Annotations of Pandas DataFrame

escola.xlsx

FromDict.ipynb

gen_names_xlsx.ipynb

gen_random_values.ipynb

import_xlsx_to_django_database.ipynb

import_xlsx_to_django_database2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import timeit\n",
    "from django.contrib.auth.models import User"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_value = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tic = timeit.default_timer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = '/tmp/example.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NOME</th>\n",
       "      <th>EMAIL</th>\n",
       "      <th>CPF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alex Swartz Wertz</td>\n",
       "      <td>[email protected]</td>\n",
       "      <td>00000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Valerie Berry Mattlin</td>\n",
       "      <td>[email protected]</td>\n",
       "      <td>00000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brooke Coley Roy</td>\n",
       "      <td>[email protected]</td>\n",
       "      <td>00000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Joe Levin Collins</td>\n",
       "      <td>[email protected]</td>\n",
       "      <td>00000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lynn Alford Timmons</td>\n",
       "      <td>[email protected]</td>\n",
       "      <td>00000000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    NOME                            EMAIL             CPF\n",
       "0      Alex Swartz Wertz      [email protected]  00000000000000\n",
       "1  Valerie Berry Mattlin  [email protected]  00000000000000\n",
       "2       Brooke Coley Roy       [email protected]  00000000000000\n",
       "3      Joe Levin Collins      [email protected]  00000000000000\n",
       "4    Lynn Alford Timmons    [email protected]  00000000000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "email = df['EMAIL']\n",
    "# Retornando os itens duplicados\n",
    "# df[email.isin(email[email.duplicated()])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>NOME</th>\n",
       "      <th>EMAIL</th>\n",
       "      <th>CPF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49991</th>\n",
       "      <td>49995</td>\n",
       "      <td>Warren Stowell Young</td>\n",
       "      <td>[email protected]</td>\n",
       "      <td>00000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49992</th>\n",
       "      <td>49996</td>\n",
       "      <td>Patricia Cheek Mitchell</td>\n",
       "      <td>[email protected]</td>\n",
       "      <td>00000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49993</th>\n",
       "      <td>49997</td>\n",
       "      <td>Linda Denton Breton</td>\n",
       "      <td>[email protected]</td>\n",
       "      <td>00000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49994</th>\n",
       "      <td>49998</td>\n",
       "      <td>Clyde Lopes Leonard</td>\n",
       "      <td>[email protected]</td>\n",
       "      <td>00000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>49999</td>\n",
       "      <td>Josefina Wilkinson Graves</td>\n",
       "      <td>[email protected]</td>\n",
       "      <td>00000000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index                       NOME                                EMAIL  \\\n",
       "49991  49995       Warren Stowell Young       [email protected]   \n",
       "49992  49996    Patricia Cheek Mitchell    [email protected]   \n",
       "49993  49997        Linda Denton Breton        [email protected]   \n",
       "49994  49998        Clyde Lopes Leonard        [email protected]   \n",
       "49995  49999  Josefina Wilkinson Graves  [email protected]   \n",
       "\n",
       "                  CPF  \n",
       "49991  00000000000000  \n",
       "49992  00000000000000  \n",
       "49993  00000000000000  \n",
       "49994  00000000000000  \n",
       "49995  00000000000000  "
      ]
     },
     "execution_count": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49996"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dffinal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_user2(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",
    "        sha_password = 'kkkkkkkkkkkkkkkkkkkk'\n",
    "        user = User(\n",
    "            first_name=first_name,\n",
    "            last_name=last_name,\n",
    "            email=email,\n",
    "            username=username,\n",
    "            password=sha_password\n",
    "        )\n",
    "        users.append(user)\n",
    "    return users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def import_users2(new_users):\n",
    "    User.objects.bulk_create(new_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_users = []\n",
    "emails = []\n",
    "emails = dffinal['EMAIL'].values.tolist()\n",
    "exist_users = User.objects.filter(email__in=emails).values_list('email', flat=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for row in dffinal.head().itertuples():\n",
    "#     print(row.NOME, row.EMAIL, row.CPF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in dffinal.head(max_value).itertuples():\n",
    "    full_name = row.NOME\n",
    "    email = row.EMAIL\n",
    "    cpf = row.CPF\n",
    "    if email not in exist_users:\n",
    "        data = dict(full_name=full_name, email=email, cpf=cpf)\n",
    "        new_users.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_users_ = create_user2(new_users)\n",
    "import_users2(new_users_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 'existentes')\n",
      "(5, '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": [
       "2.6345059871673584"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toc - tic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_query_list = User.objects.filter(email__in=new_users_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# users_query_list[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simpleuser = Group.objects.get(name='simpleuser')\n",
