Wolfram Language

Unidades e datas

Análise de dados em DateHistogram

Analise o tempo de chegada dos aviões.

In[1]:=
Click for copyable input
arrivals = TemporalData[EventSeries, {CompressedData[" 1:eJztxUENACAMALGBAixgCQkzMPX8mQ7SJpfbWSdnRIzurj4AAADAjx52whOk "], CompressedData[" 1:eJxd3TvOZjuwmOdjwJEiT8EzWLyzhiBAkYagQIAiB8dTd+6qrwPx+bHRG/02 77f6yCoW1//9P/6f//4//4//+I//+M//M//33/7Xf/6/f+j/+78e+q//Jf8y 9jox5rjfi3e/eEYH53zxtu/FOG/k+X3iedPOdj7wErkFkXsXB2n77OIFt5H3 28A5aNEcy9BNViMoaH+E7kVB28jno6DsDHAR+dqiu0h7bcK9hEYT+1vQ+noH l0i3rz5FemMNRn8Ns5oM2ZoM2Zo0YS2m2Vp03TpmdS9Z5T+A98Uc+0/cIDnv bxh5mtUy8hWDnBuTYXdm+x5mNWlvIuUuhmyvZegmq81s35t+3ts6H9b+PnTs dnXvy/jua4uCcs9HzufbHUQUnA+Zcz5647Qu0vyjGDkdEXR6EDqslWv/DIYs q0joPFRyslLOapS7WL9nM6DZzWR1mXWJhk7T2s+u/eNiT5H8VuN+Y4LLUHry utizjqR1sWeVDb2GXrOi67IBGxwiA3oXwu0qN1JMGsp8rgn74jXtZYxu2FdB i+JrF0SMxMeii49JGAqKaFQjhSw5L/oqRQOhexn6TIb79fbM9sJp6DPbE1+J lPjKnMTdyGoHaV+5kfhKhsRoZBXP+BZOcn63EDlfvzbB/oF3g0HaNon8bjAK g5w7lRyDzhnjEHlukfZmUmq1zHkNQ8/b/LEHkfcm9DAouTejGsdqXIZsvDuK QoZ7XGbOiI/IYa1iGpnRn1/bIH2Vm0BC20fatkVqlQ14qzHXFhn9ua3G7obS dTmeRD4M2TzM9pyvhF5WWW7VyOpuQ5mxMyw36Jz1SpXC8VZjte/NeXXau/rq IHNjjU8058EorMnkX0qGtVhliVRjWe6mY9dm7a93r1J4jMxkyD3hJ5L2sH5/ E+/FTrkKqERDrWTQOQkXRF7tRl/lfCXtXKSdCKhEQheTYStG9mJ173X+IFlt JvBW5uzDRNqH9btd+zuY7bkJfAs6ioLz0RvnW0amoPMxc3KLaFo65zSaf1pQ rnKjhCg4DGW2n45wO4OJdCb9nJNONOfF2j+KoLPtjY0oOIfRP8f2HpbGuZR7 /d2/fYj05B3vvGof5bZqP/hWo33xDllr7y6o8K1ka60Tub9LsjWrkYuqg5Os 5iKruckK8dXaakRew9Bp6KQa71Gr8FKrd6uWuLtI17Vt51zThpERQYnLyO8k rG69IP2cuMFr6DU0yLnRG70dEOGWaK3GEBmjPiyIHVRiM5Tm9217ty1iy5TI kPXLNBuIoDbaEhmy3DES2t9tQCLVGD0IRYwkMicTKWjQhNyLGsrMGdM6Lyu5 GP2xrNWxgexzEs3qTpE1ONgFtRG2KFh01QKQBs6PeTXZBSXS3qlUmYqROZBX k31O4jE0CJ103VQETWXOXDZByTDZ9iTahH0IVRTMYIyma399TMLlck7JN0Hm 1eK4VGjabVrW0er08xrMnOViXxymWq5mQheDshajsNirJC5D7x+kVhshs469 cViD69gbZ1HJy5xcd4iM4Faq7IY022xdEpeRzYqdTNvsZBK3oTR/d5q/Bz+4 W6mylRv5YwWeRlp3BTvo2O2u4Licz8dwn0WtYpA2rGS8aqVE9lf5q0gl4wSh 18hUMnv1nc/9o5L1AztfROYkvlO08IKLnNs28jHna85h5CDn/pG2T9L2ZeQg Mt2eOAhlJ5No52w7Z9s5aIoKSXvsK05tidfINv/Y/PhIG++y6u01RRUyZI1N Uc+9qUhvNCRhIjOnsSkqpZL4yrrETSiCMZEGNk6LvS16sh0reW3+tdxrucir RIYsJx2R0QUVkpbjYeI21AYGA5pDMkHmZLZfpJIdyVDGQkJn6yJZTXOedGxn y5RI8/tahtLATCk2arWneKnGYVl1jni9h5GD5TyQsYUbZG4Mhdv4DqHtExnf