185 lines
5.0 KiB
Plaintext
185 lines
5.0 KiB
Plaintext
{
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"start_time": "2023-05-04T09:29:48.650911Z",
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"end_time": "2023-05-04T09:29:49.231339Z"
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}
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},
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"outputs": [],
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"source": [
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"from IPython.core.interactiveshell import InteractiveShell #执行该代码可以使得当前nb支持多输出\n",
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"InteractiveShell.ast_node_interactivity = \"all\" \n",
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"import numpy as np\n",
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"import pandas as pd \n",
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"import re "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**作业1**\n",
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"\n",
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"利用pandas读入csv数据，求化合物的分子式和质量"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2023-05-04T09:31:30.790867Z",
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"end_time": "2023-05-04T09:31:34.398474Z"
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}
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The compand is composed of : ['Calcium', 'Carbon', 'Oxygen']\n",
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" symbol num name atomicMass\n",
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"0 Ca 1 Calcium 40.078\n",
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"1 C 1 Carbon 12.011\n",
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"2 O 3 Oxygen 15.999\n",
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"The atomic mass of the compound is 100.0869\n"
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]
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}
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],
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"source": [
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"# 元素周期表文件，给出任意一个化学表达式，计算其分子元素和质量。输出结果（不得再用C-O2作为输入）：\n",
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"\n",
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"import csv\n",
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"def readTable(fileObj):\n",
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" # 使用pandas reader读取文件对象\n",
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" df = pd.read_csv(fileObj, index_col='symbol')\n",
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" return df\n",
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" \n",
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"def parserElement(elementStr, table):\n",
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" # 用 split 方法分割'-'成为数组\n",
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" elements = elementStr.split('-')\n",
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" # 初始化函数作用域内变量\n",
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" names = []\n",
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" atomicMass_lst = []\n",
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" symbols = []\n",
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" numbers = []\n",
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" mass = 0\n",
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" # 遍历数组，找寻元素\n",
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" for element in elements:\n",
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" # 用正则表达式分离字母与数字 用join把列表连接\n",
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" number = ''.join(re.findall(r'\\d+', element))\n",
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" symbol = ''.join(re.findall(r'[^\\d]+', element))\n",
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" symbols.append(symbol)\n",
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" # 将元素名称加入数组\n",
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" names.append(table.loc[symbol]['name'])\n",
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" # 索引出质量\n",
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" atomicMass = eval(re.sub(r'\\([^)]*\\)', '', table.loc[symbol]['atomicMass']))\n",
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" atomicMass_lst.append(atomicMass)\n",
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" # 判断数量是否为1\n",
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" if number:\n",
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" number = int(number)\n",
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" else:\n",
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" number = 1\n",
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" numbers.append(number)\n",
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" mass += atomicMass * number\n",
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" # 新建表格\n",
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" df = pd.DataFrame()\n",
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" df['symbol'] = symbols\n",
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" df['num'] = numbers\n",
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" df['name'] = names\n",
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" df['atomicMass'] = atomicMass_lst\n",
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"\n",
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" print('The compand is composed of : ', names)\n",
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" print(df)\n",
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"\n",
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" print('The atomic mass of the compound is ', mass)\n",
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"\n",
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"fileHandle = open('periodic-table.csv','r')\n",
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"periodicTable = readTable(fileHandle)\n",
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"\n",
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"compountString = input('Input a chemical compound, hyphenated,eg. C-O2:')\n",
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"parserElement(compountString, periodicTable)\n",
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"\n",
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"fileHandle.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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"hide_input": false,
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_position": {},
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"toc_section_display": true,
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},
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"varInspector": {
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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"types_to_exclude": [
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"module",
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