Code/4-MatPlotLib/4-MatPlotLib-直方图.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MatPlotLib-直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series(np.random.randn(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.,   2.,   1.,   6.,   9.,   3.,  18.,  25.,  43.,  56.,  55.,\n",
       "         58.,  56.,  87., 100.,  78.,  89.,  68.,  60.,  51.,  31.,  37.,\n",
       "         26.,  13.,  15.,   6.,   3.,   0.,   0.,   1.]),\n",
       " array([-3.06731585, -2.85738646, -2.64745707, -2.43752768, -2.22759829,\n",
       "        -2.0176689 , -1.80773951, -1.59781012, -1.38788073, -1.17795134,\n",
       "        -0.96802195, -0.75809256, -0.54816317, -0.33823378, -0.12830439,\n",
       "         0.081625  ,  0.29155439,  0.50148378,  0.71141317,  0.92134256,\n",
       "         1.13127195,  1.34120134,  1.55113073,  1.76106012,  1.97098951,\n",
       "         2.1809189 ,  2.39084829,  2.60077768,  2.81070707,  3.02063646,\n",
       "         3.23056585]),\n",
       " <a list of 30 Patch objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# 直方图（rwidth 为宽度，bins 为分割区间，默认10） \n",
    "plt.hist(s, rwidth=0.9, bins=30)"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}