.UTC(2010, 0, 5), 16.22], [Date.UTC(2010, 1, 7), 16.60], [Date.UTC(2010, 2, 7), 17.28], [Date.UTC(2010, 3, 5), 18.06], [Date.UTC(2010, 4, 15), 18.19], [Date.UTC(2010, 6, 6), 18.48], [Date.UTC(2010, 6, 30), 17.87], [Date.UTC(2010, 8, 11), 17.15], [Date.UTC(2010, 9, 2), 17.18], [Date.UTC(2010, 10, 3), 16.72], [Date.UTC(2010, 11, 7), 16.08], [Date.UTC(2011, 0, 2), 15.82], [Date.UTC(2011, 1, 8), 14.99], [Date.UTC(2011, 2, 8), 15.26], [Date.UTC(2011, 3, 3), 16.16], [Date.UTC(2011, 4, 2), 16.17], [Date.UTC(2011, 5, 5), 16.28], [Date.UTC(2011, 5, 27), 16.28], [Date.UTC(2011, 6, 8), 17.28], [Date.UTC(2011, 7, 3), 17.39], [Date.UTC(2011, 8, 10), 18.00], [Date.UTC(2011, 9, 9), 17.71], [Date.UTC(2011, 10, 7), 17.32], [Date.UTC(2011, 11, 4), 17.06], [Date.UTC(2012, 0, 8), 16.97], [Date.UTC(2012, 1, 5), 16.52], [Date.UTC(2012, 2, 11), 17.09], [Date.UTC(2012, 3, 8), 17.55], [Date.UTC(2012, 4, 9), 17.35], [Date.UTC(2012, 5, 10), 17.72], [Date.UTC(2012, 6, 4), 18.33], [Date.UTC(2012, 7, 10), 18.94], [Date.UTC(2012, 8, 2), 19.30], [Date.UTC(2012, 9, 5), 19.82], [Date.UTC(2012, 10, 4), 19.22], [Date.UTC(2012, 11, 2), 18.70], [Date.UTC(2013, 0, 5), 17.85], [Date.UTC(2013, 1, 8), 17.08], [Date.UTC(2013, 2, 11), 17.14], [Date.UTC(2013, 3, 7), 17.86], [Date.UTC(2013, 4, 8), 18.73], [Date.UTC(2013, 5, 9), 17.81], [Date.UTC(2013, 6, 7), 17.63], [Date.UTC(2013, 6, 12), 17.63], [Date.UTC(2013, 7, 4), 15.97], [Date.UTC(2013, 8, 11), 16.98], [Date.UTC(2013, 9, 10), 17.25], [Date.UTC(2013, 10, 9), 18.16], [Date.UTC(2013, 11, 6), 17.89], [Date.UTC(2014, 0, 1), 17.94], [Date.UTC(2014, 1, 8), 18.33], [Date.UTC(2014, 2, 3), 17.53], [Date.UTC(2014, 3, 10), 17.63], [Date.UTC(2014, 4, 7), 16.93], [Date.UTC(2014, 5, 8), 16.19], [Date.UTC(2014, 6, 6), 17.14], [Date.UTC(2014, 7, 11), 16.40], [Date.UTC(2014, 8, 1), 16.72], [Date.UTC(2014, 9, 3), 17.66], [Date.UTC(2014, 10, 8), 17.47], [Date.UTC(2014, 11, 7), 17.59], [Date.UTC(2015, 0, 6), 16.70], [Date.UTC(2015, 1, 5), 16.49], [Date.UTC(2015, 2, 7), 16.64], [Date.UTC(2015, 3, 13), 15.75], [Date.UTC(2015, 4, 13), 16.85], [Date.UTC(2015, 5, 6), 16.79], [Date.UTC(2015, 6, 12), 16.15], [Date.UTC(2015, 7, 6), 14.73], [Date.UTC(2015, 8, 5), 15.62], [Date.UTC(2015, 9, 4), 16.19], [Date.UTC(2015, 10, 7), 17.15], [Date.UTC(2015, 11, 4), 16.46], [Date.UTC(2016, 0, 2), 16.04], [Date.UTC(2016, 1, 2), 15.59], [Date.UTC(2016, 2, 3), 14.60], 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[Date.UTC(2021, 6, 4), 1.53], [Date.UTC(2021, 7, 3), 1.47], [Date.UTC(2021, 8, 11), 1.80], [Date.UTC(2021, 9, 6), 2.17], [Date.UTC(2021, 10, 6), 2.11], [Date.UTC(2021, 11, 5), 1.79]]}, {name : 'Swift',data : [[Date.UTC(2014, 6, 6), 1.05], [Date.UTC(2014, 7, 11), 0.67], [Date.UTC(2014, 8, 1), 0.85], [Date.UTC(2014, 9, 3), 0.76], [Date.UTC(2014, 10, 8), 0.83], [Date.UTC(2014, 11, 7), 1.06], [Date.UTC(2015, 0, 6), 0.75], [Date.UTC(2015, 1, 5), 0.72], [Date.UTC(2015, 2, 7), 0.82], [Date.UTC(2015, 3, 13), 0.88], [Date.UTC(2015, 4, 13), 1.12], [Date.UTC(2015, 5, 6), 1.44], [Date.UTC(2015, 6, 12), 1.23], [Date.UTC(2015, 7, 6), 1.18], [Date.UTC(2015, 8, 5), 1.19], [Date.UTC(2015, 9, 4), 1.28], [Date.UTC(2015, 10, 7), 1.24], [Date.UTC(2015, 11, 4), 1.41], [Date.UTC(2016, 0, 2), 1.36], [Date.UTC(2016, 1, 2), 1.38], [Date.UTC(2016, 2, 3), 1.59], [Date.UTC(2016, 3, 7), 1.48], [Date.UTC(2016, 4, 6), 1.59], [Date.UTC(2016, 5, 5), 1.83], [Date.UTC(2016, 6, 4), 1.96], [Date.UTC(2016, 7, 6), 1.98], [Date.UTC(2016, 8, 8), 1.93], [Date.UTC(2016, 9, 7), 2.00], [Date.UTC(2016, 10, 5), 2.04], [Date.UTC(2016, 11, 4), 2.13], [Date.UTC(2017, 0, 7), 1.93], [Date.UTC(2017, 1, 8), 2.13], [Date.UTC(2017, 2, 7), 2.27], [Date.UTC(2017, 3, 9), 2.29], [Date.UTC(2017, 4, 6), 2.27], [Date.UTC(2017, 5, 3), 2.21], [Date.UTC(2017, 6, 7), 2.25], [Date.UTC(2017, 7, 2), 1.83], [Date.UTC(2017, 8, 6), 1.76], [Date.UTC(2017, 9, 5), 1.67], [Date.UTC(2017, 10, 12), 1.39], [Date.UTC(2017, 11, 9), 1.57], [Date.UTC(2018, 0, 3), 2.38], [Date.UTC(2018, 1, 8), 1.79], [Date.UTC(2018, 2, 7), 2.14], [Date.UTC(2018, 3, 1), 1.53], [Date.UTC(2018, 4, 6), 0.91], [Date.UTC(2018, 5, 10), 1.14], [Date.UTC(2018, 6, 7), 1.41], [Date.UTC(2018, 7, 1), 1.38], [Date.UTC(2018, 8, 3), 1.22], [Date.UTC(2018, 9, 5), 1.50], [Date.UTC(2018, 10, 8), 1.45], [Date.UTC(2018, 11, 2), 1.22], [Date.UTC(2019, 0, 4), 1.19], [Date.UTC(2019, 1, 6), 0.92], [Date.UTC(2019, 2, 2), 1.16], [Date.UTC(2019, 3, 7), 0.98], [Date.UTC(2019, 4, 4), 1.15], [Date.UTC(2019, 5, 9), 1.42], [Date.UTC(2019, 6, 6), 1.12], [Date.UTC(2019, 7, 5), 0.89], [Date.UTC(2019, 8, 9), 1.10], [Date.UTC(2019, 9, 5), 1.36], [Date.UTC(2019, 10, 3), 1.65], [Date.UTC(2019, 11, 6), 1.49], [Date.UTC(2020, 0, 5), 1.80], [Date.UTC(2020, 1, 4), 1.46], [Date.UTC(2020, 2, 4), 1.24], [Date.UTC(2020, 3, 2), 1.52], [Date.UTC(2020, 4, 2), 1.79], [Date.UTC(2020, 5, 1), 1.46], [Date.UTC(2020, 6, 4), 1.43], [Date.UTC(2020, 7, 2), 1.42], [Date.UTC(2020, 8, 6), 1.38], [Date.UTC(2020, 9, 4), 1.09], [Date.UTC(2020, 10, 3), 1.35], [Date.UTC(2020, 11, 3), 1.22], [Date.UTC(2021, 0, 2), 1.43], [Date.UTC(2021, 1, 6), 1.13], [Date.UTC(2021, 2, 4), 0.95], [Date.UTC(2021, 3, 4), 1.19], [Date.UTC(2021, 4, 2), 1.14], [Date.UTC(2021, 5, 5), 1.10], [Date.UTC(2021, 6, 4), 1.07], [Date.UTC(2021, 7, 3), 0.98], [Date.UTC(2021, 8, 11), 1.07], [Date.UTC(2021, 9, 6), 1.11], [Date.UTC(2021, 10, 6), 1.43], [Date.UTC(2021, 11, 5), 1.76]]}
    ]