obwajQU7UDolUufc1pJ2WCu0TImM71DmDMXIOMzJnBqidVbIjMs6Gtdyr/2M piiRiTQVFLMTebJl6lPJMPsyLSt0sr8qNHQYOgzlNyWRakzm5Jw0f3Iu6xOF cyKdMy+Lbt5uKOsXm3VhN5RfyakYWWiZEifIpii7lVFYaGwSWc5rMXPyB+cN vZZ7O6NwxxLpybtNy/6q38PcuGcZygq9SoZcY5R76UlsXj80MgL5hpUMBHIG isyr6KzfQB2dSG+Eu5EYzJzgXFZR/yDVmENEXgW6oB6LBgYnrx7HFrn9CLcf gYY50QbeS1pObSWt74sc08bPKPLiu0ITF2kxRY3vNVInsn4Tu6FLPOTMQSx/ NBqRzyUy56PxYSHKn5R3MiResL0rNJFKNo5LiVSyoZMZpUgCD6HTguYmMrqR 0fjtLtxgp1Yob8fvkPNiIzIHk/wJtdwg587RY/RuaN+E8gub+P7EJDIJ64cQ pFadVZZIP3d0qoWkRYmayBTt20pe5nNKRkNNy7IqJC1HgESmd0eRMrQ7lz1M ZOYMNCe5cxmEYh0eYzHNhotu7Fc+J05DWTjaUsds1Hk62zXrJJLz3kYOZt0x q4OuIJHpfdhd1w3BP0ha9JOJTLOD2mFoQCkkK/ST4/BLl3jJmd+yUXesQOTk /WjRZcM87keLLhrIUeYXELF5uXQxLsf/cTnRD00zo4QuaM4uyTstaC7SvvfN bt0FINQVetlsJ9pA7LC1XzbUFrnowmUVmBtyc41UibYMRVDk9gPk3J3YDGVA g931CHbXo+6egvRVXb8DaX5wKh+BUjHRJvijGWyYR6BjTLQJYZ05hicyGYKf 46zSAfkNTXzXYOI09G1+4tv8+XULQgM5P6RK4gLfa2CFG0QUzG9Z7rLca+Rr E+6mVgiKxHcEf9dVwG7oMPQdo8Rt6CG0vXqGxEPO3O5IpK8agiIFAS1KkUNW nKynJs7EZagFrUso1zmmFs+pxbOQgjCCJB7wMAkbisFEO4ed+WxhVvFumabm 0amJM8/ozMnOEX5q8Zy9M6AdbV5iEIpUmbWqXkTI5HGfIdNqOTsn60T6uSMK 5kK4zbWIrD1lakCZWj3ypPH+0v3uW4lv8zfavMRXmCcOQ4+hQSiXxAopCHtK 4jDyNHQbus2KJam1ZWpPyU0evbGnObOTKROfyArdy5xRDE6NL4lMho3acO5j 5xw751jJayUVjJura4msFK08idT5cPcjkTV4WhPpjaPoO1g95lH0HUXf4WJb IqN/2DLNw7Flnm0luVGWSG8c1IbzcPxPbIayus+dhm7THkNtYNhArq7Nwxln XvZX83LldV5F0B3T0GUoY3SHOSu+SiaBlstdtUIic3Ut8T0gzIvaITFIyy3W RIb7hmmxXCSyk7nYJmZgjJihCAqFTGCWTaSvgqsgMwbLWZvmjGVWbmxiWytu sc7yGwGZZoGOItFyD9MsuAY2Q8kQioJ4/XHqWsgrCtbHtdXEd1CWxtPEaegi Kyyeic3QTkHYQwuNfMRLWhQp6+PeSO2uiIyOcX3LgjCAru/YfPY563sdcApt 0bWf4yNnJEPiuxZWw0aQv1W0t7FgS5vzRh7oVRJfmbMm2ss1uVe/cL4rPIZe 0qLqXHWifRGFxsL5rvCdookMCr54hcwcfPF+SFZciF1aPZZmjsRJWgyvayFG Et9T6lpYPBNNy+akkMi9G/mVG7UHnCDzag1mLG59iah3Eg9p2eckBqHsRhIH BR1zPuZ8bSAXzBYugbcuDVGNoAm4BCaiRM0dMeVuFKF1AZRQboUlDkOZk1vJ sF9P20L6anMlI3GQlnuqiZcmbJugzMktIpEPQ7Zd3Ru96Drcb0+k+eebhtJ8 nP4SuWWROEWGDC+/HxIZW2oiEgkvv0JmzkE9mzg+0cjbUOTV2faGEgkfwLvw ASw0Z65kJDKChyPewunv/nN3eJAD0c9uCb77nJ81BuyGMglLIS1ucJkWOXkx viQi6y5m2fyPjr1YaZfm0US67uK8U0hapcq9FoS1NJGJdLnymngMZbhxJyyk rwLVUJ6kj8ivc2DHWeFuJLiJurR4piBkfEMxEtyyWPgeJnK9IQt9RyHP903s RGbB7o5aaWsS2p1d0NZ/cHcuXSS+zd9aavaw3IEyZJcR6MXRSIvc2IMT0NYl