Python基础 | 还不会python绘图？两万字博文教你Matplotlib库（超详细总结）
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文章目录
13.0 环境配置
【1】 要不要plt.show()
-
ipython中可用魔术方法 %matplotlib inline
-
pycharm 中必须使用plt.show()
%matplotlib inline # 配置，可以再ipython中生成就显示，而不需要多余plt.show来完成。
import matplotlib.pyplot as plt
plt.style.use("seaborn-whitegrid") # 用来永久地改变风格，与下文with临时改变进行对比
x = [1, 2, 3, 4]
y = [1, 4, 9, 16]
plt.plot(x, y)
plt.ylabel("squares")
# plt.show()
Text(0, 0.5, 'squares')
【2】设置样式
plt.style.available[:5]
['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight']
临时地改变风格，采用with这个上下文管理器。
with plt.style.context("seaborn-white"):
plt.plot(x, y)
【3】将图像保存为文件
import numpy as np
x = np.linspace(0, 10 ,100)
plt.plot(x, np.exp(x))
plt.savefig("my_figure.png")
13.1 Matplotlib库
13.1.1 折线图
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use("seaborn-whitegrid")
import numpy as np
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x))
[<matplotlib.lines.Line2D at 0x18846169780>]
- 绘制多条曲线
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.cos(x))
plt.plot(x, np.sin(x))
[<matplotlib.lines.Line2D at 0x1884615f9e8>]
【1】调整线条颜色和风格
- 调整线条颜色
offsets = np.linspace(0, np.pi, 5)
colors = ["blue", "g", "r", "yellow", "pink"]
for offset, color in zip(offsets, colors):
plt.plot(x, np.sin(x-offset), color=color) # color可缩写为c
- 调整线条风格
x = np.linspace(0, 10, 11)
offsets = list(range(8))
linestyles = ["solid", "dashed", "dashdot", "dotted", "-", "--", "-.", ":"]
for offset, linestyle in zip(offsets, linestyles):
plt.plot(x, x+offset, linestyle=linestyle) # linestyle可简写为ls
- 调整线宽
x = np.linspace(0, 10, 11)
offsets = list(range(0, 12, 3))
linewidths = (i*2 for i in range(1,5))
for offset, linewidth in zip(offsets, linewidths):
plt.plot(x, x+offset, linewidth=linewidth) # linewidth可简写为lw
- 调整数据点标记
marker设置坐标点
x = np.linspace(0, 10, 11)
offsets = list(range(0, 12, 3))
markers = ["*", "+", "o", "s"]
for offset, marker in zip(offsets, markers):
plt.plot(x, x+offset, marker=marker)
markersize 设置坐标点大小
x = np.linspace(0, 10, 11)
offsets = list(range(0, 12, 3))
markers = ["*", "+", "o", "s"]
for offset, marker in zip(offsets, markers):
plt.plot(x, x+offset, marker=marker, markersize=10) # markersize可简写为ms
颜色跟风格设置的简写 color_linestyles = [“g-”, “b–”, “k-.”, “r:”]
x = np.linspace(0, 10, 11)
offsets = list(range(0, 8, 2))
color_linestyles = ["g-", "b--", "k-.", "r:"]
for offset, color_linestyle in zip(offsets, color_linestyles):
plt.plot(x, x+offset, color_linestyle)
颜色_风格_线性 设置的简写 color_marker_linestyles = [“g*-”, “b±-”, “ko-.”, “rs:”]
x = np.linspace(0, 10, 11)
offsets = list(range(0, 8, 2))
color_marker_linestyles = ["g*-", "b+--", "ko-.", "rs:"]
for offset, color_marker_linestyle in zip(offsets, color_marker_linestyles):
plt.plot(x, x+offset, color_marker_linestyle)
其他用法及颜色缩写、数据点标记缩写等请查看官方文档，如下：
https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot
【2】调整坐标轴
- xlim, ylim # 限制x，y轴
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x))
plt.xlim(-1, 7)
plt.ylim(-1.5, 1.5)
(-1.5, 1.5)
- axis
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x))
plt.axis([-2, 8, -2, 2])
[-2, 8, -2, 2]
tight 会紧凑一点
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x))
plt.axis("tight")
(0.0, 6.283185307179586, -0.9998741276738751, 0.9998741276738751)
equal 会松一点
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x))
plt.axis("equal")
(0.0, 7.0, -1.0, 1.0)
?plt.axis # 可以查询其中的功能
Object `plt.axis # 可以查询其中的功能` not found.
