papermill_automated.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "209839c89ed14f9bbd0d44730bc9adc8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=7), HTML(value=u'')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{u'cells': [{u'cell_type': u'markdown',\n",
       "   u'metadata': {u'colab_type': u'text',\n",
       "    u'id': u'view-in-github',\n",
       "    u'papermill': {'duration': 0.021374,\n",
       "     'end_time': '2019-08-08T03:39:41.198469',\n",
       "     'exception': False,\n",
       "     'start_time': '2019-08-08T03:39:41.177095',\n",
       "     'status': 'completed'},\n",
       "    u'tags': []},\n",
       "   u'source': u'<a href=\"https://colab.research.google.com/github/SaashaJoshi/automated-report-generation/blob/master/January_sales_localxlsx.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>'},\n",
       "  {u'cell_type': u'code',\n",
       "   u'execution_count': 1,\n",
       "   u'metadata': {u'colab': {u'base_uri': u'https://localhost:8080/',\n",
       "     u'height': 74,\n",
       "     u'resources': {u'http://localhost:8080/nbextensions/google.colab/files.js': {u'data': u'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',\n",
       "       u'headers': [[u'content-type', u'application/javascript']],\n",
       "       u'ok': True,\n",
       "       u'status': 200,\n",
       "       u'status_text': u''}}},\n",
       "    u'colab_type': u'code',\n",
       "    u'id': u'm8vJn6xwaeyz',\n",
       "    u'outputId': u'54110618-9cbc-4415-d916-e2fa16f46798',\n",
       "    u'papermill': {'duration': 7.602201,\n",
       "     'end_time': '2019-08-08T03:39:48.814852',\n",
       "     'exception': False,\n",
       "     'start_time': '2019-08-08T03:39:41.212651',\n",
       "     'status': 'completed'},\n",
       "    u'tags': []},\n",
       "   u'outputs': [{'data': {u'text/plain': u\"'\\\\nUpload from Google Colab\\\\nfrom google.colab import files\\\\nuploaded = files.upload()\\\\n'\"},\n",
       "     'execution_count': 1,\n",
       "     'metadata': {},\n",
       "     'output_type': u'execute_result'}],\n",
       "   u'source': u\"import pandas as pd\\nimport matplotlib.pyplot as plt\\n%matplotlib inline\\n\\n'''\\nUpload from Google Colab\\nfrom google.colab import files\\nuploaded = files.upload()\\n'''\"},\n",
       "  {u'cell_type': u'code',\n",
       "   u'execution_count': 2,\n",
       "   u'metadata': {u'papermill': {'duration': 2.468707,\n",
       "     'end_time': '2019-08-08T03:39:51.313468',\n",
       "     'exception': False,\n",
       "     'start_time': '2019-08-08T03:39:48.844761',\n",
       "     'status': 'completed'},\n",
       "    u'tags': []},\n",
       "   u'outputs': [{'name': u'stdout',\n",
       "     'output_type': u'stream',\n",
       "     'text': u'Collecting xlrd\\r\\n'},\n",
       "    {'name': u'stdout',\n",
       "     'output_type': u'stream',\n",
       "     'text': u'  Using cached https://files.pythonhosted.org/packages/b0/16/63576a1a001752e34bf8ea62e367997530dc553b689356b9879339cf45a4/xlrd-1.2.0-py2.py3-none-any.whl\\r\\n'},\n",
       "    {'name': u'stdout',\n",
       "     'output_type': u'stream',\n",
       "     'text': u'Installing collected packages: xlrd\\r\\n'},\n",
       "    {'name': u'stdout',\n",
       "     'output_type': u'stream',\n",
       "     'text': u'Successfully installed xlrd-1.2.0\\r\\n'}],\n",
       "   u'source': u'!pip install xlrd'},\n",
       "  {u'cell_type': u'code',\n",
       "   u'execution_count': 3,\n",
       "   u'metadata': {u'colab': {u'base_uri': u'https://localhost:8080/',\n",
       "     u'height': 328},\n",
       "    u'colab_type': u'code',\n",
       "    u'id': u'xKF6YJQ8l873',\n",
       "    u'outputId': u'8014119e-5436-4dd1-e64a-a1f6eff33885',\n",
       "    u'papermill': {'duration': 0.050141,\n",
       "     'end_time': '2019-08-08T03:39:51.412175',\n",
       "     'exception': False,\n",
       "     'start_time': '2019-08-08T03:39:51.362034',\n",
       "     'status': 'completed'},\n",
       "    u'tags': [u'parameters']},\n",
       "   u'outputs': [],\n",
       "   u'source': u\"filename='Data/sales_january.xlsx'\"},\n",
       "  {'cell_type': 'code',\n",
       "   'execution_count': 4,\n",
       "   'metadata': {'papermill': {'duration': 0.842162,\n",
       "     'end_time': '2019-08-08T03:39:52.266040',\n",
       "     'exception': False,\n",
       "     'start_time': '2019-08-08T03:39:51.423878',\n",
       "     'status': 'completed'},\n",
       "    'tags': ['injected-parameters']},\n",
       "   'outputs': [],\n",
       "   'source': '# Parameters\\nfilename = \"Data/sales_february.xlsx\"\\n'},\n",
       "  {u'cell_type': u'code',\n",
       "   u'execution_count': 5,\n",
