Matplotlib 基本图表的绘制

图表类别：线形图、柱状图、密度图，以横纵坐标两个维度为主

同时可延展出多种其他图表样式

plt.plot(kind='line', ax=None, figsize=None, use_index=True, title=None, grid=None, legend=False, style=None, logx=False, logy=False, loglog=False, xticks=None, yticks=None, xlim=None, ylim=None, rot=None, fontsize=None, colormap=None, table=False, yerr=None, xerr=None, label=None, secondary_y=False, **kwds)

1.Series直接生成图表

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

% matplotlib inline

# 导入相关模块

import warnings

warnings.filterwarnings('ignore')

# 不发出警告

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))

ts = ts.cumsum()

ts.plot(kind='line',

       label = 'hehe',

       style = '--g.',

       color = 'red',

       alpha = 0.4,

       use_index = True,

       rot = 45,

       grid = True,

       ylim = [-50,50],

       yticks = list(range(-50,50,10)),

       figsize = (8,4),

       title = 'test',

       legend = True)

#plt.grid(True, linestyle = "--",color = "gray", linewidth = "0.5",axis = 'x')  # 网格

plt.legend()

# Series.plot()：series的index为横坐标，value为纵坐标

# kind → line,bar,barh...（折线图，柱状图，柱状图-横...）

# label → 图例标签，Dataframe格式以列名为label

# style → 风格字符串，这里包括了linestyle（-），marker（.），color（g）

# color → 颜色，有color指定时候，以color颜色为准

# alpha → 透明度，0-1

# use_index → 将索引用为刻度标签，默认为True

# rot → 旋转刻度标签，0-360

# grid → 显示网格，一般直接用plt.grid

# xlim,ylim → x,y轴界限

# xticks,yticks → x,y轴刻度值

# figsize → 图像大小

# title → 图名

# legend → 是否显示图例，一般直接用plt.legend()

# 也可以 → plt.plot()

输出：

2.Dataframe直接生成图表

# Dataframe直接生成图表

df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))

df = df.cumsum()

df.plot(kind='line',

       style = '--.',

       alpha = 0.4,

       use_index = True,

       rot = 45,

       grid = True,

       figsize = (8,4),

       title = 'test',

       legend = True,

       subplots = False,

       colormap = 'Greens')

# subplots → 是否将各个列绘制到不同图表，默认False

# 也可以 → plt.plot(df)

输出：

3.柱状图与堆叠图

# 柱状图与堆叠图

fig,axes = plt.subplots(4,1,figsize = (10,10))

s = pd.Series(np.random.randint(0,10,16),index = list('abcdefghijklmnop'))

df = pd.DataFrame(np.random.rand(10,3), columns=['a','b','c'])

s.plot(kind='bar',color = 'k',grid = True,alpha = 0.5,ax = axes[0])  # ax参数 → 选择第几个子图

# 单系列柱状图方法一：plt.plot(kind='bar/barh')   # dataframe里面如果有标签的话，默认以标签作为横坐标

df.plot(kind='bar',ax = axes[1],grid = True,colormap='Reds_r')

# 多系列柱状图

df.plot(kind='bar',ax = axes[2],grid = True,colormap='Blues_r',stacked=True)

# 多系列堆叠图

# stacked → 堆叠

df.plot.barh(ax = axes[3],grid = True,stacked=True,colormap = 'BuGn_r')  #横向的堆叠图 也可以这样写：df.plot(kind = 'barth')

# 新版本plt.plot.<kind>

输出：

5.柱状图的另一种画法

# 柱状图 plt.bar()

plt.figure(figsize=(10,4))

x = np.arange(10)

y1 = np.random.rand(10)

y2 = -np.random.rand(10)

plt.bar(x,y1,width = 1,facecolor = 'yellowgreen',edgecolor = 'white',yerr = y1*0.1)

plt.bar(x,y2,width = 1,facecolor = 'lightskyblue',edgecolor = 'white',yerr = y2*0.1)

# x,y参数：x，y值

# width：宽度比例

# facecolor柱状图里填充的颜色、edgecolor是边框的颜色

# left-每个柱x轴左边界,bottom-每个柱y轴下边界 → bottom扩展即可化为甘特图 Gantt Chart

# align：决定整个bar图分布，默认left表示默认从左边界开始绘制,center会将图绘制在中间位置

# xerr/yerr ：x/y方向error bar

for i,j in zip(x,y1):

    plt.text(i+0.3,j-0.15,'%.2f' % j, color = 'white')

for i,j in zip(x,y2):

    plt.text(i+0.3,j+0.05,'%.2f' % -j, color = 'white')

