├── README.md ├── code ├── .ipynb_checkpoints │ ├── 2、Seaborn 画布外观(主题风格+上下文函数)-checkpoint.ipynb │ ├── Matplotlib知识点高度总结-checkpoint.ipynb │ ├── Seaborn知识点高度总结-checkpoint.ipynb │ ├── numpy基础1-checkpoint.ipynb │ ├── 入门版1：Matplotlib 库-checkpoint.ipynb │ ├── 入门版2：Matplotlib库(条形图 + 散点图)-checkpoint.ipynb │ ├── 入门版2：Matplotlib库-checkpoint.ipynb │ ├── 入门版2：Seaborn画布外观(调色板)-checkpoint.ipynb │ ├── 入门版3：Matplotlib库(直方图(正态分布) + 箱形图)-checkpoint.ipynb │ ├── 入门版4：Matplotlib库(线形图)-checkpoint.ipynb │ ├── 入门版5：Matplotlib库(雷达图)-checkpoint.ipynb │ └── 入门版：Matplotlib库(直方图(正态分布) + 箱形图)-checkpoint.ipynb ├── Matplotlib知识点高度总结.ipynb ├── Matplotlib高度总结.txt ├── Seaborn知识点高度总结.ipynb ├── matplotlib教程.py ├── 入门版1：Matplotlib 库.ipynb ├── 入门版2：Matplotlib库(条形图 + 散点图).ipynb ├── 入门版2：Seaborn画布外观(主题风格+上下文函数).ipynb ├── 入门版2：Seaborn画布外观(调色板).ipynb ├── 入门版3：Matplotlib库(直方图(正态分布) + 箱形图).ipynb ├── 入门版4：Matplotlib库(线形图).ipynb └── 入门版5：Matplotlib库(雷达图).ipynb ├── data ├── Pokemon.csv ├── fandango_scores.csv ├── train.csv ├── unrate.csv └── womendegrees.csv ├── tmp ├── 1.png ├── subplot.png └── subplot1.png └── 可视化库Seaborn ├── .ipynb_checkpoints ├── 4-REG-checkpoint.ipynb ├── 5-category-checkpoint.ipynb ├── 6-FacetGrid-checkpoint.ipynb ├── 7-Heatmap-checkpoint.ipynb ├── Seaborn-1Style-checkpoint.ipynb ├── Seaborn-2Color-checkpoint.ipynb └── Seaborn-3Var-checkpoint.ipynb ├── 4-REG.ipynb ├── 5-category.ipynb ├── 6-FacetGrid.ipynb ├── 7-Heatmap.ipynb ├── Seaborn-1Style.ipynb ├── Seaborn-2Color.ipynb ├── Seaborn-3Var.ipynb ├── Untitled.ipynb ├── f1.png └── iris.data /README.md: -------------------------------------------------------------------------------- 1 | # Matplotlib-and-Seaborn 2 | Matplotlib、Seaborn数据可视化教程和对应的例题，可以帮助提高数据可视化能力 3 | 4 | 文件目录解释： 5 | 6 | * code 文件夹：代码 7 | * data 文件夹：练习的数据 8 | * tmp 文件夹：练习保存的图形 9 | * 可视化库Seaborn 文件夹：这是收藏网络上分享的资料 10 | 11 | Matplotlib 知识目录： 12 | 13 | * 1、入门版1：Matplotlib 库 14 | * 2、入门版2：Matplotlib库(条形图 + 散点图) 15 | * 3、入门版3：Matplotlib库(直方图(正态分布) + 箱形图) 16 | * 4、入门版4：Matplotlib库(线形图) 17 | * 5、入门版5：Matplotlib库(雷达图) 18 | 19 | 其他待更新...... 20 | 21 | Seaborn 知识目录： 22 | 23 | 24 | -------------------------------------------------------------------------------- /code/.ipynb_checkpoints/Matplotlib知识点高度总结-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [] 7 | }, 8 | { 9 | "cell_type": "code", 10 | "execution_count": 1, 11 | "metadata": { 12 | "collapsed": true 13 | }, 14 | "outputs": [], 15 | "source": [] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": { 21 | "collapsed": true 22 | }, 23 | "outputs": [], 24 | "source": [ 25 | "# ---------------------------------------------\n", 26 | "绘图步骤：\n", 27 | "1、准备数据(核心)\n", 28 | "2、创建图形\n", 29 | "3、自定义绘图(核心)\n", 30 | "4、保存图形\n", 31 | "5、显示图形" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": null, 37 | "metadata": { 38 | "collapsed": true 39 | }, 40 | "outputs": [], 41 | "source": [ 42 | "# ---------------------------------------------\n", 43 | "''' 知识点0：创建画布、子图 '''\n", 44 | "1、创建画布\n", 45 | "fig = plt.figure(figsize=(10,4)) # 第一种\n", 46 | "fig, ax = plt.subplots() # 第二种\n", 47 | "\n", 48 | "2、增加子图\n", 49 | "ax1 = fig.add_subplot(row,col,num)\n", 50 | "ax2 = fig.add_subplot(221) 等效于 add_subplot(2, 2, 1)\n", 51 | "代表添加第 2 行第 2 列的第 1 个子图\n" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": null, 57 | "metadata": { 58 | "collapsed": true 59 | }, 60 | "outputs": [], 61 | "source": [ 62 | "# ---------------------------------------------\n", 63 | "''' 知识点1：条形图 '''\n", 64 | "1、语法\n", 65 | "----------\n", 66 | "第一种：一个刻度线一个条形图\n", 67 | "bar(x, height, width, color, align='center') 纵向\n", 68 | "ax.barh(y, width, height) 横向\n", 69 | "\n", 70 | "第二种：一个刻度线多个条形图\n", 71 | "bar_width = 0.35\n", 72 | "plt.bar(x, dt2015, label = '2015', bar_width)\n", 73 | "plt.bar(x + bar_width, dt2016, label = '2016', bar_width)\n", 74 | "plt.bar(x + 2 * bar_width, dt2016, label = '2017', bar_width)\n", 75 | "\n", 76 | "第三种：y 的正轴、负轴显示条形图\n", 77 | "plt.bar(x, + dt2015, label = '2015') y 的正轴显示\n", 78 | "plt.bar(x, - dt2015, label = '2015') y 的负轴显示\n", 79 | "\n", 80 | "2、参数分析\n", 81 | "-----------\n", 82 | "x: 条形图在x轴位置, 某种情况下巧用\"整型\"索引值：df.index.tolist()\n", 83 | "height: 条形图高度\n", 84 | "width: 条形图的宽度\n", 85 | "align: 向哪里对齐，center中心对齐，edge边缘对齐" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": null, 91 | "metadata": { 92 | "collapsed": true 93 | }, 94 | "outputs": [], 95 | "source": [ 96 | "# ----------------------------------------------\n", 97 | "''' 知识点2：散点图 '''\n", 98 | "1、语法\n", 99 | "ax.scatter(x, y, color='red', marker=\".\")\n", 100 | "\n", 101 | "2、参数分析\n", 102 | "marker=\".\" 是一个标记，ax.plot(x, y, color='', marker=\"o\")" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": null, 108 | "metadata": { 109 | "collapsed": true 110 | }, 111 | "outputs": [], 112 | "source": [ 113 | "# ----------------------------------------------\n", 114 | "''' 知识点3：直方图 '''\n", 115 | "语法参数：\n", 116 | "ax.hist(x, range(1, 5), bins=20, color)\n", 117 | "x：必选参数\n", 118 | "range(1, 5)：可选/可不选参数，代表限定 x 轴的区域(X 轴从 1 开始到 5 结束)\n", 119 | "bins = 20: 可选/可不选参数,直方图的宽度\n", 120 | "\n", 121 | "作用：用于探索某类数据的集中分布，发现某类数据的核心点在哪个值的范围" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": null, 127 | "metadata": { 128 | "collapsed": true 129 | }, 130 | "outputs": [], 131 | "source": [ 132 | "# ---------------------------------------\n", 133 | "''' 知识点4：箱形图 '''\n", 134 | "# 一个画布一个箱型图\n", 135 | "ax.boxplot(x)\n", 136 | "# 一个画布多个箱型图\n", 137 | "ax.boxplot([x1, x2, x3,...])\n", 138 | "它由五个数值点组成：从下到上\n", 139 | "0%最小值(min)，25%下四分位数(Q1)，50%中位数(median)，\n", 140 | "75%上四分位数(Q3)，100%最大值(max)\n", 141 | "\n", 142 | "作用：\n", 143 | "1、直观地识别数据集中的异常值(查看离群点),离群点单独析出\n", 144 | "2、判断数据离散程度和偏向(观察盒子的长度，上下隔间的形状，以及胡须的长度)\n", 145 | "3、用于探索某类数据的集中分布" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": null, 151 | "metadata": { 152 | "collapsed": true 153 | }, 154 | "outputs": [], 155 | "source": [ 156 | "# ---------------------------------------\n", 157 | "''' 知识点5：饼图 '''\n", 158 | "1、语法：\n", 159 | "ax.pie(x, explode=None, labels=None, colors=None, shadow=False, autopct=None,\n", 160 | " radius=None, startangle=None)\n", 161 | "\n", 162 | "2、参数分析\n", 163 | " x: 一般是分类型、占比型数据，数组形式如: x=[1, 2, 4] 或 [[1], [2], [4]]\n", 164 | " explode: 各个饼图之间的距离，列表形式如: explode=[0.12, 0.1, 0.04]\n", 165 | " labels: 标签, 列表形式如: labels=['pie1', 'pie2', 'pie3'],\n", 166 | " 一般结合图例 ax.legend(loc='best')\n", 167 | " colors: 颜色，数组形式(一维二维都行)\n", 168 | " shadow: 布尔值，设置饼图边缘下面呈现一层阴影, 就会显得厚一点\n", 169 | " autopct: 控制饼图内百分比设置,可以使用format字符串或者format function\n", 170 | " '%1.1f'指小数点前后位数(没有用空格补齐)如：\n", 171 | " autopct='%.2f'(单纯显示值); autopct='%.2f%%'(显示值和百分号)\n", 172 | " radius: 饼的半径，即饼的大小,如：radius=1.2\n", 173 | " startangle: 起始绘制角度,默认图是从x轴正方向逆时针画起,\n", 174 | " 如：startangle=90 则从y轴正方向画起" 175 | ] 176 | }, 177 | { 178 | "cell_type": "code", 179 | "execution_count": null, 180 | "metadata": { 181 | "collapsed": true 182 | }, 183 | "outputs": [], 184 | "source": [ 185 | "# ---------------------------------------\n", 186 | "''' 知识点6：雷达图 '''\n", 187 | "# 导入第三方模块\n", 188 | "import numpy as np\n", 189 | "import matplotlib.pyplot as plt\n", 190 | "%matplotlib inline\n", 191 | "\n", 192 | "# 中文和负号的正常显示\n", 193 | "plt.rcParams['font.family'] = 'SimHei'\n", 194 | "plt.rcParams['axes.unicode_minus'] = False\n", 195 | "\n", 196 | "# ------------------------------------------------\n", 197 | "# 数据处理和准备\n", 198 | "values = [98, 45, 124, 83, 90, 94] # 数值\n", 199 | "labels = ['语文', '英语', '数学', '物理', '化学', '生物'] # 标签\n", 200 | "# 设置雷达图的角度，用于平分切开一个圆面\n", 201 | "angles = np.linspace(0, 2 * np.pi, len(values), endpoint=False) # 角度\n", 202 | "'''\n", 203 | "—— 拼接数组：\n", 204 | "# 注意：数组之间形状要一致才可以拼接\n", 205 | "np.concatenate((a, b), axis=0) 一维数组\n", 206 | "np.concatenate((c, d), axis=1) 二维数组\n", 207 | "'''\n", 208 | "# 为了使雷达图一圈封闭起来，需要下面的步骤, 首尾相连\n", 209 | "values = np.concatenate((values,[values[0]])) \n", 210 | "angles = np.concatenate((angles,[angles[0]])) \n", 211 | "print('\\n>>>', values, '\\n>>>', angles)\n", 212 | "\n", 213 | "\n", 214 | "# --------------------------------------------------\n", 215 | "# 绘图\n", 216 | "fig = plt.figure()\n", 217 | "\n", 218 | "''' 雷达图的核心 '''\n", 219 | "# polar=True 这里一定要设置为极坐标格式\n", 220 | "ax = fig.add_subplot(111, polar=True)\n", 221 | "ax.plot(angles, values, marker='o', color='r', linewidth=2) # 绘制折线图\n", 222 | "ax.fill(angles, values, color='b', alpha=0.25) # 填充蓝色颜色\n", 223 | "ax.set_thetagrids(angles * 180/np.pi, labels) # 添加每个特征的标签\n", 224 | "'''\n", 225 | "注意：\n", 226 | "ax.plot(x=angles, y=values, marker='o', linewidth=2)\n", 227 | "加上 x, y 显示不出线的标记\n", 228 | "'''\n", 229 | "\n", 230 | "# -------------------------------------------------\n", 231 | "# 其他设置\n", 232 | "ax.set_ylim(0,150) # 设置雷达图的范围\n", 233 | "plt.title('单个学生成绩展示') # 添加标题\n", 234 | "ax.grid(True) # 添加网格线\n" 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": null, 240 | "metadata": { 241 | "collapsed": true 242 | }, 243 | "outputs": [], 244 | "source": [ 245 | "# ---------------------------------------\n", 246 | "''' 知识点7：plt 与 ax 绘图的异同 '''\n", 247 | "1、相同：\n", 248 | "绘图函数的参数用法一样，比如：plt.plot(x, y, color='r') 与 ax.plot(x, y, color='r')\n", 249 | "其他描述性函数的参数用法一样，比如：plt.legend(loc='best') 与 ax.legend(loc='best')\n", 250 | "\n", 251 | "2、不同之处\n", 252 | "ax 使用 ax 绘图：\n", 253 | "比如：ax.plot/bar/scatter/hist等()\n", 254 | "比如：ax.legend/test/annotate/margins/axis/tick_params()\n", 255 | "比如：ax.set_xlim/ylim/title/xlabel/ylabel/xticks/yticks/xticklabel/yticklabel\n", 256 | "\n", 257 | "plt 使用 plt 绘图：\n", 258 | "比如：plt.plot/bar/scatter/hist等()\n", 259 | "比如：plt.legend/test/annotate/margins/axis/tick_params()\n", 260 | "比如：plt.xlim/ylim/title/xlabel/ylabel/xticks/yticks/xticklabel/yticklabel" 261 | ] 262 | }, 263 | { 264 | "cell_type": "code", 265 | "execution_count": null, 266 | "metadata": { 267 | "collapsed": true 268 | }, 269 | "outputs": [], 270 | "source": [ 271 | "# --------------------------------------------\n", 272 | "''' 知识点8： 线形图 '''\n", 273 | "1、语法分析：ax.plot() 等效于 plt.plot()\n", 274 | "ax.plot(X, Y, color, label, linewidth, linestyle, \n", 275 | " marker, markerfacecolor, markersize\n", 276 | " )\n", 277 | " \n", 278 | "2、参数介绍：\n", 279 | "（1）color/c：线条颜色\n", 280 | "Example：\n", 281 | "ax.plot(X, Y, color='b')\n", 282 | "========== ========\n", 283 | "'b' blue\n", 284 | "'g' green\n", 285 | "'r' red\n", 286 | "'c' cyan\n", 287 | "'m' magenta\n", 288 | "'y' yellow\n", 289 | "'k' black\n", 290 | "'w' white\n", 291 | "========== ========\n", 292 | "\n", 293 | "（2）label：设置图例的标签\n", 294 | "Example：\n", 295 | "ax.plot(X, Y, label='women')\n", 296 | "ax.legend(loc='best')\n", 297 | "label：自定义设置\n", 298 | "\n", 299 | "（3）linewidth/lw：设置线条的宽度\n", 300 | "Example：\n", 301 | "ax.plot(X, Y, linewidth=2)\n", 302 | "linewidth：自定义设置\n", 303 | "\n", 304 | "（4）linestyle/ls：线条风格\n", 305 | "['solid' | 'dashed', 'dashdot', 'dotted' , '-', '--', '-.', ':']\n", 306 | "\n", 307 | "（5）marker：线条标记\n", 308 | " markerfacecolor/mfc：标记的颜色\n", 309 | " markersize/ms：标记的大小\n", 310 | "Example：\n", 311 | "ax.plot(X, Y, marker='>', mfc='r', ms=15) \n", 312 | "\n", 313 | "character description\n", 314 | "================ =======================\n", 315 | "``'-'`` solid line style\n", 316 | "``'--'`` dashed line style\n", 317 | "``'-.'`` dash-dot line style\n", 318 | "``':'`` dotted line style\n", 319 | "``'.'`` point marker\n", 320 | "``','`` pixel marker\n", 321 | "``'o'`` circle marker\n", 322 | "``'v'`` triangle_down marker\n", 323 | "``'^'`` triangle_up marker\n", 324 | "``'<'`` triangle_left marker\n", 325 | "``'>'`` triangle_right marker\n", 326 | "``'1'`` tri_down marker\n", 327 | "``'2'`` tri_up marker\n", 328 | "``'3'`` tri_left marker\n", 329 | "``'4'`` tri_right marker\n", 330 | "``'s'`` square marker\n", 331 | "``'p'`` pentagon marker\n", 332 | "``'*'`` star marker\n", 333 | "``'h'`` hexagon1 marker\n", 334 | "``'H'`` hexagon2 marker\n", 335 | "``'+'`` plus marker\n", 336 | "``'x'`` x marker\n", 337 | "``'D'`` diamond marker\n", 338 | "``'d'`` thin_diamond marker\n", 339 | "``'|'`` vline marker\n", 340 | "``'_'`` hline marker" 341 | ] 342 | }, 343 | { 344 | "cell_type": "code", 345 | "execution_count": null, 346 | "metadata": { 347 | "collapsed": true 348 | }, 349 | "outputs": [], 350 | "source": [ 351 | "# ----------------------------------\n", 352 | "# 折线图\n", 353 | "plt.plot(sub_data.date, # x轴数据\n", 354 | " sub_data.article_reading_times, # y轴数据\n", 355 | " linestyle = '-', # 折线类型\n", 356 | " linewidth = 2, # 折线宽度\n", 357 | " color = '#ff9999', # 折线颜色\n", 358 | " marker = 'o', # 点的形状\n", 359 | " markersize = 6, # 点的大小\n", 360 | " markeredgecolor='black', # 点的边框色\n", 361 | " markerfacecolor='#ff9999', # 点的填充色\n", 362 | " label = '阅读人次') # 添加标签\n" 363 | ] 364 | }, 365 | { 366 | "cell_type": "code", 367 | "execution_count": null, 368 | "metadata": { 369 | "collapsed": true 370 | }, 371 | "outputs": [], 372 | "source": [ 373 | "# ---------------------------------------\n", 374 | "''' 知识点9：设置中文字体、字体大小 '''\n", 375 | "(注意: 这里只能用 plt 不能用 ax)\n", 376 | "1、正常显示中文字体\n", 377 | "plt.rcParams['font.family'] = 'SimHei'\n", 378 | "# 解决中文乱码，坐标轴显示不出负值的问题\n", 379 | "plt.rcParams['font.sans-serif'] =['Microsoft YaHei']\n", 380 | "plt.rcParams['axes.unicode_minus'] = False\n", 381 | "\n", 382 | "2、字体大小\n", 383 | "plt.rcParams['xtick.labelsize'] = 24\n", 384 | "plt.rcParams['ytick.labelsize'] = 20\n", 385 | "plt.rcParams['font.size'] = 15\n" 386 | ] 387 | }, 388 | { 389 | "cell_type": "code", 390 | "execution_count": null, 391 | "metadata": { 392 | "collapsed": true 393 | }, 394 | "outputs": [], 395 | "source": [ 396 | "# ---------------------------------------------\n", 397 | "''' 知识点10：基本信息设置 '''\n", 398 | "第一种方法：单一\n", 399 | "tick_positions = range(1,6)\n", 400 | "ax.set_xticks(tick_positions)\n", 401 | "ax.set_xticklabels(cols, rotation=45)\n", 402 | "ax.set_xlabel('Rating Source')\n", 403 | "ax.set_ylabel('Average Rating')\n", 404 | "ax.set_title('2015')\n", 405 | "ax.set_xlim(0, 6)\n", 406 | "ax.set_ylim(0, 6)\n", 407 | "ax.grid(True) 添加网格线\n", 408 | "ax.fill(x, y, color='b', alpha=0.25) # 填充蓝色颜色\n", 409 | "\n", 410 | "第二种方法：统一性\n", 411 | "ax.set(title='2015', # 标题\n", 412 | " xlabel='Rating Source', # X 轴标签 \n", 413 | " ylabel='Average Rating', # Y 轴标签\n", 414 | " xticks=tick_positions, # X、Y 轴刻度\n", 415 | " xlim=[0, 6], ylim=[0, 6], # X、Y 轴范围限制\n", 416 | " )\n", 417 | "ax.set_xticklabels(labels=['要设置的标签值', ''], rotation=45) X 轴刻度的标签值" 418 | ] 419 | }, 420 | { 421 | "cell_type": "code", 422 | "execution_count": null, 423 | "metadata": { 424 | "collapsed": true 425 | }, 426 | "outputs": [], 427 | "source": [ 428 | "# ---------------------------------------------\n", 429 | "''' 知识点11：设置坐标轴线 '''\n", 430 | "1、隐藏坐标轴线\n", 431 | "ax.spines['top/bottom/left/right'].set_visible(False/True)\n", 432 | "['top/bottom/left/right']: 代表哪个坐标轴线\n", 433 | "False/True: False 代表隐藏坐标线，True 代表显现坐标轴线\n", 434 | "\n", 435 | "for key,spine in ax1.spines.items():\n", 436 | " print(key, '\\t', spine)\n", 437 | " spine.set_visible(False)\n", 438 | "Out:\n", 439 | "left \t Spine\n", 440 | "right \t Spine\n", 441 | "bottom \t Spine\n", 442 | "top \t Spine\n", 443 | "\n", 444 | "2、设置底部边线的位置为 outward\n", 445 | "ax.spines['bottom'].set_position(('outward', 