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132 lines
22 KiB
132 lines
22 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"__下面我们对第四个问题的数据进行分析，这一列数据记录了“你是否通过手机、PAD等智能终端学习课程、完成作业、搜索难题？”.答案共有三个，运行代码看一下，我们的调查结果的分布是怎么样的吧__"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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VNSIU3Jd7m11vqOd43nPaFcUzyETZOGvwQf/xfoNPaZm5n9l8NVDsaYu6wmmHtT3AJ/gvi1bV3j6g/wDdtWSeRI3XLdphbYa5IFozMzeh/qm7jgyU8LpBG84M4wPfVbt2VeiuIwM1eN2grnA2a2o3K5z3Ze3MvETiY911ZJgqrxvUFU57yr8HCci1VdWVuuvIMJ6vPqErnNs0tZs1FtTUzuydSPxDdx0ZJGt6zq2a2s0q11Xu8vwMYwbLmnDantMDp9XWzShIJD7QXUeGsOG0UusXO6s8P8uYoTx/z9pwZriT6+qn9WlJ2NX6us/znd7sMWcWWLyz0k7G7j7Pb+yw4cwCJ9Q3TAm2tLynu4401gh87nWjusK5AYhrajsr3bSjUnTXkMY2Rkoint8PrmmvlGgjYJd19NDRDY2TBrS0/E13HWlKy87iOu+ttW8Uj91SUWk3K+6aiI5GdYbzXY1tZ6VZjY0TB8fjdjmTzntbR6O258wyt+6ozNddQxpao6NRneF8D7Cn+D02rTE2YVg8bhei7rgtkZLIdh0N6wunE60HPtLWfhZbWrEziFJ2NYqO0TKkBf2TrbUcd4Z+XcPE5bVMvreWafe7y71WNShOWFnHgb+p5YSVdexqcN+7r22Oc9jyWqY/UMsnVe5k+OpGxfxH69L2/T0p1jRuZDyuZaiWhrSNMnSH8yVdDa8uyee9S/fjnYXu+tZlr8aYt3+Aj6/Yj3n7Byh7NQbAsjea+MOC3tx8XC+Wv+3uG7TkpRjXzclDJH0vHS6tqByIUp4vvZGGXtTVsO5whjFksa8/fRSnZJJ7paFkUg5//Mi9RyLHDw1xqG9W5Pjhn1UJPq9JcHQovRcuPKSp6cBQs+0992EnoO3stt53mBPdjhN8G5jhZbMicOLKekTgkqm5LJyayxe1CQr7uL+rCvv4qKhzO5X/mJPHwmca6Z0DK0/vzdWrGllybJ6X5faYpRU7h541YlgLIinfUn3LQ1uoea+GQN8AB950IACb79lM0zZ39NFS34I/38/YJWOp+7iOrSu24svxMfLSkeQNzaOlroXPln/G6EWjdY5QVkVKItpGFyb8+v8zHofztQsLGJ4M4Akr6xk/6OsHEJOH+XnzRwUAvLwpzvA+PhTwvd/Xk+MTlp2Yx9D9dA9AumZcc/OYsc3Nr32Smzs71a/df05/Bs4byJYHtux5rOjyoj1/3vbYNvz57u+EyucqKfpJEc07m6l6sYrCcwqpeLqCwacM1n3o8JzOxk14Vz3jdYPDkz3kkAIfp48P8NbnLQzdz8e2GveX5LaaBEMK9v6nUUpx48sxrj8qj8UvxVh8TB7nH5bDnWvSe//aZRU7R6JUyi9pFYwrwF/QfoeslCL6dpTgEUH3AT+oZkWiKYH4hVhFjPiuOAXjC1JdVmco4HmdBegPpxN9D/jMq+bqmhQ1MbXnz6v+2cKhQ/ycelCAFWvdRQFXrG3mO+P2HlSsWNtM8YEB+vcW6pvBJ+5HfZqvIzimOT56fFPzG162Wb+hnkDfAHnD3MODwcWD+fyRz6