exec11matplotlibday11.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.         10.10204082 10.20408163 10.30612245 10.40816327 10.51020408\n",
      " 10.6122449  10.71428571 10.81632653 10.91836735 11.02040816 11.12244898\n",
      " 11.2244898  11.32653061 11.42857143 11.53061224 11.63265306 11.73469388\n",
      " 11.83673469 11.93877551 12.04081633 12.14285714 12.24489796 12.34693878\n",
      " 12.44897959 12.55102041 12.65306122 12.75510204 12.85714286 12.95918367\n",
      " 13.06122449 13.16326531 13.26530612 13.36734694 13.46938776 13.57142857\n",
      " 13.67346939 13.7755102  13.87755102 13.97959184 14.08163265 14.18367347\n",
      " 14.28571429 14.3877551  14.48979592 14.59183673 14.69387755 14.79591837\n",
      " 14.89795918 15.        ]\n"
     ]
    },
    {
     "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": [
    "#Matplotlibday-11\n",
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "s = np.linspace(10,15,50)\n",
    "print(s)\n",
    "ax.plot(s,np.cos(s));"
   ]
  },
  {
   "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": [
    "#Matplotlibday-11\n",
    "plt.plot(s, np.sin(s-0))\n",
    "\n",
    "plt.xlim(-1 ,15)\n",
    "plt.ylim(-2, 2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.pyplot' has no attribute 'xlable'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-e171c2926a9f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcos\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cosine Curve\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"x\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"cos(x)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'xlable'"
     ]
    },
    {
     "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": [
    "plt.plot(s,np.cos(s))\n",
    "plt.title(\"Cosine Curve\")\n",
    "plt.xlable(\"x\")\n",
    "plt.ylable(\"cos(x)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The above error is difference in version of system\n",
    "# As I checked in Stack overflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn-whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ce14b140a0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(1,10,50)\n",
    "y = np.log(x)\n",
    "\n",
    "plt.plot(x,y,'-.',color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ce15c2d1c0>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,'-oy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1ce149f8190>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1ce15f49910>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(7)\n",
    "x = rng.randn(100)\n",
    "y = rng.randn(100)\n",
    "\n",
    "colors = rng.randn(100)\n",
    "sizes = 1000*rng.rand(100)\n",
    "\n",
    "plt.scatter(x,y, c=colors, s=sizes, alpha=0.5, cmap='Reds')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x = np.linspace(0,10,100)\n",
    "dy = 0.1\n",
    "y = np.sin(x) + dy *np.random.randn(100)\n",
    "\n",
    "plt.errorbar(x, y, xerr=dy, fmt='.g');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-5.46312691e-01  2.54170733e-01  5.50653087e-03  6.69718557e-02\n",
      "  1.18068809e+00 -1.21639150e+00  1.45590416e+00 -5.80463425e-01\n",
      " -7.28289864e-01  2.31555583e+00  7.70805572e-01  5.78147193e-01\n",
      " -1.56998985e+00  4.50840897e-01  8.30629267e-01  9.39427255e-01\n",
      " -8.15456506e-02  1.52338295e+00 -6.12913383e-01 -4.46599958e-01\n",
      "  8.63150408e-01  1.63317904e+00  1.19541356e+00  2.01301649e-01\n",
      " -1.19066604e+00 -6.58026810e-01 -3.61341545e-01  1.90703904e-01\n",
      "  7.64624477e-01  6.84366953e-02 -1.51148018e+00  1.29417426e+00\n",
      " -1.24133232e-01 -9.79963323e-01  5.46979039e-01 -1.38610920e+00\n",
      " -5.14209168e-01 -9.30669458e-01 -9.68517638e-01 -1.76525031e-01\n",
      " -1.88336679e+00  1.63966035e-01  9.38691709e-01 -5.36921935e-01\n",
      "  1.39957124e+00 -4.22656872e-01  4.12723015e-01  5.17396532e-01\n",
      " -1.82984224e+00 -1.34648915e-01 -4.28175891e-02  1.43165708e+00\n",
      " -6.25958402e-01 -6.23135346e-01 -1.32234914e+00 -5.15243343e-01\n",
      " -6.37040595e-01  6.90474643e-02  4.59002134e-01 -1.28253232e+00\n",
      " -5.13550526e-01 -3.68285255e-01  2.67804044e-01  1.28207571e+00\n",
      "  1.02705121e-01 -9.00255326e-01 -1.29228578e+00  2.68139649e+00\n",
      "  6.48612604e-01  1.62852870e+00  7.32059520e-01 -1.71571259e+00\n",
      " -1.13992418e+00  1.73241344e-01  4.89579625e-01  6.53432757e-01\n",
      "  6.02015566e-01 -1.25422682e+00 -5.48557824e-01  2.58074984e-03\n",
      "  1.61663726e+00 -7.67674097e-01 -1.22775105e+00  2.18928596e+00\n",
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      "  1.18979264e-01 -5.88012657e-01 -2.11571267e+00  1.31656688e+00\n",
      " -3.95942062e-01  8.08450871e-01  2.27186938e-01  1.98800108e+00\n",
      "  2.34859780e-01 -1.18446190e+00  4.85702197e-01 -1.05907548e+00\n",
      " -1.56135068e-01  2.59970985e-01  6.18128661e-01 -6.07859195e-01\n",
      " -2.69571604e-02  1.28872834e-01  1.41905253e+00  3.04903634e-01\n",
