|
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 处理数据,获取信息\n",
|
|
"__经过长年观测,这些研究站的工作人员采集了大量鸟类活动数据。这些数据被多名科学家共享,他们对这些数据进行加工、分析,从而得出各种信息,为各自的科研服务。__\n",
|
|
"\n",
|
|
"__现在人们越来越多地通过计算机来表示、组织和处理数据,从而可以获取并传播有价值的信息。__\n",
|
|
"\n",
|
|
"__例如,科学技术用计算机汇总2010年10月到2012年10月的数据后得知,在实验林地共观测到鸟类44种、4823次。其中,留鸟23中,冬候鸟有9中,旅鸟有8种,夏候鸟有4种。进一步处理这些数据 ,能够得出以下鸟类居留型种数的柱状图和居留型比例的饼图。__"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# * 试试运行下方单元的代码,绘制出柱状图吧!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEHCAYAAACp9y31AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAakUlEQVR4nO3debxdVWH28d+TAVSCDBLDTJgUqBokAVEcgnEAsWLFAZywRWkV+qLWYpTX6utU9H2plrdOiCBaEdE6QMEBqSgIWAMKghShGpBBSEAhkBBI8vSPtQ453Nzce8m9557crOf7+eRz79l7n73XXRyevc/aa60t20RERDsm9bsAERExvhL8ERGNSfBHRDQmwR8R0ZgEf0REYxL8ERGNmdLvAozEVltt5ZkzZ/a7GBERE8oVV1yx2Pb0gcsnRPDPnDmTBQsW9LsYERETiqSbBluepp6IiMYk+CMiGpPgj4hoTII/IqIxCf6IiMYk+CMiGpPgj4hoTII/IqIxE2IA12jMnH9ev4vQVwtPPKTfRYiI9Uyu+CMiGpPgj4hoTII/IqIxCf6IiMYk+CMiGpPgj4hoTII/IqIxCf6IiMYk+CMiGpPgj4hoTII/IqIxCf6IiMYk+CMiGpPgj4hoTII/IqIxCf6IiMYk+CMiGpPgj4hoTII/IqIxCf6IiMYk+CMiGpPgj4hoTII/IqIxPQt+STtI+pGk6yRdK+m4unxLSRdIuqH+3KJXZYiIiDX18op/BfB3tvcE9geOkbQXMB+40PbuwIX1dUREjJOeBb/t221fWX9fAlwHbAccCpxRNzsDeHmvyhAREWsalzZ+STOBpwM/A2bYvh3KyQF44lrec7SkBZIWLFq0aDyKGRHRhJ4Hv6RpwL8Bb7d970jfZ/sU23Nsz5k+fXrvChgR0ZieBr+kqZTQ/4rtb9bFd0japq7fBrizl2WIiIhH6mWvHgFfAK6z/U9dq84Bjqy/Hwl8p1dliIiINU3p4b4PAN4A/ErSL+uy9wInAmdLOgq4GXhVD8sQERED9Cz4bV8CaC2r5/XquBERMbSM3I2IaEyCPyKiMQn+iIjGJPgjIhqT4I+IaEyCPyKiMQn+iIjGJPgjIhqT4I+IaEyCPyKiMQn+iIjGJPgjIhqT4I+IaEyCPyKiMQn+iIjGJPgjIhqT4I+IaEyCPyKiMQn+iIjGJPgjIhqT4I+IaEyCPyKiMQn+iIjGJPgjIhqT4I+IaEyCPyKiMQn+iIjGJPgjIhqT4I+IaEyCPyKiMQn+iIjGJPgjIhqT4I+IaEyCPyKiMQn+iIjGJPgjIhrTs+CXdJqkOyVd07XsA5JulfTL+u8lvTp+REQMrpdX/F8EDhpk+Sds713/nd/D40dExCCmPJqNJc0CdgZutn3lUNva/omkmetetIiI6IURXfFLmiHpo8C+wFJgtqR/lrTFOhzzWElX16agtb5f0tGSFkhasGjRonU4TEREDGbY4Jf0eOAFtt9r+1TbP7D9eeBdwGGP8nifAXYF9gZuB05a24a2T7E9x/ac6dOnP8rDRETE2ozkiv9xtr8ycKHth4DTJW0+0oPZvsP2SturgM8D+428qBERMRaGDX7bfxhi3UrbfxrpwSRt0/XyL4Br1rZtRET0xohu7kqaBqyyvVTSZMD1qn2o93wVmAtsJekW4P3AXEl7AwYWAn89irJHRMQ6GGmvnv2BAyUtB44E3gRcPNQbbB8xyOIvPKrSRUTEmBsy+CXtRLk6Xw6cCSyh9M1fKGlHYDLlm8BNvS5oRESMjeGu+LcF9gCmA/cAy4DNgOdT7g9MBlYBp/WwjBERMYaGDH7blwGXSZpN6cMPIOAG25f2unARETH2hm3jlzSDciP2xcB3gQeBm3tbrIiI6JUhu3