Reverse Dependencies of pandas-gbq

The following projects have a declared dependency on pandas-gbq:

• alo7-airflow — Programmatically author, schedule and monitor data pipelines
• am4894bq — Helper library for BigQuery
• apache-airflow — Programmatically author, schedule and monitor data pipelines
• apache-airflow-backport-providers-google — Backport provider package apache-airflow-backport-providers-google for Apache Airflow
• apache-airflow-providers-google — Provider package apache-airflow-providers-google for Apache Airflow
• apache-airflow-zack — Programmatically author, schedule and monitor data pipelines
• api2db — Python Api data collection tool
• asyncdb — Library for Asynchronous data source connections Collection of asyncio drivers.
• atscale — The AI-Link package created by AtScale
• basedosdados — Organizar e facilitar o acesso a dados brasileiros através de tabelas públicas no BigQuery.
• bigframes — BigQuery DataFrames -- scalable analytics and machine learning with BigQuery
• bqcon — Python Package for doing basic operations over google big query
• bqsc — Define schema object from bigquery schema definition json file
• bquest — Effortlessly validate and test your Google BigQuery queries with the power of pandas DataFrames in Python.
• brighter — A simple library for data-wrangling with GCP support
• cacheless-airflow — Programmatically author, schedule and monitor data pipelines
• calitp-data-analysis — Shared code for querying Cal-ITP data in notebooks, primarily.
• change_detection — package for detecting change in time-series data
• cloudops-google-bigquery — The cloudops-google-bigquery package
• cloudservice — Auto machine learning, deep learning library in Python.
• cmapBQ — Toolkit for interacting with Google BigQuery and CMAP datasets
• continual — Operational AI for the Modern Data Stack
• custom-utils — Utilities for database connectors, slack alerter, loggers etc
• custom-workflow-solutions — Programmatically author, schedule and monitor data pipelines
• dagster-gcp-pandas — Package for storing Pandas DataFrames in GCP.
• dagster-gcp-pyspark — Package for storing PySpark DataFrames in GCP
• data-science-common — UNDER CONSTRUCTION: A simple python library to facilitate analysis
• dataflowutil — no summary
• DBD — dbd is a data loading and transformation tool that enables data analysts and engineers to load and transform data in SQL databases.
• dbnd-gcp — Machine Learning Orchestration
• dbpal — A utility package for pushing around data
• dbt-table-diff — Compares models in dbt during an open PR
• deephub — no summary
• dimschema — dimschema
• droughty — droughty is an analytics engineering toolkit, helping keep your workflow dry.
• droughty-dev — no summary
• ebmdatalab — Package for ebmdatalab jupyter notebook stuff
• edu-airflow — Programmatically author, schedule and monitor data pipelines
• friktionless — Friktionless is a Python package providing simplified interfaces to Friktion data. It aims to be the fundamental building block for doing data engineering and data analysis in Python for Friktion.
• fugue-warehouses — Fugue data warehouses integration
• ga-attribution-scrape — Scrapes attribution data from GAs Model Comparison Tool through JS Network and sends to Bigquery.
• ganymede-py — Ganymede Python libraries
• gcp-data-ingestion — Utility Functions for Data Ingestion in GCP
• gcp-scraper — Scraping utils with GCP functions
• gcpts — no summary
• gdp-time-series — no summary
• geniepy — Gene-Disease relationship trend detection
• GeoFuns — A package for geo tools used to map card score
• googlewrapper — Simple API wrapper for Google Products
• gps-building-blocks — Modules and tools useful for use with advanced data solutions on Google Ads, Google Marketing Platform and Google Cloud.
• grounder — Estimate the quality and factual correctness of natural language text to help ground language models such as BERT, GPT-J, GPT-Neo, ChatGPT, and Bard.
• honeycomb — Multi-source/engine querying tool
• honeydew — Collection of connectors for ETL
• humailib — HUMAI data science framework
• instackup — A package to ease interaction with cloud services, DB connections and commonly used functionalities in data analytics.
• kedro-datasets — Kedro-Datasets is where you can find all of Kedro's data connectors.
• mcl_google_cloud_bigquery — The mcl_google_cloud_bigquery package.
• mercury-fil — Python client for accessing Filecoin chain data.
• OniPKG-Api — Helper para consumo de APIs
• openlineage-airflow — OpenLineage integration with Airflow
• pandas-pyarrow — A library for switching pandas backend to pyarrow
• pano-airflow — Programmatically author, schedule and monitor data pipelines
• pipeline-penguin — Pipeline Penguin is a versatile python library for data quality.
• polywhirl — Run pandas-profiling HTML reports for a given list of database tables.
• PREAGeoFuns — A package for geo tools used to map card score
• profiles-rudderstack — no summary
• pyFission — A tool to sync data across data sources
• pygbq — Easily integrate data in BigQuery
• pygyver — Data engineering & Data science Pipeline Framework
• santoku — Custom Python wrapper around many third party APIs, including AWS, BigQuery, Slack and Salesforce.
• shaku-database — Shaku Database util
• skt — SKT package
• terra-billing-alert — Terra Billing Alert script for Google Cloud Function
• tokyo-lineage — Tokyo Lineage
• trell-ai-utils — Trell Database connectors, slack alerter and loggers
• ultipro — Python Client for the UltiPro SOAP API
• viadot2 — A simple data ingestion library to guide data flows from some places to other places.
• vpxhw-db-job-locator — package for locating jobs in vpxhw database
• waj-bigquery — no summary
• warehouses — Python library to facilitate read/write of GIS and tabular data between Python and cloud warehouses
• wh-lookml-gen — generates lookml from a warehouse
• xedro — Kedro helps you build production-ready data and analytics pipelines