{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第4章 Matplotlibでグラフを 描画しよう\n",
    "\n",
    "### 4-4: 散布図"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vB2DGjBl0dnayYsUKAPbu3cvcuXMBcDqdDA0NsWHDBoLBID6fj9zc3HhjECJj9LB2+uD+XWFXelT/e/fI1oWy3G/sZEOV2IZevvPOO5w6dYq6ujpstpGnVFZWcvz4cfx+P/39/Zw+fZry8vLQ3w4fPgxAc3MzZWVlYWv2QujRjVqg2nICzv4JteUE6rObRy4AaTTcF6Gm98+zpl7uN9jdSXB3I8M/3URwd2NK3ndZNTOGmn1XVxcvvPAChYWFbNiwAYDi4mIee+wxli1bxsaNG1EUhZqaGrKzswGorq6mqamJ+vr60NBLIQxDJ7VAe6Gbf8fxeDMkrkBnhyablMiqmTEk++LiYvbv3x/2b/feey/33nvvhONOp5P169cnXzohMkAvtcDc1Q/i/+sfx154ikrAMxPO/M+Ex5shcUVqukr6Qlu9ZuIdkcVWzZSF0IQYJ5FaoBZt/I4Sz0hn7LjzAqij2uwB0ySuSE1XyV5oZdVMSfZCTBRnLVDL/VEjrfRo1sQVqekqFXctVl81U5K9EOPEXQvMQBu/WRNXxKYrE9y1ZJokeyHCiCeZ6qWN3wwiNV2Z4a4l0yTZC5EkGemRWma9a8k0WeJYiGTJ/qjCAKRmL0SSZKSHMAJJ9kKkgDQ9CL2TZC+EMDw9rGWkd5LshRCGpuU8BzORDlohhLFNNs9BhEiyF0IYmsxziI0keyGEoUWazyDzHMaSNnshhLHpZEVLvXcSS7IXhqD3H5LIHD3MczBCJ7Eke6F7Wm1oIcwj4/McdLLhzWSkzV7o3qQbWgihA0boJJaavdC9yTa0kOYdoQdGWAxPkr3QvYh7sU7NluYdoQ866SSeTNRkf+7cOV577TXOnDnDzp07yc/PZ3BwkHXr1uHxeEKPa2hoID8/H7/fz44dOzh//nxos/H8/HxNgxDmFnFDC0i6ndRIdwZGKqvV6KGTOJqoyT4vL4+lS5fywQcfjDnu8XhobGyc8PiDBw8ya9Ys1q9fz8mTJ9m3bx91dXWpK7GwnEgbWqj7ngv7+FjbSY0wguIGI5XVqjLeSRxF1A5at9tNRUUFWVlZY47n5eWFfXxraytLliwBYOHChbS3t6Oq4VqzhIidragE27pHsD/6NLZ1j2ArKkl+Mo2RptkbqaxClxJus798+TINDQ34fD4WL17M8uXLAfD7/djtdmpra2loaMDlcuHz+XC5XBPO4fV68Xq9AGzfvh232x17wR2OuB5vFlaMO1LMgZo6+s/9g+Guf4WO2YtLmVZThyOG96hvcCBsX4BjcIBCHbzHo+PWe1lTxYrfb0hP3Akl+5ycHOrq6pgzZw7d3d00NjYye/Zs5s6di6Io2O12SktLcTqdqKqKoihhz1NVVUVVVVXo/z094UddhON2u+N6vFlYMe6IMTuyCP6wAWVU806weg39jiyI4T0K5k6sgAAEcl26eI9Hx53usmaqfyAV328j9m0kE/fovtPJJJTsFUWhrKwMgNLSUioqKrhw4QJz587F6XQyNDTEhg0bCAaD+Hw+cnNzE3kZIaJKqp3UACMoQtJYViP3Dxi57FpLaFLVqVOn6OjoAEZq421tbdxyyy0AVFZWcvjwYQCam5spKyuLWLMXIpNsRSUo9VtQKu+BW29DqbwHRadJIZGyBrs7Ce5uZPinmwjubhyp8cbCyP0DRi67xqLW7Ldt28aVK1fo6+tj8+bNuFwuvve97/HSSy/R19eH3+9n5cqV3HTTTQBUV1fT1NREfX19aOilEHql9xEUo8VT1mRquEaYDRqJkcuutajJ/oknngh7fNOmTWGPO51O1q9fn1yphBDJSWKtFiPMBo3EyGXXmqyNI4QJJVXDrV7z2aS1G/TalzGekcuuMVkuQQgTSqaGa4TZoJEYuexak2QvhBklOXpH730Zkw2v1HvZM0WSvRAmZOYargyvTIwkeyFMyrQ1XANsFKJH0kErhDAUGV6ZGKnZCyEA4ywzIMMrEyPJXghhrHbwODqfjXIBSwdJ9kIIQ7WDx9r5bKgLWBpIshcZITUufTFaO3hMnc8GuoClgyR7kXZS49IfLdvBM3VhN9oFTGuS7EX6SY1LfxKchBUtkWfywp7IBczMd5yS7EXaxVrjuvHD6xscGNm8w0Q/PL1JZBJWTIk8kxf2OC9gZr/jlGQv0i6WGtfoH15oO74M/fDMXNsbLe5JWDEk8kw2pcR9ATP5Hacke5F+sdS4dPLDM3ttLxmxJPJMj4mP5wJm9jZ+mUEr0i6WXZd088OTnY8iipSwxxw30JLDMcVjYFKzFxkRrcaViRphuOYa3Vx09CiGOzRDLchmpD2JEyDJXuhTmn94kZpr8MwM+3iz1PaSEWsiN8qCbIa6MCVAkr3QpdE/PMfgAIEERuPE1bEaqbnGM3PkImPS2l6yjJLIY2W2eEaLmuzPnTvHa6+9xpkzZ9i5cyf5+fkAHDt2jEOHDqEoCqtXr2bBggUA+P1+duzYwfnz50Mbjt94jhDxuPHDK3S76enpieu58XasRmyW8X+KYuLanrCOqMk+Ly+PpUuX8sEHH4SOXbp0iSNHjvDMM8/g9/vZvHkz8+bNIycnh4MHDzJr1izWr1/PyZMn2bdvH3V1dZoGIcQEcY7mmayPwMy1PWEdUUfjuN1uKioqyMrKCh1raWlh0aJFOJ1OCgoKmD9/Pm1tbQC0trayZMkSABYuXEh7ezuqGu5nJIR24u5YNdCokUQEuzsJ7m5k+KebCO5uHGniEpaSUJt9b28vd9xxB3v27KGoqIji4mJ6e3uBkWYcu91ObW0tDQ0NuFwufD4fLpdrwnm8Xi9erxeA7du343a7Yy+4wxHX483CinEnEvPHxZ/Hf/ZPE45PLf48BeHO5XYT2PI8g/t3MdzXg73QTe7qB3GUeBItdtJS9VkHOjvo//l/MNz1L2CkSct+7h9Me+rnGY0vHCt+vyE9cSeU7BVFAUZq/dOnT2dgYCB0TFEU7HY7paWlOJ1OVFUN/W28qqoqqqqqQv+Pp13WnUA7rhlYMe5EYg4uXQl//eOEjtWhpSsjn8uRBffXjjwf6AfI4Hudqs86uPc51OuJ/obhrn/Rt/c5bDE2T6VrFrEVv9+QXNweT2wX7ISS/YwZM+js7GTFihUA7N27l7lz5wLgdDoZGhpiw4YNBINBfD4fubm5ibyMEAkz+zC6eCQ7V0BmEZtDQjNoKysrOX78OH6/n/7+fk6fPk15eXnob4cPHwagubmZsrKyiDV7IbRkKyrBtu4R7I8+jW3dI2lLTHprH096ZqjMIjaFqDX7bdu2ceXKFfr6+ti8eTMul4utW7eybNkyNm7ciKIo1NTUkJ2dDUB1dTVNTU3U19eHhl4KMRkzLTSmy1pwkhPUZBaxOSiqjobKdHR0xPxYadszh/HJEYDra+fcSI5Gijm4uxG15cSE40rlPTG3j9+QyriTuaCmMqZo5Ut0Ap3R6bbNXojxEk4mCaxuqec7Ab3WgpOaK6Dx0hV6Ws7azCTZi6Ql03QRb3JMRTOJlheLTC/pqwXNO7t1spx1IvRc8RhPkr1IXhI/1riTY5KJQfM2dZOunKjlLGK93g1Fo8v+mUlIshdJS+rHGmdyTPS1QjWwv7TBwMdj/5hgLTJSrU6GfMbHsHdDBrsjkWQvkpbMjzXe5JjoJtITOoHHibcWGbVWl6EfezqaFVL+Gga9GzLaHYkke5G8JH+scSXHRF4rXA1snLhrkTqs1aWjWUGL10jFctaZYLQ7Ekn2ImnpbLpI5LWi1rQSqEXqslaXjguQRq+RzHLWGWOwOxJJ9iIlItXOtWhWiLeZJFINDFcBSllFQmXSY60uHRcgXV7kMsRo/TOS7IVmdDNaIUINbPwm56k4ZyZrdem4AOnxIpdJRtrrQJK90I5O2rW1qIHpslaXjguQDi9yRhPubpc0LOssyV6kXOjL/Mf/F/bvmbjl16IGprdaXTouQLq8yBlIpLvdwJbnR5bY1pAkex3LVA0gGbEMc7TqLX86pOMCpLeLnKFEuNsd3L8rtJeCViTZ65RWNQDNx2FHG+Yot/zCwiLd1Q73aT8CSZK9XmlQA0hHh2nEJprsXJTb79T0lt9I65QIa4rUwW0vdBPU+LUl2etAuCSlSQ0gBR2m0RJqxNEat9+ZsuVwI5VLFyN/hJhMhA7u3NUPjmyDqSFJ9hkWKUnhmRn28cnUANKyPV2mRmvoZOTPZOTOQ0Tq4HaUeDTf71iSfaZFSlKemSNJMoU1gKTHSMeQUCcbraFlstP7ZB+58xA3ZKqDW5J9hkVMRv5PUVJdA0jT9nThvsxaJzvdT/YxwJ2HMDdJ9hk2WZJKdQ0g2THSSSVUrZOdzif76P3OQ5hfUsn+Jz/5CefPn2fq1KkALF++nMWLF3Ps2DEOHTqEoiisXr2aBQsWpKSwZjG6OYOp2VBYBH3dnz1AwySVqe3ptE52ep/so/s7D2F6SdfsH3nkEWbPnh36/6VLlzhy5AjPPPMMfr+fzZs3M2/ePHJycpJ9KVMIO+louhvKvzrSdKOzJDVaMgk1HclO15N9dH7nIcwv6WSfl5c35v8tLS0sWrQIp9OJ0+lk/vz5tLW18fWvfz3ZlzKHcM0ZV3pQvvy/sNU+kZkyxSHhhGrxZKf3Ow9hfkkn+1/96ldcvnyZL33pS3z3u9+lt7eXO+64gz179lBUVERxcTG9vb1hn+v1evF6vQBs374ddxxLATgcjrgerxd9gwP8O8xxx+AAhTHEY9S4cbsJbHmewf27GO7rwV7oJnf1gyMdzlEYNubx3G6Y958xP9w0ccfBijFDeuJWVFUNu9R3LC5cuEBubi7Z2dn85je/4dNPPyUrK4uKigo++ugjCgsLGRgYYHh4mG9+85tRz9fR0RHza7uNtMnBKMHdjagtJyYcVyrviWnSkdvt5vJf/5y2GqIexoYb9bNOVqxx6+EzShX5rOPn8USvMEGSNfuZMz+b+LNo0SJ27tzJV7/6VTo7O1mxYgUAe/fuZe7cucm8jLkk2ZwR6OxI23htGRuuf/IZJc9MF8vJ2BJ9Yk9PDydPniQYDHLt2jXefvttbrnlFiorKzl+/Dh+v5/+/n5Onz5NeXl5KstsaLbrm2YolffArbehVN4T1yYag/t3RRzCGOzuJLi7keGfbiK4u3HkS5yMyYZLCn2QzygpNy6WassJOPsn1JYTqM9uTv63o0MJ1+zz8vL44IMPeOONNxgYGKCsrIwHHniAqVOnsmzZMjZu3IiiKNTU1JCdnZ3KMhteMqNGIq2No16+BCmu4cnYcP2TzyhJFprslnCynzp1KjU1NWH/du+993LvvfcmempLi3ZLaS90h+3g5ZN+6L089liSX1oZG65/8hklx0oXy4SbcUTqxXJLmbv6wZE2/tGKSiB/ethzJvWlrV4T/rUsMlzSEOQzSkqki6IZL5aS7PUkhvZXR4knbJu/8rnwTTXJfGmT7V8Q2pPPKEkWuljK2jg6ktRCYxpNWtL1rFQByGeUDCtNdpNkr6F4h3Ql0/5qpS+tEKlklYulJPsERUvkCY1/TrJ2bpUvrRAifpLsExA2kbe1MOyZNdJ2Xr0moSFd6a6dR7pgxXpHosVklHDnxILT54VINUn2iQiXyIf88OFZ1A/PjtTO8wrCPjXa6Jh01c4j3XkMr62Dl5+LekeixczNSOcMbHkeHFkJnVMIMUJG4yQg6nDG7k745ErYP+lmSFekO4+9P49tRqYWMzcjnHNw/67EzymEAKRmH1a05olIHalj5E8Dm023S/pGvGBdHYzp8VpMRon03EizhoUQsZNkP05MzRPhOlLHUT73eXjgUd2Ojol4wcrJhU8nJvzxdyRazNyMdE57oZtgwmdNLassmiXMR5L9eDF0rI7pSL18CToujLTZ33C9Bq/r0TGRRv6Ma7MPHR9/R6LFuP4I58xd/SD9iZ81ZVLdTyEXDn0z2+cjyX6cRCY2GfFLMdnIn1hGBGkxcijSOR0lHtDDGucpXDRLlibWNzN+PpLsx0mkeULXNXgiX4zCXbCGRz3GHuVLrUXcen4RrLS+AAAJd0lEQVQvU9pPkcHVFo1YOUk7E66GKcl+vHBNCc6pqJc7GX5+28j/E9gYPFM/sFhqKGasxWghlf0UmVptUT7r2JhxNUzLJftoSTdie/yHZ8ecJ54fSSZ+YKE4/9IGAx+P/eP4GooJazGaSGE/RcaWJjbYZ52pSpIZl442ZbIf/wVR7/rfKO8cmdCZGm7m6+jmjeDuRtQP/xb5hcL8SMLOAE3BD+zGefsGBwjmuib90o+/uIQzuoZixlqMFlLaT6HRwnXRGOmzzuhdSIY+Hy2ZLtmH/YK0voMaHA7/hNEzX8cl/lh+AKMfE+nLmehs2nDnDW1ccv1LD8R2cRlndA3FjLUYrcTbpzBZf0kmFq4z1GedwbsQMy4saLpkH/YLEinRjzd+yQPPzKhPGfMjifTlDIYfJR7zDyzCedX/3j1ypxLjxSVkfA3FhLUYPYhWM81IZ3SGP+t4mmUyfRei58ECiTB0sg/3xUnZF6G7cyTZF5VEriWP+5FEfO0kZ9NGPO8/z4Zvj49wccFVgFJWMXk/hUlqMbqgw/bxTH7W8TbLGOouxAA0S/adnZ288MIL+Hw+vvKVr1BTU4Pdbk/Z+QOdHWFXnsQxJWWvgf9TlFE/DKZmf3Y8jmUUkp1NG9PyDKNFuLhMtoOR2WoxepDpmmkkGfus4734yR1nSmmW7Juamrjvvvv48pe/zM6dOzlx4kRKNyEf3L8r/MqTo2ey3mCzj23KcU6Fos9D96Xwj79OmVYY3w9jki9nUj+wSOf1zIQz/zOx3DpfqsEqpGY6VrwXP7njTC1Nkn1/fz/Xrl3j1ltvBWDZsmW8+uqrKU32MS2Odb3ZIjQaJ9K67ZMseRAPrb6co8/rGBwgcH00DoA6qs1+dLmlpq4DUjMdw4wTFo1Ek2Tf29tLUVERf/vb39izZw9PPfUUPWGmu3u9XrxeLwDbt2/HHccmFZ/MKPpsZEoEU2bdTOHj/znyn7sWTXyA2w3zRv4e6OxgcP8uhvt6sBe6yV394Mg0/XiNOmdKXT+vw+EgEAiEDge2PJ+acuuYw+GI67uhG253Up+PYeOOIFBTR/+5fzDc9a/QMXtxKdNq6nBcj9NsMccqHXFrkuwVRQEgJyeH0tJSVFUNHRutqqqKqqqq0P/DXRAimbbqAT79y5lJhxgGcl2xn9ORBffXAhCEkYW39LAeyzhut3tsTAYpdzImxGwkSXw+ho47HEcWwR82oIy68w1Wr6HfkRV6T0wXc4ySidvjibHykNDZo5gxYwZdXV184QtfoK6ujnPnzqX8quUo8XzWeZqiZhghhLakWSZzNEn2BQUFZGdn85e//IV58+Zx6NAhvva1r6X8dYy+8qQQQqSLZqNxvv/974eGXt52223cfffdWr0UIDUGIYSYjGbJvqSkhK1bt2p1eiGEEHGQDceFEMICJNkLIYQFSLIXQggLkGQvhBAWoKiqGtcaW0IIIYzHsDX7xx9/PNNFyAgrxm3FmMGacVsxZkhP3IZN9kIIIWInyV4IISzA/tRTTz2V6UIkavbs2ZkuQkZYMW4rxgzWjNuKMYP2cUsHrRBCWIA04wghhAVIshdCCAvQbCE0LWm9mbleHD16lLfeeovBwUEWLFjA2rVrURSFY8eOcejQIRRFYfXq1SxYsCDTRU25U6dO8bOf/Yxf//rXAKaPORAI8Morr9DW1obD4WDp0qUsXrzY1HH//e9/Z8eOHfj9fubMmcPDDz9MVlaW6WI+d+4cr732GmfOnGHnzp3k5+cDkb/Tfr+fHTt2cP78edxuN3V1daHnJEU1oM2bN6vt7e1qMBhUm5qa1GPHjmW6SCk3ODio7t+/X7127Zp67do19dFHH1Xfe+89taOjQ33sscdUv9+v9vf3qz/4wQ/UwcHBTBc3pT7++GN106ZN6oMPPqiqqmqJmA8cOKDu2rVLHR4eVv1+v3r+/HnTx71p0yb1zJkz6vDwsPrSSy+px44dM2XM3d3d6nvvvaeuW7dO/fjjj1VVnfw7vX//fvXAgQOqqqpqc3Oz+otf/CIl5TBcM87ozcwVRWHZsmW0tLRkulgpl5OTw6pVq5gyZQpTpkxh3rx59PT00NLSwqJFi3A6nRQUFDB//nza2toyXdyU2r17NytXrsThGLnxtELMXq+XVatWYbPZcDqdzJw50/RxZ2dn43a7sdlszJkzB5vNZsqY3W43FRUVZGVlhY5NFmdraytLliwBYOHChbS3t6OmYByN4ZL96M3MH3/8cYqLi02/Z2UgEOD999+nrKyM3t5ePve5z7Fnzx7eeOMNiouL6e3tzXQRU+bdd98lLy+PioqK0DGzx3zt2jUCgQCtra1s3LiRJ598kg8//ND0ca9du5Yf//jH/O53v+P06dN87WtfM33MN0wWp9/vx263U1tbS29vLy6XC5/Pl/RrGi7Zx7qZuZkcOHCA8vJyPB5PKFa328306dNNFX9/fz9vvPEG999//5jjZo4Z4OrVq/h8PhwOB1u3buUb3/gGu3btMnXcqqry5ptv8u1vf5ucnBw6Oztpb283dcyjTRanoijY7XZKS0txOp0pew8Ml+zHb2be1dWV8s3M9eTo0aNcvHiR++67DxiJv7OzkxUrVnDXXXeZKv53332XTz/9lG3btrFp0yauXLnCpk2bTB0zQH5+PlOmTOHOO+/EbrezcOFCPvroI1PHffHiRS5dusQ999zDkiVLqK+v55e//KWpYx5tsjidTidDQ0Ns2LCBvLw8fD4fubm5Sb+m4ZL96M3MVVXVbDNzPXjnnXc4deoUdXV12GwjH1VlZSXHjx/H7/fT39/P6dOnKS8vz3BJU2P58uX813/9F08//TRPP/0006dP5+mnnzZ1zAA2m40777yTEydOoKoqp06d4qabbjJ13Dk5OZw7d47Lly+jqip///vfycnJMXXMo00WZ2VlJYcPHwagubmZsrKylNTsDTmDdvTQy9tuu42amppQMjSLrq4u1q9fT2FhIVOnTgWguLiYxx57jLfeeos//OEPKIrCmjVrmD9/foZLq42HH36YF154AcD0MX/yySfs2rWLixcvkpeXx8MPP4zH4zF13M3Nzfz2t79leHiY/Px8HnjgAWbPnm26mLdt28aVK1fo6OiguLgYl8vF1q1bI8Y5NDREU1NTyodeGjLZCyGEiI+5qsNCCCHCkmQvhBAWIMleCCEsQJK9EEJYgCR7IYSwAEn2QghhAZLshRDCAiTZCyGEBfx/3LXPM82pgN0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115b9f55320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# リスト4.4.1：散布図の描画\n",
    "plt.style.use(\"ggplot\")\n",
    "# 入力値の生成\n",
    "np.random.seed(2)\n",
    "x = np.arange(1, 101)\n",
    "y = 4 * x * np.random.rand(100)\n",
    "# 散布図の描画\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anime_id</th>\n",
       "      <th>name</th>\n",
       "      <th>genre</th>\n",
       "      <th>type</th>\n",
       "      <th>episodes</th>\n",
       "      <th>rating</th>\n",
       "      <th>members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32281</td>\n",
       "      <td>Kimi no Na wa.</td>\n",
       "      <td>Drama, Romance, School, Supernatural</td>\n",
       "      <td>Movie</td>\n",
       "      <td>1</td>\n",
       "      <td>9.37</td>\n",
       "      <td>200630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5114</td>\n",
       "      <td>Fullmetal Alchemist: Brotherhood</td>\n",
       "      <td>Action, Adventure, Drama, Fantasy, Magic, Mili...</td>\n",
       "      <td>TV</td>\n",
       "      <td>64</td>\n",
       "      <td>9.26</td>\n",
       "      <td>793665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28977</td>\n",
       "      <td>Gintama°</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>TV</td>\n",
       "      <td>51</td>\n",
       "      <td>9.25</td>\n",
       "      <td>114262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9253</td>\n",
       "      <td>Steins;Gate</td>\n",
       "      <td>Sci-Fi, Thriller</td>\n",
       "      <td>TV</td>\n",
       "      <td>24</td>\n",
       "      <td>9.17</td>\n",