# 给图添加text

# zip() 函数用于将可迭代的对象作为参数，将对象中对应的元素打包成一个个元组，然后返回由这些元组组成的列表。

输出：

6.面积图

# 面积图

fig,axes = plt.subplots(2,1,figsize = (8,6))

df1 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])

df2 = pd.DataFrame(np.random.randn(10, 4), columns=['a', 'b', 'c', 'd'])

df1.plot.area(colormap = 'Greens_r',alpha = 0.5,ax = axes[0])

df2.plot.area(stacked=False,colormap = 'Set2',alpha = 0.5,ax = axes[1])

# 使用Series.plot.area()和DataFrame.plot.area()创建面积图

# stacked：是否堆叠，默认情况下，区域图被堆叠

# 为了产生堆积面积图，每列必须是正值或全部负值！

# 当数据有NaN时候，自动填充0，所以图标签需要清洗掉缺失值

输出：

7.填图

# 填图   默认和坐标轴之间做一个填充

fig,axes = plt.subplots(2,1,figsize = (8,6))

x = np.linspace(0, 1, 500)

y1 = np.sin(4 * np.pi * x) * np.exp(-5 * x)

y2 = -np.sin(4 * np.pi * x) * np.exp(-5 * x)

axes[0].fill(x, y1, 'r',alpha=0.5,label='y1')

axes[0].fill(x, y2, 'g',alpha=0.5,label='y2')

# 对函数与坐标轴之间的区域进行填充，使用fill函数

# 也可写成：plt.fill(x, y1, 'r',x, y2, 'g',alpha=0.5)

x = np.linspace(0, 5 * np.pi, 1000)

y1 = np.sin(x)

y2 = np.sin(2 * x)

axes[1].fill_between(x, y1, y2, color ='b',alpha=0.5,label='area')

# 填充两个函数之间的区域，使用fill_between函数

for i in range(2):

    axes[i].legend()

    axes[i].grid()

# 添加图例、格网

输出：

8.饼图

# 饼图 plt.pie()

# plt.pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None,

# radius=None, counterclock=True, wedgeprops=None, textprops=None, center=(0, 0), frame=False, hold=None, data=None)

s = pd.Series(3 * np.random.rand(4), index=['a', 'b', 'c', 'd'], name='series')

plt.axis('equal')  # 保证长宽相等

plt.pie(s,

       explode = [0.1,0,0,0],  #a和其他部分距离偏离0.1

       labels = s.index,

       colors=['r', 'g', 'b', 'c'],

       autopct='%.2f%%',#以二位小数点的百分号的形式显示

       pctdistance=0.6,

       labeldistance = 1.2,

       shadow = True,

       startangle=0,

       radius=1.5,

       frame=False)

print(s)

# 第一个参数：数据

# explode：指定每部分的偏移量

# labels：标签

# colors：颜色

# autopct：饼图上的数据标签显示方式

# pctdistance：每个饼切片的中心和通过autopct生成的文本开始之间的比例

# labeldistance：被画饼标记的直径,默认值：1.1

# shadow：阴影

# startangle：开始角度

# radius：半径

# frame：图框

# counterclock：指定指针方向，顺时针或者逆时针

输出：

a    0.744065

b    2.069706

c    2.159888

d    0.642984

Name: series, dtype: float64

9.直方图+密度图

# 直方图+密度图

s = pd.Series(np.random.randn(1000))

s.hist(bins = 20,

       histtype = 'bar',

       align = 'mid',

       orientation = 'vertical',

       alpha=0.5,

       normed =True)

# bin：箱子的宽度

# normed 标准化

# histtype 风格，bar，barstacked，step，stepfilled

# orientation 水平还是垂直{‘horizontal’, ‘vertical’}

# align : {‘left’, ‘mid’, ‘right’}, optional(对齐方式)

s.plot(kind='kde',style='k--')

# 密度图    #如果把直方图和密度图放在一起的话，直方图必须标准化，否则不显示密度图 标准化就是把每个值放到0和1之间

            #不标准化的化会显示实际值

输出：

10.堆叠直方图

# 堆叠直方图

plt.figure(num=1)

df = pd.DataFrame({'a': np.random.randn(1000) + 1, 'b': np.random.randn(1000),

                    'c': np.random.randn(1000) - 1, 'd': np.random.randn(1000)-2},

                   columns=['a', 'b', 'c','d'])

df.plot.hist(stacked=True,

             bins=20,

             colormap='Greens_r',

             alpha=0.5,

             grid=True)