NK2o0ySOs+4A11+/tt31LXq/byiYiICWK4pp4bJb0deDewMXAqcJekfSgngSnArcCne13QiIgYGyPpznmW7UskzbU9T9KewFOAc2wv73H5IiJijI1kyobOLJwfkSTb19n+OjCzd8WKiIheGfaK3/bS+vPCAcuv71WhIiKid0YyO+dQUydPfTSTtEVERP+NpKlniaTXSXrEtpI2Bo56NJO0RURE/42kqWeFpHOB90haRplHf2tgKnByj8sXERFjbESTtNm+l3Jzd2tgB+AC24t7WrKIiOiJR/XM3To3/1rn54+IiPXfiJ65GxERG44Ef0REYxL8ERGNSfBHRDRmRMEvaVNJr1jLulljW6SIiOilkV7xbw2cKOlNkuZK2gxA0nbA/J6VLiIixtxwz9ydDGxi+wZJfwAuAHYD3ixpB2Aa8De9L2ZERIyV4frxzwJOkPRD4DGUZ/BOpfTlv4EyPXPuE0RETCDDPYjlSknH1pfPBZ4NfAFYZvshSecBhwNf6W0xIyJirIxk5O6rgF8B2wAPAdsBZ0j6EmX6hh9K2jyTtUVETAzDPXN3FjAdmAfsCOwH7A8sB74OPBW4ELint8WMiIixMlz7/DXAB4FzgQeBnwKXANsDRwFfBF5h2z0sY0REjKHh2vhXStq/bvcnytX9JOD3lAesLwee0etCRkTE2BnJfPw/BZB0uO2Fkp4AvKPTpi/pl5Im217Z47JGRMQYGNG0zJKmAXfWl38CftFZl5u6ERETy0jn498fOFDScuBI4E3Axb0qVERE9M5wI3d3Akxpyz8TWAIcBCyUtCMwGVhl+6ZeFzQiIsbGcFf82wJ7ULp03gMsAzYDnk+5yTsZWAWc1sMyRkTEGBquV89lwGWSZgP71sUCbrB9aa8LFxERY2/YNn5JM4CFwIuB71L689/c22JFRESvDDdydzfgMOA+4A11+/tt31LXq87gGRERE8RwTT03Sno78G5gY+BU4C5J+1BOAlOAWymDuSIiYgIYSXfOs2xfImmu7XmS9qRMx3yO7eVre5Ok04CXAnfafkpdtiXwNWAmpfno1bb/OMq/ISIiHoWRzKV/Zf35EUmyfZ3tr1PCeyhfpHT97DYfuND27pTpH/L0roiIcTZs8NteWkfuXmbbkiZLmmT7+mHe9xPg7gGLDwXOqL+fAbx8XQodERHrbqRPz9qf8iSufwB+AxywjsebYft2gPrziWvbUNLRkhZIWrBo0aJ1PFxERAy03o7ctX0KcArAnDlzMu1zRMQYGe+Ru3dI2sb27ZK2YfXEbxERMU7Ge+TuOZRJ3k6sP7+zDvuIiIhR6NnIXUlfBeYCW0m6BXg/JfDPlnRU3cer1rXgERGxboZr498NeBHwBcrI3a8yYOQuMGmwh7DYPmItu503qhJHRMSoZORuRERjejZyNyIi1k8jGrkraW/gNEmbPoqRuxERsR4aycPWl0o6mNKf/32StqjvmyTJwBm2f9TjckafzJx/Xr+L0FcLTzyk30WIGHMjfebunZTmnd8An7R9G4CkqcB/SDrY9n09KmNERIyh4Xr1PA54gDIf/7eBxcBRkjYDTqoDsT6b0I+ImDiGu+I/gNKN8wZKr55XAP8FnAy8Q9L3bH+lt0WMiIixNFx3zguAC2p//rnAlygzbr6O0rd/tqQdbedRjBERE8Rwj17cQtJGlL76x1Ou/B8E/hOYCpxLmbsnIiImiOGaevakPEWrs+1xlLl6ngn8rP6+EvhVrwoYERFja7imnkslLQPeDCylPD3r0jpp2962vzAehYyIiLEzkn78vwCOkTQZeI6kWbavkLSi98WLiIixNtJ+/NSJ2C7qen1VLwoUsSHJALgMgFsfjfTRixERsYFI8EdENCbBHxHRmAR/RERjEvwREY1J8EdENCbBHxHRmAR/RERjEvwREY1J8EdENCbBHxHRmAR/RERjEvwREY1J8EdENCbBHxHRmAR/RERjEvwREY1J8EdENCbBHxHRmAR/RERjEvwREY1J8EdENCbBHxHRmCn9OKikhcASYCWwwvacfpQjIqJFfQn+6kDbi/t4/IiIJqWpJyKiMf0KfgM/kHSFpKMH20DS0ZIWSFqwaNGicS5eRMSGq1/Bf4DtfYCDgWMkPXfgBrZPsT3H9pzp06ePfwkjIjZQfQl+27fVn3cC3wL260c5IiJaNO7BL2kTSZt2fgdeBFwz3uWIiGhVP3r1zAC+Jalz/DNtf68P5YiIaNK4B7/t3wKzxvu4ERFRpDtnRERjEvwREY1J8EdENKafUzZERAxp5vzz+l