       "      <td>673572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9969</td>\n",
       "      <td>Gintama'</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>TV</td>\n",
       "      <td>51</td>\n",
       "      <td>9.16</td>\n",
       "      <td>151266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   anime_id                              name  \\\n",
       "0     32281                    Kimi no Na wa.   \n",
       "1      5114  Fullmetal Alchemist: Brotherhood   \n",
       "2     28977                          Gintama°   \n",
       "3      9253                       Steins;Gate   \n",
       "4      9969                          Gintama'   \n",
       "\n",
       "                                               genre   type  episodes  rating  \\\n",
       "0               Drama, Romance, School, Supernatural  Movie         1    9.37   \n",
       "1  Action, Adventure, Drama, Fantasy, Magic, Mili...     TV        64    9.26   \n",
       "2  Action, Comedy, Historical, Parody, Samurai, S...     TV        51    9.25   \n",
       "3                                   Sci-Fi, Thriller     TV        24    9.17   \n",
       "4  Action, Comedy, Historical, Parody, Samurai, S...     TV        51    9.16   \n",
       "\n",
       "   members  \n",
       "0   200630  \n",
       "1   793665  \n",
       "2   114262  \n",
       "3   673572  \n",
       "4   151266  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# リスト4.4.2：anime_master_csvデータの読み込み\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "base_url = \"https://raw.githubusercontent.com/practical-jupyter/sample-data/master/anime/\"\n",
    "anime_master_csv = os.path.join(base_url, \"anime_master.csv\")\n",
    "df = pd.read_csv(anime_master_csv)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>genre</th>\n",
       "      <th>type</th>\n",
       "      <th>episodes</th>\n",
       "      <th>rating</th>\n",
       "      <th>members</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anime_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32281</th>\n",
       "      <td>Kimi no Na wa.</td>\n",
       "      <td>Drama, Romance, School, Supernatural</td>\n",
       "      <td>Movie</td>\n",
       "      <td>1</td>\n",
       "      <td>9.37</td>\n",
       "      <td>200630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5114</th>\n",
       "      <td>Fullmetal Alchemist: Brotherhood</td>\n",
       "      <td>Action, Adventure, Drama, Fantasy, Magic, Mili...</td>\n",
       "      <td>TV</td>\n",
       "      <td>64</td>\n",
       "      <td>9.26</td>\n",
       "      <td>793665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28977</th>\n",
       "      <td>Gintama°</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>TV</td>\n",
