1 import pandas as pd 2 import numpy as np 3 import matplotlib.pyplot as plt 4 5 curve = pd.read_csv('curve.csv') 6 print(curve) 7 print('---') 8 # Change format to datetime format 9 curve['DATE'] = pd.to_datetime(curve['DATE']) 10 print(curve) 11 12 # matplotlib inline...
reverse(grid, 0, k - 1) # 翻转后 n - k 个数字 Solution.reverse(grid, k, length - 1) return grid @staticmethod def reverse(grid: List[List[int]], l: int, r: int): n: int = len(grid[0]) # 使用双指针翻转 while l < r: # 交换 l 和 r 位置的数字 grid[l // n][l %...
文件里从第二行开始存储了输出的变量是啥,以及网格节点数,可以看到,一共有8个变量,按照顺序依次是:rho, x, y, vx, vy, P, TC, T.那么我们需要在读取数据的函数中按照这个这个顺序修改一下代码,如下: importmathimportnumpyasnpimportmatplotlibimportmatplotlib.pyplotaspltimportmatplotlib.triastriimportstructimpo...
>>> import tkinter >>> print_mro(tkinter.Toplevel) Toplevel, BaseWidget, Misc, Wm, object >>> print_mro(tkinter.Widget) Widget, BaseWidget, Misc, Pack, Place, Grid, object >>> print_mro(tkinter.Button) Button, Widget, BaseWidget, Misc, Pack, Place, Grid, object >>> print_mro(tk...
# print(i, child) # 0 first tag # 获取父节点,祖先节点 # print(soup.a.parent) # 获取 a 标签 # print(soup.a.parents) # <generator object parents at 0x0000022F8747D570> # print(list(soup.a.parents)) # a 标签的父,父,父节点都会找出来,到html节点 # 获取兄弟节点 # print(soup.a....
print(plt.style.available)['bmh','classic','dark_background','fast','fivethirtyeight','ggplot','grayscale','seaborn-bright','seaborn-colorblind','seaborn-dark-palette','seaborn-dark','seaborn-darkgrid','seaborn-deep','seaborn-muted','seaborn-notebook','seaborn-paper','seaborn-pastel','sea...
import numpy as np a = np.random.randn() print(f'一个随机数:{a}') b = np.random.randn(3) print(f'三个数:{b}') c = np.random.randn(3, 2) print(f'3行2列:\n{c}') d = np.random.randn(3, 2, 4) print(f'3块,每块是2行4列:\n{d}') ...
importmatplotlib.pyplotasplt plt.style.use('seaborn-whitegrid') importnumpyasnp 对于所有的 Matplotlib 图表来说,我们都需要从创建图形和维度开始。图形和维度可以使用下面代码进行最简形式的创建: fig = plt.figure() ax = plt.axes() 在Matplotlib...
sns.set_style('whitegrid')sns.countplot(x='target',data=df,palette='RdBu_r')plt.show() 数据处理 探索数据集后,我发现我需要在训练机器学习模型之前将一些分类变量转换为虚拟变量并缩放所有值。 首先,我将使用该 get_dummies 方法为分类变量创建虚拟列。
add_subplot方法，给figure新增子图import matplotlib.pyplot as pltimport numpy as npx = np.arange(1, 100)fig = plt.figure(figsize=(20, 10), dpi=80)# 子图1ax1 = fig.add_subplot(2,2,1)ax1.plot(x,x)# 子图2ax2 = fig.add_subplot(2,2,2)ax2.plot(x, x**2)ax2.grid(color='r'...