Exploratory data analysis with PostgreSQL-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "framed-teaching",
   "metadata": {},
   "source": [
    "# Exploratory data analysis with PostgreSQL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "official-order",
   "metadata": {},
   "source": [
    "## Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fifth-musical",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "private-window",
   "metadata": {},
   "source": [
    "## Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "hispanic-framing",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading data from csv files into pandas dataframes\n",
    "moviesData=pd.read_csv('movie.csv')\n",
    "ratingsData=pd.read_csv('rating.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "intensive-turkish",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>Movie_id</th>\n",
       "      <th>Movie_Title</th>\n",
       "      <th>Rating</th>\n",
       "      <th>votes</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Gadhvi (He thought he was Gandhi)</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Yaaram</td>\n",
       "      <td>4.4</td>\n",
       "      <td>35</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Aur Pyaar Ho Gaya</td>\n",
       "      <td>4.7</td>\n",
       "      <td>827</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Yahaan</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1,086</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>A Question Mark</td>\n",
       "      <td>5.6</td>\n",
       "      <td>326</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  Movie_id                        Movie_Title  Rating  votes  \\\n",
       "0       1         1  Gadhvi (He thought he was Gandhi)     7.0      8   \n",
       "1       3         3                             Yaaram     4.4     35   \n",
       "2       5         5                  Aur Pyaar Ho Gaya     4.7    827   \n",
       "3       6         6                             Yahaan     7.4  1,086   \n",
       "4       8         8                    A Question Mark     5.6    326   \n",
       "\n",
       "   timestamp  \n",
       "0  964982703  \n",
       "1  964981247  \n",
       "2  964982224  \n",
       "3  964983815  \n",
       "4  964982931  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I have one column named Timestamp'' in my RatingData I've to rename it to something else before inserting data into SQL tables because the timestamp is a  data type in SQL\n",
    "ratingsData.columns = ['userId','Movie_id', 'Movie_Title','Rating' ,'votes','timestamp']\n",
    "ratingsData[:5]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "steady-chart",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>userId</th>\n",
       "      <th>Movie_id</th>\n",
       "      <th>Movie_Title</th>\n",
       "      <th>Released_Year</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Gadhvi (He thought he was Gandhi)</td>\n",
       "      <td>-2019</td>\n",
       "      <td>109 min</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Yaaram</td>\n",
       "      <td>-2019</td>\n",
       "      <td>110 min</td>\n",
       "      <td>Comedy, Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Aur Pyaar Ho Gaya</td>\n",
       "      <td>-1997</td>\n",
       "      <td>147 min</td>\n",
       "      <td>Comedy, Drama, Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Yahaan</td>\n",
       "      <td>-2005</td>\n",
       "      <td>142 min</td>\n",
       "      <td>Drama, Romance, War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>A Question Mark</td>\n",
       "      <td>-2012</td>\n",
       "      <td>82 min</td>\n",
       "      <td>Horror, Mystery, Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  Movie_id                        Movie_Title  Released_Year  \\\n",
       "0       1         1  Gadhvi (He thought he was Gandhi)          -2019   \n",
       "1       3         3                             Yaaram          -2019   \n",
       "2       5         5                  Aur Pyaar Ho Gaya          -1997   \n",
       "3       6         6                             Yahaan          -2005   \n",
       "4       8         8                    A Question Mark          -2012   \n",
       "\n",
       "  Duration                      Genre  \n",
       "0  109 min                      Drama  \n",
       "1  110 min            Comedy, Romance  \n",
       "2  147 min     Comedy, Drama, Musical  \n",
       "3  142 min        Drama, Romance, War  \n",
       "4   82 min  Horror, Mystery, Thriller  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "moviesData[:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c3896a17",
   "metadata": {},
   "outputs": [],
   "source": [
    "import psycopg2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "sunset-winter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully connected to database.\n"
     ]
    }
   ],
   "source": [
    "#importing PostgreSQLconnection class from my PostgreSQL_Database_wrapper file and creating an instance of it\n",
    "from PostgreSQL_Database_wrapper import PostgreSQLconnection\n",
    "db=PostgreSQLconnection(\"DRecommenderSystem_db\",\"postgres\",\"YASH\",\"localhost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "incorrect-measurement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table created successfully with name Movies\n",
      "Table created successfully with name Ratings\n"
     ]
    }
   ],
   "source": [
    "#sql statement to create table\n",
    "sqlstatement='''CREATE TABLE Movies(\n",
    "              userId INT,\n",
    "              Movie_id INT,\n",
    "              Movie_Title text,\n",
    "              Released_Year INT,\n",
    "              Duration VARCHAR,\n",
    "              Genre text,\n",
    "              PRIMARY KEY(Movie_id)\n",
    "              )'''\n",
    "              \n",
    "db.create_table(sqlstatement, ' Movies')\n",
    "#sql statement to create table\n",
    "sqlstatement='''CREATE TABLE Rating(\n",
    "              userId INT,\n",
    "              Movie_id INT,\n",
    "              Movie_Title VARCHAR,\n",
    "              Rating VARCHAR,\t\n",
    "              votes VARCHAR,\n",
    "\n",
    "              PRIMARY KEY(Movie_id,userId),\n",
    "              FOREIGN KEY(Movie_id) \n",
    "                  REFERENCES Movies(Movie_id)\n",
    "              )'''\n",
    "db.create_table(sqlstatement, ' Ratings')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adapted-impact",
   "metadata": {},
   "source": [
    "## Loading Data into tables from dataFrames\n",
    "After creating my tables now I've to load data from data frames into tables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "baking-pledge",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "successfully inserted data from DataFrame into table\n",
      "successfully inserted data from DataFrame into table\n"
     ]
    }
   ],
   "source": [
    "\n",
    "cursor = db.cursor\n",
    "for row in moviesData.itertuples():\n",
    "    cursor.execute('''\n",
    "                        INSERT INTO Movies(userId,Movie_id,Movie_Title,Released_Year,Duration,Genre)\n",