2Evlt44iFjvs9c8UdENCbBHxHRmAR/RERjEvwREY1J8EdENCbBHxHRmAR/RERjEvwREY1J8EdENCbBHxHRmAR/RERjEvwREY1J8EdENCbBHxHRmAR/RERjEvwREY1J8EdENCbBHxHRmAR/RERjEvwREY1J8EdENCbBHxHRmAR/RERjEvwREY1J8EdENCbBHxHRmAR/RERjEvwREY1J8EdENCbBHxHRmAR/RERjEvwREY3pS/BLOkjS9ZJulDS/H2WIiGjVuAe/pMnAp4CDgb2AIyTtNd7liIhoVT+u+PcDbrT9W9sPAmcBh/ahHBERTZLt8T2g9ErgINtvrq/fADzD9rEDtjsaOLq+fDJw/bgWdOxsBSzudyEmsNTf6KT+Rmei199OtqcPXDilDwXRIMvWOPvYPgU4pffF6S1JC2zP6Xc5JqrU3+ik/kZnQ62/fjT13ALs0PV6e+C2PpQjIqJJ/Qj+nwO7S9pZ0kbA4cA5fShHRESTxr2px/YKSccC3wcmA6fZvna8yzGOJnxzVZ+l/kYn9Tc6G2T9jfvN3YiI6K+M3I2IaEyCfxQkqf58jqRjBls3YNlO41W29ZmkrSRtOsT6fev9n+5lk4fYPvVaDVVPdX3qahCSdun6fYP//CX4R2e+pPOBZwLXSvpR5x9wmaQpAJKm1w/S0ZImS5omaZt+FrzP/hewL4CkqZJ2HLD+fwNTJM3uqs/LJW3cvVHqFSRNkfRxSY+tiz4s6RmDbNd0XUnaWNJcSc+sn6uPSJpTf38u8PSuzTf4z18/+vFvSB5j+yWS9gT+GzgSONL2hySdbHtF3e7DwD7ACuBZwOMpN7ff249C95OkzYDXA8+R9D7KAJmr6jIkzQR+CuwI/JIy2G+5pP/Lmhcqzddr7SxxKbCDpEnAzsCfJD0ZmEfpPPEAjdeV7eXARZIuoowbehylHgCW2j4E2vn8JfhHZ5KkxwO7AScAVwKn12ae7qaepZRvBV8G3gXMot26fw/wfyjdem8ETgfe3bX+dcAvKN18/xX4S0mnAV8D9pJ0o+176rZN16ukFwMfBR4D3AlsTqlfKJ+/H7G6Ppquqy6/Bk4asOxvu35v4vM3IQq5HrsLeBHw75T/6a6uyz8NnN213ap6ZfZ3wDbAgcCp41nQ9YGk/YHLbX9b0nHAMcA/2L61rt+MUo/LgWXAQ8ArKVdTjwVOtX1F1y6brlfb35d0L6XL4Z3Aa4A5lHqbBtxv+766edN11eWprPl3N/f5S/CPzteA71KuGMzqqSdmAF8ZuLHt2yRtCyyyfb2k3W3fMG6l7b/f2r68zsa6JXA3sI+k39leRam37wCdttRdgb2BD9r++4E33Doar9dNgbcCrwDuo4TUUtsXSXoppQnyYY3XFcA1wPmUE+ViSvPNq+u6Zj5/Cf7RWQl81fbHJM0DptSrsGOA27u227jeIHqYpIOBmZKe2nVVtqF7oqQXAHcAF1DaRjcHzqo3J48fsP0hwPspI733BaZLeqHtzhQfqVeYDlwMPGh7qaQVwGaSDgPulSSXwTqpq+I44BJKM42BrSknTmz/RtITu7bdYD9/Cf7RWUW5o38QsAWlF+d8YDvgPEnTKFcQA28KTQP+BZi9Pn84xpKk7YG7bZ/ZtezDwOdtv1rSlrbvlvQcSvv0VODvgc0o7dbftH15fV/qdbXH2765q8eJKTfL97F9Qe1pkrri4S7W7wf+ifJNaCnlflP31Xnn/twG/flL8I+OgFPWcsU/yfZ9kl5ue8mgb5aeNq6l7SPbtwDUm+FzgacBv6V+M7J9d91UlM/lr22vkvQm4BPA6yX92va9qdei9uK5T9IBrG6eEPBE4OmSfm/756mrh/vhv57y/+tNkj5IaZN/te3ub+dNfP7Sj390JlGu+H9E6fs7v/5+HPWkOvDDoeIlkr5F+YrelPo/zjm2P1wXfWLAJlMpdfdtSYcCP63/Y54OfLl2nU29FnsDX6c0V/y/umwvys3KD1HCanbqCig9n66j9Mx5LfBN4M+BIyWdXv/tRSOfv8zVMwqSNgF2tH3dgOXbA3fWJ4yt7b272b6x12Vc33W1QXdebwE8QOkTfVfXWAgkTbX90DD7S72OUEt1VXvs3N/9eRqwfhPb97fy+UvwR0Q0Jk09ERGNSfBHRDQmwR9jQtJutcdO5/VTVSepq68n1e50j3jPMPvcUtLUERx7RLMj1q6Nm61l3ZSB5eteN8Q+ZwyxbluVuV8GLl+jDJJ2r710kPQsSTuvpYx7d72ePVj9SDpYw8zSWbcb9O8dsM32Q6