Annotations of Pandas DataFrame

escola.xlsx

gen_names_xlsx.ipynb

gen_random_values.ipynb

import_xlsx_to_django_database.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import timeit\n",
    "import xlrd\n",
    "from django.contrib.auth.models import User"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tic = timeit.default_timer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = '/tmp/example.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>NAME</th>\n",
       "      <th>EMAIL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>Angela Brown</td>\n",
       "      <td>[email protected]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2824</th>\n",
       "      <td>Angela Brown</td>\n",
       "      <td>[email protected]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3599</th>\n",
       "      <td>Angela Jones</td>\n",
       "      <td>[email protected]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>Angela Jones</td>\n",
       "      <td>[email protected]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2605</th>\n",
       "      <td>Anthony Evans</td>\n",
       "      <td>[email protected]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               NAME                    EMAIL\n",
       "2011   Angela Brown   [email protected]\n",
       "2824   Angela Brown   [email protected]\n",
       "3599   Angela Jones   [email protected]\n",
       "1227   Angela Jones   [email protected]\n",
       "2605  Anthony Evans  [email protected]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "email = df['EMAIL']\n",
    "# Retornando os itens duplicados\n",
    "df[email.isin(email[email.duplicated()])].sort_values(by=['EMAIL']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>index</th>\n",
       "      <th>NAME</th>\n",
       "      <th>EMAIL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4957</th>\n",
       "      <td>4995</td>\n",
       "      <td>Helen Mcallister</td>\n",
       "      <td>[email protected]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4958</th>\n",
       "      <td>4996</td>\n",
       "      <td>Scott Hall</td>\n",
       "      <td>[email protected]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4959</th>\n",
       "      <td>4997</td>\n",
       "      <td>Dawn Dowling</td>\n",
       "      <td>[email protected]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4960</th>\n",
       "      <td>4998</td>\n",
       "      <td>John Campbell</td>\n",
       "      <td>[email protected]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4961</th>\n",
       "      <td>4999</td>\n",
       "      <td>Barbara Alldredge</td>\n",
       "      <td>[email protected]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index               NAME                        EMAIL\n",
       "4957   4995   Helen Mcallister   [email protected]\n",
       "4958   4996         Scott Hall         [email protected]\n",
       "4959   4997       Dawn Dowling       [email protected]\n",
       "4960   4998      John Campbell      [email protected]\n",
       "4961   4999  Barbara Alldredge  [email protected]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removendo os itens duplicados\n",
    "dffinal = df.drop_duplicates('EMAIL').reset_index()\n",
    "dffinal.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_user(new_users):\n",
    "    # Separa full_name em first_name e last_name\n",
    "    users = []\n",
    "    for new_user in new_users:\n",
    "        full_name = new_user['full_name']\n",
    "        email = new_user['email']\n",
    "        first_name = full_name.split()[0]\n",
    "        last_name = full_name.split()[1:]\n",
    "        last_name = ' '.join(last_name)\n",
    "        username = email\n",
    "        user = User(\n",
    "            first_name=first_name,\n",
    "            last_name=last_name,\n",
    "            email=email,\n",
    "            username=username\n",
    "        )\n",
    "        users.append(user)\n",
    "    return users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def import_users(new_users):\n",
    "    User.objects.bulk_create(new_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_users = []\n",
    "emails = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "emails = dffinal['EMAIL'].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "exist_users = User.objects.filter(email__in=emails).values_list('email', flat=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manuel Marshall [email protected]\n",
      "Joy Flores [email protected]\n",
      "Nicholas Waits [email protected]\n",
      "Laura Cross [email protected]\n",
      "Felipe Garland [email protected]\n"
     ]
    }
   ],
   "source": [
    "for row in dffinal.head().itertuples():\n",
    "    print(row.NAME, row.EMAIL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in dffinal.itertuples():\n",
    "    full_name = row.NAME\n",
    "    email = row.EMAIL\n",
    "    if email not in exist_users:\n",
    "        data = dict(full_name=full_name, email=email)\n",
    "        new_users.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_users_ = create_user(new_users)\n",
    "import_users(new_users_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 existentes\n",
      "4945 novos\n"
     ]
    }
   ],
   "source": [
    "print(len(exist_users), 'existentes')\n",
    "print(len(new_users), 'novos')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "toc = timeit.default_timer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1476561470044544"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toc - tic"
   ]
  }
 ],
 "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
}

import_xlsx_to_django_database2.ipynb

pandas.ipynb

remove_duplicated.ipynb

separe_email.ipynb