    "                        VALUES (?,?,?,?,?,?)\n",
    "                        ''',\n",
    "                        row.userId,\n",
    "                        row.Movie_id,\n",
    "                        row.Movie_Title,\n",
    "                        row.Released_Year,\n",
    "                        row.Duration,\n",
    "                        row.Genre\n",
    "  \n",
    "                  )\n",
    "db.db_connection.commit()\n",
    "print(\"successfully inserted data from DataFrame into table\")\n",
    "\n",
    "for row in ratingsData.itertuples():\n",
    "    cursor.execute('''\n",
    "                        INSERT INTO Rating(userId,Movie_id,Movie_Title,Rating,votes)\n",
    "                        VALUES (?,?,?,?,?)\n",
    "                        ''',\n",
    "                        row.userId,\n",
    "                        row.Movie_id,\n",
    "                        row.Movie_Title,\n",
    "                        row.Rating,\n",
    "                        row.votes                        \n",
    "\n",
    "                  )\n",
    "db.db_connection.commit()\n",
    "print(\"successfully inserted data from DataFrame into table\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4ba57d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cursor = db.cursor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "helpful-thomas",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "successfull execution of command\n"
     ]
    }
   ],
   "source": [
    "#taking Join of tables(Rating and movies) then creating View(MoviesWithRatings) of result\n",
    "command ='''\n",
    "CREATE VIEW MoviesWithRatings AS\n",
    "    SELECT Rating.userId,Rating.Movie_id,Rating.Movie_Title,Rating.rating,votes\n",
    "    FROM Rating\n",
    "    LEFT JOIN Movies\n",
    "    ON Rating.Movie_id = Movies.Movie_id;\n",
    "'''\n",
    "db.run_command(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "popular-toilet",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "    }\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>movie_title</th>\n",
       "      <th>movies_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Satyam Shivam Sundaram: Love Sublime</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hera Pheri</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kurukshetra</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Waiting Room</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nirvana13</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Login</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jeena Isi Ka Naam Hai</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mazdoor</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Aatank</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dishkiyaoon</td>\n",
       "      <td>986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kadvi Hawa</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ittefaq</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Naam</td>\n",
       "      <td>984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100 Days</td>\n",
       "      <td>983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bumboo</td>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Blood Money</td>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Naram Garam</td>\n",
       "      <td>980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Dam999</td>\n",
       "      <td>980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sonali Cable</td>\n",
       "      <td>980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>London</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             movie_title movies_count\n",
       "0   Satyam Shivam Sundaram: Love Sublime          999\n",
       "1                             Hera Pheri          998\n",
       "2                            Kurukshetra          996\n",
       "3                       The Waiting Room           99\n",
       "4                              Nirvana13           99\n",
       "5                                  Login           99\n",
       "6                  Jeena Isi Ka Naam Hai           99\n",
       "7                                Mazdoor           99\n",
       "8                                 Aatank           99\n",
       "9                            Dishkiyaoon          986\n",
       "10                            Kadvi Hawa          985\n",
       "11                               Ittefaq          985\n",
       "12                                  Naam          984\n",
       "13                              100 Days          983\n",
       "14                                Bumboo          982\n",
       "15                           Blood Money          981\n",
       "16                           Naram Garam          980\n",
       "17                                Dam999          980\n",
       "18                          Sonali Cable          980\n",
       "19                                London           98"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#executing query over view \n",
    "query = '''\n",
    "SELECT Movie_Title,votes AS movies_count\n",
    "FROM MoviesWithRatings\n",
    "GROUP BY Movie_Title,votes\n",
    "ORDER BY movies_count DESC\n",
    "LIMIT 20;\n",
    "'''\n",
    "query_result=db.run_query(query)\n",
    "query_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fiscal-humidity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "barh = plt.barh(query_result['movie_title'], query_result['movies_count'], color='blue')\n",
    "plt.title(' top ten most rated movies ')\n",
    "plt.xticks(np.arange(0, 400, 50))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "guided-commissioner",
   "metadata": {},
   "outputs": [],
   "source": [
    "genres_unique = pd.DataFrame(moviesData.Genre.str.split('|').tolist()).stack().unique()\n",
    "genres_list=genres_unique[:len(genres_unique)-1]\n",
    "genres_count=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "social-scale",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n",
      "E:\\SOFTWARES\\IMPORTANT\\PYTHON\\lib\\site-packages\\pandas\\io\\sql.py:761: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "for genre in genres_list:\n",
    "    query = '''\n",
    "    SELECT count(Movies.Movie_id) \n",
    "    FROM  Movies\n",
    "    where Movies.Genre LIKE '%'''+genre+'''%'\n",
    "    '''\n",
    "    query_result=db.run_query(query)\n",
    "    genres_count.append(query_result.iloc[0]['count'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "handy-strip",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.DataFrame({'x': genres_list, 'y': genres_count})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bearing-vitamin",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3600x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=pd.DataFrame({'x': genres_list, 'y': genres_count})\n",
    "plt.subplots(figsize=(50,8))\n",
    "sns.barplot(x=data.x, y=data.y, palette=\"rocket\")\n",
    "plt.xticks(rotation=-45)\n",
    "plt.title('Most popular genres among directors');"
   ]
  }
 ],
 "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": "3.10.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}