103 lines
2.1 KiB
Plaintext
103 lines
2.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"from IPython.display import Markdown as md\n",
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"from IPython.display import DisplayHandle\n",
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"import pandas as pd\n",
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"from pathlib import Path\n",
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"from datetime import datetime"
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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": 8,
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"metadata": {
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"tags": [
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"parameters"
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]
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},
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"outputs": [],
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"source": [
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"tribe = \"308\"\n",
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"assessment = \"161114_dm2\"\n",
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"csv_file = Path(f\"./sheets/{tribe}/{assessment}.csv\")"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"split_ass = assessment.split(\"_\")\n",
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"if len(split_ass) > 1:\n",
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" date, *assessment = assessment.split(\"_\")\n",
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" date = datetime.strptime(date, \"%y%m%d\")\n",
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" assessment = ' '.join(assessment)\n",
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"else:\n",
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" date = None\n",
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" assessment = split_ass[0]"
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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": 16,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"# dm2 (14/11/2016) pour 308"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"if date is None:\n",
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" display(md(f\"# {assessment} pour {tribe}\"))\n",
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"else:\n",
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" display(md(f\"# {assessment} ({date:%d/%m/%Y}) pour {tribe}\"))"
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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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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"celltoolbar": "Tags",
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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": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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