10))\n", 446 | "\n", 447 | "3、隐藏刻度线\n", 448 | "ax.tick_params(bottom='off', top='off', left='off', right='off')\n", 449 | "ax.tick_params(bottom='off', left='off')\n", 450 | "\n", 451 | "4、隐藏刻度线、坐标轴线、刻度值\n", 452 | "ax.axis('off')\n", 453 | "\n", 454 | "5、设置内边距\n", 455 | "ax.margins(x=0.2, y=0.3)" 456 | ] 457 | }, 458 | { 459 | "cell_type": "code", 460 | "execution_count": null, 461 | "metadata": { 462 | "collapsed": true 463 | }, 464 | "outputs": [], 465 | "source": [ 466 | "# ----------------------------------------------\n", 467 | "''' 知识点12：子图之间距离的调整 '''\n", 468 | "1、可以调整画布的大小：\n", 469 | "fig = plt.figure(figsize=(5, 10))\n", 470 | "figsize=(宽度, 高度)\n", 471 | "\n", 472 | "2、子图间距函数\n", 473 | "fig.subplots_adjust(wspace=5,\n", 474 | " hspace=0.3, \n", 475 | " left=0.125,\n", 476 | " right=0.9,\n", 477 | " top=3,\n", 478 | " bottom=0.1\n", 479 | " )\n", 480 | "\n", 481 | "3、\n", 482 | "fig.tight_layout(renderer=None, pad=1.3, h_pad=1.2, w_pad=1.2, rect=None)\n", 483 | "\n", 484 | "\n", 485 | "经验：\n", 486 | "把 top 和 right 调大到 4 左右\n", 487 | "而 bottom 和 left 应当调小至 1 左右\n", 488 | "top 控制高度，right控制宽度" 489 | ] 490 | }, 491 | { 492 | "cell_type": "code", 493 | "execution_count": null, 494 | "metadata": { 495 | "collapsed": true 496 | }, 497 | "outputs": [], 498 | "source": [ 499 | "# ------------------------------------\n", 500 | "''' 知识点13：图例设置 '''\n", 501 | "\n", 502 | "第一：设置图例标签名，plt.plot(label='图例标签名')\n", 503 | "\n", 504 | "第二：设置图例放置的位置，plt.legend(loc='放置的位置')\n", 505 | "\n", 506 | "loc 的参数有：\n", 507 | "\n", 508 | " =============== =============\n", 509 | " Location String Location Code\n", 510 | " =============== =============\n", 511 | " 'best' 0\n", 512 | " 'upper right' 1\n", 513 | " 'upper left' 2\n", 514 | " 'lower left' 3\n", 515 | " 'lower right' 4\n", 516 | " 'right' 5\n", 517 | " 'center left' 6\n", 518 | " 'center right' 7\n", 519 | " 'lower center' 8\n", 520 | " 'upper center' 9\n", 521 | " 'center' 10\n", 522 | " =============== =============" 523 | ] 524 | }, 525 | { 526 | "cell_type": "code", 527 | "execution_count": null, 528 | "metadata": { 529 | "collapsed": true 530 | }, 531 | "outputs": [], 532 | "source": [ 533 | "# ----------------------------------------\n", 534 | "''' 知识点14：图像文本、标注 '''\n", 535 | "1、文本：\n", 536 | "ax.text(x, y, s='this is text', fontsize=15)\n", 537 | "# =============\n", 538 | "x：x 轴的位置\n", 539 | "y：y 轴的位置\n", 540 | "s：设置书写的文本内容\n", 541 | "fontsize：设置字的大小\n", 542 | "\n", 543 | "2、标注：\n", 544 | "ax.annotate(\n", 545 | "s='lxz', xy=(1980, 65.1), xytext=(1970, 77.1), xycoords='data',\n", 546 | " textcoords='data', color='r', fontsize=20, \n", 547 | " arrowprops=dict(arrowstyle=\"->\",connectionstyle='arc3',color='r',)\n", 548 | ")\n", 549 | "# =============\n", 550 | "s：代表要注释的内容（必选参数）\n", 551 | "xy：（实际点）代表 x、y 轴的位置（必选参数）\n", 552 | "xytext：（\"注释内容\"的实际位置）调整\"注释内容\"的位置，不设置就默认使用 xy 的位置\n", 553 | "xycoords：（实际点）默认为 data，代表注释（可选）\n", 554 | "textcoords：（\"注释内容\"的实际样式）不设置就默认使用 xycoords 的设置（可选）\n", 555 | "color：注释内容的颜色\n", 556 | "fontsize：注释内容的字体大小\n", 557 | "# ============\n", 558 | "# 设置箭头\n", 559 | "arrowprops=dict(arrowstyle=\"->\",connectionstyle='arc3',color='r',)\n", 560 | "arrowstyle：设置箭头样式\n", 561 | "connectionstyle：连接风格\n", 562 | "color：箭头颜色\n" 563 | ] 564 | } 565 | ], 566 | "metadata": { 567 | "kernelspec": { 568 | "display_name": "Python 3", 569 | "language": "python", 570 | "name": "python3" 571 | }, 572 | "language_info": { 573 | "codemirror_mode": { 574 | "name": "ipython", 575 | "version": 3 576 | }, 577 | "file_extension": ".py", 578 | "mimetype": "text/x-python", 579 | "name": "python", 580 | "nbconvert_exporter": "python", 581 | "pygments_lexer": "ipython3", 582 | "version": "3.6.3" 583 | } 584 | }, 585 | "nbformat": 4, 586 | "nbformat_minor": 2 587 | } 588 | -------------------------------------------------------------------------------- /code/.ipynb_checkpoints/Seaborn知识点高度总结-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### 1、准备数据" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 9, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "\n" 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": {}, 24 | "source": [ 25 | "### 2、设定画布外观" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": null, 31 | "metadata": { 32 | "collapsed": true 33 | }, 34 | "outputs": [], 35 | "source": [ 36 | "# --------------------------------\n", 37 | "知识点0：展示\n", 38 | "''' \n", 39 | "设置 %matplotlib inline 和不设置的区别：\n", 40 | "1、设置，就不用使用 plt.show() 就可以展示图像\n", 41 | "2、不设置，要展示图像，必须使用 plt.show() 才能展示\n", 42 | "'''\n", 43 | "%matplotlib inline\n", 44 | "\n", 45 | "# --------------------------------\n", 46 | "知识点1：Seaborn 样式\n", 47 | "''' 设置或重置 seaborn 默认值 '''\n", 48 | "sns.set() \n", 49 | "\n", 50 | "''' 设置画布主题风格 '''\n", 51 | "sns.set_style(style=None, rc=None)\n", 52 | "\n", 53 | "参数介绍\n", 54 | "--------\n", 55 | "style: 有五种风格\n", 56 | " darkgrid, whitegrid, dark, white, ticks\n", 57 | "rc: dict\n", 58 | " \n", 59 | "Examples\n", 60 | "--------\n", 61 | ">>> set_style(\"whitegrid\")\n", 62 | ">>> set_style(\"ticks\", {\"xtick.major.size\": 8, \"ytick.major.size\": 8})\n", 63 | " \n", 64 | "''' 用with设置临时样式 '''\n", 65 | "sns.axes_style('whitegrid')\n", 66 | "\n", 67 | "# ---------------------------------\n", 68 | "知识点2：上下文函数\n", 69 | "sns.set_context(context=None, font_scale=1, rc=None)\n", 70 | "\n", 71 | "文档介绍\n", 72 | "-----------\n", 73 | "用来联系上面设置过的图形样式、风格，相当于继承上面的功能\n", 74 | "\n", 75 | "参数介绍\n", 76 | "-----------\n", 77 | "context: 用来设置怎样的画布效果\n", 78 | " paper, notebook, talk, poster\n", 79 | " \n", 80 | "例子\n", 81 | "--------\n", 82 | ">>> set_context(\"paper\")\n", 83 | ">>> set_context(\"talk\", font_scale=1.4)\n", 84 | ">>> set_context(\"talk\", rc={\"lines.linewidth\": 2})\n", 85 | "\n", 86 | "# -------------------------------------\n", 87 | "知识点3：调色板\n", 88 | "''' 3-1 '''\n", 89 | "sns.color_palette(palette=None, n_colors=None, desat=None)\n", 90 | "\n", 91 | "参数介绍\n", 92 | "----------\n", 93 | "palette: None, string, or sequence, optional\n", 94 | "可选的有：deep, muted, bright, pastel, dark, colorblind\n", 95 | " hls, husl\n", 96 | "\n", 97 | "n_colors : int, optional\n", 98 | "调色板中颜色的数量\n", 99 | "\n", 100 | "desat : float, optional\n", 101 | "按比例使每种颜色去饱和\n", 102 | "\n", 103 | "返回值\n", 104 | "-----------\n", 105 | "palette : [(R值, G值, B值), (), ()]\n", 106 | "调色板。行为类似于列表，但可以用作上下文\n", 107 | "管理器，并拥有一个' ' as_hex ' '方法转换为十六进制颜色\n", 108 | "\n", 109 | "''' 3-2 将调色板中的值绘制为水平数组 '''\n", 110 | "sns.palplot(pal, size=1)\n", 111 | "\n", 112 | "\n", 113 | "''' 3-3 控制颜色的亮度和饱和 '''\n", 114 | "sns.dark/husl/hls_palette()\n", 115 | "\n", 116 | "sns.hls_palette(n_colors=6, h=0.01, l=0.6, s=0.65)\n", 117 | "参数介绍\n", 118 | "\n", 119 | " n_colors : int，指定多少个颜色\n", 120 | " h : float，第一个颜色\n", 121 | " l : float，设置图片亮度\n", 122 | " s : float，设置图片饱和度\n", 123 | "\n", 124 | "''' 3-4 使用xkcd颜色自定义命名颜色 '''\n", 125 | "colors = [\"windows blue\", \"amber\", \"greyish\"]\n", 126 | "pal = sns.xkcd_palette(colors=colors)\n", 127 | "sns.palplot(pal=pal)\n", 128 | "\n", 129 | "''' 3-5、连续色板 '''\n", 130 | ">>>色彩随数据变换，比如数据越来越重要则颜色越来越深\n", 131 | "sns.palplot(sns.color_palette(\"Blues\"))\n", 132 | "\n", 133 | ">>>如果想要翻转渐变，可以在面板名称中添加一个_r后缀\n", 134 | "sns.palplot(sns.color_palette(\"BuGn_r\"))\n", 135 | "\n", 136 | "''' 3-6、色调线性变化 '''\n", 137 | "sns.cubehelix_palette(n_colors=6, start=0, rot=0.4, \n", 138 | " gamma=1.0, hue=0.8, light=0.85, dark=0.15, \n", 139 | " reverse=False, as_cmap=False)\n", 140 | "参数介绍\n", 141 | "----------\n", 142 | "n_colors : int\n", 143 | " Number of colors in the palette.\n", 144 | "start : float, 0 <= start <= 3\n", 145 | " 螺旋开始时的颜色\n", 146 | "rot : float\n", 147 | " 在调色板范围内围绕色相轮旋转\n", 148 | " \n", 149 | "例子\n", 150 | "----------\n", 151 | ">>>pal = sns.cubehelix_palette(8, start=.5, rot=-.75)\n", 152 | ">>>sns.palplot(pal=pal)\n", 153 | "\n", 154 | "''' 3-7 调用定制连续调色板 '''\n", 155 | "light_palette() 和dark_palette()\n", 156 | "\n", 157 | "参数介绍\n", 158 | "----------\n", 159 | "'''\n", 160 | "sns.light/dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input='rgb')\n", 161 | "\n", 162 | "Parameters\n", 163 | "----------\n", 164 | "color : base color for high values\n", 165 | " red,green,yellow,blue\n", 166 | "n_colors : int, optional\n", 167 | " number of colors in the palette\n", 168 | "reverse : bool, optional\n", 169 | " if True, reverse the direction of the blend\n", 170 | " \n", 171 | "'''\n", 172 | " \n", 173 | "例子\n", 174 | "----------\n", 175 | ">>>sns.palplot(sns.light_palette(\"green/red/green/yellow\"))\n", 176 | ">>>sns.palplot(sns.dark_palette(\"purple\"))\n", 177 | ">>>sns.palplot(sns.light_palette(\"navy\", reverse=True)) # 翻转颜色" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "metadata": {}, 183 | "source": [ 184 | "### 3、使用 Seaborn 绘图" 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": null, 190 | "metadata": { 191 | "collapsed": true 192 | }, 193 | "outputs": [], 194 | "source": [ 195 | "# ------------------------------------\n", 196 | "知识点3：使用 Seaborn 绘图" 197 | ] 198 | }, 199 | { 200 | "cell_type": "markdown", 201 | "metadata": {}, 202 | "source": [ 203 | "### 4、深度自定义" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": null, 209 | "metadata": { 210 | "collapsed": true 211 | }, 212 | "outputs": [], 213 | "source": [ 214 | "# ------------------------------------\n", 215 | "知识点1：坐标轴线设置\n", 216 | "sns.despine(fig=None, ax=None, top=True, right=True, \n", 217 | " left=False, bottom=False, offset=None, trim=False)\n", 218 | "\n", 219 | "参数介绍\n", 220 | "---------\n", 221 | "top/right/left/bottom: 布尔值，设置为 True 则删除该方向的坐标轴线\n", 222 | "offset: 整型或字典型，设置轴线的偏移量，负值向内偏移，正值向外偏移\n", 223 | "\n", 224 | "例子\n", 225 | "---------\n", 226 | ">>>\n" 227 | ] 228 | }, 229 | { 230 | "cell_type": "markdown", 231 | "metadata": {}, 232 | "source": [ 233 | "### 5、显示和保存图形" 234 | ] 235 | }, 236 | { 237 | "cell_type": "code", 238 | "execution_count": null, 239 | "metadata": { 240 | "collapsed": true 241 | }, 242 | "outputs": [], 243 | "source": [ 244 | "# ------------------------------------\n", 245 | "知识点5：显示和保存图形" 246 | ] 247 | } 248 | ], 249 | "metadata": { 250 | "kernelspec": { 251 | "display_name": "Python 3", 252 | "language": "python", 253 | "name": "python3" 254 | }, 255 | "language_info": { 256 | "codemirror_mode": { 257 | "name": "ipython", 258 | "version": 3 259 | }, 260 | "file_extension": ".py", 261 | "mimetype": "text/x-python", 262 | "name": "python", 263 | "nbconvert_exporter": "python", 264 | "pygments_lexer": "ipython3", 265 | "version": "3.6.3" 266 | } 267 | }, 268 | "nbformat": 4, 269 | "nbformat_minor": 2 270 | } 271 | -------------------------------------------------------------------------------- /code/.ipynb_checkpoints/numpy基础1-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### 导入模块" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": null, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "import numpy as np" 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": {}, 24 | "source": [ 25 | "### 创建数组" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": null, 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "# 一维数组\n", 35 | "np.array([1, 2, 3, 4]) " 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": null, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "# 二维数组，指定数据类型为浮点型\n", 45 | "np.array([(1.5, 2, 3), (4, 5, 6)], dtype=float) " 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": null, 51 | "metadata": {}, 52 | "outputs": [], 53 | "source": [ 54 | "# 三维数组\n", 55 | "np.array([[(1.5, 2, 3), (4, 5, 6), (3, 2, 1), (4, 5, 6)]], dtype = float)" 56 | ] 57 | }, 58 | { 59 | "cell_type": "markdown", 60 | "metadata": {}, 61 | "source": [ 62 | "### 初始化占位符" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": null, 68 | "metadata": {}, 69 | "outputs": [], 70 | "source": [ 71 | "# 1、创建值为 0 的数组, np.zeros((rows, columns))\n", 72 | "np.zeros((3, 4))" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": null, 78 | "metadata": {}, 79 | "outputs": [], 80 | "source": [ 81 | "# 2、创建值为 1 数组\n", 82 | "# np.ones((多少个数组，rows，columns))\n", 83 | "np.ones((2, 3, 4), dtype=np.int16)" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": null, 89 | "metadata": {}, 90 | "outputs": [], 91 | "source": [ 92 | "# 3、创建均匀间隔的数组（步进值，类似于等差数列）\n", 93 | "# np.arange(start, end, 等差值)\n", 94 | "np.arange(10, 25, 2)" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": null, 100 | "metadata": {}, 101 | "outputs": [], 102 | "source": [ 103 | "# 4、创建均匀间隔的数组（样本值）\n", 104 | "# np.linspace(允许的最小值，允许的最大值，生成样本值的个数)\n", 105 | "np.linspace(0, 2, 9)" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": null, 111 | "metadata": {}, 112 | "outputs": [], 113 | "source": [ 114 | "# 5、创建常数数组\n", 115 | "# np.full((rows, columns), 指定的常数值)\n", 116 | "np.full((3, 6), 5)" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": null, 122 | "metadata": {}, 123 | "outputs": [], 124 | "source": [ 125 | "# 6、创建 3 x 3 单位矩阵\n", 126 | "np.eye(3)" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": null, 132 | "metadata": {}, 133 | "outputs": [], 134 | "source": [ 135 | "# 7、创建随机值的数组\n", 136 | "# np.random.random((rows, columns))\n", 137 | "a = np.random.random((2, 2))\n", 138 | "a" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": null, 144 | "metadata": {}, 145 | "outputs": [], 146 | "source": [ 147 | "# 8、创建空数组\n", 148 | "b = np.empty((2, 2), dtype=np.int16)\n", 149 | "b" 150 | ] 151 | }, 152 | { 153 | "cell_type": "markdown", 154 | "metadata": {}, 155 | "source": [ 156 | "### 输入输出" 157 | ] 158 | }, 159 | { 160 | "cell_type": "markdown", 161 | "metadata": {}, 162 | "source": [ 163 | "#### 1、输出" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": null, 169 | "metadata": {}, 170 | "outputs": [], 171 | "source": [ 172 | "# 语法：np.save(fileName, 需要保存的数组)\n", 173 | "np.save('../data/my_array', [b, a]) # 保存多个要用 () 或者 [] 括起来\n", 174 | "np.savez('../data/my_array.npz', a, b)\n", 175 | "np.savetxt('../data/my_array.txt', b, delimiter=\" \")\n", 176 | "np.savetxt('../data/my_array.csv', b, delimiter=\",\")" 177 | ] 178 | }, 179 | { 180 | "cell_type": "markdown", 181 | "metadata": {}, 182 | "source": [ 183 | "#### 2、输入" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": null, 189 | "metadata": {}, 190 | "outputs": [], 191 | "source": [ 192 | "# 输入\n", 193 | "np.load('../data/my_array.npy')\n", 194 | "np.loadtxt('../data/my_array.txt')\n", 195 | "np.genfromtxt('../data/my_array.csv', delimiter=' ')" 196 | ] 197 | }, 198 | { 199 | "cell_type": "markdown", 200 | "metadata": {}, 201 | "source": [ 202 | "### 数据类型" 203 | ] 204 | }, 205 | { 206 | "cell_type": "markdown", 207 | "metadata": {}, 208 | "source": [ 209 | "#### 1、带符号的64位整数" 210 | ] 211 | }, 212 | { 213 | "cell_type": "code", 214 | "execution_count": null, 215 | "metadata": {}, 216 | "outputs": [], 217 | "source": [ 218 | "np.int64 \n", 219 | "# 指定创建的\"步进值\"数组为整数\n", 220 | "np.arange(1, 20, 4, dtype=np.int64)" 221 | ] 222 | }, 223 | { 224 | "cell_type": "markdown", 225 | "metadata": {}, 226 | "source": [ 227 | "#### 2、标准双精度浮点数" 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": null, 233 | "metadata": {}, 234 | "outputs": [], 235 | "source": [ 236 | "np.float32 \n", 237 | "# 创建均匀的样本值\n", 