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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "markdown",
  5. "metadata": {},
  6. "source": [
  7. "# 处理数据,获取信息\n",
  8. "__经过长年观测,这些研究站的工作人员采集了大量鸟类活动数据。这些数据被多名科学家共享,他们对这些数据进行加工、分析,从而得出各种信息,为各自的科研服务。__\n",
  9. "\n",
  10. "__现在人们越来越多地通过计算机来表示、组织和处理数据,从而可以获取并传播有价值的信息。__\n",
  11. "\n",
  12. "__例如,科学技术用计算机汇总2010年10月到2012年10月的数据后得知,在实验林地共观测到鸟类44种、4823次。其中,留鸟23中,冬候鸟有9中,旅鸟有8种,夏候鸟有4种。进一步处理这些数据 ,能够得出以下鸟类居留型种数的柱状图和居留型比例的饼图。__"
  13. ]
  14. },
  15. {
  16. "cell_type": "markdown",
  17. "metadata": {},
  18. "source": [
  19. "# * 试试运行下方单元的代码,绘制出柱状图吧!"
  20. ]
  21. },
  22. {
  23. "cell_type": "code",
  24. "execution_count": 2,
  25. "metadata": {},
  26. "outputs": [
  27. {
  28. "data": {
  29. "image/png": 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\n",
  30. "text/plain": [
  31. "<Figure size 432x288 with 1 Axes>"
  32. ]
  33. },
  34. "metadata": {
  35. "needs_background": "light"
  36. },
  37. "output_type": "display_data"
  38. }
  39. ],
  40. "source": [
  41. "import matplotlib.pyplot as plt\n",
  42. "import numpy as np\n",
  43. "from matplotlib.font_manager import FontProperties\n",
  44. "\n",
  45. "font = FontProperties(fname='/simsun.ttc')\n",
  46. "\n",
  47. "birds = [23,9,8,4]\n",
  48. "x = [1,2,3,4]\n",
  49. "plt.bar(x,birds)\n",
  50. "plt.xticks(x, [u\"留鸟\", u\"冬候鸟\", u\"旅鸟\", u\"夏候鸟\"], fontproperties=font)\n",
  51. "\n",
  52. "plt.xlabel(u\"林地内鸟类居流行种数柱状图\",fontproperties=font)\n",
  53. "plt.ylabel(u\"物种种数(种)\", fontproperties=font)\n",
  54. "#plt.title(u\"lindi\")\n",
  55. "plt.show()"
  56. ]
  57. },
  58. {
  59. "cell_type": "markdown",
  60. "metadata": {},
  61. "source": [
  62. "* 试试运行下方单元的代码,绘制出饼状图吧!"
  63. ]
  64. },
  65. {
  66. "cell_type": "code",
  67. "execution_count": 2,
  68. "metadata": {},
  69. "outputs": [
  70. {
  71. "data": {
  72. "image/png": 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\n",
  73. "text/plain": [
  74. "<Figure size 432x288 with 1 Axes>"
  75. ]
  76. },
  77. "metadata": {},
  78. "output_type": "display_data"
  79. }
  80. ],
  81. "source": [
  82. "import matplotlib\n",
  83. "labels=[u\"留鸟\", u\"冬候鸟\", u\"旅鸟\", u\"夏候鸟\"]\n",
  84. "plt.axes(aspect=1)\n",
  85. "patches,l_text,p_text=plt.pie(birds,labels=labels,autopct='%.0f%%')\n",
  86. "for t in l_text:\n",
  87. " t.set_fontproperties(matplotlib.font_manager.FontProperties(fname=\"/simsun.ttc\"))\n",
  88. "plt.show()"
  89. ]
  90. },
  91. {
  92. "cell_type": "code",
  93. "execution_count": null,
  94. "metadata": {},
  95. "outputs": [],
  96. "source": []
  97. }
  98. ],
  99. "metadata": {
  100. "kernelspec": {
  101. "display_name": "Python 3",
  102. "language": "python",
  103. "name": "python3"
  104. },
  105. "language_info": {
  106. "codemirror_mode": {
  107. "name": "ipython",
  108. "version": 3
  109. },
  110. "file_extension": ".py",
  111. "mimetype": "text/x-python",
  112. "name": "python",
  113. "nbconvert_exporter": "python",
  114. "pygments_lexer": "ipython3",
  115. "version": "3.7.3"
  116. }
  117. },
  118. "nbformat": 4,
  119. "nbformat_minor": 2
  120. }