tiobe202208.txt

复制方括号中的部分放到双引号中，转为字符串类型替换掉'name : '、',data '、'Date.UTC'为空字符串，再用 eval()函数将字符串写为元素为字典的列表，每个字典中只有一个元素。替换后的数据如下：

tiobe = [
{name : 'Python',data : [[Date.UTC(2001, 5, 30), 1.25], [Date.UTC(2001, 6, 30), 1.13], [Date.UTC(2001, 7, 30), 1.20], [Date.UTC(2001, 8, 28), 1.17], [Date.UTC(2001, 9, 26), 1.28], [Date.UTC(2001, 10, 28), 1.23], [Date.UTC(2001, 11, 31), 1.04], [Date.UTC(2002, 0, 30), 1.02], [Date.UTC(2002, 1, 27), 0.99], [Date.UTC(2002, 2, 29), 0.99], [Date.UTC(2002, 3, 29), 1.07], [Date.UTC(2002, 4, 29), 1.06], [Date.UTC(2002, 5, 29), 1.13], [Date.UTC(2002, 6, 31), 1.08], [Date.UTC(2002, 7, 29), 1.22], [Date.UTC(2002, 8, 29), 1.08], [Date.UTC(2002, 9, 30), 1.19], [Date.UTC(2002, 10, 29), 1.00], [Date.UTC(2002, 11, 31), 1.00], [Date.UTC(2003, 0, 31), 1.03], [Date.UTC(2003, 1, 28), 0.97], [Date.UTC(2003, 2, 31), 0.99], [Date.UTC(2003, 3, 30), 1.01], [Date.UTC(2003, 4, 30), 1.16], [Date.UTC(2003, 5, 30), 1.28], [Date.UTC(2003, 6, 31), 1.30], [Date.UTC(2003, 7, 31), 1.42], [Date.UTC(2003, 8, 30), 1.49], [Date.UTC(2003, 9, 31), 1.77], [Date.UTC(2003, 10, 30), 1.10], [Date.UTC(2003, 11, 31), 1.13], [Date.UTC(2004, 0, 31), 1.01], [Date.UTC(2004, 1, 29), 1.02], [Date.UTC(2004, 2, 31), 1.01], [Date.UTC(2004, 3, 30), 4.57], [Date.UTC(2004, 4, 30), 6.58], [Date.UTC(2004, 5, 30), 4.96], [Date.UTC(2004, 6, 31), 4.70], [Date.UTC(2004, 7, 30), 5.60], [Date.UTC(2004, 8, 30), 4.44], [Date.UTC(2004, 9, 31), 5.20], [Date.UTC(2004, 10, 30), 3.03], [Date.UTC(2004, 11, 31), 2.84], [Date.UTC(2005, 0, 31), 2.51], [Date.UTC(2005, 1, 28), 2.42], [Date.UTC(2005, 2, 31), 2.70], [Date.UTC(2005, 3, 30), 2.48], [Date.UTC(2005, 4, 31), 2.80], [Date.UTC(2005, 5, 30), 2.52], [Date.UTC(2005, 6, 31), 2.88], [Date.UTC(2005, 7, 31), 3.03], [Date.UTC(2005, 8, 30), 2.88], [Date.UTC(2005, 11, 3), 2.68], [Date.UTC(2006, 0, 4), 2.60], [Date.UTC(2006, 1, 2), 2.67], [Date.UTC(2006, 2, 1), 3.09], [Date.UTC(2006, 3, 2), 2.76], [Date.UTC(2006, 4, 1), 3.04], [Date.UTC(2006, 5, 1), 3.46], [Date.UTC(2006, 6, 2), 3.02], [Date.UTC(2006, 7, 2), 3.07], [Date.UTC(2006, 8, 2), 3.14], [Date.UTC(2006, 9, 1), 3.47], [Date.UTC(2006, 10, 2), 3.64], [Date.UTC(2006, 11, 1), 3.76], [Date.UTC(2007, 0, 2), 3.50], [Date.UTC(2007, 1, 3), 3.57], [Date.UTC(2007, 2, 3), 3.90], [Date.UTC(2007, 3, 1), 3.81], [Date.UTC(2007, 4, 5), 3.78], [Date.UTC(2007, 5, 2), 3.16], [Date.UTC(2007, 6, 2), 3.02], [Date.UTC(2007, 7, 5), 2.75], [Date.UTC(2007, 8, 2), 3.03], [Date.UTC(2007, 9, 4), 3.43], [Date.UTC(2007, 10, 4), 4.23], [Date.UTC(2007, 11, 3), 4.70], [Date.UTC(2008, 0, 3), 5.54], [Date.UTC(2008, 1, 7), 4.76], [Date.UTC(2008, 5, 1), 4.90], [Date.UTC(2008, 6, 2), 4.97], [Date.UTC(2008, 7, 3), 4.98], [Date.UTC(2008, 8, 3), 5.01], [Date.UTC(2008, 9, 6), 4.56], [Date.UTC(2008, 10, 2), 5.14], [Date.UTC(2008, 11, 3), 4.17], [Date.UTC(2009, 0, 2), 4.73], [Date.UTC(2009, 1, 1), 4.57], [Date.UTC(2009, 2, 5), 5.18], [Date.UTC(2009, 3, 7), 6.08], [Date.UTC(2009, 4, 1), 5.55], [Date.UTC(2009, 5, 4), 4.76], [Date.UTC(2009, 6, 2), 4.43], [Date.UTC(2009, 7, 1), 4.49], [Date.UTC(2009, 8, 5), 3.93], [Date.UTC(2009, 9, 2), 3.90], [Date.UTC(2009, 10, 2), 4.67], [Date.UTC(2009, 11, 2), 5.19], [Date.UTC(2010, 0, 5), 4.45], [Date.UTC(2010, 1, 7), 4.31], [Date.UTC(2010, 2, 7), 4.23], [Date.UTC(2010, 3, 5), 4.21], [Date.UTC(2010, 4, 15), 4.10], [Date.UTC(2010, 6, 6), 4.22], [Date.UTC(2010, 6, 30), 4.22], [Date.UTC(2010, 8, 11), 4.58], [Date.UTC(2010, 9, 2), 4.86], [Date.UTC(2010, 10, 3), 5.68], [Date.UTC(2010, 11, 7), 6.48], [Date.UTC(2011, 0, 2), 6.26], [Date.UTC(2011, 1, 8), 7.04], [Date.UTC(2011, 2, 8), 5.74], [Date.UTC(2011, 3, 3), 4.93], [Date.UTC(2011, 4, 2), 4.58], [Date.UTC(2011, 5, 5), 3.90], [Date.UTC(2011, 5, 27), 3.90], [Date.UTC(2011, 6, 8), 3.58], [Date.UTC(2011, 7, 3), 3.41], [Date.UTC(2011, 8, 10), 4.00], [Date.UTC(2011, 9, 9), 3.94], [Date.UTC(2011, 10, 7), 