lcVcnA4wdS8fsKhpwxxMuS2vNupCSidYlRE4a14A5tL/OioS/qFKc/Xg9APAHnHprDt8YGmD7cx4LfN/DQ35spCgpPfverG2nqmxUr1jaz6nz3sX+fmcuZTzSQ64fHzuztRdk9amnFztGnjCxsRsSTe2+jb0bpd0S/PV/3Ht2bA35xAAB1H9UR6O++LTffsxnxC4VnFxIIev5WfczrBtsSI67VOcGTgP/RXUY2O2f40FfW5eXNTeVrNu1oYtOvN+05IQSgWhTrr1rPWGcsOQP2/l2glKJ8aTlFlxexdeVWhpw6hKadTdRvqGfoWUNTWdq+JICRkZKI1hU79A9rXS8AO3QXkc2WVuw8AKV6/AC69oNa8grz/iWYANWvVtNnUh/8BX4STQn33enD/bO3XtQdTDAlnE60CXhEdxnZbES8ZfiUWOzNVL3eZ8s/Y+ONG4ltj7H+qvVUveQuXhdds/eQdrdELEH1a9UMPG4gAIPmD2LzXZv54skvGHCc5/syPep1g+0xY1gL4ATH4E5qTd/bbtLcdr9/+wmjhvdDpJfuWjRqAIZGSiLa1/w1o+cEcKIb0XzqOtsNa2kZNr0xlu0zVp40IZhgUjhdy3UXkO3+c8fOg1GqXncdGt2hu4DdTAtnGA+veVr/anBLYvCshkZt06Q0eyVSEjFmEQCzwulEW4D7dZeR7W7ZUXkoStXqrkODX+suoDWzwul6ELt/p1YDEomBRzc0ZNsaT58Cf9RdRGvmhdOJbgdW6i4j2924o+owlPpSdx0eulPnDJT2mBdO1xJs76lVv0Si//H1DcYcf/WwbcB9uotoy8xwOtFy7E0J2i3eWTkFpaK66/DAjZGSiHG7sZkZTteNQEx3Edmsb0IFT66r/7vuOnrYp8ADuotoT7fDKSJTRaTDa6GKSNG+nwU40c+Au7pal5Ua1++smipKeb5xrIcWR0oiRh5CpaLnvB7YM29KRCYnPxcmPw9q8/ybpePLYtwE7EpBjVYX7adUn+/U1mXq/p7rMeQ+2vZ0+t5aETkXuBz3hE0OEAI+bvWUA4CLgVuAKBAEpqnkSm8iskwptajDDTrBRcDSThVppVS9SN3M0SPrlchg3bWk2MmRksizuov4Op3uOZVSv1NKzVFKHQs8AcxUSh2b/PoipVSRUup54JnkY08DB4nIaBEZDfQRkaNEZHgHm7wLDRuXWl/JV6rgrJraTNsV7k8mBxO6MawVkRnASKBARMaJyHjghlZPmS8iq4FJuEPTrUqpTUBUKfWyUqpjG+g60RhwEYYsoZmtrq2qnuFT6gvddaRIPXCl7iL2pdPhFBG/iMzFHbKeCdyb/FiO+0Pv9lyy53wPqAFWSleXUnOiLwH3dOl7rZTopVTvs7+szZTtMxZHSiLlHXmiiIxq57F2l3MRkZSumdqVnnMI8CpQBdy/e0ibDGLra2J7gqjcWQ7/Tffmav4M97S3pcmiql1H+JTSvkJAN70P3NaRJyZPXN4mIgEReUhEVidHg6+LSG6r5x0rIrOBM0TkCBGZJyKnd7fQrhxzblPuWSQBFu4uOFl0sNVTT04+NjX5fX9U3Vn+34nWYYe3WuVC3g+iNVp2eU6RJuCHkZJIvIPPPwX4P7g5WZTsgK4DHgfiACIyHXdl+FOAQmA47qHcBhE5U0T6drXY7lxKEb655wwnHwunrE0nuhoDb7PKJlfsqj7Cr9SWfT/TSD/v6JSw5BB1sFJqA/BT4PDkCc2LgceUUgkRGQCciDvNcRlwN+5Cdf8P9yTmJKDLQ93uhHPPtUoR6S8ibZe2vAlAKXVf8jlDROQ6IJfuuRbY1M3XsLooF3Ivqv6yXHcdXfACnbskdzIwTER+gnvepB6YpZS6EJgnInfg5ud+3AC+jrtC