- 对数坐标
x = np.logspace(0, 5, 100)
plt.plot(x, np.log(x))
plt.xscale("log")
- 调整坐标轴刻度
plt.xticks(np.arange(0, 12, step=1))
x = np.linspace(0, 10, 100)
plt.plot(x, x**2)
plt.xticks(np.arange(0, 12, step=1))
([<matplotlib.axis.XTick at 0x18846412828>,
<matplotlib.axis.XTick at 0x18847665898>,
<matplotlib.axis.XTick at 0x18847665630>,
<matplotlib.axis.XTick at 0x18847498978>,
<matplotlib.axis.XTick at 0x18847498390>,
<matplotlib.axis.XTick at 0x18847497d68>,
<matplotlib.axis.XTick at 0x18847497748>,
<matplotlib.axis.XTick at 0x18847497438>,
<matplotlib.axis.XTick at 0x1884745f438>,
<matplotlib.axis.XTick at 0x1884745fd68>,
<matplotlib.axis.XTick at 0x18845fcf4a8>,
<matplotlib.axis.XTick at 0x18845fcf320>],
<a list of 12 Text xticklabel objects>)
x = np.linspace(0, 10, 100)
plt.plot(x, x**2)
plt.xticks(np.arange(0, 12, step=1), fontsize=15)
plt.yticks(np.arange(0, 110, step=10))
([<matplotlib.axis.YTick at 0x188474f0860>,
<matplotlib.axis.YTick at 0x188474f0518>,
<matplotlib.axis.YTick at 0x18847505a58>,
<matplotlib.axis.YTick at 0x188460caac8>,
<matplotlib.axis.YTick at 0x1884615c940>,
<matplotlib.axis.YTick at 0x1884615cdd8>,
<matplotlib.axis.YTick at 0x1884615c470>,
<matplotlib.axis.YTick at 0x1884620c390>,
<matplotlib.axis.YTick at 0x1884611f898>,
<matplotlib.axis.YTick at 0x188461197f0>,
<matplotlib.axis.YTick at 0x18846083f98>],
<a list of 11 Text yticklabel objects>)
- 调整刻度样式
plt.tick_params(axis=“both”, labelsize=15)
x = np.linspace(0, 10, 100)
plt.plot(x, x**2)
plt.tick_params(axis="both", labelsize=15)
【3】设置图形标签
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x))
plt.title("A Sine Curve", fontsize=20)
plt.xlabel("x", fontsize=15)
plt.ylabel("sin(x)", fontsize=15)
Text(0, 0.5, 'sin(x)')
【4】设置图例
- 默认
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x), "b-", label="Sin")
plt.plot(x, np.cos(x), "r--", label="Cos")
plt.legend()
<matplotlib.legend.Legend at 0x1884749f908>
- 修饰图例
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x), "b-", label="Sin")
plt.plot(x, np.cos(x), "r--", label="Cos")
plt.ylim(-1.5, 2)
plt.legend(loc="upper center", frameon=True, fontsize=15) # frameon=True增加图例的边框
<matplotlib.legend.Legend at 0x19126b53b80>
【5】添加文字和箭头
- 添加文字
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x), "b-")
plt.text(3.5, 0.5, "y=sin(x)", fontsize=15) # 前两个为文字的坐标，后面是内容和字号
Text(3.5, 0.5, 'y=sin(x)')
- 添加箭头
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x), "b-")
plt.annotate('local min', xy=(1.5*np.pi, -1), xytext=(4.5, 0),
arrowprops=dict(facecolor='black', shrink=0.1),
)
Text(4.5, 0, 'local min')
13.1.2 散点图
【1】简单散点图
x = np.linspace(0, 2*np.pi, 20)
plt.scatter(x, np.sin(x), marker="o", s=30, c="r") # s 大小 c 颜色
<matplotlib.collections.PathCollection at 0x188461eb4a8>
【2】颜色配置
x = np.linspace(0, 10, 100)
y = x**2
plt.scatter(x, y, c=y, cmap="inferno") # 让c随着y的值变化在cmap中进行映射
plt.colorbar() # 输出颜色条
<matplotlib.colorbar.Colorbar at 0x18848d392e8>
颜色配置参考官方文档
https://matplotlib.org/examples/color/colormaps_reference.html
【3】根据数据控制点的大小
x, y, colors, size = (np.random.rand(100) for i in range(4))
plt.scatter(x, y, c=colors, s=1000*size, cmap="viridis")
<matplotlib.collections.PathCollection at 0x18847b48748>
【4】透明度
x, y, colors, size = (np.random.rand(100) for i in range(4))
plt.scatter(x, y, c=colors, s=1000*size, cmap="viridis", alpha=0.3)
plt.colorbar()
<matplotlib.colorbar.Colorbar at 0x18848f2be10>
【例】随机漫步
from random import choice
class RandomWalk():
"""一个生产随机漫步的类"""
def __init__(self, num_points=5000):
self.num_points = num_points
self.x_values = [0]
self.y_values = [0]
def fill_walk(self):
while len(self.x_values) < self.num_points:
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
if x_step == 0 or y_step == 0:
continue
next_x = self.x_values[-1] + x_step
next_y = self.y_values[-1] + y_step
self.x_values.append(next_x)
self.y_values.append(next_y)
rw = RandomWalk(10000)
rw.fill_walk()
point_numbers = list(range(rw.num_points))
plt.figure(figsize=(12, 6)) # 设置画布大小
plt.scatter(rw.x_values, rw.y_values, c=point_numbers, cmap="inferno", s=1)
plt.colorbar()
plt.scatter(0, 0, c="green", s=100)
plt.scatter(rw.x_values[-1], rw.y_values[-1], c="red", s=100)
plt.xticks([])
plt.yticks([])
([], <a list of 0 Text yticklabel objects>)
13.1.3 柱形图
【1】简单柱形图
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