238 | "np.linspace(1, 6, 5, dtype=np.float32)" 239 | ] 240 | }, 241 | { 242 | "cell_type": "markdown", 243 | "metadata": {}, 244 | "source": [ 245 | "#### 3、显示为128位浮点数的复数" 246 | ] 247 | }, 248 | { 249 | "cell_type": "code", 250 | "execution_count": null, 251 | "metadata": {}, 252 | "outputs": [], 253 | "source": [ 254 | "np.complex" 255 | ] 256 | }, 257 | { 258 | "cell_type": "markdown", 259 | "metadata": {}, 260 | "source": [ 261 | "#### 4、布尔值：True值和False值" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": null, 267 | "metadata": {}, 268 | "outputs": [], 269 | "source": [ 270 | "np.bool" 271 | ] 272 | }, 273 | { 274 | "cell_type": "markdown", 275 | "metadata": {}, 276 | "source": [ 277 | "#### 5、Python 对象" 278 | ] 279 | }, 280 | { 281 | "cell_type": "code", 282 | "execution_count": null, 283 | "metadata": {}, 284 | "outputs": [], 285 | "source": [ 286 | "# 创建 4 x 4 矩阵，指定类型为 python 对象\n", 287 | "np.object\n", 288 | "np.eye(4, dtype=np.object)" 289 | ] 290 | }, 291 | { 292 | "cell_type": "markdown", 293 | "metadata": {}, 294 | "source": [ 295 | "#### 6、固定长度字符串" 296 | ] 297 | }, 298 | { 299 | "cell_type": "code", 300 | "execution_count": null, 301 | "metadata": {}, 302 | "outputs": [], 303 | "source": [ 304 | "# 创建常数数组，指定类型为字符串\n", 305 | "np.string_\n", 306 | "np.full((3, 4), 4, dtype=np.string_)" 307 | ] 308 | }, 309 | { 310 | "cell_type": "markdown", 311 | "metadata": {}, 312 | "source": [ 313 | "#### 7、固定长度 Unicode" 314 | ] 315 | }, 316 | { 317 | "cell_type": "code", 318 | "execution_count": null, 319 | "metadata": {}, 320 | "outputs": [], 321 | "source": [ 322 | "np.unicode_\n", 323 | "np.linspace(1, 12, 3, dtype=np.unicode_)" 324 | ] 325 | }, 326 | { 327 | "cell_type": "markdown", 328 | "metadata": {}, 329 | "source": [ 330 | "### 数组信息" 331 | ] 332 | }, 333 | { 334 | "cell_type": "code", 335 | "execution_count": null, 336 | "metadata": {}, 337 | "outputs": [], 338 | "source": [ 339 | "test = np.array([[1, 15, 10, 2], [2, 5, 9, 1], [6, 2, 5, 4]])\n", 340 | "test" 341 | ] 342 | }, 343 | { 344 | "cell_type": "markdown", 345 | "metadata": {}, 346 | "source": [ 347 | "#### 1、数组形状，几行几列" 348 | ] 349 | }, 350 | { 351 | "cell_type": "code", 352 | "execution_count": null, 353 | "metadata": {}, 354 | "outputs": [], 355 | "source": [ 356 | "# 查看整体形状，查看行的形状，查看列的形状\n", 357 | "a, b, c= test.shape, test.shape[0], test.shape[1]\n", 358 | "a, b, c" 359 | ] 360 | }, 361 | { 362 | "cell_type": "markdown", 363 | "metadata": {}, 364 | "source": [ 365 | "#### 2、数组长度" 366 | ] 367 | }, 368 | { 369 | "cell_type": "code", 370 | "execution_count": null, 371 | "metadata": {}, 372 | "outputs": [], 373 | "source": [ 374 | "len(test) # 也相当求行的数量" 375 | ] 376 | }, 377 | { 378 | "cell_type": "markdown", 379 | "metadata": {}, 380 | "source": [ 381 | "#### 3、几维数组" 382 | ] 383 | }, 384 | { 385 | "cell_type": "code", 386 | "execution_count": null, 387 | "metadata": {}, 388 | "outputs": [], 389 | "source": [ 390 | "test.ndim" 391 | ] 392 | }, 393 | { 394 | "cell_type": "markdown", 395 | "metadata": {}, 396 | "source": [ 397 | "#### 4、数组有多少个元素" 398 | ] 399 | }, 400 | { 401 | "cell_type": "code", 402 | "execution_count": null, 403 | "metadata": {}, 404 | "outputs": [], 405 | "source": [ 406 | "test.size" 407 | ] 408 | }, 409 | { 410 | "cell_type": "markdown", 411 | "metadata": {}, 412 | "source": [ 413 | "#### 5、数据类型" 414 | ] 415 | }, 416 | { 417 | "cell_type": "code", 418 | "execution_count": null, 419 | "metadata": {}, 420 | "outputs": [], 421 | "source": [ 422 | "test.dtype" 423 | ] 424 | }, 425 | { 426 | "cell_type": "markdown", 427 | "metadata": {}, 428 | "source": [ 429 | "#### 6、数据类型的名字" 430 | ] 431 | }, 432 | { 433 | "cell_type": "code", 434 | "execution_count": null, 435 | "metadata": {}, 436 | "outputs": [], 437 | "source": [ 438 | "test.dtype.name" 439 | ] 440 | }, 441 | { 442 | "cell_type": "markdown", 443 | "metadata": {}, 444 | "source": [ 445 | "#### 7、数据类型转换" 446 | ] 447 | }, 448 | { 449 | "cell_type": "code", 450 | "execution_count": null, 451 | "metadata": {}, 452 | "outputs": [], 453 | "source": [ 454 | "test.astype(float)" 455 | ] 456 | }, 457 | { 458 | "cell_type": "markdown", 459 | "metadata": {}, 460 | "source": [ 461 | "### 查看某个函数怎么使用，有哪些参数" 462 | ] 463 | }, 464 | { 465 | "cell_type": "code", 466 | "execution_count": null, 467 | "metadata": {}, 468 | "outputs": [], 469 | "source": [ 470 | "np.info(np.linspace)" 471 | ] 472 | }, 473 | { 474 | "cell_type": "code", 475 | "execution_count": null, 476 | "metadata": {}, 477 | "outputs": [], 478 | "source": [] 479 | } 480 | ], 481 | "metadata": { 482 | "kernelspec": { 483 | "display_name": "Python 3", 484 | "language": "python", 485 | "name": "python3" 486 | }, 487 | "language_info": { 488 | "codemirror_mode": { 489 | "name": "ipython", 490 | "version": 3 491 | }, 492 | "file_extension": ".py", 493 | "mimetype": "text/x-python", 494 | "name": "python", 495 | "nbconvert_exporter": "python", 496 | "pygments_lexer": "ipython3", 497 | "version": "3.6.3" 498 | } 499 | }, 500 | "nbformat": 4, 501 | "nbformat_minor": 2 502 | } 503 | -------------------------------------------------------------------------------- /code/Matplotlib知识点高度总结.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [] 7 | }, 8 | { 9 | "cell_type": "code", 10 | "execution_count": 1, 11 | "metadata": { 12 | "collapsed": true 13 | }, 14 | "outputs": [], 15 | "source": [] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": { 21 | "collapsed": true 22 | }, 23 | "outputs": [], 24 | "source": [ 25 | "# ---------------------------------------------\n", 26 | "绘图步骤：\n", 27 | "1、准备数据(核心)\n", 28 | "2、创建图形\n", 29 | "3、自定义绘图(核心)\n", 30 | "4、保存图形\n", 31 | "5、显示图形" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": null, 37 | "metadata": { 38 | "collapsed": true 39 | }, 40 | "outputs": [], 41 | "source": [ 42 | "# ---------------------------------------------\n", 43 | "''' 知识点0：创建画布、子图 '''\n", 44 | "1、创建画布\n", 45 | "fig = plt.figure(figsize=(10,4)) # 第一种\n", 46 | "fig, ax = plt.subplots() # 第二种\n", 47 | "\n", 48 | "2、增加子图\n", 49 | "ax1 = fig.add_subplot(row,col,num)\n", 50 | "ax2 = fig.add_subplot(221) 等效于 add_subplot(2, 2, 1)\n", 51 | "代表添加第 2 行第 2 列的第 1 个子图\n" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": null, 57 | "metadata": { 58 | "collapsed": true 59 | }, 60 | "outputs": [], 61 | "source": [ 62 | "# ---------------------------------------------\n", 63 | "''' 知识点1：条形图 '''\n", 64 | "1、语法\n", 65 | "----------\n", 66 | "第一种：一个刻度线一个条形图\n", 67 | "bar(x, height, width, color, align='center') 纵向\n", 68 | "ax.barh(y, width, height) 横向\n", 69 | "\n", 70 | "第二种：一个刻度线多个条形图\n", 71 | "bar_width = 0.35\n", 72 | "plt.bar(x, dt2015, label = '2015', bar_width)\n", 73 | "plt.bar(x + bar_width, dt2016, label = '2016', bar_width)\n", 74 | "plt.bar(x + 2 * bar_width, dt2016, label = '2017', bar_width)\n", 75 | "\n", 76 | "第三种：y 的正轴、负轴显示条形图\n", 77 | "plt.bar(x, + dt2015, label = '2015') y 的正轴显示\n", 78 | "plt.bar(x, - dt2015, label = '2015') y 的负轴显示\n", 79 | "\n", 80 | "2、参数分析\n", 81 | "-----------\n", 82 | "x: 条形图在x轴位置, 某种情况下巧用\"整型\"索引值：df.index.tolist()\n", 83 | "height: 条形图高度\n", 84 | "width: 条形图的宽度\n", 85 | "align: 向哪里对齐，center中心对齐，edge边缘对齐" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": null, 91 | "metadata": { 92 | "collapsed": true 93 | }, 94 | "outputs": [], 95 | "source": [ 96 | "# ----------------------------------------------\n", 97 | "''' 知识点2：散点图 '''\n", 98 | "1、语法\n", 99 | "ax.scatter(x, y, color='red', marker=\".\")\n", 100 | "\n", 101 | "2、参数分析\n", 102 | "marker=\".\" 是一个标记，ax.plot(x, y, color='', marker=\"o\")" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": null, 108 | "metadata": { 109 | "collapsed": true 110 | }, 111 | "outputs": [], 112 | "source": [ 113 | "# ----------------------------------------------\n", 114 | "''' 知识点3：直方图 '''\n", 115 | "语法参数：\n", 116 | "ax.hist(x, range(1, 5), bins=20, color)\n", 117 | "x：必选参数\n", 118 | "range(1, 5)：可选/可不选参数，代表限定 x 轴的区域(X 轴从 1 开始到 5 结束)\n", 119 | "bins = 20: 可选/可不选参数,直方图的宽度\n", 120 | "\n", 121 | "作用：用于探索某类数据的集中分布，发现某类数据的核心点在哪个值的范围" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": null, 127 | "metadata": { 128 | "collapsed": true 129 | }, 130 | "outputs": [], 131 | "source": [ 132 | "# ---------------------------------------\n", 133 | "''' 知识点4：箱形图 '''\n", 134 | "# 一个画布一个箱型图\n", 135 | "ax.boxplot(x)\n", 136 | "# 一个画布多个箱型图\n", 137 | "ax.boxplot([x1, x2, x3,...])\n", 138 | "它由五个数值点组成：从下到上\n", 139 | "0%最小值(min)，25%下四分位数(Q1)，50%中位数(median)，\n", 140 | "75%上四分位数(Q3)，100%最大值(max)\n", 141 | "\n", 142 | "作用：\n", 143 | "1、直观地识别数据集中的异常值(查看离群点),离群点单独析出\n", 144 | "2、判断数据离散程度和偏向(观察盒子的长度，上下隔间的形状，以及胡须的长度)\n", 145 | "3、用于探索某类数据的集中分布" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": null, 151 | "metadata": { 152 | "collapsed": true 153 | }, 154 | "outputs": [], 155 | "source": [ 156 | "# ---------------------------------------\n", 157 | "''' 知识点5：饼图 '''\n", 158 | "1、语法：\n", 159 | "ax.pie(x, explode=None, labels=None, colors=None, shadow=False, autopct=None,\n", 160 | " radius=None, startangle=None)\n", 161 | "\n", 162 | "2、参数分析\n", 163 | " x: 一般是分类型、占比型数据，数组形式如: x=[1, 2, 4] 或 [[1], [2], [4]]\n", 164 | " explode: 各个饼图之间的距离，列表形式如: explode=[0.12, 0.1, 0.04]\n", 165 | " labels: 标签, 列表形式如: labels=['pie1', 'pie2', 'pie3'],\n", 166 | " 一般结合图例 ax.legend(loc='best')\n", 167 | " colors: 颜色，数组形式(一维二维都行)\n", 168 | " shadow: 布尔值，设置饼图边缘下面呈现一层阴影, 就会显得厚一点\n", 169 | " autopct: 控制饼图内百分比设置,可以使用format字符串或者format function\n", 170 | " '%1.1f'指小数点前后位数(没有用空格补齐)如：\n", 171 | " autopct='%.2f'(单纯显示值); autopct='%.2f%%'(显示值和百分号)\n", 172 | " radius: 饼的半径，即饼的大小,如：radius=1.2\n", 173 | " startangle: 起始绘制角度,默认图是从x轴正方向逆时针画起,\n", 174 | " 如：startangle=90 则从y轴正方向画起" 175 | ] 176 | }, 177 | { 178 | "cell_type": "code", 179 | "execution_count": null, 180 | "metadata": { 181 | "collapsed": true 182 | }, 183 | "outputs": [], 184 | "source": [ 185 | "# ---------------------------------------\n", 186 | "''' 知识点6：雷达图 '''\n", 187 | "# 导入第三方模块\n", 188 | "import numpy as np\n", 189 | "import matplotlib.pyplot as plt\n", 190 | "%matplotlib inline\n", 191 | "\n", 192 | "# 中文和负号的正常显示\n", 193 | "plt.rcParams['font.family'] = 'SimHei'\n", 194 | "plt.rcParams['axes.unicode_minus'] = False\n", 195 | "\n", 196 | "# ------------------------------------------------\n", 197 | "# 数据处理和准备\n", 198 | "values = [98, 45, 124, 83, 90, 94] # 数值\n", 199 | "labels = ['语文', '英语', '数学', '物理', '化学', '生物'] # 标签\n", 200 | "# 设置雷达图的角度，用于平分切开一个圆面\n", 201 | "angles = np.linspace(0, 2 * np.pi, len(values), endpoint=False) # 角度\n", 202 | "'''\n", 203 | "—— 拼接数组：\n", 204 | "# 注意：数组之间形状要一致才可以拼接\n", 205 | "np.concatenate((a, b), axis=0) 一维数组\n", 206 | "np.concatenate((c, d), axis=1) 二维数组\n", 207 | "'''\n", 208 | "# 为了使雷达图一圈封闭起来，需要下面的步骤, 首尾相连\n", 209 | "values = np.concatenate((values,[values[0]])) \n", 210 | "angles = np.concatenate((angles,[angles[0]])) \n", 211 | "print('\\n>>>', values, '\\n>>>', angles)\n", 212 | "\n", 213 | "\n", 214 | "# --------------------------------------------------\n", 215 | "# 绘图\n", 216 | "fig = plt.figure()\n", 217 | "\n", 218 | "''' 雷达图的核心 '''\n", 219 | "# polar=True 这里一定要设置为极坐标格式\n", 220 | "ax = fig.add_subplot(111, polar=True)\n", 221 | "ax.plot(angles, values, marker='o', color='r', linewidth=2) # 绘制折线图\n", 222 | "ax.fill(angles, values, color='b', alpha=0.25) # 填充蓝色颜色\n", 223 | "ax.set_thetagrids(angles * 180/np.pi, labels) # 添加每个特征的标签\n", 224 | "'''\n", 225 | "注意：\n", 226 | "ax.plot(x=angles, y=values, marker='o', linewidth=2)\n", 227 | "加上 x, y 显示不出线的标记\n", 228 | "'''\n", 229 | "\n", 230 | "# -------------------------------------------------\n", 231 | "# 其他设置\n", 232 | "ax.set_ylim(0,150) # 设置雷达图的范围\n", 233 | "plt.title('单个学生成绩展示') # 添加标题\n", 234 | "ax.grid(True) # 添加网格线\n" 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": null, 240 | "metadata": { 241 | "collapsed": true 242 | }, 243 | "outputs": [], 244 | "source": [ 245 | "# ---------------------------------------\n", 246 | "''' 知识点7：plt 与 ax 绘图的异同 '''\n", 247 | "1、相同：\n", 248 | "绘图函数的参数用法一样，比如：plt.plot(x, y, color='r') 与 ax.plot(x, y, color='r')\n", 249 | "其他描述性函数的参数用法一样，比如：plt.legend(loc='best') 与 ax.legend(loc='best')\n", 250 | "\n", 251 | "2、不同之处\n", 252 | "ax 使用 ax 绘图：\n", 253 | "比如：ax.plot/bar/scatter/hist等()\n", 254 | "比如：ax.legend/test/annotate/margins/axis/tick_params()\n", 255 | "比如：ax.set_xlim/ylim/title/xlabel/ylabel/xticks/yticks/xticklabel/yticklabel\n", 256 | "\n", 257 | "plt 使用 plt 绘图：\n", 258 | "比如：plt.plot/bar/scatter/hist等()\n", 259 | "比如：plt.legend/test/annotate/margins/axis/tick_params()\n", 260 | "比如：plt.xlim/ylim/title/xlabel/ylabel/xticks/yticks/xticklabel/yticklabel" 261 | ] 262 | }, 263 | { 264 | "cell_type": "code", 265 | "execution_count": null, 266 | "metadata": { 267 | "collapsed": true 268 | }, 269 | "outputs": [], 270 | "source": [ 271 | "# --------------------------------------------\n", 272 | "''' 知识点8： 线形图 '''\n", 273 | "1、语法分析：ax.plot() 等效于 plt.plot()\n", 274 | "ax.plot(X, Y, color, label, linewidth, linestyle, \n", 275 | " marker, markerfacecolor, markersize\n", 276 | " )\n", 277 | " \n", 278 | "2、参数介绍：\n", 279 | "（1）color/c：线条颜色\n", 280 | "Example：\n", 281 | "ax.plot(X, Y, color='b')\n", 282 | "========== ========\n", 283 | "'b' blue\n", 284 | "'g' green\n", 285 | "'r' red\n", 286 | "'c' cyan\n", 287 | "'m' magenta\n", 288 | "'y' yellow\n", 289 | "'k' black\n", 290 | "'w' white\n", 291 | "========== ========\n", 292 | "\n", 293 | "（2）label：设置图例的标签\n", 294 | "Example：\n", 295 | "ax.plot(X, Y, label='women')\n", 296 | "ax.legend(loc='best')\n", 297 | "label：自定义设置\n", 298 | "\n", 299 | "（3）linewidth/lw：设置线条的宽度\n", 300 | "Example：\n", 301 | "ax.plot(X, Y, linewidth=2)\n", 302 | "linewidth：自定义设置\n", 303 | "\n", 304 | "（4）linestyle/ls：线条风格\n", 305 | "['solid' | 'dashed', 'dashdot', 'dotted' , '-', '--', '-.', ':']\n", 306 | "\n", 307 | "（5）marker：线条标记\n", 308 | " markerfacecolor/mfc：标记的颜色\n", 309 | " markersize/ms：标记的大小\n", 310 | "Example：\n", 311 | "ax.plot(X, Y, marker='>', mfc='r', ms=15) \n", 312 | "\n", 313 | "character description\n", 314 | "================ =======================\n", 315 | "``'-'`` solid line style\n", 316 | "``'--'`` dashed line style\n", 317 | "``'-.'`` dash-dot line style\n", 318 | "``':'`` dotted line style\n", 319 | "``'.'