cA+uVRSac1DubESei5zZjOXP0SQrdiN7IEYSp6GHnJEbiZ203OzaI2xg2CLO R6WwmCCTYaLe2ZPjUv6EvmswkVGYyJxE+mq+fseFFoRlKnEYajUQMolMwslO Zpee5cHFjqJwgwzKwmCUuI3MkC3MSXtxXColk0idF4qUvbihkfuLJnbKxSWw dh9UA0eYMoHQBPTAiWaFOSnRBqKE2YubXVu3vv37PX7R9nJc2ilGCQ1HAS3x 3oqvjR44kdHf6FW2Xn6JTMLNXuVnpAUZo80JqJC003KXBbHfyC0hC2ez38i4 zKuNbmTr9Je4TLuNfIyM3NDQXOrJCTJVDv6/+2BdqrcrwMkk1Cq9NUMnHtGC ZlANdLm/jhQplxNQ4jIyM/Zwb2Tr9JcLw8jc+SxN/QfSk1qlt1bpfRUjmqEL jYyov2hd9uWyx75sIfZFibq1Du/LDat90YvWWXGCQ3x/B/P08AqKo1XraMY6 2p5Ow4SdGIRy5+Q0th+Js4uGbqqBEqbwDe2crY6Oe4k0sOPpk0idO6bzxE5a hEwh1eC+WeKmVlwwO3rq1cGLgjguJR4qiVop0eZv23s6ac+hXLY9iYusEF+J m8jIq8R3tp+B3SqRSThwQz6jvbL9DIxNR4fBo8Pg0UPwDITMGdtyUe0WknZb KxxhEumccawkx6VCWsRx6ejlV/JYpEX68Z3JfiORBk7Us4lHpIGT+6KJNHAO 1uDk+mjiEJkbExXNmcs6c3o6Ov0d3fryB8fewEKUaM5sXX6/TuAlMhubRMtV 5iw0NkeHwcRFZMXI4spN/u5R7kKXm2i507Tocs/i6unRBzARubFw+E1kWS0u 1CVOQzeVxN59dBg8i5NXIrNuKWTK1UPc4DbyMTIDupUq2rsTh0hfacJOvJTL wwhHh8GzUQ393id4cZp2GXnRdRvLVCKrWwN3oWntnGMTuOaXuP4gWWGoOge3 vnNQs+ThgYlU2ugX0VGci+P874krkFoFd4qu3oVXM9bVjJX4SoZ6iohQ7vX9 HoUBD8h1ncILdqqB+Lp6+dWzIGZ1zOqS9nyEsie82ryuVq2r417iEPcfJC3b nkJD6ZzGqa2eJBD3H9wgzddxr658Eplj2tW49vMHBm3vtoEIqPKeNTTIircO bru2CA3zb8v/YEeq/HzVwHft/xzMXsQX7+qLd/XF+/lBgdPI08iMQudMVy5G ZkUDdc1LZG50zEk/15cXw7Rc0S9/FUMZhcEVwasvXt3w3SBNGFwRLBcFsuIQ V/fsQexWdXecnHGTSTTnac6c+K6eeoUUhGoocYgMin58dS2brLCGX83Qv6u3 IF03OR/d6drn7dl7tVlfHffuVDJo0b5atK+Oe3eiYb48NlvIOppsmeqmVxcJ xaJdt6aoJK+g/G5NgcO0dg5Hrd9lphfRP//uCYHTyKxffQDr7guROZdd3fru 4o7NXUqk1U3LxuZq/r48Y1t4yUp5tRRBi1t/P5v9i2HOSoaNl8HlsdnCIzKf eRWzkHV0NzLncsP5Bi69dStqgmEoCyc4l10NVeUgSFrc+m5wN+/q5ZfYjTyM zG4kFF/hLijcugRPLd04Nh/lTyIzRy+/G9f2oguqK2OkDZuAoerqA5gL4W1R fLw5ULfPxHe44+NqceIkMkImf7p6Fy/4NjDqSb4XUQsnWhBq4ULTvj1Zc7CL htJ8HQZDh8HQQzDx7fZoXJsJXQJz7h/Kxe4cjeUcDS+D2tMTuq0zN+gSrSR3 XXIaxR98C+pYh6MjGaLjYRS+8lqOLxukIP0HE+nYzoKtyU3oulSDPX8KhkFk VLuJy9BjaJAVF9sSu6EMd+d3PwYKjfCF2NDgGxp8Y/BsUWjhTTQrTLqJzOeB 4SZxGbopF91I+H5sIsOt/2CM+IOMglbacqQ2lNGfqB3Cx1djuhbqCvuLaALr ye4JbkORhHkcfLM6XBILvfxCE0nioSDX0cGBvR7HJjI32ONwizV0GKyfBerM Jj/0H6xnt99K6tYXlzvqhYYyny9KiUJyRnsZF+1lPZ39iYYyGS73zeLiFhSX n9TQJTCRnyeNPnH5hY2LMrOQyK7uG/YV1pYIlAP1CrdIbwT3VEtDA+I/WL/k FMSdsUSmd3CDvX72RUYwuDOWyC9soJ8sNDLTO9jVh76HobNhBMqBiLAar9yo vckzgX9blQseIz8CKv/6mjkKHxlbuMj53cYnvhrIHxL5vftReKnG656T+L4O HeXySFbHFh3TvjfKovzHyOp1oins1Pna/PdEUEid20et2ketcBgsvG+5OAwm vjvz2k4ygm3QovaKkcILvif6wmnoolaLQWnLauxPtFaHrmuHydDe/XNdZPou OMQtMvr9XfuFbYOD0Fcb8ENyfo//iYNR6JMJjPE0cZnzYuH0bRNeUVAYhB7m VT/zD1LJ15BRGG+dh8t5dLqOl0sLKZeXSwtp75i0F+/CwrNBKrmCauASWMig 4KkX9V7dW2c89X5I5NVFJCF+fIWLrF63vh9Sq3WNbCU3Q4ZNpJBVtjdDhokk PnzxCol8Xg3kDzc4O0jOp7Ho8OMrpDdOQ7bjx/dDsupMpNNZg2cgGM+wCYtZ dxRBR5lzNpPwbGTs2Uh+fAALmQz4ACZem3CZzzju5V7kY6pcRdBV5txOe28f RqbOt28jbyMj+e/gtwyXwMJhKAOKS2Ahou8Oxvcuq7GZSNfFfpVI99p1lyl6 r82/SNEbNjBYkjeoZHwIt/hYdKHMiUaLwm0PTn+lODFnJVK4dcEHsLQsjH5M Rj/mNLLVUATFZoqGMgeXwMSDUI1jue6RQhEUl7nBa6vZj4iRxG7oO+saPoCF 29BtVq+MbTyRGg0fwMJNzq9C8qeCAt+L9KWRmtT5vSpf3qFm9VpaC5eRj5GD nN/HDAvtjWNvHHvj2BvH3njvnBQuQpFXrb23/RORV0nvSmmNPVJryKvWOu1t yJx6bHWCy9Bj6DWUadbeD3gVDnDZwGudkRv15D45B+Xykmfp09sf3CDdPl+D bzQ+C1hIx07kZJvBwlkfDVyNSYg7YeEy8jHyMfI1MlMUV8RomGYKh6FMUUwz iewnG56JhTZwmXYbmS1TIot9IevqbXBCj71xbP6x+dcGXmYsjoqJYSWDRbfZ jNXL4W9WW2m2exeZOVvhtgfTe7+X4gppL16NhbQIR8Wop8KJfK3VazH5IZGD KbrDSr6a3sIl2jlxDWWMcFQsZMhwVCxEyJxGx/L9wUJadBoT6bjYz+RX40ym 6FEi4YpYOI3MCj3bFnE8rPdyKFdZd4L23o81eD+acBUyOBsWXiPTG/dVKUfj xdRCJOHt/LRdJQO+h4XM9ssuqPHRwEJE0F301T3Mq6souIoCvAt/b5CR1bXc yyTk8dWy+fBrdQNRHxzE6lVIQjv9jKW1kFWGpbVwGHkamWkWw2oMmh+zi/Qz XzaMegucrJQM4fYD38NCqxFWg7Xfv/cifXRMq1Hv5N8XX1tM4jL01eUWmvOr vC3r2bsk+3idows3ka3GYOvSMScVvsu5j1f/nPg+SRcd/8FCGjjYUdTz1x84 u2jkbeRNQa/vYWJ8pA07J6wz+6s+39cMCpf4J/K7nPtEbhSS9nVwLjxE5myV SD/PSQPne98scVmrbUHsGDvuhImcvOqdbdIiNxIXaTl5JR5qdekc/BYLm6EH 5NRWysu3JxdK40QLWowC3xAsXEZmii7OVh1nw0JmLB8NjHIsJmcUOH0dK3mZ G+siKPAfrI0363d/DPduTXx/6Dv+g9FxGPzhBoehW0TW4U5YyGTYipE9rdVE FPDRwEQ2GInmvJlme9sbp/1B6syRJ/GS8/vYXSHju5UbBwVOIuUeti79cCAq ByOyUjIcJcOZrJSDiqaQrNAw96Pc4AncRPYqHYfBxMNUOccmKApO2ATOGh2L diENvJii6q37Cw4jDyNvQ7ehtBdXxEImEubvukUyRSOvT6T5d7N+L4rfxGUo k/8eK4lepV8FRfhDzyO3ha8avPAtKJQbwWmih5IhUPz2UDJEv4bS7fFeFMkN Ikqn0WhgHh1fyTC0tQ0+GpiIvBq8e1lbz/aJpH0vxhQeI1+ywnxWSNr35ZbC aeRp5ANuG3htEdueRLoOZ8PCd/0O3AnjZ0wTjfzK54GzYfwsbyJp30txhcPI WzwUNI3MLqieN6eg+SdtkBZrWqK9wR6pTNTUGX1O4iEtGpvBxwoLmd6DXVCu VqbKxM4+ZhviK1THRP88ZmcCz74MZT5PjPLVzaTlXDb4dmEhExgnx7pf1kQr yd6sblFQjb1F5vPEBDYmsi5xGmrzOdPVuyb0Rtj8cFDC3gibHw4ZB8C6MfeG LuTkwDPxh2811mD0FzavwbcL61Iyk3Apkdam63hr94cUtJEb6zBV1qWveFy3 EKmyv2NkxOZWjPBA7u86oUglN1atgcNglI8y5bJlSrQaXAYopKBprTC71x8i