20230114
This commit is contained in:
512song@sina.com committed
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commit
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+335
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@@ -723,27 +723,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 113,
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"execution_count": null,
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"id": "7bb2e3aa-591d-4fce-983a-cde5d4c30e56",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-01-09T13:24:29.720040Z",
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"iopub.status.busy": "2023-01-09T13:24:29.719490Z",
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"iopub.status.idle": "2023-01-09T13:24:30.627490Z",
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"shell.execute_reply": "2023-01-09T13:24:30.626140Z",
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"shell.execute_reply.started": "2023-01-09T13:24:29.719971Z"
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},
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"tags": []
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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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"ok\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import openpyxl\n",
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"import json\n",
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@@ -784,27 +769,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 118,
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"execution_count": null,
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"id": "b38aef28-5d1b-4762-9c7e-70a531594fa4",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-01-09T13:32:31.923948Z",
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"iopub.status.busy": "2023-01-09T13:32:31.923410Z",
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"iopub.status.idle": "2023-01-09T13:32:32.253445Z",
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"shell.execute_reply": "2023-01-09T13:32:32.252452Z",
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"shell.execute_reply.started": "2023-01-09T13:32:31.923900Z"
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},
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"tags": []
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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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"ok!\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import openpyxl\n",
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"import json\n",
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@@ -842,10 +812,340 @@
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"print('ok!')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "638bbe8e-3770-40e1-b884-ceeeec5a053c",
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"metadata": {},
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"source": [
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"## 因素分析"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9e25a7f2-0571-438b-9aa4-e3337d9fd840",
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"metadata": {},
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"source": [
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"### 清除修改测试项目"
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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": 151,
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"id": "6e6f6a63-b297-42e8-a9ba-b2636fda5d09",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-01-14T01:56:50.048701Z",
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"iopub.status.busy": "2023-01-14T01:56:50.047669Z",
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"iopub.status.idle": "2023-01-14T01:56:50.449993Z",
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"shell.execute_reply": "2023-01-14T01:56:50.448696Z",
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"shell.execute_reply.started": "2023-01-14T01:56:50.048628Z"
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},
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"tags": []
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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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"ok\n"
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]
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}
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],
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"source": [
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"import json\n",
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"import openpyxl\n",
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"\n",
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"\n",
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"filename = 'data/天津石化人员231113.json'\n",
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"with open(filename,'r') as fl:\n",
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" dict1 = json.load(fl)\n",
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"code = '1734094'\n",
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"items = ['身高','体重']\n",
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"for item in items:\n",
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" del dict1[code][item]\n",
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"with open(filename, 'w') as fl:\n",
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" json.dump(dict1, fl, ensure_ascii=False)\n",
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"print('ok')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f96b3aeb-bf37-4a8b-bf18-d0ae8f7df653",
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"metadata": {},
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"source": [
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"### 人员情况导入"
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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": 149,
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"id": "4d77ec9b-c561-4355-98af-b18c42c1f3ca",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-01-14T01:56:10.352541Z",
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"iopub.status.busy": "2023-01-14T01:56:10.352010Z",
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"iopub.status.idle": "2023-01-14T01:56:13.111656Z",
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"shell.execute_reply": "2023-01-14T01:56:13.110214Z",
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"shell.execute_reply.started": "2023-01-14T01:56:10.352494Z"
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},
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"tags": []
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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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"ok\n"
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]
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}
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],
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"source": [
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"import openpyxl\n",
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"import json\n",
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"\n",
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"\n",
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"wb = openpyxl.load_workbook('data/员工个人基础信息20230113.xlsx')\n",
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"sheet = wb.active\n",
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"# sheets = wb.sheetnames\n",
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"person = {}\n",
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"\n",
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"for n in range(4, sheet.max_row+1):\n",
