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Parquet Metadata Examiner Tool v. 2.2
Developed by AI-Engineer Martial Terran of Albany New York (March 10, 2025)
Copyright (c) 2025 Martial Terran
License: MIT License (See LICENSE file for full text) [Contact Information - Email: mrferran1970@gmail.com] [https://huggingface.co/MartialTerran]
This tool examines the metadata of a .parquet file. It reads a filename from the clipboard (if available) or uses a default filename, and then prints detailed information about the file's metadata, schema, row groups, column chunks, and sample data.
Works Completely. This works to describe the contents of the .parquet file specified in the MS CLIPBOARD if any. Put train-00000-of-00001.parquet name into clipboard and then right-click on Examine_Parquet_Filename_In_Clipboard.V2.1.py and openwith python3. Script will launch CMD console and print the metadata in the .parquet file specified in the clipboard
HOW TO USE example: Copy train-00000-of-00001.parquet filename (not the file contents) into CLIPBOARD and then right-click on Examine_Parquet_Filename_In_Clipboard.V2.2.py and then openwith python3. The script will launch a Python CMD console terminal and print in terminal the .parquet files metadata within the .parquet file specified the clipboard.
Version [2.2] should now gracefully handle Parquet files where compressed_size or uncompressed_size attributes might be missing in the ColumnChunkMetaData. It will print NA instead of crashing, making the script more robust to variations in Parquet file formats or metadata. This should address the error you encountered and make the script fully functional for a wider range of Parquet files.
EXAMPLE OUTPUT shown in CMD.exe terminal window:
Using Parquet file from clipboard: C: ,,,, validation-00000-of-00001.parquet File Metadata: Number of Row Groups: 1 Number of Columns: 4 Total Rows: 213 Created By: parquet-cpp-arrow version 10.0.1 Format Version: 2.6
Column Names: Column 1: id Column 2: question Column 3: answers Column 4: score
Schema: Number of columns in schema: 4 Column 1: Name: id Physical Type: BYTE_ARRAY Logical Type: String Max Definition Level: 1 Max Repetition Level: 0 Column 2: Name: question Physical Type: BYTE_ARRAY Logical Type: String Max Definition Level: 1 Max Repetition Level: 0 Column 3: Name: answers Physical Type: BYTE_ARRAY Logical Type: String Max Definition Level: 1 Max Repetition Level: 0 Column 4: Name: score Physical Type: INT64 Logical Type: None Max Definition Level: 1 Max Repetition Level: 0
Row Group Metadata: Row Group 1: Number of Columns: 4 Number of Rows: 213 Total Bytes: 214909 Column Chunk 1 (id): File Offset: 2023 Encodings: ('RLE_DICTIONARY', 'PLAIN', 'RLE') Compressed Size: NA Uncompressed Size: NA Number of Values: 213 Data Page Offset: 1754 Dictionary Page Offset: 4 Statistics: Has Min-Max: True Null Count: 0 Min: 120cw9 Max: zz1i4m Column Chunk 2 (question): File Offset: 73568 Encodings: ('RLE_DICTIONARY', 'PLAIN', 'RLE') Compressed Size: NA Uncompressed Size: NA Number of Values: 213 Data Page Offset: 72742 Dictionary Page Offset: 2093 Statistics: Has Min-Max: True Null Count: 0 Min: A slurry or a flour, water mixture. What is better to thicken sauces and soups? Max: why must i change the parchment pap ...
thanks ! Column Chunk 3 (answers): File Offset: 137439 Encodings: ('RLE_DICTIONARY', 'PLAIN', 'RLE') Compressed Size: NA Uncompressed Size: NA Number of Values: 213 Data Page Offset: 137034 Dictionary Page Offset: 74206 Statistics: Has Min-Max: False Null Count: 0 Column Chunk 4 (score): File Offset: 138347 Encodings: ('RLE_DICTIONARY', 'PLAIN', 'RLE') Compressed Size: NA Uncompressed Size: NA Number of Values: 213 Data Page Offset: 138089 Dictionary Page Offset: 137656 Statistics: Has Min-Max: True Null Count: 0 Min: 3 Max: 1246
Key-Value Metadata: b'huggingface': [JSON]
b'ARROW:schema':
...
Sample Column Content (First 5 rows):
Column: id Row 1: zeooo6 Row 2: u3ignp Row 3: x2dv5j Row 4: vys6mg Row 5: kgutb0
Column: question Row 1: What do people mean when they say Row 2: Why do people seem to prefer Row 3: How to emulsify oil on the top Row 4: Drumsticks despite being well cooked Row 5: My prime rib .
Column: answers Row 1: You can think of your pans as energy buckets . Row 2: I don't know why Row 3: Skim and chill the oil. Add to a separate saucepan with flour. Heat gently to form a blonde roux. Add stew liquid slowly to roux while stirring to prevent lumps. Add back to main pot. Row 4: Look... Row 5: That’s a “sell by” date, not an expiration date. Big difference. If it’s constantly kept below 41F, there’s very little chance it would go bad before Christmas in its original packaging.
Column: score Row 1: 1246 Row 2: 1088 Row 3: 753 Row 4: 719 Row 5: 712 Type q to quit or press Enter to continue: """
license: mit
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