xbo4wxMST4Y6wcTuld0nEsq0cyU9d9AMpNsrrsHcPs80DK3CmD0ghnR5T0Okm7AzOBt0naSNKX9cjZFncGrlHXDKv130+Aj3ftaz89cpDPyyTN7qwbcOh3Ugb9HN61v8uBf6vbb6IypfengBdTRo4C7MEj6w6V8Q2TgFdKervKSNETgE0GqZqX2l5Z37eHpLetpQoPlfTndbt5ko4YUCcA75E0S9JbJR1ff58taRalJ1tMQOnHH2PlQeCxkrYCllB6RkyR9CzbF9f1S+u2H5P0bsqweSRtZXtx/X1rytD3lXXb39fl04FlAwbGjHR2xPOBvwK+B9xGmd7gncCfSfpF7VW0AviS7Y91v7Fenc/vWnQt8D5JN1JmY90OuFVlxOxySa+yvUTSy4AzKZOCXQ18Y2CPEtv3AxdLej5ljpdpkuZQRuPuVE9WP7N9L+UxpftQ+pn/qu7ih7b/JOlJwB/qdtRy7E6ZS2oJpU//9sABlK6GnbloVgGd97hu+wGVmWU7fduX2b5K0vHALsBLgNttHyGpM/YiJpgEf4yKpLmU0Y9PAg4FLqJc3b+RMrHVSkm/ogTLJJUHXiy1/VBtCpkLvBY4uu7yecDxKpOPzQAekHQPZWqHL/HIfv9Dzo5Yj/XVut1k4CPATZTw/+u62QnApbV8nVHY1P2sqD8vrfvbGbjN9nxJ+9TlLwY+Dzwb+L5t15Pf1ZST3ym23zigzg4A/rP+rSdQvom8tv4dd1HC+O667LJ63I/VOtioHut2YDdJR1AmCJsv6Y76N+5CmZL5HylTDD9U/11Emc9nGfA5ygmxY1Wtg08Dz2f1XFNWma7gauAzlBPs74gJLcEfo2L7IuB5kq4CXmd7YW36eAFlLvjLJb2CctX8BEqYL5f0GuDeOpnYvK5drgTeZvtnkt5FuVJeKOl5lCHx3YacHdH2b4FnSHoCJehPogx6+l3d/std865MBf6/7U8CSPpLyoReCyiDogAWAZ+VdDLlG8w7KZP0PY3ybeNwytxNi4HFkk4CPiVpAeVqeuv68/5alu/a/qHKoL8317/9efVY91JOHEtt/07SGyhh3jlpTKWcBG8DXmj7G7XcB1Nmi51XJ75D0v227+iuuNqEtTHwIpVRrJtTJny7qa7/je2f181vAk4D3lK3+67WMmFZTAwJ/hg1SbtSHqhynKTPAAdTriafRpmG+ZuSfky5qr+UcoL4mqQ/G2R33W3bT6IE9++HOv7aZkdUudn8ZMo3jxMpV9Dn2r5S0tOBT7J6ZsbNKJPIXUy50u8OaSQdXff5LkoAn0c5gWwO/IQyCvThJqZ68nsZ8GHbc+qyI4EFtq/t2m4vylX+UygPB+k0K/1NLcNHJW1JaeK5kNJMczkl8Petf9vCruo4iPKt4CxJrxkwOO7Jtq+vdfZlldk7f2D7BJXRrLt0jaju2IjyUBJTHkyyO/BCyok9JqgEf4yFl1PmhP8mJex/SZm07l5JL7D9w7rdKts/VpmhE8o9gekMuJEJULf5NnA98A1KM8NAQ86OSLlCfWnX6j2A30haVV8/SdK+9cp2B8qDS64Djup6z0bAW7um2d2E0iyyMeVm6w6UZpmVnZCt9ylmAefY/uOgNbbaU4BrbX9H5RGUb6GE639QmoBcf36iHutW4G2UK/8ZlG8u3XPA7wJcVpe9s4b7rvXewUP1ZPDHesJ5LuUE1qmba7TmlMIP2l6g0gPr8cBV9VseKk+ligkovXpiVGog/IzSbnwPsNz2v1OuUM+mtD2/sG6+kaRnU9rboYTnYcAFNSw7ZlNOEufb/m/gXMoNz84xp0m6EBj4jWEa8EXKVf19lPbqebYPpLTFn02Zw+Yw2wfa3q6rOWMW5clLW1Aen7mn7f0oJ7TOs5O3pTR5/BjYljKrY+ekZZXnuk4HHrB92gjqbk/gnFpX2L7Z9iX1tWz/2PYK2wso909OrfX1JZdHCS4BduicXOq3mDMpEwT+l+2T6t/+tfr3vqiG/mGU+wTdc8psAXyLchN8DXUagluAV2sCTEIWQ8sVf4zWXbZvrIE+w/a5dfmUOrvhGymh/UHgZspNwkPqNh9wmYp5V0rw/oESelfZ/mnnALZPk3QgpSmEkc6OaHtZ1+L3AJ+z/WtJ75F0NXB+V1PIStuLVbq2nyXpgbp8a2pzUG1S+j3lAR5votzU3aOWeRPKyWCO7a93ijJUxXXmeFLpnz+5qyfTZFafHDseoJxoTqdcmU+hfFuYJml727fY/kVnf2upl60pPanOt72s9jySSlfRC+sN94ckzbN9YX