Annotations of Pandas DataFrame

escola.xlsx

gen_names_xlsx.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import names\n",
    "import xlwt\n",
    "from django.utils.text import slugify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_persons(max_value):\n",
    "    persons = []\n",
    "    for _ in range(max_value):\n",
    "        full_name = names.get_full_name()\n",
    "        email = '{}@email.com'.format(slugify(full_name))\n",
    "        data = (full_name, email)\n",
    "        persons.append(data)\n",
    "    return persons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "names_ = gen_persons(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Jeremy Strange', '[email protected]'),\n",
       " ('Sherry Hampton', '[email protected]'),\n",
       " ('Daniel Litton', '[email protected]'),\n",
       " ('Kimberly Horne', '[email protected]'),\n",
       " ('Erick Cook', '[email protected]')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_xlsx(names_list):\n",
    "    # response = HttpResponse(content_type='application/ms-excel')\n",
    "    date_ = timezone.now().strftime('%y%m%d%H%M%S')\n",
    "    filename = \"/tmp/example.xlsx\"\n",
    "    # response['Content-Disposition'] = 'attachment; filename=%s' % filename\n",
    "    response = filename\n",
    "    workbook = xlwt.Workbook(encoding='utf-8')\n",
    "    sheet = workbook.add_sheet('Persons')\n",
    "    # Sheet header, first row\n",
    "    row_num = 0\n",
    "    font_style = xlwt.XFStyle()\n",
    "    font_style.font.bold = True\n",
    "    columns = ('NAME', 'EMAIL')\n",
    "    for col_num in range(len(columns)):\n",
    "        sheet.write(row_num, col_num, columns[col_num], font_style)\n",
    "\n",
    "    # Sheet body, remaining rows\n",
    "    font_style = xlwt.XFStyle()\n",
    "    rows = names_list\n",
    "    for row in rows:\n",
    "        row_num += 1\n",
    "        for col_num in range(len(row)):\n",
    "            sheet.write(row_num, col_num, row[col_num], font_style)\n",
    "\n",
    "    workbook.save(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "names_ = gen_persons(100)\n",
    "save_xlsx(names_)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Django Shell-Plus",
   "language": "python",
   "name": "django_extensions"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

gen_random_values.ipynb

import_xlsx_to_django_database.ipynb

import_xlsx_to_django_database2.ipynb

pandas.ipynb

remove_duplicated.ipynb

separe_email.ipynb