python-sdk/tutorials/dataset-uploads/upload_dataframe_register_as_dataset.ipynb

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   "source": [
    "# Check core SDK version number\n",
    "import azureml.core\n",
    "\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
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   "source": [
    "#### Connect to workspace via config.json"
   ]
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    "from azureml.core import Workspace\n",
    "\n",
    "workspace = Workspace.from_config()\n",
    "print(workspace)"
   ]
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   "cell_type": "markdown",
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   "source": [
    "Currently, following types of datastores are supported: \n",
    "* Azure Blob Container\n",
    "* Azure File Share\n",
    "* Azure Data Lake\n",
    "* Azure Data Lake Gen2\n",
    "\n",
    "\n",
    "To learn more about datatstores, refer to [datastore documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py)."
   ]
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   "source": [
    "## Upload a Pandas dataframe and register as a dataset: register_pandas_dataframe()\n",
    "\n",
    "```python\n",
    "def register_pandas_dataframe(dataframe, target, name, show_progress=True):\n",
    "    \"\"\" Create a dataset from pandas dataframe.\n",
    "        Datastore type can only be azure data lake store or azure storage store.\n",
    "\n",
    "    :param dataframe: In memory dataframe to be uploaded.\n",
    "    :type dataframe: pandas.DataFrame\n",
    "    :param target: The datastore path where the dataframe parquet data will be uploaded to.\n",
    "        A guid folder will be generated under the target path to avoid conflict.\n",
    "    :type target: azureml.data.datapath.DataPath, azureml.core.datastore.Datastore or tuple(azureml.core.datastore.Datastore, str) object\n",
    "    :param name: The name of the registered dataset.\n",
    "    :type name: str\n",
    "    :param show_progress: Indicates whether to show progress in the console. Defaults to be True.\n",
    "    :type show_progress: bool, optional\n",
    "    :return: The registered dataset.\n",
    "    :rtype: azureml.data.TabularDataset\n",
    "    \"\"\"\n",
    "```"
   ]
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   "source": [
    "You can use Pandas dataframe created by yourself or use the below sample code to get Pandas dataframe from existing dataset. \n",
    "\n",
    "```python\n",
    "datastore = workspace.get_default_datastore()\n",
    "datastore_path = [(datastore, 'weather-data-florida/*/*/data.parquet')]\n",
    "dataset = Dataset.Tabular.from_parquet_files(path=datastore_path)\n",
    "pandas_df = dataset.to_pandas_dataframe()\n",
    "```"
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   "source": [
    "# Syntax:\n",
    "# dataset=Dataset.Tabular.register_pandas_dataframe(<pandas dataframe>, <datastore>, \"<name of registered dataset>\", show_progress=True)\n",
    "# Using workspace default datastore for uploading Pandas dataframe\n",
    "import pandas as pd\n",
    "from azureml.core import Dataset\n",
    "\n",
    "# Pandas dataframe from a local CSV file\n",
    "pandas_df = pd.read_csv(\"./data/titanic.csv\")\n",
    "# Getting workspace default datastore\n",
    "datastore = workspace.get_default_datastore()\n",
    "# Uploading Pandas dataframe and registering it as a dataset\n",
    "dataset = Dataset.Tabular.register_pandas_dataframe(\n",
    "    pandas_df, datastore, \"ds_from_pandas_df\", show_progress=True\n",
    ")"
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