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@tadamatu
Created December 2, 2018 12:35
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"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": [
"# グラフのタイトル(title)\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"\n",
"x = np.arange(5) # 0~5の等差数列\n",
"y = (4, 1, 3, 2, 5)\n",
"width = 0.5\n",
"plt.bar(x, y, width, align='center') #棒グラフの描画\n",
"\n",
"plt.title('This is title') #タイトルを設定\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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": [
"# 凡例の追加 (legend)\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5]\n",
"y1 = [1, 1, 2, 3, 5]\n",
"y2 = [0, 4, 2, 6, 8]\n",
"y3 = [1, 3, 5, 7, 9]\n",
"plt.stackplot(x, y1, y2, y3) #積上げ折れ線グラフの描画\n",
"\n",
"plt.legend(['dataA', 'dataB', 'dataC']) # 凡例を設定\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# 色の凡例の追加 (colorbar)\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"delta = 0.025\n",
"x = np.arange(-4.0, 3.0, delta)\n",
"y = np.arange(-2.0, 2.0, delta)\n",
"X, Y = np.meshgrid(x, y)\n",
"Z1 = np.exp(-X**2 - Y**2)\n",
"Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)\n",
"Z = (Z1 - Z2) * 2\n",
"plt.figure()\n",
"plt.contourf(X, Y, Z)\n",
"\n",
"plt.colorbar() # 色の凡例を設定\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# 色の凡例の追加 (colorbar)\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# np.random.seed(0) # ランダム値の固定\n",
"x = np.random.randn(100000)\n",
"y = np.random.randn(100000) + 5\n",
"plt.hist2d(x, y, bins=40)\n",
"\n",
"plt.colorbar() # 色の凡例を設定\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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": [
"# ラベルの表示 (xlabel/ylabel)\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"np.random.seed(0) # ランダム値の固定\n",
"N = 50\n",
"x = np.random.rand(N)\n",
"y = np.random.rand(N)\n",
"area = (30 * np.random.rand(N))**2\n",
"\n",
"plt.scatter(x, y, s=area, alpha=0.5) # 散布図の表示\n",
"\n",
"plt.xlabel(\"Label X\") #ラベルXを設定\n",
