matplotlib 绘图 笔记

中文乱码问题

import matplotlib.pyplot as plt
import matplotlib
import random
# 防止绘图时 中文乱码
# matplotlib.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['font.sans-serif'] = ['SimHei']
# plt.rcParams['font.sans-serif']=['Songti SC']
# matplotlib.rcParams['axes.unicode_minus'] = False

解决中文乱码的参考链接, 有可能需要改一些配置文件

https://blog.csdn.net/weixin_44424296/article/details/115433307

### 常用参数
#### 常见颜色参数:(color)
基础颜色: 蓝-'b' 青-'c' 黑-'k' 绿-'g' 紫-'m' 白-'w' 红-'r' 黄-'y'
也可以使用RGB色, 如'#FF0000'为红色
#### 常用线型:(linestyle)
'-' 实线 '--' 虚线
'-.' 虚点线 ':' 点线
'.' 点 ',' 像素点
'o' 圆点 'v' 下三角点
'^' 上三角点 '<' 左三角点
'>' 右三角点 '1' 下三叉点
'2' 上三叉点 '3' 左三叉点
'4' 右三叉点 's' 正方点
'p' 五角点 '*' 星形点
'h' 六边形点1 'H' 六边形点2
'+' 加号点 'x' 乘号点
'D' 实心菱形点 'd' 瘦菱形点
'_' 横线点
#### 位置字符:( plt.legend(loc='??') )
'upper right', 'upper left', 'lower left', 'lower right', 'right', 'center left', 'center right', 'lower center', 'upper center', 'center'

### 折线图

# 生成 fig
plt.figure(figsize=(20,8))
# 准备数据
x = range(60)
# 准备上海的数据
y_上海 = [random.uniform(15, 18) for i in x]
# 准备北京的数据
y_北京 = [random.uniform(1, 4) for i in x]

# 构建中文坐标标签
x_ch = ['11点{}分'.format(i) for i in x]
y_tickets = range(40)

# 画折线图
plt.plot(x, y_上海, label='上海', color='blue') # 颜色名称: https://matplotlib.org/stable/gallery/color/named_colors.html
plt.plot(x, y_北京, label='北京', color='#000000', linestyle='--')

# plot 后再修改刻度
# 修改x y刻度
plt.xticks(x[::5], x_ch[::5])
plt.yticks(y_tickets[::5])

# 增加 x y 轴 描述
plt.xlabel('时间')
plt.ylabel('温度')

plt.title('图表的标题')

# 添加图例
plt.legend(loc='best')

plt.show()

 

 

 ### 多个子图

 

# 多个坐标系显示  子图

# 生成 fig 1行 2列
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))


# 准备数据
x = range(60)
# 准备上海的数据
y_上海 = [random.uniform(15, 18) for i in x]
# 准备北京的数据
y_北京 = [random.uniform(1, 4) for i in x]

# 构建中文坐标标签
x_ch = ['11点{}分'.format(i) for i in x]
y_tickets = range(40)

# 画折线图
ax[0].plot(x, y_上海, label='上海')
ax[1].plot(x, y_北京, label='北京', color='r', linestyle='--')

# plot 后再修改刻度
# 修改x y刻度
ax[0].set_xticks(x[::5], x_ch[::5])
ax[1].set_xticks(x[::5], x_ch[::5])
ax[0].set_yticks(y_tickets[::5])
ax[1].set_yticks(y_tickets[::5])

# 增加 x y 轴 描述
ax[0].set_xlabel('时间0')
ax[0].set_ylabel('温度0')
ax[1].set_xlabel('时间1')
ax[1].set_ylabel('温度1')

ax[0].set_title('子图0的标题')
ax[1].set_title('子图1的标题')

# 添加图例
ax[0].legend(loc='best')
ax[1].legend(loc='best')

plt.show()

 

 

### 柱状图
以电影票房统计为例

# 生成电影名及随机票房
电影序列 = list(range(5))
电影名称 = ['电影{}'.format(i+1) for i in 电影序列]
电影票房1 = [random.uniform(100, 1000) for i in 电影序列]
电影票房1 = [int(i) for i in 电影票房1]
电影票房2 = [random.uniform(100, 1000) for i in 电影序列]
电影票房2 = [int(i) for i in 电影票房2]

plt.figure(figsize=(10,8))
x = range(len(电影名称))
y = 电影票房1

plt.bar(x, y, width=0.8, color=['r','g','r','g','r'])
# 修改刻度 显示电影米杆子
plt.xticks(x, 电影名称)
plt.show()

 

