在Python中是否有办法处理解析字节的很长字符串到

2024-05-16 23:21:06 发布

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我用这篇文章通过编程生成了数千张不同的图片和情节:https://stackoverflow.com/a/45099838/10105937。我将这些输出组织在一个数据框中,然后尝试将其导出到一个HTML文件,以便使用pandas.to_HTML()方法查看。但是,我在HTML源代码中得到如下输出:

<td><img src = "data:image/png;base64,iVBORw0KGgoA...</td>

显然,这会导致图像无法加载(尽管在某个点上它们正在加载,嗯)

“…”发生在to_html步骤。我可以确认图像是否存在:当我在浏览器中粘贴长URL时,它们会加载。我认为这是因为字节很长。有什么办法可以解决这个问题吗

更小的可再现DF(我仍然得到误差):

import pandas as pd

d = {'pattern': '<img src = "/Users/sebsilas/Desktop/data-programming/coursework/Slonimsky/mid-files/midi_miner_out/pngs/slon_1.png.png"/>',
 'major_centroid_diff': '<img src = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbAAAAEgCAYAAADVKCZpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1%2B/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XtAVHXeBvCHQPAOKJDioDAMIhcHhEFRyxVdMy3JvCCkZlmpSGu5bZfdVmt7bbUst/K6mFttKKTdhjbFstItU3EAL4gXVETAGxcBUbkNv/cPX%2BeVAAWcmTNn5vn81TAHeBomng5z5vu1E0IIEBERycw9UgcgIiJqDxYYERHJEguMiIhkiQVGRESyxAIjIiJZYoEREZEsscCIiEiWWGBERCRLLDAiIpIlFhgREckSC4yIiGSJBUZERLLEAiMiIlligRERkSyxwIiISJZYYEREJEssMCIikiUWGBERyRILjIiIZIkFRkREssQCIyIiWWKBERGRLLHAiIhIllhgREQkSywwIiKSJRYYERHJEguMiIhkiQVGRESyxAIjIiJZYoEREZEsscCIiEiWWGBERCRLLDAiIpIlFhgREcmSg9QBTMXNzQ3e3t5SxyAikpUzZ86gpKRE6hitYrUF5u3tDZ1OJ3UMIiJZ0Wg0UkdoNf4JkYiIZIkFRkREssQCIyIiWZK8wGbPng0PDw8EBwc3e78QAgsWLIBKpYJarUZmZqaZExIRkSWSvMCeeOIJpKWltXj/tm3bkJubi9zcXCQmJiI%2BPt6M6YiIyFJJXmAjRoxAjx49Wrxfq9Xi8ccfh52dHSIjI1FeXo7z58%2BbMSEREVkiyQvsToqKiuDl5WW4rVAoUFRUJGEiovar1zfgcGEFhBBSRyGSPYsvsOb%2BQ7ezs2v22MTERGg0Gmg0GhQXF5s6GlGbLdIewYRVv2CRNhv6BpYY0d2w%2BAJTKBQoKCgw3C4sLISnp2ezx86ZMwc6nQ46nQ7u7u7mikjUKl9kFCI5/SwG9nFG0t6zmPtpBq7X6qWORSRbFl9g0dHR%2BPe//w0hBPbu3QtnZ2f07t1b6lhEbXLsQiVe/fowIpU98NX8YXjjkSD8cOwi4tbvRWlVjdTxiGRJ8lFScXFx2LlzJ0pKSqBQKPC3v/0NdXV1AIB58%2BZh/Pjx2Lp1K1QqFTp37oyPPvpI4sREbVNZXYf4pEx079gBH8QNgoP9PXh8qDfu7d4RC5KzMHntr/j4ycHwdusidVQiWbETVvpqskaj4SxEkpwQAvFJmfj%2B