Consolidate-csv-files-from-gcs 0.0

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Python Library for Merging CSV Files from Google Cloud Storage

This library provides a function consolidate_csv_from_gcs that simplifies the process of merging multiple CSV files from a Google Cloud Storage (GCS) bucket into a single file.

1. consolidate_csv_from_gcs(bucket_name, prefix, merged_file_name, output_bucket_name, output_bucket_name_prefix):

The consolidate_csv_from_gcs function takes the following arguments:

Parameters:

1. bucket_name = "your-bucket-name"
2. prefix = "path/to/your/csv/files/" # Include trailing slash
3. merged_file_name = "merged_data.csv"
4. output_bucket_name = "output-bucket-name"
5. output_bucket_name_prefix = "merged/data/" # Optional, defaults to prefix

Return Value:

There is no return value as the result can be seen in the GCS bucket.

Explanation:

The consolidate_csv_from_gcs function takes the following arguments:

1. bucket_name: (str) The name of the GCS bucket containing the CSV files.
2. prefix: (str) The prefix to filter files within the bucket (e.g., "path/to/your/csv/files/"). Include a trailing slash.
3. merged_file_name: (str) The desired name for the output merged CSV file.
4. output_bucket_name: (str) The name of the GCS bucket where the merged file will be saved.
5. output_bucket_name_prefix (str, optional): An optional prefix to add to the filename within the output bucket. Defaults to the prefix argument.

Function Behaviour:

1. Connects to GCS using storage.Client.
2. Retrieves the bucket specified by bucket_name.
3. Lists all files with the provided prefix and extension .csv.
4. Iterates through each CSV file:
5. Reads the content with automatic encoding detection using chardet.
6. Creates a Pandas DataFrame from the CSV content.
7. Appends the DataFrame to a list.
8. Concatenates all DataFrames vertically into a single merged DataFrame.
9. Creates a BytesIO object to hold the merged CSV data in memory.
10. Writes the merged DataFrame to the BytesIO object using to_csv.
11. Connects to the output GCS bucket using storage.Client.
12. Creates a new blob object with the desired filename within the output bucket (including any output_bucket_name_prefix).
13. Uploads the merged CSV data (from BytesIO) to the output blob as a CSV file (content_type='text/csv').

Additional Notes:

1. This library uses the google-cloud-storage and pandas libraries. Ensure they are installed (pip install google-cloud-storage pandas).
2. The library automatically detects the encoding of the CSV files using chardet.
3. The merged file is written to a memory buffer (BytesIO) before uploading to GCS for efficiency.

I hope this README clarifies the usage of these functions for managing Alteryx workflows through Python. Feel free to ask if you have any further questions.