Metadata-Version: 2.1
Name: qpd
Version: 0.3.0.dev2
Summary: Query Pandas Using SQL
Home-page: http://github.com/goodwanghan/qpd
Author: Han Wang
Author-email: goodwanghan@gmail.com
License: Apache-2.0
Description: # Query Pandas-like Dataframes Using SQL
        
        QPD let you run the same SQL (`SELECT` for now) statements on different computing frameworks with pandas-like interfaces.
        Currently, it support [Pandas](https://pandas.pydata.org/), [Dask](https://dask.org/) and [Ray](https://ray.io/)
        (via [Modin](https://github.com/modin-project/modin) on Ray).
        
        QPD directly translates SQL into pandas-like operations to run on the backend computing frameworks, so it can be significantly
        faster than some other approaches, for example, to dump pandas dataframes into SQLite, run SQL and convert the result back into
        a pandas dataframe. However, the top priorities of QPD are **correctness** and **consistency**. It ensures the results of
        implemented SQL features following SQL convention, and it ensures consistent behavior regardless of backend computing frameworks.
        A typical case is `groupby().agg()`. In pandas or pandas like frameworks, if any of the group keys is null, the default
        behavior is to drop that group, however, in SQL they are not dropped. QPD follows the SQL way.
        
        QPD syntax is a subset of the intersection of [Spark SQL](https://spark.apache.org/sql/) and [SQLite](https://www.sqlite.org/index.html).
        The correctness and consistency are extensively tested against SQLite. Practically, Spark SQL and SQLite are highly consistent
        on both syntax and behavior. So, in other words, QPD enables you to run common SQLs and get the same result on Pandas, SQLite, Spark, Dask,
        Ray and other backends that QPD will support in the future.
        
        SQL is one of the most important data processing languages. It is very *scale agnostic*, and one of the major goals of the Fugue project
        is to enrich SQL and make SQL *platform agnostic*. QPD, as a subproject of Fugue, focuses on running SQL on pandas-like frameworks, it is
        an essential component to achieve the ultimate goal.
        
        ## Installation
        
        QPD can be installed from PyPI:
        
        ```bash
        pip install qpd # install qpd + pandas
        ```
        
        If you want to use Ray or Dask as the backend, you will need to install QPD with one of the targets:
        
        ```bash
        pip install qpd[dask] # install qpd + dask[dataframe]
        pip install qpd[ray] # install qpd + ray
        pip install qpd[all] # install all dependencies above
        ```
        
        ## Using QPD
        
        ### On Pandas
        
        ```python
        from qpd_pandas import run_sql_on_pandas
        import pandas as pd
        
        df = pd.DataFrame([[0,1],[2,3],[0,5]], columns=["a","b"])
        res = run_sql_on_pandas("SELECT a, SUM(b) AS b, COUNT(*) AS c FROM df GROUP BY a", {"df": df})
        print(res)
        ```
        
        ### On Dask
        
        Please read [this](https://distributed.dask.org/en/latest/quickstart.html) to learn the best
        practice for initializing dask.
        
        ```python
        from qpd_dask import run_sql_on_dask
        import dask.dataframe as pd
        import pandas
        
        df = pd.from_pandas(pandas.DataFrame([[0,1],[2,3],[0,5]], columns=["a","b"]))
        res = run_sql_on_dask("SELECT a, SUM(b) AS b, COUNT(*) AS c FROM df GROUP BY a", {"df": df})
        print(res.compute())  # dask dataframe is lazy, you need to call compute
        ```
        
        ### On Ray
        
        Please read [this](https://docs.ray.io/en/ray-0.3.1/api.html#starting-ray) to learn the best
        practice for initializing ray. And read [this](https://modin.readthedocs.io/en/latest/using_modin.html)
        for initializing modin + ray.
        
        *Please don't use dask as modin backend if you want to use QPD, it's not tested*
        
        ```python
        import ray
        ray.init()
        
        from qpd_ray import run_sql_on_ray
        import modin.pandas as pd
        
        df = pd.DataFrame([[0,1],[2,3],[0,5]], columns=["a","b"])
        res = run_sql_on_ray("SELECT a, SUM(b) AS b, COUNT(*) AS c FROM df GROUP BY a", {"df": df})
        print(res)
        ```
        
        ### Ignoring Case in SQL
        
        By default, QPD requires users to use upper cased keywords, otherwise syntax errors will be raised.
        However if you really don't like this behavior, you can turn it off, the parameter is `ignore_case`,
        here is an example:
        
        ```python
        from qpd_pandas import run_sql_on_pandas
        import pandas as pd
        
        df = pd.DataFrame([[0,1],[2,3],[0,5]], columns=["a","b"])
        res = run_sql_on_pandas(
            "select a, sum(b) as b, count(*) as c from df group by a",
            {"df": df}, ignore_case=True)
        print(res)
        ```
        
        
        ## Things to clarify
        
        ### QPD on Spark (Koalas)?
        No, that will not happen. QPD is using Spark SQL
        [syntax file](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/antlr4/org/apache/spark/sql/catalyst/parser/SqlBase.g4).
        Spark SQL is highly optimized. If we create a Koalas backend, correctness and consistency can
        be guaranteed, but there will be no performance advantage. So for Spark, please use Spark SQL.
        If you use Fugue SQL on Spark backend, it will also directly use Spark to run the SQL statements.
        We don't see the value to make QPD run on Spark.
        
        
        ## Update History
        
        * 0.2.6: Update pandas indexer import
        * 0.2.5: Update antlr to 4.9
        * 0.2.4: Fix a bug: set operations will alter the input dataframe to add columns
        * 0.2.3: Refactor and extract out PandasLikeUtils class
        * 0.2.2: Accept constant select without `FROM`, `SELECT 1 AS a, 'b' AS b`
        * <= 0.2.1: Pandas, Dask, Ray SQL support
        
Keywords: pandas sql
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dask
Provides-Extra: ray
Provides-Extra: all