       "   u'metadata': {u'papermill': {'duration': 1.800062,\n",
       "     'end_time': '2019-08-08T03:39:54.108803',\n",
       "     'exception': False,\n",
       "     'start_time': '2019-08-08T03:39:52.308741',\n",
       "     'status': 'completed'},\n",
       "    u'tags': []},\n",
       "   u'outputs': [{'data': {u'text/html': u'<div>\\n<style scoped>\\n    .dataframe tbody tr th:only-of-type {\\n        vertical-align: middle;\\n    }\\n\\n    .dataframe tbody tr th {\\n        vertical-align: top;\\n    }\\n\\n    .dataframe thead th {\\n        text-align: right;\\n    }\\n</style>\\n<table border=\"1\" class=\"dataframe\">\\n  <thead>\\n    <tr style=\"text-align: right;\">\\n      <th></th>\\n      <th>Sales</th>\\n    </tr>\\n    <tr>\\n      <th>Employee Name</th>\\n      <th></th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>Paul</th>\\n      <td>250</td>\\n    </tr>\\n    <tr>\\n      <th>Oscar</th>\\n      <td>4000</td>\\n    </tr>\\n    <tr>\\n      <th>Richard</th>\\n      <td>3250</td>\\n    </tr>\\n    <tr>\\n      <th>Thomas</th>\\n      <td>100</td>\\n    </tr>\\n    <tr>\\n      <th>Ursula</th>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>Gabriela</th>\\n      <td>260</td>\\n    </tr>\\n    <tr>\\n      <th>Allison</th>\\n      <td>890</td>\\n    </tr>\\n    <tr>\\n      <th>Louis</th>\\n      <td>990</td>\\n    </tr>\\n  </tbody>\\n</table>\\n</div>',\n",
       "      u'text/plain': u'               Sales \\nEmployee Name        \\nPaul              250\\nOscar            4000\\nRichard          3250\\nThomas            100\\nUrsula              0\\nGabriela          260\\nAllison           890\\nLouis             990'},\n",
       "     'execution_count': 5,\n",
       "     'metadata': {},\n",
       "     'output_type': u'execute_result'}],\n",
       "   u'source': u'data=pd.read_excel(filename, index_col=0)\\ndata'},\n",
       "  {u'cell_type': u'code',\n",
       "   u'execution_count': 6,\n",
       "   u'metadata': {u'colab': {u'base_uri': u'https://localhost:8080/',\n",
       "     u'height': 345},\n",
       "    u'colab_type': u'code',\n",
       "    u'id': u'XLZdePjYmhH_',\n",
       "    u'outputId': u'dcfced7f-79df-4df1-9494-e63eeda221cd',\n",
       "    u'papermill': {'duration': 0.609641,\n",
       "     'end_time': '2019-08-08T03:39:54.734603',\n",
       "     'exception': False,\n",
       "     'start_time': '2019-08-08T03:39:54.124962',\n",
       "     'status': 'completed'},\n",
       "    u'tags': []},\n",
       "   u'outputs': [{'data': {u'text/plain': u'<matplotlib.axes._subplots.AxesSubplot at 0x7fb504594cd0>'},\n",
       "     'execution_count': 6,\n",
       "     'metadata': {},\n",
       "     'output_type': u'execute_result'},\n",
       "    {'data': {u'image/png': u'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\\n',\n",
       "      u'text/plain': u'<Figure size 432x288 with 1 Axes>'},\n",
       "     'metadata': {u'needs_background': u'light'},\n",
       "     'output_type': u'display_data'}],\n",
       "   u'source': u\"data.plot(kind='bar', title='Sales report from {}'.format(filename))\"}],\n",
       " u'metadata': {u'accelerator': u'GPU',\n",
       "  u'celltoolbar': u'Tags',\n",
       "  u'colab': {u'include_colab_link': True,\n",
       "   u'name': u'January_sales_localxlsx.ipynb',\n",
       "   u'provenance': [],\n",
       "   u'version': u'0.3.2'},\n",
       "  u'kernelspec': {u'display_name': u'Python 2',\n",
       "   u'language': u'python',\n",
       "   u'name': u'python2'},\n",
       "  u'language_info': {u'codemirror_mode': {u'name': u'ipython', u'version': 2},\n",
       "   u'file_extension': u'.py',\n",
       "   u'mimetype': u'text/x-python',\n",
       "   u'name': u'python',\n",
       "   u'nbconvert_exporter': u'python',\n",
       "   u'pygments_lexer': u'ipython2',\n",
       "   u'version': u'2.7.15rc1'},\n",
       "  u'papermill': {'duration': 14.758275,\n",
       "   'end_time': '2019-08-08T03:39:55.293632',\n",
       "   u'environment_variables': {},\n",
       "   'exception': None,\n",
       "   u'input_path': 'sales_template.ipynb',\n",
       "   u'output_path': 'Outputs/February_sales.ipynb',\n",
       "   u'parameters': {'filename': 'Data/sales_february.xlsx'},\n",
       "   'start_time': '2019-08-08T03:39:40.535357',\n",
       "   u'version': '1.0.1'}},\n",
       " u'nbformat': 4,\n",
       " u'nbformat_minor': 1}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import papermill as pm\n",
    "pm.execute_notebook('sales_template.ipynb', 'Outputs/February_sales.ipynb', \n",
    "                   parameters=dict(filename='Data/sales_february.xlsx'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}