matplotlib基本用法介绍

于 2022-01-11 11:02:11 发布
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分类专栏： Python 机器学习 文章标签： python 几何学 深度学习
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本文链接：https://blog.youkuaiyun.com/qq_33521184/article/details/122427056
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原文

0 代码import的包

from matplotlib import pyplot as plt
from matplotlib.pyplot import savefig
import numpy as np
import pandas as pd
from statsmodels.graphics.tsaplots import plot_acf
import matplotlib.gridspec as gs

1 散点图

1.1 normal scatter

def scatter():
    x = np.random.randint(1,20,10)
    y = np.random.randint(1,10,10)
    plt.scatter(x,y)
    plt.xlabel("x values") # 设置x轴的显示
    plt.ylabel("y values") # 设置y轴的显示
    plt.title("scatter test") #设置标题
    savefig("img/scatter.png") #保存图片  该语句必须放在plt.show()之前，否则会是空白
    plt.show() #显示

1.2 add a colorbar

def scatter():
    x = np.random.randint(1,80,40)
    y = np.random.randint(1,100,40)
    plt.scatter(x,y,c=y,cmap="Spectral")  # ket code
    plt.colorbar() # key codde
    plt.xlabel("x values") # 设置x轴的显示
    plt.ylabel("y values") # 设置y轴的显示
    plt.title("scatter with colorbar") #设置标题
    savefig("img/scatter_colorbar.png") #保存图片  该语句必须放在plt.show()之前，否则会是空白
    plt.show() #显示

2 折线图

2.1 draw a function

def line():
    x = np.arange(-5,5,0.02)
    plt.plot(x,np.power(x,2))
    plt.xlabel("x values")  # 设置x轴的显示
    plt.ylabel("y values")  # 设置y轴的显示
    plt.title("line test")  # 设置标题
    savefig("img/line.png")  # 保存图片  该语句必须放在plt.show()之前，否则会是空白
    plt.show()

2.2 draw stuck data

def line():
    data = pd.read_csv("data/000001.SZ.csv")
    x = pd.to_datetime([str(i) for i in data["trade_date"].values])
    y = data["close"]
    plt.figure(figsize=(16,12))
    plt.plot(x,y)
    plt.xlabel("year")  # 设置x轴的显示
    plt.ylabel("close data")  # 设置y轴的显示
    plt.title("stuck data chart")  # 设置标题
    savefig("img/line-stuck.png")  # 保存图片  该语句必须放在plt.show()之前，否则会是空白
    plt.show()

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-eHGjpTge-1641870079788)(https://jums.club/images/article/line-stuck.png)]

2.3 marker

'''
marker, 指定标签，相关标签样式见下表
markevery, 指定间隔多少个值画一个标签
'''
plt.plot([i for i in range(1,30)], marker="x", markevery=2)
plt.plot([i for i in range(3,32)], marker="o", markevery=2)
plt.legend(loc="best")
plt.show()
markersymboldescription
"."m00point
","m01pixel
"o"m02circle
"v"m03triangle_down
"^"m04triangle_up
"<"m05triangle_left
">"m06triangle_right
"1"m07tri_down
"2"m08tri_up
"3"m09tri_left
"4"m10tri_right
"8"m11octagon
"s"m12square
"p"m13pentagon
"P"m23plus (filled)
"*"m14star
"h"m15hexagon1
"H"m16hexagon2
"+"m17plus
"x"m18x
"X"m24x (filled)
"D"m19diamond
"d"m20thin_diamond
"|"m21vline
"_"m22hline

3 柱状图

3.1 simple bar chart

def bar():
    x = [1,2,3,4,5]
    y = [15,8,10,17,12]
    plt.bar(x,y)
    plt.xlabel("x values")  # 设置x轴的显示
    plt.ylabel("y values")  # 设置y轴的显示
    plt.title("bar test")  # 设置标题
    savefig("img/bar.png")  # 保存图片  该语句必须放在plt.show()之前，否则会是空白
    plt.show()

3.2 add color

def bar():
    x = [1,2,3,4,5]
    y = [15,8,10,17,12]
    plt.bar(x,y,color="rgb")
    plt.xlabel("x values")  # 设置x轴的显示
    plt.ylabel("y values")  # 设置y轴的显示
    plt.title("bar test")  # 设置标题
    savefig("img/bar.png")  # 保存图片  该语句必须放在plt.show()之前，否则会是空白
    plt.show()

3.3 add target

def bar():
    x = [1,2,3,4,5]
    y = [15,8,10,17,12]
    plt.bar(x,y,color="rgb",tick_label=["mon","thu","wen","tu","fri"])
    plt.xlabel("x values")  # 设置x轴的显示
    plt.ylabel("y values")  # 设置y轴的显示
    plt.title("bar test")  # 设置标题
    savefig("img/bar_target.png")  # 保存图片  该语句必须放在plt.show()之前，否则会是空白
    plt.show()