3.62], [Date.UTC(2011, 11, 4), 3.49], [Date.UTC(2012, 0, 8), 3.21], [Date.UTC(2012, 1, 5), 3.15], [Date.UTC(2012, 2, 11), 3.29], [Date.UTC(2012, 3, 8), 3.66], [Date.UTC(2012, 4, 9), 3.82], [Date.UTC(2012, 5, 10), 3.85], [Date.UTC(2012, 6, 4), 4.00], [Date.UTC(2012, 7, 10), 3.88], [Date.UTC(2012, 8, 2), 3.86], [Date.UTC(2012, 9, 5), 3.90], [Date.UTC(2012, 10, 4), 4.06], [Date.UTC(2012, 11, 2), 3.85], [Date.UTC(2013, 0, 5), 4.17], [Date.UTC(2013, 1, 8), 4.95], [Date.UTC(2013, 2, 11), 4.39], [Date.UTC(2013, 3, 7), 4.44], [Date.UTC(2013, 4, 8), 4.32], [Date.UTC(2013, 5, 9), 4.18], [Date.UTC(2013, 6, 7), 4.03], [Date.UTC(2013, 6, 12), 4.03], [Date.UTC(2013, 7, 4), 3.60], [Date.UTC(2013, 8, 11), 3.17], [Date.UTC(2013, 9, 10), 3.11], [Date.UTC(2013, 10, 9), 3.11], [Date.UTC(2013, 11, 6), 2.21], [Date.UTC(2014, 0, 1), 2.37], [Date.UTC(2014, 1, 8), 2.16], [Date.UTC(2014, 2, 3), 2.02], [Date.UTC(2014, 3, 10), 1.99], [Date.UTC(2014, 4, 7), 3.06], [Date.UTC(2014, 5, 8), 2.71], [Date.UTC(2014, 6, 6), 2.66], [Date.UTC(2014, 7, 11), 3.12], [Date.UTC(2014, 8, 1), 2.78], [Date.UTC(2014, 9, 3), 2.33], [Date.UTC(2014, 10, 8), 2.59], [Date.UTC(2014, 11, 7), 2.29], [Date.UTC(2015, 0, 6), 2.61], [Date.UTC(2015, 1, 5), 2.88], [Date.UTC(2015, 2, 7), 2.61], [Date.UTC(2015, 3, 13), 2.69], [Date.UTC(2015, 4, 13), 3.72], [Date.UTC(2015, 5, 6), 4.00], [Date.UTC(2015, 6, 12), 4.26], [Date.UTC(2015, 7, 6), 4.07], [Date.UTC(2015, 8, 5), 3.66], [Date.UTC(2015, 9, 4), 4.51], [Date.UTC(2015, 10, 7), 3.77], [Date.UTC(2015, 11, 4), 4.43], [Date.UTC(2016, 0, 2), 3.85], [Date.UTC(2016, 1, 2), 4.18], [Date.UTC(2016, 2, 3), 4.26], [Date.UTC(2016, 3, 7), 3.33], [Date.UTC(2016, 4, 6), 3.79], [Date.UTC(2016, 5, 5), 3.90], [Date.UTC(2016, 6, 4), 4.17], [Date.UTC(2016, 7, 6), 4.40], [Date.UTC(2016, 8, 8), 4.30], [Date.UTC(2016, 9, 7), 3.77], [Date.UTC(2016, 10, 5), 3.57], [Date.UTC(2016, 11, 4), 4.24], [Date.UTC(2017, 0, 7), 3.46], [Date.UTC(2017, 1, 8), 4.04], [Date.UTC(2017, 2, 7), 3.92], [Date.UTC(2017, 3, 9), 3.46], [Date.UTC(2017, 4, 6), 3.55], [Date.UTC(2017, 5, 3), 4.33], [Date.UTC(2017, 6, 7), 3.54], [Date.UTC(2017, 7, 2), 3.69], [Date.UTC(2017, 8, 6), 2.98], [Date.UTC(2017, 9, 5), 3.80], [Date.UTC(2017, 10, 12), 4.48], [Date.UTC(2017, 11, 9), 3.78], [Date.UTC(2018, 0, 3), 4.68], [Date.UTC(2018, 1, 8), 5.17], [Date.UTC(2018, 2, 7), 5.87], [Date.UTC(2018, 3, 1), 5.80], [Date.UTC(2018, 4, 6), 5.19], [Date.UTC(2018, 5, 10), 5.76], [Date.UTC(2018, 6, 7), 6.36], [Date.UTC(2018, 7, 1), 6.99], [Date.UTC(2018, 8, 3), 7.65], [Date.UTC(2018, 9, 5), 7.16], [Date.UTC(2018, 10, 8), 7.68], [Date.UTC(2018, 11, 2), 8.38], [Date.UTC(2019, 0, 4), 8.29], [Date.UTC(2019, 1, 6), 7.57], [Date.UTC(2019, 2, 2), 8.26], [Date.UTC(2019, 3, 7), 8.17], [Date.UTC(2019, 4, 4), 7.83], [Date.UTC(2019, 5, 9), 8.53], [Date.UTC(2019, 6, 6), 9.26], [Date.UTC(2019, 7, 5), 10.02], [Date.UTC(2019, 8, 9), 9.87], [Date.UTC(2019, 9, 5), 9.09], [Date.UTC(2019, 10, 3), 9.84], [Date.UTC(2019, 11, 6), 10.31], [Date.UTC(2020, 0, 5), 9.70], [Date.UTC(2020, 1, 4), 9.35], [Date.UTC(2020, 2, 4), 10.11], [Date.UTC(2020, 3, 2), 9.31], [Date.UTC(2020, 4, 2), 9.12], [Date.UTC(2020, 5, 1), 8.36], [Date.UTC(2020, 6, 4), 9.09], [Date.UTC(2020, 7, 2), 9.69], [Date.UTC(2020, 8, 6), 10.47], [Date.UTC(2020, 9, 4), 11.28], [Date.UTC(2020, 10, 3), 12.12], [Date.UTC(2020, 11, 3), 12.21], [Date.UTC(2021, 0, 2), 11.72], [Date.UTC(2021, 1, 6), 10.86], [Date.UTC(2021, 2, 4), 10.31], [Date.UTC(2021, 3, 4), 11.03], [Date.UTC(2021, 4, 2), 11.87], [Date.UTC(2021, 5, 5), 11.84], [Date.UTC(2021, 6, 4), 10.96], [Date.UTC(2021, 7, 3), 11.86], [Date.UTC(2021, 8, 11), 11.67], [Date.UTC(2021, 9, 6), 11.27], [Date.UTC(2021, 10, 6), 11.77], [Date.UTC(2021, 11, 5), 12.90], [Date.UTC(2022, 0, 1), 13.58], [Date.UTC(2022, 1, 2), 15.33], [Date.UTC(2022, 2, 2), 14.26], [Date.UTC(2022, 3, 5), 13.92], [Date.UTC(2022, 4, 3), 12.74], [Date.UTC(2022, 5, 4), 12.20], [Date.UTC(2022, 6, 2), 13.44], [Date.UTC(2022, 7, 2), 15.42]]}, 