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" code = int(sheet.cell(n, 1).value)\n",
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" person.setdefault(code, {})\n",
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" dict1 = {}\n",
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" dict1['name'] = sheet.cell(n, 2).value\n",
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" dict1['sex'] = sheet.cell(n, 3).value\n",
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" dict1['unit'] = sheet.cell(n, 6).value\n",
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" dict1['sub_unit'] = sheet.cell(n, 7).value\n",
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" if sheet.cell(n,10).value is not None:\n",
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" dict1['daoban'] = '是'\n",
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" else:\n",
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" dict1['daoban'] = '否'\n",
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" dict1['age'] = sheet.cell(n, 13).value\n",
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" dict1['gl'] = sheet.cell(n, 14).value\n",
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" dict1['jhgl'] = sheet.cell(n, 15).value \n",
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" person[code] = dict1\n",
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"filename = 'data/天津石化人员231113.json'\n",
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"with open(filename, 'w') as fl:\n",
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" json.dump(person, fl, ensure_ascii=False)\n",
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"print('ok')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a5c58b77-69b6-4d76-8927-6e734556e848",
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"metadata": {},
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"source": [
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"### 人员体测得分导入合并"
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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": 150,
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"id": "db473c01-7e1a-48c4-a0d7-e44cdeb9e24b",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-01-14T01:56:24.524792Z",
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"iopub.status.busy": "2023-01-14T01:56:24.524267Z",
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"iopub.status.idle": "2023-01-14T01:56:24.967568Z",
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"shell.execute_reply": "2023-01-14T01:56:24.966212Z",
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"shell.execute_reply.started": "2023-01-14T01:56:24.524745Z"
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},
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"tags": []
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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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"ok\n"
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]
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}
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],
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"source": [
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"import json\n",
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"import openpyxl\n",
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"\n",
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"items = ['身高','体重','肺活量','握力','坐位体前屈','纵跳','俯卧撑','一分钟仰卧起坐','单脚站立','选择反应时','台阶指数']\n",
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"\n",
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"filename = 'data/result_天津.json'\n",
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"with open(filename,'r') as fl:\n",
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" dict1 = json.load(fl)\n",
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"\n",
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"filename = 'data/天津石化人员231113.json'\n",
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"with open(filename,'r') as fl:\n",
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" dict2 = json.load(fl)\n",
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"for k, v in dict2.items():\n",
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" if k in dict1.keys():\n",
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" for item in dict1[k].keys():\n",
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" dict2[k][item] = dict1[k][item]\n",
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"filename = 'data/天津石化人员231113.json'\n",
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"with open(filename, 'w') as fl:\n",
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" json.dump(dict2, fl, ensure_ascii=False)\n",
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"print('ok')\n",
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" "
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]
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},
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{
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"cell_type": "markdown",
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"id": "cc53ec68-cfc4-449a-9905-095a079dc8ab",
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"metadata": {},
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"source": [
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"### 筛选体测人员，计算得分"
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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": 152,
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"id": "224cdee4-153f-424a-aaaa-92ef58943edb",
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"metadata": {
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"execution": {
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||||
"iopub.execute_input": "2023-01-14T01:56:53.363160Z",
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||||
"iopub.status.busy": "2023-01-14T01:56:53.362570Z",
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||||
"iopub.status.idle": "2023-01-14T01:56:53.912576Z",
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||||
"shell.execute_reply": "2023-01-14T01:56:53.911169Z",
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||||
"shell.execute_reply.started": "2023-01-14T01:56:53.363112Z"
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||||
},
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"tags": []
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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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"ok\n"
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]
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}
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],
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"source": [
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"import json\n",
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"\n",
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"items = ['体重','肺活量','握力','坐位体前屈','纵跳','俯卧撑','一分钟仰卧起坐','单脚站立','选择反应时','台阶指数']\n",
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"\n",
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"filename = 'data/result_天津.json'\n",
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"with open(filename,'r') as fl:\n",
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" dict1 = json.load(fl)\n",
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"\n",
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"filename = 'data/天津石化人员231113.json'\n",
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"with open(filename,'r') as fl:\n",
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" dict2 = json.load(fl)\n",
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"dict3 = {}\n",
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"for k, v in dict2.items():\n",