KzdwNiy0N5Qbmw1VIotuc/Nn8F5uoZ0Tdixm96HZfWhnL7wgc1I7+zgN4XYw gQ3t7EM7e+l5yQqze2I38jAyq5u3dgsZwaM0O0qzo/g6izE6277ads5himqj T2RQNMon2iLF1wnLVaposq/v375p7+uznCOPzm1+KNkmHpF18fbNKvGSFsFY j8yLW3yXZGIQyvZy4iBZ+HbObOi9J182LAwiszWdmvymJr+Jv2TUE/SEclVg 8qHDxG17jy1670AW2qJji5CxhRTEObTEE5GvLcJcWC9agWzVEpuh71r4bShA +orvHv7wrXPH1lZIWlRhvxcXXpzWin1dfVuGrDilzo6trVziCL12DnuzevPV yEFk7GUT58pCC+IMW/h2Dl9FLKS9gz1h4hTp58EWcfLdw0Rk3W8fB77nstzW MZ/r9jdIbwzUaBNPzMJu5GnoMnRR7uuFXWg1FEGDvdnkBeBCZuz8GP35MXMm yvncxjK+eGLG7xPcID05uTlQmgJwWattNdiqTd4WLqRzJgr2ietlIaMwlUgT bfyciqCpzOFt4broY29cphkfSYy5FBTa6BNpvib7qck+cRlK8zXoJzJj8bUs ZAIvlGxTc//k5eFCFvtaFrTpusW+LpEJrH1/8tRwIfNKc//U3J+4/yBZhc1X fC0uas7FjrFOaRvkV3J/y8jbyDRwt+8PvpXcbOTmVl5tJRKvFpffD0O2F6Ow N7U6KLrnVT5r8ZyBJnDiAVrIkAUmg1wKDBk+noWMYCj68AAtvIYy6+IygnGt BkbMQmrF5Yf1YRQoU4yh79xYWi0Tl6GbrDi01pM/GwyyYjO2+ApkIrKu3qok lD1SvVdP5GutuKhZ79UTGf3kwpm08F10C4fQRETQasic3LjQ7Y2TZiI92dgF 1cP4ZvVOs9VQdiW+m4S6b0XOqMIWX5AsXOIxsu09m1B2Qat/jEJHbiy8R2N1 BMXqnDQTh6FbvETmaLn43GTiNGekyk9hA9LeznXK37tTL17LZUkuvEdj8Xpw 1K/TGzpQZdeTqBdkxuIuWngMpYF4j8bS3L9wJv0habHiLS8DLD43Wcj4jm0T 2MksrwosLwMUGooI0vqfeAy9NJDj0vIyQKI9GfYkniCLt4ULmSqTk1ciC3ai sFpTyTAX82q6YCe+HolUcuLcURtxQrH+J1pn9iqJzNjfJ45enKZluCenp7UU BQuzXSJZLRf7Qsu0eB+40LT8LK7DtfPaXr3V4F3TslGS1VUiXW4drMvJa+E8 G7/PnILMyTvNeX6GMvp3DiMPI2+RyaBdcuFLG4tPZEY99UFk7k8uvGVj4QAb PydX8W1+cPJawclr4Tz7wwsyZMExbeE8Gz+r5ovDgpSEfF4z/p0XQSOzCwou TC4cb6PURjSBKwoLX9pE7iTkTvxt/k/dDr7VKO37Bl8xkjhIy62DxG3aY2iQ lnPZ1rV26zy7+fhmbL1lt96yJZ6oBt5n+9umRUm+dZ7dOs/+bBEiaVEN7Y8b kom0iK9tFk5DKbehM098xfVuKLp34+705nuaiWi26/3Ft5INQZG4DL3gMZS1 vxva6cTVRSJjEds8NfxzMu/iBekcDfqJy6wOkXH92B0tcSLzqvcpMhl4W7iQ bu+s7s1Tw4VMhs5l6a25P9HO4VCzeYi4kEnYERS7H5vPCWh7GWB37jFu3haO OnZP8BX1W3P/HkqVgcdr4hSPkS8Fce968y5xIStlcC1q6/C7B5a4PTadMzjU JDKBBxubxGOodUYJU7bEFyc6ir15ZGBvFFZb/9+9bQIvlyayodq8Lxr11v2L B41NIkOmjS8ReXW4NJV4jHyNTK0O6uitH+4+ij7NhVuDYD2ML9KxB5Xy1my3 D+eyfbYt4mZmIZGPBXEVs64EEjnsySDnizfHvhjI6johkdnJlIF3g0jgi8J5 6x67dY/dOsBuPpFZyOq+iqCrzOEzl4nYvOpGpKHTUFuEwrmQUDTMW0/bfVHg JNrtir6LAmfreLt1vN162u74+F0IBWNwvWGH8ioUUDHMeZiVu6AY7Oti8gOk H+4O7kElMp8D1dCObbnbJijNgvNR4jKUlaJrbSI/McEV0MKnklmlJr4DenTL PbrWlh6QyBiqUrY10iI3EheRudF9eIi4cBg6TbupFVLl8MXMwk3OYc7YrQ4v