/HPAamAocIdSqssL13V5DaHkXUH5Sqmdya+LgGr1DTMZRMQH0K3hLYATnAeswuzbDzNWHOLTQ6O2xjt6t5d+VcBhkZLI5535JhHpDYwHvgBiSqnK5ON9gQVKqQeTXxcCC4AI7pWJecDtqpsr6ZuzwFdnOcFS3BsdLA3u69f31bv695uju44OOitSEvlDZ74hGcwlwDjcw7VPgGOB0bgnJsNKqaUi0g+3o6gDBuP2sHVAH+BkpVSXV41P33ACOMHHgLN1l5GNWqBlemjU5maR/XXXsg+3RUoiHb8jLUlE+gPfAR5XSjWIiF8p1SIiS5RS17d6Xh/g35VSi0XkLOAfSqkPROSXwK1KqS7Pdkn3YeGFQLbMOTSKH/xX7Kru2I0k+jyHe46iK0LAGOA0EZlGq3MsACIyNnk5pXWGBPCJyOFAYXeCCenecwI4wVG4G5xq3/km2yQgMX30qE+bfHKA7lra8RFwRKQk0qX5qMnhalwpVSsiNwJHAW2PIRcDbwOHK6VeSd53vh7YCJytlLq36+VnQjgBnOAc3O3BPdmIx/rKb/vu90bZwAFH6q6jjWrcYG7wstHkpZUapVRKpqCl+7DW5URfBX6iu4xsdO6XtTN7JRKehmAfmoHveR1MAKVUVaqCCZkSTgAnej/27K3nBOSaqmpT5twmgPMjJZFVugtJhcwY1rbmBG8FrtFdRraZMXrkPxp8voM1lqCAiyMlkYc01pBSmdNz7uZEr6WDNzZbqXNd5S7di1AvyqRgQiaGE8CJLsKgPS+ywWm1ddMLEokPNDW/OFISuV1T2z0mM8MJ4ESvxC4Q5qlf7Kxq1NDsdZGSiKOh3R6XececbTnBe4C2N+VbPWRW0cj3a/y+wzxoKgFcFimJZOzeOpnbc37lx8DNuovIFjfsrPRiS4Mm4OxMDiZkQ8+5mxM8D3eTpGzetdkTc4tGvFft90/uoZevA87IlMsl3yQbek6XE/0t7qyCTFmkylg37ajsznI036QcmJ0NwYRsCieAE30TmAGs1V1KJjuqoXHSwJaWVE9IeAGYFimJZM3/XXaFE8CJbgZmY9hejJmmrGJnd1e8aO12YH6kJFKZwtc0XvYcc7blBAVwcBdw6uj2EFYnzBs1/J2KQGBaN16iDrg0UhIxdsuEnpS94dzNCc4EVgAH6S4l07zTK+/DHxYOndDFb18DfD9SEvl4n8/MUNk3rG3LPQ6dDNyJXXYzpaY1xiYUxuNvdfLb4rgjmjnZHEywPefenOCxuJv2jtZdSqZYm5f70fmFQw+iY6v9b8DtLTsb6Ixke87W3HVxDwMe1l1KppgUaxo3Mh5fs4+nNeHeKDLFBvMrtuf8Ok7wRNyFgg/VXUq6+zA355PvDR82huTSqG08B/xbtg9h22PD+U2coA+4AHf3tBF6i0lv3x5R+Hp5bs6sVg9tAq6KlESe0lWT6Ww4O8IJ5gNX4a7k1uW9L7LZhpycT88cMawIkRrgVuCOSEmkfl/fl81sODvDCQ4GfgFcgl1MrLNqFg4dvOSN/N4PREoi1bqLSQc2nF3hBMcCVwPfB/I1V2O6atzLVHfgRKt0F5NObDi7wwn2w92W8MeA6Sufe20j7iY/9+JEu7R2bLaz4UwF98TRKcC/4W5ik61iwFPAA8BqnKh9c3WDDWeqOcEJuD3pAmCQ5mq8sg53ruxKO3RNHRvOnuIEA7jzRxcApwMD9RaUcptwN4pdgRPd100GVhfYcHrBDeps3KHvt3G3lUs3MeAl3JsGnsWJrtdcT8az4dTBPds7F3fi9wxgIuZdmkkAHwN/AZ4F/ooTtdclPWTDaQIn2AuYwldhnQGM9bCCatxdmdcC7yc/1uFE6zyswWrDhtNUbmBHAKOAke18LgTycCeKB5IffvaeON4M7AR2tPmoSH7eAkSSq0NYhrHhzERO0A2sE43pLsXqOhtOyzKUnc9pWYay4bQsQ9lwWpahbDgty1A2nJZlKBtOyzKUDadlGcqG07IMZcNpWYay4bQsQ9lwWpahbDgty1A2nJZlKBtOyzKUDadlGcqG07IMZcNpWYay4bQsQ9lwWpahbDgty1A2nJZlKBtOyzKUDadlGcqG07IMZcNpWYay4bQsQ9lwWpah/hfbY3FYK9h3mwAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib\n",
|
|
"import pandas\n",
|
|
"import numpy as np \n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import pandas as pd\n",
|
|
"data = pd.read_csv(\"中学生移动学习调查问卷答卷汇总.csv\")\n",
|
|
"data1=data.values[:,9]\n",
|
|
"freq=[0,0,0]\n",
|
|
"for item in data1:\n",
|
|
" if item == '很少':\n",
|
|
" freq[0]+=1\n",
|
|
" elif item=='有时':\n",
|
|
" freq[1]+=1\n",
|
|
" else:\n",
|
|
" freq[2]+=1\n",
|
|
"x = [1,2,3]\n",
|
|
" \n",
|
|
"labels=[u\"很少\", u\"有时\", u\"经常\"]\n",
|
|
"plt.axes(aspect=1)\n",
|
|
"patches,l_text,p_text=plt.pie(freq,labels=labels,autopct='%.0f%%')\n",
|
|
"for t in l_text:\n",
|
|
" t.set_fontproperties(matplotlib.font_manager.FontProperties(fname=\"/simsun.ttc\"))\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"__同样的我们对第七个问题的数据进行分析，这一列数据记录了“你每周使用移动学习软件的时间一般有多久？”.答案共有三个，运行代码看一下，我们的调查结果吧__"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"data2 = data.values[:,12]\n",
|
|
"\n",
|
|
"time=[0.,0.,0.]\n",
|
|
"for item in data2:\n",
|
|
" if item == '小于1小时':\n",
|
|
" time[0]+=1\n",
|
|
" elif item=='1~5小时':\n",
|
|
" time[1]+=1\n",
|
|
" else:\n",
|
|
" time[2]+=1\n",
|
|
"x = [1,2,3]\n",
|
|
"\n",
|
|
"from matplotlib.font_manager import FontProperties\n",
|
|
"\n",
|
|
"font = FontProperties(fname='/simsun.ttc')\n",
|
|
"\n",
|
|
"plt.bar(x,time)\n",
|
|
"plt.xticks(x, [u\"小于1小时\", u\"1~5小时\", u\"大于5小时\",], fontproperties=font)\n",
|
|
"\n",
|
|
"plt.xlabel(u\"中学生每周移动学习时间\",fontproperties=font)\n",
|
|
"#plt.ylabel(u\"百分比\", fontproperties=font)\n",
|
|
"#plt.title(u\"lindi\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|