"plt.ylabel(\"Label Y\") #ラベルYを設定\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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dI+rz6boDjod8YaHlgY/i2ZycybSbo0usJQxs14ThnUJ5efEuro2O9Cgs9qWf4MkF27j/og60C/VNraQ08zemkJmTX+LlccvGdbjhvBa8u2o/tw9uXeE5i37pmYXbaFynJrcPKbnrX3lNOr8Nn6xN4p2V+7jnQmf+cJxNYaFl8icbMMbw9DXdS7z56ednaFa/Fs3q1/ppoNfY3uGMe30FN8+I5ePfDqRROT5nH6zeT3ZuwVm7TZZXrZr+DG7fhMHtmwBw/FQ+q/ceYeXudFbsSeflRTsptBAcGECHsGAu79GcTmHBdAyrR8emwY5NHhcaHMTz1/Xkpumx/P2LzTx5dXdHtlsu1lqvf/Xp08eeSUFBoX3u6+225eR5dvRLS23S0Wx7OCvHjn0lxrZ6eJ6dtnTPGderymbE7LEtJ8+z21IyHd3us8X/T2/8sKvUZXcczLStH55n//b5pgrv73BWjr3g6UU/nZu8/IIKb6syXfPaMjv0mcW2sLCwxGXSsnJs58cW2N+8HefIPpfuSLMtJ8+z0yvh8zlxxirb+x9f25O5+Y5v+5feWp5gW06eZ2et2leh9VftSbftH51vr3g5xp44lVemdfLyC+yAx7+1176+vEL7rKiM7FybdDT7rJ8TJz25YKttOXmenbv+gEfbAeJsOfO2So149fMz/HFkB6bdHM3ewye4/L8xjH11OZuTM3n1+t7c7uBfem+5vEdzAvwMcxyc5uCL+GRe+m4n4/pEcGcZrhDaNw3mur6RvLtyH/vSyz/I5mRuAXe8HUfKsRzuGtqWjQeOMXVp1Rns8aNdh7JYvfdoqfO1N6kbyKTz2/LV5oOs3e/ZHCjWWp5ZuI3wBrW4ob/zN9cmnd+W9BO5fFLJYy72pZ/gifnbuKBDCNf1jazQNvq1bsR/J/RiQ1IGd727lryC0nvNLdh0kORjOZXSnHk29WvVILxBLa/N63/fyA70btGAP83ZWKHfQU9UqZD/0YjOTZl79yAa16nJ8VP5vH9nfy7p1szXxaqQJnUDuaBDCHPXJTvSDS0+MYMHPor/WU+asvjjiA7U8Pfj6a+2l2t/BYVFPS3WJ2bw4vheTB7ViUu6hvH8tzuq3CRss2ITqeFfNFVAae4Y0pomdQN5cv42j7q4Ldx8kPikY9w7onLGaPRv04juEfWZumRPpXVjLCy0PPjRBgL8DU9e3c2j4Lu4SxiPj+3GDzvSeOjjDaV2350ek0CrxrW5sJO7pw+u4e/HSxN64Wfg/2atq1C38YqqkiEPRUO95987hKUPDaNPy4a+Lo5HruodwcHMHFbsTvdoOweP5XDn23E0qRvIa7/oSVOa0HpB3DmkDV9uTClz7dVay9+/2MzXW1L56+jOjOpa9MCGf1zRlTo1/Xnw43iv9J8ui5y8Aj5Zm8RFncPKNJVFncAA7h3Rnti9R1i07VCF9llQaPnP1ztoG1KHqyqpi6oxht+c35a96dl8U0ljLmYsSyB27xH+enkXj7p+/mh8vxY8cFEHPl13gCcWbC1xuTX7jrI+MYNbB5Vt8FN1F9GwNk9f050NScd4+qvyjUY/cSqf5bsqNrCxyoY8FP31q+NBH9eq4sKoUIKDAjxqsjmZW8Cdb8dx4lQ+0ydGV2hOnknntyEkOJDHv9xaptrrlCV7eHvFPu4c0pqJg/7XVBYSHMjfxnRh3f4M3lxWNZptFm4+SEZ2Xrn6I4/vG0nrJnV46qttFfpjNWdtErsOHefBizsSUEJ/aSeM6hpGi0a1eWPJHscH1uxOO84zC7czIiqUq3s794fq98PaccuAlkxdmsCUEgZ1zYhJoF5QANeU4crLLUZ1bcZN/VsyLSaBRdtSS1zOWsuW5Exe/2E3E6aspOc/vub6aasqtM8qHfJuEVTDn9Hdm/HVpoNk5+aXvsIvFBZa7vtwPZuSj/Hi+F50CqvYdMl1AgO4b2QH4vYdZWEp09p+Hp/MEwu2cVn3ZjxySdSv3h/TozkjokJ5ZuH2KjGZ1qzY/bRoVJuB5XiYRA1/Px68uCM7Uo+Xu807NTOHF77dSY+I+lxcyY+k8/cz3DGkNev2ZxDn4DzqBcU9tIJq+PP4WM+aaX7JGMNfLu/CZd2b8fj8bXzyiylMEo9ks2BTChP6tXBFRa48Hr0sik5hwdz/YTwHj+X89POjJ3L5PD6ZBz6K57zHv+PSl5by5IJtHM3O5bbBrXn9xj4V2t+59b/rQ2N7RTArNpGFmw8ythzDtgH+9eVWFmw6yJ8vi2JE56YelWNcnwhmxCTw1FfbuTCq6RlH7K3Ync4DH8bTr3Ujnh3X44yX0sYY/j22GyOf+4HJH29g9qT+Prvk3pN2nJV7jvDQqI7lLsMlXcPoEdmA57/ZwZgezc86YCy/oJAfdqQxKzaRxduLmnj+M66HV27ejesTyfPf7OCNH/bQt5UzjwycunQP6/Zn8OL4npXyXGJ/P8Nz1/YgIzuXhz7ZQKM6NRlW3Pb+1vK9GGO4ZWArx/db1QXV8Ofl63tz+X9j+L9ZaxnQtglLdqQRn5SBtUU3