       "      <td>51</td>\n",
       "      <td>9.25</td>\n",
       "      <td>114262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9253</th>\n",
       "      <td>Steins;Gate</td>\n",
       "      <td>Sci-Fi, Thriller</td>\n",
       "      <td>TV</td>\n",
       "      <td>24</td>\n",
       "      <td>9.17</td>\n",
       "      <td>673572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9969</th>\n",
       "      <td>Gintama'</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>TV</td>\n",
       "      <td>51</td>\n",
       "      <td>9.16</td>\n",
       "      <td>151266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      name  \\\n",
       "anime_id                                     \n",
       "32281                       Kimi no Na wa.   \n",
       "5114      Fullmetal Alchemist: Brotherhood   \n",
       "28977                             Gintama°   \n",
       "9253                           Steins;Gate   \n",
       "9969                              Gintama'   \n",
       "\n",
       "                                                      genre   type  episodes  \\\n",
       "anime_id                                                                       \n",
       "32281                  Drama, Romance, School, Supernatural  Movie         1   \n",
       "5114      Action, Adventure, Drama, Fantasy, Magic, Mili...     TV        64   \n",
       "28977     Action, Comedy, Historical, Parody, Samurai, S...     TV        51   \n",
       "9253                                       Sci-Fi, Thriller     TV        24   \n",
       "9969      Action, Comedy, Historical, Parody, Samurai, S...     TV        51   \n",
       "\n",
       "          rating  members  \n",
       "anime_id                   \n",
       "32281       9.37   200630  \n",
       "5114        9.26   793665  \n",
       "28977       9.25   114262  \n",
       "9253        9.17   673572  \n",
       "9969        9.16   151266  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# リスト4.4.3：anime_master_csvデータの再読み込み\n",
    "df = pd.read_csv(anime_master_csv, index_col=\"anime_id\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115bafce208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# リスト4.4.4：membersとratingの値で散布図を作成\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(df[\"members\"], df[\"rating\"], alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>members</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anime_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>Death Note</td>\n",
       "      <td>1013917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16498</th>\n",
       "      <td>Shingeki no Kyojin</td>\n",
       "      <td>896229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11757</th>\n",