`` point marker\n", 320 | "``','`` pixel marker\n", 321 | "``'o'`` circle marker\n", 322 | "``'v'`` triangle_down marker\n", 323 | "``'^'`` triangle_up marker\n", 324 | "``'<'`` triangle_left marker\n", 325 | "``'>'`` triangle_right marker\n", 326 | "``'1'`` tri_down marker\n", 327 | "``'2'`` tri_up marker\n", 328 | "``'3'`` tri_left marker\n", 329 | "``'4'`` tri_right marker\n", 330 | "``'s'`` square marker\n", 331 | "``'p'`` pentagon marker\n", 332 | "``'*'`` star marker\n", 333 | "``'h'`` hexagon1 marker\n", 334 | "``'H'`` hexagon2 marker\n", 335 | "``'+'`` plus marker\n", 336 | "``'x'`` x marker\n", 337 | "``'D'`` diamond marker\n", 338 | "``'d'`` thin_diamond marker\n", 339 | "``'|'`` vline marker\n", 340 | "``'_'`` hline marker" 341 | ] 342 | }, 343 | { 344 | "cell_type": "code", 345 | "execution_count": null, 346 | "metadata": { 347 | "collapsed": true 348 | }, 349 | "outputs": [], 350 | "source": [ 351 | "# ----------------------------------\n", 352 | "# 折线图\n", 353 | "plt.plot(sub_data.date, # x轴数据\n", 354 | " sub_data.article_reading_times, # y轴数据\n", 355 | " linestyle = '-', # 折线类型\n", 356 | " linewidth = 2, # 折线宽度\n", 357 | " color = '#ff9999', # 折线颜色\n", 358 | " marker = 'o', # 点的形状\n", 359 | " markersize = 6, # 点的大小\n", 360 | " markeredgecolor='black', # 点的边框色\n", 361 | " markerfacecolor='#ff9999', # 点的填充色\n", 362 | " label = '阅读人次') # 添加标签\n" 363 | ] 364 | }, 365 | { 366 | "cell_type": "code", 367 | "execution_count": null, 368 | "metadata": { 369 | "collapsed": true 370 | }, 371 | "outputs": [], 372 | "source": [ 373 | "# ---------------------------------------\n", 374 | "''' 知识点9：设置中文字体、字体大小 '''\n", 375 | "(注意: 这里只能用 plt 不能用 ax)\n", 376 | "1、正常显示中文字体\n", 377 | "plt.rcParams['font.family'] = 'SimHei'\n", 378 | "# 解决中文乱码，坐标轴显示不出负值的问题\n", 379 | "plt.rcParams['font.sans-serif'] =['Microsoft YaHei']\n", 380 | "plt.rcParams['axes.unicode_minus'] = False\n", 381 | "\n", 382 | "2、字体大小\n", 383 | "plt.rcParams['xtick.labelsize'] = 24\n", 384 | "plt.rcParams['ytick.labelsize'] = 20\n", 385 | "plt.rcParams['font.size'] = 15\n" 386 | ] 387 | }, 388 | { 389 | "cell_type": "code", 390 | "execution_count": null, 391 | "metadata": { 392 | "collapsed": true 393 | }, 394 | "outputs": [], 395 | "source": [ 396 | "# ---------------------------------------------\n", 397 | "''' 知识点10：基本信息设置 '''\n", 398 | "第一种方法：单一\n", 399 | "tick_positions = range(1,6)\n", 400 | "ax.set_xticks(tick_positions)\n", 401 | "ax.set_xticklabels(cols, rotation=45)\n", 402 | "ax.set_xlabel('Rating Source')\n", 403 | "ax.set_ylabel('Average Rating')\n", 404 | "ax.set_title('2015')\n", 405 | "ax.set_xlim(0, 6)\n", 406 | "ax.set_ylim(0, 6)\n", 407 | "ax.grid(True) 添加网格线\n", 408 | "ax.fill(x, y, color='b', alpha=0.25) # 填充蓝色颜色\n", 409 | "\n", 410 | "第二种方法：统一性\n", 411 | "ax.set(title='2015', # 标题\n", 412 | " xlabel='Rating Source', # X 轴标签 \n", 413 | " ylabel='Average Rating', # Y 轴标签\n", 414 | " xticks=tick_positions, # X、Y 轴刻度\n", 415 | " xlim=[0, 6], ylim=[0, 6], # X、Y 轴范围限制\n", 416 | " )\n", 417 | "ax.set_xticklabels(labels=['要设置的标签值', ''], rotation=45) X 轴刻度的标签值" 418 | ] 419 | }, 420 | { 421 | "cell_type": "code", 422 | "execution_count": null, 423 | "metadata": { 424 | "collapsed": true 425 | }, 426 | "outputs": [], 427 | "source": [ 428 | "# ---------------------------------------------\n", 429 | "''' 知识点11：设置坐标轴线 '''\n", 430 | "1、隐藏坐标轴线\n", 431 | "ax.spines['top/bottom/left/right'].set_visible(False/True)\n", 432 | "['top/bottom/left/right']: 代表哪个坐标轴线\n", 433 | "False/True: False 代表隐藏坐标线，True 代表显现坐标轴线\n", 434 | "\n", 435 | "for key,spine in ax1.spines.items():\n", 436 | " print(key, '\\t', spine)\n", 437 | " spine.set_visible(False)\n", 438 | "Out:\n", 439 | "left \t Spine\n", 440 | "right \t Spine\n", 441 | "bottom \t Spine\n", 442 | "top \t Spine\n", 443 | "\n", 444 | "2、设置底部边线的位置为 outward\n", 445 | "ax.spines['bottom'].set_position(('outward', 10))\n", 446 | "\n", 447 | "3、隐藏刻度线\n", 448 | "ax.tick_params(bottom='off', top='off', left='off', right='off')\n", 449 | "ax.tick_params(bottom='off', left='off')\n", 450 | "\n", 451 | "4、隐藏刻度线、坐标轴线、刻度值\n", 452 | "ax.axis('off')\n", 453 | "\n", 454 | "5、设置内边距\n", 455 | "ax.margins(x=0.2, y=0.3)" 456 | ] 457 | }, 458 | { 459 | "cell_type": "code", 460 | "execution_count": null, 461 | "metadata": { 462 | "collapsed": true 463 | }, 464 | "outputs": [], 465 | "source": [ 466 | "# ----------------------------------------------\n", 467 | "''' 知识点12：子图之间距离的调整 '''\n", 468 | "1、可以调整画布的大小：\n", 469 | "fig = plt.figure(figsize=(5, 10))\n", 470 | "figsize=(宽度, 高度)\n", 471 | "\n", 472 | "2、子图间距函数\n", 473 | "fig.subplots_adjust(wspace=5,\n", 474 | " hspace=0.3, \n", 475 | " left=0.125,\n", 476 | " right=0.9,\n", 477 | " top=3,\n", 478 | " bottom=0.1\n", 479 | " )\n", 480 | "\n", 481 | "3、\n", 482 | "fig.tight_layout(renderer=None, pad=1.3, h_pad=1.2, w_pad=1.2, rect=None)\n", 483 | "\n", 484 | "\n", 485 | "经验：\n", 486 | "把 top 和 right 调大到 4 左右\n", 487 | "而 bottom 和 left 应当调小至 1 左右\n", 488 | "top 控制高度，right控制宽度" 489 | ] 490 | }, 491 | { 492 | "cell_type": "code", 493 | "execution_count": null, 494 | "metadata": { 495 | "collapsed": true 496 | }, 497 | "outputs": [], 498 | "source": [ 499 | "# ------------------------------------\n", 500 | "''' 知识点13：图例设置 '''\n", 501 | "\n", 502 | "第一：设置图例标签名，plt.plot(label='图例标签名')\n", 503 | "\n", 504 | "第二：设置图例放置的位置，plt.legend(loc='放置的位置')\n", 505 | "\n", 506 | "loc 的参数有：\n", 507 | "\n", 508 | " =============== =============\n", 509 | " Location String Location Code\n", 510 | " =============== =============\n", 511 | " 'best' 0\n", 512 | " 'upper right' 1\n", 513 | " 'upper left' 2\n", 514 | " 'lower left' 3\n", 515 | " 'lower right' 4\n", 516 | " 'right' 5\n", 517 | " 'center left' 6\n", 518 | " 'center right' 7\n", 519 | " 'lower center' 8\n", 520 | " 'upper center' 9\n", 521 | " 'center' 10\n", 522 | " =============== =============" 523 | ] 524 | }, 525 | { 526 | "cell_type": "code", 527 | "execution_count": null, 528 | "metadata": { 529 | "collapsed": true 530 | }, 531 | "outputs": [], 532 | "source": [ 533 | "# ----------------------------------------\n", 534 | "''' 知识点14：图像文本、标注 '''\n", 535 | "1、文本：\n", 536 | "ax.text(x, y, s='this is text', fontsize=15)\n", 537 | "# =============\n", 538 | "x：x 轴的位置\n", 539 | "y：y 轴的位置\n", 540 | "s：设置书写的文本内容\n", 541 | "fontsize：设置字的大小\n", 542 | "\n", 543 | "2、标注：\n", 544 | "ax.annotate(\n", 545 | "s='lxz', xy=(1980, 65.1), xytext=(1970, 77.1), xycoords='data',\n", 546 | " textcoords='data', color='r', fontsize=20, \n", 547 | " arrowprops=dict(arrowstyle=\"->\",connectionstyle='arc3',color='r',)\n", 548 | ")\n", 549 | "# =============\n", 550 | "s：代表要注释的内容（必选参数）\n", 551 | "xy：（实际点）代表 x、y 轴的位置（必选参数）\n", 552 | "xytext：（\"注释内容\"的实际位置）调整\"注释内容\"的位置，不设置就默认使用 xy 的位置\n", 553 | "xycoords：（实际点）默认为 data，代表注释（可选）\n", 554 | "textcoords：（\"注释内容\"的实际样式）不设置就默认使用 xycoords 的设置（可选）\n", 555 | "color：注释内容的颜色\n", 556 | "fontsize：注释内容的字体大小\n", 557 | "# ============\n", 558 | "# 设置箭头\n", 559 | "arrowprops=dict(arrowstyle=\"->\",connectionstyle='arc3',color='r',)\n", 560 | "arrowstyle：设置箭头样式\n", 561 | "connectionstyle：连接风格\n", 562 | "color：箭头颜色\n" 563 | ] 564 | } 565 | ], 566 | "metadata": { 567 | "kernelspec": { 568 | "display_name": "Python 3", 569 | "language": "python", 570 | "name": "python3" 571 | }, 572 | "language_info": { 573 | "codemirror_mode": { 574 | "name": "ipython", 575 | "version": 3 576 | }, 577 | "file_extension": ".py", 578 | "mimetype": "text/x-python", 579 | "name": "python", 580 | "nbconvert_exporter": "python", 581 | "pygments_lexer": "ipython3", 582 | "version": "3.6.3" 583 | } 584 | }, 585 | "nbformat": 4, 586 | "nbformat_minor": 2 587 | } 588 | -------------------------------------------------------------------------------- /code/Matplotlib高度总结.txt: -------------------------------------------------------------------------------- 1 | # --------------------------------------------- 2 | 绘图步骤： 3 | 1、准备数据 4 | 2、创建图形 5 | 3、自定义绘图 6 | 4、保存图形 7 | 5、显示图形 8 | 9 | # --------------------------------------------- 10 | # 知识点0：创建画布、子图 11 | 创建子图 12 | fig = plt.figure(figsize=(10,4)) # 第一种 13 | fig, ax = plt.subplots() # 第二种 14 | add_subplot(row,col,num) 15 | add_subplot(221) 等效于 add_subplot(2, 2, 1) 16 | 代表添加第 2 行第 2 列的第 1 个子图 17 | 18 | # --------------------------------------------- 19 | # 知识点1：条形图 20 | ax.bar(条形图在x轴位置，条形图高度，条形图的宽度) 纵向 21 | ax.barh() 横向 22 | 23 | # --------------------------------------------- 24 | # 知识点2： 25 | 第一种方法：单一 26 | # tick_positions = range(1,6) 27 | # ax.set_xticks(tick_positions) 28 | # ax.set_xticklabels(cols, rotation=45) 29 | # ax.set_xlabel('Rating Source') 30 | # ax.set_ylabel('Average Rating') 31 | # ax.set_title('2015') 32 | # ax.set_xlim(0, 6) 33 | # ax.set_ylim(0, 6) 34 | 35 | 第二种方法：统一性 36 | ax.set(title='2015', # 标题 37 | xlabel='Rating Source', # X 轴标签 38 | ylabel='Average Rating', # Y 轴标签 39 | xticks=tick_positions, # X、Y 轴刻度 40 | xlim=[0, 6], ylim=[0, 6], # X、Y 轴范围限制 41 | ) 42 | ax.set_xticklabels(['要设置的标签值', ''], rotation=45) X 轴刻度的标签值 43 | 44 | # ---------------------------------------------- 45 | # 知识点3：散点图 46 | ax.scatter(x, y, color='red', marker=".") 47 | marker="." 是一个标记，ax.plot(x, y, color='', marker="o") 48 | 49 | # ---------------------------------------------- 50 | # 知识点4：直方图 51 | 语法参数： 52 | ax.hist(x, range(1, 5), bins=20) 53 | x：必选参数 54 | range(1, 5)：可选/可不选参数，代表限定 x 轴的区域(X 轴从 1 开始到 5 结束) 55 | bins = 20: 可选/可不选参数,直方图的宽度 56 | 57 | 作用：用于探索某类数据的集中分布，发现某类数据的核心点在哪个值的范围 58 | 59 | # ---------------------------------------------- 60 | # 知识点5：子图之间距离的调整 61 | 1、可以调整画布的大小： 62 | fig = plt.figure(figsize=(5, 10)) 63 | figsize=(宽度, 高度) 64 | 65 | 2、子图间距函数 66 | fig.subplots_adjust(wspace=5, 67 | hspace=0.3, 68 | left=0.125, 69 | right=0.9, 70 | top=3, 71 | bottom=0.1 72 | ) 73 | 74 | # --------------------------------------------- 75 | # 知识点6：设置坐标轴线 76 | 1、隐藏坐标轴线 77 | ax.spines['top/bottom/left/right'].set_visible(False/True) 78 | ['top/bottom/left/right']: 代表哪个坐标轴线 79 | False/True: False 代表隐藏坐标线，True 代表显现坐标轴线 80 | 81 | for key,spine in ax1.spines.items(): 82 | print(key, '\t', spine) 83 | spine.set_visible(False) 84 | Out: 85 | left Spine 86 | right Spine 87 | bottom Spine 88 | top Spine 89 | 90 | 2、设置底部边线的位置为 outward 91 | ax.spines['bottom'].set_position(('outward', 10)) 92 | 93 | 3、隐藏刻度线 94 | ax.tick_params(bottom='off', top='off', left='off', right='off') 95 | ax.tick_params(bottom='off', left='off') 96 | 97 | 4、隐藏刻度线、坐标轴线、刻度值 98 | ax.axis('off') 99 | 100 | 5、设置内边距 101 | ax.margins(x=0.2, y=0.3) 102 | 103 | # --------------------------------------- 104 | # 知识点7：箱形图 105 | # 一个画布一个箱型图 106 | ax.boxplot(x) 107 | # 一个画布一个箱型图 108 | ax.boxplot([x1, x2, x3,...]) 109 | 它由五个数值点组成：从下到上 110 | 0%最小值(min)，25%下四分位数(Q1)，50%中位数(median)， 111 | 75%上四分位数(Q3)，100%最大值(max) 112 | 113 | 作用： 114 | 1、直观地识别数据集中的异常值(查看离群点),离群点单独析出 115 | 2、判断数据离散程度和偏向(观察盒子的长度，上下隔间的形状，以及胡须的长度) 116 | 117 | # --------------------------------------- 118 | # 知识点8：plt 与 ax 绘图的异同： 119 | 1、相同： 120 | 绘图函数的参数用法一样，比如：plt.plot(x, y, color='r') 与 ax.plot(x, y, color='r') 121 | 其他描述性函数的参数用法一样，比如：plt.legend(loc='best') 与 ax.legend(loc='best') 122 | 123 | 2、不同之处 124 | ax 使用 ax 绘图： 125 | 比如：ax.plot/bar/scatter/hist等() 126 | 比如：ax.legend/test/annotate/margins/axis/tick_params() 127 | 比如：ax.set_xlim/ylim/title/xlabel/ylabel/xticks/yticks/xticklabel/yticklabel 128 | 129 | plt 使用 plt 绘图： 130 | 比如：plt.plot/bar/scatter/hist等() 131 | 比如：plt.legend/test/annotate/margins/axis/tick_params() 132 | 比如：plt.xlim/ylim/title/xlabel/ylabel/xticks/yticks/xticklabel/yticklabel 133 | 134 | # --------------------------------------- 135 | # 知识点9：设置中文字体、字体大小 136 | (注意: 这里只能用 plt 不能用 ax) 137 | 1、正常显示中文字体 138 | plt.rcParams['font.family'] = 'SimHei' 139 | 140 | 2、字体大小 141 | #plt.rcParams['xtick.labelsize'] = 24 142 | #plt.rcParams['ytick.labelsize'] = 24 143 | 144 | # -------------------------------------------- 145 | # 知识点10：ax.plot() 或 plt.plot() 146 | 1、语法分析： 147 | ax.plot(X, Y, color, label, linewidth, linestyle, 148 | marker, markerfacecolor, markersize 149 | ) 150 | 151 | 2、参数介绍： 152 | （1）color/c：线条颜色 153 | Example： 154 | ax.plot(X, Y, color='b') 155 | ========== ======== 156 | 'b' blue 157 | 'g' green 158 | 'r' red 159 | 'c' cyan 160 | 'm' magenta 161 | 'y' yellow 162 | 'k' black 163 | 'w' white 164 | ========== ======== 165 | 166 | （2）label：设置图例的标签 167 | Example： 168 | ax.plot(X, Y, label='women') 169 | ax.legend(loc='best') 170 | label：自定义设置 171 | 172 | （3）linewidth/lw：设置线条的宽度 173 | Example： 174 | ax.plot(X, Y, linewidth=2) 175 | linewidth：自定义设置 176 | 177 | （4）linestyle/ls：线条风格 178 | ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] 179 | 180 | （5）marker：线条标记 181 | markerfacecolor/mfc：标记的颜色 182 | markersize/ms：标记的大小 183 | Example： 184 | ax.plot(X, Y, marker='>', mfc='r', ms=15) 185 | 186 | character description 187 | ================ =============================== 188 | ``'-'`` solid line style 189 | ``'--'`` dashed line style 190 | ``'-.'`` dash-dot line style 191 | ``':'`` dotted line style 192 | ``'.'`` point marker 193 | ``','`` pixel marker 194 | ``'o'`` circle marker 195 | ``'v'`` triangle_down marker 196 | ``'^'`` triangle_up marker 197 | ``'<'`` triangle_left marker 198 | ``'>'`` triangle_right marker 199 | ``'1'`` tri_down marker 200 | ``'2'`` tri_up marker 201 | ``'3'`` tri_left marker 202 | ``'4'`` tri_right marker 203 | ``'s'`` square marker 204 | ``'p'`` pentagon marker 205 | ``'*'`` star marker 206 | ``'h'`` hexagon1 marker 207 | ``'H'`` hexagon2 marker 208 | ``'+'`` plus marker 209 | ``'x'`` x marker 210 | ``'D'`` diamond marker 211 | ``'d'`` thin_diamond marker 212 | ``'|'`` vline marker 213 | ``'_'`` hline marker 214 | 215 | # ---------------------------------------- 216 | # 知识点11：图像文本、标注 217 | 1、文本： 218 | ax.text(x, y, s='this is text', fontsize=15) 219 | # ============= 220 | x：x 轴的位置 221 | y：y 轴的位置 222 | s：设置书写的文本内容 223 | fontsize：设置字的大小 224 | 225 | 2、标注： 226 | ax.annotate( 227 | s='lxz', xy=(1980, 65.1), xytext=(1970, 77.1), xycoords='data',textcoords='data', color='r', fontsize=20, arrowprops=dict(arrowstyle="->",connectionstyle='arc3',color='r',) 228 | ) 229 | # ============= 230 | s：代表要注释的内容（必选参数） 231 | xy：（实际点）代表 x、y 轴的位置（必选参数） 232 | xytext：（"注释内容"的实际位置）调整"注释内容"的位置，不设置就默认使用 xy 的位置 233 | xycoords：（实际点）默认为 data，代表注释 234 | textcoords：（"注释内容"的实际样式）不设置就默认使用 xycoords 的设置 235 | color：注释内容的颜色 236 | fontsize：注释内容的字体大小 237 | # ============ 238 | # 设置箭头 239 | arrowprops=dict(arrowstyle="->",connectionstyle='arc3',color='r',) 240 | arrowstyle：设置箭头样式 241 | connectionstyle：连接风格 242 | color：箭头颜色 243 | 244 | # --------------------------------------------- 245 | # 246 | 247 | 248 | 249 | 250 | -------------------------------------------------------------------------------- /code/Seaborn知识点高度总结.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### 1、准备数据" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 9, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "\n" 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": {}, 24 | "source": [ 25 | "### 2、设定画布外观" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": null, 31 | "metadata": { 32 | "collapsed": true 33 | }, 34 | "outputs": [], 35 | "source": [ 36 | "# --------------------------------\n", 37 | "知识点0：展示\n", 38 | "''' \n", 39 | "设置 %matplotlib inline 和不设置的区别：\n", 40 | "1、设置，就不用使用 plt.show() 就可以展示图像\n", 41 | "2、不设置，要展示图像，必须使用 plt.show() 才能展示\n", 42 | "'''\n", 43 | "%matplotlib inline\n", 44 | "\n", 45 | "# --------------------------------\n", 46 | "知识点1：Seaborn 样式\n", 47 | "''' 设置或重置 seaborn 默认值 '''\n", 48 | "sns.set() \n", 49 | "\n", 50 | "''' 设置画布主题风格 '''\n", 51 | "sns.set_style(style=None, rc=None)\n", 52 | "\n", 53 | "参数介绍\n", 54 | "--------\n", 55 | "style: 有五种风格\n", 56 | " darkgrid, whitegrid, dark, white, ticks\n", 57 | "rc: dict\n", 58 | " \n", 59 | "Examples\n", 60 | "--------\n", 61 | ">>> set_style(\"whitegrid\")\n", 62 | ">>> set_style(\"ticks\", {\"xtick.major.size\": 8, \"ytick.major.size\": 8})\n", 63 | " \n", 64 | "''' 用with设置临时样式 '''\n", 65 | "sns.axes_style('whitegrid')\n", 66 | "\n", 67 | "# ---------------------------------\n", 68 | "知识点2：上下文函数\n", 69 | "sns.set_context(context=None, font_scale=1, rc=None)\n", 70 | "\n", 71 | "文档介绍\n", 72 | "-----------\n", 73 | "用来联系上面设置过的图形样式、风格，相当于继承上面的功能\n", 74 | "\n", 75 | "参数介绍\n", 76 | "-----------\n", 77 | "context: 用来设置怎样的画布效果\n", 78 | " paper, notebook, talk, poster\n", 79 | " \n", 80 | "例子\n", 81 | "--------\n", 82 | ">>> set_context(\"paper\")\n", 83 | ">>> set_context(\"talk\", font_scale=1.4)\n", 84 | ">>> set_context(\"talk\", rc={\"lines.linewidth\": 2})\n", 85 | "\n", 86 | "# -------------------------------------\n", 87 | "知识点3：调色板\n", 88 | "''' 3-1 '''\n", 89 | "sns.color_palette(palette=None, n_colors=None, desat=None)\n", 90 | "\n", 91 | "参数介绍\n", 92 | "----------\n", 93 | "palette: None, string, or sequence, optional\n", 94 | "可选的有：deep, muted, bright, pastel, dark, colorblind\n", 95 | " hls, husl\n", 96 | "\n", 97 | "n_colors : int, optional\n", 98 | "调色板中颜色的数量\n", 99 | "\n", 100 | "desat : float, optional\n", 101 | "按比例使每种颜色去饱和\n", 102 | "\n", 103 | "返回值\n", 104 | "-----------\n", 105 | "palette : [(R值, G值, B值), (), ()]\n", 106 | "调色板。