      " -9.08320919e-01  7.73983199e-01  7.43081238e-01 -2.14655927e-01\n",
      "  8.22983465e-01  1.28806434e+00  5.18222748e-01 -7.40500414e-01\n",
      " -1.58025276e+00 -8.20661505e-01 -1.10570462e-02 -6.24663549e-01\n",
      " -1.56500875e-01  5.01063076e-02  4.41878790e-01 -1.46139639e+00\n",
      "  1.26755007e+00 -2.14619218e-01 -3.64462039e-01 -1.62207184e+00\n",
      "  7.73099007e-01 -1.54213709e+00  5.12693994e-01 -1.98425591e-01\n",
      " -1.57066615e+00  1.16335260e+00 -1.45256546e+00 -1.03568866e-01\n",
      " -1.47594792e+00  6.99843840e-01  4.15829421e-01 -6.56330010e-02\n",
      " -7.43496930e-01  9.16727092e-01  1.60918885e+00 -2.56048993e+00\n",
      "  3.99288904e-01 -1.88018397e-01 -1.94242244e+00 -3.87266681e-01\n",
      " -3.44095214e-01 -1.00715814e+00 -5.63347249e-01 -1.47794628e+00\n",
      " -6.64496365e-01  4.26850335e-01 -8.90006545e-01 -4.71478812e-01\n",
      "  5.63359686e-01 -3.00216792e-02  5.21021932e-01 -1.79571819e+00\n",
      " -5.32800352e-01  1.09611944e+00 -1.69499654e-01  1.72557942e+00\n",
      " -1.01425548e+00  7.79306592e-01 -1.04938594e+00 -1.20132368e+00\n",
      " -8.00145914e-01  7.70218199e-02 -8.29139182e-01  5.63708678e-01\n",
      " -1.21555056e+00  2.85778703e-01  5.07851275e-01  1.25069921e-01\n",
      "  4.10699519e-01 -9.07276798e-01 -1.92211743e+00  1.96312595e+00\n",
      " -3.80499100e+00 -6.01068777e-02  1.24857234e+00 -2.07923299e+00\n",
      " -1.68847186e+00 -1.13787213e-01 -1.11397936e+00  4.01157934e-01\n",
      " -9.31685902e-01 -1.47542736e+00  2.81689448e-01  6.94439056e-02\n",
      "  6.73859499e-01 -2.18137314e-01  1.08114659e+00 -1.00203602e+00\n",
      " -1.37530207e+00  1.83818539e+00 -6.51119620e-01 -2.13899044e-01\n",
      " -9.53142741e-01  4.54709289e-01 -2.10299092e+00  1.91604455e+00\n",
      " -1.33758468e+00  6.14741386e-01 -1.37935202e+00  2.39492233e-01\n",
      "  1.90829367e+00  5.31432787e-01  2.72740632e-01  6.69856578e-01\n",
      "  4.33514530e-01  9.68163265e-01  2.24127103e-01  2.40553390e-01\n",
      " -1.20560400e+00 -2.32291192e-02 -8.85853987e-01  9.72534549e-02\n",
      "  1.54173364e-01  1.96033723e+00 -4.63971786e-02  4.42380672e-01\n",
      "  4.47442896e-01  5.52243303e-01 -1.00328384e+00 -1.19185147e-01\n",
      "  2.30700914e-01  7.62257155e-01  7.55364327e-01 -8.29505012e-01\n",
      " -5.29505888e-01  2.25764316e-01  1.22096338e+00  2.29100847e+00\n",
      "  2.51065666e+00  1.58612643e+00  2.01242274e-01 -6.02323297e-01\n",
      "  4.40001138e-01  1.06625182e-01  5.93139534e-01  7.78750513e-01\n",
      "  1.48592900e-01 -1.21586368e-01 -1.01170090e+00  2.84070349e-01\n",
      "  1.30764448e+00 -9.88473110e-01  7.07115305e-01 -8.96506963e-01\n",
      " -1.26648761e+00 -6.04373908e-01  6.62640986e-01  1.21018529e+00\n",
      "  1.55440686e-01  1.90559312e-01  3.06625692e-01  1.99440690e-01\n",
      " -1.30987236e+00 -2.09591981e+00  9.63023248e-01  1.45221621e+00\n",
      " -7.13702304e-01  2.08284031e+00 -2.63514663e-01 -2.05359268e+00\n",
      "  7.77746059e-01 -2.57719779e-01 -4.84616926e-01 -1.64476482e-01\n",
      "  1.15056760e+00 -1.75277918e+00 -5.52207479e-01  7.10997382e-01\n",
      " -2.06244474e-01  2.35010085e+00 -6.71323531e-01 -5.75657007e-01\n",
      "  1.38776968e+00 -5.12991578e-01  1.06573352e+00  3.61040621e-01\n",
      "  4.98985014e-03 -2.27798401e-01  1.32617658e+00 -6.26416227e-02\n",
      "  3.90484200e-01 -5.90121094e-02 -8.69366540e-01 -5.88204583e-01]\n"
     ]
    }
   ],
   "source": [
    "plt.style.use('seaborn-white')\n",
    "data = np.random.randn(1000)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Polygon' object has no property 'normed'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-51-86eac47a0e1d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m plt.hist(data, bins=5, normed=True, alpha=0.6,\n\u001b[0m\u001b[0;32m      2\u001b[0m          histtype='stepfilled')\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mhist\u001b[1;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)\u001b[0m\n\u001b[0;32m   2683\u001b[0m         \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'vertical'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2684\u001b[0m         label=None, stacked=False, *, data=None, **kwargs):\n\u001b[1;32m-> 2685\u001b[1;33m     return gca().hist(\n\u001b[0m\u001b[0;32m   2686\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdensity\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdensity\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2687\u001b[0m         \u001b[0mcumulative\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcumulative\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbottom\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1436\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1437\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1438\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1439\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1440\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mhist\u001b[1;34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[0;32m   