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" if k in dict1.keys():\n",
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" dict3[k] = dict2[k]\n",
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" i = 0\n",
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" score =0\n",
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" for item in dict2[k].keys(): \n",
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" if item in items:\n",
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" score = score + int(dict2[k][item]['得分'])\n",
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" i+=1\n",
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" dict3[k]['score'] = score\n",
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" dict3[k]['item_num'] = i\n",
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" dict3[k]['avg'] = round(score/i,2)\n",
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"filename = 'data/天津石化人员231113_1.json'\n",
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"with open(filename, 'w') as fl:\n",
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" json.dump(dict3, fl, ensure_ascii=False)\n",
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"print('ok')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dd014e41-18f2-44e7-9f20-1190df4e7b45",
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"metadata": {},
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"source": [
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"### 因素分析"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c63bca31-f003-49e9-a20d-1931c050c740",
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"metadata": {},
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"source": [
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"#### 倒班因素分析"
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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": 168,
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"id": "2a964412-8408-4039-a788-8cc1fc7cd518",
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"metadata": {
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"execution": {
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||||
"iopub.execute_input": "2023-01-14T03:54:06.706899Z",
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||||
"iopub.status.busy": "2023-01-14T03:54:06.706358Z",
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||||
"iopub.status.idle": "2023-01-14T03:54:06.815555Z",
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||||
"shell.execute_reply": "2023-01-14T03:54:06.814512Z",
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||||
"shell.execute_reply.started": "2023-01-14T03:54:06.706851Z"
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||||
},
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||||
"tags": []
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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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"ok!\n"
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]
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}
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],
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"source": [
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"import json\n",
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"import openpyxl\n",
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"\n",
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"filename = 'data/天津石化人员231113_1.json'\n",
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"with open(filename,'r') as fl:\n",
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" dict1 = json.load(fl)\n",
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"unit = set()\n",
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"\n",
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"for k, v in dict1.items():\n",
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" unit.add(dict1[k]['unit'])\n",
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"#print(unit)\n",
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"dict2 = {}\n",
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"for k, v in dict1.items():\n",
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" dict2.setdefault(v['unit'],{})\n",
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" dict2[v['unit']].setdefault('是',{})\n",
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" dict2[v['unit']].setdefault('否',{})\n",
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" dict2[v['unit']]['是'].setdefault('num',0)\n",
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" dict2[v['unit']]['否'].setdefault('num',0)\n",
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" dict2[v['unit']]['是'].setdefault('score',0)\n",
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" dict2[v['unit']]['否'].setdefault('score',0)\n",
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" dict2[v['unit']][v['daoban']]['num'] = dict2[v['unit']][v['daoban']]['num'] + 1\n",
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" dict2[v['unit']][v['daoban']]['score'] = dict2[v['unit']][v['daoban']]['score'] + v['avg']\n",
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"for k, v in dict2.items():\n",
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" \n",
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" if v['是']['num'] > 0:\n",
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" dict2[k]['是']['avg'] = round(v['是']['score']/v['是']['num'],2)\n",
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" else:\n",
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" dict2[k]['是']['avg'] = 0\n",
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" if v['否']['num'] > 0:\n",
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" dict2[k]['否']['avg'] = round(v['否']['score']/v['否']['num'],2)\n",
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" else:\n",
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" dict2[k]['否']['avg'] = 0\n",
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"list1 = [] \n",
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"for k, v in dict2.items():\n",
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" list2 = []\n",
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" unit = k\n",
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" for k1, v1 in v.items():\n",
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" daoban = k1\n",
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" num = v1['num']\n",
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" avg = v1['avg']\n",
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" list2 = [k,daoban,num,avg]\n",
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" list1.append(list2)\n",
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" \n",
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"filename = 'data/天津倒班因素分析表.xlsx' \n",
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"wb = openpyxl.Workbook()\n",
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"sheet = wb.active\n",
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"\n",
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"for row in list1:\n",
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" sheet.append(row)\n",
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" \n",
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"wb.save(filename)\n",
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"print('ok!')"
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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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||||
"id": "a4c24e30-f4e0-46e9-a22e-5460a73a14c8",
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||||
"id": "4fdc1484-ea69-4779-9a75-c52784cc9c7c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
|
||||
Reference in new issue
Block a user