hYe0b8IFHUI4v0MITT08N6rJe0l0y4ZENKzFnLUHyrXetKV7mLEsgVsHtXKkd1GAvx+PXNqJhMMneH/Vryfr2pFaNDCtRePaTL0p+qyh17ReEI+N7kzs3iO8vWKvx2WrqNmrEwnwMxW67DfG8MglnUg5lsPM5XvPuEzS0Wye+3o7g59azO1vxbE+MYNJ57fhu/su8Npj6GrV9OemAa34dmsquw55PoX0jtQsnvt6B6O6hFXqvE+BAf68fmMfopoFc9d7a1i7/yjHT+XzwepELu3WjOYNPL8HUB21C63LP6/syuq9R3l50U6MgXsvbM+nvxvI2sdG8vL1vRkXHelxwINq8l7j52e4qlc4Ly/eRWpmTplO3rwNyfzry61c0jWMP1/W2bEa47COoQxo05gXv9vJ2N7h1Cueljk1M4eJM2IJquHPzFv7lmlAyDV9IvhyYwpPfbWd4Z2a0qKxd+dZOZVfwMdrkhgR1bTCU0f0b9OYYR1DeHXxLsb3jaRB7Zrk5hfy3dZUZq1OZOnOoqdKXdAhhL+N6cKFUaElzllSmW4Z0JI3ftjN1CUJPHVNxQfV5BUUcv+H8dQNCihXD62KCg6qwZsT+3HN68u5beZqxvRoTtap/GrZJdpJ1/SJoEPTukQ2rF2pI5pVk/eisb0jKLRFM0aWZtWedO77oKir5PPX9XR0hK8xhkcvi+LIiVxe/77oplhWTh4T31zNsZN5vDmxb5mHphtjeHxsN/z9DJM/Kb3LnNO+2ZLKkRO5TDjPsz7qD43qRNapfB6fv5UnFmxl4JPfcdd7a9mVmsU9w9sTM3k4M2/tx6iuYT4JeCh6/N246Ag+XXeAQ5k5pa9Qgte/383GA0Wjyr31UJ2Q4EDeue08Avz8eHvFPvq0bEjPyAZe2XdV1j2iQaVPWaGQ96LWTerQq0WDUptsdqZmcefbcUQ2qsXUm8/eZFJRXcPrM7ZX0QRm+9Oz+d17a9mRmsWrN/Yp09OMTte8QS0evSyKFXvSeT+2YvO1Z+fml2nwzC/Nit1PeINaDGnXpEL7/VFUs3pc1SuCD+OSmLY0gd4tGvLmxL4snTycP47sQHgVaVa4Y3Ab8goLS2xaKs2W5ExeWrSTy3s051Ivjz1p0bg2b93Wl3ahdfnjiLI/UEU8o+YaL7uqVziPzd3MluRMOjf/dS+Z1MwcJr65msAa/sy8tR8NalfeX/n7L+rAlxtTGPNKDBnZeTx9TXcu6HD2J/KUZHzfSL7ckMIT87cyrFNomUNxf3o2ry/ZzcdrkgipG8hjo6O4uEtYmZoQ9qWfYNmudO4f2cGRm76PjY4iulVDLuwUWik3Ip3QqkkdRnUJ452V+/jdsHblmkY3N7+Q+z+Kp36tmvzDyzNb/qhL8/p8e98FPtn3uUo1eS8rmoPanPHRgD82mWRk5/LmxL6VPo94RMPa3DqoFRnZefxhRHuuja7YcHYoarZ54qpuWODhTzaU2p97a0om98xax9D/LObjuCSu7NmcuoEB/Pbdtdw0PbZMNxdnr07E388wzoNyn65B7ZpM6Neiygb8jyad34asnHxml/GqyVpLbMIRJr0Tx9aUTJ64qpvXZrUU31NN3ssa1qnJsI6hzI1P5uFLOv00iCY3v5C73i1qMpkxsW+5m0wq6oGLOjK8Yyj9WnveLS+yUW0euaQTj83dzEdxSVx7hjlQ4vYe4dXvd7No2yHq1PTnjiFtuH1wa5rWCyK/oJB3V+7juW92MOqFpUwc2Ip7R7Q/4/N68woK+SguiWEdQwmrX7VD2Wm9WjSkX+tGzIhJ4JaBrUq8R5CTV8AX8cnMXL6XzcmZ1K9Vg4cv6cRID7vhSvWikPeBq3qH8/WWVJbtTueCDiFYa3l4zgZidh3mGQ+aTCqihr8f57VxrhvgDee15MuNKfzzyy2c3yGEsPpBWGv5fkcary3eTezeIzSsXYP7RnbglgGtftaDJ8Dfj4mDWnN5j+Y8s3A705cl8Nn6ZCaP6sjVvSN+1iTz3dZUDh8/xfXnOVOLr25+c34bbn8rji83pPzqqV+pmTm8u3If76/aT/qJXDo0rcsTV3Xjyp7hHs9nLtVPlXhoyLnmVH4B/f79HUM7hvDi+F78Z+F2Xl68i/tGdvDKvOGVbV/6CS5+YQkD2zbhqt7hvLp4N1tSMmlWv2j+nPH9Isv0wJcNSRn8Ze5m1idm0KtFA/4+pgvdI4p6ZNw8I5adqVnETB5eLZ4t4LTCQstFLyyhhr8f8+8ZjDGGtfuPMnPZXuZvTKHAWi7s1JTbBrViQNvGXpttUSqXzx4aYowZBbwI+APTrLVPOrFdtwoMKJrm4JO1SUxdsoeXi/tn/99wzx8bVxW0bFyHBy/uxD/nbWHRtkO0CanD09d058qe4dQMKPttoO4RDZhz10DmrDvAkwu2ccUry7guOpIJ/VqwdGca917Y/pwMeCgadzFpSBse+mQDTy/czvLd6cQnZhAcGMAtA1txy4BWXh+zIFWTxzV5Y4w/sAMYCSQBq4EJ1totJa1zrtfkoWgGvqtfWw7AsI4hTL05ulInufK2gkLLK4t30T60Lhd1CfM4jDNz8njp253MXL6X/EKLn4GYycPP2RGTUHRFOOSpxRzKOkWbJnWYOKgVV/eOOOfmgjmX+Kom3w/YZa3dU1yI2cAVQIkhL9C7RQOimtWjZoAfL1/f21UBD0XzljjZ9FQvqAZ/Ht2Z8f0ieWL+NsIb1jqnAx6KrgjfvLUvR0/kMbBt43Niul4pPydCPhxIPO11EnDeLxcyxkwCJgG0aOH8E3SqG2MMn9w1gKAAf/1ylkO70GCmT+zr62JUGV2ae6cXllRfTlQfz5RQv2oDstZOsdZGW2ujQ0K813ukKqtdM0ABLyKVyomQTwJO78cWASQ7sF0REfGQEyG/GmhvjGltjKkJjAc+d2C7IiLiIY/b5K21+caYu4GFFHWhnGGt3exxyURExGOO9LWy1s4H5juxLRERcY67+u2JiMjPKORFRFxMIS8i4mIKeRERF1PIi4i4mEJeRMTFFPIiIi6mkBcRcTGFvIiIiynkRURcTCEvIuJiCnkRERdTyIuIuJhCXkTExRTyIiIuppAXEXExhbyIiIsp5EVEXEwhLyLiYgp5EREXU8iLiLiYQl5ExMUU8iIiLqaQFxFxMYW8iIiLKeRFRFxMIS8i4mIehbwxZpwxZrMxptAYE+1UoURExBme1uQ3AVcBSxwoi4iIOCzAk5WttVsBjDHOlEZERBzltTZ5Y8wkY0ycMSYuLS3NW7sVETmnlVqTN8Z8C4Sd4a1HrbVzy7oja+0UYApAdHS0LXMJRUSkwkoNeWvtCG8UREREnKculCIiLuZpF8qxxpgkYADwpTFmoTPFEhERJ3jau+ZT4FOHyiIiIg5Tc42IiIsp5EVEXEwhLyLiYgp5EREXU8iLiLiYQl5ExMUU8iIiLqaQFxFxMYW8iIiLKeRFRFxMIS8i4mIKeRERF1PIi4i4mEJeRMTFFPIiIi6mkBcRcTGFvIiIiynkRURcTCEvIuJiCnkRERdTyIuIuJhCXkTExRTyIiIuppAXEXExhbyIiIsp5EVEXEwhLyK1/bb0AAAHR0lEQVTiYh6FvDHmGWPMNmPMBmPMp8aYBk4VTEREPOdpTf4boKu1tjuwA3jE8yKJiIhTPAp5a+3X1tr84pcrgQjPiyQiIk5xsk3+NmBBSW8aYyYZY+KMMXFpaWkO7lZEREoSUNoCxphvgbAzvPWotXZu8TKPAvnAeyVtx1o7BZgCEB0dbStUWhERKZdSQ95aO+Js7xtjbgFGAxdaaxXeIiJVSKkhfzbGmFHAZOACa222M0USERGneNom/zIQDHxjjFlvjHndgTKJiIhDPKrJW2vbOVUQERFxnka8ioi4mEJeRMTFFPIiIi6mkBcRcTGFvIiIiynkRURcTCEvIuJiCnkRERdTyIuIuJhCXkTExRTyIiIuppAXEXExhbyIiIsp5EVEXEwhLyLiYgp5EREXU8iLiLiYQl5ExMUU8iIiLqaQFxFxMYW8iIiLKeRFRFxMIS8i4mIKeRERF1PIi4i4mEJeRMTFFPIiIi7mUcgbY/5pjNlgjFlvjPnaGNPcqYKJiIjnPK3JP2Ot7W6t7QnMA/7iQJlERMQhHoW8tTbztJd1AOtZcURExEkBnm7AGPNv4GbgGDDM4xKJiIhjSq3JG2O+NcZsOsPXFQDW2kettZHAe8DdZ9nOJGNMnDEmLi0tzbkjEBGREhlrnWlhMca0BL601nYtbdno6GgbFxfnyH5FRM4Vxpg11tro8qzjae+a9qe9HANs82R7IiLiLE/b5J80xnQECoF9wG89L5KIiDjFo5C31l7tVEFERMR5GvEqIuJiCnkRERdTyIuIuJhCXkTExRTyIiIuppAXEXExhbyIiIsp5EVEXEwhLyLiYgp5EREXU8iLiLiYQl5ExMUU8iIiLqaQFxFxMYW8iIiLKeRFRFxMIS8i4mIKeRERF1PIi4i4mEJeRMTFFPIiIi6mkBcRcTGFvIiIiynkRURcTCEvIuJiCnkRERdTyIuIuJhCXkTExRwJeWPMA8YYa4xp4sT2RETEGR6HvDEmEhgJ7Pe8OCIi4iQnavLPAw8B1oFtiYiIgwI8WdkYMwY4YK2NN8aUtuwkYFLxy1PGmE2e7LuKawIc9nUhKpGbj8/NxwY6vuquY3lXMNaevQJujPkWCDvDW48CfwIustYeM8bsBaKttaX+Bxtj4qy10eUtbHWh46u+3HxsoOOr7ipyfKXW5K21I0rYWTegNfBjLT4CWGuM6WetPVieQoiISOWocHONtXYjEPrj6/LU5EVExDt81U9+io/26y06vurLzccGOr7qrtzHV2qbvIiIVF8a8Soi4mIKeRERF6v0kDfGzDDGHDq9X7wxppEx5htjzM7ifxtWdjkqSwnH9zdjzAFjzPrir0t9WcaKMsZEGmMWG2O2GmM2G2PuLf65K87fWY7PLecvyBgTa4yJLz6+vxf/vLUxZlXx+fvAGFPT12Utr7Mc20xjTMJp566nr8vqCWOMvzFmnTFmXvHrcp87b9TkZwKjfvGzh4HvrLXtge+KX1dXM/n18QE8b63tWfw138tlcko+cL+1NgroD/zeGNMZ95y/ko4P3HH+TgHDrbU9gJ7AKGNMf+Apio6vPXAUuN2HZayoko4N4MHTzt163xXREfcCW097Xe5zV+khb61dAhz5xY+vAN4q/v4t4MrKLkdlKeH4XMFam2KtXVv8fRZFH7ZwXHL+znJ8rmCLHC9+WaP4ywLDgY+Lf14tz99Zjs01jDERwGXAtOLXhgqcO1+1yTe11qZA0S8ap/W3d5G7jTEbiptzqmVzxumMMa2AXsAqXHj+fnF84JLzV3y5vx44BHwD7AYyrLX5xYskUU3/sP3y2Ky1P567fxefu+eNMYE+LKKnXqBoXrDC4teNqcC5043XyvEa0Jaiy8gU4FnfFsczxpi6wCfAH6y1mb4uj9POcHyuOX/W2gJrbU+KRqT3A6LOtJh3S+WMXx6bMaYr8AjQCegLNAIm+7CIFWaMGQ0cstauOf3HZ1i01HPnq5BPNcY0Ayj+95CPylEprLWpxR/AQmAqRb9c1ZIxpgZFAfietXZO8Y9dc/7OdHxuOn8/stZmAN9TdO+hgTHmx9HuEUCyr8rlhNOObVRxE5y11p4C3qT6nrtBwJjimQRmU9RM8wIVOHe+CvnPgVuKv78FmOujclSKHwOw2FigWs64WdwGOB3Yaq197rS3XHH+Sjo+F52/EGNMg+LvawEjKLrvsBi4pnixann+Sji2badVPgxF7dXV8txZax+x1kZYa1sB44FF1tobqMC5q/QRr8aYWcBQiqYATQX+CnwGfAi0oOhhI+OstdXy5mUJxzeUokt9C+wFfvNjG3Z1YowZDCwFNvK/dsE/UdRuXe3P31mObwLuOH/dKbo5509Rhe5Da+0/jDFtKKodNgLWATcW13yrjbMc2yIghKKmjfXAb0+7QVstGWOGAg9Ya0dX5NxpWgMRERfTjVcRERdTyIuIuJhCXkTExRTyIiIuppAXEXExhbyIiIsp5EVEXOz/AV3ZcSlvjmMUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# 軸の描画範囲 (xlim/ylim)\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"\n",
"# np.random.seed(0) # ランダム値の固定\n",
"x = np.random.rand(50)\n",
"plt.plot(x) # 折れ線グラフを表示\n",
"\n",
"plt.xlim(10, 40) # X軸の描画範囲\n",
"plt.ylim(-4.0, 2.0) # Y軸の描画範囲\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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": [
"# 平行・垂直の線を引く(axhline/axvline)\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.axhline(y=0.5, xmin=0.1, xmax=0.9) # 水平線を引く\n",
"plt.axvline(x=0.5, ymin=0.1, ymax=0.9, color='r', linewidth=4) # 垂直線を引く(赤線、少し太く)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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": [
"# 平行・垂直の平行線を引く(hlines/vlines)\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.hlines([2, 4, 6], xmin=1, xmax=10) # 水平平行線を引く\n",