### 多个系列数据的柱形图

# 多个系列数据
plt.figure(figsize=(10,8))
x = range(len(电影名称))
y1 = 电影票房1
y2 = 电影票房2

plt.bar(x, y1, width=0.2, label='首日票房')
plt.bar([i + 0.3 for i in x], y2, width=0.2, label='周均票房')
# 修改刻度 显示电影米杆子
plt.xticks([i + 0.1 for i in x], 电影名称)
plt.legend(loc='upper center')
plt.show()

 ### 散点图和折线图 画在一起

# 散点图 和 折线图 可以画在一起
plt.figure(figsize=(20,8))
y标签 = range(1000)
plt.scatter(x, y1,color='b')
plt.plot(x,y2,color='k')
plt.yticks(y标签[::100])
plt.show()

 

### 直方图
体现概率分布

# 生成随机的 0-100的1000数字
序号 = range(1000)
随机数字 = [random.uniform(0, 100) for i in 序号]

# 绘图
plt.figure(figsize=(20,8))

# 分组
组距 = 10
组数 = int((max(随机数字) - min(随机数字)) / 组距 )
组数 = 100

# 画直方图
plt.hist(随机数字, 组数)

# 指定刻度范围及步长
plt.xticks(list(range(100))[::5])
plt.yticks(list(range(30)))
plt.grid(True, linestyle='-', alpha=0.8, color='k')

plt.show()

 ### 饼图

数据名 = ['数据1','数据2','数据3']
数据值 = [16,10,12]
plt.pie(数据值, labels=数据名, autopct='%1.2f%%',colors=['r','b','y'])

# 显示正圆
plt.axis('equal')
plt.legend(loc='best')
plt.show()

 

可以使用plt.annotate('注释文本',
xy=(1,2), # 箭头位置
arrowprops=dict(arrowstyle='->'), # 自定义箭头样式
xytext=(3,4) # 文本位置
) # 生成带指向的注释

 