行为类似于列表，但可以用作上下文\n", 107 | "管理器，并拥有一个' ' as_hex ' '方法转换为十六进制颜色\n", 108 | "\n", 109 | "''' 3-2 将调色板中的值绘制为水平数组 '''\n", 110 | "sns.palplot(pal, size=1)\n", 111 | "\n", 112 | "\n", 113 | "''' 3-3 控制颜色的亮度和饱和 '''\n", 114 | "sns.dark/husl/hls_palette()\n", 115 | "\n", 116 | "sns.hls_palette(n_colors=6, h=0.01, l=0.6, s=0.65)\n", 117 | "参数介绍\n", 118 | "\n", 119 | " n_colors : int，指定多少个颜色\n", 120 | " h : float，第一个颜色\n", 121 | " l : float，设置图片亮度\n", 122 | " s : float，设置图片饱和度\n", 123 | "\n", 124 | "''' 3-4 使用xkcd颜色自定义命名颜色 '''\n", 125 | "colors = [\"windows blue\", \"amber\", \"greyish\"]\n", 126 | "pal = sns.xkcd_palette(colors=colors)\n", 127 | "sns.palplot(pal=pal)\n", 128 | "\n", 129 | "''' 3-5、连续色板 '''\n", 130 | ">>>色彩随数据变换，比如数据越来越重要则颜色越来越深\n", 131 | "sns.palplot(sns.color_palette(\"Blues\"))\n", 132 | "\n", 133 | ">>>如果想要翻转渐变，可以在面板名称中添加一个_r后缀\n", 134 | "sns.palplot(sns.color_palette(\"BuGn_r\"))\n", 135 | "\n", 136 | "''' 3-6、色调线性变化 '''\n", 137 | "sns.cubehelix_palette(n_colors=6, start=0, rot=0.4, \n", 138 | " gamma=1.0, hue=0.8, light=0.85, dark=0.15, \n", 139 | " reverse=False, as_cmap=False)\n", 140 | "参数介绍\n", 141 | "----------\n", 142 | "n_colors : int\n", 143 | " Number of colors in the palette.\n", 144 | "start : float, 0 <= start <= 3\n", 145 | " 螺旋开始时的颜色\n", 146 | "rot : float\n", 147 | " 在调色板范围内围绕色相轮旋转\n", 148 | " \n", 149 | "例子\n", 150 | "----------\n", 151 | ">>>pal = sns.cubehelix_palette(8, start=.5, rot=-.75)\n", 152 | ">>>sns.palplot(pal=pal)\n", 153 | "\n", 154 | "''' 3-7 调用定制连续调色板 '''\n", 155 | "light_palette() 和dark_palette()\n", 156 | "\n", 157 | "参数介绍\n", 158 | "----------\n", 159 | "'''\n", 160 | "sns.light/dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input='rgb')\n", 161 | "\n", 162 | "Parameters\n", 163 | "----------\n", 164 | "color : base color for high values\n", 165 | " red,green,yellow,blue\n", 166 | "n_colors : int, optional\n", 167 | " number of colors in the palette\n", 168 | "reverse : bool, optional\n", 169 | " if True, reverse the direction of the blend\n", 170 | " \n", 171 | "'''\n", 172 | " \n", 173 | "例子\n", 174 | "----------\n", 175 | ">>>sns.palplot(sns.light_palette(\"green/red/green/yellow\"))\n", 176 | ">>>sns.palplot(sns.dark_palette(\"purple\"))\n", 177 | ">>>sns.palplot(sns.light_palette(\"navy\", reverse=True)) # 翻转颜色" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "metadata": {}, 183 | "source": [ 184 | "### 3、使用 Seaborn 绘图" 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": null, 190 | "metadata": { 191 | "collapsed": true 192 | }, 193 | "outputs": [], 194 | "source": [ 195 | "# ------------------------------------\n", 196 | "知识点3：使用 Seaborn 绘图" 197 | ] 198 | }, 199 | { 200 | "cell_type": "markdown", 201 | "metadata": {}, 202 | "source": [ 203 | "### 4、深度自定义" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": null, 209 | "metadata": { 210 | "collapsed": true 211 | }, 212 | "outputs": [], 213 | "source": [ 214 | "# ------------------------------------\n", 215 | "知识点1：坐标轴线设置\n", 216 | "sns.despine(fig=None, ax=None, top=True, right=True, \n", 217 | " left=False, bottom=False, offset=None, trim=False)\n", 218 | "\n", 219 | "参数介绍\n", 220 | "---------\n", 221 | "top/right/left/bottom: 布尔值，设置为 True 则删除该方向的坐标轴线\n", 222 | "offset: 整型或字典型，设置轴线的偏移量，负值向内偏移，正值向外偏移\n", 223 | "\n", 224 | "例子\n", 225 | "---------\n", 226 | ">>>\n" 227 | ] 228 | }, 229 | { 230 | "cell_type": "markdown", 231 | "metadata": {}, 232 | "source": [ 233 | "### 5、显示和保存图形" 234 | ] 235 | }, 236 | { 237 | "cell_type": "code", 238 | "execution_count": null, 239 | "metadata": { 240 | "collapsed": true 241 | }, 242 | "outputs": [], 243 | "source": [ 244 | "# ------------------------------------\n", 245 | "知识点5：显示和保存图形" 246 | ] 247 | } 248 | ], 249 | "metadata": { 250 | "kernelspec": { 251 | "display_name": "Python 3", 252 | "language": "python", 253 | "name": "python3" 254 | }, 255 | "language_info": { 256 | "codemirror_mode": { 257 | "name": "ipython", 258 | "version": 3 259 | }, 260 | "file_extension": ".py", 261 | "mimetype": "text/x-python", 262 | "name": "python", 263 | "nbconvert_exporter": "python", 264 | "pygments_lexer": "ipython3", 265 | "version": "3.6.3" 266 | } 267 | }, 268 | "nbformat": 4, 269 | "nbformat_minor": 2 270 | } 271 | -------------------------------------------------------------------------------- /code/matplotlib教程.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # ############## 1. 2D图表—散点图和线图 ############## 4 | # # 1-1 导入模块 5 | # import numpy as np 6 | # import matplotlib as mpl 7 | # import matplotlib.pyplot as plt 8 | # # 1-2 通过rcParams设置全局横纵轴字体的大小 9 | # mpl.rcParams['xtick.labelsize'] = 24 10 | # mpl.rcParams['ytick.labelsize'] = 24 11 | # # 设置字体样式，这里设置为中文黑体 12 | # plt.rcParams['font.family'] = 'SimHei' 13 | # # 1-3 x、y轴采样点的设置 14 | # # 代表取100个值在0—5之间的数 15 | # x = np.linspace(0, 5, 100) # x轴的采样点 16 | # # print('测试2--\n', x) #设置测试的目的是为了分析和调试 17 | # # 通过下面曲线加上噪声(随机生成)生成y轴的采样点y_data 18 | # y = 2 * np.sin(x) + 0.3 * x ** 2 19 | # # size=100代表取100个值 20 | # y_data = y + np.random.normal(scale=0.3, size=100) 21 | # # print('测试3--\n', y_data) #设置测试的目的是为了分析和调试 22 | # """ 23 | # 1-4 制作散点图的操作 24 | # """ 25 | # # 1-4-1 散点图的文件名称和图表的标题设置 26 | # plt.figure('散点图') # figure()指定图标名称 27 | # plt.title('散点图的一个示例') # title()指定图标的标题 28 | # # 1-4-2 绘制散点图 29 | # # ' . '标明画散点图，每个散点的形状是一个个圆点来的 30 | # plt.plot(x, y_data, '.') 31 | # """ 32 | # 1-5 制作线形图的操作 33 | # """ 34 | # # 1-5-1 线形图名称和标题 35 | # plt.figure('线形图') # figure()指定图标名称 36 | # plt.title('线形图的一个示例') # title()指定图标的标题 37 | # # 1-5-2 绘制线型图 38 | # # x,y代表x,y轴； 39 | # # 'r - -'代表线是红色虚线，'k'代表黑色实线 40 | # # lw=3, 代表线的宽度为3 41 | # plt.plot(x, y, color='k', lw=3) 42 | # """ 43 | # 1-6 制作散点图和线形图结合在一起的混合曲线 44 | # """ 45 | # # 1-6-1 混合曲线名称和标题 46 | # # figure()指定图标名称，将img1与img2混合在一起 47 | # plt.figure('散点图 & 线形图') 48 | # plt.title('混合图') # title()指定图标的标题 49 | # # 1-6-2 添加绘制的线形图 50 | # plt.plot(x, y, color='r', lw=3) 51 | # # 1-6-3 添加绘制的散点图（这种方式更方便） 52 | # plt.scatter(x, y_data) 53 | # # 1-7 将当前的figure图（混合图）保存到指定路径 54 | # # 注意这里只能保存一个，需要的保存其他曲线图的， 55 | # # 可以在其后面添加 56 | # plt.savefig('hunhe.png') 57 | # # 1-8 将图表显示在屏幕上 58 | # plt.show() 59 | # 60 | # ################ 2. 2D图表—柱形图（条形图） ############ 61 | # import matplotlib.pyplot as plt 62 | # import matplotlib as mpl 63 | # mpl.rcParams['font.family'] = 'SimHei' 64 | # # 制作几部电影所获得的奥斯卡金像奖的数目 65 | # # 2-1 建立x轴和y轴 66 | # movies = ["Annie Hall", "Ben-hur", "Gasablanca", \ 67 | # "Candhi", "West Side Story"] 68 | # num = [5, 11, 3, 8, 10] 69 | # # 2-2 设置y轴标志、条形图的标题和名称 70 | # plt.figure('柱形图') 71 | # plt.ylabel("获得的奥斯卡金像奖的数目") 72 | # plt.title('最受欢迎de电影') 73 | # # 2-3 条形的默认宽度是0.8，因此我们对左侧坐标加上0.1， 74 | # # 这样每个条形就被放置在中心了 75 | # xs = [i + 0.1 for i, _ in enumerate(movies)] 76 | # # 2-4 使用电影的名字标记x轴，位置在x轴上条形的中心 77 | # plt.xticks([i + 0.15 for i, _ in enumerate(movies)], movies) 78 | # # 2-5 使用x坐标[xs]和高度[num]画条形图 79 | # plt.bar(xs, num) 80 | # # 2-6 展示条形图 81 | # plt.show() 82 | # 83 | # ################ 3. 2D图表—饼形图 ############ 84 | # import numpy as np 85 | # import matplotlib as mpl 86 | # import matplotlib.pyplot as plt 87 | # # 3-1 设置字体样式,（中文黑体） 88 | # mpl.rcParams['font.family'] = 'SimHei' 89 | # # 3-2 随机产生5个在3—11之间的数据值，作为样本数据 90 | # # 5个样本值，也就是说有5个饼块图 91 | # data = np.random.randint(3, 11, 5) 92 | # # print('测试---', data) 93 | # # 3-3 设置饼图的名称和标题 94 | # plt.figure('饼图练习') 95 | # plt.title('欢迎关注【python是一个时代】') 96 | # # 3-4 画一个饼图，自定义饼块的偏移量 97 | # # 格式：pie(样本值，偏移量设置) 98 | # plt.pie(data,explode=[0, 0.2, 0, 0, 0]) 99 | # # 3-5 将饼图显示在屏幕上 100 | # plt.show() 101 | # 102 | # 显示中文的属性 103 | """ 104 | plt.rcParams['font.family'] = 'SimHei' 105 | 黑体 SimHei 106 | 微软雅黑 Microsoft YaHei 107 | 微软正黑体 Microsoft JhengHei 108 | 新宋体 NSimSun 109 | 新细明体 PMingLiU 110 | 细明体 MingLiU 111 | 标楷体 DFKai-SB 112 | 仿宋 FangSong 113 | 楷体 KaiTi 114 | 仿宋_GB2312 FangSong_GB2312 115 | 楷体_GB2312 KaiTi_GB2312 116 | """ 117 | # # 关于保存图标的问题： 118 | # """ 119 | # 注意一个plt.savefig()只能保存一个图表， 120 | # 需要的保存其他曲线图的， 121 | # 可以在其后面一个个的添加 122 | # plt.savefig('hunhe.png') 123 | # """ 124 | 125 | # ############ 1.3D（三维）图表—立体柱形图（条形图） ########### 126 | # # 1-1 导入模块 127 | # import matplotlib.pyplot as plt 128 | # import numpy as np 129 | # # 导入3D三维绘图工具包 130 | # from mpl_toolkits.mplot3d import Axes3D 131 | # # 设置中文黑体字体，保证中文在图表中正常显示 132 | # plt.rcParams['font.family'] = 'SimHei' 133 | # # 1-2 创建Axes3D对象 134 | # fig = plt.figure('3D（三维）图表—立体柱形图（条形图）') 135 | # # 1-2-1 第一种创建方法（推荐，容易理解） 136 | # ax = Axes3D(fig) 137 | # # 1-2-2 第二种创建方法 138 | # # ax = fig.add_subplot(111, projection='3d') 139 | # # 1-3 自定义图标的标题 140 | # ax.set_title('欢迎关注【python是一个时代】\n更多精彩内容等着你') 141 | # # 1-4 （核心）立体柱形图的设置 142 | # # zip函数接受任意多个（包括0个和1个）序列作为参数，返回一个tuple列表 143 | # for c, z in zip(['r', 'g', 'b', 'y'], [30, 20, 10, 0]): 144 | # xs = np.arange(20) # 1-4-1 生成x轴的值 145 | # print('测试点：xs', xs) # 注意：为了分析和调试专用的 146 | # ys = np.random.rand(20) # 1-4-2 生成y轴的值 147 | # print('测试点：ys', ys) # 注意：为了分析和调试专用的 148 | # cs = [c] * len(xs) # 1-4-3 生成不同的颜色 149 | # print('测试点：cs', cs) # 注意：为了分析和调试专用的 150 | # cs[0] = 'c' 151 | # # 1-4-4 bar()是生成柱形图的函数 152 | # ax.bar(xs, ys, zs=z, zdir='y', color=cs, alpha=0.8) 153 | # # 1-5 设置x,y,z轴的标签 154 | # ax.set_xlabel('【这是X轴】') 155 | # ax.set_ylabel('【这是Y轴】') 156 | # ax.set_zlabel('【Z轴】') 157 | # # 1-6 将图表展示到屏幕 158 | # plt.show() 159 | 160 | ################## 2. 3D图表—长方体 ####################### 161 | # 导入模块 162 | import numpy as np 163 | import matplotlib.pyplot as plt 164 | # 2-1 导入3D三维绘图工具包 165 | from mpl_toolkits.mplot3d import Axes3D 166 | # 设置中文黑体字体，保证中文在图表中正常显示 167 | plt.rcParams['font.family'] = 'SimHei' 168 | # 2-2 创建Axes3D对象 169 | fig = plt.figure('3D图表—长方体') 170 | ax = Axes3D(fig) 171 | # 自定义图标的标题 172 | ax.set_title('欢迎关注【python是一个时代】\n更多精彩内容等着你') 173 | # 2-3 随机生成2行100列的随机数 174 | x, y = np.random.rand(2, 100) * 4 175 | # print(x,'y\n\n', y) 176 | # 2-4 创建直方图hist与随机bin(容器)内容： 177 | hist, xedges, yedges = np.histogram2d(x, y, bins=4, range=[[0, 4], [0, 4]]) 178 | # print('测试--\n', hist,'\n\n\n', xedges, '\n',yedges) 179 | # 2-5 从一个坐标向量xedges、yedges中返回一个坐标矩阵 180 | xpos, ypos = np.meshgrid(xedges[:-1] + 0.25, yedges[:-1] + 0.25) 181 | # 2-6 将多个列表中的元素合成一个列表的元素 182 | xpos = xpos.flatten('F') 183 | ypos = ypos.flatten('F') 184 | # print('xpos/ypos:\n', xpos,'\n\n\n', ypos) # 为了分析而设置的输出 185 | zpos = np.zeros_like(xpos) 186 | dx = 0.5 * np.ones_like(zpos) 187 | dy = dx.copy() 188 | dz = hist.flatten() 189 | # 2-7 bar3d是生成长方体的函数 190 | ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', zsort='average') 191 | # 2-8 将图表显示在屏幕上 192 | plt.show() 193 | 194 | ################ 3. 3D图表—自定义图表：######################## 195 | # 导入模块 196 | import numpy as np 197 | import matplotlib.pyplot as plt 198 | # 3-1 导入3D三维绘图工具包 199 | from mpl_toolkits.mplot3d import Axes3D 200 | # 设置中文黑体字体，保证中文在图表中正常显示 201 | plt.rcParams['font.family'] = 'SimHei' 202 | # 3-2 创建Axes3D对象 203 | fig = plt.figure('3D图表') 204 | ax = Axes3D(fig) 205 | # 3-3 自定义图标的标题 206 | ax.set_title('欢迎关注【python是一个时代】\n更多精彩内容等着你') 207 | # 3-4 创建x,y,z轴的数据 208 | # 生成[-6，6] 间隔0.25的数列，间隔越小，曲面越平滑 209 | # 这里的数值影响图表的样子，可自定义 210 | X = np.arange(-12, 12, 0.5) 211 | Y = np.arange(-12, 12, 0.5) 212 | # 格点矩阵，原来的x行向量向下复制len(y)此形成 213 | X, Y = np.meshgrid(X, Y) 214 | # 数组开平方处理 215 | R = np.sqrt(X ** 2 + Y ** 2) 216 | Z = np.sin(R) 217 | # 3-5 cmap指color map 218 | surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.cm.hot) 219 | ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.cm.hot) 220 | ax.set_zlim(-2, 2) 221 | # 3-6 添加右侧的色卡条 222 | # shrink表示整体收缩比例，aspect值越大，bar越窄 223 | fig.colorbar(surf, shrink=0.6, aspect=8) 224 | # 3-7 savefig('D:/文件夹/plot3d_ex.png',dpi=48) # 可保存 225 | # 3-8 将图表显示在屏幕上 226 | plt.show() 227 | 228 | ################# 4. 3D图表 229 | # import numpy as np 230 | # import matplotlib.pyplot as plt 231 | # from mpl_toolkits.mplot3d import Axes3D 232 | # from matplotlib import cm 233 | # from matplotlib.ticker import LinearLocator, FormatStrFormatter 234 | # 235 | # fig = plt.figure(figsize=(16, 12)) 236 | # ax = fig.gca(projection="3d") 237 | # 238 | # # 准备数据 239 | # x = np.arange(-5, 5, 0.25) # 生成[-5，5] 间隔0.25的数列，间隔越小，曲面越平滑 240 | # y = np.arange(-5, 5, 0.25) 241 | # x, y = np.meshgrid(x, y) # 格点矩阵，原来的x行向量向下复制len(y)此形成 242 | # # len(y)*len(x)的矩阵，即为新的x矩阵；原来的y列向量向右复制len(x)次，形成 243 | # # len(y)*len(x)的矩阵，即为新的y矩阵；新的x矩阵和新的y矩阵shape相同 244 | # r = np.sqrt(x ** 2 + y ** 2) 245 | # z = np.sin(r) 246 | # 247 | # surf = ax.plot_surface(x, y, z, cmap=cm.coolwarm) # cmap指color map 248 | # 249 | # # 自定义z轴 250 | # ax.set_zlim(-1, 1) 251 | # ax.zaxis.set_major_locator(LinearLocator(20)) # z轴网格线的疏密，刻度的疏密，20表示刻度的个数 252 | # ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f')) # 将z的value子符串转为float，保留2位小数 253 | # 254 | # # 设置坐标轴的label和标题 255 | # ax.set_xlabel('x', size=15) 256 | # ax.set_ylabel('y', size=15) 257 | # ax.set_zlabel('z', size=15) 258 | # ax.set_title("Surface plot", weight='bold', size=20) 259 | # 260 | # # 添加右侧的色卡条 261 | # fig.colorbar(surf, shrink=0.6, aspect=8) # shrink表示整体收缩比例，aspect仅对bar的宽度有影响， 262 | # # aspect值越大，bar越窄 263 | # plt.show() 264 | 265 | """ 266 | # 行实现设置坐标标记点 267 | ax.set_xticks([0,25,50,75,100]) 268 | # 设置图片的标题 269 | ax.set_title('Demo Figure') 270 | # 设置横轴的名称 271 | ax.set_xlabel('Time') # x轴标签 272 | ax.set_ylabel('Time') # y轴标签 273 | """ 274 | ################## 画直方图 ##################### 275 | """ 276 | matplotlib库中提供了一个画直方图的工具，它的常用参数如下 277 | plt.hist(x, bins=None, range=None) 278 | 参数x代表要使用的数据，bins表示要划分区间数，range表示区间的范围 279 | 280 | 具体使用如下 281 | import matplotlib.pyplot as plt 282 | import numpy as np 283 | data=np.random.randn(1000)#产生1000个正太分布的随机点 284 | plt.hist(x=data,bins=100,range=(-5,5),label='aa')#在[-5,5]上划分100个区间 285 | plt.show() 286 | 287 | 288 | ###################### 画散点图 ################### 289 | # 注意参数要写成这种形式 color='blue'， 不能直接 'r'， 否则报错 290 | plt.scatter(dataset_x, dataset_y, color='blue') # 散点图 291 | plt.show() 292 | """ 293 | -------------------------------------------------------------------------------- /data/Pokemon.