3btgO/VQEnSzLlv1dMQAn+GJWupowpPPLhMxvV9bepDEz6kO0/AEhaKukySo8dKE0W/9y1n0d0e1Tpa34sZS71znHXmB2Rcm/hLcAZddljKd8engT8a/32gO1/lPRXwPdqm/0kSohDOTkd4jpNdL2pfFfXoS6i9Gw5p3alXEIJ6QfrPt7fte3kuo/HUJqcDqT2EBrgtlqGTvCb0pzU+du2oNyUNuVewvOBI4DP1vd8TtKJneCnfLN6JWWcxIPA1BreUyn/Hb5ft5tEueG+le3P1WUfB86vJ6IfAFfYHnQshaQ3D7Y81n+ZpC3GRO06eVPXwKFdaq+addnPnQP66yPpKbavGcH7H54dUaUP/r3u04dc0k62O71kpgJ/YfvsYd422H4enhWynkQ39+rnFwy2/bZdvZWG2u8OlKaiSwcsnzaw/mPDkuCPiGhMbu5GRDQmwR8R0ZgEf0REYxL8ERGNSfBHRDQmwR8R0Zj/AX6SHgyS5kGUAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np\n",
|
|
"from matplotlib.font_manager import FontProperties\n",
|
|
"\n",
|
|
"font = FontProperties(fname='/simsun.ttc')\n",
|
|
"\n",
|
|
"birds = [23,9,8,4]\n",
|
|
"x = [1,2,3,4]\n",
|
|
"plt.bar(x,birds)\n",
|
|
"plt.xticks(x, [u\"留鸟\", u\"冬候鸟\", u\"旅鸟\", u\"夏候鸟\"], fontproperties=font)\n",
|
|
"\n",
|
|
"plt.xlabel(u\"林地内鸟类居流行种数柱状图\",fontproperties=font)\n",
|
|
"plt.ylabel(u\"物种种数(种)\", fontproperties=font)\n",
|
|
"#plt.title(u\"lindi\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* 试试运行下方单元的代码,绘制出饼状图吧!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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v3N+7engb89KpzSOsMy2gP5gydG3fX4bT0jjdt6lutmxomu3b0HOsbA2Nlt3DC+gcJ8JIYKQZme5hSyA46HX97cUyYcm1gQkLX0guW/R8crpkYd/6fY27ebCpCQV8pGg4ny4u5s6dDTzf2sbxeWGqx+h5vseam2lO9vKpEcWZlnQo1ppqeCAYMfSV/sfWXux/ftkY2T28gI5xPmEEtujBoGjy+9I2vPjzGb7Tn5orO6t+1/vS2N2ZS0hZ29XFg01NPDApQlCEJVu3cMqQIbzW0cHSyZP5cl0db3Z1MjEY4pE9zdw1Pi35N0dDO1BnqvGBYMTQ0eAfa4F3mWrfa/RATyLNa+BNQ6X0i1cEShe8nlxxRYYSUtZ3dzEnP598n970d3L+EJ5qbaVHKZRSdKkkAYS7d+9m8fARBMXY0vn68prVjj81A0xVLKlq7gY2GGnbg2wMBrcikpE95s/M8Z1y2Rf9BWvG8ZyCtH6op4XCvNreTlNvLx3JJM+1tdLYm+B9Qwu5eFMt44JBCv1+VnZ28J7CwnQ2PVBc0d0GsxFyNXCswfY9wyq9ZJWxJJGOPBn2tU8Hzpq7PvnfL/85WZiuhJQp4TCfLR7J5Vs2M8Tn47hwHgERLh85kstH6mmTr22v5+qSUh5qamJZexvHhcNcOTLr86Brst3g0WLy9MnXDLbtKVaGQ1nZY/6fKb7Zldf7x66YJs8q6EnHPRcOH86fI5O5b+Ikivx+JgX3bTBZ1an3Q0RCIR7d08wPxo5jbVcXtd3d6Wh6ILyS7QaPFpOGXmawbU+xJhTK2uAyEZDw9y7xn31zpX9jR4hVg73frkQCgLqeHv7R2sIFw/aVm/vxWzu5pqSEhFIkU519H0JnMjnYZgfK8mw3eLSY7HK/jN5jauuLDZItgXfusso06UpIua5uG029vQRFuKVsFEV+/XH4R0sLM/PyKQvorNQ5+fl8eONGjg2HOT4vqwU4t5TXrK7PZoODwUjFkrepKvoPMMecAG8wNzJha6/IeFPtj96ttnzj/t4GjyakPFhes3qRaRH9xWSXG+BFw+27nm7o6jV8qkMqIWXeH8/yvaCg0aSWDPCyaQEDwRra5WwIBbchYvr/EYCHT/edseQaf6KumJdMa0kjrvq7mP4g2ImxQbI6FHrLtIa+NOuElPk/u8C3oldwzdjzEDQDK0yLGAhmDV3VvBGoMarB5cSztGQ1UDKZkJJFniqvWZ0YzA1E5Jg+P58sIqEDXj/kpLCI9LtA5F5MR2iApaYFuJlsLlkNlL0JKd9Z5Iv3+F2ZGfh/R7pARMIiskBE5ovIPBH5loiclPr5LOCEPpffAgRSrz0tIk8Dy0UkfMA9S1PGXyIifhEZKiL9Sr01O8sNUFX0Lly0zuc0zp447t+7/X7H198OJFTXFx9NvnTym66pkKKAseU1q7f352IReSb1niHsq8jTrpSqSL0eARYBj6FTSQNKqS4R+S5wq1Kqo8+9fgGcCCTQ1UaHAU8opW4+kg4nbI5Ygd7J4orzd51Gs8/nisoriYCEv7fQv2BKvVr7td/3dg/pZoZpTUdgRX/NnGIVcOcBz13T5+dPorMjPwbcD1wqIncDDwDTRWSdUqo5dW07MB+4D7gRvbTbL6+a73JXNSvgUdMy3EhqycqRRQsPxfoxMu3S6/3lT5wozypnV38d6FBwFvCrAx4lACJShD7soAt9FFQPcEnqmp8AJ/QxM0BSKZUAbkDXtzuHfuaTmze0xo6jj4L1oeBWpyxZDQQl4vv1ef6zr73C39hYwL9M6zkISeB3A3zPSuD7QBT4LLAEvQEJdP26vkFrCjAXeFUpdSrw24PdUClVhy63vFMptUZEph1JhPkxNEBVURCox1YmGRAPDS14+bbSke8yrWOwXLQs+cLHnkvOyEaFlH7yVHnN6vcO5A2pSawX0N1lhY6sVymlnkm9fgYQBk4CylJ/TgPeRNe+OzdlYETkJ3DQIUkEmKWUaj2UDmd8u1c19wC/MS3DbawMh11xPMuReCSVkLKt2DGJRr8ZyMUiIsDX0RH6y8DVwBvsv49aUo9g6poL0WPpqFJqhlKqLjWb/RTvNPPQlKbHD2dmcMak2F7uAq7HnujQb9aEg874Qk4DzUOl9EtXBEoX/De54oq/Gj2ypwV4uL8Xp9aRFwN3KaU2icg30GPeRUqpvok1gvbbKqVUUkQ+A/wAWCwiq5RSe5RSrSJyoVKq5RBtzT6SHud8IKqa3wSeNi3DTWwLBIyW8cgEz8z2nXLZl/xDDSakPFRes3ogJZHz0GPl6SLyCfSXwQeBShG5J/WYjo7MAWCpiHwYWJYy/D3AfSJSDnCgmUVzgYg8Ajx/JDHOGEPvpapoEXoa3yhbmpN8emkH21sVPoElJwa57tQwuzsUH32ondomRWS48KdLhjAiX1i2OcFVsU7CAfjDwiFMLfbR1Kmv/dsnhyAZqoU1NzKhrlfEs8t9c9Yn//uVPyeHBns55shXp43Ty2tW97vrn5rBbkvNSh/s9QKlVJuIjGDfmvKuvteLSFApddiCESIyVSl1xFLCTjN0ENiC4VMt6luS1LcqThzjp6VLMe+uNpZ+LJ/f/KeH4nwhekaY6he6aOxQ3HFuHhc/0M4d7w1T26T427oEd56Xxw1PdPKh4wKcHcnMqKZTpOPkSePzMvZt4RACCdV13aPJl07JTkLKS+U1q0/LcBsZxTldbtg7OXa3aRljCn2cOEan2BaGhfJSH9v2KB5dk6Byjv5MVc4JsnSN/pIN+qEjAe09iqAf1u9Osq0lmTEzA6zXhQE9bWbQCSl3LvQvuPkz/tr2EG9kuLnvZfj+GcdZhtb8GAcdrl3blOS1+l7eNd7PjtYkYwr1P9mYQh8NbboUzk1nhFnyeCc/fLmbq08J8dV/dvLNc8KHu+2geSMccsVpiOlib0LK3zKXkLIWD+RDOM/QVc31wC9MywBo7VYs/FM7P3x/HsPChw6Gc0f7Wf7ZAp6uLGBDY5KxhT4U8NGH2ln8cAc7WtNfA2tlONSV9ps6HCXiuztzCSnfL69ZnfViZenGeYbW3IHhKN3Tq838yVlBLi7X3exRQ33Ut+j/8/qWJGUF+//zKaW4/bkuvnZWmNue7eK2BWEWzw7yo5fTX6XyzZB3lqwGyo5iGX/FtYF5fzjbtyyZnlMhd+KRPAhnfigMR2mlFJc/1kl5iZ/r5+/rOn/o2AD3vq4nI+99vYcPH7f/GPne13uomBZgRL7Q3gM+0Y/2tBS83R8vLlkNlEdO851+xTX+3jQkpNxRXrPaMcO8weCsWe6+VBWNQZ+ukdUSjwAvbE5w5j3tzCrz4Uv1tL/9njDvGudn0UMdbG5WTCwSHvzIEIrz9QXtPYqK37fz5OIhBP3C85sSfP6vnYT88