jupyter 笔记文件,  后缀改为 ipynb 后使用

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# matplotlib 绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 中文乱码问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import random\n",
    "# 防止绘图时 中文乱码\n",
    "# matplotlib.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "# plt.rcParams['font.sans-serif']=['Songti SC']\n",
    "# matplotlib.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 常用参数\n",
    "#### 常见颜色参数:(color)\n",
    "    基础颜色: 蓝-'b' 青-'c' 黑-'k' 绿-'g' 紫-'m' 白-'w' 红-'r' 黄-'y'\n",
    "    也可以使用RGB色, 如'#FF0000'为红色\n",
    "#### 常用线型:(linestyle)\n",
    "    '-'\t实线\t'--'\t虚线\n",
    "    '-.'\t虚点线\t':'\t点线\n",
    "    '.'\t点\t','\t像素点\n",
    "    'o'\t圆点\t'v'\t下三角点\n",
    "    '^'\t上三角点\t'<'\t左三角点\n",
    "    '>'\t右三角点\t'1'\t下三叉点\n",
    "    '2'\t上三叉点\t'3'\t左三叉点\n",
    "    '4'\t右三叉点\t's'\t正方点\n",
    "    'p'\t五角点\t'*'\t星形点\n",
    "    'h'\t六边形点1\t'H'\t六边形点2\n",
    "    '+'\t加号点\t'x'\t乘号点\n",
    "    'D'\t实心菱形点\t'd'\t瘦菱形点\n",
    "    '_'\t横线点\n",
    "#### 位置字符:( plt.legend(loc='??') )\n",
    "    'upper right', 'upper left', 'lower left', 'lower right', 'right', 'center left', 'center right', 'lower center', 'upper center', 'center'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成 fig\n",
    "plt.figure(figsize=(20,8))\n",
    "# 准备数据\n",
    "x = range(60)\n",
    "# 准备上海的数据\n",
    "y_上海 = [random.uniform(15, 18) for i in x]\n",
    "# 准备北京的数据\n",
    "y_北京 = [random.uniform(1, 4) for i in x]\n",
    "\n",
    "# 构建中文坐标标签\n",
    "x_ch = ['11点{}分'.format(i) for i in x]\n",
    "y_tickets = range(40)\n",
    "\n",
    "# 画折线图\n",
    "plt.plot(x, y_上海, label='上海', color='blue') # 颜色名称: https://matplotlib.org/stable/gallery/color/named_colors.html\n",
    "plt.plot(x, y_北京, label='北京', color='#000000', linestyle='--')\n",
    "\n",
    "# plot 后再修改刻度\n",
    "# 修改x y刻度\n",
    "plt.xticks(x[::5], x_ch[::5])\n",
    "plt.yticks(y_tickets[::5])\n",
    "\n",
    "# 增加 x y 轴 描述\n",
    "plt.xlabel('时间')\n",
    "plt.ylabel('温度')\n",
    "\n",
    "plt.title('图表的标题')\n",
    "\n",
    "# 添加图例\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多个子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i7xUrhravZcuiU8Nryxapf3/7QuEf/wj9+dnZ9hoNGWKz/sowAkYAAACIvn797Pf48cE/x3sLGJ14oqXtFOSc1Ly53f7hB+mFF6SOHaWJE0vdXBQiJ8eKHA8ebAGZefOkv/0t//ElS+zb/CVL7DXo0CH2NUD27bOZOieeaCmL774buyW/f//dZr6NG1e6/dxxh7R7t/Ttt5FpVyRMnizVq2dFjkO1fbvNPjn9dAvqloVV1MaOteD4zTcf+Njxx9tMo19+iX27SmPmzPzA4f7BIsk+hy++OPSA0bXXSrNnR6Z9eX75RerRw4LVhx9uX1SEasECC9j26mUrG/72W2TbeBAhYAQAAIDoa99eGjYs+BkKkrRypRW7zgs2FadDB7ugqVHDtn/iCQtwIHK2bctfKn7SJKl+/cDHnbP+b9HC6tfMmyedemps27h4sRUU7t3bLkbT06VRo2Jz7PHj7Zzr3790++nSxYINse67onhvgYJevQJnywSrenW7eL/qKgswHgqrhJVk2DArBn7aaYU//t57B1cdo/R0SzG84oqiU4DXrrXZPMHWlPvxR2nCBKlaNfv3woWRaeuiRRakHD/egtfvvRf6PrZts9p7vXpJxx4r/eEPkWnbQYiAEQAAAKIvKcnSUvJWVArGEUdYWsBFFwW3fZs20qxZltJzzz12PJTe0qV2kVizps0G+Pe/D1z1qygZGbGb4SNZ8d1t2yzY0qmTBRJfeSU2x/76aytu3K1b6fdVp4793rmz9PsqrcxM6fzz7Sdc5ctbEFc69FPTfvjBAqo33lj4zEjJ6rpJFuA8GNJoH3rIgi8vvyylpha+zcKF0p/+FPwMu+eft8+R666zlTDbtbPFDcK1dKn9Puss6ddfrVZUXrHxUJ14ov09jRrZ/yeffmpfXpRBBIwAAAAQGzt22GyPUOoY1atnMxSCVaWK9M47Vh/kqqtCbyMCffSRpfk9+KD9u3bt4GeZZGRIDRtKf/1r9NpXmIoVLUDhnM0ymjvXUkyi7euvpT59ir6gDtVNN9lFbyRTuH7+OfQUoLQ0q600eHDpjl2njs2eyktrSiSrVtnsp337Sr+vrCwLWF5zTfHbbdtms1fuv7/0x4ymOXOsb666Sjr55KK369PHalcFs1ra779bUelLLrHPlDPPtEUNLr/cCuaHIi9VtlUrm2Uq5QeKvJeuvFJ6/PHg9+e91TDKc911dl+sAs8JhoARAAAAYmP9ertA+PjjkrfNyZEuvNCKxobKOemWW2y1JslmG02YEPp+yronnrDVj1q2lG64IfTnlytnF8Tvvx+bujWZmZYO9skn+fddcok0dap0zDHRPfaWLbbqVWnT0fbXrZvNVvnii8jtc+pUmzERysqCy5ZFJpAiWcH0zz6LzL4iZcMGS2O9887gPptK0rGjzUjZvyB8YapXt6LKr78e3RUFvZc+/zwwCBKsrCwLfNWpY0HD4qSlSQMHWh+WdKyRI+39csst9u8KFSw43aaNdM45wRfL37vXZqA+9pily3XsGPi4c1Z/6P33g9ufZClttWpZAFiSmja1emgjRlh/lDEEjAAAABAbzZvbjJNgCl9//72lKWzYULpjZmbafm67jZpGoZgzxwJt559vq341ahTefgYPzl/NLtrmzrV0mP1rqFSvbgVww6m9E4patSy16PrrI7fPCy+098tTT0VunzVr2gV0KIHYAQOsoHEkNGwopaREZl+RsH27/X1r1lgtnVACC4WZOlVavTr47e+9V6pUyX5Hy9df24ynN94I/bneW82yl16y+mQlGTRI2rhRmjGj+O2GDrWZRO3b599XrZoFRw8/3Aqkl5TK+u67toLZ6NH2HhkxovDZfX37Wj21YAvwT5pk58X+Bd6HDrX3dyLOjosyAkYAAACIDeds8J5XHLg433xjv4MpeF2c1FRb3nr+fJvdgODcdZeliowYYeld4TrrLKtfVdoL8WBMmmS/CxZW373bLviisXz3/pzLr00TCWlp0q232opzeak24crKssK9RxxhQaO33w7ueWvXWj2Ynj1Ld/z9PfqonV+JoFIlm4k4ZowFHUpTpyknx2a5hBJcO+wwWxXvgw+i8/m0caOlNXbrZqlve/aE9vzUVJsFdOaZwW0/cKDNFiopQJyWVnh6W716Fsx8/vkDU5H37rXgUF5dr9WrbRbj2LHWh0UFhfv2tcBX3udDSSZPtsBmkyb59512mv0f0rdvcPs4hBAwAgAAQOz062fpOyVdUHzzjXT00QeuxBWOiy6yfT3wQHhpGUXx3lJPwg2GZGZaYdyJEyPXpkh59lnptdesJklp1K5tAZxwVioK1aRJVsekbt3A+ytWtPNp2LDoHDc724prv/Za5Pd93XU28+K550q3n1Gj7O9ftsyCImPHSrt2lfy8KVPsdyirG5bk11/jn96TkWHBlJQUafhwm2U0ZIgFOMP15Zf2t4WavnnrrVbwOS+YE8n0zQcekI480tJL166V/vnP4J/7/POhn9PVq1u/Dh1a9DaXXmp9XpRmzfIXOvjuO0unu+46CyZdcIGlrkkWyJo7t+RgVpcuFhgMZmZrXmCpd+/AAFRKSuBsqDKEgBEAAABiJ2/GUHEpC/v22be8pZ1dlCc52WYZ/fRT5JZY//lnS/M4++ySa5UU5YknpBdftEKvu3dHpl2llXex2qZN0UuCh+qRR2xltWjWMcrOlr791i70Csorfj15sr1ukTZnjqVQlmYmVlGqVLEL5BdeCH8fWVn2GrRvbylDl1xiwYkPPyz5uZMnS5UrW0AsUk47zYoeB1unJtKysy1ocdxxB864WbrUAhThGDbMghqhFgevXNkKkefNTHrmGftMKO2qXJmZFqjt29cKUg8aZJ85mzaV/Ny1a6W77w6vflblykU/tmCBzW7bvr3k/ezYIZ1xhn3O/uc/9lk7bpylakpFr0BXUFqaFb5u3LjkbX/+2dKgC/scycmxwt8PPxzccQ8RBIwAAAAQOw0a2IVQEPdbpQAAHZlJREFUcbVe1q+3IsXFrcgTqnPOsdk8rVuXbj+7dlltn7ZtrV7JU0/ZhacU+uylU0+1i8RVq6QnnyxduyJl+HD7Fj+SAazjj7eUpmjWEdq61V6HAQMKf/yKK+wCMxorHeUVx41UgLOgXr1sllG43n7bZr785S+WHtizp3T77XYOl2TyZOvXSNYd6t/fXot4FL/23j573nvPPg8qVgx8/MEHLZgU6uynX3+1QNP114eXlpiaaq+NJKWnW+pVixZW22jHjtD3J9msui1b8gMsTzxhKXB5y88X54EHLOAUzgqHGRkWpCqsSPZzz1nKWkkryEk2u/HTTy1YtGGDFQfv1y/4QNH+hg2ztLWSVKxon+8nnXTgY0lJ1p//+ldoReMPdt77hPw59thjPQAAOHRJmu0TYMzBD+OvkJx1lveS91dc4f26dXZfdrb3F1/s/U03BbePnJzAf198sfflynm/bFlEmxqyHTu8r1PH+xNOOLCNpTVrlvePPRbZfYZq0CDv69b1ft++yO63d2/vO3SI7D4LmjrV+27dvN+wIbTnZWZ6f+SR3nfsGN5rOmmS91OmhP68kvTu7X27dpHfb3Fycry/7TZ7/95/f+HbjBljj48bF9q+X3/d+woVvF+zpvTt9N77FSu8v+QSa0udOt5/8EHo+7j8cu+rV/c+PT3/vqyskp+3YIH3SUne33pr6MfM06mT9z16BN63aZP35ct7P2RI+PstjcxM77duLd0+PvnEXpN3341Mm+Io2DEYM4wAAAAQWytW2CyWWbMKfzya396uXm2zK9LTg3/OokX5aRx/+YvV1Xj9dUs/keyb5zp1LG0or1h3cR5/3Gpy5M1ieOop+ya/YcNQ/pLIe+opqz/y9NORnw00aZLNlli2LLL7zRNMMd8bb7SZZiXV7hk3zooyB3OO7NplM8369w+uneGqUcPSOENNTcvIsL/50UcPfE1nzSq5EHCvXjZDLNIuvNBmEZbmve69zYTbfwbOlCk2K2rSpPyfX3+1x156SfrHP6z490MPFb7PAQNslkmodcmuuMLSuCJRc02y4uRvvWXFzlu1splBoUhPt0La55xjhaHzJCfbOZFXB6gwd95ps3vuuy+8tkuW/jZ9euAqlyNGWLtuuSX8/YbLe6vlVNwsI+/t/Nl/lcWCTjnFUttefjnybUxUwUSV4vHDN1wAABzaxAyjhPuJ2fhryxbvnfP+4YcPfGznTu8rV/Z++PDoHHvcOO8l7597Lrjtn3nG++Rk72++ufjt9uzxvkUL7xs39n779qK3++EH71NTvb/gguDbXBqvvur9Qw+VPLtk9WqbIXHhhdFpx7Jl1u9PPRXc9i+95P3ddwe3bXa297VqeX/ffeG3z3ub1XHOOdZOyfunny75OevX24yJb78t3bGDMWiQ9zVrer9rV2T216GD9126FP34J594/9VXkTlWpPTq5X2zZtYPycn2Ol11Vf7jKSn5r1/ezx/+YI9t2uT9I4/Y+VKcwYO9r1ev5O3ybNsW3t8SrHBmhuXkeD9vnvc//XTgY089Zf0yfXrhzxs71vs33wz9mPubP9+OMWJE/n0TJ3p/772l229pnHWW902bFv143mfUsGHF7+eRR2y7X36JbPtiLNgxWNwHJkX9EDACAODQRsAo8X5iOv7q1MnSUgr69FMfVkpIKE480VKTirvwzsnx/s9/tracfbZdbJZk2jRL5bj22sIfz8z0/thjva9d2/uNGw98/MMPve/bNzIpU3v32t9wzTX2N1x3nR2/KEOHep+WFt20uE6dLK2qJBMmWEBRslSskuRdnL7xRsnb5uRYcCcvnTDPyJGWLlOhgvePPmr/3r275P3F0rff+pCCnZ984v3nnxcdcHj6advfkiWFP965c+Hv0UjJyfF+7drQnnPFFZbCeeON9v588knvv/wy//Fvvsn/GT/efor6+4oyapQFo374oeRt9+2zIHGwwc1w7dhh7+H33ovMvurWteBbpFNP8+TkeH/EEd6ffnp09h+O556z872oz7jXXrPHS3rd1671/k9/ilz6YZwQMAIAAAmNgFHi/cR0/HX77RagKHhRfuutVs9nz57oHTvvwvvJJwt/PCvL++uv9/8LtART9yPPXXfZ7IfCas08/rjtc/Towp/78cf2+D/+EfzxinLttd736WOzJPICX2ecUXQQZOPG8OqkhOKxx6wdq1YVvc2mTd7Xr2+ztQ47zPvzzit5v88+a/tdsaLkbZcvt20fecQuavfutfvnz/f+/PO9/+23wO3T04u/qF60KPiZKJHQs6f3bduWfE5mZHjfpIkFfYpq/+rVFph74IEDH9uxw4KfRdX6iYR77/W+UqXAGjtFWbo0eu0oaM8emwUZjFGj7Hz66KPotikry/tWrbw/5piSz7edO+39v3Bh0du8+OKB7X77bZull5ERmTYPG+b988/b7WefDe79GU0LF9rf/MorhT9+1VX22R3L93McETACAAAJjYBR4v3EdPz1+efeS4GzA7y3Qrh9+0b/+AMH2sVBYelja9ZYSso994T+DXx6euGzJnbutADIuecW/dycHGtX1aqW6hSudessGDd0aP59L7xgwYHu3Q+8IIzWLIOCliyx1LGvvy56m+3bbRbJ3Lnez5hh/VaSc86x2QzB6tvXZoWccor3l15a9HYrVljB6HfeKfzxtWt9xAJ8wfrsM+//9S97zbKzi37tRoywtn3ySfH769fP/saC+/niC3t+ca9VaeUVEC74GVDQ/Pl27pY2TSrScnJsxmDLlrEJMowcaf314YfFb5cXxJo8ueht9u2zoOzRR9vMw927vW/QwPuuXSP/eTB7trXnmWciu99Q5eRYAfFLLin88WbNLG0tGNnZdt5OmlS69sRRsGMwil4DAAAg9k44QerY0Qqw5tm4UVqwIHrLk+/vkUeks84KPP6ePVJOjhWu/eEHK04davHncuWkww+3/Xz+ef79lStLs2cXX7TYOemf/7Siq/fcE9px9zdsmBUTvu22/PtuvNEK+Z55ZuCy319+KXXvLq1aFf7xgtWihRXBLWzJaskqzlStagXFO3aUuna1fsvJKXqf3luR4z59gm/HtddKK1daserOnW0fhWnYUKpeXfq//5O2bz/w8XHj7Hcoxy6tgQOloUPtXHnlFXsfzZkTuM2+fVbkumtX6dRTi9/fJZfY+27FisD7J0+WUlKkHj0i2vwAJ54olS9vS6cX5/777bw4/fTotaWgn36SeveW5s4tepvJk63vb7vNCt9H24UXWuHmRx4p+pyVpHfekRo0kI47ruhtUlOlJ56w99eGDdIzz0hr1kS+4P22bdLFF9txrroqcvsNh3PSc8/ZZ2FBq1dbQf5evYLf3w032AIKeYsXhGLFCitsftNNxX++JYJgokrx+GGGEQAAhzYxwyjhfuI+/tq0ydK2ikuliJbNm+3b9TvuiMz+8mZ4fPCB1cQI5dvkO++05y5aFPpxd+70vkYNq7tUnGnTvJ8509KbmjWLXBpKMHJyDqynNHeuFWAumHr04482g6Oo2RIZGZZeM2FC8MfPyrJ6RwXrGBVm1iyb3ZJXOHl/l15q9ajilcIyapTNmHDOalXlzUp7+WU7fz77rOR97N1bePrngAHB1ZsqrYEDvT/qqKIfnz7d/pZHH41+W/a3ebPVMfrzn4veZvBge/2jmT5b0L//Xfxru3WrFdW/7baS95WTYz/r19tCA8HOrgnFySdbe2+4IfL7jqTMTPtMDKUu0Ztv2t8WTF/vb88e7zt2tNdJsvduHD5Dgh2DxX1gUtRP3AcsAAAgqggYJd5PXMZf+/ZFpshzuGbO9P5vf/O+dWurnVRSukewMjLsouCww2y/jz0W/HN37LCVisJJWXjhBV9iseicHCtAnbfKVFE1laJh40arrfPii/n37dzpffPmlhKzeXPg9rt3W4HeE0+MXRsLuvlmq+cza1b+fTk5lrYYrVXlgrVtm9UDS031vkoVC4SNHGkBw1DOn7zgQZ6srNKlRQZr2DBfbOHtk06y99COHdFvS0H9+lmwsqh+3LrV+ylTYtumjAx7vZcvL/zxV1+1/pw5M/h9/v3vxb8GpfHZZ7bvxYsjv+9w5ORYmuW0aZHZ3803298XSrrkypXet2ljKZn332+fw6G8XhES7BiMlDQAAADEx6JFUs2a0ief2L+/+KLw1J9o+uc/pTvusJSsL76QBg2KzH7T0qQ33rC/p1Il6Zprgn9ulSqWOuacpReF4qqrpHffLT6VyDnp44+lDh0s/e/cc0M7Rmkcdpilw7z/fv59N98sLV0qvf22VKtW4PYVK0p33y1NmCBNmnTg/iZOlH77LapN1qOPSnXqSCNG5N+3cKG0fr3Uv390j12SatWkv/3N2tO7t1S7tnTppdKYMcGnFv36q9SmjZ3/eZKTpbp1o9Pm/Z19tqVQ1a9/4GOrV0szZ1p6ZpUq0W9LQeecIy1ZIv34Y+GP16ghHX98bNuUlmavd5MmhT++b5+lonXuHPw+09Ptc7BFi4g0McDAgVJ2ttSyZeT3Ha5rrrHUu/099pg0bVro+/rHPywl9brrpOXLg3tOo0bS/PnSaadJDz9sadhduoR+7BghYAQAAID4aN7c6jd8843VdBg40IIssfTYYxYkmjAh8rVojjnG6hh99VV4F99vvmkXWtu2Bf+cChWCCwDVry/NmmU1jCJZs6QkzkmDB1t/b9kivfWWveb3328Bj8Jcf73VhXrwwcD7vbe6LvfeG902V6smffut9OKL+fcdeaQFOmNZV6c4LVpYEHDgwNCf27ixBb/eesv+PXx47Gqr1K8vXXCB1bgpqGFDuwi/4Ybot6MwZ59t5+uYMYH3b9pkQZkZM+LTLsneu4XVObv+ejtXQ3lP//nP0h//GLm2FRSL+k7Bck7q29c+f/LO7w0bpPvuk6ZMCX1/qakWoH/xxaKDeHmmTJGuvNJq5aWk5N/furX9fv996YorwquJFEUJ9OoBAACgTElLsyKj48db0EiKTcHr/TVpIn34oXTssdHZf79+4e+7TRubZTFokF1kFMd76fzzpdGjg9+/czaTJNYGD7ZZB2PHWnDihBPsgq0oFSrYLKNJkwIv6n7+2S72igo0RdKRR9qF78aN9lOxos0QqFMn+seOtrQ0O3c+/FDatcvOoe++i92Ffl6x5V278u/buNHO6Zo17fWPh8MPtwLJzZsH3v+vf1nB9GrV4tMuyV6fJ54IfD9s2JD4BZQTQd++0tatNrNHyu/DUApe7++ww2xmp3M2I23/hRTyrFljgfxp02xBgsIsW2ZfElx6aUIFjQgYAQAAIH769bMViUaOlOrVy/+2FRZoGjnSLmjOPrvwC5E8EyfaN92hzEaKl2OPtRWCxoyx2Vfvvx/4jXthhgyx1KWePfPvy0tRi0XASLLV69q3tyDCI49YKteh4pJLLCj53nt2URvuxXM4fvnF0kLzVp3z3lZ3i2WqZFGGDbNZbHn27rX7Tj9datUqfu0aMsTSDx99NP++886TBgyIX5sOFieeaL/Hj7ffkyZZ2nBpvzRYv95SAf/wh8BV7DIyLEi+Z4/0wQdFBxrvuMPSDf/7X+mii4oOLMUYASMAAADET9++9nvSJAsexTI96mBw4YXSv/9tgZULLrCZOYV5+mmb7XL55bFtXzickx5/3NJnype3C9+SlC9vf//+M6ImTbIgY8EZINFSoYItaf/++9IDDxRd2+ZgdNxxFsT705/swjaWAaPjjpOqVpU++8z+/eGHtlz9GWfErg3F+f13q7cmWQB382brp3iqWNHa8NVXVudp9WoLLMcqeHowa9jQUjinTrV/T5pkgejU1NLtt1496ZZbrNbZSy/l33/LLZa++PrrJX8hcvvtVhfpvfeK/7yPoRJC+QAAAEAUdehg34y/+27s09EOFldfbRfx27cXnkK2aJFdbD/8sAVWDgYXXxze80aMsNpBH35oF3q9e8c2yHjXXVace9myyNe8iqekJJut8uyzlq5zwgmxO3ZqqnTyyXYOZ2dbPauWLS01JxHk1YWaOlX6+9+lTp0SIzBz443SU0/Z65Z3Ll5wQVybdND4+murn7V3r6U/nn9+ZPb7yCPS999bkKh1a0tlHT3aUmoHDw5uH7feap/zRX3exxgBIwAAAMRPUpJNwX/wwcJXSoK5+eb827/9ZoWK8wIlTz9tMw5uvDE+bYulrCzpo4/sgm/mzJJrO0Va+fKWSrdokc2KOZRceqldPFerFpsV0vZ36qk2q+Kee6xv33mn5DTFWDnzTCusvnKldOed9jmVCDMhq1SxmW47dkijRllKVaxm2x3sGje23ykp0rp1oa9GWZSkJAsod+tmKZVz51oAqUGD0PZzyy2RaU8EOL9/fl0C6dy5s589e3a8mwEAAKLEOTfHex/C2r+INsZfB4FVq6yOzuWXS888YxeuY8daECmBLjKiJiPDLoobNLAZH4lw4Y7SW7/eLuIzM6V27aR58xJnda0lS6xe0XPPWX2aRPPrr/ae+NvfLKUJJcvJsZk87dpJ11wT+f0vWSK9/LIVJk9Li/z+IyDYMViCvAsBAAAAlKhhQ1t6+dlnLXVHslXUykKwSJLKlbMV1aZPDyxGjINbvXqWCpeRYemGiRIskiw9TrL3WKxntAXjiCMsne+SS+LdkoNHUpKltF57rQXaIq1lS6tFlKDBolAk0DsRAAAAQLGcswuRa6+VHnvMVvfZvDnerYqtK6+03zt3xrUZiLDKle0Cu2nTeLfkQI88Yr8TZOWqAKmpVmfp8MPj3ZKDS96qaGXt8zNEBIwAAACAg4lztgrPWWfZjIfly+PdothKS5O2bbPVyoBYuPdeKT296CXRcfDp399+J0IB8wSWIJXEAAAAAAQtOdkCJmvWSI0axbs1sceFO2LJOUuHxKHjwgutHtzRR8e7JQmNGUYAAADAwSgpqWwGiwAgEggWlYiAEQAAAAAAAAIQMAIAAAAAAEAAAkYAAAAAAAA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      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 多个坐标系显示  子图\n",
    "\n",
    "# 生成 fig 1行 2列\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
    "\n",
    "\n",
    "# 准备数据\n",
    "x = range(60)\n",
    "# 准备上海的数据\n",
    "y_上海 = [random.uniform(15, 18) for i in x]\n",
    "# 准备北京的数据\n",
    "y_北京 = [random.uniform(1, 4) for i in x]\n",
    "\n",
    "# 构建中文坐标标签\n",
    "x_ch = ['11点{}分'.format(i) for i in x]\n",
    "y_tickets = range(40)\n",
    "\n",
    "# 画折线图\n",
    "ax[0].plot(x, y_上海, label='上海')\n",
    "ax[1].plot(x, y_北京, label='北京', color='r', linestyle='--')\n",
    "\n",
    "# plot 后再修改刻度\n",
    "# 修改x y刻度\n",
    "ax[0].set_xticks(x[::5], x_ch[::5])\n",
    "ax[1].set_xticks(x[::5], x_ch[::5])\n",
    "ax[0].set_yticks(y_tickets[::5])\n",
    "ax[1].set_yticks(y_tickets[::5])\n",
    "\n",
    "# 增加 x y 轴 描述\n",
    "ax[0].set_xlabel('时间0')\n",
    "ax[0].set_ylabel('温度0')\n",
    "ax[1].set_xlabel('时间1')\n",
    "ax[1].set_ylabel('温度1')\n",
    "\n",
    "ax[0].set_title('子图0的标题')\n",
    "ax[1].set_title('子图1的标题')\n",
    "\n",
    "# 添加图例\n",
    "ax[0].legend(loc='best')\n",
    "ax[1].legend(loc='best')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 柱状图\n",