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaozhiagi/Matplotlib-and-Seaborn/73a5d3af5e92f0b326ca55eea8edcd073ccdc54e/data/Pokemon.csv -------------------------------------------------------------------------------- /data/fandango_scores.csv: -------------------------------------------------------------------------------- 1 | FILM,RottenTomatoes,RottenTomatoes_User,Metacritic,Metacritic_User,IMDB,Fandango_Stars,Fandango_Ratingvalue,RT_norm,RT_user_norm,Metacritic_norm,Metacritic_user_nom,IMDB_norm,RT_norm_round,RT_user_norm_round,Metacritic_norm_round,Metacritic_user_norm_round,IMDB_norm_round,Metacritic_user_vote_count,IMDB_user_vote_count,Fandango_votes,Fandango_Difference 2 | Avengers: Age of Ultron (2015),74,86,66,7.1,7.8,5,4.5,3.7,4.3,3.3,3.55,3.9,3.5,4.5,3.5,3.5,4,1330,271107,14846,0.5 3 | Cinderella (2015),85,80,67,7.5,7.1,5,4.5,4.25,4,3.35,3.75,3.55,4.5,4,3.5,4,3.5,249,65709,12640,0.5 4 | Ant-Man (2015),80,90,64,8.1,7.8,5,4.5,4,4.5,3.2,4.05,3.9,4,4.5,3,4,4,627,103660,12055,0.5 5 | Do You Believe? (2015),18,84,22,4.7,5.4,5,4.5,0.9,4.2,1.1,2.35,2.7,1,4,1,2.5,2.5,31,3136,1793,0.5 6 | Hot Tub Time Machine 2 (2015),14,28,29,3.4,5.1,3.5,3,0.7,1.4,1.45,1.7,2.55,0.5,1.5,1.5,1.5,2.5,88,19560,1021,0.5 7 | The Water Diviner (2015),63,62,50,6.8,7.2,4.5,4,3.15,3.1,2.5,3.4,3.6,3,3,2.5,3.5,3.5,34,39373,397,0.5 8 | Irrational Man (2015),42,53,53,7.6,6.9,4,3.5,2.1,2.65,2.65,3.8,3.45,2,2.5,2.5,4,3.5,17,2680,252,0.5 9 | Top Five (2014),86,64,81,6.8,6.5,4,3.5,4.3,3.2,4.05,3.4,3.25,4.5,3,4,3.5,3.5,124,16876,3223,0.5 10 | Shaun the Sheep Movie (2015),99,82,81,8.8,7.4,4.5,4,4.95,4.1,4.05,4.4,3.7,5,4,4,4.5,3.5,62,12227,896,0.5 11 | Love & Mercy (2015),89,87,80,8.5,7.8,4.5,4,4.45,4.35,4,4.25,3.9,4.5,4.5,4,4.5,4,54,5367,864,0.5 12 | Far From The Madding Crowd (2015),84,77,71,7.5,7.2,4.5,4,4.2,3.85,3.55,3.75,3.6,4,4,3.5,4,3.5,35,12129,804,0.5 13 | Black Sea (2015),82,60,62,6.6,6.4,4,3.5,4.1,3,3.1,3.3,3.2,4,3,3,3.5,3,37,16547,218,0.5 14 | Leviathan (2014),99,79,92,7.2,7.7,4,3.5,4.95,3.95,4.6,3.6,3.85,5,4,4.5,3.5,4,145,22521,64,0.5 15 | Unbroken (2014),51,70,59,6.5,7.2,4.5,4.1,2.55,3.5,2.95,3.25,3.6,2.5,3.5,3,3.5,3.5,218,77518,9443,0.4 16 | The Imitation Game (2014),90,92,73,8.2,8.1,5,4.6,4.5,4.6,3.65,4.1,4.05,4.5,4.5,3.5,4,4,566,334164,8055,0.4 17 | Taken 3 (2015),9,46,26,4.6,6.1,4.5,4.1,0.45,2.3,1.3,2.3,3.05,0.5,2.5,1.5,2.5,3,240,104235,6757,0.4 18 | Ted 2 (2015),46,58,48,6.5,6.6,4.5,4.1,2.3,2.9,2.4,3.25,3.3,2.5,3,2.5,3.5,3.5,197,49102,6437,0.4 19 | Southpaw (2015),59,80,57,8.2,7.8,5,4.6,2.95,4,2.85,4.1,3.9,3,4,3,4,4,128,23561,5597,0.4 20 | Night at the Museum: Secret of the Tomb (2014),50,58,47,5.8,6.3,4.5,4.1,2.5,2.9,2.35,2.9,3.15,2.5,3,2.5,3,3,103,50291,5445,0.4 21 | Pixels (2015),17,54,27,5.3,5.6,4.5,4.1,0.85,2.7,1.35,2.65,2.8,1,2.5,1.5,2.5,3,246,19521,3886,0.4 22 | "McFarland, USA (2015)",79,89,60,7.2,7.5,5,4.6,3.95,4.45,3,3.6,3.75,4,4.5,3,3.5,4,59,13769,3364,0.4 23 | Insidious: Chapter 3 (2015),59,56,52,6.9,6.3,4.5,4.1,2.95,2.8,2.6,3.45,3.15,3,3,2.5,3.5,3,115,25134,3276,0.4 24 | The Man From U.N.C.L.E. (2015),68,80,55,7.9,7.6,4.5,4.1,3.4,4,2.75,3.95,3.8,3.5,4,3,4,4,144,22104,2686,0.4 25 | Run All Night (2015),60,59,59,7.3,6.6,4.5,4.1,3,2.95,2.95,3.65,3.3,3,3,3,3.5,3.5,141,50438,2066,0.4 26 | Trainwreck (2015),85,74,75,6,6.7,4.5,4.1,4.25,3.7,3.75,3,3.35,4.5,3.5,4,3,3.5,169,27380,8381,0.4 27 | Selma (2014),99,86,89,7.1,7.5,5,4.6,4.95,4.3,4.45,3.55,3.75,5,4.5,4.5,3.5,4,316,45344,7025,0.4 28 | Ex Machina (2015),92,86,78,7.9,7.7,4.5,4.1,4.6,4.3,3.9,3.95,3.85,4.5,4.5,4,4,4,672,154499,3458,0.4 29 | Still Alice (2015),88,85,72,7.8,7.5,4.5,4.1,4.4,4.25,3.6,3.9,3.75,4.5,4.5,3.5,4,4,153,57123,1258,0.4 30 | Wild Tales (2014),96,92,77,8.8,8.2,4.5,4.1,4.8,4.6,3.85,4.4,4.1,5,4.5,4,4.5,4,107,50285,235,0.4 31 | The End of the Tour (2015),92,89,84,7.5,7.9,4.5,4.1,4.6,4.45,4.2,3.75,3.95,4.5,4.5,4,4,4,19,1320,121,0.4 32 | Red Army (2015),96,86,82,7.4,7.7,4.5,4.1,4.8,4.3,4.1,3.7,3.85,5,4.5,4,3.5,4,11,2275,54,0.4 33 | When Marnie Was There (2015),89,89,71,6.4,7.8,4.5,4.1,4.45,4.45,3.55,3.2,3.9,4.5,4.5,3.5,3,4,29,4160,46,0.4 34 | The Hunting Ground (2015),92,72,77,7.8,7.5,4.5,4.1,4.6,3.6,3.85,3.9,3.75,4.5,3.5,4,4,4,6,1196,42,0.4 35 | The Boy Next Door (2015),10,35,30,5.5,4.6,4,3.6,0.5,1.75,1.5,2.75,2.3,0.5,2,1.5,3,2.5,75,19658,2800,0.4 36 | Aloha (2015),19,31,40,4,5.5,3.5,3.1,0.95,1.55,2,2,2.75,1,1.5,2,2,3,67,12255,2284,0.4 37 | The Loft (2015),11,40,24,2.4,6.3,4,3.6,0.55,2,1.2,1.2,3.15,0.5,2,1,1,3,80,21319,811,0.4 38 | 5 Flights Up (2015),52,47,55,6.8,6.1,4,3.6,2.6,2.35,2.75,3.4,3.05,2.5,2.5,3,3.5,3,6,2174,79,0.4 39 | Welcome to Me (2015),71,47,67,6.9,5.9,4,3.6,3.55,2.35,3.35,3.45,2.95,3.5,2.5,3.5,3.5,3,33,8301,56,0.4 40 | Saint Laurent (2015),51,45,52,6.8,6.3,3.5,3.1,2.55,2.25,2.6,3.4,3.15,2.5,2.5,2.5,3.5,3,8,2196,43,0.4 41 | Maps to the Stars (2015),60,46,67,5.8,6.3,3.5,3.1,3,2.3,3.35,2.9,3.15,3,2.5,3.5,3,3,46,22440,35,0.4 42 | I'll See You In My Dreams (2015),94,70,75,6.9,6.9,4,3.6,4.7,3.5,3.75,3.45,3.45,4.5,3.5,4,3.5,3.5,14,1151,281,0.4 43 | Timbuktu (2015),99,78,91,6.9,7.2,4,3.6,4.95,3.9,4.55,3.45,3.6,5,4,4.5,3.5,3.5,37,6246,74,0.4 44 | About Elly (2015),97,86,87,9.6,8.2,4,3.6,4.85,4.3,4.35,4.8,4.1,5,4.5,4.5,5,4,23,20659,43,0.4 45 | The Diary of a Teenage Girl (2015),95,81,87,6.3,7,4,3.6,4.75,4.05,4.35,3.15,3.5,5,4,4.5,3,3.5,18,1107,38,0.4 46 | Kingsman: The Secret Service (2015),75,84,58,7.9,7.8,4.5,4.2,3.75,4.2,2.9,3.95,3.9,4,4,3,4,4,1054,272204,15205,0.3 47 | Tomorrowland (2015),50,53,60,6.4,6.6,4,3.7,2.5,2.65,3,3.2,3.3,2.5,2.5,3,3,3.5,262,42937,8077,0.3 48 | The Divergent Series: Insurgent (2015),30,61,42,5.4,6.4,4.5,4.2,1.5,3.05,2.1,2.7,3.2,1.5,3,2,2.5,3,206,89618,7123,0.3 49 | Annie (2014),27,61,33,4.8,5.2,4.5,4.2,1.35,3.05,1.65,2.4,2.6,1.5,3,1.5,2.5,2.5,108,19222,6835,0.3 50 | Fantastic Four (2015),9,20,27,2.5,4,3,2.7,0.45,1,1.35,1.25,2,0.5,1,1.5,1.5,2,421,39838,6288,0.3 51 | Terminator Genisys (2015),26,60,38,6.4,6.9,4.5,4.2,1.3,3,1.9,3.2,3.45,1.5,3,2,3,3.5,779,85585,6272,0.3 52 | Pitch Perfect 2 (2015),67,68,63,5.7,6.7,4.5,4.2,3.35,3.4,3.15,2.85,3.35,3.5,3.5,3,3,3.5,192,56333,4577,0.3 53 | Entourage (2015),32,68,38,5.2,7.1,4.5,4.2,1.6,3.4,1.9,2.6,3.55,1.5,3.5,2,2.5,3.5,96,21914,4279,0.3 54 | The Age of Adaline (2015),54,68,51,7.4,7.3,4.5,4.2,2.7,3.4,2.55,3.7,3.65,2.5,3.5,2.5,3.5,3.5,100,45510,3325,0.3 55 | Hot Pursuit (2015),8,37,31,3.7,4.9,4,3.7,0.4,1.85,1.55,1.85,2.45,0.5,2,1.5,2,2.5,78,17061,2618,0.3 56 | The DUFF (2015),71,68,56,6.4,6.6,4.5,4.2,3.55,3.4,2.8,3.2,3.3,3.5,3.5,3,3,3.5,69,33594,2273,0.3 57 | Black or White (2015),39,68,45,7.9,6.6,4.5,4.2,1.95,3.4,2.25,3.95,3.3,2,3.5,2.5,4,3.5,24,4857,1862,0.3 58 | Project Almanac (2015),34,46,47,5.4,6.4,4,3.7,1.7,2.3,2.35,2.7,3.2,1.5,2.5,2.5,2.5,3,95,40057,1834,0.3 59 | Ricki and the Flash (2015),64,53,54,7,6.2,4,3.7,3.2,2.65,2.7,3.5,3.1,3,2.5,2.5,3.5,3,37,1769,1462,0.3 60 | Seventh Son (2015),12,35,30,3.9,5.5,3.5,3.2,0.6,1.75,1.5,1.95,2.75,0.5,2,1.5,2,3,126,41177,1213,0.3 61 | Mortdecai (2015),12,30,27,3.2,5.5,3.5,3.2,0.6,1.5,1.35,1.6,2.75,0.5,1.5,1.5,1.5,3,144,31878,1196,0.3 62 | Unfinished Business (2015),11,27,32,3.8,5.4,3.5,3.2,0.55,1.35,1.6,1.9,2.7,0.5,1.5,1.5,2,2.5,39,14346,821,0.3 63 | American Ultra (2015),46,52,50,6.8,6.5,4,3.7,2.3,2.6,2.5,3.4,3.25,2.5,2.5,2.5,3.5,3.5,42,3017,638,0.3 64 | True Story (2015),45,41,50,5.7,6.3,3.5,3.2,2.25,2.05,2.5,2.85,3.15,2.5,2,2.5,3,3,37,16069,540,0.3 65 | Child 44 (2015),26,44,41,5.3,6.4,4,3.7,1.3,2.2,2.05,2.65,3.2,1.5,2,2,2.5,3,73,19220,308,0.3 66 | Dark Places (2015),26,33,39,7.9,6.3,4,3.7,1.3,1.65,1.95,3.95,3.15,1.5,1.5,2,4,3,18,9856,55,0.3 67 | Birdman (2014),92,78,88,8,7.9,4,3.7,4.6,3.9,4.4,4,3.95,4.5,4,4.5,4,4,1171,303505,4194,0.3 68 | The Gift (2015),93,79,77,8.3,7.6,4,3.7,4.65,3.95,3.85,4.15,3.8,4.5,4,4,4,4,121,10891,2680,0.3 69 | Unfriended (2015),60,39,59,5.8,5.9,3,2.7,3,1.95,2.95,2.9,2.95,3,2,3,3,3,130,22348,2507,0.3 70 | Monkey Kingdom (2015),94,77,72,7.5,7.3,4.5,4.2,4.7,3.85,3.6,3.75,3.65,4.5,4,3.5,4,3.5,15,883,701,0.3 71 | Mr. Turner (2014),98,56,94,6.6,6.9,3.5,3.2,4.9,2.8,4.7,3.3,3.45,5,3,4.5,3.5,3.5,98,13296,290,0.3 72 | Seymour: An Introduction (2015),100,87,83,6,7.7,4.5,4.2,5,4.35,4.15,3,3.85,5,4.5,4,3,4,4,243,41,0.3 73 | The Wrecking Crew (2015),93,84,67,7,7.8,4.5,4.2,4.65,4.2,3.35,3.5,3.9,4.5,4,3.5,3.5,4,4,732,38,0.3 74 | American Sniper (2015),72,85,72,6.6,7.4,5,4.8,3.6,4.25,3.6,3.3,3.7,3.5,4.5,3.5,3.5,3.5,850,251856,34085,0.2 75 | Furious 7 (2015),81,84,67,6.8,7.4,5,4.8,4.05,4.2,3.35,3.4,3.7,4,4,3.5,3.5,3.5,764,207211,33538,0.2 76 | The Hobbit: The Battle of the Five Armies (2014),61,75,59,7,7.5,4.5,4.3,3.05,3.75,2.95,3.5,3.75,3,4,3,3.5,4,903,289464,15337,0.2 77 | San Andreas (2015),50,58,43,5.5,6.5,4.5,4.3,2.5,2.9,2.15,2.75,3.25,2.5,3,2,3,3.5,199,45723,9749,0.2 78 | Straight Outta Compton (2015),90,94,72,7.3,8.4,5,4.8,4.5,4.7,3.6,3.65,4.2,4.5,4.5,3.5,3.5,4,90,15982,8096,0.2 79 | Vacation (2015),27,55,34,6.2,6.3,4,3.8,1.35,2.75,1.7,3.1,3.15,1.5,3,1.5,3,3,72,8179,3815,0.2 80 | Chappie (2015),30,57,41,7.4,7,4,3.8,1.5,2.85,2.05,3.7,3.5,1.5,3,2,3.5,3.5,637,125088,3642,0.2 81 | Poltergeist (2015),31,24,47,3.7,5,3,2.8,1.55,1.2,2.35,1.85,2.5,1.5,1,2.5,2,2.5,142,21372,2704,0.2 82 | Paper Towns (2015),55,57,56,6.2,6.9,4,3.8,2.75,2.85,2.8,3.1,3.45,3,3,3,3,3.5,51,14156,1750,0.2 83 | Big Eyes (2014),72,69,62,7.5,7,4,3.8,3.6,3.45,3.1,3.75,3.5,3.5,3.5,3,4,3.5,127,39152,1501,0.2 84 | Blackhat (2015),34,25,51,5.4,5.4,3,2.8,1.7,1.25,2.55,2.7,2.7,1.5,1.5,2.5,2.5,2.5,80,27328,1430,0.2 85 | Self/less (2015),20,51,34,8.4,6.6,4,3.8,1,2.55,1.7,4.2,3.3,1,2.5,1.5,4,3.5,77,5626,1415,0.2 86 | Sinister 2 (2015),13,34,31,5,5.5,3.5,3.3,0.65,1.7,1.55,2.5,2.75,0.5,1.5,1.5,2.5,3,37,3200,973,0.2 87 | Little Boy (2015),20,81,30,5.9,7.4,4.5,4.3,1,4.05,1.5,2.95,3.7,1,4,1.5,3,3.5,38,5927,811,0.2 88 | Me and Earl and The Dying Girl (2015),81,89,74,8.4,8.2,4.5,4.3,4.05,4.45,3.7,4.2,4.1,4,4.5,3.5,4,4,41,5269,624,0.2 89 | Maggie (2015),54,32,52,6.5,5.6,3.5,3.3,2.7,1.6,2.6,3.25,2.8,2.5,1.5,2.5,3.5,3,90,18986,95,0.2 90 | Mad Max: Fury Road (2015),97,88,89,8.7,8.3,4.5,4.3,4.85,4.4,4.45,4.35,4.15,5,4.5,4.5,4.5,4,2375,292023,10509,0.2 91 | Spy (2015),93,82,75,6.3,7.3,4.5,4.3,4.65,4.1,3.75,3.15,3.65,4.5,4,4,3,3.5,318,66636,9418,0.2 92 | The SpongeBob Movie: Sponge Out of Water (2015),78,55,62,6.5,6.1,3.5,3.3,3.9,2.75,3.1,3.25,3.05,4,3,3,3.5,3,196,26046,4493,0.2 93 | Paddington (2015),98,81,77,8.2,7.2,4.5,4.3,4.9,4.05,3.85,4.1,3.6,5,4,4,4,3.5,149,38593,4045,0.2 94 | Dope (2015),87,86,72,7.2,7.5,4.5,4.3,4.35,4.3,3.6,3.6,3.75,4.5,4.5,3.5,3.5,4,43,4911,2195,0.2 95 | What We Do in the Shadows (2015),96,86,75,8.3,7.6,4.5,4.3,4.8,4.3,3.75,4.15,3.8,5,4.5,4,4,4,69,39561,259,0.2 96 | The Overnight (2015),82,65,65,8.6,6.9,3.5,3.3,4.1,3.25,3.25,4.3,3.45,4,3.5,3.5,4.5,3.5,13,867,110,0.2 97 | The Salt of the Earth (2015),96,90,83,7.8,8.4,4.5,4.3,4.8,4.5,4.15,3.9,4.2,5,4.5,4,4,4,13,6605,83,0.2 98 | Song of the Sea (2014),99,92,86,8.2,8.2,4.5,4.3,4.95,4.6,4.3,4.1,4.1,5,4.5,4.5,4,4,62,14067,66,0.2 99 | Fifty Shades of Grey (2015),25,42,46,3.2,4.2,4,3.9,1.25,2.1,2.3,1.6,2.1,1.5,2,2.5,1.5,2,778,179506,34846,0.1 100 | Get Hard (2015),29,48,34,3.8,6.1,4,3.9,1.45,2.4,1.7,1.9,3.05,1.5,2.5,1.5,2,3,145,50022,5933,0.1 101 | Focus (2015),57,54,56,6.2,6.6,4,3.9,2.85,2.7,2.8,3.1,3.3,3,2.5,3,3,3.5,167,101264,4933,0.1 102 | Jupiter Ascending (2015),26,40,40,4.5,5.5,3.5,3.4,1.3,2,2,2.25,2.75,1.5,2,2,2.5,3,503,105412,4122,0.1 103 | The Gallows (2015),16,27,30,7,4.4,3,2.9,0.8,1.35,1.5,3.5,2.2,1,1.5,1.5,3.5,2,80,5511,1896,0.1 104 | The Second Best Exotic Marigold Hotel (2015),62,63,51,6.1,6.6,4,3.9,3.1,3.15,2.55,3.05,3.3,3,3,2.5,3,3.5,41,12940,1870,0.1 105 | Strange Magic (2015),17,50,25,5.3,5.7,3.5,3.4,0.85,2.5,1.25,2.65,2.85,1,2.5,1.5,2.5,3,41,3658,1117,0.1 106 | The Gunman (2015),17,34,39,4.3,5.8,3.5,3.4,0.85,1.7,1.95,2.15,2.9,1,1.5,2,2,3,49,16663,996,0.1 107 | Hitman: Agent 47 (2015),7,49,28,3.3,5.9,4,3.9,0.35,2.45,1.4,1.65,2.95,0.5,2.5,1.5,1.5,3,67,4260,917,0.1 108 | Cake (2015),49,47,49,6.4,6.5,3.5,3.4,2.45,2.35,2.45,3.2,3.25,2.5,2.5,2.5,3,3.5,44,19627,482,0.1 109 | The Vatican Tapes (2015),13,21,37,5.4,4.6,3,2.9,0.65,1.05,1.85,2.7,2.3,0.5,1,2,2.5,2.5,5,952,210,0.1 110 | A Little Chaos (2015),40,47,51,7,6.4,4,3.9,2,2.35,2.55,3.5,3.2,2,2.5,2.5,3.5,3,7,4778,83,0.1 111 | The 100-Year-Old Man Who Climbed Out the Window and Disappeared (2015),67,69,58,4.6,7.1,4,3.9,3.35,3.45,2.9,2.3,3.55,3.5,3.5,3,2.5,3.5,5,17237,63,0.1 112 | Escobar: Paradise Lost (2015),52,52,56,6.9,6.6,4,3.9,2.6,2.6,2.8,3.45,3.3,2.5,2.5,3,3.5,3.5,7,7819,48,0.1 113 | Into the Woods (2014),71,50,69,6.1,6,3.5,3.4,3.55,2.5,3.45,3.05,3,3.5,2.5,3.5,3,3,307,81679,13055,0.1 114 | It Follows (2015),96,65,83,7.5,6.9,3,2.9,4.8,3.25,4.15,3.75,3.45,5,3.5,4,4,3.5,551,64656,2097,0.1 115 | Inherent Vice (2014),73,52,81,7.4,6.7,3,2.9,3.65,2.6,4.05,3.7,3.35,3.5,2.5,4,3.5,3.5,286,44711,1078,0.1 116 | A Most Violent Year (2014),90,69,79,7,7.1,3.5,3.4,4.5,3.45,3.95,3.5,3.55,4.5,3.5,4,3.5,3.5,133,32166,675,0.1 117 | While We're Young (2015),83,52,76,6.7,6.4,3,2.9,4.15,2.6,3.8,3.35,3.2,4,2.5,4,3.5,3,65,17647,449,0.1 118 | Clouds of Sils Maria (2015),89,67,78,7.1,6.8,3.5,3.4,4.45,3.35,3.9,3.55,3.4,4.5,3.5,4,3.5,3.5,36,11392,162,0.1 119 | Testament of Youth (2015),81,79,77,7.9,7.3,4,3.9,4.05,3.95,3.85,3.95,3.65,4,4,4,4,3.5,15,5495,127,0.1 120 | Infinitely Polar Bear (2015),80,76,64,7.9,7.2,4,3.9,4,3.8,3.2,3.95,3.6,4,4,3,4,3.5,8,1062,124,0.1 121 | Phoenix (2015),99,81,91,8,7.2,3.5,3.4,4.95,4.05,4.55,4,3.6,5,4,4.5,4,3.5,21,3687,70,0.1 122 | The Wolfpack (2015),84,73,75,7,7.1,3.5,3.4,4.2,3.65,3.75,3.5,3.55,4,3.5,4,3.5,3.5,8,1488,66,0.1 123 | The Stanford Prison Experiment (2015),84,87,68,8.5,7.1,4,3.9,4.2,4.35,3.4,4.25,3.55,4,4.5,3.5,4.5,3.5,6,950,51,0.1 124 | Tangerine (2015),95,86,86,7.3,7.4,4,3.9,4.75,4.3,4.3,3.65,3.7,5,4.5,4.5,3.5,3.5,14,696,36,0.1 125 | Magic Mike XXL (2015),62,64,60,5.4,6.3,4.5,4.4,3.1,3.2,3,2.7,3.15,3,3,3,2.5,3,52,11937,9363,0.1 126 | Home (2015),45,65,55,7.3,6.7,4.5,4.4,2.25,3.25,2.75,3.65,3.35,2.5,3.5,3,3.5,3.5,177,41158,7705,0.1 127 | The Wedding Ringer (2015),27,66,35,3.3,6.7,4.5,4.4,1.35,3.3,1.75,1.65,3.35,1.5,3.5,2,1.5,3.5,126,37292,6506,0.1 128 | Woman in Gold (2015),52,81,51,7.2,7.4,4.5,4.4,2.6,4.05,2.55,3.6,3.7,2.5,4,2.5,3.5,3.5,72,17957,2435,0.1 129 | The Last Five Years (2015),60,60,60,6.9,6,4.5,4.4,3,3,3,3.45,3,3,3,3,3.5,3,20,4110,99,0.1 130 | Mission: Impossible – Rogue Nation (2015),92,90,75,8,7.8,4.5,4.4,4.6,4.5,3.75,4,3.9,4.5,4.5,4,4,4,362,82579,8357,0.1 131 | Amy (2015),97,91,85,8.8,8,4.5,4.4,4.85,4.55,4.25,4.4,4,5,4.5,4.5,4.5,4,60,5630,729,0.1 132 | Jurassic World (2015),71,81,59,7,7.3,4.5,4.5,3.55,4.05,2.95,3.5,3.65,3.5,4,3,3.5,3.5,1281,241807,34390,0 133 | Minions (2015),54,52,56,5.7,6.7,4,4,2.7,2.6,2.8,2.85,3.35,2.5,2.5,3,3,3.5,204,55895,14998,0 134 | Max (2015),35,73,47,5.9,7,4.5,4.5,1.75,3.65,2.35,2.95,3.5,2,3.5,2.5,3,3.5,15,5444,3412,0 135 | Paul Blart: Mall Cop 2 (2015),5,36,13,2.4,4.3,3.5,3.5,0.25,1.8,0.65,1.2,2.15,0.5,2,0.5,1,2,211,15004,3054,0 136 | The Longest Ride (2015),31,73,33,4.8,7.2,4.5,4.5,1.55,3.65,1.65,2.4,3.6,1.5,3.5,1.5,2.5,3.5,49,25214,2603,0 137 | The Lazarus Effect (2015),14,23,31,4.9,5.2,3,3,0.7,1.15,1.55,2.45,2.6,0.5,1,1.5,2.5,2.5,62,17691,1651,0 138 | The Woman In Black 2 Angel of Death (2015),22,25,42,4.4,4.9,3,3,1.1,1.25,2.1,2.2,2.45,1,1.5,2,2,2.5,55,14873,1333,0 139 | Danny Collins (2015),77,75,58,7.1,7.1,4,4,3.85,3.75,2.9,3.55,3.55,4,4,3,3.5,3.5,33,11206,531,0 140 | Spare Parts (2015),52,83,50,7.1,7.2,4.5,4.5,2.6,4.15,2.5,3.55,3.6,2.5,4,2.5,3.5,3.5,7,47377,450,0 141 | Serena (2015),18,25,36,5.3,5.4,3,3,0.9,1.25,1.8,2.65,2.7,1,1.5,2,2.5,2.5,19,12165,50,0 142 | Inside Out (2015),98,90,94,8.9,8.6,4.5,4.5,4.9,4.5,4.7,4.45,4.3,5,4.5,4.5,4.5,4.5,807,96252,15749,0 143 | Mr. Holmes (2015),87,78,67,7.9,7.4,4,4,4.35,3.9,3.35,3.95,3.7,4.5,4,3.5,4,3.5,33,7367,1348,0 144 | '71 (2015),97,82,83,7.5,7.2,3.5,3.5,4.85,4.1,4.15,3.75,3.6,5,4,4,4,3.5,60,24116,192,0 145 | "Two Days, One Night (2014)",97,78,89,8.8,7.4,3.5,3.5,4.85,3.9,4.45,4.4,3.7,5,4,4.5,4.5,3.5,123,24345,118,0 146 | Gett: The Trial of Viviane Amsalem (2015),100,81,90,7.3,7.8,3.5,3.5,5,4.05,4.5,3.65,3.9,5,4,4.5,3.5,4,19,1955,59,0 147 | "Kumiko, The Treasure Hunter (2015)",87,63,68,6.4,6.7,3.5,3.5,4.35,3.15,3.4,3.2,3.35,4.5,3,3.5,3,3.5,19,5289,41,0 148 | -------------------------------------------------------------------------------- /data/train.