1. 繪制圖表組成元素的主要函數
1.1 plot()——展現量的變化趨勢
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Qt5Agg')
x = np.linspace(0.05, 10, 1000)
y = np.cos(x)
plt.plot(x, y, ls="-", lw=2, label="plot figure")
plt.legend()
plt.show()
1.2 scatter()——尋找變量之間的關系
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Qt5Agg')
x = np.linspace(0.05, 10, 1000)
y = np.random.rand(1000)
plt.scatter(x, y, label="scatter figure")
plt.legend()
plt.show()
1.3 xlim()——設置x軸的數值顯示范圍
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Qt5Agg')
x = np.linspace(0.05, 10, 1000)
y = np.random.rand(1000)
plt.scatter(x, y, label="scatter figure")
plt.legend()
plt.xlim(0.05, 10)
plt.ylim(0, 1)
plt.show()
1.4 xlabel()——設置x軸的標簽文本
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Qt5Agg')
x = np.linspace(0.05, 10, 1000)
y = np.sin(x)
plt.plot(x, y, ls="--", lw=2, c="c", label="plot figure")
plt.legend()
plt.xlabel("x-axis")
plt.ylabel("y-axis")
plt.show()
1.5 grid()——繪制刻度線的網格線
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Qt5Agg')
x = np.linspace(0.05, 10, 1000)
y = np.sin(x)
plt.plot(x, y, ls="-.", lw=2, c="c", label="plot figure")
plt.legend()
plt.grid(linestyle=":", color="r")
plt.show()
grid()函數的主要參數為grid(b, which, axis, color, linestyle, linewidth， **kwargs)：
b：布爾值。就是是否顯示網格線的意思。官網說如果b設置為None， 且kwargs長度為0，則切換網格狀態which：取值為major,minor，both。 默認為majoraxis：取值為both，x，y。就是想繪制哪個方向的網格線color：這就不用多說了，就是設置網格線的顏色。或者直接用c來代替color也可以linestyle：也可以用ls來代替linestyle， 設置網格線的風格，是連續實線，虛線或者其它不同的線條
1.6 axhline()——繪制平行於x軸的水平參考線
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Qt5Agg')
x = np.linspace(0.05, 10, 1000)
y = np.sin(x)
plt.plot(x, y, ls="-.", lw=2, c="c", label="plot figure")
plt.legend()
plt.axhline(y=0.0, c="r", ls="--", lw=2)
plt.axvline(x=4.0, c="r", ls="--", lw=2)
plt.show()
1.7 axvspan()——繪制垂直於x軸的參考區域
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Qt5Agg')
x = np.linspace(0.05, 10, 1000)
y = np.sin(x)
plt.plot(x, y, ls="-.", lw=2, c="c", label="plot figure")
plt.legend()
plt.axvspan(xmin=4.0, xmax=6.0, facecolor="y", alpha=0.3)
plt.axhspan(ymin=0.0, ymax=0.5, facecolor="y", alpha=0.3)
plt.show()
1.8 annotate()——添加圖形內容細節的指向型注釋文本
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Qt5Agg')
x = np.linspace(0.05, 10, 1000)
y = np.sin(x)
plt.plot(x, y, ls="-.", lw=2, c="c", label="plot figure")
plt.legend()
plt.annotate(s="maximum",
xy=(np.pi / 2, 1.0),
xytext=((np.pi / 2) + 1.0, 0.8),
weight="bold",
color="b",
arrowprops=dict(arrowstyle="->", connectionstyle="arc3", color="b")
)
plt.show()
xy：被注釋圖形內容的位置坐標
xytext：注釋文本的位置坐標
weight：注釋文本的字體粗細風格
color：注釋文本的字體顏色
arrowprops：指示被注釋內容的箭頭的屬性字典
1.9 text()——添加圖形內容細節的無指向型注釋文本
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Qt5Agg')
x = np.linspace(0.05, 10, 1000)
y = np.sin(x)
plt.plot(x, y, ls="-.", lw=2, c="c", label="plot figure")
plt.legend()
plt.text(x=3.10, y=0.09, s="y=sin(x)", weight="bold", color="b")
plt.show()
1.10 title()——添加圖形內容的標題
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
x = np.linspace(-2, 2, 1000)
y = np.exp(x)
plt.plot(x, y, ls="-", lw=2, color="g")