    "以电影票房统计为例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成电影名及随机票房\n",
    "电影序列 = list(range(5))\n",
    "电影名称 = ['电影{}'.format(i+1) for i in 电影序列]\n",
    "电影票房1 = [random.uniform(100, 1000) for i in 电影序列]\n",
    "电影票房1 = [int(i) for i in 电影票房1]\n",
    "电影票房2 = [random.uniform(100, 1000) for i in 电影序列]\n",
    "电影票房2 = [int(i) for i in 电影票房2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "x = range(len(电影名称))\n",
    "y = 电影票房1\n",
    "\n",
    "plt.bar(x, y, width=0.8, color=['r','g','r','g','r'])\n",
    "# 修改刻度 显示电影米杆子\n",
    "plt.xticks(x, 电影名称)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 多个系列数据\n",
    "plt.figure(figsize=(10,8))\n",
    "x = range(len(电影名称))\n",
    "y1 = 电影票房1\n",
    "y2 = 电影票房2\n",
    "\n",
    "plt.bar(x, y1, width=0.2, label='首日票房')\n",
    "plt.bar([i + 0.3 for i in x], y2, width=0.2, label='周均票房')\n",
    "# 修改刻度 显示电影米杆子\n",
    "plt.xticks([i + 0.1 for i in x], 电影名称)\n",
    "plt.legend(loc='upper center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 散点图 和 折线图 可以画在一起\n",
    "plt.figure(figsize=(20,8))\n",
    "y标签 = range(1000)\n",
    "plt.scatter(x, y1,color='b')\n",
    "plt.plot(x,y2,color='k')\n",
    "plt.yticks(y标签[::100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 直方图\n",
    "    体现概率分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 50.88921923664634,\n",
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       " 2.4746900454202136,\n",
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       " 29.0156985248621,\n",
       " 58.74962053381558,\n",
       " 93.71384527791147]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成随机的 0-100的1000数字\n",
    "序号 = range(1000)\n",
    "随机数字 = [random.uniform(0, 100) for i in 序号]\n",
    "随机数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘图\n",
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "# 分组\n",
    "组距 = 10\n",
    "组数 = int((max(随机数字) - min(随机数字)) / 组距 )\n",
    "组数 = 100\n",
    "\n",
    "# 画直方图\n",
    "plt.hist(随机数字, 组数)\n",
    "\n",
    "# 指定刻度范围及步长\n",
    "plt.xticks(list(range(100))[::5])\n",
    "plt.yticks(list(range(30)))\n",
    "plt.grid(True, linestyle='-', alpha=0.8, color='k')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "数据名 = ['数据1','数据2','数据3']\n",
    "数据值 = [16,10,12]\n",
    "plt.pie(数据值, labels=数据名, autopct='%1.2f%%',colors=['r','b','y'])\n",
    "\n",
    "# 显示正圆\n",
    "plt.axis('equal')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用plt.annotate('注释文本', \n",
    "               xy=(1,2), # 箭头位置\n",
    "               arrowprops=dict(arrowstyle='->'), # 自定义箭头样式\n",
    "               xytext=(3,4) # 文本位置\n",
    "                ) # 生成带指向的注释\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}