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 3 | 2,1,1,"Cumings, Mrs. John 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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S 891 | 890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C 892 | 891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q 893 | -------------------------------------------------------------------------------- /data/unrate.csv: -------------------------------------------------------------------------------- 1 | DATE,VALUE 2 | 1948-01-01,3.4 3 | 1948-02-01,3.8 4 | 1948-03-01,4.0 5 | 1948-04-01,3.9 6 | 1948-05-01,3.5 7 | 1948-06-01,3.6 8 | 1948-07-01,3.6 9 | 1948-08-01,3.9 10 | 1948-09-01,3.8 11 | 1948-10-01,3.7 12 | 1948-11-01,3.8 13 | 1948-12-01,4.0 14 | 1949-01-01,4.3 15 | 1949-02-01,4.7 16 | 1949-03-01,5.0 17 | 1949-04-01,5.3 18 | 1949-05-01,6.1 19 | 1949-06-01,6.2 20 | 1949-07-01,6.7 21 | 1949-08-01,6.8 22 | 1949-09-01,6.6 23 | 1949-10-01,7.9 24 | 1949-11-01,6.4 25 | 1949-12-01,6.6 26 | 1950-01-01,6.5 27 | 1950-02-01,6.4 28 | 1950-03-01,6.3 29 | 1950-04-01,5.8 30 | 1950-05-01,5.5 31 | 1950-06-01,5.4 32 | 1950-07-01,5.0 33 | 1950-08-01,4.5 34 | 1950-09-01,4.4 35 | 1950-10-01,4.2 36 | 1950-11-01,4.2 37 | 1950-12-01,4.3 38 | 1951-01-01,3.7 39 | 1951-02-01,3.4 40 | 1951-03-01,3.4 41 | 1951-04-01,3.1 42 | 1951-05-01,3.0 43 | 1951-06-01,3.2 44 | 1951-07-01,3.1 45 | 1951-08-01,3.1 46 | 1951-09-01,3.3 47 | 1951-10-01,3.5 48 | 1951-11-01,3.5 49 | 1951-12-01,3.1 50 | 1952-01-01,3.2 51 | 1952-02-01,3.1 52 | 1952-03-01,2.9 53 | 1952-04-01,2.9 54 | 1952-05-01,3.0 55 | 1952-06-01,3.0 56 | 1952-07-01,3.2 57 | 1952-08-01,3.4 58 | 1952-09-01,3.1 59 | 1952-10-01,3.0 60 | 1952-11-01,2.8 61 | 1952-12-01,2.7 62 | 1953-01-01,2.9 63 | 1953-02-01,2.6 64 | 1953-03-01,2.6 65 | 1953-04-01,2.7 66 | 1953-05-01,2.5 67 | 1953-06-01,2.5 68 | 1953-07-01,2.6 69 | 1953-08-01,2.7 70 | 1953-09-01,2.9 71 | 1953-10-01,3.1 72 | 1953-11-01,3.5 73 | 1953-12-01,4.5 74 | 1954-01-01,4.9 75 | 1954-02-01,5.2 76 | 1954-03-01,5.7 77 | 1954-04-01,5.9 78 | 1954-05-01,5.9 79 | 1954-06-01,5.6 80 | 1954-07-01,5.8 81 | 1954-08-01,6.0 82 | 1954-09-01,6.1 83 | 1954-10-01,5.7 84 | 1954-11-01,5.3 85 | 1954-12-01,5.0 86 | 1955-01-01,4.9 87 | 1955-02-01,4.7 88 | 1955-03-01,4.6 89 | 1955-04-01,4.7 90 | 1955-05-01,4.3 91 | 1955-06-01,4.2 92 | 1955-07-01,4.0 93 | 1955-08-01,4.2 94 | 1955-09-01,4.1 95 | 1955-10-01,4.3 96 | 1955-11-01,4.2 97 | 1955-12-01,4.2 98 | 1956-01-01,4.0 99 | 1956-02-01,3.9 100 | 1956-03-01,4.2 101 | 1956-04-01,4.0 102 | 1956-05-01,4.3 103 | 1956-06-01,4.3 104 | 1956-07-01,4.4 105 | 1956-08-01,4.1 106 | 1956-09-01,3.9 107 | 1956-10-01,3.9 108 | 1956-11-01,4.3 109 | 1956-12-01,4.2 110 | 1957-01-01,4.2 111 | 1957-02-01,3.9 112 | 1957-03-01,3.7 113 | 1957-04-01,3.9 114 | 1957-05-01,4.1 115 | 1957-06-01,4.3 116 | 1957-07-01,4.2 117 | 1957-08-01,4.1 118 | 1957-09-01,4.4 119 | 1957-10-01,4.5 120 | 1957-11-01,5.1 121 | 1957-12-01,5.2 122 | 1958-01-01,5.8 123 | 1958-02-01,6.4 124 | 1958-03-01,6.7 125 | 1958-04-01,7.4 126 | 1958-05-01,7.4 127 | 1958-06-01,7.3 128 | 1958-07-01,7.5 129 | 1958-08-01,7.4 130 | 1958-09-01,7.1 131 | 1958-10-01,6.7 132 | 1958-11-01,6.2 133 | 1958-12-01,6.2 134 | 1959-01-01,6.0 135 | 1959-02-01,5.9 136 | 1959-03-01,5.6 137 | 1959-04-01,5.2 138 | 1959-05-01,5.1 139 | 1959-06-01,5.0 140 | 1959-07-01,5.1 141 | 1959-08-01,5.2 142 | 1959-09-01,5.5 143 | 1959-10-01,5.7 144 | 1959-11-01,5.8 145 | 1959-12-01,5.3 146 | 1960-01-01,5.2 147 | 1960-02-01,4.8 148 | 1960-03-01,5.4 149 | 1960-04-01,5.2 150 | 1960-05-01,5.1 151 | 1960-06-01,5.4 152 | 1960-07-01,5.5 153 | 1960-08-01,5.6 154 | 1960-09-01,5.5 155 | 1960-10-01,6.1 156 | 1960-11-01,6.1 157 | 1960-12-01,6.6 158 | 1961-01-01,6.6 159 | 1961-02-01,6.9 160 | 1961-03-01,6.9 161 | 1961-04-01,7.0 162 | 1961-05-01,7.1 163 | 1961-06-01,6.9 164 | 1961-07-01,7.0 165 | 1961-08-01,6.6 166 | 1961-09-01,6.7 167 | 1961-10-01,6.5 168 | 1961-11-01,6.1 169 | 1961-12-01,6.0 170 | 1962-01-01,5.8 171 | 1962-02-01,5.5 172 | 1962-03-01,5.6 173 | 1962-04-01,5.6 174 | 1962-05-01,5.5 175 | 1962-06-01,5.5 176 | 1962-07-01,5.4 177 | 1962-08-01,5.7 178 | 1962-09-01,5.6 179 | 1962-10-01,5.4 180 | 1962-11-01,5.7 181 | 1962-12-01,5.5 182 | 1963-01-01,5.7 183 | 1963-02-01,5.9 184 | 1963-03-01,5.7 185 | 1963-04-01,5.7 186 | 1963-05-01,5.9 187 | 1963-06-01,5.6 188 | 1963-07-01,5.6 189 | 1963-08-01,5.4 190 | 1963-09-01,5.5 191 | 1963-10-01,5.5 192 | 1963-11-01,5.7 193 | 1963-12-01,5.5 194 | 1964-01-01,5.6 195 | 1964-02-01,5.4 196 | 1964-03-01,5.4 197 | 1964-04-01,5.3 198 | 1964-05-01,5.1 199 | 1964-06-01,5.2 200 | 1964-07-01,4.9 201 | 1964-08-01,5.0 202 | 1964-09-01,5.1 203 | 1964-10-01,5.1 204 | 1964-11-01,4.8 205 | 1964-12-01,5.0 206 | 1965-01-01,4.9 207 | 1965-02-01,5.1 208 | 1965-03-01,4.7 209 | 1965-04-01,4.8 210 | 1965-05-01,4.6 211 | 1965-06-01,4.6 212 | 1965-07-01,4.4 213 | 1965-08-01,4.4 214 | 1965-09-01,4.3 215 | 1965-10-01,4.2 216 | 1965-11-01,4.1 217 | 1965-12-01,4.0 218 | 1966-01-01,4.0 219 | 1966-02-01,3.8 220 | 1966-03-01,3.8 221 | 1966-04-01,3.8 222 | 1966-05-01,3.9 223 | 1966-06-01,3.8 224 | 1966-07-01,3.8 225 | 1966-08-01,3.8 226 | 1966-09-01,3.7 227 | 1966-10-01,3.7 228 | 1966-11-01,3.6 229 | 1966-12-01,3.8 230 | 1967-01-01,3.9 231 | 1967-02-01,3.8 232 | 1967-03-01,3.8 233 | 1967-04-01,3.8 234 | 1967-05-01,3.8 235 | 1967-06-01,3.9 236 | 1967-07-01,3.8 237 | 1967-08-01,3.8 238 | 1967-09-01,3.8 239 | 1967-10-01,4.0 240 | 1967-11-01,3.9 241 | 1967-12-01,3.8 242 | 1968-01-01,3.7 243 | 1968-02-01,3.8 244 | 1968-03-01,3.7 245 | 1968-04-01,3.5 246 | 1968-05-01,3.5 247 | 1968-06-01,3.7 248 | 1968-07-01,3.7 249 | 1968-08-01,3.5 250 | 1968-09-01,3.4 251 | 1968-10-01,3.4 252 | 1968-11-01,3.4 253 | 1968-12-01,3.4 254 | 1969-01-01,3.4 255 | 1969-02-01,3.4 256 | 1969-03-01,3.4 257 | 1969-04-01,3.4 258 | 1969-05-01,3.4 259 | 1969-06-01,3.5 260 | 1969-07-01,3.5 261 | 1969-08-01,3.5 262 | 1969-09-01,3.7 263 | 1969-10-01,3.7 264 | 1969-11-01,3.5 265 | 1969-12-01,3.5 266 | 1970-01-01,3.9 267 | 1970-02-01,4.2 268 | 1970-03-01,4.4 269 | 1970-04-01,4.6 270 | 1970-05-01,4.8 271 | 1970-06-01,4.9 272 | 1970-07-01,5.0 273 | 1970-08-01,5.1 274 | 1970-09-01,5.4 275 | 1970-10-01,5.5 276 | 1970-11-01,5.9 277 | 1970-12-01,6.1 278 | 1971-01-01,5.9 279 | 1971-02-01,5.9 280 | 1971-03-01,6.0 281 | 1971-04-01,5.9 282 | 1971-05-01,5.9 283 | 1971-06-01,5.9 284 | 1971-07-01,6.0 285 | 1971-08-01,6.1 286 | 1971-09-01,6.0 287 | 1971-10-01,5.8 288 | 1971-11-01,6.0 289 | 1971-12-01,6.0 290 | 1972-01-01,5.8 291 | 1972-02-01,5.7 292 | 1972-03-01,5.8 293 | 1972-04-01,5.7 294 | 1972-05-01,5.7 295 | 1972-06-01,5.7 296 | 1972-07-01,5.6 297 | 1972-08-01,5.6 298 | 1972-09-01,5.5 299 | 1972-10-01,5.6 300 | 1972-11-01,5.3 301 | 1972-12-01,5.2 302 | 1973-01-01,4.9 303 | 1973-02-01,5.0 304 | 1973-03-01,4.9 305 | 1973-04-01,5.0 306 | 1973-05-01,4.9 307 | 1973-06-01,4.9 308 | 1973-07-01,4.8 309 | 1973-08-01,4.8 310 | 1973-09-01,4.8 311 | 1973-10-01,4.6 312 | 1973-11-01,4.8 313 | 1973-12-01,4.9 314 | 1974-01-01,5.1 315 | 1974-02-01,5.2 316 | 1974-03-01,5.1 317 | 1974-04-01,5.1 318 | 1974-05-01,5.1 319 | 1974-06-01,5.4 320 | 1974-07-01,5.5 321 | 1974-08-01,5.5 322 | 1974-09-01,5.9 323 | 1974-10-01,6.0 324 | 1974-11-01,6.6 325 | 1974-12-01,7.2 326 | 1975-01-01,8.1 327 | 1975-02-01,8.1 328 | 1975-03-01,8.6 329 | 1975-04-01,8.8 330 | 1975-05-01,9.0 331 | 1975-06-01,8.8 332 | 1975-07-01,8.6 333 | 1975-08-01,8.4 334 | 1975-09-01,8.4 335 | 1975-10-01,8.4 336 | 1975-11-01,8.3 337 | 1975-12-01,8.2 338 | 1976-01-01,7.9 339 | 1976-02-01,7.7 340 | 1976-03-01,7.6 341 | 1976-04-01,7.7 342 | 1976-05-01,7.4 343 | 1976-06-01,7.6 344 | 1976-07-01,7.8 345 | 1976-08-01,7.8 346 | 1976-09-01,7.6 347 | 1976-10-01,7.7 348 | 1976-11-01,7.8 349 | 1976-12-01,7.8 350 | 1977-01-01,7.5 351 | 1977-02-01,7.6 352 | 1977-03-01,7.4 353 | 1977-04-01,7.2 354 | 1977-05-01,7.0 355 | 1977-06-01,7.2 356 | 1977-07-01,6.9 357 | 1977-08-01,7.0 358 | 1977-09-01,6.8 359 | 1977-10-01,6.8 360 | 1977-11-01,6.8 361 | 1977-12-01,6.4 362 | 1978-01-01,6.4 363 | 1978-02-01,6.3 364 | 1978-03-01,6.3 365 | 1978-04-01,6.1 366 | 1978-05-01,6.0 367 | 1978-06-01,5.9 368 | 1978-07-01,6.2 369 | 1978-08-01,5.9 370 | 1978-09-01,6.0 371 | 1978-10-01,5.8 372 | 1978-11-01,5.9 373 | 1978-12-01,6.0 374 | 1979-01-01,5.9 375 | 1979-02-01,5.9 376 | 1979-03-01,5.8 377 | 1979-04-01,5.8 378 | 1979-05-01,5.6 379 | 1979-06-01,5.7 380 | 1979-07-01,5.7 381 | 1979-08-01,6.0 382 | 1979-09-01,5.9 383 | 1979-10-01,6.0 384 | 1979-11-01,5.9 385 | 1979-12-01,6.0 386 | 1980-01-01,6.3 387 | 1980-02-01,6.3 388 | 1980-03-01,6.3 389 | 1980-04-01,6.9 390 | 1980-05-01,7.5 391 | 1980-06-01,7.6 392 | 1980-07-01,7.8 393 | 1980-08-01,7.7 394 | 1980-09-01,7.5 395 | 1980-10-01,7.5 396 | 1980-11-01,7.5 397 | 1980-12-01,7.2 398 | 1981-01-01,7.5 399 | 1981-02-01,7.4 400 | 1981-03-01,7.4 401 | 1981-04-01,7.2 402 | 1981-05-01,7.5 403 | 1981-06-01,7.5 404 | 1981-07-01,7.2 405 | 1981-08-01,7.4 406 | 1981-09-01,7.6 407 | 1981-10-01,7.9 408 | 1981-11-01,8.3 409 | 1981-12-01,8.5 410 | 1982-01-01,8.6 411 | 1982-02-01,8.9 412 | 1982-03-01,9.0 413 | 1982-04-01,9.3 414 | 1982-05-01,9.4 415 | 1982-06-01,9.6 416 | 1982-07-01,9.8 417 | 1982-08-01,9.8 418 | 1982-09-01,10.1 419 | 1982-10-01,10.4 420 | 1982-11-01,10.8 421 | 1982-12-01,10.8 422 | 1983-01-01,10.4 423 | 1983-02-01,10.4 424 | 1983-03-01,10.3 425 | 1983-04-01,10.2 426 | 1983-05-01,10.1 427 | 1983-06-01,10.1 428 | 1983-07-01,9.4 429 | 1983-08-01,9.5 430 | 1983-09-01,9.2 431 | 1983-10-01,8.8 432 | 1983-11-01,8.5 433 | 1983-12-01,8.3 434 | 1984-01-01,8.0 435 | 1984-02-01,7.8 436 | 1984-03-01,7.8 437 | 1984-04-01,7.7 438 | 1984-05-01,7.4 439 | 1984-06-01,7.2 440 | 1984-07-01,7.5 441 | 1984-08-01,7.5 442 | 1984-09-01,7.3 443 | 1984-10-01,7.4 444 | 1984-11-01,7.2 445 | 1984-12-01,7.3 446 | 1985-01-01,7.3 447 | 1985-02-01,7.2 448 | 1985-03-01,7.2 449 | 1985-04-01,7.3 450 | 1985-05-01,7.2 451 | 1985-06-01,7.4 452 | 1985-07-01,7.4 453 | 1985-08-01,7.1 454 | 1985-09-01,7.1 455 | 1985-10-01,7.1 456 | 1985-11-01,7.0 457 | 1985-12-01,7.0 458 | 1986-01-01,6.7 459 | 1986-02-01,7.2 460 | 1986-03-01,7.2 461 | 1986-04-01,7.1 462 | 1986-05-01,7.2 463 | 1986-06-01,7.2 464 | 1986-07-01,7.0 465 | 1986-08-01,6.9 466 | 1986-09-01,7.0 467 | 1986-10-01,7.0 468 | 1986-11-01,6.9 469 | 1986-12-01,6.6 470 | 1987-01-01,6.6 471 | 1987-02-01,6.6 472 | 1987-03-01,6.6 473 | 1987-04-01,6.3 474 | 1987-05-01,6.3 475 | 1987-06-01,6.2 476 | 1987-07-01,6.1 477 | 1987-08-01,6.0 478 | 1987-09-01,5.9 479 | 1987-10-01,6.0 480 | 1987-11-01,5.8 481 | 1987-12-01,5.7 482 | 1988-01-01,5.7 483 | 1988-02-01,5.7 484 | 1988-03-01,5.7 485 | 1988-04-01,5.4 486 | 1988-05-01,5.6 487 | 1988-06-01,5.4 488 | 1988-07-01,5.4 489 | 1988-08-01,5.6 490 | 1988-09-01,5.4 491 | 1988-10-01,5.4 492 | 1988-11-01,5.3 493 | 1988-12-01,5.3 494 | 1989-01-01,5.4 495 | 1989-02-01,5.2 496 | 1989-03-01,5.0 497 | 1989-04-01,5.2 498 | 1989-05-01,5.2 499 | 1989-06-01,5.3 500 | 1989-07-01,5.2 501 | 1989-08-01,5.2 502 | 1989-09-01,5.3 503 | 1989-10-01,5.3 504 | 1989-11-01,5.4 505 | 1989-12-01,5.4 506 | 1990-01-01,5.4 507 | 1990-02-01,5.3 508 | 1990-03-01,5.2 509 | 1990-04-01,5.4 510 | 1990-05-01,5.4 511 | 1990-06-01,5.2 512 | 1990-07-01,5.5 513 | 1990-08-01,5.7 514 | 1990-09-01,5.9 515 | 1990-10-01,5.9 516 | 1990-11-01,6.2 517 | 1990-12-01,6.3 518 | 1991-01-01,6.4 519 | 1991-02-01,6.6 520 | 1991-03-01,6.8 521 | 1991-04-01,6.7 522 | 1991-05-01,6.9 523 | 1991-06-01,6.9 524 | 1991-07-01,6.8 525 | 1991-08-01,6.9 526 | 1991-09-01,6.9 527 | 1991-10-01,7.0 528 | 1991-11-01,7.0 529 | 1991-12-01,7.3 530 | 1992-01-01,7.3 531 | 1992-02-01,7.4 532 | 1992-03-01,7.4 533 | 1992-04-01,7.4 534 | 1992-05-01,7.6 535 | 1992-06-01,7.8 536 | 1992-07-01,7.7 537 | 1992-08-01,7.6 538 | 1992-09-01,7.6 539 | 1992-10-01,7.3 540 | 1992-11-01,7.4 541 | 1992-12-01,7.4 542 | 1993-01-01,7.3 543 | 1993-02-01,7.1 544 | 1993-03-01,7.0 545 | 1993-04-01,7.1 546 | 1993-05-01,7.1 547 | 1993-06-01,7.0 548 | 1993-07-01,6.9 549 | 1993-08-01,6.8 550 | 1993-09-01,6.7 551 | 1993-10-01,6.8 552 | 1993-11-01,6.6 553 | 1993-12-01,6.5 554 | 1994-01-01,6.6 555 | 1994-02-01,6.6 556 | 1994-03-01,6.5 557 | 1994-04-01,6.4 558 | 1994-05-01,6.1 559 | 1994-06-01,6.1 560 | 1994-07-01,6.1 561 | 1994-08-01,6.0 562 | 1994-09-01,5.9 563 | 1994-10-01,5.8 564 | 1994-11-01,5.6 565 | 1994-12-01,5.5 566 | 1995-01-01,5.6 567 | 1995-02-01,5.4 568 | 1995-03-01,5.4 569 | 1995-04-01,5.8 570 | 1995-05-01,5.6 571 | 1995-06-01,5.6 572 | 1995-07-01,5.7 573 | 1995-08-01,5.7 574 | 1995-09-01,5.6 575 | 1995-10-01,5.5 576 | 1995-11-01,5.6 577 | 1995-12-01,5.6 578 | 1996-01-01,5.6 579 | 1996-02-01,5.5 580 | 1996-03-01,5.5 581 | 1996-04-01,5.6 582 | 1996-05-01,5.6 583 | 1996-06-01,5.3 584 | 1996-07-01,5.5 585 | 1996-08-01,5.1 586 | 1996-09-01,5.2 587 | 1996-10-01,5.2 588 | 1996-11-01,5.4 589 | 1996-12-01,5.4 590 | 1997-01-01,5.3 591 | 1997-02-01,5.2 592 | 1997-03-01,5.2 593 | 1997-04-01,5.1 594 | 1997-05-01,4.9 595 | 1997-06-01,5.0 596 | 1997-07-01,4.9 597 | 1997-08-01,4.8 598 | 1997-09-01,4.9 599 | 1997-10-01,4.7 600 | 1997-11-01,4.6 601 | 1997-12-01,4.7 602 | 1998-01-01,4.6 603 | 1998-02-01,4.6 604 | 1998-03-01,4.7 605 | 1998-04-01,4.3 606 | 1998-05-01,4.4 607 | 1998-06-01,4.5 608 | 1998-07-01,4.5 609 | 1998-08-01,4.5 610 | 1998-09-01,4.6 611 | 1998-10-01,4.5 612 | 1998-11-01,4.4 613 | 1998-12-01,4.4 614 | 1999-01-01,4.3 615 | 1999-02-01,4.4 616 | 1999-03-01,4.2 617 | 1999-04-01,4.3 618 | 1999-05-01,4.2 619 | 1999-06-01,4.3 620 | 1999-07-01,4.3 621 | 1999-08-01,4.2 622 | 1999-09-01,4.2 623 | 1999-10-01,4.1 624 | 1999-11-01,4.1 625 | 1999-12-01,4.0 626 | 2000-01-01,4.0 627 | 2000-02-01,4.1 628 | 2000-03-01,4.0 629 | 2000-04-01,3.8 630 | 2000-05-01,4.0 631 | 2000-06-01,4.0 632 | 2000-07-01,4.0 633 | 2000-08-01,4.1 634 | 2000-09-01,3.9 635 | 2000-10-01,3.9 636 | 2000-11-01,3.9 637 | 2000-12-01,3.9 638 | 2001-01-01,4.2 639 | 2001-02-01,4.2 640 | 2001-03-01,4.3 641 | 2001-04-01,4.4 642 | 2001-05-01,4.3 643 | 2001-06-01,4.5 644 | 2001-07-01,4.6 645 | 2001-08-01,4.9 646 | 2001-09-01,5.0 647 | 2001-10-01,5.3 648 | 2001-11-01,5.5 649 | 2001-12-01,5.7 650 | 2002-01-01,5.7 651 | 2002-02-01,5.7 652 | 2002-03-01,5.7 653 | 2002-04-01,5.9 654 | 2002-05-01,5.8 655 | 2002-06-01,5.8 656 | 2002-07-01,5.8 657 | 