Numpy技巧和提示.py.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  \"自动\"重定义数组形状\n",
    "import numpy as np\n",
    "a = np.arange(30)\n",
    "a.shape = 2,-1,3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 5, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2],\n",
       "        [ 3,  4,  5],\n",
       "        [ 6,  7,  8],\n",
       "        [ 9, 10, 11],\n",
       "        [12, 13, 14]],\n",
       "\n",
       "       [[15, 16, 17],\n",
       "        [18, 19, 20],\n",
       "        [21, 22, 23],\n",
       "        [24, 25, 26],\n",
       "        [27, 28, 29]]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 2 4 6 8]\n",
      " [0 1 2 3 4]]\n",
      "------------------------------\n",
      "[0 2 4 6 8 0 1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "# 向量堆叠\n",
    "import numpy as np\n",
    "x = np.arange(0,10,2)\n",
    "y = np.arange(5)\n",
    "m = np.vstack([x,y])  # 垂直方向堆叠\n",
    "print(m)\n",
    "print(\"---\"*10)\n",
    "xy = np.hstack([x,y]) # 水平方向堆叠\n",
    "print(xy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.29517369 2.54997857 1.52817365 ... 1.05479102 2.31722613 1.82795658]\n"
     ]
    }
   ],
   "source": [
    "# 直方图\n",
    "# NumPy的 histogram 函数应用于一个数组，并返回一对向量：数组的histogram和向量的bin\n",
    "# matplotlib 也具有构建histograms的函数（在Matlab中称为 hist ），它与NumPy中的不同。\n",
    "# 主要区别是 pylab.hist 自动绘制histogram，而 numpy.histogram 仅生成数据。\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mu,sigma = 2,0.5\n",
    "v = np.random.normal(mu,sigma,10000)\n",
    "print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aA8NdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QG/R+Uwvmla8AX8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a9760e8c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matplotlib.pyplot的版本\n",
    "plt.hist(v,bins=50,normed=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00137197 0.         0.         0.         0.00548789 0.00548789\n",
      " 0.01234775 0.01783564 0.01783564 0.04390311 0.049391   0.05076297\n",
      " 0.09055017 0.11936158 0.15640483 0.18521625 0.25107091 0.32378544\n",
      " 0.34299305 0.42256744 0.51448958 0.5776003  0.6173875  0.72851725\n",
      " 0.72714527 0.7998598  0.86571447 0.74498091 0.76418852 0.75184077\n",
      " 0.67226638 0.64482694 0.56525255 0.52409339 0.44177505 0.34710897\n",
      " 0.35259686 0.25793078 0.17835639 0.14268511 0.11524567 0.10564186\n",
      " 0.04664706 0.04253114 0.04253114 0.01920761 0.01097578 0.01097578\n",
      " 0.00548789 0.00548789]\n",
      "[0.03311421 0.10600198 0.17888976 0.25177753 0.3246653  0.39755308\n",
      " 0.47044085 0.54332862 0.6162164  0.68910417 0.76199194 0.83487972\n",
      " 0.90776749 0.98065527 1.05354304 1.12643081 1.19931859 1.27220636\n",
      " 1.34509413 1.41798191 1.49086968 1.56375745 1.63664523 1.709533\n",
      " 1.78242077 1.85530855 1.92819632 2.0010841  2.07397187 2.14685964\n",
      " 2.21974742 2.29263519 2.36552296 2.43841074 2.51129851 2.58418628\n",
      " 2.65707406 2.72996183 2.8028496  2.87573738 2.94862515 3.02151293\n",
      " 3.0944007  3.16728847 3.24017625 3.31306402 3.38595179 3.45883957\n",
      " 3.53172734 3.60461511 3.67750289]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a9762fabe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(n,bins) = np.histogram(v,bins=50,normed=True)\n",
    "print(n)\n",
    "print(bins)\n",
    "plt.plot(.5*(bins[1:]+bins[:-1]),n)\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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}