The use of matplotlib for python data analysis

The previous article has completed the basic learning of numpy. Data visualization needs to be performed during data analysis. Therefore, this article mainly focuses on the basic operations of matplotlib and the drawing of common charts, such as: line charts, scatter charts, etc.

# coding=utf-8
"""
created on:2018/4/23
author:DilicelSten
target:learn matplotlib
"""
import matplotlib.pyplot as plt
import numpy as np


# -----------------------------line-----------------------------
#
x = np.linspace(-np.pi, np.pi, 256,endpoint=True)
c, s = np.cos(x), np.sin(x)  # 定义余弦和正弦函数
plt.figure(1)
plt.plot(x, s, color="blue", linewidth=1.0, linestyle="-", label="COS", alpha=0.5)  # 绘图及属性
plt.plot(x, c, "r*", label="SIN")
plt.title("COS & SIN")  # 加标题
ax = plt.gca()  # 轴的编辑器
# 出现横轴和纵轴
ax.spines["right"].set_color("none")
ax.spines["top"].set_color("none")
ax.spines["left"].set_position(("data", 0))
ax.spines["bottom"].set_position(("data", 0))
ax.yaxis.set_ticks_position("left")
ax.xaxis.set_ticks_position("bottom")
# 扩大横纵轴上的数标
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
           [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])
plt.yticks(np.linspace(-1, 1, 5, endpoint=True))
for label in ax.get_xticklabels()+ax.get_yticklabels():
    label.set_fontsize(16)
    label.set_bbox(dict(facecolor="white", edgecolor="None", alpha=0.2))
# 出现图例
plt.legend(loc="upper left")
# 出现网格线
plt.grid()
# 限制范围
# plt.axis([-1, 1, -0.5, 1])
# 填充功能
plt.fill_between(x, np.abs(x) < 0.5, c, c > 0.5, color="green", alpha=0.5)
# 注释功能
t = 1
plt.plot([t, t], [0, np.cos(t)], 'y', linewidth=3, linestyle="--")
plt.annotate("cos(1)", xy=(t, np.cos(1)), xycoords="data", xytext=(+10, +30), textcoords="offset points", arrowprops=dict(connectionstyle="arc3,rad=0.2"))
plt.show()

# ----------------------many type of figures-------------------------
# 散点图
fig = plt.figure()  # 建立表格
fig.add_subplot(3, 3, 1)  # 3行3列
n = 128
X = np.random.normal(0, 1, n)
Y = np.random.normal(0, 1, n)
T = np.arctan2(Y, X)  # 上色
# plt.axes([0.025, 0.025, 0.95, 0.95])  # 确定显示范围
plt.scatter(X, Y, s=75, c=T, alpha=0.5)  # 画散点
plt.xlim(-1.5, 1.5)
plt.xticks([])
plt.ylim(-1.5, 1.5)
plt.yticks([])
plt.axis()
plt.title("scatter")
plt.xlabel("x")
plt.ylabel("y")


# 柱状图
fig.add_subplot(3, 3, 2)
n = 10
X = np.arange(n)
Y1 = (1 - X/float(n)) * np.random.uniform(0.5, 1.0, n)
Y2 = (1 - X/float(n)) * np.random.uniform(0.5, 1.0, n)
plt.bar(X, +Y1, facecolor="#9999ff", edgecolor="white")
plt.bar(X, -Y2, facecolor="#ff9999", edgecolor="white")
# 添加注释
for x, y in zip(X, Y1):
    plt.text(x + 0.4, y + 0.05, '%.2f' % y, ha="center", va="bottom")
for x, y in zip(X, Y2):
    plt.text(x + 0.4, -y - 0.05, '%.2f' % y, ha="center", va="top")


# 饼图
fig.add_subplot(333)
n = 20
Z = np.ones(n)
Z[-1] *= 2
plt.pie(Z, explode=Z * .05, colors=['%f' % (i / float(n)) for i in range(n)], labels=['%2f' % (i / float(n)) for i in range(n)])
plt.gca().set_aspect('equal')  # 正圆
plt.xticks([])
plt.yticks([])


# 极坐标
fig.add_subplot(334, polar=True)
n = 20
theta = np.arange(0.0, 2 * np.pi, 2 * np.pi / n)
radii = 10 * np.random.rand(n)  # 半径
# plt.plot(theta, radii)
plt.polar(theta, radii)

# 热图
fig.add_subplot(335)
from matplotlib import cm  # 上色
data = np.random.rand(3, 3)
cmap = cm.Blues
map = plt.imshow(data, interpolation='nearest', cmap=cmap, aspect='auto', vmin=0, vmax=1)


# 3D图
from mpl_toolkits.mplot3d import Axes3D
ax = fig.add_subplot(336, projection="3d")
ax.scatter(1, 1, 3, s=100)


# 热力图
fig.add_subplot(313)
def f(x, y):
    return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)
n = 256
x = np.linspace(-3, 3, n)
y = np.linspace(-3, 3, n)
X, Y = np.meshgrid(x, y)
plt.contourf(X, Y, f(X, Y), 8, alpha=.75, cmap=plt.cm.hot)
plt.savefig('fig.png')
plt.show()