2002-08-01,5.7 658 | 2002-09-01,5.7 659 | 2002-10-01,5.7 660 | 2002-11-01,5.9 661 | 2002-12-01,6.0 662 | 2003-01-01,5.8 663 | 2003-02-01,5.9 664 | 2003-03-01,5.9 665 | 2003-04-01,6.0 666 | 2003-05-01,6.1 667 | 2003-06-01,6.3 668 | 2003-07-01,6.2 669 | 2003-08-01,6.1 670 | 2003-09-01,6.1 671 | 2003-10-01,6.0 672 | 2003-11-01,5.8 673 | 2003-12-01,5.7 674 | 2004-01-01,5.7 675 | 2004-02-01,5.6 676 | 2004-03-01,5.8 677 | 2004-04-01,5.6 678 | 2004-05-01,5.6 679 | 2004-06-01,5.6 680 | 2004-07-01,5.5 681 | 2004-08-01,5.4 682 | 2004-09-01,5.4 683 | 2004-10-01,5.5 684 | 2004-11-01,5.4 685 | 2004-12-01,5.4 686 | 2005-01-01,5.3 687 | 2005-02-01,5.4 688 | 2005-03-01,5.2 689 | 2005-04-01,5.2 690 | 2005-05-01,5.1 691 | 2005-06-01,5.0 692 | 2005-07-01,5.0 693 | 2005-08-01,4.9 694 | 2005-09-01,5.0 695 | 2005-10-01,5.0 696 | 2005-11-01,5.0 697 | 2005-12-01,4.9 698 | 2006-01-01,4.7 699 | 2006-02-01,4.8 700 | 2006-03-01,4.7 701 | 2006-04-01,4.7 702 | 2006-05-01,4.6 703 | 2006-06-01,4.6 704 | 2006-07-01,4.7 705 | 2006-08-01,4.7 706 | 2006-09-01,4.5 707 | 2006-10-01,4.4 708 | 2006-11-01,4.5 709 | 2006-12-01,4.4 710 | 2007-01-01,4.6 711 | 2007-02-01,4.5 712 | 2007-03-01,4.4 713 | 2007-04-01,4.5 714 | 2007-05-01,4.4 715 | 2007-06-01,4.6 716 | 2007-07-01,4.7 717 | 2007-08-01,4.6 718 | 2007-09-01,4.7 719 | 2007-10-01,4.7 720 | 2007-11-01,4.7 721 | 2007-12-01,5.0 722 | 2008-01-01,5.0 723 | 2008-02-01,4.9 724 | 2008-03-01,5.1 725 | 2008-04-01,5.0 726 | 2008-05-01,5.4 727 | 2008-06-01,5.6 728 | 2008-07-01,5.8 729 | 2008-08-01,6.1 730 | 2008-09-01,6.1 731 | 2008-10-01,6.5 732 | 2008-11-01,6.8 733 | 2008-12-01,7.3 734 | 2009-01-01,7.8 735 | 2009-02-01,8.3 736 | 2009-03-01,8.7 737 | 2009-04-01,9.0 738 | 2009-05-01,9.4 739 | 2009-06-01,9.5 740 | 2009-07-01,9.5 741 | 2009-08-01,9.6 742 | 2009-09-01,9.8 743 | 2009-10-01,10.0 744 | 2009-11-01,9.9 745 | 2009-12-01,9.9 746 | 2010-01-01,9.8 747 | 2010-02-01,9.8 748 | 2010-03-01,9.9 749 | 2010-04-01,9.9 750 | 2010-05-01,9.6 751 | 2010-06-01,9.4 752 | 2010-07-01,9.4 753 | 2010-08-01,9.5 754 | 2010-09-01,9.5 755 | 2010-10-01,9.4 756 | 2010-11-01,9.8 757 | 2010-12-01,9.3 758 | 2011-01-01,9.1 759 | 2011-02-01,9.0 760 | 2011-03-01,9.0 761 | 2011-04-01,9.1 762 | 2011-05-01,9.0 763 | 2011-06-01,9.1 764 | 2011-07-01,9.0 765 | 2011-08-01,9.0 766 | 2011-09-01,9.0 767 | 2011-10-01,8.8 768 | 2011-11-01,8.6 769 | 2011-12-01,8.5 770 | 2012-01-01,8.3 771 | 2012-02-01,8.3 772 | 2012-03-01,8.2 773 | 2012-04-01,8.2 774 | 2012-05-01,8.2 775 | 2012-06-01,8.2 776 | 2012-07-01,8.2 777 | 2012-08-01,8.1 778 | 2012-09-01,7.8 779 | 2012-10-01,7.8 780 | 2012-11-01,7.7 781 | 2012-12-01,7.9 782 | 2013-01-01,8.0 783 | 2013-02-01,7.7 784 | 2013-03-01,7.5 785 | 2013-04-01,7.6 786 | 2013-05-01,7.5 787 | 2013-06-01,7.5 788 | 2013-07-01,7.3 789 | 2013-08-01,7.3 790 | 2013-09-01,7.3 791 | 2013-10-01,7.2 792 | 2013-11-01,6.9 793 | 2013-12-01,6.7 794 | 2014-01-01,6.6 795 | 2014-02-01,6.7 796 | 2014-03-01,6.7 797 | 2014-04-01,6.2 798 | 2014-05-01,6.2 799 | 2014-06-01,6.1 800 | 2014-07-01,6.2 801 | 2014-08-01,6.2 802 | 2014-09-01,6.0 803 | 2014-10-01,5.7 804 | 2014-11-01,5.8 805 | 2014-12-01,5.6 806 | 2015-01-01,5.7 807 | 2015-02-01,5.5 808 | 2015-03-01,5.5 809 | 2015-04-01,5.4 810 | 2015-05-01,5.5 811 | 2015-06-01,5.3 812 | 2015-07-01,5.3 813 | 2015-08-01,5.1 814 | 2015-09-01,5.1 815 | 2015-10-01,5.0 816 | 2015-11-01,5.0 817 | 2015-12-01,5.0 818 | 2016-01-01,4.9 819 | 2016-02-01,4.9 820 | 2016-03-01,5.0 821 | 2016-04-01,5.0 822 | 2016-05-01,4.7 823 | 2016-06-01,4.9 824 | 2016-07-01,4.9 825 | 2016-08-01,4.9 826 | -------------------------------------------------------------------------------- /data/womendegrees.csv: -------------------------------------------------------------------------------- 1 | Year,Agriculture,Architecture,Art and Performance,Biology,Business,Communications and Journalism,Computer Science,Education,Engineering,English,Foreign Languages,Health Professions,Math and Statistics,Physical Sciences,Psychology,Public Administration,Social Sciences and History 1970,4.22979798,11.92100539,59.7,29.08836297,9.064438975,35.3,13.6,74.53532758,0.8,65.57092343,73.8,77.1,38,13.8,44.4,68.4,36.8 1971,5.452796685,12.00310559,59.9,29.39440285,9.503186594,35.5,13.6,74.14920369,1,64.55648516,73.9,75.5,39,14.9,46.2,65.5,36.2 1972,7.42071022,13.21459351,60.4,29.81022105,10.5589621,36.6,14.9,73.55451996,1.2,63.6642632,74.6,76.9,40.2,14.8,47.6,62.6,36.1 1973,9.653602412,14.7916134,60.2,31.14791477,12.80460152,38.4,16.4,73.50181443,1.6,62.94150212,74.9,77.4,40.9,16.5,50.4,64.3,36.4 1974,14.07462346,17.44468758,61.9,32.99618284,16.20485038,40.5,18.9,73.33681143,2.2,62.41341209,75.3,77.9,41.8,18.2,52.6,66.1,37.3 1975,18.33316153,19.13404767,60.9,34.44990213,19.68624931,41.5,19.8,72.80185448,3.2,61.64720641,75,78.9,40.7,19.1,54.5,63,37.7 1976,22.25276005,21.39449143,61.3,36.07287146,23.4300375,44.3,23.9,72.16652471,4.5,62.14819377,74.4,79.2,41.5,20,56.9,65.6,39.2 1977,24.6401766,23.74054054,62,38.33138629,27.16342715,46.9,25.7,72.45639481,6.8,62.72306675,74.3,80.5,41.1,21.3,59,69.3,40.5 1978,27.14619175,25.84923973,62.5,40.11249564,30.52751868,49.9,28.1,73.19282134,8.4,63.61912216,74.3,81.9,41.6,22.5,61.3,71.5,41.8 1979,29.63336549,27.77047744,63.2,42.06555109,33.62163381,52.3,30.2,73.82114234,9.4,65.08838972,74.2,82.3,42.3,23.7,63.3,73.3,43.6 1980,30.75938956,28.08038075,63.4,43.99925716,36.76572529,54.7,32.5,74.98103152,10.3,65.28413007,74.1,83.5,42.8,24.6,65.1,74.6,44.2 1981,31.31865519,29.84169408,63.3,45.24951206,39.26622984,56.4,34.8,75.84512345,11.6,65.83832154,73.9,84.1,43.2,25.7,66.9,74.7,44.6 1982,32.63666364,34.81624758,63.1,45.96733794,41.94937335,58,36.3,75.84364914,12.4,65.84735212,72.7,84.4,44,27.3,67.5,76.8,44.6 1983,31.6353471,35.82625735,62.4,46.71313451,43.54206966,58.6,37.1,75.95060123,13.1,65.91837999,71.8,84.6,44.3,27.6,67.9,76.1,44.1 1984,31.09294748,35.45308311,62.1,47.66908276,45.12403027,59.1,36.8,75.86911601,13.5,65.74986233,72.1,85.1,46.2,28,68.2,75.9,44.1 1985,31.3796588,36.13334795,61.8,47.9098841,45.747782,59,35.7,75.92343971,13.5,65.79819852,70.8,85.3,46.5,27.5,69,75,43.8 1986,31.19871923,37.24022346,62.1,48.30067763,46.53291505,60,34.7,76.14301516,13.9,65.98256091,71.2,85.7,46.7,28.4,69,75.7,44 1987,31.48642948,38.73067535,61.7,50.20987789,46.69046648,60.2,32.4,76.96309168,14,66.70603055,72,85.5,46.5,30.4,70.1,76.4,43.9 1988,31.08508746,39.3989071,61.7,50.09981147,46.7648277,60.4,30.8,77.62766177,13.9,67.14449816,72.3,85.2,46.2,29.7,70.9,75.6,44.4 1989,31.6124031,39.09653994,62,50.77471585,46.7815648,60.5,29.9,78.11191872,14.1,67.01707156,72.4,84.6,46.2,31.3,71.6,76,44.2 1990,32.70344407,40.82404662,62.6,50.81809432,47.20085084,60.8,29.4,78.86685859,14.1,66.92190193,71.2,83.9,47.3,31.6,72.6,77.6,45.1 1991,34.71183749,33.67988118,62.1,51.46880537,47.22432481,60.8,28.7,78.99124597,14,66.24147465,71.1,83.5,47,32.6,73.2,78.2,45.5 1992,33.93165961,35.20235628,61,51.34974154,47.21939541,59.7,28.2,78.43518191,14.5,65.62245655,71,83,47.4,32.6,73.2,77.3,45.8 1993,34.94683208,35.77715877,60.2,51.12484404,47.63933161,58.7,28.5,77.26731199,14.9,65.73095014,70,82.4,46.4,33.6,73.1,78,46.1 1994,36.03267447,34.43353129,59.4,52.2462176,47.98392441,58.1,28.5,75.81493264,15.7,65.64197772,69.1,81.8,47,34.8,72.9,78.8,46.8 1995,36.84480747,36.06321839,59.2,52.59940342,48.57318101,58.8,27.5,75.12525621,16.2,65.93694921,69.6,81.5,46.1,35.9,73,78.8,47.9 1996,38.96977475,35.9264854,58.6,53.78988011,48.6473926,58.7,27.1,75.03519921,16.7,66.43777883,69.7,81.3,46.4,37.3,73.9,79.8,48.7 1997,40.68568483,35.10193413,58.7,54.99946903,48.56105033,60,26.8,75.1637013,17,66.78635548,70,81.9,47,38.3,74.4,81,49.2 1998,41.91240333,37.59854457,59.1,56.35124789,49.2585152,60,27,75.48616027,17.8,67.2554484,70.1,82.1,48.3,39.7,75.1,81.3,50.5 1999,42.88720191,38.63152919,59.2,58.22882288,49.81020815,61.2,28.1,75.83816206,18.6,67.82022113,70.9,83.5,47.8,40.2,76.5,81.1,51.2 2000,45.05776637,40.02358491,59.2,59.38985737,49.80361649,61.9,27.7,76.69214284,18.4,68.36599498,70.9,83.5,48.2,41,77.5,81.1,51.8 2001,45.86601517,40.69028156,59.4,60.71233149,50.27514494,63,27.6,77.37522931,19,68.57852029,71.2,85.1,47,42.2,77.5,80.9,51.7 2002,47.13465821,41.13295053,60.9,61.8951284,50.5523346,63.7,27,78.64424394,18.7,68.82995959,70.5,85.8,45.7,41.1,77.7,81.3,51.5 2003,47.93518721,42.75854266,61.1,62.1694558,50.34559774,64.6,25.1,78.54494815,18.8,68.89448726,70.6,86.5,46,41.7,77.8,81.5,50.9 2004,47.88714025,43.46649345,61.3,61.91458697,49.95089449,64.2,22.2,78.65074774,18.2,68.45473436,70.8,86.5,44.7,42.1,77.8,80.7,50.5 2005,47.67275409,43.10036784,61.4,61.50098432,49.79185139,63.4,20.6,79.06712173,17.9,68.57122114,69.9,86,45.1,41.6,77.5,81.2,50 2006,46.79029957,44.49933107,61.6,60.17284465,49.21091439,63,18.6,78.68630551,16.8,68.29759443,69.6,85.9,44.1,40.8,77.4,81.2,49.8 2007,47.60502633,43.10045895,61.4,59.41199314,49.00045935,62.5,17.6,78.72141311,16.8,67.87492278,70.2,85.4,44.1,40.7,77.1,82.1,49.3 2008,47.570834,42.71173041,60.7,59.30576517,48.88802678,62.4,17.8,79.19632674,16.5,67.59402834,70.2,85.2,43.3,40.7,77.2,81.7,49.4 2009,48.66722357,43.34892051,61,58.48958333,48.84047414,62.8,18.1,79.5329087,16.8,67.96979204,69.3,85.1,43.3,40.7,77.1,82,49.4 2010,48.73004227,42.06672091,61.3,59.01025521,48.75798769,62.5,17.6,79.61862451,17.2,67.92810557,69,85,43.1,40.2,77,81.7,49.3 2011,50.03718193,42.7734375,61.2,58.7423969,48.18041792,62.2,18.2,79.43281184,17.5,68.42673015,69.5,84.8,43.1,40.1,76.7,81.9,49.2 -------------------------------------------------------------------------------- /tmp/1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaozhiagi/Matplotlib-and-Seaborn/73a5d3af5e92f0b326ca55eea8edcd073ccdc54e/tmp/1.png -------------------------------------------------------------------------------- /tmp/subplot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaozhiagi/Matplotlib-and-Seaborn/73a5d3af5e92f0b326ca55eea8edcd073ccdc54e/tmp/subplot.png -------------------------------------------------------------------------------- /tmp/subplot1.png: 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4.9,3.1,1.5,0.1,Iris-setosa 11 | 5.4,3.7,1.5,0.2,Iris-setosa 12 | 4.8,3.4,1.6,0.2,Iris-setosa 13 | 4.8,3.0,1.4,0.1,Iris-setosa 14 | 4.3,3.0,1.1,0.1,Iris-setosa 15 | 5.8,4.0,1.2,0.2,Iris-setosa 16 | 5.7,4.4,1.5,0.4,Iris-setosa 17 | 5.4,3.9,1.3,0.4,Iris-setosa 18 | 5.1,3.5,1.4,0.3,Iris-setosa 19 | 5.7,3.8,1.7,0.3,Iris-setosa 20 | 5.1,3.8,1.5,0.3,Iris-setosa 21 | 5.4,3.4,1.7,0.2,Iris-setosa 22 | 5.1,3.7,1.5,0.4,Iris-setosa 23 | 4.6,3.6,1.0,0.2,Iris-setosa 24 | 5.1,3.3,1.7,0.5,Iris-setosa 25 | 4.8,3.4,1.9,0.2,Iris-setosa 26 | 5.0,3.0,1.6,0.2,Iris-setosa 27 | 5.0,3.4,1.6,0.4,Iris-setosa 28 | 5.2,3.5,1.5,0.2,Iris-setosa 29 | 5.2,3.4,1.4,0.2,Iris-setosa 30 | 4.7,3.2,1.6,0.2,Iris-setosa 31 | 4.8,3.1,1.6,0.2,Iris-setosa 32 | 5.4,3.4,1.5,0.4,Iris-setosa 33 | 5.2,4.1,1.5,0.1,Iris-setosa 34 | 5.5,4.2,1.4,0.2,Iris-setosa 35 | 4.9,3.1,1.5,0.1,Iris-setosa 36 | 5.0,3.2,1.2,0.2,Iris-setosa 37 | 5.5,3.5,1.3,0.2,Iris-setosa 38 | 4.9,3.1,1.5,0.1,Iris-setosa 39 | 4.4,3.0,1.3,0.2,Iris-setosa 40 | 5.1,3.4,1.5,0.2,Iris-setosa 41 | 5.0,3.5,1.3,0.3,Iris-setosa 42 | 4.5,2.3,1.3,0.3,Iris-setosa 43 | 4.4,3.2,1.3,0.2,Iris-setosa 44 | 5.0,3.5,1.6,0.6,Iris-setosa 45 | 5.1,3.8,1.9,0.4,Iris-setosa 46 | 4.8,3.0,1.4,0.3,Iris-setosa 47 | 5.1,3.8,1.6,0.2,Iris-setosa 48 | 4.6,3.2,1.4,0.2,Iris-setosa 49 | 5.3,3.7,1.5,0.2,Iris-setosa 50 | 5.0,3.3,1.4,0.2,Iris-setosa 51 | 7.0,3.2,4.7,1.4,Iris-versicolor 52 | 6.4,3.2,4.5,1.5,Iris-versicolor 53 | 6.9,3.1,4.9,1.5,Iris-versicolor 54 | 5.5,2.3,4.0,1.3,Iris-versicolor 55 | 6.5,2.8,4.6,1.5,Iris-versicolor 56 | 5.7,2.8,4.5,1.3,Iris-versicolor 57 | 6.3,3.3,4.7,1.6,Iris-versicolor 58 | 4.9,2.4,3.3,1.0,Iris-versicolor 59 | 6.6,2.9,4.6,1.3,Iris-versicolor 60 | 5.2,2.7,3.9,1.4,Iris-versicolor 61 | 5.0,2.0,3.5,1.0,Iris-versicolor 62 | 5.9,3.0,4.2,1.5,Iris-versicolor 63 | 6.0,2.2,4.0,1.0,Iris-versicolor 64 | 6.1,2.9,4.7,1.4,Iris-versicolor 65 | 5.6,2.9,3.6,1.3,Iris-versicolor 66 | 6.7,3.1,4.4,1.4,Iris-versicolor 67 | 5.6,3.0,4.5,1.5,Iris-versicolor 68 | 5.8,2.7,4.1,1.0,Iris-versicolor 69 | 6.2,2.2,4.5,1.5,Iris-versicolor 70 | 5.6,2.5,3.9,1.1,Iris-versicolor 71 | 5.9,3.2,4.8,1.8,Iris-versicolor 72 | 6.1,2.8,4.0,1.3,Iris-versicolor 73 | 6.3,2.5,4.9,1.5,Iris-versicolor 74 | 6.1,2.8,4.7,1.2,Iris-versicolor 75 | 6.4,2.9,4.3,1.3,Iris-versicolor 76 | 6.6,3.0,4.4,1.4,Iris-versicolor 77 | 6.8,2.8,4.8,1.4,Iris-versicolor 78 | 6.7,3.0,5.0,1.7,Iris-versicolor 79 | 6.0,2.9,4.5,1.5,Iris-versicolor 80 | 5.7,2.6,3.5,1.0,Iris-versicolor 81 | 5.5,2.4,3.8,1.1,Iris-versicolor 82 | 5.5,2.4,3.7,1.0,Iris-versicolor 83 | 5.8,2.7,3.9,1.2,Iris-versicolor 84 | 6.0,2.7,5.1,1.6,Iris-versicolor 85 | 5.4,3.0,4.5,1.5,Iris-versicolor 86 | 6.0,3.4,4.5,1.6,Iris-versicolor 87 | 6.7,3.1,4.7,1.5,Iris-versicolor 88 | 6.3,2.3,4.4,1.3,Iris-versicolor 89 | 5.6,3.0,4.1,1.3,Iris-versicolor 90 | 5.5,2.5,4.0,1.3,Iris-versicolor 91 | 5.5,2.6,4.4,1.2,Iris-versicolor 92 | 6.1,3.0,4.6,1.4,Iris-versicolor 93 | 5.8,2.6,4.0,1.2,Iris-versicolor 94 | 5.0,2.3,3.3,1.0,Iris-versicolor 95 | 5.6,2.7,4.2,1.3,Iris-versicolor 96 | 5.7,3.0,4.2,1.2,Iris-versicolor 97 | 5.7,2.9,4.2,1.3,Iris-versicolor 98 | 6.2,2.9,4.3,1.3,Iris-versicolor 99 | 5.1,2.5,3.0,1.1,Iris-versicolor 100 | 5.7,2.8,4.1,1.3,Iris-versicolor 101 | 6.3,3.3,6.0,2.5,Iris-virginica 102 | 5.8,2.7,5.1,1.9,Iris-virginica 103 | 7.1,3.0,5.9,2.1,Iris-virginica 104 | 6.3,2.9,5.6,1.8,Iris-virginica 105 | 6.5,3.0,5.8,2.2,Iris-virginica 106 | 7.6,3.0,6.6,2.1,Iris-virginica 107 | 4.9,2.5,4.5,1.7,Iris-virginica 108 | 7.3,2.9,6.3,1.8,Iris-virginica 109 | 6.7,2.5,5.8,1.8,Iris-virginica 110 | 7.2,3.6,6.1,2.5,Iris-virginica 111 | 6.5,3.2,5.1,2.0,Iris-virginica 112 | 6.4,2.7,5.3,1.9,Iris-virginica 113 | 6.8,3.0,5.5,2.1,Iris-virginica 114 | 5.7,2.5,5.0,2.0,Iris-virginica 115 | 5.8,2.8,5.1,2.4,Iris-virginica 116 | 6.4,3.2,5.3,2.3,Iris-virginica 117 | 6.5,3.0,5.5,1.8,Iris-virginica 118 | 7.7,3.8,6.7,2.2,Iris-virginica 119 | 7.7,2.6,6.9,2.3,Iris-virginica 120 | 6.0,2.2,5.0,1.5,Iris-virginica 121 | 6.9,3.2,5.7,2.3,Iris-virginica 122 | 5.6,2.8,4.9,2.0,Iris-virginica 123 | 7.7,2.8,6.7,2.0,Iris-virginica 124 | 6.3,2.7,4.9,1.8,Iris-virginica 125 | 6.7,3.3,5.7,2.1,Iris-virginica 126 | 7.2,3.2,6.0,1.8,Iris-virginica 127 | 6.2,2.8,4.8,1.8,Iris-virginica 128 | 6.1,3.0,4.9,1.8,Iris-virginica 129 | 6.4,2.8,5.6,2.1,Iris-virginica 130 | 7.2,3.0,5.8,1.6,Iris-virginica 131 | 7.4,2.8,6.1,1.9,Iris-virginica 132 | 7.9,3.8,6.4,2.0,Iris-virginica 133 | 6.4,2.8,5.6,2.2,Iris-virginica 134 | 6.3,2.8,5.1,1.5,Iris-virginica 135 | 6.1,2.6,5.6,1.4,Iris-virginica 136 | 7.7,3.0,6.1,2.3,Iris-virginica 137 | 6.3,3.4,5.6,2.4,Iris-virginica 138 | 6.4,3.1,5.5,1.8,Iris-virginica 139 | 6.0,3.0,4.8,1.8,Iris-virginica 140 | 6.9,3.1,5.4,2.1,Iris-virginica 141 | 6.7,3.1,5.6,2.4,Iris-virginica 142 | 6.9,3.1,5.1,2.3,Iris-virginica 143 | 5.8,2.7,5.1,1.9,Iris-virginica 144 | 6.8,3.2,5.9,2.3,Iris-virginica 145 | 6.7,3.3,5.7,2.5,Iris-virginica 146 | 6.7,3.0,5.2,2.3,Iris-virginica 147 | 6.3,2.5,5.0,1.9,Iris-virginica 148 | 6.5,3.0,5.2,2.0,Iris-virginica 149 | 6.2,3.4,5.4,2.3,Iris-virginica 150 | 5.9,3.0,5.1,1.8,Iris-virginica 151 | --------------------------------------------------------------------------------