plt.title("center demo")
plt.title("left demo", loc="left",
fontdict={"size": "xx-large",
"color": "r",
"family": "Times New Roman"})
plt.title("right demo", loc="right",
family="Comic Sans MS", size=20,
style="oblique", color="c")
plt.show()
主要參數都在上面代碼里體現了
1.11 legend()——表示不同圖形的文本標簽圖例
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
x = np.arange(0, 2.1, 0.1)
y = np.power(x, 3)
y1 = np.power(x, 2)
y2 = np.power(x, 1)
plt.plot(x, y, ls="-", lw=2, label="$x^3$")
plt.plot(x, y1, ls="-", lw=2, label="$x^2$")
plt.plot(x, y2, ls="-", lw=2, label="$x^1$")
plt.legend(loc="upper left",fontsize="x-large", bbox_to_anchor=(0.05, 0.95), ncol=3,
title="power function", shadow=True, fancybox=True)
plt.show()
loc參數控制圖例的位置，可選值為：
bestupper rightupper leftlower leftlower rightrightcenter leftcenter rightlower centerupper centercenter
fontsize控制圖例字體大小，可選值為：
intfloatxx-smallx-smallsmallmediumlargex-largexx-large
frameon：True或False，是否顯示圖例邊框edgecolor：圖例邊框顏色facecolor：圖例背景顏色,若無邊框,參數無效title：設置圖例標題fancybox：True表示線框直角，False表示線框圓角shadow：True或False，是否顯示陰影
2. 常用配置參數
2.1 線型
linestyle或ls
-：實線--：虛線-.：點划線:：點線
2.2 線寬
linewidth或lw
- 浮點數
2.3 線條顏色
color或c
b：blue，藍色g：green，綠色r：red，紅色c：cyan，藍綠m：magenta，洋紅y：yellow，黃色k：black，黑色w：white，白色
也可以對關鍵字參數color賦十六進制的RGB字符串如 color='#900302'
2.4 點標記類型
marker，只能用以下簡寫符號表示
.：point marker,：pixel markero：circle markerv：triangle_down marker^：triangle_up marker<：triangle_left marker>：triangle_right marker1：tri_down marker2：tri_up marker3：tri_left marker4：tri_right markers：square markerp：pentagon marker*：star markerh：hexagon1 markerH：hexagon2 marker+：plus markerx：x markerD：diamond markerd：thin_diamond marker|：vline marker_：hline marker
特別地，標記還有mathtext模式
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.use('Qt5Agg')
mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['font.serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False # 解決保存圖像是負號'-'顯示為方塊的問題,或者轉換負號為字符串
x = np.arange(1, 13, 1)
y = np.array([12, 34, 22, 30, 18, 13, 15, 19, 24, 28, 23, 27])
fig, ax = plt.subplots(2, 2)
ax[0, 0].scatter(x, y * 1.5, marker=r"$\clubsuit$", c="#fb8072", s=500)
ax[0, 0].locator_params(axis="x", tight=True, nbins=11)
ax[0, 0].set_xlim(0, 13)
ax[0, 0].set_xticks(x)
ax[0, 0].set_title('顯示樣式{}的散點圖'.format(r"$\clubsuit$"))
ax[0, 1].scatter(x, y - 2, marker=r"$\heartsuit$", c="#fb8072", s=500)
ax[0, 1].locator_params(axis="x", tight=True, nbins=11)
ax[0, 1].set_xlim(0, 13)
ax[0, 1].set_xticks(x)
ax[0, 1].set_title('顯示樣式{}的散點圖'.format(r"$\heartsuit$"))
ax[1, 0].scatter(x, y + 7, marker=r"$\diamondsuit$", c="#fb8072", s=500)
ax[1, 0].locator_params(axis="x", tight=True, nbins=11)
ax[1, 0].set_xlim(0, 13)
ax[1, 0].set_xticks(x)
ax[1, 0].set_title('顯示樣式{}的散點圖'.format(r"$\diamondsuit$"))
ax[1, 1].scatter(x, y - 9, marker=r"$\spadesuit$", c="#fb8072", s=500)
ax[1, 1].locator_params(axis="x", tight=True, nbins=11)
ax[1, 1].set_xlim(0, 13)
ax[1, 1].set_xticks(x)
ax[1, 1].set_title('顯示樣式{}的散點圖'.format(r"$\spadesuit"))
plt.suptitle("不同原始字符串作為標記類型的展示效果", fontsize=16, weight="black")
plt.show()
官網有一張屬性表，先貼在這，以后有空會再補充內容的