    "simpleuser.id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_groups_list = []\n",
    "# https://stackoverflow.com/a/34093544\n",
    "for user in users_query_list:\n",
    "    user_groups = User.groups.through(user_id=user.pk, group_id=simpleuser.pk)\n",
    "    user_groups_list.append(user_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<User_groups: User_groups object>,\n",
       " <User_groups: User_groups object>,\n",
       " <User_groups: User_groups object>,\n",
       " <User_groups: User_groups object>,\n",
       " <User_groups: User_groups object>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "User.groups.through.objects.bulk_create(user_groups_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "toc = timeit.default_timer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3119399547576904"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toc - tic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UserCPF\n",
    "exist_usercpfs = UserCPF.objects.filter(user__email__in=emails).values_list('user__email', flat=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[email protected]\n",
      "[email protected]\n",
      "[email protected]\n",
      "[email protected]\n",
      "[email protected]\n"
     ]
    }
   ],
   "source": [
    "new_usercpfs = []\n",
    "for row in dffinal.head(max_value).itertuples():\n",
    "    email = row.EMAIL\n",
    "    cpf = row.CPF\n",
    "    # Verifica se UserCPF ja existe\n",
    "    if email not in exist_usercpfs:\n",
    "        print(email)\n",
    "        user = User.objects.get(email=email)\n",
    "        data = dict(user=user, cpf=cpf)\n",
    "        new_usercpfs.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# função create_usercpf(new_usercpfs)\n",
    "new_usercpfs2 = []\n",
    "for user in new_usercpfs:\n",
    "    new_usercpfs2.append(UserCPF(user=user['user'], cpf=user['cpf']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<UserCPF: Alex Swartz Wertz>,\n",
       " <UserCPF: Valerie Berry Mattlin>,\n",
       " <UserCPF: Brooke Coley Roy>,\n",
       " <UserCPF: Joe Levin Collins>,\n",
       " <UserCPF: Lynn Alford Timmons>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UserCPF.objects.bulk_create(new_usercpfs2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "toc = timeit.default_timer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.112852096557617"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toc - tic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Employee\n",
    "firm = Company.objects.get(name='Empresa')\n",
    "exist_employees = Employee.objects.filter(user__email__in=emails).values_list('user__email', flat=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Company: Empresa>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_employees = []\n",
    "for row in dffinal.head(max_value).itertuples():\n",
    "    email = row.EMAIL\n",
    "    if email not in exist_employees:\n",
    "        user = User.objects.get(email=email)\n",
    "        data = dict(user=user, firm=firm, department='14')\n",
    "        new_employees.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'department': '14', 'firm': <Company: Empresa>, 'user': <User: [email protected]>}\n",
      "{'department': '14', 'firm': <Company: Empresa>, 'user': <User: [email protected]>}\n",
      "{'department': '14', 'firm': <Company: Empresa>, 'user': <User: [email protected]>}\n",
      "{'department': '14', 'firm': <Company: Empresa>, 'user': <User: [email protected]>}\n",
      "{'department': '14', 'firm': <Company: Empresa>, 'user': <User: [email protected]>}\n"
     ]
    }
   ],
   "source": [
    "# função create_employee(new_employees)\n",
    "new_employees2 = []\n",
    "for user in new_employees:\n",
    "    print(user)\n",
    "    new_employees2.append(Employee(user=user['user'], firm=user['firm'], department=user['department']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Employee: Alex Swartz Wertz>,\n",
       " <Employee: Valerie Berry Mattlin>,\n",
       " <Employee: Brooke Coley Roy>,\n",
       " <Employee: Joe Levin Collins>,\n",
       " <Employee: Lynn Alford Timmons>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Employee.objects.bulk_create(new_employees2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Permissões\n",
    "def create_permissions():\n",
    "    # Criando permissão view_manage_career_pages\n",
    "    content_type = ContentType.objects.get_for_model(Company)\n",
    "    permission = Permission.objects.get(\n",
    "        content_type=content_type,\n",
    "        codename='view_manage_career_pages'\n",
    "    )\n",
    "    # Habilitando permissão para simpleuser\n",
    "    g = Group.objects.get(name='simpleuser')\n",
    "    g.permissions.add(permission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_permissions_quiz():\n",
    "    # Criando permissão add_quiz\n",
    "    content_type = ContentType.objects.get_for_model(Quiz)\n",
    "    permission = Permission.objects.get(\n",
    "        content_type=content_type,\n",
    "        codename='add_quiz'\n",
    "    )\n",
    "    # Habilitando permissão para simpleuser\n",
    "    g = Group.objects.get(name='simpleuser')\n",
    "    g.permissions.add(permission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_permissions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_permissions_quiz()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "toc = timeit.default_timer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.437417984008789"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toc - tic"
   ]
  }
 ],
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OrcamentosNumOrcNovo.ipynb

pandas.ipynb

remove_duplicated.ipynb

separe_email.ipynb

Código que substitui da célula [6] em diante do separe_email.ipynb:

emails_df = df.email_.str.split(";").apply(pd.Series).stack().reset_index(level=1, drop=True).to_frame('emails')

dffinal = df.drop("email_", axis=1).merge(emails_df, left_index=True, right_index=True)

Um detalhe é a falta de um índice único dos registros. Então, supondo o índice automático decorrente da importação do arquivo Excel como ID válido, esse ID é o usado no "join" (merge).

Para transformar Query do Django em DataFrame:
df = pd.DataFrame(list(User.objects.all().values()))

Pra mostrar somente algumas colunas:
df[['id', 'email']]

Transformando todos os campos do DataFrame num jSON
df.T.apply(dict).tolist()

Renomeando colunas
df.rename(columns={'old_name': 'new_name'})

Selecionando as colunas de 10 em 10
df.iloc[:, :10]
com transposição
df.T.iloc[:10]

Novo df com algumas colunas.
new = old.filter(['A','B','D'], axis=1)

Como não inserir chaves com valor nulo no dicionário:
df.T.apply(lambda x: dict(x.dropna())).tolist()

Retornando os valores cujo tamanho da string seja maior que...
df[df['foo'].str.len() > 50]['foo']

Mostrando o tamanho de cada célula
df[df['foo'].str.len() > 50]['foo'].str.len()

Mostrando o tamanho de cada célula da coluna

df['foo_len'] = df['foo'].apply(len)
df[['foo_len', 'foo']]

Ou

df['foo'].str.len()

Suponha que você tenha Cliente e Obra.

Pegando o ID do Cliente que está em Obra e trocando pelo nome do Cliente que está no outro DataFrame.

# JSON do Cliente com IDCliente e Cliente
dict_cliente = df_cliente[['IDCliente', 'Cliente']].T.apply(dict).tolist()

[
 {'IDCliente': 288, 'Cliente': 'Cliente Um'},
 {'IDCliente': 1, 'Cliente': 'Cliente Dois'},
 {'IDCliente': 959, 'Cliente': 'Cliente Três'},
]

Montando o dicionário que será usado como busca de cada Cliente a partir do seu ID.

_dict_cliente = {}
for item in dict_cliente:
    _dict_cliente[item['IDCliente']] = item['Cliente']

_dict_cliente

{
 288: 'Cliente Um',
 1: 'Cliente Dois',
 959: 'Cliente Três',
}

A partir desse dicionário fazemos a busca no outro DataFrame.

for row in df.itertuples():
    nome_cliente = _dict_cliente.get(row.IDCliente)
    print(row.IDCliente, nome_cliente)

288 Cliente Um
1   Cliente Dois
959 Cliente Três

Retornando o valor máximo agrupado por ano.

df.groupby(['Ano'])['NumeroOrcamento'].max()
df.reset_index()

Verificando data vazia:

for row in df.itertuples():
    if row.DataOrcamento is pd.NaT:
        print('Vazio')
    else:
        print(row.DataOrcamento)

Definindo vários fillna diferentes por coluna:

values = {'last_name': '', 'occupation': '', 'age': 0}
df = df.fillna(value=values)
df.head()

Se tiver problema com liblzma, faça um downgrade do Pandas para pandas==0.24.2.
https://stackoverflow.com/a/57115325

Retorna o tamanho do maior objeto de cada coluna.

dict_sizes = {}
for col in df.columns:
    try:
        print(f'{col} max length: {df[col].map(len).max()}\n')
        dict_sizes[col] = df[col].map(len).max()
    except Exception as e:
        raise e
dict_sizes

from_dict doc

dtype example
df['estoque'] =df['estoque'].fillna(0).astype(int)

Pandas Dataframe df to Django
https://www.laurivan.com/save-pandas-dataframe-as-django-model/

Produto.objects.bulk_create(
    Produto(**item) for item in df.to_dict('records')
)

Definindo os tipos das colunas com dtype

dict_types_annot = {
    'produto': str,
    'ncm': str,
    'preco': float,
    'estoque': 'Int64',
}

# Define os tipos das colunas
dff = df.astype(dict_types_annot, errors='ignore')

# Troca 'nan' por None e float por None.
dff = dff.replace({'nan': None, float('nan'): None})

dff.to_dict('records')

Produto.objects.bulk_create(
    Produto(**item) for item in dff.to_dict('records')
)

Intersecção de dataframes

import pandas as pd
import numpy as np
import datetime
from random import randint

df1 = pd.DataFrame({
    'letters': ['A', 'B', 'C', 'D', 'E', 'J', 'K', 'M'],
    'B': np.random.randint(0, 10, 8),
})
df1

df2 = pd.DataFrame({
    'letters': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'U', 'Z'],
    'B': np.random.randint(0, 10, 26),
})
df2

# Retorna o que tem de comum nos dois dataframes.
pd.merge(df1, df2, how='inner', on='fruits')

# Retorna o que tem de comum, considerando o df1.
pd.merge(df1, df2, how='left', on='fruits')

# Retorna o que tem de comum, considerando o df2.
pd.merge(df1, df2, how='right', on='fruits')