"plt.vlines([2, 4, 6], ymin=1, ymax=10, color='r') # 垂直平行線を引く(赤線)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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": [
"# 矩形の描画(axhspan/axvspan)\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.axhspan(1.0, 3.0, facecolor='g', alpha=0.5) # 矩形の描画(1.0〜3.0で塗りつぶし)\n",
"plt.axvspan(2.0, 5.0, facecolor='b', alpha=0.5) # 矩形の描画(1.0〜3.0で塗りつぶし)\n",
"\n",
"plt.xlim(0, 10) # X軸の描画範囲(分かりやすいように)\n",
"plt.ylim(0, 10) # Y軸の描画範囲(分かりやすいように)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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": [
"# テキストの追加(text)\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.text(0.2, 0.5, \"Test\", size=20, rotation=20., ha=\"center\", va=\"center\")\n",
"plt.text(0.6, 0.5, \"Sample\", size=20, rotation=20., ha=\"center\", va=\"center\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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": [
"# 注釈の追加(annotate)\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"\n",
"t = np.arange(0.0, 5.0, 0.01)\n",
"s = np.cos(2*np.pi*t)\n",
"line, = ax.plot(t, s, lw=2)\n",
"\n",
"# xycoordsがデフォルトの'data'なので、\n",
"# 座標(4, 1)のデータに対して座標(3, 1.5)にテキストを表示して\n",
"# 矢印で線を引っ張る\n",
"arrow=dict(facecolor='black', shrink=0.05)\n",
"ax.annotate('max', xy=(4, 1), xytext=(3, 1.5), arrowprops=arrow)\n",
"\n",
"ax.set_ylim(-2,2)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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": [
"# グリッド(格子)の表示(grid)\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"mu, sigma = 100, 15\n",
"x = mu + sigma * np.random.randn(100)\n",
"plt.hist(x, 50, density=True, alpha=0.75) # ヒストブラムの用事\n",
"\n",
"plt.grid(linestyle='-', linewidth=1) #グリッドの表示\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# 複数のグラフを表示(figure/axes)\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"x = np.linspace(-3, 3, 20)\n",
"y1 = x\n",
"y2 = x ** 2\n",
"y3 = x ** 3\n",
"y4 = x ** 4\n",
"\n",
"fig = plt.figure()\n",
"\n",
"# 左上\n",
"ax1 = fig.add_subplot(2, 2, 1)\n",
"ax1.plot(x, y1)\n",
"\n",
"# 右上\n",
"ax2 = fig.add_subplot(2, 2, 2)\n",
"ax2.plot(x, y2)\n",
"\n",
"# 左下\n",
"ax3 = fig.add_subplot(2, 2, 3)\n",
"ax3.plot(x, y3)\n",
"\n",
"# 右下\n",
"ax4 = fig.add_subplot(2, 2, 4)\n",
"ax4.plot(x, y4)\n",
"\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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