<jupyter_start><jupyter_text>Input  auto_read_file<jupyter_code>dataflow = dprep.auto_read_file(path='./data/train.csv')
dataflow.head()<jupyter_output><empty_output><jupyter_text>read_csv<jupyter_code>import azureml.dataprep as dprep

# Read CSV files
dataflow = dprep.read_csv(path='./data/*.csv', infer_column_types=True)
dataflow.head()
dataflow.dtypes<jupyter_output><empty_output><jupyter_text>read_jsonNotice how the array from the first record get's split into multiple rows?<jupyter_code>import azureml.dataprep as dprep

# Read JSON files
dataflow = dprep.read_json(path='./data/*.json')
dataflow.head()<jupyter_output><empty_output><jupyter_text>read_parquet<jupyter_code># Read single paqrquet files individually
dataflow = dprep.read_parquet_file(path='./data/*.parquet')
dataflow.head()<jupyter_output><empty_output><jupyter_text>read_parquet_datasetNotice how the last column contains the directory information? This is called a paration column.<jupyter_code># Read nested folders of parquet files
dataflow = dprep.read_parquet_dataset(path='./data/warehouse/telemetry')
dataflow.head()<jupyter_output><empty_output><jupyter_text>Output<jupyter_code>dataflow = dprep.auto_read_file(path='./data/train.csv')
# Write data as Parquet file
dataflow.write_to_parquet(file_path='./outputs/train.parquet', single_file=True).run_local()
# Write all partitions as parquet folder with _SUCCESS file
dataflow.write_to_parquet(directory_path='./outputs/parquet').run_local()<jupyter_output><empty_output>
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