{name : 'C',data : [[Date.UTC(2001, 5, 30), 20.24], [Date.UTC(2001, 6, 30), 20.77], [Date.UTC(2001, 7, 30), 20.75], [Date.UTC(2001, 8, 28), 20.77], [Date.UTC(2001, 9, 26), 19.75], [Date.UTC(2001, 10, 28), 19.21], [Date.UTC(2001, 11, 31), 20.14], [Date.UTC(2002, 0, 30), 18.83], [Date.UTC(2002, 1, 27), 19.89], [Date.UTC(2002, 2, 29), 19.85], [Date.UTC(2002, 3, 29), 19.82], [Date.UTC(2002, 4, 29), 19.99], [Date.UTC(2002, 5, 29), 19.57], [Date.UTC(2002, 6, 31), 19.25], [Date.UTC(2002, 7, 29), 18.16], [Date.UTC(2002, 8, 29), 18.70], [Date.UTC(2002, 9, 30), 18.73], [Date.UTC(2002, 10, 29), 17.50], [Date.UTC(2002, 11, 31), 17.26], [Date.UTC(2003, 0, 31), 18.25], [Date.UTC(2003, 1, 28), 18.54], [Date.UTC(2003, 2, 31), 17.21], [Date.UTC(2003, 3, 30), 18.02], [Date.UTC(2003, 4, 30), 18.50], [Date.UTC(2003, 5, 30), 18.52], [Date.UTC(2003, 6, 31), 17.97], [Date.UTC(2003, 7, 31), 17.78], [Date.UTC(2003, 8, 30), 18.31], [Date.UTC(2003, 9, 31), 17.00], [Date.UTC(2003, 10, 30), 18.47], [Date.UTC(2003, 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7), 4.00], [Date.UTC(2015, 3, 13), 3.01], [Date.UTC(2015, 4, 13), 2.72], [Date.UTC(2015, 5, 6), 2.87], [Date.UTC(2015, 6, 12), 2.89], [Date.UTC(2015, 7, 6), 2.73], [Date.UTC(2015, 8, 5), 2.53], [Date.UTC(2015, 9, 4), 2.56], [Date.UTC(2015, 10, 7), 3.25], [Date.UTC(2015, 11, 4), 2.79], [Date.UTC(2016, 0, 2), 2.71], [Date.UTC(2016, 1, 2), 2.77], [Date.UTC(2016, 2, 3), 2.77], [Date.UTC(2016, 3, 7), 2.99], [Date.UTC(2016, 4, 6), 2.99], [Date.UTC(2016, 5, 5), 3.23], [Date.UTC(2016, 6, 4), 3.27], [Date.UTC(2016, 7, 6), 3.17], [Date.UTC(2016, 8, 8), 2.85], [Date.UTC(2016, 9, 7), 2.74], [Date.UTC(2016, 10, 5), 3.13], [Date.UTC(2016, 11, 4), 2.92], [Date.UTC(2017, 0, 7), 2.56], [Date.UTC(2017, 1, 8), 3.07], [Date.UTC(2017, 2, 7), 3.01], [Date.UTC(2017, 3, 9), 3.38], [Date.UTC(2017, 4, 6), 2.69], [Date.UTC(2017, 5, 3), 2.77], [Date.UTC(2017, 6, 7), 3.09], [Date.UTC(2017, 7, 2), 2.29], [Date.UTC(2017, 8, 6), 2.21], [Date.UTC(2017, 9, 5), 2.79], [Date.UTC(2017, 10, 12), 1.90], [Date.UTC(2017, 11, 9), 1.59], [Date.UTC(2018, 0, 3), 2.53], [Date.UTC(2018, 1, 8), 3.42], [Date.UTC(2018, 2, 7), 4.01], [Date.UTC(2018, 3, 1), 4.22], [Date.UTC(2018, 4, 6), 3.32], [Date.UTC(2018, 5, 10), 2.88], [Date.UTC(2018, 6, 7), 2.83], [Date.UTC(2018, 7, 1), 2.93], [Date.UTC(2018, 8, 3), 2.78], [Date.UTC(2018, 9, 5), 2.79], [Date.UTC(2018, 10, 8), 2.38], [Date.UTC(2018, 11, 2), 2.44], [Date.UTC(2019, 0, 4), 2.68], [Date.UTC(2019, 1, 6), 2.27], [Date.UTC(2019, 2, 2), 2.42], [Date.UTC(2019, 3, 7), 2.24], [Date.UTC(2019, 4, 4), 2.49], [Date.UTC(2019, 5, 9), 2.57], [Date.UTC(2019, 6, 6), 2.17], [Date.UTC(2019, 7, 5), 2.08], [Date.UTC(2019, 8, 9), 1.86], [Date.UTC(2019, 9, 5), 1.91], [Date.UTC(2019, 10, 3), 1.72], [Date.UTC(2019, 11, 6), 2.05], [Date.UTC(2020, 0, 5), 2.41], [Date.UTC(2020, 1, 4), 2.02], [Date.UTC(2020, 2, 4), 2.02], [Date.UTC(2020, 3, 2), 2.37], [Date.UTC(2020, 4, 2), 2.49], [Date.UTC(2020, 5, 1), 2.26], [Date.UTC(2020, 6, 4), 1.90], [Date.UTC(2020, 7, 2), 2.24], [Date.UTC(2020, 8, 6), 2.49], [Date.UTC(2020, 9, 4), 2.09], [Date.UTC(2020, 10, 3), 1.79], [Date.UTC(2020, 11, 3), 2.12], [Date.UTC(2021, 0, 2), 1.99], [Date.UTC(2021, 1, 6), 1.75], [Date.UTC(2021, 2, 4), 2.07], [Date.UTC(2021, 3, 4), 1.84], [Date.UTC(2021, 4, 2), 1.86], [Date.UTC(2021, 5, 5), 2.21], [Date.UTC(2021, 6, 4), 2.58], [Date.UTC(2021, 7, 3), 2.19], [Date.UTC(2021, 8, 11), 1.85], [Date.UTC(2021, 9, 6), 2.10], [Date.UTC(2021, 10, 6), 1.81], [Date.UTC(2021, 11, 5), 1.50], [Date.UTC(2022, 0, 1), 1.40], [Date.UTC(2022, 1, 2), 1.79], [Date.UTC(2022, 2, 2), 1.92], [Date.UTC(2022, 3, 5), 1.64], [Date.UTC(2022, 4, 3), 1.52], [Date.UTC(2022, 5, 4), 1.25], [Date.UTC(2022, 6, 2), 1.20], [Date.UTC(2022, 7, 2), 1.39]]}]

