tutorials/023 - Flexible Partitions Filter.ipynb

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   "cell_type": "markdown",
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   "source": [
    "[![AWS SDK for pandas](_static/logo.png \"AWS SDK for pandas\")](https://github.com/aws/aws-sdk-pandas)\n",
    "\n",
    "# 23 - Flexible Partitions Filter (PUSH-DOWN)\n",
    "\n",
    "* `partition_filter` argument:\n",
    "\n",
    "        - Callback Function filters to apply on PARTITION columns (PUSH-DOWN filter).\n",
    "        - This function MUST receive a single argument (Dict[str, str]) where keys are partitions names and values are partitions values.\n",
    "        - This function MUST return a bool, True to read the partition or False to ignore it.\n",
    "        - Ignored if `dataset=False`.\n",
    "        \n",
    "\n",
    "*P.S. Check the [function API doc](https://aws-sdk-pandas.readthedocs.io/en/3.8.0/api.html) to see it has some argument that can be configured through Global configurations.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "stdout",
     "output_type": "stream",
     "text": [
      " ············\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "\n",
    "bucket = getpass.getpass()\n",
    "path = f\"s3://{bucket}/dataset/\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Dataset (Parquet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>boo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   id value\n",
       "0   3   bar\n",
       "1   2   boo\n",
       "2   1   foo"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"id\": [1, 2, 3],\n",
    "        \"value\": [\"foo\", \"boo\", \"bar\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "wr.s3.to_parquet(df=df, path=path, dataset=True, mode=\"overwrite\", partition_cols=[\"value\"])\n",
    "\n",
    "wr.s3.read_parquet(path, dataset=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parquet Example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>boo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   id value\n",
       "0   2   boo\n",
       "1   1   foo"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_filter(x):\n",
    "    return x[\"value\"].endswith(\"oo\")\n",
    "\n",
    "\n",
    "wr.s3.read_parquet(path, dataset=True, partition_filter=my_filter)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parquet Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>boo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   id value\n",
       "0   2   boo\n",
       "1   1   foo"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from Levenshtein import distance\n",
    "\n",
    "\n",
    "def my_filter(partitions):\n",
    "    return distance(\"boo\", partitions[\"value\"]) <= 1\n",
    "\n",
    "\n",
    "wr.s3.read_parquet(path, dataset=True, partition_filter=my_filter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Dataset (CSV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>bar</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>boo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   id value\n",
       "0   3   bar\n",
       "1   2   boo\n",
       "2   1   foo"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"id\": [1, 2, 3],\n",
    "        \"value\": [\"foo\", \"boo\", \"bar\"],\n",
    "    }\n",
    ")\n",
    "\n",
    "wr.s3.to_csv(\n",
    "    df=df, path=path, dataset=True, mode=\"overwrite\", partition_cols=[\"value\"], compression=\"gzip\", index=False\n",
    ")\n",
    "\n",
    "wr.s3.read_csv(path, dataset=True)"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "## CSV Example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>boo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   id value\n",
       "0   2   boo\n",
       "1   1   foo"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_filter(x):\n",
    "    return x[\"value\"].endswith(\"oo\")\n",
    "\n",
    "\n",
    "wr.s3.read_csv(path, dataset=True, partition_filter=my_filter)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CSV Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>boo</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   id value\n",
       "0   2   boo\n",
       "1   1   foo"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from Levenshtein import distance\n",
    "\n",
    "\n",
    "def my_filter(partitions):\n",
    "    return distance(\"boo\", partitions[\"value\"]) <= 1\n",
    "\n",
    "\n",
    "wr.s3.read_csv(path, dataset=True, partition_filter=my_filter)"
   ]
  }
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