tutorials/012 - CSV Crawler.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![AWS SDK for pandas](_static/logo.png \"AWS SDK for pandas\")](https://github.com/aws/aws-sdk-pandas)\n",
    "\n",
    "# 12 - CSV Crawler\n",
    "\n",
    "[awswrangler](https://github.com/aws/aws-sdk-pandas) can extract only the metadata from a Pandas DataFrame and then add it can be added to Glue Catalog as a table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import awswrangler as wr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enter your bucket name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " ············\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "\n",
    "bucket = getpass.getpass()\n",
    "path = f\"s3://{bucket}/csv_crawler/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>string</th>\n",
       "      <th>float</th>\n",
       "      <th>date</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>bool</th>\n",
       "      <th>par0</th>\n",
       "      <th>par1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaT</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>boo</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>2020-01-02 00:00:01</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id string  float        date           timestamp   bool  par0 par1\n",
       "0   1    foo    1.0  2020-01-01 2020-01-01 00:00:00   True     1    a\n",
       "1   2   None    NaN        None                 NaT   None     1    b\n",
       "2   3    boo    2.0  2020-01-02 2020-01-02 00:00:01  False     2    b"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = lambda x: datetime.strptime(x, \"%Y-%m-%d %H:%M:%S.%f\")  # noqa\n",
    "dt = lambda x: datetime.strptime(x, \"%Y-%m-%d\").date()  # noqa\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"id\": [1, 2, 3],\n",
    "        \"string\": [\"foo\", None, \"boo\"],\n",
    "        \"float\": [1.0, None, 2.0],\n",
    "        \"date\": [dt(\"2020-01-01\"), None, dt(\"2020-01-02\")],\n",
    "        \"timestamp\": [ts(\"2020-01-01 00:00:00.0\"), None, ts(\"2020-01-02 00:00:01.0\")],\n",
    "        \"bool\": [True, None, False],\n",
    "        \"par0\": [1, 1, 2],\n",
    "        \"par1\": [\"a\", \"b\", \"b\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting the metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_types, partitions_types = wr.catalog.extract_athena_types(\n",
    "    df=df, file_format=\"csv\", index=False, partition_cols=[\"par0\", \"par1\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'bigint',\n",
       " 'string': 'string',\n",
       " 'float': 'double',\n",
       " 'date': 'date',\n",
       " 'timestamp': 'timestamp',\n",
       " 'bool': 'boolean'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'par0': 'bigint', 'par1': 'string'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partitions_types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "wr.catalog.create_csv_table(\n",
    "    table=\"csv_crawler\",\n",
    "    database=\"awswrangler_test\",\n",
    "    path=path,\n",
    "    partitions_types=partitions_types,\n",
    "    columns_types=columns_types,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column Name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Partition</th>\n",
       "      <th>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id</td>\n",
       "      <td>bigint</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>string</td>\n",
       "      <td>string</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>float</td>\n",
       "      <td>double</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>date</td>\n",
       "      <td>date</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>timestamp</td>\n",
       "      <td>timestamp</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bool</td>\n",
       "      <td>boolean</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>par0</td>\n",
       "      <td>bigint</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>par1</td>\n",
       "      <td>string</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Column Name       Type  Partition Comment\n",
       "0          id     bigint      False        \n",
       "1      string     string      False        \n",
       "2       float     double      False        \n",
       "3        date       date      False        \n",
       "4   timestamp  timestamp      False        \n",
       "5        bool    boolean      False        \n",
       "6        par0     bigint       True        \n",
       "7        par1     string       True        "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wr.catalog.table(database=\"awswrangler_test\", table=\"csv_crawler\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can still using the extracted metadata to ensure all data types consistence to new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>string</th>\n",
       "      <th>float</th>\n",
       "      <th>date</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>bool</th>\n",
       "      <th>par0</th>\n",
       "      <th>par1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id string  float       date   timestamp  bool  par0 par1\n",
       "0   1      1      1 2020-01-01  2020-01-02     1     1    a"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"id\": [1],\n",
    "        \"string\": [\"1\"],\n",
    "        \"float\": [1],\n",
    "        \"date\": [ts(\"2020-01-01 00:00:00.0\")],\n",
    "        \"timestamp\": [dt(\"2020-01-02\")],\n",
    "        \"bool\": [1],\n",
    "        \"par0\": [1],\n",
    "        \"par1\": [\"a\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = wr.s3.to_csv(\n",
    "    df=df,\n",
    "    path=path,\n",
    "    index=False,\n",
    "    dataset=True,\n",
    "    database=\"awswrangler_test\",\n",
    "    table=\"csv_crawler\",\n",
    "    partition_cols=[\"par0\", \"par1\"],\n",
    "    dtype=columns_types,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## You can also extract the metadata directly from the Catalog if you want"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = wr.catalog.get_table_types(database=\"awswrangler_test\", table=\"csv_crawler\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = wr.s3.to_csv(\n",
    "    df=df,\n",
    "    path=path,\n",
    "    index=False,\n",
    "    dataset=True,\n",
    "    database=\"awswrangler_test\",\n",
    "    table=\"csv_crawler\",\n",
    "    partition_cols=[\"par0\", \"par1\"],\n",
    "    dtype=dtype,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>string</th>\n",
       "      <th>float</th>\n",
       "      <th>date</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>bool</th>\n",
       "      <th>par0</th>\n",
       "      <th>par1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id string  float  date  timestamp  bool  par0 par1\n",
       "0   1      1    1.0  None 2020-01-02  True     1    a\n",
       "1   1      1    1.0  None 2020-01-02  True     1    a"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = wr.athena.read_sql_table(database=\"awswrangler_test\", table=\"csv_crawler\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                    Int64\n",
       "string               string\n",
       "float               float64\n",
       "date                 object\n",
       "timestamp    datetime64[ns]\n",
       "bool                boolean\n",
       "par0                  Int64\n",
       "par1                 string\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning Up S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "wr.s3.delete_objects(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning Up the Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wr.catalog.delete_table_if_exists(database=\"awswrangler_test\", table=\"csv_crawler\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.14",
   "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": "3.9.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}