# 使用DataFrame.plot.hist()和Series.plot.hist()方法绘制

# stacked：是否堆叠

df.hist(bins=50)

# 生成多个直方图

输出：

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001F92A9B9940>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000001F92AA016A0>],

       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001F92AA4B8D0>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000001F92AA8A400>]], dtype=object)

11.散点图

# plt.scatter()散点图 散点图会用到很多 因为图片就是散点图

# plt.scatter(x, y, s=20, c=None, marker='o', cmap=None, norm=None, vmin=None, vmax=None,

# alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None, **kwargs)

plt.figure(figsize=(8,6))

x = np.random.randn(1000)

y = np.random.randn(1000)

plt.scatter(x,y,marker='.',

           s = np.random.randn(1000)*100,

           cmap = 'Reds',

           c = y,

           alpha = 0.8,)

plt.grid()

# s：散点的大小

# c：散点的颜色

# vmin,vmax：亮度设置，标量

# cmap：colormap

输出：

12.散点矩阵

# pd.scatter_matrix()散点矩阵

# pd.scatter_matrix(frame, alpha=0.5, figsize=None, ax=None,

# grid=False, diagonal='hist', marker='.', density_kwds=None, hist_kwds=None, range_padding=0.05, **kwds)

df = pd.DataFrame(np.random.randn(100,4),columns = ['a','b','c','d'])

pd.scatter_matrix(df,figsize=(10,6),

                 marker = 'o',

                 diagonal='kde',

                 alpha = 0.5,

                 range_padding=0.1)

# diagonal：({‘hist’, ‘kde’})，必须且只能在{‘hist’, ‘kde’}中选择1个 → 每个指标的频率图

# range_padding：(float, 可选)，图像在x轴、y轴原点附近的留白(padding)，该值越大，留白距离越大，图像远离坐标原点

输出：

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000002A61B496E10>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A61C9FD550>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CA45F28>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CA80BE0>],

       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CACAE10>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CB06BA8>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CB4ECF8>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CB5EFD0>],

       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CD5E4E0>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CDA9438>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CDE7240>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CE32C18>],

       [<matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CE6E2E8>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CEBBA58>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CEF9128>,

        <matplotlib.axes._subplots.AxesSubplot object at 0x000002A61CF42278>]],

      dtype=object)

13.箱型图

# 箱型图

# plt.plot.box()绘制

fig,axes = plt.subplots(2,1,figsize=(10,6))

df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])

color = dict(boxes='DarkGreen', whiskers='DarkOrange', medians='DarkBlue', caps='Gray')

# 箱型图着色

# boxes → 箱线

# whiskers → 分位数与error bar横线之间竖线的颜色

# medians → 中位数线颜色

# caps → error bar横线颜色

df.plot.box(ylim=[0,1.2],

           grid = True,

           color = color,

           ax = axes[0])

# color：样式填充

df.plot.box(vert=False,

            positions=[1, 4, 5, 6, 8],

            ax = axes[1],

            grid = True,

           color = color)

# vert：是否垂直，默认True

# position：箱型图占位

输出：

14.箱型图另一种画法

# 箱型图

# plt.boxplot()绘制

# pltboxplot(x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, bootstrap=None,

# usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None,

# labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_xticks=True, autorange=False,

# zorder=None, hold=None, data=None)

df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])

plt.figure(figsize=(10,4))

# 创建图表、数据

f = df.boxplot(sym = 'o',  # 异常点形状，参考marker

               vert = True,  # 是否垂直

               whis = 1.5,  # IQR，默认1.5，也可以设置区间比如[5,95]，代表强制上下边缘为数据95%和5%位置

               patch_artist = True,  # 上下四分位框内是否填充，True为填充

               meanline = False,showmeans=True,  # 是否有均值线及其形状

               showbox = True,  # 是否显示箱线

               showcaps = True,  # 是否显示边缘线

               showfliers = True,  # 是否显示异常值

               notch = False,  # 中间箱体是否缺口

               return_type='dict'  # 返回类型为字典

              )

plt.title('boxplot')

print(f)

for box in f['boxes']:

    box.set( color='b', linewidth=1)        # 箱体边框颜色

    box.set( facecolor = 'b' ,alpha=0.5)    # 箱体内部填充颜色

for whisker in f['whiskers']:

    whisker.set(color='k', linewidth=0.5,linestyle='-')

for cap in f['caps']:

    cap.set(color='gray', linewidth=2)

for median in f['medians']:

    median.set(color='DarkBlue', linewidth=2)

for flier in f['fliers']:

    flier.set(marker='o', color='y', alpha=0.5)