遍历列表，将日期部分拼接为一个字符串，根据日期和当月热度可绘制折线图：

import matplotlib.pyplot as plt

with open('images/ch9/tiobe202208.txt', 'r', encoding='utf-8') as fr:
        tiobe = fr.read()  # 读文件到字符会串
tiobe = eval(tiobe.replace('name : ', '').replace(',data ', '').replace('Date.UTC', ''))

# print(tiobe)
for line in tiobe:
    lan_name = list(line.keys())[0]  # 当前处理的语言名，字符串
    data = line[lan_name]  # 当前处理的语言的热度数据列表
    date_of_lan = [f'{x[0][0]}-{x[0][1]:02}-{x[0][2]:02}' for x in data]
    rank = [x[1] for x in data]
    plt.plot(date_of_lan, rank, label=lan_name)
    plt.legend(loc='lower left')
plt.xticks(
    ['2002-00-30', '2004-00-31', '2006-00-04', '2008-00-03', '2010-00-05', '2012-00-08', '2014-00-01', '2016-00-02', '2018-00-03', '2020-00-05', '2022-00-01'],
    ['2002', '2004', '2006', '2008', '2010', '2012', '2014', '2016', '2018', '2020', '2022'])  # , rotation=-45
plt.show()

import matplotlib.pyplot as plt
import matplotlib as mpl

mpl.rcParams['figure.figsize'] = (20,10)  # 修改图片大小
plt.rcParams['figure.dpi'] = 300          # default for me was 75

def read_txt():
    """读文本文件中的数据为字符串，替换转为字典，返回字典"""
    with open('images/ch9/tiobe202208.txt', 'r', encoding='utf-8') as fr:
        tiobe_str = fr.read()  # 读文件到字符会串
    tiobe_dic = eval(tiobe_str.replace('name : ', '').replace(',data ', '').replace('Date.UTC', ''))
    return tiobe_dic


def plot_ranking_curve(tiobe_dic):
    for line in tiobe_dic:               # 遍历字典，逐条绘制
        lan_name = list(line.keys())[0]  # 当前处理的语言名，字符串
        data = line[lan_name]            # 当前处理的语言的热度数据列表
        date_of_lan = [f'{x[0][0]}-{x[0][1]:02}-{x[0][2]:02}' for x in data]  # 格式化日期，月和日用2位数，不足时补0
        rank = [x[1] for x in data]
        plt.plot(date_of_lan, rank, label=lan_name, linewidth=2)


def draw_label():
    plt.title('TIOBE Programming Community index')
    plt.xlabel('year')
    plt.ylabel('Ratings(%)')
    plt.legend(loc='lower left')
    plt.xticks(
    ['2002-00-30', '2004-00-31', '2006-00-04', '2008-00-03', '2010-00-05', '2012-00-08', '2014-00-01', '2016-00-02', '2018-00-03', '2020-00-05', '2022-00-01'],
    ['2002', '2004', '2006', '2008', '2010', '2012', '2014', '2016', '2018', '2020', '2022'])  # , rotation=-45
    


if __name__ == '__main__':
    tiobe_dict = read_txt()
    plot_ranking_curve(tiobe_dict)
    draw_label()
    plt.show()

plt.xticks(date_of_lan[::12],[x[:4] for x in date_of_lan[::12]],rotation=-45)

# 修改x刻度
plt.xticks(
    ['2002-00-30', '2004-00-31', '2006-00-04', '2008-00-03', '2010-00-05', '2012-00-08', '2014-00-01', '2016-00-02', '2018-00-03', '2020-00-05', '2022-00-01'],
    ['2002', '2004', '2006', '2008', '2010', '2012', '2014', '2016', '2018', '2020', '2022'])  # , rotation=-45

# 改变图例位置
plt.legend(bbox_to_anchor=(0., -0.13, 1., -.13), loc=8,
           ncol=5, mode="expand", borderaxespad=0.)
# 增加横向主网格
plt.grid(which='major', axis='y', color='k', linestyle='-.', linewidth=0.7)

import matplotlib.pyplot as plt
import matplotlib as mpl

mpl.rcParams['figure.figsize'] = (20,10)  # 修改图片大小
plt.rcParams['figure.dpi'] = 300          # default for me was 75

def read_txt():
    """读文本文件中的数据为字符串，替换转为字典，返回字典"""
    with open('images/ch9/tiobe202208.txt', 'r', encoding='utf-8') as fr:
        tiobe_str = fr.read()  # 读文件到字符会串
    tiobe_dic = eval(tiobe_str.replace('name : ', '').replace(',data ', '').replace('Date.UTC', ''))
    return tiobe_dic


def plot_ranking_curve(tiobe_dic):
    for line in tiobe_dic:
        lan_name = list(line.keys())[0]  # 当前处理的语言名，字符串
        data = line[lan_name]  # 当前处理的语言的热度数据列表
        date_of_lan = [f'{x[0][0]}-{x[0][1]:02}-{x[0][2]:02}' for x in data]
        rank = [x[1] for x in data]
        plt.plot(date_of_lan, rank, label=lan_name, linewidth=2)


def draw_label():
    plt.title('TIOBE Programming Community index')
    plt.xlabel('year')
    plt.ylabel('Ratings(%)')