DxfSG41rnEd/2KMl/Wg6Pw2r1tFb9mg6rw+kGMoYNVRSidvQQzWWBbFlOtrZ j3b20xBBpx3bi82rXOIoF7Vwos1HyCQu09resL0c0+pzGyLN72y3EpmEHQNZ 4rvoTsdQdXjTuJCO5RHjwmkoDZzIyaNL71kcHo+2tnpjH7RWC/3V4VucUc/m UxAnvqPzbD2Mb9pXTtbL6KRlX1d7BEMZUE1vR9NbbiiaSOds9jm1+zCU9uot m7jEQ1b4053NZZ6a3CKDoj9sIj252TId7WXZUzYfzXYic3Lj+19INa4tupYb tghNUY4Q8/ngg3/4gGYhq+zgkl9IZFTZR3/Y8mboItVA+XMO58FEWsSHOwuZ Zoe7l4kIc51nz2G7lUjHHu57J9JXesv+vhIvbpDmXw6AR+fZRFbZxYKfyBjx 0c9E7OyHD3fWQx6M/mWfc3h5uN5ftFxMYInDyEzRu60GWqZzlSoXvffho5+F 1upaDXZQeaJjbgTW8BPKnHDbE1yJPIGX/eFLnfXAJE3QD/fEsBroruvmB5EV MqGQCcVIuCnS0/Zo0T5atA8vHhcw+eNarvur4B5jfQWDnK+dg3G8XtDs4hO5 PpkhbpB7BYX7RfZIiYvIyKvLe8iF09B3uBMPBSG+fsdQkchsxhKHkadZLcrd tog90v0wriUO8f3VuDytXEgTGlqm+mQGoah2f8df8W2vfsd5OG4igzIRjPXk /gWXoe8P0NUd+E4uIJUZh6x4UeRqELx8bLTwmPM19JIzGvXETkHsCX9XicEg LWLzagEsKyzVQPRd3YHvfL9EU2hvhM3ndcq7UDrdhdKplvoEG5HZfdXPApG5 G1BIZCRhvcBPWh4nuToaXx2N63P1hOIIU58upFwEY33/+I28UZLXc/0TvEZ+ herdHA8vXxctRKrwddF6V+v9ESmnRnLmIlB9eJjIyrrNNYOrd/Ddir6tgNoo rApJe2wCB8CrS2/iMLItunY7263Lt0frdX5adD5axLvThUyzwyMD9VnEDlqQ QuYMy1WMHNRK9ygZjqLgcDy8fJk0/n2j60G2PYn01cFd5R7XPu9dF24jI830 /716+F49fOsRlDery0WCy9dF42rBr2/tEFlBoUH/arK/2ujvxRL3+1QJyJLU Rn+vYoS3suPfB0UexOXkXrZbiTbwmjMqqXsvU4X3ruMGVrwbChmN8jfQXdcn BzbI+OpoXE//E7lbDVx660MepEUHdYNz2Y1lzm4SNNnfwGPu8jh2If3MW9lx A5XU5TXsQlZo8Mzav3eK/zd2+qq+oTBfRIqGfsehITI0F5bHPqHs3BLXJ5Iz G7ngq6YRmhoL39Dxfp+9sBv6SobQDTk0U9bL6IbSOZop//nlPsiFq9CV+Odc Cgblcp2yfC8NpXN0NA6tlqEhspzT6ByuNwQfSK1rIpaL+j0GTy4kXpG+mlgP E5k5E5FbSGQuS4deyaEbcuiG/M8x5kVGYaK6/3c7/EUrySsKoWdxojmjq68D LmnRfSUO0qIKSwxDGd+JyE20N3D6K7vNm/PiaFnXWEWav7APho7GdZ30D1IQ WrW6MWlkWrSWlUR1HzoaB58xLaT5i3NZ+Ox2TV+qcW0+B89Y1+Yrc7YyZ/Os XPC91Ah9lkvwkxU7qNB3OJHl7PveidSZr5omor8K3/dOZJVtPFBCN+S6WECt jnU+iL7NUbpE+Zv2sPuqD1hNkEnovYLw5kB4cyB0NA49iwsNHVQDBXvoHVxo KLLuoOxKRBTwJdZCy11mte2cbefggVKabvBYkDLnKGQOjiGl6qXcy+h7Y6FU nyCeID/F2YNeUUhkLfigd1zFiJcQ6vMxIPfMI54f+joKX/FRwSW+BtDC56bE Dw/4+OIVPpcfCp9N0Q+PGGT17Al/2Mh5W6tH9P1D01qrR+3/D4n8uLr8sH3g JjQs6Nm57bIj0/z+0XWvIbLwubL+wz5BBuX9MOsPL5Efo8AP6ZzXO/iH1vl5 KK/w2ar9kBHsy4KWBT2X4X9IP/dNx/bn9YYfbpFp1h+h+sNGzvcTmTk9LDeY /K//b2Gjr8ajzvrh7WC8BY1B143RDR2UOyz38cX7hxs8RJ6fyGwfk1EYi54c m1k3NuM7ru29jNFQbrwfZv3hInLYorAJwdp/nYV/yGJ/P9ta2OnJ+ZxhfxiE PnLyh8zJOWngnDRwThbsfMyUhYuenI+i7B8SWXn1ftS18HTRagRZredcVqig