IeF+Rw7Mr3FTedEJtQn+7lHNhc4O5585cpYcpxfDXjX3ibguPKa1Z4YwjjX0ABVRT8ErjMtw2l0iLSfMml8fi7Mcg+E/C7VctOfel87bitnSv8zDj9VXrP6/owKyyLO7HLv41t4r4rkoFkXDG6zZn4nHWEpvPVTgbO+vcgX7/azvh9veY2B76pyNM6O0ABVRVcBPzMtw0k8UDh0+e0lxaem415bf72Vlv+0EBgWYNq39O68jk0d1N1bh+pR4Iexnx7LkGOG0La2jbp76/AFfYy/cjzhUWF623rZ8vMtTLphkqO+YwIJ1XXdY8nlp6xRpx0mIeW95TWrn8qqsAzj9AgNenLsVdMinMTKcChtEzgjzhhB5IbIfs9t/9N2yi4sY+o3pzLqolFsf0AX7tj1t11MvHoioxaOYvc/9eRyw2MNlH6g1FFmhlRCysX+s286dELKX71mZnCDoauak8Dn0ZvOLcCaUChtM2wFxxXgL9j/diJCskP/c/d29BIckQpwflA9imR3EvELXQ1dJBoTFByf9ZN4+s2GfQkpz/VJSGkHvmBSV6ZwvqEBqppfQZdrsQB1Af+wI1919Iz+xGi2P7Cdmutr2P7H7Yy6RKfWl1aUsu2ebex6chcj3zuShocaKLu4LJNS0kIqIeWsa6/0NzYW8CrwjfKa1bWmdWUCdxhacxPgqKLypmjx+UZn8v67/7mb0R8fzfHfP54xnxjDtru3AZA/KZ8pt05hcnQy3Tu7CYzQ6/Cbf7aZLb/YQqJ5UCWsM86OETL+imsD/gfPENfnbB8K9xi6qnk3uuud07SLtCVFMrobrWlZE8NO0p2AYScPo2PD/rX8lVI0PNZA2YfKaFjawKgLRzF8/nB2/d3x6eU9wKW3/mpVr2khmcI9hgaoan4QvSE8Z1kXCm7NdBvB4UHaavRws211G6FR+x32QNMLTRTOKcRf4CfZndSfIh/6Z2fz7Xhl/HXTIjKJk0oQ9ZdrgNOBY00LMcHKUCgductvs+XnW2iraSPRmqDmSzWUXVjG2EvHUv+7ekiCBIVxl457+/pkV5KmZU1EbowAUHJeCZt/shnxCxOumpBOaenmeeCbpkVkGuevQx+MqqIT0acCho50qde4uaT4mccLhy4wrcNlvAXMjVfGt5kWkmnc1eXeS1Xzv9HnBOUca0MhN/aqTKKAT+WCmcGthtZ8D3jStIhsUxfw5/wuqwFyR7wy/jfTIrKFew2tj9D5GP08IsQrtPp8dodV/3mOHOvJudfQAFXNjUAF4Pj1knTQKtKSFHF+JoczWAdcHK+Me3aJ6mC429AAVc3rgYuA9JcFcRjrQsGcGAemgbeA8+OV8Zz4ou+L+w0NUNX8PPqAME+zMhy2W0mPTCfw4Xhl/Ig1rL2INwwNUNV8H3C7aRmZZGU45PleyCBRQGW8Mu6UM7KyjncMrbkV+KVpEZliXTCY3jpG3uP6eGX8T6ZFmMRbhtYz31cA95qWkgnqAoGM7rJyOTfGK+M/NC3CNN4yNOw19WXA701LSTetPlsU8BDcGK+M32lahBPwnqFhb1GET+GhSN0iskeJlJrW4UCsmfuQMUOLSImIHDKrSUROTp163/e5Q44RRWTSgARoU1+KR8bUb4ZCdaY1OJAbrJn3J5MR+lrgZNDHZYrIxANevwUIiMg8EXlaRJ4GlovIfodCiUhpyvhLRMSfOuW+f13PfWNq1++yWRVO7y4rl9MNLI5Xxr9vWojTyMhuq9SZua+hi5gDlACvK6UWp16PAIuAx9CHhAWUUl0i8l3gVqVUR597/QI4EUiw73zdJ5RSNw9IVFXRp9BljFy5Q+t/Skc+89ehBQtM63AAjcBF8cr4s6aFOJFMReibgNvQhdjOA/4L/E+f1z8JrETnYk8GbhWRKejD3qenvhD20g7MB2qBxcDXgOUDVqTXqd+LS9NE14aCdpcVbAROs2Y+NGk3tIicCixXSt0LnAv8AB11t6VeLwKGA11AB7oszCXo6PkT4ASlVHOfWyZTp93fAIwGzuFoN2TojLJ3HfX7DVLvDxQd+SpP8zJwarwyXmNaiJPJRITeoJRaKiLTgWL06YAnisjetkYBj/a5fgowF3hVKXUq8NuD3VQpVYc+3mSnUmqNiEw7KnU693s+Ltt62eaTgZ7Z5CV+BJwVr4w3mBbidNI+hhaRmcBsYAc6CifQEfkyIB/4CjASCAMnAWWpP6cBbwKlwLkpAyMiPwFmHKSpCDBLKdV6VEKrigQd9b/NoU9WcAR7fNJ8+qQJuRihm4DL4pXxR0wLcQtpNbSIjEd3kev6PHc78Eul1CYRKVZK7RaRM9GGPhVtqCL0uPthpdTy1PuGoiP5gb2Ioeiu+Tyl1LWDFl1VNA+dhOLYGmWv5oVXXTpm1HTTOrLMK8BH45XxjaaFuIm0TrQopbYCiMgwYAE6Um8A6lOv7116kVTbq5RSSRH5DHqsvVhEViml9iilWkXkQqVUy8HaEpHZaRFd1fyvVI2yH6F7EY5jZSjUZFpDFkmiPws3xyvjdjPKAMnILHfKkI8ppfbufvrBAZcE0YZeKiIfBpYpperRJXrvE5Hy1H32M7NoLhCRR9BVHNNDVXMbVc2Xo5fSHFfM/43c2WUVB+bHK+M3WjMfHVmp+ikiovo0JCIj2LemvCs1i733taBSqucI95uqlMrMfteqomL0MOBzOCQ19qJxo5etC4VON60jg3Sht77eEa+MH/b/3jb+V28AAAQISURBVHJ43FnGNxtUFZ0M/ByYZ1rK/EnjV7b6fDNN68gQy4DP2uWo9GANfTiqinzo1NFvASNMyZgdmbBbiRSbaj9DrEePk3N6/3K6sYbuD1VFpcDNaHPnZ7PpZp+v6YxJ44dns80MsxOdW/+/tnudfqyhB0JV0Wh0CmvWjL0iL/zG5WNGHWwd3m20Az9Ej5P3mBbjVayhj4YsGvvuosJlPyge4eYJsUZ03sCP4pVxx60geA1r6MGgjX0tev06I0e83lBW8uyTBUPOzsS9M8xG9Nr+r+OV8YPmEljSjzV0OqgqCgIXoiP2u9GJM2nhwnFjlq0PBd0SoXuBv6OLSjyaa0XunYA1dLqpKpoGLAE+g94HPihOnTT+jTafz+lj6FXock/3xyvjtrKKQayhM4WO2gvQkfvDwLjDXn8IZkcmNCkRJ85yb0Hn2v82Xhl/xbQYi8YaOhvonV0no819EXB8f97W6PPtPmvSeKesP/eiz+SOAbF4ZTyerhuLiF8pdcjuuYhMUkptOtTrln1YQ5ugqigCnA6clnrMAt5RIHF5XviNz5lbsuoB/oMuLLAMeDJeGU9LXTMRCaDTa7+ulOoQke8AS5VSLx9wXSnQDHwdfYhCPlCYyvu3HARb1sYEVc216JJKv9O/FxWiK6mchq6fdjww5Y1wOFu7rNrQmVurgRXoEk//jlfGOzPRmFIqISIvAhNShS8mA00ichzwHuBupVQnOr97bz2500jVk0Mn+VgOgjW0E6hqbgH+kXqknisK1gf8xwBTgWPQH/pSdBWYYnSRiGJ08YhDlT9OoNeBd6Erx+z9czu6OOM6YG02J7JE5Dx0dM4DGtD6b9r7MvA0+z6Xe+vJ3QfcCMzBfmYPi+1yu5xZ987au7f8HTg1tVJE5gN3oWfHr0FXrKlBF69oU0qtT113p1LqBhEZC4xBF5X8lVLKdTXhsoX9tnM58cq4Qo933UQhcBVwMdCK7k63K6WeEZEPoLv/b6OUqkuZ+u16ckqptVlX7QKsoS0mKEUXqOhWSrWLSAIoEpGFwJ4+++fDqQMY3kZEzgciInL09eQ8jCM28FtyjmFKqc3ounKgz3V+HVBKqb8DBSLyFO8sDjkU+A3wuDXzwbER2pJVUrParSJyOvsMLejqryeIyBal1CtZqSfnQWyEtmSbucCD6EMTvpd6bjqwDb1PerGIzMtaPTmPYWe5La4io/XkPIA1tMXiIWyX22LxENbQFouHsIa2WDyENbTF4iGsoS0WD2ENbbF4CGtoi8VDWENbLB7CGtpi8RDW0BaLh7CGtlg8hDW0xeIhrKEtFg9hDW2xeAhraIvFQ1hDWywewhraYvEQ1tAWi4ewhrZYPIQ1tMXiIayhLRYP8f8BldxttIOTpGUAAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib\n",
|
|
"labels=[u\"留鸟\", u\"冬候鸟\", u\"旅鸟\", u\"夏候鸟\"]\n",
|
|
"plt.axes(aspect=1)\n",
|
|
"patches,l_text,p_text=plt.pie(birds,labels=labels,autopct='%.0f%%')\n",
|
|
"for t in l_text:\n",
|
|
" t.set_fontproperties(matplotlib.font_manager.FontProperties(fname=\"/simsun.ttc\"))\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": "3.7.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|