    # 修改x刻度
    plt.xticks(
    ['2002-00-30', '2004-00-31', '2006-00-04', '2008-00-03', '2010-00-05', '2012-00-08', '2014-00-01', '2016-00-02', '2018-00-03', '2020-00-05', '2022-00-01'],
    ['2002', '2004', '2006', '2008', '2010', '2012', '2014', '2016', '2018', '2020', '2022'])  # , rotation=-45
    
    # 改变图例位置
    plt.legend(bbox_to_anchor=(0., -0.13, 1., -.13), loc=8, ncol=5, mode="expand", borderaxespad=0.)
    
    # 增加横向主网格
    plt.grid(which='major', axis='y', color='k', linestyle='-.', linewidth=0.7)


if __name__ == '__main__':
    tiobe_dict = read_txt()
    plot_ranking_curve(tiobe_dict)
    draw_label()
    plt.show()

import matplotlib.pyplot as plt
import matplotlib as mpl

mpl.rcParams['figure.figsize'] = (20,10)  # 修改图片大小
plt.rcParams['figure.dpi'] = 300          # default for me was 75

def read_txt():
    """读文本文件中的数据为字符串，替换转为字典，返回字典"""
    with open('images/ch9/tiobe202208.txt', 'r', encoding='utf-8') as fr:
        tiobe_str = fr.read()  # 读文件到字符会串
    tiobe_dic = eval(tiobe_str.replace('name : ', '').replace(',data ', '').replace('Date.UTC', ''))
    return tiobe_dic


def plot_ranking_curve(tiobe_dic):
    """逐条绘制排名前4的程序设计语言曲线，Python语言曲线宽度设为4，突出显示"""    
    for line in tiobe_dic[:4]:  # 只绘制排名前4的语言
        lan_name = list(line.keys())[0]  # 当前处理的语言名，字符串
        if lan_name == 'Python':
            width = 4
        else:
            width = 2
        data = line[lan_name]  # 当前处理的语言的热度数据列表
        date_of_lan = [f'{x[0][0]}-{x[0][1]:02}-{x[0][2]:02}' for x in data]
        rank = [x[1] for x in data]
        plt.plot(date_of_lan, rank, label=lan_name, linewidth=width)


def draw_label():
    plt.title('TIOBE Programming Community index')
    plt.xlabel('year')
    plt.ylabel('Ratings(%)')

    # 修改x刻度
    plt.xticks(
    ['2002-00-30', '2004-00-31', '2006-00-04', '2008-00-03', '2010-00-05', '2012-00-08', '2014-00-01', '2016-00-02', '2018-00-03', '2020-00-05', '2022-00-01'],
    ['2002', '2004', '2006', '2008', '2010', '2012', '2014', '2016', '2018', '2020', '2022'])  # , rotation=-45
    
    # 改变图例位置
    plt.legend(bbox_to_anchor=(0., -0.13, 1., -.13), loc=8, ncol=5, mode="expand", borderaxespad=0.)
    
    # 增加横向主网格
    plt.grid(which='major', axis='y', color='k', linestyle='-.', linewidth=0.7)


if __name__ == '__main__':
    tiobe_dict = read_txt()
    plot_ranking_curve(tiobe_dict)
    draw_label()
    plt.show()

2. 面向对象方法¶

2.1 matplotlib.figure.Figure¶

matplotlib.pyplot.figure(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True, FigureClass=<class 'matplotlib.figure.Figure'>, clear=False, **kwargs)

该函数的功能就是为了创建一个图形figure，或者激活一个已经存在的图形figure

1. num：int or str, optional¶

合法值为整数或者一个字符串，可选。该关键字是一个figure独一无二的ID标识，当num是整数的时候，该整数即为Figure.number属性的值，如果是字符串，则Figure.number属性的值自动加1。下面看一个例子：该例子展示了plt.figure()中关键字num的作用，当存在多个figure的时候，就可以通过调用plt.figure()，传入对应的ID标识即可激活对应的figure，其中figure是有对应的编号的，可以传入数字来激活。

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-np.pi, np.pi, 256)
y1 = np.sin(x)
y2 = np.cos(x)
 
fig1 = plt.figure(num='first')
fig1.suptitle('first figure')
plt.plot(x, y1)
 
fig2 = plt.figure(num='second')
fig2.suptitle('second figure')
plt.plot(x, y2)
 
plt.figure(num=1)  #plt.figure(num='first')
plt.plot(x, y2)
plt.show()

2. figsize：(float, float)¶

合法值为一个元组(float, float), default: rcParams["figure.figsize"] (default: [6.4, 4.8])，分别表示宽和高，整型会被转化为浮点型float。

import matplotlib.pyplot as plt

fig1 = plt.figure(figsize=(2,1))
fig1.suptitle('fig1')
plt.plot([1,2,3,4])
 
fig2 = plt.figure(figsize=(4,2))
fig2.suptitle('fig2')
plt.plot([1,2,3,4])
 
plt.show()

3. facecolor¶

背景色（background color），default: rcParams["figure.facecolor"] (default: 'white')，默认白色。

import matplotlib.pyplot as plt

fig1 = plt.figure(figsize=(2,1),facecolor='b')
fig1.suptitle('fig1')
plt.plot([1,2,3,4])
 
fig2 = plt.figure(figsize=(4,2),facecolor='g')
fig2.suptitle('fig2')
plt.plot([1,2,3,4])
 
plt.show()

4.clear¶

合法值为bool类型，default: False，主要是用来清除已经存在的figure。

5. 返回值¶

返回一个图形figure句柄。

将图形保存为文件¶

fig.savfig('my_figure.png')

matplotlib支持许多图形格式，具体格式由操作系统已安装的图形显示接口决定，可以通过canvas对象的方法查看系统支持的文件格式。需要注意的是，当你保存图形文件时，不需要使用plt.show()了。

import matplotlib.pyplot as plt
fig = plt.figure()
fig.canvas.get_supported_filetypes()

2.2 matplotlib.axes.Axes¶

matplotlib.axes相关属性设置（绘图方式、坐标轴、坐标刻度、文本等）,其继承关系如下：

在绘制子图过程中，对于每一个子图的不同设置，ax 可以直接实现对于单个子图的设定，因此掌握必要的 ax 设置命令尤为重要！

参数传递，返回新的 AXES 对象：

matplotlib.axes.Axes（fig，rect，*，facecolor = None，frameon = True，sharex = None，sharey = None，label = ‘’，xscale = None，yscale = None，box_aspect = None，** kwargs）

2.2.1 基本绘图¶

Axes.plot 将y对x绘制为线条或标记。 Axes.errorbar 将y与x绘制为带有错误栏的线和/或标记。 Axes.scatter y与y的散点图 Axes.plot_date 绘制强制轴以将浮点数视为日期的图。 Axes.step 绘制一个阶梯图。 Axes.loglog 在x轴和y轴上使用对数缩放绘制图。 Axes.semilogx 在x轴上绘制具有对数比例的图。 Axes.semilogy 用y轴上的对数比例绘制图。 Axes.fill_between 填充两条水平曲线之间的区域。 Axes.fill_betweenx 填充两条垂直曲线之间的区域。 Axes.bar 绘制条形图。 Axes.barh 绘制水平条形图。 Axes.bar_label 标记条形图。 Axes.stem 创建一个茎图。 Axes.eventplot 在给定位置绘制相同的平行线。 Axes.pie 绘制饼图。 Axes.stackplot 绘制堆积面积图。 Axes.broken_barh 绘制矩形的水平序列。 Axes.vlines 在每个x上绘制从ymin到ymax的垂直线。 Axes.hlines 在从xmin到xmax的每个y上绘制水平线。 Axes.fill 绘制填充的多边形。