3.4 stack bar

def bar():
    x = [1,2,3,4,5]
    y = [15,8,10,17,12]
    y2 = [3,2,5,4,3]
    plt.bar(x,y,color="red",label="money")
    plt.bar(x,y2,color="b",bottom=y,label="cash",tick_label=["mon","thu","wen","tu","fri"])
    plt.legend(loc="best")
    plt.xlabel("x values")  # 设置x轴的显示
    plt.ylabel("y values")  # 设置y轴的显示
    plt.title("bar test")  # 设置标题
    savefig("img/bar_stack.png")  # 保存图片  该语句必须放在plt.show()之前，否则会是空白
    plt.show()

3.5 paralleling bar

def bar():
    x = [1,2,3,4,5]
    y = [15,8,10,17,12]
    y2 = [3,2,5,4,3]
    width = 0.4
    plt.bar(x,y,color="red",label="money",width=width)
    for i in range(len(x)):
        x[i] += width #增加X轴的距离
    plt.bar(x,y2,color="b", width=width,  label="cash",tick_label=["mon","thu","wen","tu","fri"])
    plt.legend(loc="best")
    plt.xlabel("x values")  # 设置x轴的显示
    plt.ylabel("y values")  # 设置y轴的显示
    plt.title("bar test")  # 设置标题
    savefig("img/bar_paralleling.png")  # 保存图片  该语句必须放在plt.show()之前，否则会是空白
    plt.show()

3.6 bar-type bar chart

def bar():
    x = [1,2,3,4,5]
    y = [15,8,10,17,12]
    y2 = [3,2,5,4,3]
    height = 0.9
    plt.barh(x,y,color="red",label="money",height=height,tick_label=["mon","thu","wen","tu","fri"])
    plt.legend(loc="best")
    plt.xlabel("y values")  # 设置x轴的显示
    plt.ylabel("x values")  # 设置y轴的显示
    plt.title("bar test")  # 设置标题
    savefig("img/bar_type.png")  # 保存图片  该语句必须放在plt.show()之前，否则会是空白
    plt.show()

4 饼图

4.1 normal

def pie():
    x = ["mon","thu","wen","tu","fri"]
    y = [190,180,200,222,230]
    color = ["cyan","green","red","pink","yellow"]
    plt.pie(y,labels=x,colors=color,shadow=True,autopct="%1.2f%%")
    plt.legend(loc="best")
    plt.title("pie chart")
    savefig("img/pie.png")
    plt.show()

4.2 exploded

import matplotlib

matplotlib.rcParams['font.family'] = 'Simsun'
matplotlib.rcParams['font.size'] = 20
x = [u"交通事故", u"水灾事故", u"其他事故"]
y = [4005, 3625, 1206]
color = ["cyan", "green", "red", "pink", "yellow", "b"]
exploed = (0, 0.1, 0)  ## 关键代码
plt.pie(y, labels=x, colors=color, shadow=True, autopct="%1.2f%%", explode=exploed)
plt.tight_layout()
plt.savefig("img/pie2.png")
plt.show()

5 子图

5.1 subplot

def subpl():
    plt.figure(1)
    # sub 1
    plt.subplot(221)# 将画面分成2行2列共4个plot，下面的数据显示在第一个plot中
    data = pd.read_csv("data/000001.SZ.csv")
    x = pd.to_datetime([str(i) for i in data["trade_date"].values])
    y = data["close"]
    plt.plot(x, y)
    plt.xlabel("year")  # 设置x轴的显示
    plt.ylabel("close data")  # 设置y轴的显示
    plt.title("stuck data chart")  # 设置标题
    # sub 2
    plot_acf(y.diff().dropna(),ax=plt.subplot(222))
    # sub 3
    plt.subplot(223)
    x = [1, 2, 3, 4, 5]
    y = [15, 8, 10, 17, 12]
    y2 = [3, 2, 5, 4, 3]
    height = 0.4
    labels = ["mon", "thu", "wen", "tu", "fri"]
    plt.bar(x, y, color="red", label="money", width=height, tick_label=labels)
    for i in range(len(x)):
        x[i] += height
    plt.bar(x,y2,width=height,color="pink",label="cash")
    plt.legend(loc="best")
    plt.xlabel("y values")  # 设置x轴的显示
    plt.ylabel("x values")  # 设置y轴的显示
    plt.title("bar test")  # 设置标题
    # sub 4
    plt.subplot(224)
    y = [190, 180, 200, 222, 230]
    color = ["cyan", "green", "red", "pink", "yellow"]
    plt.pie(y, labels=labels, colors=color, shadow=True, autopct="%1.2f%%")
    plt.title("pie chart")
    savefig("img/subplot.png")
    plt.show()