       "      <td>Sword Art Online</td>\n",
       "      <td>893100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  members\n",
       "anime_id                             \n",
       "1535              Death Note  1013917\n",
       "16498     Shingeki no Kyojin   896229\n",
       "11757       Sword Art Online   893100"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# リスト4.4.5：メンバ数80万人以上の作品\n",
    "# membersの値でデータを絞り込み\n",
    "df.loc[df[\"members\"] >= 800000, [\"name\", \"members\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anime_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5114</th>\n",
       "      <td>Fullmetal Alchemist: Brotherhood</td>\n",
       "      <td>9.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9253</th>\n",
       "      <td>Steins;Gate</td>\n",
       "      <td>9.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1575</th>\n",
       "      <td>Code Geass: Hangyaku no Lelouch</td>\n",
       "      <td>8.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>Death Note</td>\n",
       "      <td>8.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16498</th>\n",
       "      <td>Shingeki no Kyojin</td>\n",
       "      <td>8.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      name  rating\n",
       "anime_id                                          \n",
       "5114      Fullmetal Alchemist: Brotherhood    9.26\n",
       "9253                           Steins;Gate    9.17\n",
       "1575       Code Geass: Hangyaku no Lelouch    8.83\n",
       "1535                            Death Note    8.71\n",
       "16498                   Shingeki no Kyojin    8.54"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# リスト4.4.6：メンバ数60万人以上かつレーティング8.5以上のデータ\n",
    "# membersとratingの値でデータを絞り込み\n",
    "df.loc[(df[\"members\"] >= 600000) & (df[\"rating\"] >= 8.5), [\"name\", \"rating\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Movie', 'TV', 'OVA', 'Special', 'Music', 'ONA'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# リスト4.4.7：typeの重複のないリスト\n",
    "types = df[\"type\"].unique()\n",
    "types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115baf10c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# リスト4.4.9：配給種別ごとにグループ化されたデータの散布図の作成\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "ax = fig.add_subplot(111)\n",
    "for t in types:\n",
    "    x = df.loc[df[\"type\"] == t, \"members\"]\n",
    "    y = df.loc[df[\"type\"] == t, \"rating\"]\n",
    "    ax.scatter(x, y, alpha=0.5, label=t)\n",
    "ax.set_title(\"配給種別ごとにグループ化した散布図\")\n",
    "ax.set_xlabel(\"Members\")\n",
    "ax.set_ylabel(\"Rating\")\n",
    "ax.legend(loc=\"lower right\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}