6818\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpatch\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6819\u001b[0m                 \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6820\u001b[1;33m                 \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6821\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mlbl\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6822\u001b[0m                     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlbl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m    994\u001b[0m                     \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mf\"set_{k}\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    995\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 996\u001b[1;33m                         raise AttributeError(f\"{type(self).__name__!r} object \"\n\u001b[0m\u001b[0;32m    997\u001b[0m                                              f\"has no property {k!r}\")\n\u001b[0;32m    998\u001b[0m                     \u001b[0mret\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Polygon' object has no property 'normed'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data, bins=5, normed=True, alpha=0.6,\n",
    "         histtype='stepfilled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Polygon' object has no property 'normed'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-3b5da5cf6abb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'stepfilled'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnormed\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mhist\u001b[1;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)\u001b[0m\n\u001b[0;32m   2683\u001b[0m         \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'vertical'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2684\u001b[0m         label=None, stacked=False, *, data=None, **kwargs):\n\u001b[1;32m-> 2685\u001b[1;33m     return gca().hist(\n\u001b[0m\u001b[0;32m   2686\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdensity\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdensity\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2687\u001b[0m         \u001b[0mcumulative\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcumulative\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbottom\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1436\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1437\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1438\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1439\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1440\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mhist\u001b[1;34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[0;32m   6818\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpatch\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6819\u001b[0m                 \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6820\u001b[1;33m                 \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6821\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mlbl\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6822\u001b[0m                     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlbl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m    994\u001b[0m                     \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mf\"set_{k}\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    995\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 996\u001b[1;33m                         raise AttributeError(f\"{type(self).__name__!r} object \"\n\u001b[0m\u001b[0;32m    997\u001b[0m                                              f\"has no property {k!r}\")\n\u001b[0;32m    998\u001b[0m                     \u001b[0mret\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Polygon' object has no property 'normed'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.normal(0, 0.8, 1000)\n",
    "y = np.random.normal(-2, 1, 1000)\n",
    "\n",
    "kwargs = dict(histtype='stepfilled', alpha=0.5, normed=True, bins=50)\n",
    "\n",
    "plt.hist(x, **kwargs)\n",
    "plt.hist(y, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 16 272 586 122   4]\n"
     ]
    }
   ],
   "source": [
    "counts, bin_edges = np.histogram(data, bins=5)\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-54-dfbab873304b>:4: RuntimeWarning: covariance is not positive-semidefinite.\n",
      "  x,y = np.random.multivariate_normal(mean, cov, 10000).T\n"
     ]
    }
   ],
   "source": [
    "mean = [5,2]\n",
    "cov = [[3,1],[2,0.5]]\n",
    "\n",
    "x,y = np.random.multivariate_normal(mean, cov, 10000).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(x, y, bins=20, cmap= 'Reds')\n",
    "cb = plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hexbin(x, y, gridsize=20, cmap='Reds')\n",
    "\n",
    "cb = plt.colorbar()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}