6EUkPxOJwT6Nr7jNPHsZT39y43m6YZYGg/q6ShGTyEBOvzst/k%2BIRHK24Zc8pB25gJcf9G9SXgAQ1tcVX8QPQ7eODohbvxff51yUICWRPLHAiExk/5kyLN12DA8G9cIz9ytbPM7HrQu%2BiB8G/3u7Ye6nOiTtzTdjSiL5YoERmUDxlRokbMyEl2snvD1V3eJbP25y6%2BqE5DmRiPL3wF%2B/zsZbacfQwMvsiW6LBUZkZPX6BixIzkJldR3WzghH944dWvV5nR0d8M%2BZ4XhsSF%2Bs3XkKf9x8ALX1DSZOSyRfkl%2BFSGRtVnx/AntOl%2BKdqSEI6N29TZ/rYH8P3pwYjD4unbB8%2B3FculKDdTNbX4JEtoRnYERGtCPnItbsPIW4wV6YEq5o19ews7NDQpQKK2JCkJ5Xhph1e3C%2B4rqRkxLJHwuMyEjOll7DHzcfQHCf7nhtQtBdf71JYQp8Mnswii5fx6Orf8WxC5VGSElkPVhgREZQXadH/MYMAMDa6eHo2MHeKF93uMoNm%2BcNBQBMXbsHv54sMcrXJbIGLDAiI3g99QiOnKvEP6aFwqtHZ6N%2B7YDe3fHl/GHo7dIRsz5Kh/YAtzEQASwworu2RVeAlP0FmD/SF6MD7jXJ9/B06YQt84YhvJ8rnks5gDU7T3IlC9k8FhjRXcg5V4m/fp2Nocqe%2BOOY/ib9Xs6dOuCT2YMRHeKJt9OOcyUL2TxeRk/UTpXVdZi/MQPOnf5/SK%2BpOTnY471pofB06YR1u07hQkUNVsYNQidH47zmRiQnPAMjagchBP60%2BSAKLl/H6ulhcO/mZLbvfc89dnhl3ACuZCGbxwIjaof1P5/GdzkX8edxAxDh3XRIrzk8PtQb62aE4%2Bj5Skxe%2ByvOlFyVJAeRVFhgRG2073Qp3ko7jvEDe%2BGp%2B3wkzTI2qBeS50Sisroek9b%2BiqyzlyXNQ2ROLDCiNrh0pRrPJmehX4/OeGvynYf0mgNXspCtYoERtVK9vgF/2JSFK9V1WDMjDN0saD7hb1eyfMqVLGQDWGBErfTOdyewL68Mf390IAb0atuQXnO4dSXLIq5kIRvAAiNqhe%2BOXMC6Xafw2JC%2BmBTWviG95sCVLGRL%2BD4wojvIL72KF7YcxMA%2Bzlj8cKDUce6IK1nIVvAMjOg2quv0mJeUiXvs7LBmepjRhvSaGleykC1ggRHdxmJtNo6er8Q/poUYfUivOXAlC1kzFhhRCzbvL8BmXSGejVJh1ADTDOk1B65kIWvFAiNqxpFzFVikzcZwVU8sNPGQXnP47UqWr7O4koXkjwVG9BsV1%2BsQn5QJ186OeD92EOzvkf7NysZw60qW5z/jShaSPxYY0S2EEPjTloM4V34dq6cPgltX8w3pNQeuZCFrwsvoiW7xz/%2Bexvc5F7H44UCE95NmSK%2BpcSULWQuegRH9n72nS/F22jE8pO6NJ4d7Sx3HpLiShawBC4wIwKXKajy7KQvebl0sZkivOXAlC8kZC4xsXr2%2BAc8mZ%2BFqTT3WzQhHVyfb%2Bss6V7KQXLHAyOYt334c6XllWDppIPrf203qOJLgShaSI4sosLS0NPj7%2B0OlUmHZsmVN7j979iyioqIwaNAgqNVqbN26VYKUZI3Ssi/gn/89jRmRfTFxUB%2Bp40iKK1lIbiQvML1ej4SEBGzbtg05OTlITk5GTk5Oo2OWLFmCmJgYZGVlISUlBfPnz5coLVmTvJKreHHLQYQonLFIBkN6zYErWUhOJC%2Bw9PR0qFQqKJVKODo6IjY2FlqtttExdnZ2qKy8McOtoqICnp6eUkQlK3K9Vo/4pAzY29th9fQwODnwEvKbuJKF5ELyV6uLiorg5eVluK1QKLBv375Gx7z%2B%2But44IEHsHLlSly9ehU7duwwd0yyIkIILNJm4/jFK/jXExFQuMpvSK%2BpcSULyYHkZ2DNjbL57SXMycnJeOKJJ1BYWIitW7di5syZaGho%2Bn%2BEiYmJ0Gg00Gg0KC4uNllmkrfP9hfg84xC/CFKhSh/D6njWCyuZCFLJ3mBKRQKFBQUGG4XFhY2%2BRPhhg0bEBMTAwAYOnQoqqurUVLSdKL2nDlzoNPpoNPp4O7ubtrgJEvZRRVYnHoE9/u54bnfy39IrzlMClPg4ycHo5ArWcjCSF5gERERyM3NRV5eHmpra5GSkoLo6OhGx/Tt2xc//PADAODo0aOorq5mQVGbVVyrQ/zGDPTs4oj3poVazZBec7jPzw1buJKFLIzkBebg4IBVq1Zh7NixCAgIQExMDIKCgrB48WKkpqYCAN59912sX78eISEhiIuLw8cff2wzkxLIOBoaBF7YcgAXKqqxenoYelrZkF5z4EoWsjR2wkr3KWg0Guh0OqljkIVYs/Mk3k47jtcnBOKJ4T5Sx5G1iut1mPupDntPl%2BGlB/0R/ztf/g%2BlFZHT707Jz8CITO3XUyV4Z/txTAjxxKxh3lLHkT2uZCFLIfll9ESmdLGyGguSs%2BDj1gXLJg3kmYKRcCULWQKegZHVqtM34NlNmbhWq8e6GeHoYmNDek2NK1lIaiwwslpvpx3D/jOXsXTSQPjZ6JBec%2BBKFpIKC4ys0rbD57H%2B5zw8PrQfHgm17SG95sCVLCQFFhhZndPFVXjx80MI8XLBqw8FSB3HZnAlC5kbC4ysyvVaPeZvzEQHezus4ZBes%2BNKFjInFhhZDSEEXv36MI5fvIL3Ygehj0snqSPZJK5kIXNhgZHVSE4vwJeZRVgwyg%2B/689RY1LiShYyB15XTFbhcGEFXv%2B/Ib0LRvtJHYfAlSxkejwDI9krv1aL%2BI0ZcOvqiPdjB3FIrwXhShYyJRYYyVpDg8AfNx/ExcpqrJkRjh5dHKWORM3gShYyBRYYydraXafw47FLWPRwIEK9XKSOQ7fBlSxkbCwwkq3dJ0vw7nfHER3iiZmR/aSOQ63AlSxkTCwwkqULFTeG9Crdu2Iph/TKiqdLJ2yZNwzh/Vzx/GcHsGbnSVjpVicyMRYYyc7NIb3X6/RYNyOMQ3pliCtZyBj4Xz7JzrJtx6DLv4yVcYOg8uCQXrniSha6WzwDI1n59tB5bPglD08M88aEEE%2Bp49Bd4koWuhssMJKNU8VVeOnzgxjU1wV/Gc8hvdaEK1moPVhgJAvXausRn5QBpw72WP1YGBwd%2BNS1NlzJQm3F3wJk8YQQePWrbOReqsL7sTdeMyHrxJUs1BYsMLJ4G/edxVdZRXh%2BdH/c78chvdaOK1motVhgZNEOFZbjjW9yMNLfHX8YpZI6DpkJV7JQa7DAyGJdvlqL%2BKRMuHdzwj9iQnEPh/TaFK5koTvh%2B8DIIjU0CCzcfADFV2qwZd5QuHJIr03iSha6HZ6BkUVa/dNJ7DxejEUTAhHCIb02jStZqCUsMLI4v%2BSWYMWOE5gY6okZQ/pKHYcsBFey0G%2BxwMiinK%2B4jgUpWfDz6Iq/c0gv/QZXstCtWGBkMWrrG5CwMRM1dXqsnRGOzo58iZaa4koWuokFRhbj71uPIvNsOd6eEgJf965SxyELxpUsBLDAyEJ8c/AcPv71DJ4c7o2H1L2ljkMywJUsZBEFlpaWBn9/f6hUKixbtqzZYzZv3ozAwEAEBQXhscceM3NCMqWTl6rwyheHENbXBX8exyG91Ho3V7LM%2B50vkvaexdxPM3C9Vi91LDITyV9k0Ov1SEhIwPfffw%2BFQoGIiAhER0cjMDDQcExubi6WLl2K3bt3w9XVFZcuXZIwMRnT1ZpbhvRO55BearubK1k8XTritdQjiFu/FxtmadCzq5PU0cjEJP9tkZ6eDpVKBaVSCUdHR8TGxkKr1TY6Zv369UhISICrqysAwMPDQ4qoZGRCCPzlq8M4WVyFD2IHobczh/RS%2B3Eli%2B2RvMCKiorg5eVluK1QKFBU1PiqohMnTuDEiRMYPnw4IiMjkZaWZu6YZAJJe/OhPXAOL4zpj/v83KSOQ1ZgbFAvbHomEhXX67iSxQZIXmDNXTn02/f%2B1NfXIzc3Fzt37kRycjKefvpplJeXN/m8xMREaDQaaDQaFBcXmywz3b0DBeV44z85GDXAA/NHckgvGU94P1d8OX84V7LYAMkLTKFQoKCgwHC7sLAQnp6eTY555JFH0KFDB/j4%2BMDf3x%2B5ublNvtacOXOg0%2Bmg0%2Bng7s61G5bq8tVaJGzMxL3dO2JFTAiH9JLRcSWLbZC8wCIiIpCbm4u8vDzU1tYiJSUF0dHRjY6ZOHEifvrpJwBASUkJTpw4AaVSKUVcuksNDQLPf3ZjSO%2Ba6WFw6cwhvWQaXMli/SQvMAcHB6xatQpjx45FQEAAYmJiEBQUhMWLFyM1NRUAMHbsWPTs2ROBgYGIiorC8uXL0bNnT4mTU3us/PEkdp0oxmvRgVArOKSXTIsrWaybnbDSt69rNBrodDqpY9At/nuiGLM%2BSsejoX3wbkwI5xyS2QghsGbnKSzffhzDfHtyJcttyOl3p%2BRnYGQbzpVfx3MpWejv0Q1vPsohvWReXMlinVhgZHK19Q2YvzETdXqBtTPC0MnRXupIZKO4ksW6sMDI5N78NgcHCsrx9hQ1lBzSSxLjShbrwQIjk0o9eA6f7MnHU/f5YPxADukly8CVLNaBBUYmk3vxCl754hA0/VzxyrgBUschaoQrWeSPBUYmcbWmHvEbM9HZ0R6rHgtDB3s%2B1cjycCWLvEk%2BjZ6sjxACr3x5GKeLq5D01BD0cu4odSSiFt1cyeLp0gnrdp3ChYoarIwbxIuNZID/W0xG9%2B89%2Bfjm4Dm88IA/hqk4pJcs382VLG88EoQfjl1E3Pq9KK2qkToW3QELjIwq8%2BxlLPk2B6MHeCD%2Bd75SxyFqE65kkRcWGBlN2dVaPLsxE72cO2JFTCiH9JIscSWLfLDAyCj0DQLPpWSh5Got1k4Ph3Nnjukh%2BeJKFnlggZFRfPBDLn7OLcHfooMQ3MdZ6jhEd40rWSwfC4zu2s7jl/DBj7mYHKZAbITXnT%2BBSCa4ksWyscDorhRevobnPzsA/3u7YcnEYA7pJavDlSyWi%2B8Do3arqdcjYWMm9HqBtTPC%2Bb4ZsloO9vfgzYnB6OPSCcu3H8elKzVcyWIBeAZG7bbkP0dxsLACy6eq4ePWReo4RCbFlSyWhwVG7aI9UIRP9%2Bbjmft98GAwh/SS7eBKFsvBAqM2O3HxCl754jAivF3x0oMc0ku2hytZLAMLjNqkqqYe85Iy0MXJgUN6yaZxJYv0%2BNuHWk0IgZe/OIQzJVexMm4Q7u3OIb1k27iSRVosMGq1j389g28PnceLYwdgqG9PqeMQWQSuZJEOL6OnVsnIv4w3vz2K3wfci3m/U0odh8iicCWLNHgGRndUWlWDZzdlwtOlE96NCeGblYmawZUs5scCo9u6MaT3AEqv1mLN9DA4d%2BIbN4luhytZzIcFRrf1/o4T%2BOVkCf7nEQ7pJWotrmQxDxYYtein45fwwY8nMTVcgWkRfaWOQyQrXMlieiwwalZB2TUs/OwAAnp3x/9MDJY6DpEscSWLabHAqImaej0SNv3fkN7pYejYgVdSEbUXV7KYDguMmnjjmxwcKqzAOzEh8OaQXqK7xpUspsH3gVEjX2UVYuO%2Bs5g7QomxQb2kjkNkNbiSxfgs4gwsLS0N/v7%2BUKlUWLZsWYvHff7557Czs4NOpzNjOttx/MIV/PnLwxjs0wMvjvWXOg6R1eFKFuOSvMD0ej0SEhKwbds25OTkIDk5GTk5OU2Ou3LlCj744AMMGTJEgpTW70p1HeKTMtCtYwesemwQHDikl8hkuJLFOCT/LZWeng6VSgWlUglHR0fExsZCq9U2OW7RokV46aWX0LEjB8ga280hvfll17AqbhA8uvExJjK1%2B/zcsHnuUAgIrmRpJ8kLrKioCF5eXobbCoUCRUWN1xJkZWWhoKAADz/8sLnj2YR/7T6DrYcv4KWx/hii5JBeInMJ9OyOr%2BYP50qWdpK8wJpbPXDrrL2GhgYsXLgQ77777h2/VmJiIjQaDTQaDYqLi42a01rpzpRh6dajeCDwXswZwSG9RObGlSztJ3mBKRQKFBQUGG4XFhbC09PTcPvKlSvIzs7GyJEj4e3tjb179yI6OrrZCznmzJkDnU4HnU4Hd3d3s%2BSXs5KqGiRsykQf105YPpVDeomkwpUs7SP5ZfQRERHIzc1FXl4e%2BvTpg5SUFGzatMlwv7OzM0pK/v9vwyNHjsQ777wDjUYjRVyrcWNIbxbKr9Xhq/mDOaSXSGJcydJ2kp%2BBOTg4YNWqVRg7diwCAgIQExODoKAgLF68GKmpqVLHs1r/%2BP4Edp8sxf9MDEagZ3ep4xARuJKlreyElf6xVaPR8P1iLfjx2EXM/liHaRovvDVFLXUcImrG9iMXsCA5C72cO%2BKTJwebbSqOnH53Sn4GRuZVUHYNz6ccQGDv7vjbI0FSxyGiFtxcyVLJlSwtYoHZkOo6PeI3ZkAAWDcjnEN6iSwcV7LcHgvMhvztmxxkF1ViRUwo%2BvbsLHUcImoFrmRpGQvMRnyRUYjk9LOIH%2BmLMYH3Sh2HiNqAK1maxwKzAccuVOLVrw9jqLInXhjTX%2Bo4RNQOXMnSlOTvAyPTqqyuQ3xSJrp37IAP4jikl0jOuJKlMf42s2JCCLy05RDOll3DqsfC4N7NSepIRHSXfruSZeraPThXbpsrWVhgVmzDL3lIO3IBrzw4AIN9ekgdh4iM6OZKlqLy65i0xjZXsrDArNT%2BM2VYuu0YHgzqhafv95E6DhGZgK2vZGGBWaHiKzVI2JgJL9dOeHuqmkN6iayYLa9kYYFZmXp9AxYkZ6Gyug5rZ9jui7tEtuS3K1m0B2yjxFhgVmbF9yew53QplkwciIDeHNJLZCturmSJH%2BmLkf09pI5jFryM3orsyLmINTtPIW6wF6aEK6SOQ0Rm5uRgj5cfHCB1DLPhGZiVOFt6DQs3H0Bwn%2B54bQKH9BKR9WOBWYGbQ3rtAKydziG9RGQb%2BCdEK/B66hEcOVeJDbM08OrBIb1EZBt4BiZzW3QFSNlfgIQoX4wO4JBeIrIdLDAZyzlXib9%2BnY1hvj3xxzH%2BUschIjIrFphMVVbXYf7GDLh0vjGk1/4evlmZiGwLXwOTISEE/rT5IAovX0fKnEi4deWQXiKyPTwDk6H1P5/GdzkX8cq4AdB4c0gvEdkmFpjM7DtdirfSjmP8wF546j4O6SUi28UCk5FLV6rxbHIW%2BvXojLcmc0gvEdk2vgYmE/X6BvxhUxauVNfh06cGoxuH9BKRjWOBycQ7353AvrwyrIgJwYBeHNJLRMQ/IcrAd0cuYN2uU3hsSF9MCuOQXiIigAVm8fJLr%2BKFLQcxsI8zFj8cKHUcIiKLwQKzYNV1esxLysQ9dnZYMz2MQ3qJiG7B18As2GJtNo6er8RHT0RwSC8R0W/wDMxCbd5fgM26QvxhlApRA2xjuyoRUVuwwCzQkXMVWKTNxn0qNzz/%2B/5SxyEiskgWUWBpaWnw9/eHSqXCsmXLmty/YsUKBAYGQq1WY/To0cjPz5cgpXlUXK9DfFImXDs74v3YUA7pJSJqgeQFptfrkZCQgG3btiEnJwfJycnIyclpdMygQYOg0%2Blw6NAhTJkyBS%2B99JJEaU1LCIE/bTmIc%2BXXsXp6GHpySC8RUYskL7D09HSoVCoolUo4OjoiNjYWWq220TFRUVHo3PnGRQyRkZEoLCyUIqrJ/fO/p/F9zkX8ZXwAwvu5Sh2HiMiiSV5gRUVF8PLyMtxWKBQoKipq8fgNGzZg3Lhx5ohmVntPl%2BLttGN4SN0bTw73ljoOEZHFk/wyeiFEk4%2B1NKQ2KSkJOp0Ou3btavb%2BxMREJCYmAgCKi4uNF9LELlVW49lNWfB268IhvURErST5GZhCoUBBQYHhdmFhITw9PZsct2PHDrz55ptITU2Fk1Pzrw3NmTMHOp0OOp0O7u7uJstsTPX6BjybnIWrNfVYNyMcXZ0k/38KIiJZkLzAIiIikJubi7y8PNTW1iIlJQXR0dGNjsnKysLcuXORmpoKDw/rek/U8u3HkZ5XhqWTBqL/vd2kjkNEJBuSF5iDgwNWrVqFsWPHIiAgADExMQgKCsLixYuRmpoKAHjxxRdRVVWFqVOnIjQ0tEnByVVa9gX887%2BnMSOyLyYO6iN1HCIiWbETzb0IZQU0Gg10Op3UMVqUV3IV0St/gdK9CzbPGwonB845JCLpWfrvzltJfgZmi67X6hGflAF7ezusnh7G8iIiagdeMWBmQggs0mbj%2BMUr%2BOiJCChcOaSXiKg9eAZmZp/tL8DnGYX4wyg/jPS3rgtSiIjMiQVmRtlFFVicegT3%2B7nhudF%2BUschIpI1FpiZVFyrQ/zGDPTs4oj3YwdxSC8R0V3ia2Bm0NAg8MKWA7hQUY3P5g5Fjy6OUkciIpI9noGZwbr/nsKOo5fw6vgAhPXlkF4iImNggZnYr6dK8M7245gQ4olZw7yljkNEZDVYYCZ0sbIaC5Kz4OPWBcsmDeSQXiIiI%2BJrYCZSp2/As5syca1Wj%2BRnItGFQ3qJiIyKv1VN5K1tx7D/zGW8HxsKPw7pJSIyOv4J0QS2HT6PD3/Jw%2BND%2B%2BGRUA7pJSIyBRaYkZ0ursKLnx9CqJcLXn0oQOo4RERWiwVmRNdr9Zi/MRMdOKSXiMjk%2BBqYkQgh8OrXh3H84hV88uRg9HHpJHUkIiKrxjMwI0lOL8CXmUV4brQfRvR3lzoOEZHVY4EZweHCCryeegQj%2BrtjwSgO6SUiMgcW2F0qv1aL%2BI0ZcOvqiPemheIeDuklIjILvgZ2FxoaBP64%2BSAuVlZjy7xhHNJLRGRGPAO7C2t3ncKPxy5h0cOBCPVykToOEZFNYYG10%2B6TJXj3u%2BOIDvHEzMh%2BUschIrI5LLB2uFBxY0iv0r0rlnJILxGRJPgaWBvV6RuQsCkT1%2Bv0%2BGxGGIf0EhFJhL9922jp1mPIyL%2BMlXGDoPLgkF4iIqnwT4ht8O2h8/jX7jw8McwbE0I8pY5DRGTTWGCtdKq4Ci99fhBhfV3wl/Ec0ktEJDUWWCtcq61HfFIGnDrYY/X0MDg68GEjIpIaXwO7AyEEXv0qG7mXqvDv2YPR25lDeomILAFPJe5g476z%2BCqrCAt/3x/3%2B3FILxGRpWCB3cahwnK88U0ORvq749koldRxiIjoFhZRYGlpafD394dKpcKyZcua3F9TU4Np06ZBpVJhyJAhOHPmjMkzXb5ai/ikTLh3c8I/Yjikl4jI0kheYHq9HgkJCdi2bRtycnKQnJyMnJycRsds2LABrq6uOHnyJBYuXIiXX37ZpJkaGgQWbj6A4is1WDM9DK4c0ktEZHEkL7D09HSoVCoolUo4OjoiNjYWWq220TFarRazZs0CAEyZMgU//PADhBAmy7T6p5PYebwYiyYEIoRDeomILJLkBVZUVAQvLy/DbYVCgaKiohaPcXBwgLOzM0pLS02S55fcEqzYcQITQz0xY0hfk3wPIiK6e5JfRt/cmdRvh%2BO25hgASExMRGJiIgCguLi4XXlyzlfA/95u%2BDuH9BIRWTTJz8AUCgUKCgoMtwsLC%2BHp6dniMfX19aioqECPHj2afK05c%2BZAp9NBp9PB3b19l7zPGeGLrxOGo7Oj5N1ORES3IXmBRUREIDc3F3l5eaitrUVKSgqio6MbHRMdHY1PPvkEAPD5559j1KhRJj076tjB3mRfm4iIjEPy0wwHBwesWrUKY8eOhV6vx%2BzZsxEUFITFixdDo9EgOjoaTz31FGbOnAmVSoUePXogJSVF6thERCQxO2HKy/kkpNFooNPppI5BRCQrcvrdKfmfEImIiNqDBUZERLLEAiMiIlligRERkSyxwIiISJas9ipENzc3eHsPjGgPAAAIWElEQVR7t%2Btzi4uL2/1GaFNirrZhrrZhrraz1Gx3k%2BvMmTMoKSkxciLTsNoCuxuWehkpc7UNc7UNc7WdpWaz1FzGxj8hEhGRLLHAiIhIluxff/3116UOYYnCw8OljtAs5mob5mob5mo7S81mqbmMia%2BBERGRLPFPiEREJEs2V2BpaWnw9/eHSqXCsmXLmtxfU1ODadOmQaVSYciQIThz5ozhvqVLl0KlUsHf3x/bt283a64VK1YgMDAQarUao0ePRn5%2BvuE%2Be3t7hIaGIjQ0tMkqGlPn%2Bvjjj%2BHu7m74/h9%2B%2BKHhvk8%2B%2BQR%2Bfn7w8/MzrMMxV66FCxcaMvXv3x8uLi6G%2B0z1eM2ePRseHh4IDg5u9n4hBBYsWACVSgW1Wo3MzEzDfaZ8rO6Ua%2BPGjVCr1VCr1Rg2bBgOHjxouM/b2xsDBw5EaGgoNBqNWXPt3LkTzs7Ohp/VG2%2B8YbjvTj9/U%2BZavny5IVNwcDDs7e1RVlYGwLSPV0FBAaKiohAQEICgoCC8//77TY6R6jkmGWFD6uvrhVKpFKdOnRI1NTVCrVaLI0eONDpm9erVYu7cuUIIIZKTk0VMTIwQQogjR44ItVotqqurxenTp4VSqRT19fVmy/Xjjz%2BKq1evCiGEWLNmjSGXEEJ06dLFKDnak%2Bujjz4SCQkJTT63tLRU%2BPj4iNLSUlFWViZ8fHxEWVmZ2XLd6oMPPhBPPvmk4bapHq9du3aJjIwMERQU1Oz93377rXjwwQdFQ0OD2LNnjxg8eLAQwrSPVWty7d692/D9tm7dasglhBD9%2BvUTxcXFRsvSllw//fSTeOihh5p8vK0/f2PnulVqaqqIiooy3Dbl43Xu3DmRkZEhhBCisrJS%2BPn5Nfn3luo5JhWbOgNLT0%2BHSqWCUqmEo6MjYmNjodVqGx2j1Woxa9YsAMCUKVPwww8/QAgBrVaL2NhYODk5wcfHByqVCunp6WbLFRUVhc6dOwMAIiMjUVhYaJTvfbe5WrJ9%2B3aMGTMGPXr0gKurK8aMGYO0tDRJciUnJyMuLs4o3/t2RowY0eym8Ju0Wi0ef/xx2NnZITIyEuXl5Th//rxJH6vW5Bo2bBhcXV0BmO%2B51ZpcLbmb56Wxc5nruQUAvXv3RlhYGACgW7duCAgIQFFRUaNjpHqOScWmCqyoqAheXl6G2wqFoskT4NZjHBwc4OzsjNLS0lZ9rilz3WrDhg0YN26c4XZ1dTU0Gg0iIyPx9ddfGyVTW3J98cUXUKvVmDJlCgoKCtr0uabMBQD5%2BfnIy8vDqFGjDB8z1eN1Jy3lNuVj1Va/fW7Z2dnhgQceQHh4OBITE82eZ8%2BePQgJCcG4ceNw5MgRAKZ9brXFtWvXkJaWhsmTJxs%2BZq7H68yZM8jKysKQIUMafVwOzzFjknwjszmJZi64tLOza9UxrflcU%2Ba6KSkpCTqdDrt27TJ87OzZs/D09MTp06cxatQoDBw4EL6%2BvmbJNWHCBMTFxcHJyQnr1q3DrFmz8OOPP1rM45WSkoIpU6bA3t7e8DFTPV53IsVzqy1%2B%2BuknbNiwAb/88ovhY7t374anpycuXbqEMWPGYMCAARgxYoRZ8oSFhSE/Px9du3bF1q1bMXHiROTm5lrM4/XNN99g%2BPDhjc7WzPF4VVVVYfLkyXjvvffQvXv3RvdZ%2BnPM2GzqDEyhUBjOEACgsLAQnp6eLR5TX1%2BPiooK9OjRo1Wfa8pcALBjxw68%2BeabSE1NhZOTk%2BHjN49VKpUYOXIksrKyzJarZ8%2BehizPPPMMMjIy2vTvZKpcN6WkpDT5E4%2BpHq87aSm3KR%2Br1jp06BCefvppaLVa9OzZ0/Dxmzk8PDzw6KOPGu3P5q3RvXt3dO3aFQAwfvx41NXVoaSkxCIeL%2BD2zy1TPV51dXWYPHkypk%2BfjkmTJjW535KfYyYhyStvEqmrqxM%2BPj7i9OnThhd/s7OzGx2zatWqRhdxTJ06VQghRHZ2dqOLOHx8fIx2EUdrcmVmZgqlUilOnDjR6ONlZWWiurpaCCFEcXGxUKlURntBuzW5zp07Z/jnL7/8UgwZMkQIceNFY29vb1FWVibKysqEt7e3KC0tNVsuIYQ4duyY6Nevn2hoaDB8zJSPlxBC5OXltfji/3/%2B859GL7BHREQIIUz7WLUmV35%2BvvD19RW7d%2B9u9PGqqipRWVlp%2BOehQ4eKbdu2mS3X%2BfPnDT%2B7ffv2CS8vL9HQ0NDqn7%2BpcgkhRHl5uXB1dRVVVVWGj5n68WpoaBAzZ84Uzz33XIvHSPkck4JNFZgQN67S8fPzE0qlUixZskQIIcSiRYuEVqsVQghx/fp1MWXKFOHr6ysiIiLEqVOnDJ%2B7ZMkSoVQqRf/%2B/cXWrVvNmmv06NHCw8NDhISEiJCQEDFhwgQhxI0ryIKDg4VarRbBwcHiww8/NGuuV155RQQGBgq1Wi1Gjhwpjh49avjcDRs2CF9fX%2BHr6yv%2B9a9/mTWXEEK89tpr4uWXX270eaZ8vGJjY0WvXr2Eg4OD6NOnj/jwww/F2rVrxdq1a4UQN34BzZ8/XyiVShEcHCz2799v%2BFxTPlZ3yvXUU08JFxcXw3MrPDxcCCHEqVOnhFqtFmq1WgQGBhoeZ3PlWrlypeG5NWTIkEYF29zP31y5hLhx9e20adMafZ6pH6%2Bff/5ZABADBw40/Ky%2B/fZbi3iOSYWTOIiISJZs6jUwIiKyHiwwIiKSJRYYERHJEguMiIhkiQVGRESyxAIjIiJZYoEREZEsscCIiEiWWGBERCRLLDAiIpIlFhgREckSC4yIiGSJBUZERLLEAiMiIlligRERkSyxwIiISJZYYEREJEssMCIikiUWGBERyRILjIiIZOl/ARZWOZEUjahCAAAAAElFTkSuQmCC"/>',
 }
popcorn = pd.DataFrame.from_records([d])

popcorn.to_html('popcorn.html', escape=False)

Tags: to图像imagesrcpandasimgdatapng
1条回答
网友
1楼 · 发布于 2024-05-16 23:21:06

我无法再现你的错误。可能是特定于机器/版本的。html在我的mac中呈现良好

(根据this):

What you are seeing is pandas truncating the output for display purposes only.

解决方案:

with pd.option_context('display.max_colwidth', -1): 
    popcorn.to_html('popcorn.html', escape=False)

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