# boxes, 箱线

# medians, 中位值的横线,

# whiskers, 从box到error bar之间的竖线.

# fliers, 异常值

# caps, error bar横线

# means, 均值的横线,

输出：

{'boxes': [<matplotlib.patches.PathPatch object at 0x000002A61CBCBA20>, 
<matplotlib.patches.PathPatch object at 0x000002A61CBDCA90>, <matplotlib.patches.PathPatch object at 0x000002A61CBF1940>, <matplotlib.patches.PathPatch object at 0x000002A61CC098D0>,
 <matplotlib.patches.PathPatch object at 0x000002A61CC1F860>], 'means': [<matplotlib.lines.Line2D object at 0x000002A61CBD44A8>, <matplotlib.lines.Line2D object at 0x000002A61CBEC390>,
 <matplotlib.lines.Line2D object at 0x000002A61CC03320>, <matplotlib.lines.Line2D object at 0x000002A61CC192B0>, <matplotlib.lines.Line2D object at 0x000002A61CB92438>], 
'medians': [<matplotlib.lines.Line2D object at 0x000002A61CBC3EF0>, <matplotlib.lines.Line2D object at 0x000002A61CBE6B38>, <matplotlib.lines.Line2D object at 0x000002A61CBFEAC8>,
 <matplotlib.lines.Line2D object at 0x000002A61CC14A58>, <matplotlib.lines.Line2D object at 0x000002A61CB96470>], 'caps': [<matplotlib.lines.Line2D object at 0x000002A61CBCEBE0>,
 <matplotlib.lines.Line2D object at 0x000002A61CBCECF8>, <matplotlib.lines.Line2D object at 0x000002A61CBE2AC8>, <matplotlib.lines.Line2D object at 0x000002A61CBE69B0>, 
<matplotlib.lines.Line2D object at 0x000002A61CBF7A58>, <matplotlib.lines.Line2D object at 0x000002A61CBFE940>, <matplotlib.lines.Line2D object at 0x000002A61CC0DF98>, 
<matplotlib.lines.Line2D object at 0x000002A61CC148D0>, <matplotlib.lines.Line2D object at 0x000002A61CB9DF28>, <matplotlib.lines.Line2D object at 0x000002A61CB9D208>], 
'fliers': [<matplotlib.lines.Line2D object at 0x000002A61CBD4B70>, <matplotlib.lines.Line2D object at 0x000002A61CBECB00>, <matplotlib.lines.Line2D object at 0x000002A61CC03A90>,
 <matplotlib.lines.Line2D object at 0x000002A61CC19A20>, <matplotlib.lines.Line2D object at 0x000002A61CC24EB8>], 'whiskers': [<matplotlib.lines.Line2D object at 0x000002A61CBCBE80>, 
<matplotlib.lines.Line2D object at 0x000002A61CBCBFD0>, <matplotlib.lines.Line2D object at 0x000002A61CBDCFD0>, <matplotlib.lines.Line2D object at 0x000002A61CBE2940>, 
<matplotlib.lines.Line2D object at 0x000002A61CBF1F98>, <matplotlib.lines.Line2D object at 0x000002A61CBF78D0>, <matplotlib.lines.Line2D object at 0x000002A61CC09F28>, 
<matplotlib.lines.Line2D object at 0x000002A61CC0D860>, <matplotlib.lines.Line2D object at 0x000002A61CC1FEB8>, <matplotlib.lines.Line2D object at 0x000002A61CC247F0>]}

# 箱型图

# plt.boxplot()绘制

# 分组汇总

df = pd.DataFrame(np.random.rand(10,2), columns=['Col1', 'Col2'] )

df['X'] = pd.Series(['A','A','A','A','A','B','B','B','B','B'])

df['Y'] = pd.Series(['A','B','A','B','A','B','A','B','A','B'])

print(df)

df.boxplot(by = 'X')

df.boxplot(column=['Col1','Col2'], by=['X','Y'])

# columns：按照数据的列分子图

# by：按照列分组做箱型图

输出：

       Col1      Col2  X  Y

0  0.661114  0.164637  A  A

1  0.483369  0.361403  A  B

2  0.954009  0.786664  A  A

3  0.173198  0.500602  A  B

4  0.156583  0.047123  A  A

5  0.852358  0.672986  B  B

6  0.823713  0.625156  B  A

7  0.705710  0.632264  B  B

8  0.940125  0.091521  B  A

9  0.230993  0.753328  B  B