WI/u64fHUDpnKTeWcmN15tVSbqznOlbhYJWtwZCtZWTX/nvroNDVvQ7DvQ6z bh2bf5iE69lu/fCY1s4517RM0XUZ33XtnGAE98fc2I9tsVAxshUFezBGezKB 96QnzybneG51Fi469v1e6v59k+rJqn3fW1B9YeG+2FsXDX3HqL1eqz/c5NyP WR0ij6+Lhg5DA9yG7rc36gEVqsGiS1xikPZ+pL22907KvbS3fU2k21sj58aC rS8sTJD2NhZdfWGBtGMSOi2IH/r6aAKR+aFvbV3wmBXLqlpAna9ZRfuDZBV2 TtiimEZ+5XMdaTbIFO3fMfK7yrKbmWa90e29sTRef9jCPog8BpFZku31eP3h NvSS87ZFuxnaSbstl8Xe3g+z/pCC3s+n/pCVMtohVMkwBuM7OBG01x+2cFHJ 4YIdLthxmAzDNThuM9ScL81/n7D+h0Y+ZsUEfr1Wf8hkGPHuKHLaMCfnR19N fqzb+yr1DxmjOZgbcyCRpgv2fbP6h0zvuZjP09U9Xd1zIWTeN6t/Xm2DJrh+ Z7AWFr+Dbbkk1zcMRYqujzFart/3VeofDkMZlNXp59Vp0Zp0zpo0/33gutAF u1ywyzW4NsNdyrwXDwVtNr3tNQfvf+fdB+/Hz8RVXt3BSrnsGdrrxFq4KOi6 K7js+dtrtyoM1uANxvdGN/I0lFUWH7/7YW+Em4TXFPVD5mRM5mTMbugSGdCY DOj7Bdgf0qJYTOD3k6//kGoo3MLdSChzImxgmDbetKW5nuAQX4nUP0RQ/UgS mRNB/4ZZIUbKfZCsOO/X3Swic/zv37ZWnPfrq56GXnI+H1mxye+v52nhtUXX OrMb6Q2J1N9Prxa2d4oWXnCRtgWR+/zEDdL8huogcYHzktWin9u2CftQycMI vq6mP9yGWqug21uMP2jk9weov99p/SGd05F1tZ8S3x/cnzbrxUlPds7svXNI 7+9T0j+kn/vaRj5E5kRf34M11FqhY+wdHWN/fUt/yCp7rcM/tDeCrhsfIzg+ RnB8jOBrtC3k6NFfG+4Paf5AG5DIoIxO88ewGmgD+pj082v/LWRzUoNPrZQq QzEyUA7014b7z4uerNAT1idRDEXGDvY5fbKF6HN2QucylKkyFzJnLkZ/8nNc L6O/tZpo1fpEq5aTna5bnOhLdyvSOYvf0L5cdMvpvfwB2ihhaim87d1BX72m qEI2cv21LhWiDOlnvT/l/XVT/aEFbebzYTPWj4LxbAs6NOEoNt+3Z39I55yw 3GAd3Q/JcBu/C7ctQ2nRRQnTL1qX/n6Y9YcWpGC8SrO7mbEXI0jhBq3V4df5 3leojo/xHR8/MSmQiNzaB/Z3OY+GWqmeuicU1VDiJjKbwNHWAbdp2ZuNxjm0 Hokg9DayQls7GsfDLOXtyaFJaLwPxv6Q9nYk8Ohoe8brqlbIVm1oixmdTdHo d4k0vweD8r7Fuuu943cS1hcH3iaMTns1kQxNJLXXJBTBmBgghoyx5h+kVuqB h3rguiX04uYwNTZnybH7uzQKN8hU2fxMjPcBxh8yvhsbUP2ykdW+1OowGfa1 Vmh7Eum6/Vx9STzszcb5Xmk2DvbfcXqArsGDCBpnmhYDynj9vArZqiUG6Ao9 x6yuLbrHULruBJP/BDP2hL0Rtsjl/H5C8YcM2cX+Oy5btdx9sBaui/39ZuIP qfPtrNDbrdWwGuMQeVruRHxdF87F/jtep7DC9f1BslpI/ouxeFz2dYlBKBai cbHUjItpJndqpmW/Md7nRhNDeRXfe7Ya8bEGA7XSCI54tSUkZwVFKCgCU/KI YTUWcyPQ9A4NNyM2oxCHCRyKAq08I67VwMI7Am1PIis0/LEOxUhg0p3f93bd fB27fniNHERG2zO//q7fenJffJdG4iJndkHzm+0Ticz1lflxWpwft1nmh3qn 7mkauil3W437UQ1011P70fzQTpdGmeZzWpxf2M9hrZBm8/UCK+QAWJ8J+MBp aBDKAXBqqJptLPEQeVgNDoCzITcSl6EMd0Oq1DcFSLsZ7rYnWaFlqm8KEMoW YmqZynlDVvMjdH6G2levj1ihDVQ5PydH2qk2vj5NOUE6Z7KRm6ru5wwria2t PNXetAtb21SxP9/3J3/IwlnorvM0zJxciM257NjFQTuR5q9l2mXaxSRUdT/X tgnbJjzXCwsPc/J1KPshQ7ZQss2FUJ2L0+LvFglIQRs73Xy/PvkPN4g0e73P CjsiaHO0TGTItnJyo0Kfm33d3MsWoVUrJCsOYnNz6y+R4d7b3tj2xma2b27R JDL6mzPs3NdqcIibR9F32vtzPA9XbuZp/A6ejnx+vc9+OAylCWewYM+gnw87 t3nYuc3DVm2e3UWaf1C/z4OCvdBQyz12zrFzFJvn2vzLz9NBG1+X/cmZfd28 KNjnVW5ctluJy8ishcu5LJEGXtTvv68xiBtEPl+u6iXS/Kv4uij25502kOPw vNzrmxfL47zHci/r94btVQRdFPuFpqW9mu3m67n2w2EoP0+BfrI+E2faY2R2 jNEY39cJrpA7gTMwGcyYRlZ8BefQqckvcYlXZO2H0iwUULGt1baBmAzq0wZU EpPBDG4rzeB60oywIM6w9Us+X0ReFRKZc2j9zpMWEbS+eUjLeXB9963V0kSy OpcuVuei9Rqo0dZgf7UGAjm3j6ZFiq7Bcbjet57gICuOw4nLyPsTTRuEclhe Wj3W4MLk0giyBrbUepKd0Mv4Dq5U1SvrpA17I96JlK2j+ZM7GIUXDCKj+E1k QGdnuCdSNNGsuLGwJnJyTS48L80rS/PKmpgM1sRkUM+5U9CyN7aVxGSwXle1 wmtazs5rYhTIfSt1Xuzc1kLWJdKExb5uLZTka3Xm5OIqZiIjuNDmFRp6KGha DXZ9eSqjNxan40Sm6GJvthZXMROvoYzR4ii9FrfCCo08DbVzLrNuo9xLpFZb sbmxlq7NNq8+EGooPblR362NR0YiE3hzlF5bmbOVKpuz89ocLdfm/Ls29zZz iKyGYmQHvXE44q2DV8U6CorD3culQXAdblmsgzZ+HRw01sHcsA67vnVw0Mhp 9YmMr8bEdTgt1pQEUdCtw6WLQrI6yPaDkq1ugb21uqjfc/4yCldRcLmGvTQI JvLzdLl3XS6ef5CsXOwXbfz62fj+f7aSKEM= "], 1, {"Discrete", 1}, { "Discrete", 1}, 1, {ResamplingMethod -> None}}, True, 11.];
mostre o input completo da Wolfram Language
In[2]:=
Click for copyable input
opts = {ChartElementFunction -> ChartElementDataFunction["GradientScaleRectangle", "ColorScheme" -> "SolarColors"], ImageSize -> 400, PlotTheme -> {"Grid", "LargeLabels"}, ChartStyle -> EdgeForm[GrayLevel[0.5]], PlotLabel -> "Arrival Time O`Hare Dec 2015"};
In[3]:=
Click for copyable input
DateHistogram[arrivals, opts]
Out[3]=

Terças e quintas são os dias mais movimentados, enquanto sábado é o menos movimentado.

In[4]:=
Click for copyable input
DateHistogram[arrivals, "Day", DateReduction -> "Week", PlotLabel -> "Weekly Analysis", opts]
Out[4]=

O aeroporto fica ocupado por volta das 7 da manhã e permanece ocupado durante todo o dia.

In[5]:=
Click for copyable input
DateHistogram[arrivals, "Hour", DateReduction -> "Day", PlotLabel -> "Daily Analysis", opts]
Out[5]=

Veja a intensidade dos tempos de chegada em um dia.

In[6]:=
Click for copyable input
DateHistogram[arrivals, "Hour", "Intensity", DateReduction -> "Day", AxesLabel -> {None, "%"}, PlotLabel -> "Daily Arrival Intensity", opts]
Out[6]=

Exemplos Relacionados

de en es fr ja ko ru zh