2.2.2 跨度，光谱，填充，2D数组¶

Axes.axhline 在轴上添加一条水平线。 Axes.axhspan 在轴上添加水平跨度（矩形）。 Axes.axvline 在轴上添加一条垂直线。 Axes.axvspan 在轴上添加垂直跨度（矩形）。 Axes.axline 添加无限长的直线。

Axes.acorr 绘制x的自相关。 Axes.angle_spectrum 绘制角度光谱。 Axes.cohere 绘制x和y之间的相干性。 Axes.csd 绘制交叉光谱密度。 Axes.magnitude_spectrum 绘制幅度谱。 Axes.phase_spectrum 绘制相位谱。 Axes.psd 绘制功率谱密度。 Axes.specgram 绘制频谱图。 Axes.xcorr 绘制x和y之间的互相关。

Axes.clabel 标注等高线图。 Axes.contour 绘制轮廓线。 Axes.contourf 绘制填充轮廓。

Axes.imshow 将数据显示为图像，即在2D常规栅格上。 Axes.matshow 将2D矩阵或数组的值绘制为颜色编码的图像。 Axes.pcolor 创建具有非规则矩形网格的伪彩色图。 Axes.pcolorfast 创建具有非规则矩形网格的伪彩色图。 Axes.pcolormesh 创建具有非规则矩形网格的伪彩色图。 Axes.spy 绘制2D阵列的稀疏模式。

2.2.3 坐标轴¶

Axes.axis 获取或设置某些轴属性的便捷方法。 Axes.set_axis_off 关闭x和y轴。 Axes.set_axis_on 开启x和y轴。 Axes.set_frame_on 设置是否绘制轴矩形补丁。 Axes.get_frame_on 获取是否绘制了轴矩形补丁。 Axes.set_axisbelow 设置轴刻度线和网格线是在图上方还是下方。 Axes.get_axisbelow 获取轴刻度和网格线是在图上方还是下方。 Axes.grid 增加网格线。 Axes.get_facecolor 获取轴的表面色。 Axes.set_facecolor 设置轴的表面色。

2.2.4 轴的范围、方向、标签、标题、图例¶

Axes.invert_xaxis 反转x轴。 Axes.xaxis_inverted 返回x轴是否沿“反”方向定向。 Axes.invert_yaxis 反转y轴。 Axes.yaxis_inverted 返回y轴是否沿“反”方向定向。 Axes.set_xlim 设置x轴范围。 Axes.get_xlim 返回x轴范围。 Axes.set_ylim 设置y轴范围。 Axes.get_ylim 返回y轴范围。 Axes.set_xbound 设置x轴的上下边界。 Axes.get_xbound 以递增顺序返回x轴的上下边界。 Axes.set_ybound 设置y轴的上下边界。 Axes.get_ybound 以递增顺序返回y轴的上下边界。

Axes.set_xlabel 设置x轴的标签。 Axes.get_xlabel 获取xlabel文本字符串。 Axes.set_ylabel 设置y轴的标签。 Axes.get_ylabel 获取ylabel文本字符串。 Axes.set_title 为轴设置标题。 Axes.get_title 获取轴标题。 Axes.legend 在轴上放置一个图例。 Axes.get_legend 返回Legend实例，如果未定义图例，则返回None。 Axes.get_legend_handles_labels 返回图例的句柄和标签

Axes.set_xscale 设置x轴比例。 Axes.get_xscale 返回xaxis的比例尺（以str表示）。 Axes.set_yscale 设置y轴比例。 Axes.get_yscale 返回yaxis的比例尺（以str表示）。

Axes.set_xticks 设置xaxis的刻度位置。 Axes.get_xticks 返回数据坐标中xaxis的刻度位置。 Axes.set_xticklabels 使用字符串标签列表设置xaxis的标签。 Axes.get_xticklabels 获取xaxis的刻度标签。 Axes.get_xmajorticklabels 返回xaxis的主要刻度标签，作为的列表Text。 Axes.get_xminorticklabels 返回xaxis的次刻度标签，作为的列表Text。 Axes.get_xgridlines 返回xaxis的网格线作为Line2Ds的列表。 Axes.get_xticklines 以x的列表形式返回xaxis的刻度线Line2D。 Axes.xaxis_date 设置轴刻度和标签，以将沿x轴的数据视为日期。 Axes.set_yticks 设置yaxis的刻度位置。 Axes.get_yticks 返回数据坐标中yaxis的刻度位置。 Axes.set_yticklabels 使用字符串标签列表设置yaxis标签。 Axes.get_yticklabels 获取yaxis的刻度标签。 Axes.get_ymajorticklabels 返回yaxis的主要刻度标签，作为的列表Text。 Axes.get_yminorticklabels 返回yaxis的次要刻度标签，作为的列表Text。 Axes.get_ygridlines 返回yaxis的网格线作为Line2Ds的列表。 Axes.get_yticklines 返回yaxis的刻度线作为Line2Ds的列表。 Axes.yaxis_date 设置轴刻度和标签，以将沿y轴的数据视为日期。 Axes.minorticks_off 去除轴上的细小滴答声。 Axes.minorticks_on 在轴上显示较小的刻度。 Axes.ticklabel_format 配置ScalarFormatter默认情况下用于线性轴。 Axes.tick_params 更改刻度线，刻度线标签和网格线的外观。

2.2.5 投影¶

Axes.get_xaxis_transform 获取用于绘制x轴标签，刻度线和网格线的转换。 Axes.get_yaxis_transform 获取用于绘制y轴标签，刻度线和网格线的转换。 Axes.get_data_ratio 返回缩放数据的纵横比。

2.多子图绘制¶

2.1 matplotlib.pyplot api 绘制子图¶

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示
plt.rcParams['axes.unicode_minus'] = False


def read_csv(filename):
    """接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。"""
    with open(filename, 'r', encoding='utf-8') as fr:
        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表
    return data_lst


def draw_dos(data_lst):
    """接收二维列表为参数，绘制数据曲线。"""
    cl = ['r', 'g', 'b', 'purple']
    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线
        x = [float(ls[2 * i]) for ls in data_lst]  # 生成x的列表
        y = [float(ls[2 * i + 1]) for ls in data_lst]  # 生成y的列表
        plt.subplot(eval(f'22{i+1}'))  # 分2x2，小于10的数可合成3位数
        plt.plot(x, y, color=cl[i])  # cl[i]根据i值取颜色




if __name__ == '__main__':
    file = 'images/ch9/9.5 PDOS.csv'
    dos_in_lst = read_csv(file)
    draw_dos(dos_in_lst)
    plt.show()