5.2 subplots

def subplots():
    # subplots显示subplot的数据
    fig, ax = plt.subplots(2,2) # 画2行2列共4个plot，返回的是一个元组型的数据，fig是画布，ax是子图
    data = pd.read_csv("data/000001.SZ.csv")
    x = pd.to_datetime([str(i) for i in data["trade_date"].values])
    y = data["close"]
    # sub 1
    ax[0][0].plot(x, y)
    ax[0,0].set_xlabel("year")
    ax[0,0].set_ylabel("close")
    ax[0,0].set_title("stuck data")
    # sub 2
    plot_acf(y.diff().dropna(),ax=ax[0,1])
    ax[0,1].set_title("one diff")
    # sub 3
    x = [1, 2, 3, 4, 5]
    y = [15, 8, 10, 17, 12]
    y2 = [3, 2, 5, 4, 3]
    height = 0.4
    labels = ["mon", "thu", "wen", "tu", "fri"]
    ax[1,0].bar(x, y, color="red", label="money", width=height, tick_label=labels)
    for i in range(len(x)):
        x[i] += height
    ax[1,0].bar(x, y2, width=height, color="pink", label="cash")
    ax[1,0].legend(loc="best")
    ax[1,0].set_xlabel("y values")  # 设置x轴的显示
    ax[1,0].set_ylabel("x values")  # 设置y轴的显示
    ax[1,0].set_title("bar test")  # 设置标题
    # sub 4
    y = [190, 180, 200, 222, 230]
    color = ["cyan", "green", "red", "pink", "yellow"]
    ax[1,1].pie(y, labels=labels, colors=color, shadow=True, autopct="%1.2f%%")
    ax[1,1].set_title("pie chart")
    savefig("img/subplots.png")
    plt.show()

5.3 subplot2gird

def subgrid():
    shape = (3,3)
    ax1 = plt.subplot2grid(shape,(0,0),rowspan=1,colspan=3)
    data = pd.read_csv("data/000001.SZ.csv")
    year = pd.to_datetime([str(i) for i in data["trade_date"].values])
    clo = data["close"]
    # sub 1
    ax1.plot(year,clo)
    ax1.set_xlabel("year")
    ax1.set_ylabel("close")
    ax1.set_title("stuck data")
    # sub 2
    ax2 = plt.subplot2grid(shape,(1,0),rowspan=1,colspan=2)
    plot_acf(clo.diff().dropna(),ax=ax2)
    ax2.set_title("one diff")
    # sub 3
    ax3 = plt.subplot2grid(shape,(1,2))
    x = [1, 2, 3, 4, 5]
    y = [15, 8, 10, 17, 12]
    height = 0.4
    labels = ["mon", "thu", "wen", "tu", "fri"]
    ax3.bar(x, y, color="red", label="money", width=height, tick_label=labels)
    ax3.set_title("bar chart")
    ax3.set_xlabel("week")
    ax3.set_ylabel("money")
    #sub 4
    ax4 = plt.subplot2grid(shape,(2,0),rowspan=1,colspan=3)
    ax4.plot(year,clo.diff())
    ax4.set_title("one diff")
    ax4.set_xlabel("year")
    ax4.set_ylabel("close")
    savefig("img/subplot2grid.png")
    plt.show()

5.4 gridspec

def gridspec():
    plt.figure(1)
    g = gs.GridSpec(3,3) # 定义3行3列
    ax1 = plt.subplot(g[0,:]) # 使用索引进行标记位置，第一个0表示第一行，第二个:号，表示所有列
    data = pd.read_csv("data/000001.SZ.csv")
    year = pd.to_datetime([str(i) for i in data["trade_date"].values])
    clo = data["close"]
    # sub 1
    ax1.plot(year, clo)
    ax1.set_xlabel("year")
    ax1.set_ylabel("close")
    ax1.set_title("stuck data")
    # sub 2
    ax2 = plt.subplot(g[1,0])
    x = [1, 2, 3, 4, 5]
    y = [15, 8, 10, 17, 12]
    height = 0.4
    labels = ["mon", "thu", "wen", "tu", "fri"]
    ax2.bar(x, y, color="red", label="money", width=height, tick_label=labels)
    ax2.set_title("bar chart")
    ax2.set_xlabel("week")
    ax2.set_ylabel("money")
    # sub 3
    ax3 = plt.subplot(g[1,1:])
    plot_acf(clo.diff().dropna(), ax=ax3)
    ax3.set_title("one diff")
    # sub 4
    ax4 = plt.subplot(g[2,:])
    ax4.plot(year, clo.diff())
    ax4.set_title("one diff")
    ax4.set_xlabel("year")
    ax4.set_ylabel("close")
    savefig("img/gridspec.png")
    plt.show()

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