2.2 面向对象方式绘制子图¶

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示
plt.rcParams['axes.unicode_minus'] = False


def read_csv(filename):
    """接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。"""
    with open(filename, 'r', encoding='utf-8') as fr:
        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表
    return data_lst


def draw_dos(data_lst):
    """接收二维列表为参数，绘制数据曲线。"""
    my_dpi = 80
    fig, axs = plt.subplots(2, 2, figsize=(720 / my_dpi, 480 / my_dpi), dpi=my_dpi,
                            sharex=False,  # x轴刻度值共享开启
                            sharey=False,  # y轴刻度值共享关闭                        
                            )
    cl = ['r', 'g', 'b', 'purple']

    x0 = [float(ls[0]) for ls in data_lst]  # 生成x的列表
    y0 = [float(ls[1]) for ls in data_lst]  # 生成y的列表
    axs[0][0].plot(x0, y0, color=cl[0])
    
    x1 = [float(ls[2]) for ls in data_lst]  # 生成x的列表
    y1 = [float(ls[3]) for ls in data_lst]  # 生成y的列表
    axs[0][1].plot(x1, y1, color=cl[1])
    
    x2 = [float(ls[4]) for ls in data_lst]  # 生成x的列表
    y2 = [float(ls[5]) for ls in data_lst]  # 生成y的列表
    axs[1][0].plot(x2, y2, color=cl[2])
    
    x3 = [float(ls[6]) for ls in data_lst]  # 生成x的列表
    y3 = [float(ls[7]) for ls in data_lst]  # 生成y的列表
    axs[1][1].plot(x3, y3, color=cl[3])


if __name__ == '__main__':
    file = 'images/ch9/9.5 PDOS.csv'
    dos_in_lst = read_csv(file)
    draw_dos(dos_in_lst)
    plt.show()

2.3 matplotlib.pyplot add_subplot方式添加子图¶

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示
plt.rcParams['axes.unicode_minus'] = False


def read_csv(filename):
    """接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。"""
    with open(filename, 'r', encoding='utf-8') as fr:
        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表
    return data_lst


def draw_dos(data_lst):
    """接收二维列表为参数，绘制数据曲线。"""
    cl = ['r', 'g', 'b', 'purple']
    my_dpi = 80
    fig = plt.figure(figsize=(640/my_dpi,480/my_dpi),dpi=my_dpi)
    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线
        x = [float(ls[2 * i]) for ls in data_lst]  # 生成x的列表
        y = [float(ls[2 * i + 1]) for ls in data_lst]  # 生成y的列表
        fig.add_subplot(eval(f'22{i+1}'))
        plt.plot(x, y, color=cl[i])  # cl[i]根据i值取颜色



if __name__ == '__main__':
    file = 'images/ch9/9.5 PDOS.csv'
    dos_in_lst = read_csv(file)
    draw_dos(dos_in_lst)
    plt.show()

2.4 matplotlib.gridspec.GridSpec方式添加子图¶

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示
plt.rcParams['axes.unicode_minus'] = False


def read_csv(filename):
    """接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。"""
    with open(filename, 'r', encoding='utf-8') as fr:
        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表
    return data_lst


def draw_dos(data_lst):
    """接收二维列表为参数，绘制数据曲线。"""
    cl = ['r', 'g', 'b', 'purple']
    fig = plt.figure(dpi=100,constrained_layout=True)  # 类似于tight_layout，使得各子图之间的距离自动调整【类似excel中行宽根据内容自适应】
    gs = GridSpec(3, 3, figure=fig)  # GridSpec将fiure分为3行3列，每行三个axes，gs为一个matplotlib.gridspec.GridSpec对象，可灵活的切片figure

    x0 = [float(ls[0]) for ls in data_lst]  # 生成x的列表
    y0 = [float(ls[1]) for ls in data_lst]  # 生成y的列表
    ax1 = fig.add_subplot(gs[0, 0:1])
    plt.plot(x0, y0, color=cl[0])


    x1 = [float(ls[2]) for ls in data_lst]  # 生成x的列表
    y1 = [float(ls[3]) for ls in data_lst]  # 生成y的列表
    ax2 = fig.add_subplot(gs[0, 1:3])#gs[0, 0:3]中0选取figure的第一行，0:3选取figure第二列和第三列
    plt.plot(x1, y1, color=cl[1])
    plt.xlim(-5, 5)

    x2 = [float(ls[4]) for ls in data_lst]  # 生成x的列表
    y2 = [float(ls[5]) for ls in data_lst]  # 生成y的列表
    ax3 = fig.add_subplot(gs[1, 0:2])
    plt.plot(x2, y2, color=cl[2])
    plt.xlim(-5, 5)

    x3 = [float(ls[6]) for ls in data_lst]  # 生成x的列表
    y3 = [float(ls[7]) for ls in data_lst]  # 生成y的列表
    ax4 = fig.add_subplot(gs[1:3, 2:3])
    plt.plot(x3, y3, color=cl[3])
    plt.xlim(-5, 5)

    ax5 = fig.add_subplot(gs[2, 0:1])
    plt.scatter([1, 2, 3], [4, 5, 6], marker='*')

    ax6 = fig.add_subplot(gs[2, 1:2])
    plt.bar([1, 2, 3], [4, 5, 6])
    fig.suptitle("GridSpec", color='r')

if __name__ == '__main__':
    file = 'images/ch9/9.5 PDOS.csv'
    dos_in_lst = read_csv(file)
    draw_dos(dos_in_lst)
    plt.show()

2.5 任意位置绘制子图(plt.axes)¶

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimSun']  # 支持中文显示
plt.rcParams['axes.unicode_minus'] = False


def read_csv(filename):
    """接收文件名为参数，读取文件中的数据到二维列表中，返回二维列表。"""
    with open(filename, 'r', encoding='utf-8') as fr:
        data_lst = [line.strip().split(',') for line in fr]  # 数据转列表
    return data_lst


def draw_dos(data_lst):
    """接收二维列表为参数，绘制数据曲线。"""
    cl = ['r', 'g', 'b', 'purple']
    loc = [[0.15, 0.7, 0.2, 0.15],[0.7, 0.7, 0.2, 0.15],[0.15, 0.3, 0.2, 0.15],[0.7, 0.3, 0.2, 0.15]]
    for i in range(4):  # 每次循环读相邻两列数据绘制一条曲线
        x = [float(ls[2 * i]) for ls in data_lst]  # 生成x的列表
        y = [float(ls[2 * i + 1]) for ls in data_lst]  # 生成y的列表
        plt.subplot(eval(f'22{i+1}'))  # 分2x2，小于10的数可合成3位数
        plt.plot(x, y, color=cl[i])  # cl[i]根据i值取颜色
        plt.axes(loc[i])   ## [left, bottom, width, height]四个参数(fractions of figure)可以非常灵活的调节子图中子图的位置
        plt.plot(x, y, color=cl[i])  # cl[i]根据i值取颜色
        plt.xlim(-10,10)


if __name__ == '__main__':
    file = 'images/ch9/9.5 PDOS.csv'
    dos_in_lst = read_csv(file)
    draw_dos(dos_in_lst)
    plt.show()

9.1.5 数据文化绘图.ipynb @master — view markup · raw · history · blame